diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
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--- /dev/null
+++ b/feature_crosses.ipynb
@@ -0,0 +1,1571 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "76856ae1-6e96-443f-fc87-4f4e4042bed8"
+      },
+      "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": 11,
+      "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       2648.4           540.9   \n",
+              "std         2.1        2.0                12.6       2136.6           416.6   \n",
+              "min        32.5     -124.3                 1.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1465.0           299.0   \n",
+              "50%        34.2     -118.5                28.0       2145.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3167.0           654.0   \n",
+              "max        42.0     -114.3                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1432.4       502.5            3.9               2.0  \n",
+              "std        1128.7       379.9            1.9               1.1  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         793.0       282.0            2.6               1.5  \n",
+              "50%        1170.0       410.0            3.6               1.9  \n",
+              "75%        1725.0       608.0            4.8               2.3  \n",
+              "max       35682.0      5050.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2648.4</td>\n",
+              "      <td>540.9</td>\n",
+              "      <td>1432.4</td>\n",
+              "      <td>502.5</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>2136.6</td>\n",
+              "      <td>416.6</td>\n",
+              "      <td>1128.7</td>\n",
+              "      <td>379.9</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>11.0</td>\n",
+              "      <td>3.0</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>1465.0</td>\n",
+              "      <td>299.0</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>28.0</td>\n",
+              "      <td>2145.0</td>\n",
+              "      <td>435.0</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>3167.0</td>\n",
+              "      <td>654.0</td>\n",
+              "      <td>1725.0</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>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5050.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.8       2632.3           535.8   \n",
+              "std         2.1        2.0                12.5       2280.9           433.0   \n",
+              "min        32.6     -124.3                 2.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           293.0   \n",
+              "50%        34.2     -118.5                29.0       2095.0           429.0   \n",
+              "75%        37.7     -118.0                37.0       3103.2           636.0   \n",
+              "max        42.0     -114.5                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1422.9       498.2            3.9               2.0  \n",
+              "std        1192.6       395.4            2.0               1.3  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         781.0       278.0            2.6               1.5  \n",
+              "50%        1158.0       405.0            3.5               1.9  \n",
+              "75%        1707.2       594.0            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>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>2632.3</td>\n",
+              "      <td>535.8</td>\n",
+              "      <td>1422.9</td>\n",
+              "      <td>498.2</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>2280.9</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1192.6</td>\n",
+              "      <td>395.4</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.3</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>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1453.0</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>781.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2095.0</td>\n",
+              "      <td>429.0</td>\n",
+              "      <td>1158.0</td>\n",
+              "      <td>405.0</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>3103.2</td>\n",
+              "      <td>636.0</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>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</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": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.5\n",
+              "std                 115.6\n",
+              "min                  15.0\n",
+              "25%                 120.0\n",
+              "50%                 180.9\n",
+              "75%                 265.5\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.5</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>120.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                206.8\n",
+              "std                 116.8\n",
+              "min                  15.0\n",
+              "25%                 118.3\n",
+              "50%                 179.1\n",
+              "75%                 264.0\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>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "0957a246-1f75-43cc-e70b-91f91b8d7070"
+      },
+      "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",
+        "    \n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 265.60\n",
+            "  period 01 : 183.05\n",
+            "  period 02 : 112.45\n",
+            "  period 03 : 150.36\n",
+            "  period 04 : 119.82\n",
+            "  period 05 : 110.19\n",
+            "  period 06 : 135.82\n",
+            "  period 07 : 110.52\n",
+            "  period 08 : 111.86\n",
+            "  period 09 : 112.46\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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MThZnujuaiIhIg6ACcxV6dbRhMv1vGilO90YSERGpUyowVyE4wJeYFqEcyCgk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\npIARQgghRND5/9I3PVye9VwOAAAAAElFTkSuQmCC\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",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\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",
+        "  \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",
+        "  \n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0FfUytOTNJhL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "aa8e14a5-4a1f-4f2f-e9fb-6b1753c2efdf"
+      },
+      "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": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.68\n",
+            "  period 01 : 143.34\n",
+            "  period 02 : 126.76\n",
+            "  period 03 : 115.57\n",
+            "  period 04 : 107.67\n",
+            "  period 05 : 101.91\n",
+            "  period 06 : 97.48\n",
+            "  period 07 : 93.93\n",
+            "  period 08 : 91.13\n",
+            "  period 09 : 88.77\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Yp7szke3DKcny4te0wxzLOGnrSCIiItWOGhgbGHpTM5xTggD4Mvbbcs3ALSIi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\nK38Is/fK6QAAAABJRU5ErkJggg==\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "AFJ1qoZPlQcs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Feature Crosses\n",
+        "\n",
+        "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n",
+        "\n",
+        "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n",
+        "\n",
+        "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-Rk0c1oTYaVH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train the Model Using Feature Crosses\n",
+        "\n",
+        "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n",
+        "\n",
+        "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-eYiVEGeYhUi",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xZuZMp3EShkM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "a3c457ee-9b41-4028-a177-b2ba9b7de867"
+      },
+      "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": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.18\n",
+            "  period 01 : 134.98\n",
+            "  period 02 : 117.97\n",
+            "  period 03 : 106.69\n",
+            "  period 04 : 98.96\n",
+            "  period 05 : 93.29\n",
+            "  period 06 : 88.97\n",
+            "  period 07 : 85.59\n",
+            "  period 08 : 82.94\n",
+            "  period 09 : 80.71\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RERGpYzZs+LZK93v11X+SlHT2src//vjD1RWp2qnAiIiI1CHJyUmsXbu6SvedNWs2YWFN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\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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..9549a1a
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1524 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "751e59de-45a9-404b-ba42-015a550ba81e"
+      },
+      "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       2656.5           542.2   \n",
+              "std         2.1        2.0                12.6       2209.0           425.8   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1469.0           298.0   \n",
+              "50%        34.3     -118.5                28.5       2140.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3159.2           651.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1441.2       504.5            3.9               2.0  \n",
+              "std        1181.2       389.4            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         791.0       282.0            2.6               1.5  \n",
+              "50%        1168.0       410.0            3.5               1.9  \n",
+              "75%        1729.0       608.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2656.5</td>\n",
+              "      <td>542.2</td>\n",
+              "      <td>1441.2</td>\n",
+              "      <td>504.5</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>2209.0</td>\n",
+              "      <td>425.8</td>\n",
+              "      <td>1181.2</td>\n",
+              "      <td>389.4</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>1469.0</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>791.0</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>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2140.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3159.2</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1729.0</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>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.7       2612.9           532.8   \n",
+              "std         2.1        2.0                12.5       2108.6           410.9   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1442.0           294.0   \n",
+              "50%        34.2     -118.5                29.0       2105.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3118.0           644.0   \n",
+              "max        41.9     -114.5                52.0      30405.0          4952.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1401.8       493.4            3.9               2.0  \n",
+              "std        1063.1       372.4            1.9               1.2  \n",
+              "min           6.0         2.0            0.5               0.2  \n",
+              "25%         785.8       279.0            2.6               1.5  \n",
+              "50%        1163.5       407.0            3.6               2.0  \n",
+              "75%        1700.0       597.0            4.8               2.3  \n",
+              "max       15037.0      4616.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2612.9</td>\n",
+              "      <td>532.8</td>\n",
+              "      <td>1401.8</td>\n",
+              "      <td>493.4</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>12.5</td>\n",
+              "      <td>2108.6</td>\n",
+              "      <td>410.9</td>\n",
+              "      <td>1063.1</td>\n",
+              "      <td>372.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</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>1442.0</td>\n",
+              "      <td>294.0</td>\n",
+              "      <td>785.8</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2105.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1163.5</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>3118.0</td>\n",
+              "      <td>644.0</td>\n",
+              "      <td>1700.0</td>\n",
+              "      <td>597.0</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>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30405.0</td>\n",
+              "      <td>4952.0</td>\n",
+              "      <td>15037.0</td>\n",
+              "      <td>4616.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.1\n",
+              "std                 116.3\n",
+              "min                  15.0\n",
+              "25%                 118.9\n",
+              "50%                 179.8\n",
+              "75%                 265.0\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>207.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.3</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>179.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.0</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                207.8\n",
+              "std                 115.3\n",
+              "min                  15.0\n",
+              "25%                 121.3\n",
+              "50%                 181.3\n",
+              "75%                 264.7\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>121.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "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": "7d345295-aadf-4926-b29f-0a141e49bcaa"
+      },
+      "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>-0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</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>-0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    latitude  longitude  housing_median_age  total_rooms  \\\n",
+              "latitude                 1.0       -0.9                 0.0         -0.0   \n",
+              "longitude               -0.9        1.0                -0.1          0.0   \n",
+              "housing_median_age       0.0       -0.1                 1.0         -0.4   \n",
+              "total_rooms             -0.0        0.0                -0.4          1.0   \n",
+              "total_bedrooms          -0.1        0.1                -0.3          0.9   \n",
+              "population              -0.1        0.1                -0.3          0.9   \n",
+              "households              -0.1        0.1                -0.3          0.9   \n",
+              "median_income           -0.1       -0.0                -0.1          0.2   \n",
+              "rooms_per_person         0.1       -0.1                -0.1          0.1   \n",
+              "target                  -0.2       -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                         0.1        -0.0         0.1            0.7   \n",
+              "\n",
+              "                    rooms_per_person  target  \n",
+              "latitude                         0.1    -0.2  \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     0.1  \n",
+              "population                      -0.1    -0.0  \n",
+              "households                      -0.0     0.1  \n",
+              "median_income                    0.2     0.7  \n",
+              "rooms_per_person                 1.0     0.2  \n",
+              "target                           0.2     1.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RQpktkNpia2P",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n",
+        "\n",
+        "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n",
+        "\n",
+        "Use this information to try removing features.  You can also try developing additional synthetic features, such as ratios of two raw features.\n",
+        "\n",
+        "For convenience, we've included the training code from the previous exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bjR5jWpFr2xs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jsvKHzRciH9T",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        "    \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "\n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g3kjQV9WH3pb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "varLu7RNH3pf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DSgUxRIlH3pg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "57108087-5322-4c9c-9c73-dd5afd32ecce"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# Your code here: add your features of choice as a list of quoted strings.\n",
+        "#\n",
+        "minimal_features = [\n",
+        "  \"population\",\n",
+        "  \"households\",\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=0.00005,\n",
+        "    steps=1000,\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": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 208.17\n",
+            "  period 01 : 188.09\n",
+            "  period 02 : 178.69\n",
+            "  period 03 : 175.69\n",
+            "  period 04 : 175.39\n",
+            "  period 05 : 176.95\n",
+            "  period 06 : 178.97\n",
+            "  period 07 : 180.70\n",
+            "  period 08 : 182.85\n",
+            "  period 09 : 182.78\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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qEexTD4qD2X5iJ8Vlp8wuSURE5IqlAFNFrFYLXX6+GqnMKGN7VprZJYmIiFyx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\n/1fycFo434XQAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IGINhMIJ5Wyt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BAGoXFPZ5ZE3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "minimal_features = [\n",
+        "  \"median_income\",\n",
+        "  \"latitude\",\n",
+        "]\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RidI9YhKOiY2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Make Better Use of Latitude\n",
+        "\n",
+        "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n",
+        "\n",
+        "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hfGUKj2IR_F1",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "82987289-0417-4da6-d1bd-774f52b3ff34"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f2f413ec1d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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gjlw/V8Vfwc6dO9Hc3Iw5c+ZIvh6TsVpyf0/F682tIAYfFnDwhLJmL00Bs902\n3HXjXLAmEz69qg5/++J7uDQsLZpvYRl8dvV8eDzjisdtWuhKu38djUZx1w3zcNcN8/Dif5/B4dOX\nlG8oh1wc9OELT+wCH75SijLgDeD1A2fhD4Qkw89CWIDTLh8mFELhtM+p2HG77ZruYdNN83HHtXMm\n7XUOD09gwOtPLL5SGRnnceHiCEIKe53pnnlgIogTHcqLYSGSfeSCpoAlc6qyNsiDIwH0fDyUtu3k\nsTPy4f3kY8g99+mE1jFLUEav56pk1BWXjW+99Rb27NmDzZs347e//S1+9atfwWq1IhiMG6tLly6h\npqYGNTU1GBwcTHxuYGAANTU1WV94tqgJHUdjwPkBH3a8dRYA8Nr+HlljDAA3LJ0JK5d+30vsnORQ\n8Hb4cBQv7+qAEI2h49xI2mPmkmAoOskYJyOGQlND2SRMqIxYM5z8HLJpwSgeU+mZB/gIRiaU94vH\n/JE0V56eaAxo6xrI+jhq204qCZCkHkPquRMIpYCih/z0008n/v+Xv/wlZs+ejba2NuzatQuf+cxn\n8MYbb2D16tVYvnw5HnvsMYyNjYFhGLS2tuKHP/xhzi8+HWp0pkXaOgdx143z0yZj3dI0S9W5xRKo\nu26cjx89/55sUo3Y4WakiOt/vePBRDeq1GQaLd2LpjNK2f9qFzFyz/yetQuwfW9P3pIFuy+MZX0M\nJS11tcmRZPFHMAoZK3V961vfwiOPPILt27ejtrYWmzZtgtlsxne+8x08+OCDoCgK3/zmN2G3F34P\ngzMzaG6onpRtKod3PIgLA7602aiZ7kXZrSxWLa5RDF+fOecFa6ZlPdRCw6Z0o0rNpCZNATIj1aBW\n2TgsnufAptVXqfq8XMeobbs7dWurqIZsMrcZGmDNJkUt9XQRripb/LdFFn8Eo6DaIH/rW99K/P9v\nfvObKa/ffvvtuP322/W5Kh1R6yg47HE5SwtLy/Z45cw03FVlGV/DlvX18Acjsi0Wi02xayrSTzFZ\napM0BVCPaFA3rV6AV97sxJlzXk1NPpKfuZ8P40B7fmvbWTMN1kRhPJD5nrQQBQJ8PHQuVyqnWMpk\n4/Djr1wDu1U/9TECodAYOvWQDws40TWY/o2Ih71YMwMo6GfdsHSGJu+PoWk8cNsiOLMsEykENC3f\nhJ5IbWbHzgNncfBUvy5Sj9ve7AIv8z3lCj4chdWivo44Hamlckr75SsXuzM2xkRNjlDsGLa5BKAu\nqYsz07hx2SxsWV+PodEgeAUBkY2r5mq+Fs7MoMxiBnQU988H0SjAmmiEIlMneyK4oJ109cSZNPng\nwwI+/Fi6lCrXBHQU3JFS1FIqF+9BAAAgAElEQVSTo5CuuxNRkyOUCoY2yGqSuvhwFO3dg2BoCptW\nXwWOZSRVvSwskxDOyJS4jGEX+gaLt/yCgnx4n5IJGuQqmWY6tM/TU+px1MfD61PWqa6wmjHm10/L\nWmR8Qr9jSi3w5PbLgfSGVhxHu94/P2lvfbqryRGKF0MbZLV61uIPNC6moH966va93XlNttGC0l2H\nI1HcuHQmOs6N5DSTerp4Mn4+gp0HPgJFQVKAJtPIQ6WNg1OhtaLTzmJ5gzsnY7DKzsErI1SSKUoL\nPKkcBTnZ1lgsBoqi0NbpwdAYDzkRPtJulFBsGNogA/GQlyBEVfUpPt45KLtfyocEzUL4mfZlLgQV\nVjOCoQhCkakWwmG34IHbFgGIe2NlnAkBPoKIEIOeAkhG18UWFxzvtF+UHWdA5pEHzsxgxSL5TP4V\ni2ouL2ootHUOYng8CAr6lEY1zqnEkdOZ1yPPqbGBDwsYHAloWuAp/a4OnuyfFOWSu0/SdIJQbBje\nIDM0jc3rG3DoVH/asqKRCR5VMl1xnBXa9ku19GUuBGP+MCwsDSlfWTQQQjSK3ccu5MSD1XNPtVhJ\nXXCkQlPAmpbZqgxTalh/y/p6RGMxHEoyRpyZxg1LZya+HzH023VhBE9tP6HbfWXK+pWz8blbG1Dl\nKEfPx0OatiaUfldqG8mQHAhCsWF4gwzEf7whFTW+TrsFTQudkt601v3STMRJCo3otVlYBqGwMMVz\nyaUHa/T2eWoiJbEYcNs1cxQXN0ph/XvX1mN1Uy3+8O4n6LowghFfCCd7hrCd6U4YZc7M6Bqx6To/\nCpqOJ/+pJSJEMTQaRJWjXPN3qsfvigiKEIqNaWGQ1f54WxqrsWn1AoTCUZy5rEpVaWPR0qBtv1T0\nYprqq4t+DzmZcosJP7x/BdxJ8oO59mCN3j5PTaRETRRGblHUcW4E/mB4yvNLXTTxYUG2uYUWvOO8\nbNKfHO+c6MOB431wO8rQtNClKcKilB8ipyVAU/H4j5OoyRGKlGlhkNMld7kqLGhucCEai+FHzx3B\n0BgPzkyDNdEY9YXQ3jMEJsnLSEeqF+Ows5hVbUXfYG6baeiFd5wHe1nsQyTXHqwekpLFjJpFYbr7\nVFoUnR/wKZ5fXDTpvYVSUW7GaIaZ1uKe7oA3kEimvO2aORmHruVKoqKxGPZKqPOtaa7FbdfONXT2\nPqG0mRYGGQA2rV6AQDCCM+e88I7zcNgtaKp3YcPKOjgrLHhtfw/2JBmD5P3mTEOzqV7M8HiopOqP\npTzSfHiwRtbFVvboGNzcNCvtfWZjTMVFk95bKHNqyjH6UXaNUfa39WJfay9cGeYkyJVECdEoaIqS\nHEdGytYnGA/DG2Qpb/X6q2di68aGRNcmtZnQakKzpZJVrYSUp2ZiKFgtZsmJXC8PVqnm1AjIaVgn\nj0UlsjGm4qJJbSmgWga88p3R1CJ6zGoWvlI16qklUUYfRwTjYniDLOWtHjrVDxMD3Hn9fFTaONWe\nh5rQbKlkVUthYWnc3FQr6alt39stGRadU2PT3YMtRV1sNWImUoYCiI8ZMeFKiWyMafKiadPqq+AL\nhnH09CVICLBlxOBo9gY5FamFr5Ya9VIcR4TpjaENspK3+vaJfrx9oh+uCg5N9dVwKAgriKgJzZZS\nVrXYps9pZ7F4nlPWU1N6jv6g/vXIpYRWQ+GqtGgSQdmyvh6BYCTRtjMdyWFgIRrFy290orVjAOOB\n7HsiA7lp8yi18DV6jTqBABjcIKvxVofGeOxr7cWcGltag6wm6WbUx6NpoUuVEEmhiQH43n3NWDC7\nUvK+xPsJRaKGLknKBq2GQuvnGJrG/bctwoefDKcdrwDQtNCFrRsaEYpE8N1nDsGnkyHOJakL3+lQ\no04gAAY3yJl4q54RP9Y0z8Kps14MjwXBsfEfuFQ9bipSXtJsdzn6BidkPQjOTIEP56GLvAKxGDBv\nVsWkyYwPC+gfnsAfj5xH13kvvOMhOOysrMa3EUqStKLVUGRrYNIpcyXT3jMMPizgp/92rCSMMTB1\n4Wv0GnUCQcTQBpkzM1i60In9bX1p3xsMRRGJxPDTr103ZX8vXVKIlLcDKC8CHHYL+ocD6m4kh/yv\nl47iia9cCwB4dU/XFNlBAIqemBFKkrSi1VDoYWCSE8SGx4KyWuTD40F80jeGix79G5vUucsx7g9l\nXPYkh9POYcUi95SFr9Fr1AkEEUMbZACqFLpEzpzzAsCkyTD5/6USd5S8HXGPNhULyxSFMQaAi4N+\nbHuzEwxDY49E7WYyFpZBucWUKBszSkmSVrQaCj0MTHKCmMfrx893tEseLxYD/s/vTuagZQrgC4RR\nU2XF6MRo1seiKOCvNi9Hnds25TWj16gTCCKGNsh8WEDHJ17V7/eO85LeiVLijpK3I5/wIv2CUgvE\nXHKswwNGRVZWKCzgh/evAGtmSCkJtBsKPQ0MZ2ZQV2NXjATlKlQ94gshHNGnH7LTboG7qkz2dSPX\nqBMIIoY2yKM+Ht4MBDnkvBOlBJy71yyU9Xacdg7LG6rR3j2UmEQWz62SzZAt1I6y2j65Drtlkpxm\nPinWHslaDYVeBkbstX345CVtN5AlE0F9DPLiuVWKr2dTW1ysY4dASMXQBjnTEiQp70RNAo6ct7Ni\nkTuuH7xOmLQvfeact6jKokw0VNWjFiI8WOw9krUaCj3EK4RoFD954Wha2cxS4OCpfpw559VcWyxl\ndIt97BAIqRjaIKsVUlCSLlSTgJOJt8OZmaJrNpHOGLMmCrc0q2sLqDelUn9aCBGKbW92GsIYi2j5\nbpWMbqmMHQJBxNAGGUgODXpkvdJyiwl3r1kouWq2WVlwMt1jxBC3kqbutt1xIYbh8RAqrCbYyzn4\ng/pkpeaLB//0U7hm8Yy8n9fI9afZem98WEBb12AerjT/ZPLdyhldIRpDe7f08yn1sUMwLoaP2zA0\njXvWLoBZIWlJTOaS4rX9PZLGGJgawhW9JPFvr+zpwu6jFxJlQ2P+CHo9ExntaxcDrorClJWoiU6U\nKqIhGRrjEcMVQ7J9b7eqz4/6eIz41I0jCsCqxW7tF5tn1H63Sgu2452DsgvwUh87BONieIMMAH/3\nb63o98qXGUklcwnRKF7adQb726RDyxaWxqbVV8kekw8LOHQyff1zsUPTwGy3XdNn+bCAAa8ffFhb\n4o+YAyBFKdefpvP81TyvShuneqEUA/DRxTFw5gwbFxcItd+t4oLNx0Pubkt57BCMjeFD1uP+EHo9\nyvtszQ2uKeGr7Xu7FeUvg6EofP6wbJcej9cv61mXEjc3zdKUbKRHMo1R60/1EAZRejZS5XPFlESY\nDrXfbaWNU9Sgl6taKOWxQzA2hjfIFwZ8aQXwU19W20KxjFN4fFRpeCPp2LByTsaf0TOZxoj1p3op\nT6WqddmtZgjRGAJ8BLHCqrJmDEXFa5Ez+W45M4PysvRNYURoCljTUpjkRAJBDYY3yHU1NlnFLJET\nXUO4d62QWDV7vH5VHsWoj4fdykq+5q4qA2emwWegFFaM7GvrxQOfXqT6/dkmYqWWr4gJc3fdOB8X\nBnyoq7HJPvNSQcm7tVpMMDFXFnNKNbSpyYT/5z9P4kIOJDJzjdPO4a82L7/8m1HvufJhIaMEyVgM\nuO2aOaTkiVC0GN4g260sZrttiuUhYpgwuSWeGkKCvLHlzPFSqnRylMVOe/cQ+HWC6olSazhWLsx9\nz9oF2PHWWcPVkm5ZX4+OcyNTxuX5AR+27+1OlO0o3XeysS7jTLg4WHrGGACaG6sTkpmZiHhk2nvc\nWRGPPhChEEKxYniDDADfv78Fj/zqXfiC0hKCYpgwNdSaDtak/GO+79YGxAAcOtmX2E+2sAyuXVyD\nw6cvIZRtd/g8MDQWRNeFETTUVane19MSjpULc6caLaPUkkaEmKx319Y5CEGITsphSL5vKWM9b4Y9\nJ72J8wEFdXkHqYY0U+GfZQsdeG1/j+EWdwTjMC0M8u/e/kjWGAPxMKEQjar2jAGAM9OK2rvA5d61\nGxfh3rX18Hj9AEXBXVUGj9ePt9tLJwP7qe0nJjW6V5q8tCRiKYW55RLySr2WVMm7Gx4PytYYyxnr\ndEap0mrCqL842y8e7xpCNBrLaAHS0ujGptULsHiuQ1aKNpVgOIrDSXrfRlncEYyD4Q2ymgSt8wM+\nbHuzK6Pw1w1LZ6o2BmIDABGhBD2ZTCYvtYlYoscTikQzbtBR6n1wlby7qnIOXpk6WSVjLQdNAUvm\nu3D4dGH0rtORdgESjU1SthPH4jvtFxEMRcGa6LTRJlcFh06ZRjOlvrgjGAfDG2S1+0xnPhkGqzIJ\na06NDZ/fqH1Fvf946e4rq5m8UlsDipEB0bNODU867Cw4lpnSh1kJ1szAZpUuOSsFlCIJzY3VaO+W\nFrZQMtZyRGPA4dOXYJFRnCs0iguQsSCOd0oba/Fe1Gz9LJ7rwCEZT7rUF3cE42B4g5yuVlFEbekE\nZ6bxyOdXaN5zKnXBELWTlxCNyu7Xpe4Xq332yQRDAnYe+KikQ41KkQSGpjI21iLybTyLsxRvWb0T\nH5wdlrynShuLkSxVtWY5rdh0ywLZpi5EKIRQLBjeIJsYCpzZBEAfucpwJAqfPwSrUg2yDEI0iuf/\ncBqhSAnGrC+jdvLSojFsYRmUW0wYHudV1dGWeqhRqeOTFmMtIvfoQmEBKxqq0VpkGtg3L5sF1sRI\n5x00VKO9ZygrYZO+YT/+5v8ehrvKKnkcIhRCKBYMb5C37+1G37Bft+NV2ThNq2mjtMpTM3ml0xiW\nC0+GwgJ+eP8KgKLw9H8cT+s5GyXUKNUpKp2xPvOJV7bmmKIguaBx2C343IYG1QaZogAzQ+V8AfnM\n705h1SI31q+cjRNdQ1MXIExm1Q9SBENRnB/wYU6NDf5gxDAiMwRjYWiDrFZxKxMWz3NoWk2//EZp\ntsrjzDTCkWhGk5fSvv3IBI8qGyvZGMFht8B9uTnHikU1aSdhOW/dSHWmUsY6IsTVuOSQiy60NFYr\nfk7qOKFITFXSVDaM+kLYc6wXG1bV4adfu27Kd3fn9fOyNsgi/mAEj39pFQJ8xBDjg2AsDG2Qh8eC\numr4WlgGWzc2ZPQZIRrFtjc78fYJeV3sYuZ7W1tgs5gzmryUMoiddguaFjoldcKTve/kkO3QWFDy\nPKne+nRpSJ+pIIar4spiqm8o82hRvurlxS2I1AVIn46CJ97xIAJ8pOSjKgRjYmiDvPvo+Yw/oySz\neXPTLNlmEnKka1JR7Dz/Xx/ip1+9PqPPpKtFjochacWyqOSQbf/wBP545Dy6zo9gxMfLeuvTpSF9\nJoIYVTYWj39pFexWFkI0in2t+niauUBuC6LGUZZW/lYtJIGLUMwY1iDzYQHtPUMZf+6W5lpsXDUH\nb7x/Hie6BzHqC03ytDK9Br1D5vnm4qAf4/5QxvrRyklJ8vujyeFmE0NNydS+4eqZ+NzGxilJddlq\naJcSnJlBU331pNpcOcYmQgjwEditLF7d01XUi8NUY5kc8VBjjC0qSuea6qd2diMQigXDGuRMw3oJ\nKGBv6wUcOX0p8eP2BUKIXt6Yy6XWbrHScd6LVYtmZPQZJaMrkrw/KhVutlrMU2QzD57qR5nFNMXj\n1aOlYSmxYWWdKoMcr9dmwYcFHDypTtGqUKRuQWQqZXvTspmYCEQUBVA2rKzL6hoJhFxiWIOcqc6t\nyOFTl6assvlwDHuP9aLr/Cj8wbDq/Um1NdDFDpVF/apUUpIUUuFmue9OyuPVq6VhqeCssMClYnzH\n67XP4pbltRkJr+SbtS21kyJQmUSXLCyDm5bNRAxA53lpNS4gvpfurLBke6kEQs4wTqZLCuI+ZqYo\nTVrnB3wYGuMRw5X9ye17uxWvwWopXTUpkcY5VQDik+SA1w8+rO/EnmloX/R4k1H6vo1YZyqGrdXQ\n1jmIULg4daxFTAyNiBBLjK9MoktWzoRoNL5oVlr8GnEcEIyFYT1kQF2mbra0dXpwS9OsRLmOiJhd\nXaot8URmVZfBajFh2+5OCXH/q+Dzh7MuH8k0tC/n8arV0DYKasPW3vEgWLOpaKUzAWBf6wUcO3MJ\nI74wnBUcli10gTVT4MPpN4+9Pl5R39ulMQeEQMg3VCymRhMpN3g843k5j7jiVtPAnVOpZ51Kajek\nbbs7daudLCR/+9VrsP94n+S9WFgGfEjIuryIDwt47NnDqrcXNqyqU8ya1rMO2e22522cZoqfj+C7\nz7yT1si6Kjj89GvX47dvdWNviffnlsJhk9fCpgA88ZVrJjV3MTrFPGZLGb2eq9stPxYNG7JOhjMz\ncFVa0DCnUvY9DB2f6G9umqXpHMkhbCNkVwPxia7CysneSzAkqA7fK6EUbp5TY4OrwgKaiu8BblhV\nl9bTEfetjR6e/N3bPao83sVz42I2n7u1AWtaavNwZfmlubEargrpHAFnRVxshkAoBQwdsk5m+95u\n7GuVL/mIxYC7bpwPqyX+SA6e7E/sJ1tYBtVVFlwYSB9+buscxC3Law2RXd18WdlJ7b1kU16kFG6O\nCDHDKG/phdqsaYYGNt/acPn/adBUcTaY0IqFZUBTwPKGaknvn+wbE0qJaWGQ+bCA1o4BxfdEY8CF\nAR+WzHfi8xsX4Z619fCMBBAKR8CaTXBWWLDzwFm0dQ5ieDwoK084PBbE8Gig5LOr59TYsHVDAyJC\nTHW2ejblRUplUgwNQ5UspZJJiF18ry8YVpU1LUSB3x+Md8Xiw4JsK8NSJRgSsOdYL25dORsbVtVN\nm/wBgjFJa5ADgQC+//3vY2hoCDzP4xvf+AYWL16Mhx9+GIIgwO1248knnwTLsnj99dfx4osvgqZp\nbN68Gffee28+7kERIRrFS7s6VBnHuhpb4v9NDIW3T1ycksj0xIPXYHiMl21+QFHAz3ecBMeW7qq8\n3MLg+/e3gKFpMDRkVbdS0aO8SG2ZlBHIROoz9b2VNvVCLWLkYtTHZ93KsFg53jWEn37tOsW6dwKh\n2Em7h7xv3z4sXboUL7/8Mp5++mn87Gc/wy9+8Qts3boV27Ztw7x587Bjxw74/X4888wzeOGFF/DS\nSy/hxRdfxMjISD7uQZHte7tlG5On+9zuoxemlDntPPAR6tw2rFhUI/m5aCze/q6Yaz7TMREU8Pcv\ntSb+vWV9PTasqkvs5VpkFhskPJgZcmNMai8+9b1SzTnk8I4HMTwWxK73zsFgEesEYnQmeUGXixI9\nAiGXpPWQ77zzzsT/9/X1YcaMGThy5AieeOIJAMC6devw/PPP46qrrsKyZctgt8czyFasWIHW1las\nX78+R5eenkyTq8SQtRoZxnvWLkDHuRH0eny6aOwWGxc8E3jhjx/igU8vmhJOtlnZRPher/CgkTo0\nqSETqc9skwQddgt2H7tQENlMmgaiMnlnnJlGS6Mbhz+Yqqw1p8aWUXe0uCKZOW3UYbqNM0JpoXoP\n+b777kN/fz/+5V/+BV/+8pfBsvGQmcvlgsfjweDgIJxOZ+L9TqcTHo/yJOJwWGEy5e5H8XHfmOpS\nGpoCli+ZCY5lcOEj+YboQ2NBwMTgD4c+Lsl2ipnw9vE+2K0cvn738sTfROHBb39uJYKhCLxjPBwV\nHCystnQEQYji+d9/gMOn+uAZCcBdVYbrl87CV+66GgxzJYCjx7m0olSmoJW+wQkMj8tLfTKsGe7q\n8rTvVcOqJTU4+qFyDkWukDPGAHBjUy2+vaUl8f0PjgRQffn7/+KdS/DiHz7E4VN9GPAG0p4nGBLw\n/w6fA2tiJBuMWCxm0BSFd09ehGckCHeVBTcsq50yzoxCLsYsIffPVfXM9uqrr+LDDz/E9773PSSX\nLsuVMaspb/Z6M28FpwYhGsWre7rwTnuf6s/Mclnxwu9Poa3Tk9aIv/LH0/jgI3mJvlJBTQed/zr0\nMe64bi6snEnSuzABGB8NQGt1Xmq99oA3gNcPnIU/EMLWDY0Fb6mYq5pOISzAaZeX+hRC4cR5ld4r\nhYWlEQpH4bDH9cAPn+rD6ERY1+vPFs5MY9Oahei7NIZNN83HHdfOmTS2RkcDuOPaOVjVWA0hGsXb\nJ/pwosujmAvyx3c/AWeWHhNvHvlkkr6AZySI1w+chc/P4/6Ni3S/v0JC6pBzQz7qkNMa5FOnTsHl\ncmHWrFlYsmQJBEFAeXk5gsEgLBYLLl26hJqaGtTU1GBw8EoG58DAAJqbm7O+eC1s39uNPRkKIMyb\nWaFayKO9ZxijGezhFSsVVhNsVi6tWMoL//0hqmwcWjsGMDwegtPOYsWimqyVutSEbV/b32PIlorp\nWlQmP0+l90oRiwE//sq12NdamDC1GmKxGL791P5JgjqVNi5lSyS+CHPYWYQiUfgC6eU/5UR95P5+\n6GQ/7l1bT8LXhKIgrUE+evQoent78eijj2JwcBB+vx+rV6/Grl278JnPfAZvvPEGVq9ejeXLl+Ox\nxx7D2NgYGIZBa2srfvjDH+bjHibBhwW0athva+9RXw4y4guhysZKJtbo1bc1H4xMRPDtzS345Y4T\nip7H8a5BRIQrNzU8HsLuoxdw4MRFhMJRzV5rug5NHq/fcC0VxSiDzcoiFotNUYbjWBqxWAxCNDrp\nWd55/TzVBpkPRxHW2H40GTXtDNVSWW6e5KWHIvHxJC6wOs6NJBq3cCkSn7ksHwyGBHi8/mml5EUo\nXtIa5Pvuuw+PPvootm7dimAwiMcffxxLly7FI488gu3bt6O2thabNm2C2WzGd77zHTz44IOgKArf\n/OY3Ewle+URry8MJFavvZAK89PvXtMzGuuZa/HxHe8adpgrBW229uG9DI371u1Oy70k2xsmIhkSr\n15quQxMoyjAtFVND76lGR4QPRbHnWC8oipr0LM/1ZxYq+/3Bj7Mef3pWCowHlEPmyfkYedfbNmrq\nOaHkSGuQLRYL/umf/mnK33/zm99M+dvtt9+O22+/XZ8r00gZZ9LkpbIZalinvldsAXffrQ1gaDqj\nEGMhaev0YG2zNrnQqcfKzGtNF7Z1V5UZpqVianvJdEZHfJYmhsL2vd041pFZ1Od4zxAYOi4MUgwo\nJXflArWNNCwsA3dVWR6uiEBIj+HSCwN8RFPIWEtDiWSCIQEURSXCjJtWLwDHFv/jHfOH8U/bT+hy\nLKm2iOlIrXFO1qs2SktFLWVL4rMUDblXQ5Z1sRjjQmC1mFFZnr716U3LZmY9jnLVlpQw/TCcdGal\njYOzQLKVyR6izx8CX6St7lJRSpYxMxQo6sqenxJavFYlyUzAGC0VtWyjOOwWlHEmQzQpKQTecV5W\n3hYAqmwsVi2uyWocFboCgGA8DGeQOTOD5kZ3QdrMDY3FFZFmucrBloj3lg7WTOO6T83AXoXGHCKZ\neq2pZVRS+8HpDHYpoLRXLkdLmsYeFID/8dmlqJ9diR/8+l3JCE8pJRhqRS7xzGnnEIvFJBfmVTYW\nT3zlWtit6uVHpUjdhjBKBQChcBhyGVfIFI3dx+I/yNfe6ingVejHRFCAEI1dDitzoKh4f905NTY4\n7VxGbRFFhGgUL73RgR/++jB+8OvDeOzZw9i2uxOCwkZjKbdU5MwMljdUZ/SZiCDAZjXDqdBWcNkC\nF35/6CPZ7Zbay8IipUh1pSXte+bU2HDTspmSr7U0umXlbVctrsnaGKcr2SPha4IWDOch82EBx7sK\n19GmvXsI4zeHcOZc6QuHiBzvGsTPvn7jFC81nQyh1OtCNIqfvHB0UlbtdPAsMl0kvtXWBxMjn/S2\neG4VQmEBbx+Xj1x8b2sL/vcrx0tSUW7x/Coc/dCjmOntD0bw2VsWgqIoxS0N8bUqG4fF8xzYtPqq\nrCU005XslVIFAKF4MJxB1lr2pBfe8SAuDPgM0Q9ZZHQinJhgkicZuTCz0t7atjc7ZQ1Ea4cHtyyv\nTWS9lmqIOhWti8S2zkE88eC1if/3jgcvb4XEcPBUP059NKSYuOUZCeDxL63Ctjc78f6ZAVXCGsXC\nhx+N4LpPzcB+hQXH0Fjc8CltaWzd0IhNqxfglTc78eEnwzh0qh/HOgZAUfFMd1GYZNPqBfD5Q6rH\nW7qSvVKqACAUD4YzyFr26/TEYbegrsZW0GvQG1uZKTHBqPEs5PbWBCGKNgXDNDzO40fPvXe5dWVs\n0oRZyokyWheJw+NB+PyhhMF5aVfHpM5l6eQwfROh+B78xkb4AiG8f6Z0eiEPjQVxvCt9Qtvuo+fx\nwG2LFdt2/ufbPTiY9NySQ/zi2Hyn/SL4kHqRm0yU1ggEtRjOIHNmBlaLOWNjKCbAqK1flGN5gwt2\nK1sydchqWLrACRNDYdvuzrQZpYp7a12DadsGprauzDScXYzdfLQuEqvKuUmeVkeG2yC2chZ8WMBr\n+3tKyhiLqNHfbu8ZwoWBcbhl8gv4sIBDJ9Nr2ou/+UzGmxEqAAjFheEMMh8WMBHIvORJzEa97uoZ\noCka+1q1ZWmLe4Vb1tcjHIkqhtxKheb6atUZpUre4KgvBIeNgzfDWmUgvehIMZegcGYGTfXVGY+p\n5iRPS4uX/XcvHoOzgsNEsLgaS+jJ0BiPx59/Hw6bGSsXz5jyfXu8fk0LbDUiN0aoACAUF6UZA1Rg\n1MfDm0UN8qkeL+5esxCz3doSMlo7BjHuj4cK77hurubrKCb8gbDqjNJKG3c55DwVjmWwrN4p+Vo6\n0omOiAuGoTEeMVxZMGzf263pfHqzYWVd+jclUVdTjq0bGhL/Fr3sTBCfQ96lKDVAU3EFS4fGvVev\nL4zdRy/g33d3Jv7GhwUMjwW1HS8DkZtSrgAgFBeGM8haJq5khseCeOXNTvR6praGrHFYUGlTLpfw\n+nj86Pn3sG13J2xWM1xZXEuxwEdiaTNKJyNf/LquJTPDJKKUKFMKJSjOCktGY2HRnKpJnp6SapkR\niMWA725pxo+/ck1Wv5m32y5iwOvHc//vNB579jCe3nFS03FIYhahEBjOIGc7cVEUcOrjYcnXBCGG\nxXMdaY8x4ot3Q9r2ZqoYRbgAACAASURBVBea6jOrPy1Grppph8MuvRCpsk3e5xz1yXtkfEgAc7mO\nOVOUEmWGx4Ky+7Na5DxzQabj8njX0JSFxBWZUeMZCmeFBQtmVybyL7QSjQHf//VhHDzVn1VSJUnM\nIhQCwxlkANiwcrbmz0ZjkO11PDTG48jpS7CwDBgVT+7QqX6c6PJglrO06xHrZthQXiZtkMvLzJMm\nrkobJ2swKm0sKm1cRhOuGtGR3UfPy75WTJ7OlvX1WNdSC1pFUbLUQkLcs2xa6Mr43MXezyjZAIoL\nD1tZ/lJcODOtSeSGQNATwyV1AUD3hbGcHl/MAp7ltCIQiihmDsel+0JT+t6WEq+91SObKDcRCIEP\nC4nJVKkcZMQXwk9eeB/LG6qxtmUW3mpTzn793n3NWDC7UtFT4dP0/W2qdxWNp8PQNB64bTFAUWkT\nvOQWEnxYwInuzDOmzWYKoXBx6mjetGzGJAMoLjxuu2YOvvfP7+blGso4Ex59YLlstjaBkA8M5SGL\nXVe0JmRlSigSxaMPrERVmn1lAKBKuOdqa6dHtlnH8HhoiieX3MEplaExHnuP9cLEMLilSb7to+ty\nCBOAYieddNnHmSZT5YOtGxrShp7lQqajPl5T45RiNcYAcNu18yQz4V2VZahz6yf/qfQLFBfVxBgT\nCokhPOTUkpeqPIUoveNBCNEYVi2uSVtzHAwJaK53ob1nqOQE/5XqQWkq7l0kI3o4d904Hz9+/n3J\nMidRheqj/nFJ5a7lDS68tr8nbRmTUo2v085BiMYmefDFgPh8BCGKfW1Ty+Lm1NhkQ6aVNg5V5SxG\nJvLfzSxnKLRleuyLK/Hj37yH/iFt2dLJUDQQUwhS/XxHe9GUyhGmJ4YYdaklL1rqXLXgsMcTmkSP\nkDPLP06aAo53l54xTkc0Fu9BLUWAj2BE5rvwXlahevxLq7CupRZVNhYUruzhUYCqMialZCk/H8GP\nnntPVfOKfKMUavcHI4gI0gOFMzNoWWSsbGulPX7WZMKYT5866nRff7GVyhGmHyXvIWtp/q4Xi+c6\nEp7X3WsWorVjAHxY2nMxmiEWcaRkWSejRu9X3FfdvP6KwhYAPPbsYcljvtPeh02rr4KVu9J8PlUx\niTXHW/KJe/3F2LxCKdQ+NBZE/7Af82bYp7wmRKOJml2lfr+lRICPTOq+lKy2NjwagJ/Xt2yNM9Pg\nzAzG/NKGXo0oCIGQC0reQy5UMwkLy+BzGycrVGUjSFLMKNVeNyuUhyh5r6l7pMniCkrfaTAkYNub\nXZP+JoaAH//SKvzPu5umhNBFiqUmGUhfL/9Pr7RJ/v3VPV3Yc6zXMMbYVXFlQSdEo9i2uxOPPXsY\n3//1Yfz1Lw/gR785qvs5w5EovvInS2T3lIulVI4w/Sh5D1nJC3NVWDB3RjnauuSzcLVyc9MsWJMm\n/kI3tcglcmVgc2psuHvNQgx4/bKygVr0fittHBwVnKxRPvOJd9K+cHIOgdLzL6a2eJyZweL5Dhxs\n75d83ReMYGg0AFdlWeJvfFjAwZPS70+HqNVebDQtvJIFnyrPmiuFMdbMYO4MO+nWRCg6St4gK5XZ\nNNW7cPOyWZoNsr3MhPFAZMpkxtBxIyBEo4nkD6XrMBpVNhbNDS7QNI0fPXdEMelKi94vZ2bAmeTf\n4x3nJxnW1IlcjmKbaBfNqZI1yADQcW4ENy67YpA9IwHF/sBSOGwslsx3wmyisP94+iYL+eZT852J\nqEW+tp6CIQG/P/QxFs91TOoCJUJEQQiFouQNMiDlhXGwWsw40eXR3CQCAMwmGrOcVvQNT5bRFKLA\nvtaLCWOTfB3+QBiHPrik+ZzFTpWNxRNfuRa/P/SxqmYTIkrt8VLhwwL4sHzvXtZMT2oHqXYiL7aJ\ndkka1bcPz3tx3dUzEBFiGPXxCCk8k1Sa6p2wW1icOefFu6f64bCzKDNTCBRZ+dMzvzsFC0ujudGd\n0dZTfFGs/bz723oT3d0ACqGwQLo1EQqOIQxyqhe2671zkuUkmSKKesjR2uGZlPzB0DRMCpnWRmB0\nIgTPaEBROzrbhJhMam3T5RBQFOAs0onWZlWuXz/Y3o9z/T74g+FEFIKm02cLA0BlOYsDJ654f1pq\nl/NFMBTF4VOXMhLPycYYA1ciXmJY/MalM/HAbYuKasFGmH4YwiCLcGYGlTZOUblJT5JDp/GElC68\nc6L4woJ6EosBv9zRLlubrMc+baWNQ5WNlVVAC0WiiXOkq0P+q83L4a4qK8qJ1uOd2sAkleQabbX5\nCTaLCac/yqx3cjEQjhSuLK3j3EjBzk0giBjOnctn1rXDzqGMM2HA68e23V3Y19pblIkzeqMkFKLH\nPi1nZtDSIN+Uw5l0DqVM7hWL3Khz24rSGAOIu+85gKGpkkwuLORvR1xIimp/xZKNT5heGMpDBvKb\n7VxeZsZPXngfQ2O8qoYB0wG99mm33FqP988MwBeYum+aeg4tmdy5JrmWVu55lMn0jc6WUX8YFJSa\nYBJSqbJx2PX+ebR3DyomKRIIucRwBjkf2c4MDcxylU8KJ04Hz1gKh43D6ASftRFMNWA73joraYyT\nZSWTP5NpJneuSJVxVZrYewcncnYd03Q4aqaMYyYlgBajmAzB+BjOIANXPKZjHR54x/X3lO1lZlm5\nyOmEq8KCx7+0CgE+otkIShmwpoUuRVlJPixg5wFpo1foGuPUEiyliV1JahUA8XLzSO+g9H4+Ue0i\n5BNDx2LCGZSJZMLIRLgg6mDFRktjNexWNqGwpYVUHfKhMR772i7Kbjl4x4PY9maXKp3rfKNUgiWl\nEibXY1qEGOPCQ1S7CPnEkAb5lT3xCdsXzE1ihsPGglXwbigKqloyliqV5WyiiXs2STBKBkxuT95s\nonHmk2HJ1wotjamUUDg8HsTZ3tFJ11dZrjxGnHb9xlA5Rzw8LRSbmAzB2BguZM2HBRw6mdvSo+aG\narz7gbTCEmem8egXVqGynMUj/3JIUv6PZSiEZLr5lALfumcZ5s2wq94rlUPJgMntyfPhqGwDj0JL\nYyolFFIAnnz1OFxJzymd59Uwx4Ejp/URmYkaRfw6zxSbmAzB2BjOQ/Z4/TnTwKUpYE3zLGxYNUf2\nHKFwFKyJht3K4sZlsyTfc+3SGTm5vnzAmSn4JkJ46Y2OrMPGSg0WHDb2sorSVOS850J7M5yZgdVi\nlnxNXGBMek5pyp5uv25O2raeagnk6DdhVGgKWLdidtGJyRCMjeEMci4dz2gMOHV2GLuPXZANJzor\nrhiFz93agA2r6uC0c6AQF6rYsKoOX7xtMZgSffJ8OIand5zE2zK6yKlh4+SQdmp4W6mGePE8h+yi\nR857LrQ3w4cFTATUKWK1dQ6ispyFRab0ycIycFfFdaxj0zWFv4Csaa7FA59eREqeCHnFcCHrt09k\nL5mpxNAYj32tvbJeWrJRkGusMOLjs5b+yzdquwWJYWNXpWVSSJtjGQAxBEPRSWFbuRriSAYPyFVR\n+LpjILMWnN7xIAJ8BDctm4k9x6bqrd+0bCZ2HvhoWjQrKQaoyyntTpVjSarOPPlvAApegkcoPQxl\nkPmwgPbuwbycK9U4WVgGNzfNkqyRTW2s8MIfPszLNepFuYVBQGWTeDFsPLWV3pXPp5YCpS5aAOCx\nZw+rOl+VjcXjX1o1qcF9ochElMZht8BmNSOGeIMDMRrAmWmsXFSDP7lhPv7u3/TvBUyQ57v3NWPB\n7EpFAypVpre8oRoUgONdgxga4xMNK/iQQARGCBlhKIM86uMLJhlo5Uy4e81CAMC23Z1T6mo3rJoD\nZ4UFAHC2b6wg16iVOrcNHedHVb3XajFBiMZUdWBKrvFMXrQMeP2qy8rGJkII8JGiMMiZiNK0NFZj\n54GPsDfFO+bDURw61Y/THw/Lanlrpc5djgue3ImRlDIOGwu7VXr/PxmpOvPU7zB5q4UIjBAywVBL\nNrEpQSEQG03I1dU++uwRPPbsYby8q0NSgaqYufO6earfe37Ah1fe7FRlUOVqPJWSvVKpsnFFVZay\nZX09Nqyqg6vCApoCXBUc5tTY4LRzl/9twYZVddi0egFaOwZkj6O3MZ5TY8ODf7pE12MaieHxEB5/\n/n08+q/vYtvuTggSLbUyafWZSqFL8gilgaE8ZLEpgR6tFzOl0saijDMp/mCHxngcPNU/KURZCryy\nrwsMTUFQmVx05pwXDjubtuWfGN5ODe9n4mkunhfvKTzg9RfFfp1c3kDqPQ54/XltiegPRsCr3HYo\nBmY6regfTt8NS2+Gx0OyHm02jWsKXZJHKA0MZZABYOvGRnT3jk3SmdaDdDKGyxtcCPARVT/YUsuZ\n7R8KIJPeGd5xHtdfPROHTknXaossb3Dhtf09krXMV5K9PLLbEJyZBmum8dizh4uuIUBq3kDqv8s4\n9T89pVaUahkeC+KP753L6hj5pH/YDwtLIxaD6h7JeiIlmZlN45pCl+QRSgNDhayBuIfy+JdWYU1z\nLSrK0+8JqSUGoKXeJft694Ux2KxmVaFWPhRFmQ61pflEzf6aiMNuwT1rF8iW9NAUsK6lFhQgW8ss\neppPPHgdZjmlvYoahxVvXZbZLCYJTTWolWN0VVjw0J8vzfp8HMvgeHd++oTrRTAUBR+OZrQY1Aup\ndoxKZXrpKHRJHqE0MJyHLGZBnjo7hLGJMMo5ChO8Pj5pvzcg+1qvZwKvvdWjOtQaKMCqXys0FQ8N\nv/eh/J5nMi2N1QiFo+BD0iHSGIB1K+rw89+ekHw92TvZeeAs+iRCl3XucviD0n2ZS6IhgMpeyEsX\nOjDbbYdTxRaAMqUWl7lCIa7cVmbCfx85h1NnhyZFX+5ZuwAA8E5736TKgWTEhWgoLBRFK1BC6WA4\ng5yaBamXMQaAviHlPa3WrkH83deuBxA3CkNjQd3OXUhmu234wu2L0d4zKLn3TVPxSdOZNPlEhJhs\neM9ptwCxmGx4X/ROKm2c7J78RCACr4yXWYz7dak1qkA85J4uHBsKR8GZGZSXZWeQSylnoRgY80ew\n//iVXBQx+iJEY9i8rh5tnR5Jg+ywcfjxV64Ba2ZIHTIhYwxlkLPJgtSDUV8IPn8okdQzPBbE7w9+\nhMOn1XmWxUiduxyPfmEFWJMJNzfVSnr/1y6ZgTtvmAd3VVmSKApkowUtjdVwO6yyBlus0X1pV4fs\nft3IBC+7t1pM+3WpdavJAily0qDJdHwygnF/SDYaoBa1wi4EZfa39cIflM8VGZ3gE2V4xbQgJJQG\npbWRmYZssiD1wGY1JwwBZ2Ywy1WOL96xRNeuPfnmoT9fBtYUX7ellvRYWAYWlsbh05fw89+ewGv7\neyaVi0wtAbIkukQp7ce1NFbjtf09iklhTrsFLQ3Vsp8HoLkLlZ6klsEFQ0LCWxX/q5R/NuLj8cml\n8azr64kx1odoDDhy+tLlhdVUimkxSCg9DOUh26wsuAKWFC1b6JwSnuLMDFYsqilZCUQ26X4iQgwb\nVtbhrhvnY/ve7kkGU0oAQa4ESCQ5k3p4nIfTHt+n27R6Af7nz99WvC4xNM4w9CTZzeUNLsRisaLI\nvFYbsam0mjERFBCKTB23DrsFR04rZ6urgQJQW12OvqEJYpyToChASyOsmMyHSPIWIRsMZZB3Hjhb\n0L2yYx8OoJwzT5n8t6yvhyBEsf/4xZKbDHe81YMv37l4ilzgRAYJVaklP6nEYjHEYlcmuYuDPkjo\nMiS4dklN4hmnGvwdb3VP0oYWFwqxWAyf37gow7vPDrURm9GJMG64eiYOSkQEmupdigIiaokB6B0s\nHZWuFQ3V6B+ewMUh+UTKbKEpYPXyWVjbMhtRIYZnfndS9T49H47ipqUzcebcyCQNdpK8RcgGwxhk\nvfaPs9lrC0VikqICDE3jgdsWAxSFfa1TGwkUM2c+8WLb7q5J160UPk1OqJIS4E8mNQFPFGW4lEYQ\nYtVi96QFj2jw+bCAgyelvcmDJ/txz9r6vHovautWHXYLPrexEWUW0yRvv7nBhUAwgtGJ7PaPkymF\nveTKcjMq7Rxau3KrSx+NAfuP98FsYrB1Q2NGkSwKwOb19SR5i6ArqgzyP/7jP+LYsWOIRCL4y7/8\nSyxbtgwPP/wwBEGA2+3Gk08+CZZl8frrr+PFF18ETdPYvHkz7r333lxffwK99o9nuspAU/QkzV+G\npuCusqB/WN1qXa7sZuuGBjA0lZh0zab0WbaFxjvO43in+okxnpDFTtHzTg0bKy2gzl0aVzyHs8Ii\naew9IwHZUpRgSIBnJIA6ty3tPUhlRGtBreJYS2M1rJxpirf/2v4eHPrgUlbXkEqxG2MAKC8z43hn\n/hIhxd+r6N0qlTSJxABcGPBhwexKkrxF0I20Bvnw4cPo6urC9u3b4fV68dnPfhY33HADtm7dijvu\nuANPPfUUduzYgU2bNuGZZ57Bjh07YDabcc8992Djxo2oqqrKx31kpaKTzPAYPyXsLURjAKWuTAWQ\nL7tJDrF6RgJ4+j+Ogw/nTz5RC5U2FiMqRSwAsWnC2SkC/KmRA6UF1Jg/LOvJUVTc223vHpxi7NNu\nBqZ5XaqTz03LZ+OuG+Zq3n9ObS8p7snHOwFNDXMme/u5qBhwVXCwcCb0FnGTiYuD+ZXMHB678nvd\ntPoqHFDRwpUC8OSrxye1Ei20Ohyh9ElrkK+55ho0NTUBACoqKhAIBHDkyBE88cQTAIB169bh+eef\nx1VXXYVly5bBbrcDAFasWIHW1lasX78+h5d/hUz0j5WQ24PuHwrAxKgTc0iXacmZGbAmWnXv3EIS\njgiyCx0Ly8DKmTDi4xN7aJtWX4X/3967x7dRnnnfP81IM7Is2ZJsOT7m6EMgiRM7AXKEJDiE8sLT\n7EIJuKRQaLvPW3i33Q9toZDl1NKW0qcvSw9bShcoyaaEht08pcuzISEHAkkgiR07SbEdO5CDE8ey\nLR9kSSNppOcPZWRJnhmNpNHR8/18dksseXR7dM993fd1+F1P/9unvNcK9RwU6mlBvWuTnsb8OSZ8\neGKy+7msSDfJfc5953feNEdQJ1xLkbBEOcnwdfL5y8GzcDjdcXfq4YtzA5ikcz04Eq7FnayKAZ1W\nI7usbLZDaYjg97Jt9xlJm25ua6d0c1KQk6gGmSRJ6HSBhWzHjh248cYb8dFHH4GiAqU8RUVFsFqt\nGBgYgNlsDv6e2WyG1Zq6mmDW54PP7w9bkCm1Cn4/4GHl8dN5JV6nnifbOpJCPR13hmcqGXcF1IaA\nycZhZX3ZpAxqsdaJoZ4DMbGL/DwNNt0yF5RajeMdV2CzT5yY+wTEWThjv3xB2aR2eACwfEGp6Hci\ndiKVQ/krMrGtxKQD6/MJturU52mEhVUKaPh9Ptjs0mPLJAGsXFiOkynqF55NMB4f3t57BneunoOO\nc0NxXSMr1OFCiJbfoZAeJCd17dmzBzt27MBrr72GW265JfhzofR/oZ+HYjLpoFbLMxle3Xly0kLs\n9qbH2n15dTUsFoPoe0bsTFbE8wBg3OXBbctn4thnVzAw7ESxMQ9L55fhwTvmgSQJVIa811CYhyKj\nFgPDk1XKigq1mDOzCFpKDZfbK1gjzHhYGE35+M69i/Hrt1ux65PzwXsldM9sYy6QlAb/uLEReh2N\nwycvYWDYhWKjFssWlAfHKsTlgXEMjQlvJEhKA0txvuDvx8OrO09OOpHva72Efa2XYDHloVDPb5Dr\npptRkK/Brk+kN4tgfYHNtU3mto65wr7WSyASuD+2MRe8KgKkioCpgIaWSm++rND6w7I+vPbuaRw5\ndRnWYScsEc+ygjjR1vVEkTRrDh48iN/97nf4wx/+AIPBAJ1OB5fLBa1WiytXrqCkpAQlJSUYGJjY\nfff392PRokWi17XZ5IkVMR4WH7dlTvbywKAdBQLdfLidqdBJLxMZHnPjxgWluGPZjLBd9dAQfxxy\nWNCwMej5YhCFehojdgZWAW3wgWEnus4OYM/xizjQKu17NRm0YN0eDA35sGHFTHzp+ipJY+VgPSzM\nBmHlMNbtgdUqnmwWC9HmrNXmhNXmRIUlHy6GxeCoCyQRMKyHT10GEUfHhbaufhjzNTGdrKcSrZ1X\nQMWZaElpSDzz+0Npr30HAkZDaK5u29MVtgnstzkTDstMFcTua6zXESLqbBkbG8PPf/5zvPLKK8EE\nreXLl2PXrl0AgPfffx+rVq3CwoULcfLkSYyOjmJ8fBwtLS1YsmRJwoOXQroVuiKhNJONMeee3Pzq\nEfzwlSP4467ONIwsPkwGOmjYOHezEIMjTkHXvpf14/FXjmDzq0ew6+gFmAQUzCgNid3HLmBfS69k\nL0KkIIOUsYYSTTlMbrfeiJ2RlIDYax3HvFlGlJl1YEPsRDzelcFRBg5X5vdEXhxnR6VEGRpzx131\n4HKzGd91LFpYJt2qdgoSTsjvvfcebDYbvvvd7wZ/9rOf/QybN2/G9u3bUV5ejg0bNkCj0eDRRx/F\nQw89BJVKhYcffjiY4JVs5MqwlguLMS/s34yHxdZdnWHCDzaBU2QmMneGSbJB6jw/HPU9g6MM9rX0\noqpEzxtDdrlZHJFY7mM20Giss8giyBCZEW0yaLFiYTnuWDY94WtHkkerJdcEf9iWuFIXB8OjBpZJ\nFOZrsLi2GMfTqEkvBeJq/ofJQGOc8YDhSSRs7bJmVFxZ7OCSiQ1ZpiJRDfLGjRuxcePGST9//fXX\nJ/3s1ltvxa233irPyGJArgxruXAyXtAaMlhG09LZn2DrvPRBEio0r6vhfW3M4cbFfjsqS/Qw6AKn\n3brp0svcHC4PaI0KjGeyVYpWBwoESk++e/dCWIx5GBxxJZygwpcRXVlulNVVzeFkvFmTQ5BKRsY9\neGtfZp0s+Wiss2DNogpoKRI/evM473sGR5mMMnJiBxdFgzszyBmlro1rq+FweUUbEqSKre934pG/\nr59URpONaNTEpDiY2+vF82+2oNdqh88fqA2uKM7H5vsXo6gwD/o8NexOb9RrD40xiWWZq4BX/nIa\nTpcHtjG3bLG7aFKfclCop2HSK/FcPkZlVCZLFu3dAzjeYRUMu3DkCeSSpAOxg4uiwZ0Z5ExaXUCe\nsg5FBenf5X3RN4bBESeOdWRv20UOxh1IQgvlR388jgv99uAJz+8HLlrH8b3fHALr8+GF/3cZ9HnR\nFyKzgRbshKUV6KYTit8fiLEOjbkzOnbHB60hodelf64qxIfb64cfiOr5sg47MqLrGIdYBzaF9JM5\n2zcZoDUkFtYU89ahppKhUQY/+uNxjDqy000dirlgwpXF+nz44//pEFR5sju92PJ+Jx649Rq8/J0b\nMTjixGfnbXjvyDn08TQJ4JKo+HbsyxeUglCp8MGxi4j1EJ0NNaGMh024x7FC5vPr/ziFEbt83ptE\nidaBTSG95JRBBgJxxUwgF4wxMOHKYn0+PPfGsagqTye6BsHczILWkCgqzMP5Kxd4jXFViT64K3d7\nWZzoGsSow42iEDlJh8uLD+Jw+aczQUWq4ELSlLjoQI23Ep/ODIav1jVnmqJXKsIyCrGTUwaZ8bA4\nkeQOMVOJNQ3lQaO5bXeXJMnFUYc7rNuTUJmFw+WFk/HgxT+1hcWi82gSd62eDZIgcLHfHvPpGEhP\nggqfDrbYiahQT4MS0EZXqQBjPo1hOwNVDN2ZCBXgYKLH7hXSRzZ4bxTSR87EkIHMq0fOdtZfH2iq\nwHhYtErc6BSFuLijlVn8dEsrbyz6+TdbAACVJfq4PB4La4pSvuBxCXyx1KJ6Wf4SJL8fsNkD14nl\npKvKFPeQTGRrrwaDTvicw3lvFBT4yNIpPxnW58OuT88nbVGS0lhimlkLLZUbt5QkAL1OAyBgWIcl\nSgqGZmtyZRZ8GA00+gT6Hvda7Ri2M3j30BcgRCSpSIHXPvvcltKTYjyCC1abAwL2OG7ErqdCIImO\nUmfP/PSloWRaS5HBZKfVjeUoNeVF/6UI7A4vhFQolfIiBTGy5+mMwva93djXeilpsTMpjSVcDJvx\nzSKkwvqAd/b3ALhqWKOUdwDAmsaKSa0EhdSvZpYaBN3RPj+wdVcn9hy7GGh9KfQ+gdcuDznw6K8P\nYtueLrApWNWlCC5MIoXH2aICGs8+eB2e/9ZSzI2hTnwqkkcReObr1+HH37wBaoJAn4C8qxh+CG+O\nsrW8iPGwGZUtnqvkRAw5Wb1jY2Vk3JMxSWVy0HpmAHevZUU7M3HotWpsuqUOQHhiE5/6VUNtMW5b\nOgMnzgzwbqAIFfBF32jU8YntfRiPP2VJNPEILliMedBSpCQBlERpqLWgsiSgmnfH8ploPxtfR6Op\ngM3uCfaslmNNMekpjIy7g/M+28qLYs2NUEiMnDDImRI7LtBpwHjYuPVwM41huxtne0dQWaLHuFPc\nZe1gvBi2u/DekfO8Dy9fmUWFRc+bKDbNpBN0Z8dKKpJo4hFcoDUkViwoxQcyl+hdP7cEPZdGwzY/\noUagclpq5GyzGZJQybamNK+rRVWJPmvLi/h6hGdStniukRNbHLFYZSrR6zSCiTrZyotvncD3f3so\nqgBCwM3cxZvYtG13F2+zhye/1oiqEn2wcxGhCpRDPb6pUdL3KWWDnqokmsmCCzRWzC/FhlWzBX/n\nnptrUFWil3UcnRdsmDfLiO/evRBPPbAEzU21YScZWkPiumvS07whW+i3OZFHq1Gojx6miYbJQIXN\n+2xy/SajGUU2/f3pICdOyJmiZT3u8MieqJMJuCU2JOju5W8sceDEJUClwp03zYHd4Q6eFkiCQN10\nI8adbgyNuWHUU6ibbgRJEKitMuJwlAYTfh9QZtbhsshpOtJlLHdj9tDrNTfVYsOqWdi2+ww6zg3h\n0Kk+dJy3Cbr4vKxfdnGQkXEPPmzrw4dtfSgScC/ed0sdjn6W/hBPJqICcPh0H07/1SY5kVEMizFQ\n65uNrl85m1Fk49+fDnLCIAPAhlWzcLDtUlrdxSOO+BZXY74aJEmmvVuVCuJx2WiMOvgzm31+YF9L\nLw6fugzG7Qs+uCTp8wAAIABJREFUjH6/P8xlOzTmxp5jF/FR+yW4eLrnRGIu0OLJ+xfjnf09+PhU\nH9w83z3X7ELuBUHoen6/P0xPXczFl+xQi9Bn7zz4edI+M9vxA/jopHx6+Bf77ZhdUYh3DvRknetX\nzmYUiutbGjljkO0OD++CnGootQpu72SzplEDRoMWVptr0mvD414A6Rd0KMynMDyePIUxzshyD6OQ\nXrUUYwwE4rM6WoNN6+fiztVzrp5MbbCNMaCvXvvwqT50nrdBp9WExasTXRCEFhihv4kvlp2qtqGh\nn814WBzvkNbaUiFxXnzrBIoKaIwLeEIyWShErmYU0Vzfmfr3p4Oc8RXodRToDKgBNhfw1y3euLAC\nP/nmUqxpKIdRhthUMkimMeYj3gzjAh01qcRKR2vwjduvxfPfWorl80vhcrNwudlgLFtIZSyeWJjY\nAiP0N/HFssXKwuQk9LNH7IygJ0MhOQyOMoKbTL55kUlxVjmaUcRVFjhFyZkT8s6DZyWfrJJJvy0Q\nz+Saz5sNFBrrSoKu0U3r5+LutSxe/6+/4dOOzIrjaUgVPBLqrdOJShWQ52zvHgBJqHhdzh3nbZKv\nF4/udTyuZiEX38a11WB9frR2WWWJWUb77EI9DaOeStpnKcRG6HeTiXFWOZpRKH2YpZP+I6UMZEod\nMjAhdcj974I5xZMyXQGg4zx/AlQ6yXRjDCAovCIkTRmrsYxnQRDL6hdyWfO5+LgFuL17IKkGMvSz\naQ2JJXNLkvZZCrER+t3EI7+aKviqJGL5XSFPULYKpSSLnDDII3Ym7QlRQhxsu4QtuzrCFKNG7AzG\n4kwAm6oIKWhGupxjLYGLZ0EQW2CWLyjF2sUVYYZZS5Hw+/2TVMNCF2A5oNQEblxUFtW9uHFtNW5c\nWCbLZypIR0uRMBto3u9GPM5qxcX+sYxwYccD42GxpqECaxor0t6HOZPCAXzkhMs6j1YHXcSZhs8P\n7Gu9BJIkgslDqUrmSRWUWgWCIGRVnVIhkEVdP8eMhloLfrm9jfd9kS5nsUSUqhI9HC6voGhGLAgp\nkG1cW43te7vD7oXLzeKD471QqVTBOZAMrw7r8+Pem2uBmyHqXgyETurQeWEIV4ZyYw7KgVBCplys\nrC/DnTfNgXXYCfj9sJh0Qc+ZmGdncJTBU68dFSxjSxfRSgj5XPD1c4rQtKQK5gJtSk/GmRgO4CMn\nDLKTyfz+r6HZhLSGxKKaYtlVmtJFYBFjccM1JejuHZFlo3H/rXW4YV5pMDO4SGADY9TTk1zOYsbS\ny/plqUMWiq05GA8+ar/M+zuhcyAZJU+sz4++oXHMmFaAQj0t+ndu39utGOMQTHoa//zAYvx5X0/U\n+vd4WdNQji27OtFxbgi2MXeYUZCySc+EUiHGw2Jo1IU9xy6gvWdQ1LjxVSJEHk5SRbaUXeWEQRbq\n+iMX+jw17M7EMlNDT3KMh4XTlXuZrsc6+7FyYTkOtF5K+FrzZpnD4p5Cp14H48U7B3rCFgOxRBSS\ngKyN2SMbvW/bfSZqpnWJSYdCPQ2TQVwfPB7e++Q8CnWU6Ekgk3IuMoXGumIY9Vpo6eQtic++dhTu\nkDyNSKNQX12MfS3RN+npKBUKPWFGbhr4jFsmlTpl0liikTln9QQQU2qSg0SNMRCo8dXrNNi2pwtP\n/v4wDiVpF55OWB8w7vSgaUllwm0oI7s8ceUXkUlTLjcrmPiSSCJKPDgYL4539gu+btRTwdM817BD\nbtrPDERNDMoU7fdMwsF4wXhYtHdL6/sdD26BpEkuD6JpcaWk6wyNumC1Jb7mxRJPlZLvEJrPkUml\nTpk0lmjkhEE+lgVCBx7Wh50HP8eeYxdlPxVlEsc7rXB7WeQJZBtLoahgshuaJAjcedMc5Gv5TzDx\nauvKyZ92d4kqxdEadZimsdyymYHr8n/+R+2Xgz2iM0X7PZNo7x6EddiZlo0KZxTMBVoUSfhe/AD+\nZUd73O1FWZ8P2/Z0YfOrR/DDV45g86tHRK8l1aMSatzE5liqS50yaSzRyHqDnOxdrVw4XN4poZDk\n9wMfnrgMmz1+Y7Owhj/zWcpOd8zhxmdfDGHMkdpND+Nho9Y/u72spBNEMnC5WWzb3RX8d3Wl0hc5\nlHGXF/D707JR4YxCLEIxiZRExVpeJXWuhho3NamCTqvhfV+qS52yqewq62PII3bmqvRkZuPzIyEj\nNaXw87v2CvU0aIEewpSGwK/+4yQuD4zD5w+USVVY9Hjya42g1Mmf5lIWLdsYExZDTkamPa0hBE/J\nxzqugKZItHcP5EyGv1yYDTSgUmHBnCLslyEHIhZCjUJkQiKlCZTMCX2nscZA44mnSp2rkTXVfOp4\nlZb8tPSEFkv0zCSy3iDnJTEJQ04IFVCYr1GMcgiUmuDtJHXo1BV8ZU2NwCLDb6xdbh96rePBf/v8\nwIV+O55/swXPPni9XEOeBONhYbU54GZ9URet0BMErSGxsKYYe2XKtC8qCCwwYw43Pvkbfxzb7fVL\nShqaioyMu/H0v30KjTq5CaKhaCkSK+vLwoxCaEKi1eYAVCp4PCx+/OZx3pkfq9JcPB2conXT4+ae\nlJrqSwPj2La7C83rJoslJRM5FMdSQXZYMxEyKSAvRoVFj7rpxrS3iMwUppnzcGXIyfuay83COuxE\npSW8V/DQqCtmedReqx1jDjcMOnkTqFifD3/64AwOnbwcHBMZZX2JdI/5ZKrV+9Yd12LeLDOcjBej\n44ygQVYQhksiTGYdspYi4fawMBlozJ1uwr3raqHjOVCwPh/eOdATzJQvzKdACXg+Yo2Bcn2e+ZTh\nxK61cW01/H4/Pj7ZF/RQ0RoCjbUWfPWWWujoCfe0mNHn02VIJZFVEZlG1htkj8RevamkuJDGwMjE\nhCQJFWoqC3DX6tlgWR/2pdgllklwruRNt9bgJ2+2Cr7vvcNf4KHbrw3bRe86ej7mz/P5Ay3wrplp\njme4vDAeFlt2dYa1WQQQ7IWtpQi43L4QPXMajXWWsJMQ42HRdmZQlvH89fAX2LG/O1jbShLIyb7c\n2czyedPQfEtdWD9wISJrZsWavkiNgYaWLQnJtIpdiyQIqFSqsHAR4/Hh8OkryM/ThBlXKS7uTCs3\nyhSy3iD7U+dhkkzk7pD1+bG35RIIgsDda2tw+HRfRjTCSCUmA40vr5iJebPMKCrMw5ZdHaLvP/K3\nfuh1FJqbagNZobu78FEbv+CGGIQKqCzRR3+jBLhFraWzXzRTntaQ+P49DSg25sHJeHkX4BE7g2GZ\nvDuXBiZKYMQWQZoiwEyxeZcp0LQauqv/J0a0jGZaQ8Dj9cUcA4008qFEupxjHVekcaU1JOrnFIke\nPOJp6jIVyHqDTEXzE6YBIU9ka9cAbqwvy2ljLBQXdjIevPHfndBrNZhfbcYZCc01uAf9nQM9Ub0K\npaY89Nkmu8ArLHrZ3NVii1ooI+MevPxOO+bNKkLzOv5YeLLlU7UUiTyKxLDdDdPVE7qXZbG/NfZN\njULitHcPglnDRj0RRksO1FIknvzaEliMebIkcpn0NJ56YEnUZ0RsXEOjLpztHcHsikKoSVWgYUqP\nuPcn08qNMoWsN8gWky5jdawjGRp1ASqVoAxkLuDx+rB8fik6zw8Hs0QDvYkDRtru8uDIKWnlX7Yx\nF6zDTrSIiG0AgUXqsfsa8Mvt7ei12idlWctBrOpWI+MeHDrVh5YuK5bNL0XT4sqgfi8nPxhQhUrO\nPHB7WDyxaTEoNQG9jsLOg2dxsmcoKZ+lEB2pJ8JAe0waNgHvyei4B5SaiMnVK2ZMR8YZOBlvVIMs\npiynUgG/eOsEzAU0dFqNYO/xUDKt3ChTyHqDTGtI1FYVouP8SLqHEpVCPQWLMU80YzFTiHeTYy6g\nsXFtNUbsDDxeH37znyfjbjphMmgBvz+qkIrLzeK/Dp/Hsw9ejzGHGxf77agske9kDMRfN+xys9jX\n0ot9Lb0wGyjk51FwuDxJ35BRGjJ4itq2pyvj51uuI/VESGtILKoVltCkKTLudqHx9iPmkswcDP9z\n7AtpiSo0r7n1xKSnsSgDy40yhczz98bBkjppxfTxIGeIuqGmGABw48JyVFryBVsKZgIlJh1KTXkx\n/14ercZzbxzF068dxa/+42RCqmQNtcWSFx9Oqcugo3DNTLPsWdVyqFsNjblxod+eUu+IoludGcRy\nIrzzpjkiGft+WGNsH5ioMAYXqkmkm5vPDxh0GtjsDNq7B7B9b3dcKmO5TtafkAMkx7KZDTQe/rv5\neGFbS8LlEJUl+VARKmx+9UhWuKv7hhww6WM3ahdDaoGFsjlD0dEknAwL+qrUZqAsZCLJ5PLAeJQr\nBBgadQXrC5NRZxitFjPTYNyB1nhur0/RrU4jtIbA0nmlWNNQAcYTPYYMAHaHWzBL3uX2xdWKMV5h\nDDk3dFwP+EzttJQJ5IRBrq1KjgxgY50FxcY8+KGCkCAFHwQBcJs/WkNg2fxSEIRKNhGIVGGTYFAT\n5a7Vs3HtzKLgSXiSMVVJ22xRGgK7jl5Ae/dA0vqd8i1q9XPMaFpShT3HLqSlnE0otGAy0Nh19AJO\ndPXHMHMVhCCIgDtRapWlSgWoCRUYjw8H2y7hwIlLko1ooZ6OmmcSq1GLVxgjmRKv2VD6FNrzORXk\nhEHe9ekFWa9XVKDFopoi+Px+PP3apzHXOj/+1UZoNSSgUsFiDLh9N796RNYx5grTSwvCEl0ik14s\nxjxoBeQyQ/Gy4SpUydiFiy1qd66eg49PXk6qsAQfQnH+/DyNosolIyY9DYfLC69Et63fD3iudncK\njbFKmZNSyoY4YjVqsQpjxFMNUGnJh5NhYRtzoTBfOEEtk0ufQuu2uQ3+ioUVuGPZ9KQqjGV9DJnx\nsDj9hTzZo3kUiee/eQN+/M0boFIFTrRS3K6RaCk1KksMqLTok9aMPlcoNefz/pxrDQcAS+eVRL1O\nZLtGjmR0gYps6xiokz6TcmM8MR4CWoqECoHN5JqG8qR0kprKDI0xcCYQQw1FypxsWlIl6VpcqCZZ\ncJsDKXA5MQ6XB/VzzPjRN27AMw9eJ9jBSiihLJa2kMmCrwHHXw6ejauZRyxk/Qk5ILAgj2vV5/cH\nJ0i8cRM1qUJhfnjsNdk1p9nMzoNnw04LfDvTmqrCuK+fil349r3dk1S7UgknqbhifinuW1+HETuT\n8gYJuU5sQStxhq6W80VKw4ZCqaWdlVQqYNfRC2huqpH95MbJwx4+LW1uc3vioTF3mDymUO5FZEIZ\n37Mvd9hJCmMON453xNaAQy6y/oRcqKdhNsiTUct4fNi6qzOhE62X9eO5N46G9ReNpa3aVKO1ywrG\nwwZ3xdv2nJm0Mz1yOro2s5bin8pGPQ231xfTbjuWHXomZTEf77KC9fmVfsdJQE6dA78feOntE4I9\niLl+xVLHta+lNyknt+17u7H3eG/cQkacJ2Dj2mo0LalEUYEWhCrgxWlaUjkpoSzWtpByw933p1/7\nNKqbPVmQzzzzzDNJu3oUHDL0rFWTBKwjTnx+eUyGEQW6kTAeL+xOD5wCdXfRcDIszl4ahZPxYsHs\ngLvn2pkm2B1unL8ypiTZhOBiWAyNMfjzvm68e+gczvfFd38W11rQy5OR7fP78cGxizh8ug8DIy5c\nO9MEQiBRjPX58NYHZ7Btdxf+eugcDp/ug3XYiZrKAsHfOX9lFLs+zYzMay/rx4idwXXXTMPAiAtn\nL42me0gKAjjdk9cIjrc+OINDEsVzOEbsbty0qBxqkkB+Ph3T2soJ1ajVBNRX660YD4t/f78zITe9\nk/Fi+fxSFOTTWDC7CDctKsfKBWW4bdkMNNRYwp4pxhPo18235ob+bcnkrQ/ORC3vMhdocduyGQmN\nJT9feLOc9S5rQD5XEhDYcX7Y1oeqEn3CLuZQ9wZJENi0fi6gUinJNiFQGiLM3RvvSeSW66fDkE+h\ntcsa9r1x7lwpCTWR0phc3GjM7sKaxkq4PV5QGjUsxjyoVH48/2YLeq3RVYlSyakvhjDmcGPDqtlw\nurz47NxQQrXgCskl0gXKeNioynR8xBOaEXMRj9gZWebNnuMXsemWOgDhCWWh2cvR8mxSEXaS6ulK\ntsJY1hvkQNecAdmvO+70YE1jhWh3lGjwTaRArEeFj9ovJ1RonytIrGqKyoETF3Hb0plwe7z4sE04\n5iUUAxJ7IPe1XgrLeKU1BDRqAnanV/L4THoaHpaN6XfiYcTuxndf/ijYSMJkoLB8finuWj0bdqcX\nw2MMfvl2W1LHoCCdyDViaNQVlyGMRxuabwPK/fvOm+bALCCVGQuRGt5Cm4ANq2bHrCYWadQTIVqY\n0qSnsaohkGWdTLLeICcrg9k2xmBJrQVNjRXY/IdP4zqF800krnRmw6pZ+PfdXTje0Z+27Nx0oyYh\nW6ONj9r7cLCtL6qB57JSI3fbscwjxuPj7U0rxqiDSVlLRD8m7uvQmBuHTvVBp1WjuakW5/vkCe0o\nyEPkGrHneHzhj1hPblK6NzXWlSQshBPaeILWkKKbgHQmf4kl3hr1FJ558DrMnlEEqzW5z0/WG+Rk\nZTBzgulGPR23SzxyIoXu6HS0BvlazZQ1xgDgZQOTnc8DQagChsVsCNSEf3ZuCJcGJndz4uBc3f4o\nt1NICzjZmfDp7k/MLbKzygzpHYhCGKFrBONh0d4tzdtHEAD8iLkNI0e07k0jdgYb11bD5/fj47bL\nYOLsOx/aeKK+uhhtZ4Q3Ac8+dF3wv8XUxMSMeqyaA6FrstCGYMncEtmleIXIeoOcLElDboEXyrYT\nI7K/KN+Orr66GCe6Yo8VZTKxNqQgVED9HDOvi/mmReVYf/106HUavHPgLPoGhY2xHGSbNGascK5R\nMgPblU4VQhX8uG5kd62eHXw9Fi+Nzwcsn1+KTevr4nLXRuvexJVS3beuDrcvm4nv//bjuDaVoaIo\nYrkztjEX7A5PVDWxWPoyi8G3Ji+qKcbaxRVoOzMYk7yonGS9QQYCkoYerw8HTqS/9rIwXzOpvyjf\nji4XE7tiTcjy+YH1188ApVHz7opJgsC2PV2y3ituR8yXIBIqjTk06oIqS9p6SoHSTHgGCnUkRhxK\n/kKqCa1w8vmBC/127Ng/UYcfq5emU6CnuMvtRb/NIRhbldK9aV9LL0hCheamWrx35JxsHh5hqdcJ\n172YmphcyV98a/IHx3ux9NppeOqBJXAyXtn18KWQEwaZJAhcP7ck6QaZUhNwR3HdjIx7YHd6ggY5\nk+pUUwVJqASVs0LRUgTMBVrBXbGD8eCj9suyjq0wnxJMfomUxtx19ELObJz8V335tIbEddeW5awn\nINsIPdXF6qUZijBA3KmvvWcQVptTMLYaaYzExrb+uioc65DPkye0LEiNgSfaShIQX5OP/O0KWs9Y\nsbK+DPfcXBP1WnIjyX/V1dWFpqYmbN26FQBw+fJlbNq0Cc3NzfjOd74Dtzvg9vjLX/6CO++8E1/5\nylfw5z//OXmj5qGyRJ/UdoZFBVq8+O3l+O5d9VHfG5qYMRVlMw15aiydNw1mAx0lyWrixUg5SgDY\ntvuM7JnoLrcX7xzoEW39xo2luakGt6+cBTKT+2RKhPH4goIG/2PFjDSPRoEjUmhi49pqrGmsQKGE\nTmuUmggzQJyh7bc5BYU1YjkgDI668Nwbx2RTQgQCOSM3LiyNKhIiRKKtJIHoazLj8eGD48kRW4lG\nVIPscDjwox/9CMuWLQv+7OWXX0ZzczO2bduGGTNmYMeOHXA4HPjNb36DN954A1u2bMEf//hHDA/z\nu1SSgUFHoUJEii5RGmqLYdBRqK4yCqpCcRw+1QcHE9AS5mI1U4mRcQ82rJyF57+1FP9090LB93Et\nAnlf87DoOBe7RnlRAQ0dLez4cbl9ktV/SILA/f/PtTDkZW43GqkQqkCvasbD4rW/dqR7OFmJRk2g\n1KSV9ZpGPR00qsETbvcARqQYwZDTZrTYKqc6F+sBYcwpryb6sN2N05/bUF9dhB99I9A3oLmpVnJ2\nNONhsaahAmsaK+I26lKV7DgVwVQS9S5QFIVXX30VJSUTAv+ffPIJbr75ZgDAmjVrcPjwYbS1tWHB\nggUwGAzQarVobGxES0tL8kYegdvrDbrl5ISmiLAve+fBs1FLdVxuFtt2nwn8vobE3Blm2ceVyZgM\nVND1LLYbNxloQRdTPMIEjbXF8Pl8cDDRa32PdfRjTIKakW2UwfB4cmuHU4HPD7z5fgf+8aUDONEz\nmO7hZCUerw99Npes13Qw3qvxXA9ef68jKB0pBcbrQ/eF4WBeRLTYKiDdGCUTLodmz/GLkmO0nKzl\n5lePYPOrn6C9eyDYwCJWoy5VynhojEmqTCYfUWPIarUaanX425xOJygqcOorKiqC1WrFwMAAzOYJ\nw2M2m2G1pi52+uM/HsdFq7Rm9rGgo8LLlqS6ezrO2YINyZvX1eB4Z3/MtavZyjUzzKA1ZKCMQ2Tx\n12nVgg9koZ4WLIkSoqVLukDMsN2NZ147isVzxesXc8FdzXG8Q34BHYXomA00dHlqDAw7J23mXW4W\ne45dxIcnLkXNT+Hjf73dhqICGvVziiTFVjOpmuBAay/g96N5XXRjypsYG9LAIlY2rq0G6/PjQGuv\nYFzbLHJgSBYJJ3UJnUqlnFZNJh3U6sTdgUMjTtmMcVmxDpcHHMF/2+we7Dl2Ebo8CrevnI2hMWk7\nJtsYA5LSwFIcaC+4YmEF9h6Tt2+zHGgpQjZxDo5v/X09zIV5uDwwLrrbdzIsDIV50FL803B5fTne\nO/SFpM+MpxuPzc4Ev9tvbljA+x6hukkFBSn8072N6Dw3FHUex2OMOTjjNLu8gPd5W7GwHJXlxuC/\nH7m7Abo8CkdOXcbAsBPFxjzMn1Oc8vXJ5w+o4Bn0WsHnDwjkfQht7Nt7BvEPdwqvIUKwrA+GfBqU\nRrjX+oqFFWH3DQAsluTW8cdlkHU6HVwuF7RaLa5cuYKSkhKUlJRgYGBiB97f349FixaJXsdmc4i+\nLpWX/nxClusAwJVB/jF93HYJNzeUw2yQVpZQqKfAuj1BZZe/XzULh9rj75ySDKpK9KipKsTe4/Jm\nEl++MgrW7YWb8YLSqOD28JvKoTEXer4YFCxT+LuVM3GyewAX+qPrRScSrPi47RK+dH0V72l9ZllB\nzPXVCgpAIG5fbqTx5n/JWykgxPAYgzWNFTj9+RAGhp3BEsI7lk2fpDC1YcVMfOn6KliHnYDfjzxa\njf3HL6Rlnkc+f5GSmP02B6w2fh2CgWGn6BoixLY9XYJeAi1FYsWC0kn3zWIxyKLUJWbU4zLIy5cv\nx65du/DlL38Z77//PlatWoWFCxdi8+bNGB0dBUmSaGlpwRNPPBH3oKXCeFh8LmNXG6EJaRtzwcl4\nJbt7GmrCM/50tBor68szwlXEYR124nv3NoBQyaetrUIgeQgA3tnfLWiMgYAKV6hLKPJBJAkCTz2w\nBK/871M41pk8d6uQnCYQcJ1XWPSSNgUKCqFUWPRgff6U9UEftjNYf10Vvv2VRej5YjD4HDEeFoMj\n4XXJXC0yJ4xRqKfStunkYtxFhVps23MGJ7oGMGxPTOdaDLHQo1FP4dkHr0+ZMlckUQ3yqVOn8MIL\nL6C3txdqtRq7du3CL37xCzz++OPYvn07ysvLsWHDBmg0Gjz66KN46KGHoFKp8PDDD8NgSL5M34id\nSbpgPzDxxd+1ejY6ztlEXeSVlnw0r5sc1+ASw1o6rZJd38nE5Wbx9gdn8NDt1+K2pTPwg99+jAQ8\nZwACJ1W704OdH30etS68bnrAHSSkTXvX6tnYsf+sbK01hdCoVaIP9eP3NeCxfz0cNs+UU3N6Uami\ny6SmE5IAvnfvItjHU9dpi8vY1lJqlJh0cDAe/OGvneg4NwTbmDusLjkyJitnaVOsmAxa6HUaPPfG\nsbCNbzw611IQS4AbHXfDyXgz1yDPnz8fW7ZsmfTz119/fdLPbr31Vtx6663yjEwiydYg5uC++G17\nuqLGqx+6/RreJIVQ4Ymtuzrx8SnhrkSpouN8IPnM7WETNsYAYNJT2HP8oiRBjUOn+tB53gYtrUZv\nyD3lHsTO88OST6YqFbCqvhSnPx/G4Ki8mbD/+eHnkzZ9ijFOL5lsjIGAKte297twPIZEw0TJz9OA\n1pBg2UBGcqTXi3uuWNYnmmyZauqri/DO/h7BZz0WnWspyCEukiyyXqmL1pCory5OmqKSliKxdF4J\n1jRUYMzhlpRlHS1jkNaQeOC2uWjrGUjJ6V4M29XU/kI9jSIZNjaLaoolC+QDuPp5/J8ZS69h/1UZ\nznub6jA06sJPtxyH3SXt3rq9fkGXtcvtnXJKawqJQxDAJ5+lVqt+3OkO1Jm/e1o0NNZ6RlqdM0mk\npinK8mun4Tc7Twm+PiRR51oqYpnmye53HI2cUJq/cWF50q7tcrM4crofm1/9BE+/9mlUg0VTBOD3\nRy0oH3O4026MgYkdodTaPDEqS/KxdnGFbN6KWE+hu4+dB60h8f7R85KNMRDQH88TEBOxjcqvtKYm\n01dKZcjL+j14VpCO7l5DY2688X86cOSUeBLZsN0tSQksVX/DuMsr6jI35tOI1LmOZjQZD4t+m0Nw\nHeYU0Uz6gJpgrOIiySInns5kr2+c20dSnMUPPP3a0ag9Orf8d6fcw4yL0B0hNxk/ar8UVza408Vi\n99H0lXYdOtWHv1s1B4dPXYnp90bGPXjujaO835dOq0ZhPoVhGWKBhfkaLKwpRnv3YFpidg3VZmy6\ndS6efeOYNCUohbhQE5Al/BMPn/xN2tzP12rSGjfmIAggX6uGWaDzFAAsinJqDU0GZX0+bNt9hjdu\nzj3XoYpoNjsDo55C/RxzQv2U5SInDLLFpEtKPW0scK3VOPEPsR6djIfF376wpXyMoRjzNVh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Ous8/wwGI/01XxhjQWbbqmblCiwdVcnbyepSGxjbrxzoCcYQ6M1pGDbR6ksuioI0m9ziJZtGfUU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\nBllBQUFBQSED+L8J3PIlqLh08QAAAABJRU5ErkJggg==\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "d3926663-9686-4b02-d5e4-5d2b6003d1d1"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n",
+        "#\n",
+        "def select_and_transform_features(source_df):\n",
+        "\n",
+        "  LATITUDE_RANGES = zip(range(32, 39), range(33, 42))\n",
+        "\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "\n",
+        "  return selected_examples\n",
+        "\n",
+        "\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=1000,\n",
+        "    batch_size=1,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 218.06\n",
+            "  period 01 : 198.71\n",
+            "  period 02 : 180.11\n",
+            "  period 03 : 162.17\n",
+            "  period 04 : 144.96\n",
+            "  period 05 : 129.54\n",
+            "  period 06 : 119.05\n",
+            "  period 07 : 109.70\n",
+            "  period 08 : 102.87\n",
+            "  period 09 : 96.62\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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6euLu7s6VK3/dz+f8+fMEBgYSGBhIXNz/v7JsTEwMgYGBtoxVaAV6eTMm/EXc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\nBRgRERGJOgowIiIiEnX+H0mYeY9GlWnMAAAAAElFTkSuQmCC\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb
new file mode 100644
index 0000000..8be871a
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,1764 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "c4fc88b1-77be-41f4-deaf-2510a434a6aa"
+      },
+      "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>2080</th>\n",
+              "      <td>-117.3</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2912.0</td>\n",
+              "      <td>566.0</td>\n",
+              "      <td>2188.0</td>\n",
+              "      <td>518.0</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>90.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4531</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>2143.0</td>\n",
+              "      <td>427.0</td>\n",
+              "      <td>1107.0</td>\n",
+              "      <td>416.0</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>252.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10202</th>\n",
+              "      <td>-119.9</td>\n",
+              "      <td>36.8</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>1649.0</td>\n",
+              "      <td>323.0</td>\n",
+              "      <td>919.0</td>\n",
+              "      <td>316.0</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>74.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10685</th>\n",
+              "      <td>-120.6</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>291.0</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>124.0</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>154.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7386</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>1368.0</td>\n",
+              "      <td>322.0</td>\n",
+              "      <td>617.0</td>\n",
+              "      <td>303.0</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>440.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10378</th>\n",
+              "      <td>-120.2</td>\n",
+              "      <td>39.2</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1703.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>354.0</td>\n",
+              "      <td>163.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>146.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5128</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>2676.0</td>\n",
+              "      <td>427.0</td>\n",
+              "      <td>1022.0</td>\n",
+              "      <td>395.0</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>295.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5364</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>3362.0</td>\n",
+              "      <td>799.0</td>\n",
+              "      <td>1939.0</td>\n",
+              "      <td>754.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>305.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5392</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1590.0</td>\n",
+              "      <td>350.0</td>\n",
+              "      <td>1299.0</td>\n",
+              "      <td>335.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>163.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8888</th>\n",
+              "      <td>-118.8</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>1143.0</td>\n",
+              "      <td>179.0</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>180.0</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>356.7</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",
+              "2080      -117.3      34.1                29.0       2912.0           566.0   \n",
+              "4531      -118.0      34.1                30.0       2143.0           427.0   \n",
+              "10202     -119.9      36.8                34.0       1649.0           323.0   \n",
+              "10685     -120.6      37.6                36.0        291.0            48.0   \n",
+              "7386      -118.3      34.1                52.0       1368.0           322.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "10378     -120.2      39.2                18.0       1703.0           360.0   \n",
+              "5128      -118.1      34.2                46.0       2676.0           427.0   \n",
+              "5364      -118.2      34.0                31.0       3362.0           799.0   \n",
+              "5392      -118.2      33.9                35.0       1590.0           350.0   \n",
+              "8888      -118.8      34.1                 9.0       1143.0           179.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "2080       2188.0       518.0            3.3                90.6  \n",
+              "4531       1107.0       416.0            4.2               252.2  \n",
+              "10202       919.0       316.0            2.9                74.5  \n",
+              "10685       124.0        47.0            5.7               154.2  \n",
+              "7386        617.0       303.0            5.4               440.9  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "10378       354.0       163.0            3.7               146.9  \n",
+              "5128       1022.0       395.0            6.4               295.5  \n",
+              "5364       1939.0       754.0            3.5               305.8  \n",
+              "5392       1299.0       335.0            4.0               163.2  \n",
+              "8888        647.0       180.0            6.8               356.7  \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": "c24d75cc-16e9-401b-c459-686c945df15b"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "e984c592-bab8-4bdf-a82d-8a3d9ecad56f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "9b0e3328-2ac3-4a4f-c722-2bf030929bba"
+      },
+      "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": "aa31de9d-277f-49be-c80d-074bc25cff8b"
+      },
+      "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": "4fe000f6-d2fe-4717-e5eb-c3eb2d73e759"
+      },
+      "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": "fe7d388a-bc3f-4575-981e-608fd58d2cfd"
+      },
+      "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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UrO/n8oVIoTi3NGOXHJnzwooovygO5AcOHIDdbse8efPwH//xH9i7dy++//3v\n84CTHJAvSWRS6/uLL78Aqze25vSFSKGIXppZtf4gth84nfCYdPf+E9HgUBzIn3rqKfzsZz/Drl27\nsH//fjz66KN48skn8dprr6nZviElnRGpkiSygWiHElLr+6s3tubFhUghMRl0uOXaC1Fs1mctcZKI\nBofiQG4ymTB69GisWbMGS5cuxdixY6HlaCkrMpkaV5JEVjsA7UhF9Po+s9kHT74mThJRLMXfzm63\nG++++y42btyIr3/96+jq6oLT6VSzbUNGeGq8wykghHMj0jWbDid9bjiJTEz8Wmf47HGpM7wzaUe6\nlFyISPF4/RmdoU79whdWDOJE+UnxiPyHP/whXnvtNfzrv/4rLBYL/uu//gs333yzik0rfIIvAHuX\nGy0H20TvVzIiVZJEFggEk65BD9bIOJ1s9vDMwb4jHbA73FxTJ6IhTXEgnzlzJmbOnAkACAaDuPvu\nu1VrVKGLn8JOPM29n9JtQMmKxKz801+TrkEP1j7vdKrV5UtyHxHRQFAcyCdOnBhz7rhGo4HVasXO\nnTtVaVghiw9EUpRuA5Jb6xR8Aew4cEr0edEjbbX3eYcT6OJLwQq+AOZNG4FAIIh9RzoVHbjCNXV1\ncPsfUX5SHMg///zzyP/7fD5s374dBw8eVKVRhUwuEMVLdRuQWJGYbpcAe5db9PHRI201jiIFYmcf\nOpznDmepsBhgKTahz+OLTPdPuqAKM8bbMOp8q2S5WFaIy758qENARNLSOsbUYDBg7ty5WLlyJf7l\nX/4l220qaMnqXWsAVJZmbxtQmcUEW3kR2hyJwTx+pJ3tOu5A4uxD8Ow6gsPlg8Pli9ze4RTwwd6T\n+GDvyZgT2OIDCSvEZR+XKojym+JAvnbt2pifT58+jTNnzmS9QYVOLhBVlZpw7+J62LKYQWwy6HDJ\npGFYt/WLhPviR9rZ3o6UyuxDNLlAotbMwVDFpQqi/Kc4kO/evTvmZ4vFgueffz7rDcp1ma4jygci\nG2prrNloZox/vvZr2HOwDSfsLgRDgFYDjLBZsPjyCyTbmI3p6WSzD8lIBZLwDMG+Ix1o73KzkEkG\nuFRBlP8UB/Knn34aANDV1QWNRoOysjLVGpWLsrmOmO4UdroXEa++8xmOtbkiPwdDwLE2F9Zu+ULV\nqVOlp21JkQok4ZmDOxYV4chXHUzOygCXKojyn+JA3tLSgvvvvx+9vb0IhUIoLy/Hs88+i8mTJ6vZ\nvpyRzXXEVKewM7mIUJq1rgalp21JSRZIzEY9R4sZ4lIFUf5THMh/8Ytf4Fe/+hXGj+8PWn/729/w\nk5/8BK+//rpqjcsVaq0jKp0GUoOwAAAgAElEQVTCzuQiQmnWulriT9sKZ60b9Rp4/VI76PsxkAwM\nNZIciWjgKA7kWq02EsSB/n3lOt3Q+JJNto5od/TBeHYvtlTgSXdaPNOLiFSy1tUQP/tQZNKj2yXg\nP9fuk5xyr7Sa0DDBxkAyQHK15jr3tRMpk1Ig37BhA2bPng0A+Mtf/jJkArncOqLRoMN/rt0nOeWd\n6dp6NpKRJtVVY9OuYwm3D+SIN3r2wS34Jfuk0QA/WDoFtTbLgLSLzslWkmOmuK+dKDWKA/mKFSvw\n4x//GA8//DA0Gg2mTp2KFStWqNm2nCG3jujxBuDx9h/aITblnenaerrJSPGFWMxGLQANvL7AgEyd\nyo2m5PpUaTXDVl6kWrso93FfO1FqFAfy0aNH46WXXlKzLTktfh2x3GJCn+CPBPFo0eeAZ7q2nm4y\n0ur3WrF5z8nIzx5vEAAwe9L5WH7VBNVG4kpGU0ywIinc106UOsWB/KOPPsJrr72Gnp4ehELnkpSG\nQrIbkLiO6PUH8fhLH4s+Nvr4Takp5A6nB51OD4ZVlSR971SSkQLBIFZvPIQtUUE82sGjXUnfLxPJ\nRlPhkXrTnDEAmGBFsbivnSh1KU2t33XXXTj//PPVbE/OC68jCr6AoilvuX3UG3cfx/IrJyR9z1SS\nkf73/UPY3HJC8rXU+jIUfAHYHX0yoyn72YNROmJG6itumwlXn5cJTQSA+9qJ0qE4kI8YMQLXXXed\nmm3JK0qnh+vrqmKmuKPtO9wBYV5AcQBLlowk+ALYvl98z3hYtr8M49fipXQ4hZjfA9c9SQyXXYhS\nlzSQHzvWn+08Y8YMrFmzBjNnzoRef+5pI0eOVK91g8Dj9aPN0adohKhkyrtxxkjJQJ7t0bHd0RdZ\nC5ci9WUYHlFDo4GtvEjxF6bSI1mlcN2T4nFfO1Fqkgbyf/7nf4ZGo4msi//P//xP5D6NRoP3339f\nvdYNoPDIct+RDtgdbkVbXpRMeVeWmlGVpanCpPtqo86LF3N+ZVGkvnr4tSzFRvzxL0ewff+pyEWA\n2ajDpZPPxw1XjJPd7pPuoSjRuO5J8XJ1XztRrkoayDdt2pT0Rd566y00NTVlpUGDJZMtL3JT3tmY\nKlS6r9ZWXgSzUSeaSQ8ApzvdeHPzEWg0mshrmYzahFG8xxvA+7tPIBiC7Bp+t0tIu456GNc9SUqu\n7GsnynVZqa7wxz/+MRsvM2iSbXkRfOKBUall88eicUYtqkrN0GqAqlIzGmfUSk4VCr4A2hx9kfcN\nX2R0OAWEcO4iY82mwzHPMxn6R9Jytu0/HfNaclPxH+w5gVXrP0cgKP6YMosJ5Raj7Pslw3VPIqLM\nKE52kxO9HS0fqb3lRelUodjIu76uCvuOdIi+7u7P7Vg4ezSsxeeC6Q1XjINbCGD7gdOiz5EarYsJ\nhoDNe05Cp9OKzkqYDDpMG1ctmQMgxmzUpVWUhuU6iYjEZSWQa5Kszea6gdrykmyqUGx6Xy5IOlwC\nHl/5MWZcWBOZZtdptVh+1QTsPtgGwSef+KaUXEJa84LxOHzCGXNMatjIGgv6PP6YhKWmOWP6L5pC\nIdgqipOW3JRbViAioiwF8nyXC1te0k0c63J5Rdfys3ltJTcrodNq8djNM7B64yHsbW1HV6+AyqjR\ntj8Qioyk9TpNyjW0pXIX+jx+/Ou3p2evk0REeYqB/KzwCG/fkQ60d7kHfMuL3PS+EtGj5m6XkHQb\nWjytFpBYCk86K6HTarH8yglYOm9swvS3TovIBcDqja0pJRTKXdxsP3Aah36+CVPqqniYBhENaVkJ\n5BZL/p9UFV7HvmNREY581THga7Fy0/thGg0glY4QPWous5gkt7zF02qAuVOHY9HldVi1vhU7/3Ym\n4TFKZyXklg7SqaGd7OLG7nCzqAwRDXmKA7ndbsc777yD7u7umOS2e++9F7/61a9UadxgMBv1g7Ll\nRW56PywUAkqLjXD2eRPuix41mww61I+tli3VGnlNAFfNHIVikwHf/cbXYC02ZFyIQywxLZ2EQiUX\nNwCLyhDR0KY4kN9xxx2YMGECRowYoWZ7hrRl88ciEAzhgz39e7jjVZWaUT+2SjRAx4+aG6fXKgrk\nlVEXAP5ACI3Ta7Fw9mi4BX/KsxJyiWnpJBQqubgBWFSGiIY2xYG8uLgYTz/9tJptGfLCa80IhUSz\n1evrKtE4vRZAf512uVGzXEW5aNPGV0Ov02D1xtaMM8OTFdVJJ6Gwac4Y9Hn8+OyrTjhciTMRAIvK\nENHQpjiQT5kyBUeOHEFdXZ2a7SH0b+nS6bQxZ58XmfXYc6gdm/ecRNXZ/eWNM0aistQsGgSTjWar\nSs9dAGRS1S5Mfg3cjsvqh6FpzgVnf1Z2HGv86H5YZTFOdfYlPJZFZYhoKFMcyLdu3YpXXnkFFRUV\n0Ov1CIVC0Gg02LJli4rNG5qiC8h0Oj349VsHcNzeG7k/vL88ulCL2Lq0WCZ+fV0lLps6HDqNBraK\nYvgDoZSS0KQKs8itgXc4BTy28hNURY4uvRiuPp/s1L3YxQUQuze9urwI9Wez1omIhirFgfzXv/51\nwm1Op1P2OT//+c+xe/du+P1+3HHHHZg8eTLuv/9+BAIB2Gw2PPvsszAajVi3bh1effVVaLVaLF26\nFEuWLEm9J3lGSaUyk0GHjbuPxwTxaHta29E05wK8tfULyb3Z0Zn4lmIj3tr6Bf77D/sjj71wVIXk\n9Hv02nOyeu9KEtOUjvTlRvd9Hj8eu3kG3IIfdaOr0NPtlnydfCP4AjjV3ouAT/nRtkREKZ1Hfvjw\nYTgcDgCA1+vFU089hXfffVf08Tt27MChQ4ewZs0aOBwOfPOb38SsWbPQ3NyMa665Bs899xzWrl2L\npqYmvPDCC1i7di0MBgMWL16MBQsWoLy8PDs9zDFKD0AB+r/Y97a2S75Wp9OD/32vFduiyrGKBctw\nJr7YPu5tB07DLHJwChC79pxs+l1pYhqQPMs8WYa7W/CjpqIYZqMePUnfLffFfCZ6BFRakxfKISIK\nUxzIn3rqKWzbtg3t7e0YNWoUjh07hltvvVXy8RdffDHq6+sBAKWlpXC73di5cydWrFgBAJg3bx5W\nrlyJMWPGYPLkybBarQCAhoYGtLS0YP78+Zn0K2elsh7d7RLQ5ZIe4ZaWGPD5UYfoffHBUr5ynHgZ\nuPqxVeh2CSgy6RVNv0efI93Z41G0513MQJXMzRXZyFEgoqFLcSDfv38/3n33XSxfvhyrVq3CgQMH\n8N5770k+XqfTobi4/4t67dq1uOyyy/Dhhx/CaOw/4KOqqgp2ux3t7e2orKyMPK+yshJ2u3yp0oqK\nYuj16k092mxWVV7X4/VLHoCy70gH7lhUBLOx/08SCASx9i9fQKMFQhIV16ZOqMFf9ohvMXP0eKAz\nGmCrLgEA6IwGdPaIXxR4fQFcMWMk9h9pR3uXG9XlRbAUGXDgiw5s2XMCFVaT7Ag5+n3uvXE6PF4/\nTnf04snf7oC9y5PwnOryItSNror0VcylU0Zg3dYvRG4fjtrh52Zr1PpbDZRUPhP5Lt//VmIKsU9A\nYfarEPsUpvgbIhyAfT4fQqEQJk2ahGeeeSbp8zZu3Ii1a9di5cqVuPLKKyO3S52YpuQkNYcjMXM5\nW2w2K+x2dSZs2xx9sDvE13Tbu9w48lWHZDnTeCNrLFgy9wIcONwuOnItKzHB3euBPRSEzWZFwOtD\npVV6lLt47gVYPPcCdLsErP/4aMz2N7nqahVWMwJeX8LvrESvxZSx1aJ9qK/rX9uW+y0vnDUKfW5v\nQob7wlmjIu+l5t9qoKTymchnhfC3ileIfQIKs1+F0Ce5CxHFgXzMmDF4/fXXMWPGDNxyyy0YM2YM\nenrkfzFbt27Fb37zG/z2t7+F1WpFcXExPB4PzGYzzpw5g5qaGtTU1KC9/dw6cFtbG6ZOnaq0WXlF\n6ZSx3DS4RgNcNmU4brpyPHRareS6tMMl4MlXPsG08Tbcs3Qa9DoNisx6QOS9p46rikzBl1lMkiNE\nMXJbv6Kn2lOtFKf06Nd8l41lBB7xSjS0KQ7kK1asQHd3N0pLS/F///d/6OjowB133CH5+J6eHvz8\n5z/HK6+8Eklcmz17NtavX4/rr78eGzZswJw5czBlyhQ88sgjcDqd0Ol0aGlpwUMPPZR5zwaJ3Jeq\nXqdBsdkg+qVdX1cZU85UKvs7FAL8/nNz7dHBssMZO40dXmstLjKiz+3F8Tbx7PfoOZBk9c0rLCZ0\n9wqKgnI2gnGyo1/zXSYn76WSOElEhStpIP/b3/6GiRMnYseOHZHbqqurUV1djS+//BLnn3++6PPe\neecdOBwO/OAHP4jc9rOf/QyPPPII1qxZg+HDh6OpqQkGgwH33XcfbrvtNmg0Gtx9992RxLd8ouRL\ndc2mw6LndgP966GrN7ZGyplKZZIDwLYDp1Fk1qO5sX9Uvmz+WHj9AWzdewpiCxMbdv4dZqN0QNhz\n0I7Lp46ArbxIdoRYVWqObP1KJSiLBWOOIs9Jd+aCSXJEBCgI5G+99RYmTpwoejCKRqPBrFmzRJ+3\nbNkyLFu2LOH2l19+OeG2q6++GldffbWS9uasZF+qyc4bj378orl1kMokD4vOFl+z6TD+sveU5GM9\n3gA83oDk/Q6XF4+/9HHk4mPKuGps2i1ez91abIS12CjbNjmZjiKjLwAKRfTMhc5oQMDrS3pxk85p\nckRUmJIG8vA096pVq1RvTL5S8qWq9LzxPa3tuGzKcAgygRc4t4WrzGKSvUBQKoRzFxNXTB+Bxhm1\nGZ+CJibdUaTYBcClU0Zg4axRBTONbDLoYKsuUZSUk85pckRUmJIG8uXLl0OjkR4dvvbaa1ltUD5S\n8qWq9EhOR48HCIWSPjacCKX0AiEVew914Knb/zGltW0lU+WZjCLFLgDWbf0CfW7vkJxGHmp77YlI\nWtJAftdddwHo30am0WhwySWXIBgMYvv27SgqKlK9gflAyZeq0spnFVYzbBXFSR8bToRSeoGQiugR\nXbJRXSpT5emOIjmNnCiTJLnBxvwIouxKGsjDa+AvvfQSfvvb30Zuv/LKK/G9731PvZblEaVfqnIZ\n5mH1Y6tiqqS1HGxDZ48XWg0QDCFy8Ej4/lRKo0bToL8yXHevL+G+VEZ0SqfKBV8AXl8grVEkp5HF\nZbK9bzAwy55IHYq3n50+fRpffvklxowZAwA4evQojh07plrD8o2SL9X4U8027j6OfWcLuoQD9aeH\n7NBpNVh8+QUxr19WYsDXRldh8eUXwOsLwh8IQadNfO9OpwemsxnqgjcAzdnXjWcy6iQT4MIXE8n0\nCT58uE88yS48UtbrNDFf3iaj+Be23CiS08ji8m2vPbPsidShOJD/4Ac/wM033wxBEKDVaqHVavN6\nv3cmxKYGU/lSNRl0GFZVguVXTsCqUAib95yMBNvOHi827jqOg0e7YraqOVw+bD9wGi2tdgjegOgp\nZ9HvDfSPZNd/cgybWxIz0OWy2MMXE+HXlpoKXbW+VfJ1wiPljbuPx3x5h7fUmY06eH0BRaPIfJ5G\nHgj5sNfe4/VzeYRIJYoDeWNjIxobG9HV1YVQKISKigo125WTlEwNpvKlKvgCklXUjkvsNw8HTrHR\nTPx711QUo7lxHKwlJmz79OTZmQITej0+yT3qwLmLifCZ8/H9XXz5BViz6Qg++eyM5GtUWE3QaTXY\n/bn4l3exSY+Hlk+HrbxI0Re42IzHpVOGY+GsUUmfS4PP4eTyCJFaFAfyEydO4JlnnoHD4cCqVavw\n5ptv4uKLL8bo0aNVbF5uyfbUoNzab/KK8/2SjWZ0Wi1ub5qMa2aORLdLgNcXwOMrP1H02tv2n44Z\ncYf7Gz9bIMao1+Enq3ajy+UVvb/LJcCo1yoehYnNOtQOL8/7+slDRUUpl0eI1KI4w+TRRx/F9ddf\nHznUZPTo0Xj00UdVa1iuSZY5Lfjk932LvZ7XH0SFNf3iKgDQ2ePBFye6k75/eLRuqyhGZamyL02p\nafMTdvkgrtUApzr7JIM4kP6Xd7gfnIbNL2ajHtPG20Tv4/IIUWYUj8h9Ph+uuOIKvPLKKwD6zxsf\nSrKVOR0/PW+USP5SLAQ8+/u9qLQa0TChJmkGsMmgQ31dVczpZqkSS55L5X5gaH55D/VtV/mWZU+U\nL1I66NjpdEaKwxw6dAiCkN1CJLks1cxpqS/t+Ol5QWKt2qTXQvBLr2OHhWNmeF07GArhpgUTZJ/T\nOGOkokAuVe9dK5EJr4TJoMXX64cNqS9vbrvql29Z9kT5QnEgv/vuu7F06VLY7XYsXLgQDocDzz77\nrJptyylKM6flvrT9gZDicqpSQdxs1EHwBSB1bPsHe07gW5fVQafVSNYkryw1o0qmiEyl1YSGCTYE\nQyHRmusjbJaka+RSBF8QGo1mSAUwbruKlQ9Z9kT5RPfEE088oeSBBoMBGo0G9fX18Pv9uOSSS2C3\n2zFz5kyVm5ior0967TVTJSUmydefOLoCbsGPbpcXgtePylIzLp18PpbNHwvt2ZmK379/CBt3HYdb\n6F9fdgsBfHHSCbfgx4jqEry9/e9ptUurAWprLHj8losxobYcO/4mnjEeCvUXkdm46xje3v53fPTX\n07B3uTGutjTSRr1Oi/ZuD7446Ux4/qWTzse/LpuKaeNsuGhMpWh/77x+IjzeALpdXngEf8p96XZ5\nMXfqcOh1mQVzub9VrhB8Aax+rzXyeYgm9ntI1ifBF0Cn0wO9Xpvx728g5cPfKlWF2CegMPtVCH0q\nKZHOKVI8Ir/99ttx0UUX4bzzzsPYsf3Ton5/6l/i+SzZ1GCyhLiFs0fLFmKREwwBx9pc+NO2r3DZ\nlOGyjz3tcEf+X6omudx6ZXi0LNff8O12Rx/+c+2+lErEDqXtRmrlVgzV6XkiSqQ4kJeXl+Ppp59W\nsy15Q2pqUMmXtvKNZeIiFwQK19Cjnxe9TS3VAjZi/TUZdKitsUouORj1Gnj9if0dStuNslWVjtPz\nRCRF8aX8ggULsG7dOhw7dgwnT56M/Ef9km0nq7CaAY1GthCLEo4eD9yCH/848byUn9d/IRErG9u5\nls0fi8YZtagqNUOD/nV8s1EnGsSBoZWxHs6tEKP095DtrY9EVFgUj8gPHjyIP/3pTygvL4/cptFo\nsGXLFjXalTfipzzDdc7jTRtfDVt5kWSSWVWpCWNry7FTYu07rNxiwvpPjuHAl+IV4aRUWM0oMunR\n5uhTnC2sdLtU9Oj+d+sPYtuB06KPqyrN3e1Gam4Ny3TbFQ+NISI5igP5p59+ik8++QRGY2YFTApN\n/JRneP1brJa4TquVnIa+cFQFblwwHtZig+zpaCVFBtHa6ckUm/V48pVPIuurF46qwKLL6+D1BRKC\nl9h6bP3YajROr0VlqVk20H1+1CF6e4XFhMdungFrcfLPT6pBNZMgPBBrz5luu+KhMUQkR3EgnzRp\nEgRBYCCPIjflWWLW46GbGmCLm7aOH50ZDToAIWw7cBqfH3Vg2ngbHv5OA47be7Hr8zM48IUDjh4B\nFVYTpoytkqzNLme4rSRmu1iHU8C2A6cjI+equOAlth67ueUENrecSHhsNLmRY3evALfglw3kqQbV\nQCCI1RtbMwrCA7n2nO62Kx4aQ0RyFAfyM2fOYP78+airq4NOd+6L4/XXX1elYfnA3uWWmfIUYDTo\nEr5k/YEQGqfXYuHs0Viz6TC2R01Dh4PIh/tOQfAGzk7ThxACoNH078GWej85ybaIRQevRXPrZPe6\nywW6TEeOqQbVlX/6a0ZBONnacy6dyMWqaEQkRXEgv/POO9VsR14JjxxbDrZJ5qDHr0nHn8tdWdp/\nCpmY8PR8/IEl2w+chk6rQSCFsmomg1Zx8N/T2o7L6ocperxYoMtk5JhqUBV8Aew4IH8WerIgnE9r\nz6yKRkRSFAfywSj8kqviR45i4teki82GhOntdEgF8dqaEhxv6024fdak8/G3rxxoi9pbLsXR4wE0\nGslRdfxjxQJd4sixfz2+ac4Fsq+XalDtdgmwd4n3SWkQzse1Z1ZFI6J4rCSRIrmRY5hO21+8pcMp\nIIT+oJ1uSVMlzEYd/u3GhrNbwEzQaPrXvRtn1OLbC8bjkknDFL1OhdUMW3kR6uuqFD1WLNCFR44r\nbrsYl1x0PkKhELYfOI3HX9qJ1RtbEQiKb78LB1Wl71VmMcFWXpRS2+KFD5ARw7VnKmSCL4A2Rx+3\nLhaIlA5NIfmRY1ggs63iKfP6AnB7fJJTr7cuvAh9bi8+3HdKtqrc1HFV+MMHRyIJdXKHoyQLdG9t\n/VJ0/R8QX79OdVreZNDhkknDsG7rFym3DTi3PBLf13Cdea49UyFihcDCxECeIrnp2HSYjToUm/To\ncvUnx6VTvjXZCFSn02Lh7NGYMLIMb245gvYuT0yADh+BGgyF8H5UIA0/ptZWArcQSJpkFd4GptNq\nsPtz+fVuADEXHIFgEMFQKObENbNRh0suOg/zpo2A4AtEgnP4fb591QT0ub1pJYDFL4+E+zplXDUr\npVHBYoXAwsRAniK5kWM6vl4/LDKKLjIb8OzqFhy3J651y5k2vhp6nQarN7ai5WAbOnu8keD8zcvG\n4N5fbMZXp5ySo+sp42xYNLcOj7y4Q/R+txDAYzfPgFvwiyZZBYJBrH6vFXsOtaPL5YUG0oVoHT0e\nrFp/EAePOmJGBGInrXm8Aez462l8sOckKktNmDKuGhoAew+1o9MpwFbRvwyw4raZcPV5U9p3LrU8\nsu9wB4R5AU6rU8HJp10alBoG8jREJ3R1Oj3QKDyfe2SNBX0ev+ghJTUVxVi9sTVpEL9k4nk4dLw7\n4TX+9/1DMYEwfD75jgOn4fLIbz/bd7gD86YOl002cwt+0SSrQDCIJ1/ZFZMDIPerMBp0olPuZqP4\ntF54dN7hFBICfZvDndZoIp+y1YmyhZ/7wsVAnob4rUDrPzkmWm1NrLqbPxBK+eS0aNfO+gfYyoti\nXkPwBbB9v/hWrGRBHEierV5hNcHrC8RMb4f9bkNriol84mE+kxr0cqMJsapv+ZitTpQpfu4LFwN5\nBsJbgZobx0Gn1SSs1TbNuSBhylenRconp4WZjTrYyosStiDZHX0ZBcJwtrrUkkGvx4fHV34SkxgD\nAKvfa8VfPlV2cE6FxYSJoysk67BnQmw0IZfUw0ppNBTxc1+4GMizQKpYRypbO5Qk0V06+fzI68aM\nMjWajNof/kcsVj7W4w3ETG9Hfwls3qMsiJdbjHji1othNOjw+VFH1hIFw8RGE8mSelgpjYYifu4L\nEwN5FoVHyoFg6jXA5a6WzUYtvl4/HIsvv0D0dZvmjIHZmHrGe/xpZNEXJPYuN55/Y6/oa+5ptSMU\nUl5dbsaFNZEa60oTBXVa5dv44kcTSpN6WCmNhhpWCCxMDOQqkBoNBgJBXDVzlOQ/HqmqaDcuGI9i\nkx6rN7ZKjjIvnXw+3t+d/FQ0rQYYVl2CO6+fiNJiE9yCH/5ACLqo6wuTQQejXgtHj1f0NTp7BCiN\n4zqtBsFgEIFgEDqtVnGiYLnFhPq6Kuw70hkZOUwZV3U2a70Djh4Pqs8Wr4kfTaSS1MNKaTQU8XNf\nWBjIs0xuNPjB3pPYcnYrldgIXe5qOdkoc8VtM+EPhPDBXvHpbg2A731zEiaMLEexWZ+0KITcVH+l\n1YRQKIROiUAfLRAMYVPLSWjP9i26j1+c6Ma//36v6PMcPQKumjkKS+ePS/hdLL68f2mhbnQVeroT\ny7QyqYeIhhKW8skyudFgMIRIydaNu45jzabDoo8LXy1Hj9qTjTJdfV7MvLBGsl0hACUmPazFxsiM\nQXQJ2fj2hKf6xUwbb0PDBOn3ErOn1R6TM2Ay6HDBiLKkZVnFfhfh28xG8etQ+bYzqYeICgsDeZbJ\n1QyPt6e1XXFCnJJa5LU1Fmgl8t60GqC2xpJ0ZB/dnmXzx56t326GVtO/pt44oxbL5o+N3FdpNUED\noKzEINv+zh4B3a7YC5FMA67H65esFy3XdiKiQsKp9SxLpfJbKkUYlGwdMRl0GGGziO7rLjbrUWzW\no6Pbo3j9WG6qP3z4SThhXqvVwmTQQPCJL55XWk2iU9rpZNFG10m3O9yiSwNM6iGioYKBXAVKE7rk\nCq0ke12poPfwdxrwoxe2w+WOLQTjcvuxZtNhLJpbhwqrUXR9W2r9WCwxJj6hz9Ejv6Vs2nibaB/T\nCbip1ItmUg8RFToGchUorfwmVmhF7gQiJUEvFNLAZNAlBHIAaDloRyAYQp8gPp1fX1epKJjKTc+b\nDNqzjzl38Mmlk89POqWtNOCyXjQRUSwGchWZDDpUlZmh0SDmVC+dVoNAMCRaaEVJzXC5oCeXFNfZ\nI4heUJgM/bXe9x3pwJY9J1FuMWHq+OqzFesSLyzk3sPnD+KJW2cCoRCg0UQq0WUL60UTEcVispvK\n1mw6jE27T8SUUA1InLDSctAumrgl+AKSSV3xikx6lFmMovdJFoDTAMfaXJEsdoerP+A/+cquyFp4\ntGSJd7byItTWWFFrs2R9dKwk6Y+IaCjhiFxFSg9CCevsEfC79Qdx87UXQqfVytYLjx8pRz+2yyW+\nv1uqiIsgUaf9WJsLq99rxfKrLoy5faBrNseXpGW9aCKicxjIVaTkIJR42w6cRpFZj+bG8SkldcU/\nNlv2HGrH0vmJyXgDUbNZ6kJm8eUXAAD2HelAe5eb9aKJaEhjIFdRmcWEcosBDpcvpeftaW3Hwtmj\nFSd1yY38tQrPSpfS7fKKrjsPxPauZBcydywqwpGvOri1jIiGNK6Rq8hk0MFslC+UIsbR48HxNlfS\npK6wZNXkMlFZKr/ubDLoUGYxodslpHTaWzJKCteYjfqEqm9DQSo5E0RU+DgiV5HgC0DwJW4DC5Ma\nLVdYzaitsSiuFy5XWzzTEbncunMqa/ipUpKdXpvRO+QfNX/fRJS/VP3X39raisbGRvzud78DAJw6\ndQrLly9Hc3Mz7r33XpwjAa4AABvdSURBVHi9/UlZ69atw6JFi7BkyRK8+eabajZJddGjpW6XIHmC\nGACcJ7FNatr4aliLjbLlSwFE3keu1OkIm0Vx20fYSlBuMUIDZSVNldRsTxez0xOp+fsmovyl2oi8\nr68PP/7xjzFr1qzIbb/85S/R3NyMa665Bs899xzWrl2LpqYmvPDCC1i7di0MBgMWL16MBQsWoLy8\nXK2mqUJstFQ/tlqyiprZqMODy6fjT9u+lEwYE0somzKuCqFQCI+8uEM0ASz+tRZffgHWbT+Kd7Z/\nJdn2SqsJDRP6R3b+QCjjojDZKMzC7PRYLIRDRFJUC+RGoxEvvvgiXnzxxchtO3fuxIoVKwAA8+bN\nw8qVKzFmzBhMnjwZVqsVANDQ0ICWlhbMnz9fraapQiwxa3PLCYyssYgG8q/XD4O1yIDmxvFYOHs0\njre5UFNRhEAwFDkfPDqhzO7oAzQabG45jk17Tsa8T3QCmFjy2S0LL8KmXUdj9rKHGfVaPH7LxbAW\n9+8912mhqKDKQBRmGYjM+HyRL4Vw4rcKEpH6VAvker0een3sy7vdbhiN/QGjqqoKdrsd7e3tqKys\njDymsrISdrvyvde5QG601OfxYd604dh3pDMhGAWCQazeeAh7DtrR1euNrGdXRa19AsAfPjgSGelL\nFXXZ9VkbFs4eDWuxMeEL3eEURIM40F+JzS34I4FcqYE485sHn5yT62esc/2eaPAMWrJbSKI6idTt\n0SoqiqHXq/eFbrNZU3r8qfZedEocGuLoEXDj1RNxV6kJDqeAilITzEY9AoEgfvj8B/jipDPy2HBS\nWniUXVzUH1yjR/pSv56uXi/u/812LJj5D/judZOg05378vR4/aipKEKbw53Y14oi1I2ukjzbW86l\nU0Zg3dYvRG4fjtrh2V0akUpsS/VvlQ+k+jSQv+9UvfjWftGtgsVFRtzeNBmAsr+Vx+uP+XeS6wrx\n8wcUZr8KsU9hA/ovpbi4GB6PB2azGWfOnEFNTQ1qamrQ3t4eeUxbWxumTp0q+zoOR59qbbTZrLDb\ne1J6TsAXQKVVarRkQsDrQ093EHoAPd1u9ABYtf7zmCAu5sO9J6TLqooQvEG8/eGX8Hh8MQVjbDYr\n6uuqRNeb6+uqIm1K1cJZo9Dn9iZMfS+cNSrl32E60vlbDbRUp5rl+jTYv28pgi+AbZ8m1vAHgG2f\nnsQ1M0eidni5bBvzcUQf/lsV2nJCPvy7SlUh9EnuQmRAA/ns2bOxfv16XH/99diwYQPmzJmDKVOm\n4JFHHoHT6YROp0NLSwseeuihgWxWxuQSs3o9Pvxu/UHcuGA8ik39v27BF8CeQ+0Jj42X7GhQKS0H\n7Vg0tw5A/9qqtaxIlfVmTn1LUyMw5ervOxtbBVOpYpgrAoEgVm9szauLDypMqgXyAwcO4JlnnsGJ\nEyeg1+uxfv16/Pu//zsefPBBrFmzBsOHD0dTUxMMBgPuu+8+3HbbbdBoNLj77rsjiW/5JBwQP9x3\nCh7vuUIdHm8Q2w6cxu7WNsyaNAyN02sRCIYk66FHq7CaoNFAdKQvJ1yz/fOjDnQ6BdgqilBfV4Vl\n88dmJQiERyBFJj3cgh9lFlNOJFrlEjUDU66dsZ7p+n2+ZuSv/NNf8+7igwqTaoF80qRJWLVqVcLt\nL7/8csJtV199Na6++mq1mjIgdFotFs2tQ8vBtphAHubxBrG55QQ2t5xApdUIs1En+rhoDRP694aL\njfRNBm3kzG+x+7YdOB35uc3hjvmCSScICL4AOp0ebNx9HPsOt6PDKUSS8yqtRjRMqOFI5Kx8DUzp\nynSrYL5k5EcTfAHsOHBK9L5C/BtTbsv9bJI8kqwATJjYdrRo8VnrQOKUeDAUwqbd4uuSUtL5gome\nIo4fcYWT8zp7vENqJJJsTTQfA1OmMlm6yfWMfDHdLgH2rsTkUaBw/8aUuxjIs0juC0mM2ahDiVmP\nTqeAMosR9XWVuGrmP6Cy1BwTIMTWRQPBIFqPdeF4W2/C60qN1NP5gknlVLVCH4koXffOx8CUqUzW\n7/Ox+E+ZxQRbufhOkEL9G1PuYiDPIrkvJDFeXwAP3dQA49mDR+S+sOLXRf2BENwe8TrucjXc5b5g\n4keaqZ6nXugjEaXr3vkYmLIl3fX7fCv+YzLocMmkYaLbAQv9b0y5h4E8y6SS3sRUWM2wRZ3epWQb\nS/gxXn8w5RPP4r9gwq9lKTbgra1fJow0500bkdJ56tkaieTidp5U173zLTANtlzNyJdz68KLRLcD\n8m9MA42BXCGlwSX8hdQ0ZwxWv3cIn//dIVksJhxYlUzZxj+mwmqESSJhrqrUhPq6qkg1ueryc1nr\nYq8V/zrhkWYgGEppqSDTkUgu7yVOdd07HwNTLsi1jHw5Oh3/xpQbGMiTkAouTXMugKvPK/mPt9hk\nwHe/MfFctveuY6JlWgHpKVu3x4+brpoAk0GX8Bi5hLlp421obhwfufioG91f9CUs/rWkZg72He5A\nfV0VNkfVdhejAVBbY4kc3JKuXN5LnO66dz4FJkoP/8Y02BjIk5AKLh/uOwnBG0w6ajQZdBhWVYLl\nV10oOqqXm7LdduA0Pvt7J6aMs+HTQ+KPCSfMOXqEhAuE8BeM2aiPVG4TfAG0HGxT1HdHjweNM0ZC\np9PGTB+aTTqcsJ9LsgsBONbmwtotX6QdcHN9y9ZQXvcmotzGQC5DLriEDyFJZdQoduUuN2UL9I+8\nN7dIbzMLJ8x5A0G4er0YM7xMdhq62yUk3f4WVmE1o7LUnHBC289ebxF9fCYBNx+2bHHdm4hyEQO5\njGRBNlq6QUzpljWpTHRLkR7//f/2o73Lg2Co/3HnVRTjweXTYS0yJDy+yKSXfK1408ZXQ6/TxJSh\nLLMYJavSZRJw82HLFte9iSgXsQyXjCKTHuUKA0g4iKUiPNVeP7Y66WOlAq+zz482hydyfzAEnOrs\nww//aytWb2xFIBi7p9wt+GWDuAZAVakZjTNqsWz+2MjSQodTQAiQLS2bScANT12LybWp6/DMSi61\niYiGLo7IRUQnuDkUBudUglh8Al1ZiRG1thK4Bb/kyDw6E73D6VHwHudKu9574/TI7WUWE6okRr5m\noxYPNDfg/KqStPaRZxpwOXVNRJQ6BnIRctXMpGqkpxLE4l+/q9eLrl4vhlcX4/zKIpzuTKwWFc5E\n7+nz4vGXPkZXr7J17j2t7fB4zxWOkUva8niD+HD/KXx7wQQAyZcWKiwmdPcmJtmli1PXRESpYyCP\n4/H6JUeh5RYjHrv5Yryz4+9pjxrlRrkn28XPWR9ZY4m8vlvwKw7iQP+Uv8MpIBCVMd80Zww2t5xA\nQGSOfdv+01h8+ViYzlabk1q3rio147GbZ0ROP8tmwOV2HiIi5RjI4zic0qNQZ68XXl8gpVFj/Jaz\nVBLowvo8fvgDIei0Z5PCrEbFmeflFhPe+uAwdh44FdkHP662TDSIA/17yu1dbtTaLEm3XFmLjbAW\nG1PqCxERZRcDeZyKUmXZ08lGjXKFZMotJsVr70BsNrjJoEPDhBrF9dxNBh3e2f5V5OcOp4COv8nv\nI/f6zk3Fc92aiCi3MZDHMRv1WSn8IVelbPLYSvxlr/hZxmLiE+mWzR+LYCiE7ftPR9brjXoNSktM\n8PsD6O71wWjQIhQK4VSn+HS9nF+/9deYIjdctyYiyl0M5CKkRqFNc8agzdGnaDpdah18676TMOlT\n2/UXfwGh02px04IJWHL5WNgdfYBGA1t5EUwGHfoEP37y6q60AniYWJEbrlsTEeUmBnIR8aNQo0GH\nP2w5gsdf+ljRYR5y6+CCNwjBK35eeFi5xQhnrzfpNLbJoENtjTXmtj98cERREK+0mjBlXDW27TsJ\nr198vTwXSqMSEZE8BnIZep0GG3f311X3RAXfZGVZlVZrEyOXDd7R7cbBo12YMKocVWVFCc8VfAHs\nbW1X9D4NE2xYNLcOLQfb4PX7RB/TmSOlUYmISBoDuQy5/eSA9IhVLts7mSnjqhKywd1eHx749Udw\nuc8loVmK9Hjme7NQZDxXhrXbJaArSRJdVdRsQke3B9294kEcAMpLTDlRGpWIiKSxRKsEJVXN5Mqy\nNs0ZA7Mx9Slpjcht8UEcAFxuPx749Ucxt4VnAqT848Tz8NTtl6C5cTx0Wm2kypuUqTlWGpWIiBIx\nkEtQst9briyrq88HQeKcbzl7D3VA8J17Xke3OyGIR97D7UdH1DnjcvXKR9ZY8N1vfC0mMCd7fHPj\nuJTbT0REA4tT6xKUrHPLbUdLd508/gSxg0e7ZB9/8GgXZk8+t14enXHf2eNBeYkJs+qH4ZtfHy2a\nmBfzeKcHZRYjpo2rRvOC8bLHoRIRUW5gIJcgt85tNurw9fphskVRkq2TazRASCRZPH6UP2FUuWw7\n4+8X2/ddO7wcdnuP6PO5T5yIKL8xkMuI309ebjHhwn+oQPOCcSg2JZ71nez5FVYz6sdWoXF6LTbu\nPo7NLScSnhM/yq8qK4KlSC86vW4p0otmrwOp7/vmPnEiovzEQC4j09Gq3PObG8dBp9UoKn36zPdm\nSWatExHR0MZArkCmo1Wx56dykVBkNOCX916WdB85ERENPQzkgyyVi4SqsqKYxDYiIiKmJRMREeUx\nBnIiIqI8xkBORESUxxjIiYiI8hgDORERUR5jICciIspjDORERER5jIGciIgojzGQExER5TEGciIi\nojzGQE5ERJTHGMiJiIjyGAM5ERFRHmMgJyIiymMM5ERERHmMgZyIiCiPMZATERHlMQZyIiKiPKYf\n7AaE/fSnP8Wnn34KjUaDhx56CPX19YPdJCIiopyXE4H8448/xt///nesWbMGR44cwUMPPYQ1a9YM\ndrOIiEhMKJS9n7P5WlKvXaQBXC5ooGK7dTqESsswGHIikH/00UdobGwEANTV1aG7uxsulwsWi2Xg\nG7NiBUp37z33czb/0Nn8EMX9rJF7rFGPMsGX5LXimxqSvnOg2p3sZ4MO5V4/4h6QQbviX0r53zbl\nfkj9TnVaVPgDqb1WBp/J5O0GZG9Q+oWs1aAyEFTeziSvnbTdqXxm0/1caICqUGqvndBuNQNLBj9X\ni7Y1v9kG4D1cK34K9/fuGYB3ipUTgby9vR0XXXRR5OfKykrY7XbJQF5RUQy9Xpf9hvj9wIsvwnTi\nRPZfe5AZB7sB8TQa8f9P8WdDssdn8NpJf1bptfUqvnb2Xyv6Z43sY3X6HGp3ll5bm+T+rLdzgN5L\nI3Z/HrR7UF9bp4Plynmw2KwYaDkRyOOFklwJOhx9qr237cgRtH9xMvbGhD8kktyfvQ9RKP7N0ngv\nm80Ku71ncP8xqCDSrwJSiH0CCrNfhdgnoDD7NaB9Uul9bDIXCDkRyGtqatDe3h75ua2tDTbbQEyE\niDCZEKquHpz3VovZDJh9yR9HRER5Jye2n1166aVYv349AOCvf/0rampqBmd9nIiIKM/kxIi8oaEB\nF110EW644QZoNBo8/vjjg90kIiKivJATgRwAfvSjHw12E4iIiPJOTkytExERUXoYyImIiPIYAzkR\nEVEeYyAnIiLKYwzkREREeYyBnIiIKI8xkBMREeUxBnIiIqI8pgklO6GEiIiIchZH5ERERHmMgZyI\niCiPMZATERHlMQZyIiKiPMZATkRElMcYyImIiPIYA/lZP/3pT7Fs2TLccMMN2Ldv32A3R7Gf//zn\nWLZsGRYtWoQNGzbg1KlTWL58OZqbm3HvvffC6/UCANatW4dFixZhyZIlePPNNwEAPp8P9913H268\n8UbcdNNNOHbs2GB2JYbH40FjYyP++Mc/Fkyf1q1bh+uuuw7f+ta3sGXLlrzvV+//3979x1RV/3Ec\nf/LrZiIooBeDyiVZODPQpCDAskRLzFZDhnZtpkVqlswZKmPhJosfUUtpjKY2N6kRoQuZZU0Lx/JK\nY2zMNNbwx9LL4oeC4I1fF97fP4xbFGpEX6/H3o//zudzuOf9OueON5/D3T12O2vXrmXZsmUkJSVR\nWVlJXV0dSUlJJCUlkZGR4dx3586dJCQksHjxYo4cOQJAR0cHycnJLFmyhJUrV9LW1uaqKAD89NNP\nzJ07l6KiIoB/5fpc7Xy4Otfy5cuxWCwsX76c5uZmw+X6c6YBlZWV3H///c5tI2UaEVFSVVUlycnJ\nIiJSX18viYmJLq7o77FarfLyyy+LiMjFixflsccek02bNskXX3whIiLvvvuufPzxx2K322XevHnS\n3t4unZ2dEh8fL62trbJv3z7ZsmWLiIhUVlbKunXrXJblz9577z15/vnnZe/evbdEposXL8q8efOk\no6NDGhsbJT093fC59uzZI3l5eSIi8ssvv8j8+fPFYrFIbW2tiIisX79eKioq5Oeff5bnnntOuru7\n5cKFCzJ//nxxOBySn58vO3bsEBGR4uJiyc3NdVkWu90uFotF0tPTZc+ePSIi/8r1Gep8uDpXamqq\nHDhwQEREioqKJCcnx1C5hsokItLV1SUWi0Wio6Od+xkl00jpihywWq3MnTsXgJCQEC5dusTly5dd\nXNX1RUREsG3bNgB8fX3p7OykqqqKJ598EoA5c+ZgtVqpra1l+vTp+Pj4MGrUKGbOnElNTQ1Wq5W4\nuDgAHn30UWpqalyW5Y9OnTpFfX09jz/+OMAtkclqtRIVFcWYMWMwm81s3brV8Ln8/Pycq+j29nbG\njRuHzWbjwQcfBH7PVFVVRWxsLCaTCX9/f4KDg6mvrx+UaWBfVzGZTOzYsQOz2ewcG+n16enpGfJ8\nuDpXRkYG8+fPB36/hkbKNVQmgMLCQpYuXYrJZAIwVKaR0kYOtLS04Ofn59z29/d33m66mXl4eDB6\n9GgASktLmT17Np2dnc43ckBAAM3NzbS0tODv7+/8uYF8fxx3d3fHzc3NefvQlXJycti0aZNz+1bI\ndP78ebq6uli1ahVLly7FarUaPld8fDwNDQ3ExcVhsVhITU3F19fXOT+cTAEBATQ1Nd3wDAM8PT0Z\nNWrUoLGRXp+WlpYhz8eNNFSu0aNH4+HhQV9fH5988gnPPPOMoXINlenMmTPU1dXx9NNPO8eMlGmk\nPF1dwM1IDPattYcOHaK0tJSPPvqIefPmOcevlmO44zfS559/Tnh4OHfdddeQ80bMNKCtrY0PPviA\nhoYGXnzxxUG1GTFXWVkZQUFB7Nq1i7q6Ol577TV8fHyc88Op/WbIcy3/xvW5mTL29fWRmppKZGQk\nUVFRlJeXD5o3Wq6srCzS09OvuY/RMg2HrsgBs9lMS0uLc7upqYkJEya4sKK/r7KyksLCQnbs2IGP\njw+jR4+mq6sLgMbGRsxm85D5BsYH/urs7e1FRJwrEFepqKjg8OHDJCYm8tlnn1FQUGD4THDlL/wZ\nM2bg6enJ3Xffjbe3N97e3obOVVNTQ0xMDAChoaF0d3fT2trqnL9apj+OD2QaGLuZjPR9N2HChEEf\n4LuZMm7evJlJkyaxdu1aYOjfgUbJ1djYyOnTp9mwYQOJiYk0NTVhsVgMnWm4tJED0dHRfPXVVwCc\nOHECs9nMmDFjXFzV9XV0dJCbm8uHH37IuHHjgCv/8xnI8vXXXxMbG0tYWBjHjx+nvb0du91OTU0N\ns2bNIjo6moMHDwLw7bff8sgjj7gsy4D333+fvXv3UlJSwuLFi1mzZo3hMwHExMRw7Ngx+vv7aW1t\n5ddffzV8rkmTJlFbWwuAzWbD29ubkJAQqqurgd8zRUZGUlFRQU9PD42NjTQ1NXHvvfcOyjSw781k\npNfHy8uLyZMn/+V8uNr+/fvx8vLijTfecI4ZOVdgYCCHDh2ipKSEkpISzGYzRUVFhs40XPr0s9/k\n5eVRXV2Nm5sbGRkZhIaGurqk6/r000/Jz8/nnnvucY5lZ2eTnp5Od3c3QUFBZGVl4eXlxcGDB9m1\naxdubm5YLBYWLVpEX18f6enpnD17FpPJRHZ2NnfccYcLEw2Wn59PcHAwMTExbNy40fCZiouLKS0t\nBWD16tVMnz7d0LnsdjtpaWlcuHABh8PBunXrmDBhAm+99Rb9/f2EhYWxefNmAPbs2UN5eTlubm6k\npKQQFRWF3W7nzTffpK2tDV9fX955551Bt+ZvpB9++IGcnBxsNhuenp4EBgaSl5fHpk2bRnR96uvr\nhzwfrsx14cIFbrvtNudiJSQkhC1bthgm11CZ8vPznYuZJ554gm+++QbAMJlGShu5UkopZWB6a10p\npZQyMG3kSimllIFpI1dKKaUMTBu5UkopZWDayJVSSikD00aulFJKGZg2cqUMrqys7JrzR44cue4j\nQpctW8bRo0f/zbKUUjeINnKlDKyvr4+CgoJr7rN7924uXbp0gypSSt1o+tAUpQwsLS0Nm83GihUr\nWLBgAcXFxdx+++0EBASQmZnJ/v37qa6uZsOGDWRlZXHmzBl27tyJyWSir6+P3Nxc7rzzzuse5/z5\n86xevZr77ruPKVOm8Morr/D2229z4sQJACIjI0lJSQGgoKCAiooKPD09mTJlCunp6TQ2NvLqq68S\nHR1NdXU1fn5+LFq0iLKyMmw2G9u2bSM0NJS8vDyOHTuGyWQiMDCQnJwcl3+nvFI3vf/3A8+VUv8/\n586dk9jYWLHZbDJ79mzp6OgQEZHs7GzJz88XEZE5c+bI2bNnRUSktLRUbDabiIgUFhZKdna2iIhY\nLBb57rvvrnmcqVOnyqlTp0REpLy8XJKTk6W/v18cDockJCRIVVWV1NTUyLPPPis9PT0iIvL666/L\nvn37nD9/+vRpZ00D9W3fvl0yMzOlra1NwsPDxeFwiIjIgQMHnLUqpa5OV+RK3QJOnjzJtGnTnN+f\n/fDDD1NcXPyX/caPH8/GjRsREZqbm5kxY8bfPsbYsWOZPHkyALW1tURFReHm5oaHhwezZs3i+PHj\neHh4EBERgZeXl7OO48ePExERgZ+fn/O5AIGBgcycOROAiRMn0tDQwNixY4mNjcVisRAXF8eCBQuY\nOHHiiM6LUv8F+j9ypW5BIoKbm9ugsd7eXlJSUti6dStFRUUsW7ZsWK850JyBv7z2wPGuNg7g4eEx\naO6P2/LbIx+2b99OZmYmABaLhR9//HFYNSr1X6SNXCkDc3d3x+Fw8MADD3DixAkuX74MwNGjRwkL\nCwOuNF2Hw4Hdbsfd3Z3g4GC6u7s5fPgwPT09/+i44eHhHD16FBHB4XDw/fffExYWRnh4OFVVVfT2\n9gJgtVqddVzPuXPn2L17NyEhIaxYsYK4uDjq6ur+UX1K/ZforXWlDMxsNjN+/HjWrFlDcnIyL730\nEiaTiYkTJ7J+/XrgyrPQV61aRU5ODgsXLiQhIYGgoCBWrlxJamoqX3755bCP+9RTT1FTU8OSJUvo\n7+9n7ty5PPTQQwDEx8fzwgsv4O7uzrRp01i4cCENDQ3Xfc3AwEBOnjxJQkIC3t7ejB07lrVr1w67\nNqX+a/QxpkoppZSB6YpcKQVcubWdlpY25FxaWhpTp069wRUppf4OXZErpZRSBqYfdlNKKaUMTBu5\nUkopZWDayJVSSikD00aulFJKGZg2cqWUUsrA/geFPCh8RpvD9gAAAABJRU5ErkJggg==\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": "82cbdf5e-93d3-451d-ce2a-d887b6fb0e0d"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=1000,\n",
+        "    batch_size=10\n",
+        ")"
+      ],
+      "execution_count": 16,
+      "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 : 204.24\n",
+            "  period 03 : 194.97\n",
+            "  period 04 : 187.23\n",
+            "  period 05 : 180.94\n",
+            "  period 06 : 175.44\n",
+            "  period 07 : 171.65\n",
+            "  period 08 : 169.08\n",
+            "  period 09 : 167.84\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         113.4    207.3\n",
+              "std           93.5    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           62.7    119.4\n",
+              "50%           91.3    180.4\n",
+              "75%          135.2    265.0\n",
+              "max         1627.6    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>113.4</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>93.5</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>62.7</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>91.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>135.2</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1627.6</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 167.84\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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QXk1mzRJC1Cunn52P6ehxGt85meCRQ6q0jirjNNqEdSgGX2yDbwKNG094FQUK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72C0stA+/cXoNRX7bxQH/4WZVYK9tge2G8Z4NxO6xD96YjUAjVX8nVoVRI9o03o1e5JvZEkmXW7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\nnOq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sSujNdugJRCQ69SKLRxAEQRD6SLSb+Id7prDjYBMvb6rmtQ+OseNAE48sH88Yt+1K\nH54gXBYiKHER+pYqnH1bidVLCpAjSm8wYqT0rK6qQ5z+8a/QJToZvyIXjaQSmX8fktHA3DEqKhLR\nBDdfvUOP3WZlb6OFsCKRlySTZJZpaj8T8DhwIsof3w2hKHDfIiPlE4duLrlnXwf/+dRJOvxR5s9y\n8dXVmciRKHJEOev0kUBQ4aW19by7uRlVhYU3JfLwfVmj0u3e3xXl3c3N/G1jMx3+KAa9htsWpbDi\n1lRSk6/NRkHRqMq+w51sr/Cyo9JHmzcCxJv5lZc5mFXmZNoUx3Wbyt73c3G295oweoIhhfrGPlMu\nugMPDR6ZaHRAyYUE6alGigsTzmQ9uOM9H6yW6/N9KgiCIAijQaPRUF6cxsTcRNa8f4RP9jXy4xd2\ncWt5NituysUgvhsJ1znxzfEi3b8wH0WJUXmkBZ8/jNGgJRQefOVQo4GfvlRJVzBMe2eYRLuR0sIU\n7l+YPyg9S+n0c/Tv/g9qOELhI3MwGRSiZctRkzOh7QQqKluOa1hXcZBgGJYvvAmrVSLHGeKjXQep\nrG6mrUMm0W4kOzWH0w0utFr4yh0minMHv+SxmMprbzey5q0GtFoNjz+YRbvSzv/3x529+xnuWFVV\n5bPdXv7wci1t3giZbiNf/3IOk8Zf+shua3uYt99rYsOWFkJyDKtFy713uLltccqoT/EYDcGQwp59\nHWyr8LK7qoOuQPx9k2DVsvCmRGaWOZlabL+upoQM1DO9pu97drj3mnBpqKpKuy96JtuhPh58qGsI\n0dIWGbS92SQxNtt8puSiO/DgTjWKJqqCIAiCcAklmPU8ekcxsya6eWH9Id7dVsPuw808vGw8E8a4\nrvThCcKoEUGJi9CzoKo61orPH8aZYKSkIAlJ0vDp3sZ+wYlQOMbpJn/vv1s75N4Mi9WLC3tvV1WV\nE//078gnTpOxai5JaSrKmEkoRTPBexJUhZ21Ei9uqcdoMHDrzXOwWq3sPXiED5pq+z2GP5DIqXoX\nkqTw6J1mCrIHv9wdnVH+6+mTVO7rICXJwD9+I5edx+p4f3fdWY+1qUXmqT+dZndVB3qdhi+uTGfV\n8rRLXhteUxvg2ZdPseXTNqKKSqJTzxdXpLN0QfI1N+LS1xFh5+c+dlT6+Hx/R2/Dv5QkAzfPSWRW\nmZMJBQlotTdGivvA6TXDvdeE8xeNqniaZWobQ7T72qk+5usetSkTCA4OnCa59JQU28jsW3LhNuJy\nDl3qJQiCIAjC6JiYm8iPHy3njY+Os3HXaX7+SiXzS9K595Z8rKZr70KcIJyNCEpchIELqna/zJbK\nem4py8RiHDpjYqDK6hbuXpDXm7Le8sqbtP11AwmT8sidbiXmSCE6awV01IISJmp08dq24+j1OhbP\nn4XTYePgkeNU7jtE31Jtsz4Hk95NLCbjCx7md2/GKC1MYfXigt4r0NXHuvj5747T0hZh2hQ7//DY\nWAxGDU+9O/REkZ5j1Wok3t7YxJo3G5DDMSZPsPG1h7LJdJ9774lzceREF2+s87CtwouqxidM3LU8\njQWzE6+ppnhNLTLbK3xsq/By6IifWHcWfE6mqXdixrgc8w238Btpes3Az4UwvEBQGdTnobYhRGOT\n3DudpYdOqyE9zciUYhuZbmN34CEehLjWAnyCIAiCcD0zGrR8cVEB5cVpPLfuEB9+3sDnR1v50pJC\npo9PvdKHJwiXlAhKnIOh6t1HWlDtqW6h3R8+p323d4Z6991ceZCGH/wcrd3K+C/kojGZicx/AEJt\nEAmAwUabYqejK8qi+bNIcjk4cvwUO/fsB+he7GqwGsZh0CWhxAJ0yodR1QheP3xQUcfRWh//9+Fp\nbPiglefX1BGLqXxpVQarbktDkjQ0tQeGnSjS3hmicr+XNWubOFkbxG7T8fWHs1kwK/GSLahVVaXq\nQCdr13moOtgJQFF+AiuWpjCzzIn2GmiSp6oqp2qDbO8e3XmiJgjES3iK8qzxQESpg/S0SxvEudaM\nNL2m53MhxmLFqapKa3ukT+DhTN+Hnv4jfVnMWvLGWrtLLowUF7lIsKi4U4w3TBaOIAiCIFwPctPt\n/L9HprN+ew1vfXKS3/51H2WFKXxpSSEu27XZS00QBhJBiRGMVO8+0oLK2yXjTDDgPYfAhMtmZMOO\nGvYfauDmp39BYkgm+4vTMTv0ROasQtVroMsLOhM4MkkIqyxZMIvkpESOn6pl2+6q3n1JGgmLoQC9\n1kFE6aRLrkal/6XSmkY/3/m3fdSeVrAlaPn7x8YwfYqz9+fDTRSJKRqUDis/+80pVBUWz0/iy/dk\nXrLGi0pMZXuFl7XveDh2Kj72sqTYxqrb0lg4P4OWFv9Z9nBlKTGVw0e74qM7K714muOvvU6noWyy\nnfJSJzNKHbgcIuWux0jTa1w2E46EG+8/2kg0RqMnXnJRWx+irlHuLrkIEZJjg7ZPSTJQOslOptvY\nO14zK92Ew67rFyhMSbHR3Nx5OX8VQRAEQRAuEZ1W4o45Y5lWlMIL6w9TUd3MwVPt3HdLHvNKMpBu\nsGxb4fojghIjGKne/e4FecMuqBJtJqbkJfJBZf1ZH8Ni0vNBZT0LNr1GYpsH3cwisifYqbJOpCg1\nK162IenAkU0MiaNtRpKTdJyqbeCTnXvo6YevQY/TMh5VNROOttMVPgr075avyBL+BivesILJqqBN\n8bHmYz/VTWcaCw6cKKKqEPHrCTSZURWJ9DQDX/tyNiUTHBf2pA4QicTY8lkbb7zrocEjo9HA7OlO\nVi1PIz/XGv/drtITbTgSo+pAJ9srvezc48PXEQXijQHnznRRXuagbLIDi0iLH9JI02tKC5Ov69KN\nrkA0nu1Qf6bkoq4hRGOzTGxA7EGv05DhNnY3mDSR3T3pIsNtxGS8fp8jQRAEQRD6S0+y8r3VpXz4\neT2vfXCUF9YfZtt+Dw8vH487UWSXCtcuEZQYxrnUu4+0oLp/YT4AW/fU9/YQ6EvSwNySdPYda6Xg\nUAUTDuwk7E7hphVj2BdysVE3hsKOOjQaCZw5xCQ9BzxG2gI6XOYo9REPiTYjbR0hTAYLBm0Bqmok\nGmsmqp5iYEBC7tAT8FhA1WB0hTAlh9Bohm4s2HPsO/a2Un9MIhLQo5FUEjMjhCxe/vSBj/11Fzch\nIRhU2LC1hbc2NNHui6DTalg8P4mVy9IueW+KS6kroFBRFe8PUbG3o/fqtcOuY8n8JMrLnEyZYLum\nel5cST3vtcrqFto7Q7hspn6fn2tZLKbS0hamru+Ize6/vd0BrL4SrFoKx1nPTLno/pOabLgmypYE\nQRAEQRh9kkbDzVMzKclL5k/vHabySAv/7w87WDF3LLfOzLnShycIF0QEJYZxLvXuIy2ooorK9KJU\ntgyTLaEC5ePT+HxLFfM+WEvUYGDWgxNow8LL4cl8Z05CfCt7FqrWxKEmIy1dOpwmhUlumZKMAu5e\nMI6n3zrOsdpkJI2eYKSOUCQ+NcOdaKaxLYgag2CzGdlnBEnF6u7CYBtcg963sWAsBkbZTv2hLiIR\nlVS3lpCpHdUQX4BfzIQEb0eEdzY18+7mZroCCiajxIplqdy5JJUkl+G89nW5tHkj7NzjZXuFj70H\nO4kq8YCPO9VIeZmD8lInhXlWsXC8AFpJYvXiQu5ekDeob8u1IhyJ0eAZHHioa5SRw/3THjQaSE0y\nUDbZ3ht46AlC2G3idCwIgiAIwrlx2Yx8a9Vkdh9u5qWN1fxl63F2HGzi63dPwW03XrWZxoIwFPEt\neBjnUu8+1IJKp9X09qFo7ZCRNPESiIE0wI6qGpZteBlDJMyY+8swJCfwn97JPH5rGnazlog5FZ3B\nRnWzgSa/DrtRYVJ6CG33RfgjpxVO1KWiQaIrfJJwtKl3/+FIjFSblaP7NCiyDq1BwZrRhdYwuC4d\nzgRaWppjPPliDafrQjjtOr78cAbvVB4m0jn4fuczIcHTLPPmhibe/6iFcETFnqBj9V3pLF+YQoL1\n6nsb1ntC8f4QFT6qj3f1vobjxpgpL41PzMjJNIkT/iVi1Guv+qaWHf5ov6BDbXfgoalZHpQNZTBo\n4uUW7p6sByNZ6SbS00wYDSKLRhAEQRCEi6fRaJg+PpUJY138efNRPqpq4F9+9yk5aQksnZHNzAlp\n6LTie4dw9bv6VoNXifOpd++7oHp5U3W/+wxVutFze/S3z+Ly1GGZlktOWRrPegu5fX4OGS4dnxwL\nk1tgpiOgo9GvJ8GgMDk9hK77vFJxOMIrG8Ooqoau8FEiSnu//Tc1KihtVhQ5RkJiFH2inySnka5Q\nhFB4cIDBbjax5o0mNn/chkYDt96czEP3ZNAlh/nThxc+IeFUbZC16xr5eEc7sVi8Md/KZWksmpuE\n0Xj1nCRVVeXYyUDvxIzT9SEgXmYzsSiB8lInM0sdpCbfeM0XbyRKTKWlNdw7VvNMEEKmwz+45MJh\n1zG+IKFPyUU8+JCcaEASmTOCIAiCIFwGVpOer9w2gflTM9jyeQOfVtXzzN8O8tqWYywsy+LmqRnY\nLFdnRrIggAhKjOhMeUYzbZ0yibYz0zeGMlIfioFyj+5lctWnkOpi6soCtnSlk11SRHGGkcpTIZ79\n0MvUVh+TJ6QSCQeZlKOg7x7lt7UyzFsfhTEaIBg+QkTx9e5XVSHUaiLUZkKvU/nmIznMm+3qzeT4\ny9Zj/YImqgrhTj31p8yckNsYm2Xm6w/nUJQXbzKp02suaELCgWo/a9c1sruqA4CcTBOrbnNz0wwX\nOt3VsVhTFJX91f7ujAgvre3xshaDXsOMqQ5mlTmZXuIQafXXIVmOUe8ZHHio94QIR/pHEiUNpKUY\nKcyz9Cu5yHSbLtn0GUEQBEEQhIuVl+FgVkkWB480sWl3LR9V1fPGh8f526cnmTPJzZLp2WQkW6/0\nYQrCIOIb9TlQVRVVjf89Ep9fHnLxPpCto42b33+dmE7H9AcnUoODhqyprBpv5WRLhN9v9TGpqIDJ\nEwro6PSzYcunNNel8MCiAt75NMwHuyPYrRoy0zx8uu9MQCIW1dDVYCEa1GNN0PBv3y1i3Jh4FkNP\nNkPfPhgtrWHkFivBTi1Gg4Yv35vBnUtS+wUNzidjJBZT2V3Vwdp1jRw62gXAhAIrq25zM22K/aoo\ndZDlGHv2d7Ctwsuuz334u+IjU60WLTfPTmRmmYPSSXYx1eA6oKoqvs5ov6BDvOQiRHNreFBZldEg\nkZVh6tdoMivdRHqqUTQuFQRBEAThmpHsNPPFRQWsmJvLx1UNbNx1mq176tm6p57J45JYOiOb4rGu\nq+K7uSCACEoMSY4o+PwyG3bU9Bvr2dYZHrHBoyPBiMkgDVke0UNSFBa/+zJGOci4uycTS3HxvqGM\nh2Y6afMr/HpjO3m5YymdPB5/V4CNW7cRDMlUHG4hFs2mslohxanhkdsN/Meaht79RoNa/PVWVEXC\naIvwnz+cSoprcBaDVpK4d0E++BN4Y4+HaFRl2hQ7TzyYPWxpwtkmJESjKh/vbOONdR5q6uJlD9NL\n7Ny13E1xYcJZnu3R1+GPsutzHzsqvFTu7yAcjq9Gk1x65pUnMqvMQXGh7arJ4BDOj6KoNLXI3SUX\ncneTyXggoifo1JfLoWdiUUJvtkNPICLRqRclF4IgCIIgXDfMRh1LZmSzaFoWlUda2Lizhr3HW9l7\nvJXMFCtLpmcze2Iaep24GCdcWSIo0YcSi/U2qWzrkBkueDhyg8eh76SVNCgxlZmfrSfNU4O9JAv3\n9ExeVKZw/4IMQpEY/7WxnRR3FjOmTiIQDPHe1s/oCgYBiXA4h8pqhZw0iUe/YKYzEKC1Q0ZVQfYa\nCTbHx2iak4OYE2VUBi/GAPYd7uTJF2qoa5RJdOp5bHUWs6Y5R4yUDjchQZZjbPi4ib+ub6K5NYwk\nwYLZidy1PI0xWeaRnupR19waZkell20VXg5U+4l1x4my0k3xiRllTvLHWkSE+BoSDCnUDxivWdsY\nosEjE40OKLmQID3VSHFhwpmsB3e854PVIk57giAIgiDcOCRJw7SiFKYVpXCioYONO0+z81ATz797\niL9sPcYtpZncUpaFwyr6TghXhvh23seazUcH9VsYynANHn1+GTk8dDBAiamMqz3M1IqtSEk2Jt49\ngb3OEu4oLUAnwa82edHb0pg1bQohWWbj1s/wdwXQoCPBWIhOmwB0kOjqwmzM442PalEV6PJYiPgN\naLQxrOld6C0KifbBvR46OqO88OdaNn8Sb2R5+6IUVq/KwGI+98hoT0NPf1eUt9Y38LeNzXT4oxj0\nGm5blMKKW1OvWCNIVVU5XX9mYsaxU4HenxXmWSkvjY/uzEw3XZHjE86Nqqq0+6Jnsh3q44GHuoYQ\nLW2DR9maTRJjs81nSi66Aw/uVCN6nSi5EARBEARB6Cs33c4TX5jIPTfnsbmijq176njrk5Os23aK\nWcVuls7IJiv1ymc6CzcWEZTodj5NKodr8DjSGFGr38e8da+iaiVKvjSZfbhJn1qMzaThxU99+NQk\n5s8sJRyJsHHrNnydfiSNgQRjEVrJjBxtJhA+yQcVKhBjZ1UrHTU2YhEtOnMEa3oASRePokzJS+zN\n4lBVlQ8+aeP5P9fS6VcYlxNvZFmQe/5Nblrbw7z9XhMbtrQQkmNYLVruvcPNbYtTcNr1572/ixWL\nqVQf74oHIip9NHjiz7tWC1Mn2igvczJzqoNEl4j6Xm2iURVPs3xmykVjT/aDTCA4OLCX5NJTUmwj\ns2/JhduIy6kX2S6CIAiCIAjnKdFu4p6b87hzzlg+3dfAe7tq+XhvAx/vbWDCGBdLZ2QzOS8JSXzP\nEi4DEZTo5vPLtJ1Dk0oY3OCxx3BNITWxGIs2vII51EXeimL8aW6kKbNwmjVEDE4sKYnMn5iLElXY\nvrMCq0FBY3OgRHKRJAOhSD3ByJl9fvRZO801RlA1mFwhTMmhfqUmi6dnA1DbEOLJP9aw/7Afk1Hi\nK1/M5PZFqWi153dyqWsI8df1HrZ82kZUUUl06vniinSWLkjGfB6ZFpdCJBpj78FOtlf62Fnppd0X\nH9NoMkrMnu5kVpmTaVPsIkX/KhEIKr3lFn0zHxqbZJQBsQedVkN6mpEpxTYy3cbuwEM8CHG532eC\nIAiCIAg3AqNByy1lWSwozaTqWCsbd57m4Kl2Dp5qx51oYcn0LOZMSsdoEN/FhNEjVm7dRspykDTx\nUo5Ee/8Gj0MZ2BTSYTWSt+ltMuqO45zoJrF8LBXZs5iWbaOyJoQqfrdqAAAgAElEQVQzJ5nkdCca\nINfhZ/4DxdR64Jm3A6iShkD4FHLUA4Aag0CTmXCHAUmrYk7zY0iI9nv8JLuJBLOBl9+o5411HqKK\nSnmpg8e+lE1y4vllDBw50cUb6zxsq/CiqpCeZmTV8jQWzE68rNMIAoEon+xoZ1uFl4q9PgLBeIMI\nu03H4nlJzCx1UjLRhkFMSLgiVDXeaLJqfwd1jSFO14eoa4w3nGzzDi65sJi15I21dpdcGLtLLky4\nU4znHTATBEEQBEEQLp6k0TA1P5mp+cnUeDrZuOs02w94ePG9atZ+eJybSzNZWJaFy3ZlSrWF65sI\nSnQbafTlgtJMbp2R3dvgcSQDm0LGKvZw6ifvo3VaGH/PJLbZpzJzspuTLRE2HTNSnugEFSalyyRa\n9FQdjfLShhCqqkEjnQlIKGGJrgYLiqzDaIlx8y0WdhzxDXr8DLuTf/pxNQ0emSSXnscfzKa81HnO\nz4OqqlQd6GTtOg9VBzsByBtj4e7b05hZ5kR7maYTeH0Rdn7uY3uFl6oDnUS6GxmmJhtYNC+eEVGU\nb71sxyPEs1QaPTK13RkPPYGH2oYQIXnwxJmUJAOlk+xkuo294zWz0k047DpRciEIgiAIgnCVykmz\n8ejtxdyzII8PKuvYXFHHO5+dYv32GmZMSGXpjGzGuu1X+jCF64gISvQx0uhLrXR+V+GNei0uJcS+\n7/4IjaRh0uop7E3IZ+bc8bT5Ff64I8rcm8qJqTDJLZNoUfi0KsLaLTIGPTx8u4nd1Xo27YKwX0eg\n0Yoa02BwyNy+LJHVSwqwb9b1HqvNZCLmS+DDzSEkDdy5NJUHVqSfc9q7ElPZXuFl7Tue3iaRJcU2\nVt2WxuQJtsuyiGxskrv7Q3g5dLSrt9Fofq6VaZPjPSLGZpvFgnaU+bui3SUXcu9ozdqGEJ5muXeK\nSQ+9TkOG28i4MTaSE7Vkd0+6yHAbMRlFmp8gCIIgCMK1ypFgZOW8cdw2awzbDnjYuPM02/Z72Lbf\nQ2GWg6Uzc5ianyxGqgsXTQQl+hhu9OVI5Igy5LZqLMbxv/9XIk2t5N5WRKSwkMKbZhKKxHjtc5W5\nc+YgSRITUmWSLFHWbwuzcUeEBLOGx1aYyE7VkpueR1WlzOH6CGhUUseGWTAnqTdIsnpxIXfNG8e7\nm5tY+04zXYEo+WMtfP3hHPLGWEY46jMikRhbPmvjjXc9NHjiY1BnT3eyanka+RfQDPN8qKrKiZog\n2yu9bK/wcqo2BIBGAxMKEigvczBzqpPJE5Npbu4c1WO50cRiKi1tYeoa+zSb7P7j7YgO2j7BqqVw\nnPXMlIvuP6nJBrSShpQUm3iNBEEQBEEQrkMGvZb5JRnMm5LO/pNtvLfzNPuOt1Fdu5cUp4nF07OZ\nOzkds1EsLYULI945Q+gZfTkSJRZjzeajVFY309Yhk2g3UlqY0hswaPjtH/Ft+QxXUQoZSyYTmT0P\nVa/Dq3EzaWomkZhEUYpMsjXK65tltu2PkmTX8MRKM8lOiTZvhP948gSHqyO4Uw088eUMigsc/QIf\nNXVBfvdCDYeOdmE2STz+pSxuvSXlnEoagkGFDVtbeGtDE+2+CDqthsXzk1i5LI1M9+iNzVQUlYNH\n/WzfHZ+Y0dwaBuJX3KeX2CkvdTJ9quOKTPO4HoUjMRo8gwMPdY0ycrh/2oNGA6lJBsom23sDDz1B\nCLtNnCoEQRAEQRBuZBqNhkm5SUzKTaKupYtNu07z6b5GXtl0hL9+dJz5JRksmpZFssN8pQ9VuMaI\nlcYFWrP5aL/+E60dcu+/73QEqf3pbzE4TBTeP4Vo2XwwGomY3RxtjQckCpJlki0RXngnxP4TCpkp\nEo+vMGGzSOw71Ml/PHkCb0eU2dOdfOsrY7D0KcOQ5Riv/a2Bv673oCjxzIZHH8gi6RxGX3o7Iryz\nqZl3NzfTFVAwGSVWLEvlziWp53T/CyGHY1Qd6GBbhY9de3x0+ONX4i1mLfNnuSgvc1I60S4mLFyE\nDn+0u89DqLfPQ21DiKaWcG8ZTA+DQRNvLunuyXowkpVuIj3NhNEgmoUKgiAIgiAII8tMtvLwsvGs\nmj+OLd19JzbsOM3GnbWUFaWwdEY2+ZmOK32YwjVCBCUugBxRqKxuHvJn+z4/xYRX/gtiKkVfnII0\nYz6Kw0nUmEhFWyayIjEuMYzLGOHJN4KcbIhRkK3lkdtMGPSwdl0jL/2lHo0EX/1iFncsSenXQ6Fi\nr4+nXjyNpyVMSpKBJx7MZnrJ2T/wTS0yf13fxPsftRCOqNgTdKy+K53lC1NIsF76t4G/K8quKh87\nKnxU7uvobYTocui59eZkZpU5mTg+Ab1OLILPlRJTaW4J9+vzUNfd+6En0NOXw65jQkFCn5KLePAh\nOdEgav8EQRAEQRCEi2azGLjzplyWlY9hx8F434ldh5rYdaiJvAw7S2ZkM60o5bz78wk3FhGUuAA+\nv0zbEKNDUVWmvvEnIvUecpbkY5tXTjRrLDF9AhW+sYSiEmNcYew6mf9+PYSnLcbUAh0PLDESkhV+\n+ftT7NzjI8ml53//XS7j8xN6d93mjfDcq7V8vKMdSYK7lqdx3xfcZ20meKo2yNp1jXy8o51YLD4R\nYeWyNBbNTcJovLQnh9b2MDsq4xMz9h3uRFHit2ekGSkvc1Je5qQg1yIWxGchyzHqPYMDD/WeEOFI\n/7QHSQNpKUYK8yz9Si4y3SZsCeLjLQiCIAiCIIw+vU7ipsnpzJnk5nCNl/d2nubzoy08+eZ+kuxG\nFk3LZn5JOhaTKNEWBhOrlgvgSDCSaDfSOiAwMenzTxh7fD+OvESyVs4kOn4qqs5MpT+PQERLliOC\nKSbzm9eCeP0q86bq+cI8Aydqgvz8f47jaQlTUmzjfz0xtrenQiym8t7WFl58vZ5AUKEwz8rffTmb\nsdkj97w4UO1n7bpGdld1AJCTaWLVbW5umuFCp7t0QYHahlB8YkaFlyMnAr235+daKC91Ul7mICvd\nJCZmDKCqKr7OaG+pRV3Dmb4PPX02+jIaJLIyTP0aTWalm0hPNaLXi8izIAiCIAiCcOVpNBrGj3Ex\nfowLT1uAjbtO8/HeBv78wVHe/PgEc6eks2R61ln79wk3FhGUuABGvZbSwpR+PSWSm2qZ/ck76KwG\nih6cgTJ1DqrBzN5gPp1hAxn2CFI4yP+8HSQow+1zDNxcpmPTh60889JpIlGVe+90c/+K9N5GlSdP\nB/jdH09TfawLi1nL1x7KZumC4cfuxGIqu6s6WLuukUNHuwCYUGBl1W1upk2xX5LAQCymcvREoHdi\nRl1jPDAjSTBlQnxs58xSB8mJo9Of4lqjKCqeFrk7+CCfCUI0hvB3KYO2dzn0TBqf0Jvt0BOISHTq\nRYaJIAiCIAiCcM1IS7Tw4NIi7po/jg/31LNpdy3v765l8+5aphYks3RGNoXZTnHxUhBBiQt1/8J8\nACqrW/C3eFm+4SW0isL4+0uRbroZxergsJxPm2wmzRYh3BHg2Q0hFAXuX2ykJE/Lb56tYcunbSRY\ntfzTt8YybUq8N0RIVljzZgNvvddELAZzZ7r46gNZuBxDpztFoyof72zjjXUeauriYzWnl9i5a7mb\n4sKEIe9zPqJRlX2HO9le4WVHpY82bwSIN0wsL3Mwq8zJtCmOG7pcIBhSqB8wXrO2MUSDRyYaHVBy\nIUH6/9/encdHVZ79H//MkpnseyYJCWELuyyJAiIgblBBfqXFHQMudaVSq7WCPLTio1Wx9PFpsXZB\nfbSIhYq0WhXcUYSwCUQWEcMasi+TPTPJzJzfHwlDYtCCYiaQ7/v14hVm5mTmOnPPgXOuua/rdtgZ\n1C/8+KyHpOaeD2GhXfc9FBEREZGzT1hwEJPO78GEEd359ItS3tlyhO1flrH9yzJ6JEYwcUR3Rgx0\nYLVo9m9XpSugb8liNjP9sn5Mu7A3+++aR52znNSLehMx6SI8jhQOentT5IogIcxDVWkdr37gxmKB\nm6cEExXiYc5vvuTwURfpvUL55V29cMTbAdiyo4oly/IoLW8kMd7GHTPTyDgn8oQxuN0+3v+kjH+t\nKaG0vBGzGcaPjuXHkxLpkfrdluJpcHnZsauajdsq+fSzaurqm7/VDw+zcMmYWEZmRjN8UORp70vR\nmRmGgbOqdclFc+Ihv9BFWUVTu+1Dgs307B5yvOSiJfGQ5LCrwaeIiIiIdClWi5lRgxIZOdBBbn4V\n72zJY9u+Upa8sYdX1uZySWYqF2WkEB6ivhNdjZIS31H1K29Qt/oDItKi6X7dhXjTz6HQl8qR+lji\nQj0U5tWwOruR0GC49f+FkJ9fzaPPH6bB5WPSJQncfG0KQUFmyp2NPPfyUbI/rcRigSuvSOTqKckn\nvOivrfOw+oNS3ni3lOpaD7YgE5MvTWDqDxz+5Ma3UVXdxJacKjZvryJnd7W/qWJCnI2LLojl/Mxo\nBvYNx2I5u6dYeTwGRaVtSy2OLbdZ3+Brt31cTBDDBkWQ0rrkIslOTHSQpqOJiIiIiLRiMpnomxpN\n39RoSisbeP/To3ycU8Cqjw/w+vpDDOgRzfD0eIb1iScuKjjQ4UoHUFLiO6jfm8vh+QuxhgTR/5YL\n8GaOoZxE9tUlEx3s4csvqln/WRMxESZumRLMmvcL+fc7JQTbzdx3e0/GnR+L12fw5nslLFtVQIPL\nx4D0MO66MY20lPYzHcqdjfz7nRLeXluGy+0jLNTC1VOSmHxZgr8x5qkqKXOzaVsVG7dVsvfLWnwt\nlQZpKcH+FTN6p4WclRfXdfVe8ltmOhxLPBwtclFU4vavHHKM1WIiOdHO0EHBpCTZWxIPzUmIkJBv\nXgFFRERERETaS4gO4bpL+zJ1bC/WfVbIJ58VsutABbsOVPAS+0hNCGd43ziGpcfTKzkS81l4TSJK\nSnxr3voGcm97AMPdRN8bz8Vy8QSqbQnsrOlBpN3Lzs+q2PGlh6Q4M1eNt7B4SS57c+tITQ7mgVm9\n6J4SwoHD9fzpxSPkHqonPMzCrJvSuHRsXLuGhvmFLv61ppi1GyrweA1io4O4bmoyE8fHn/IFsWEY\nHD7awKaWpTsPHmkAwGSC/n3CmhMRGVEkJ54dWUnDMCh3NvlnPTTPfGieBXGsN0ZroSEW+vQMaym5\nsLeUXASTlGA/62eIiIiIiIgEQojdysQR3Zk4ojtlVQ18tr+cHbll7D3s5I0Ntbyx4TCRoUEM7RPP\nsPR4BveKIdimS9mzhUbyWzr8Xwtx7T9CtzE9iLp6CvVRqeyoSScsyMeWrZXsO+KhdzczI9M9LPht\nLtU1HsaNiuGuG9MAeH75Ud58twSf0dwH4qZrU9rNdsg9WMeqt4rZuK0Sw4DkRDvTJiUyfnTsKS0D\n6fUZfJFb17x05/ZKikubl5y0Wk1kDolkVEY0IzKivraR5pmgyeOjqNjdLvFwtNCFy92+5CIhzkbG\nOZGkJNn9y2umJgcTFWk9K2eFiIiIiIicCeKjQrgkM5VLMlNxNXrYc8jJjtwyPttfzic7C/lkZyFW\ni4kBaTEMS49nWHoc8VHfrZ+eBJaSEi3cTV6qat1EhduxB33z7IOylW9StuINwlMi6X7rJBpTB7Ct\nth82K6zPdpJX7OWc3hZCPJU8vrgQi9nE7VndufzieDbvqGLJS3mUO5tIdti5Y0Z3hg0+3sjSMAw+\n21PDqreK+ezzGgD69AjlyisSGZkZ7V8u9D9pbPLx2Z4aNm2vZMuOKqqqPUBz88WxI2MYlRlF5pAo\nQs+w0oPaOk9Lk0l3c8lFS+KhuNSN7yu5hyCriW5Jx2c7dG9Z6aJbkp1g+5m13yIiIiIiXU2wzUpm\nvwQy+yXgMwwOFdawI7eMnNwydh2sYNfBCpa9C6kJYQxLj2f4sTKPk7xmks6hyyclvD4fS/61k/U5\n+VRUu4mNtJPRL4FrL0nHYm4/G6Fh/2EOzXkMi91Cv9svwjt0NDvq+oHJwkefVFBc7uPc/hZy9+Sz\nfWc1CXE27r+rF7HRQTzx9AE2b6/CajFxzQ+TuPKKJGwtMx68PoNN2ypZ9WYx+w/XAzB0YATTJicy\ndFDESX17X1fvZdtnzf0htu2s9s8QiIq0MuHCOEZlRjN0YMQpzbIIBJ/PoKyikfyWJTbLnYXkHqwh\nv9BFZUtypbXwMAv9eocdX+Wi5Y8j3nbSSRwREREREem8zCYTvbtF0rtbJNMu7E15lYvP9pexI7ec\nzw87OZp9mDezDxMRGsTQPnEMT49nUM9YQuxd/pK30+vyI7Tig1ze23rUf7u82u2/Pf2yfm229bnc\n7L/tfnwNbvrPOA/LxEnsdPfDZQSzdl05ziof5/WDD9/bT2l5I5lDIpl9Sw8+3lTB3/9ZiMvtY3D/\ncO6cmUZqcnPPhqYmH2uzK/jn6mIKi92YTDD6vGimTUokvVfYf4y/orKJLTsq2bStip2f1+DxNneq\nTHLYGZUZxaiMaPr1CeuUF+eNTT4Ki93+BpP5hceaTrpxN7ad9mAygSPORuaQSH/i4VgSIjKiy3+M\nRURERES6lLioYC7OTOXizFTcjV72HKponkWxv5z1O4tYv7MIq8VE/7SY5tU8VObRaXXpqzl3k5ft\n+0pP+Nj2fWVcOb5Pm1KOIw8ton7vQRJHdif6xqvZZxpIpSeStevKqa7xMaBbI6tePYzXZzD9x8kM\nHRzBI0/lcuBIAxHhFm67oQcXj4nFZDLR0ODl7Y/KeP3tEpxVTVgtJi67MI4fXZ5IStI3N5ksKHY1\n94fYVsW+A3UYLStm9O4RwqiM5hUz0lKCO01vhOpaj39JzdYNJ0vKGv2xH2OzmZrLLZKOzXqwM2RQ\nHME2L3Zb557hISIip8++ffuYNWsWN910E1lZWfzsZz/D6XQCUFlZyfDhw3nkkUd49tlnWbNmDSaT\nibvvvpvx48cHOHIREelodpuFjH4JZLQq88hpKfPYfbCC3S1lHikJYS0Jinh6q8yj0+jSSYmqWjcV\n1e4TPuascVFV68YREwpAxb/fpWTpPwlNDKfHvVeTFzmMAlcCH66roKHeR4ylirfeKiYy3MpPb04j\nZ08ND/5mH4YBl4yJ5cZrUomMsFJZ3cSb75Wy+oNS6uq9BNvNTL3cwf+b4CAuxnbCWAzDYP+hev+K\nGXkFLgDMJhjcP5xRGdGMzIjCEW//ft6ok+D1GZSWNbbp85Df0vuhurZ9yUVUpJWBfcNblVzYSU0O\nJj7W1u4fh4SEcEpLazpqV0REJMDq6+t55JFHGD16tP++P/zhD/6/P/jgg1x99dXk5eXx1ltvsXz5\ncmpra5k+fTpjx47FYlHfIBGRrqp1mcePL+xNRbWLnP3l5OSWseeQkzdbl3n0jmtZzUNlHoHUpd/5\nqHA7sZF2yk+QmIiJCCYqvPki330kn4P3PYw5yEy/u39AWfqF5NansvYTJ40uLw2lxew7UkX/9DAu\nviCWvyzNo6KyiZQkO3fOTOOcARGUlLlZ/loh768ro7HJIDLcyvQfJzPpkgTCw9oPg9drsHtfbcuM\niErKnc3LV9qCTIwYHsX5mdGcNyyqw0sX3G4fBcWur5RcuCkodtHY1Hbag9kEiQl2+vUJbVNykZIU\nTER4l/7oiYjIN7DZbCxZsoQlS5a0e+zAgQPU1NQwdOhQVq5cybhx47DZbMTGxpKSkkJubi79+/cP\nQNQiItIZxUYGc3FGChdnpDSXeRyuaJlFUc76XUWs31WExWxiQFq0v1lmfLTKPDpSl74ytAc1T/Np\n3VPimIx+8diDLPgam8i95V68dS7SbziPxonT2FPfm4/XV9LkaiLvizxqq11cOi4OZ2Ujf/5bHkFW\nE9f/KJkfT0qkoNjNU389yCebnfh8zUtR/ujyRC4dG4fd3rYcwe32sWN3NRu3VbI1p4raOi8AYaEW\nLhody8jMKDLOifzeV44wDIOqGk+b2Q7HZj+Ulje2295uM5PaLbhNo8nU5GCSHfZO31RTREQ6H6vV\nitV64lOUv/3tb2RlZQFQVlZGbGys/7HY2FhKS0u/MSkRExOK1fr9/D+akBDxvTyvnDyNQeBpDAJP\nY/DNUlOimXhBb3w+g9yjlWzZU8zmPUXsPuRk9yEnL7/3JWlJEYwclMTIQUn06xFzyv35NAanpksn\nJQCuvSSd0BAb63MKcNa4iIkIJqNfPNdekg5A/n8vom7PARIyUwi//Wa2uAfz8cYaGmrd5O48TJDF\nx/jRsXyyyYm70cfQgRHcMbM7lVUeFv7xAJ9+Vg1AWkow0yYnMWZEDFbr8Q91da2HrTlVbN5Wyfbd\n1TQ2Ns82iIsJYtyoWM7PjGJQv4g2v3O6eL0GxWXulj4P7uNJiCKXPyHSWkxUEOcMCPfPdjiWiIiN\nDlI9loiIfO8aGxv59NNPWbBgwQkfN77aqOgEnM760xxVs4SECJUaBpjGIPA0BoGnMTg1MSFWJp6b\nwsRzU6iodvHZ/nJ25Jbx+WEnKz/4kpUffEl4yPHVPE6mzENjcGLflKjp8kkJi9nMbT8awqSR3amq\ndRMVbvc3t6x8+0MKn3+VkPhQ0ubdwqfmUXy8sR5naS2HPs/DEWvFbDbzUXYFkRFW7ryxO6HBFhY/\nd5i9uXUADEgP48orkjh3aKS/8WRpeSObt1eycVsle/bV4mtZaCI1Obh5xYzMaNJ7hp62RpUNLi8F\nRe42vR6OFrkoLHbj8Xyl5MIMyQ47g/qFH5/1kNTc8yEstMt/XEREJIC2bNnC0KFD/bcdDgcHDx70\n3y4uLsbhcAQiNBEROcPFRgZzUUYKF2Wk4G5qXs0jJ7ecnP1lbNhVxIaWMo/+rco8ElTmcVroKrOF\nPcjib2oJ0JhfxIHZv8JkNdP3Fz9iV/IkPtzUSP4RJwX7C0hOsFFQ3NyL4tJxcaT3DOGfbxVzJL+5\nCeW5QyOZNjmJQf3CMQyDvILjK2bsP3z8W5p+fcIYldG8dGdK8jevuvFNDMPAWdW65KI58XC0wOXv\nR9FaSLCZnt1DjpdctCQekhx2gqwquRARkc5n586dDBgwwH/7/PPP5//+7/+YPXs2TqeTkpIS0tPT\nAxihiIicDexBFjL6JpDRt3k1j8NFNf4+FHsOOdlzyMnf3/uSbvFhDEtvnkXRp1uUZo9/S0pKnIDh\n8bD/lnvw1Lrofd1Ijoy/hXc3m9i/t4SK/GKCbWYKit2kJgeTcU4EG7dV8f66csxmGD86lh9PSqR7\nt2D2HajjxX8cZdP2KgpbEhgWCwwfHMGozGhGDo8i9mtW3Pg6Ho9BUam7zdKax0ou6ht87baPiwli\n2KAIUlqXXCTZiYkO6jRLhoqIiLS2a9cuFi5cSH5+PlarlbfffpvFixdTWlpKWlqaf7tu3bpxzTXX\nkJWVhclkYsGCBZjNSqyLiMjpYzaZ6JUcSa/kSH40rjfOGrd/udE9h52s3niE1RuPEB4SxJDecWQO\nTCQ0yExiTAjREXbMuub6j0zGyRRgdjKnu0bnq3U/+Q89Qf6SlcQN7UbI4j+w4rNUdm7Lp66sgsYm\ng6AgE4P6hnPgcD01dV5sQSYuuzCeyZfGU1zayKbtVWzZXomzqnkpzGC7mYwhkZyfGc25QyNPqgyi\nrt5LfsvqFq1LLopK3Hi/0u7BajGRnGhvSTzYWxIPzUmIkJAzf1k01WV1fhqjzk3j07mdaeNzpjfv\n+r7e6zNtHM9GGoPA0xgEnsagY7mbvHx+yEnO/uYkRWVt20UBbFYzjpgQEmNCccSGkBQTSmJsKIkx\nIUSG2brUl8TqKXEKqt9bS/6zK7HHhBD33/fzt10p7Nh0hJrySgwDHHE2KmuayNlTQ1iohR9d7iA5\nMZidn9fwwCNf+GcrREZYuWxcHCMzohk2OALbCVahMAyDcmfT8aRDoYv8IjdHC1w4q9qXXISGWOjT\nM6yl5MLeUnIRTFKCHYul63ygRUREREREAs0eZGF433iG943HMAyOFNdS5fKQe6SC4ooGiivqKXY2\ncLS0rv3v2iwktiQsEmNbfrYkLyJCutasdiUlWmkqKmb/7F9hMpnoOfcGlpeMZvO6g9RV1hJkBa8X\nSsobiY6yct7QcOobvLzxXqm/WaQj3sal46I5PzOa/ulh/qVjmjw+juQ3tEs85Be5cLnbl1wkxNnI\nOCeSlCS7f3nN1ORgoiKtXerDKSIiIiIiciYwmUz0SIogISGCoT1j/PcbhkFVXaM/QVHsrKekovln\nUXk9R4pr2z1XiN3anLBomVXR/LM5eREWHNSRu9UhlJRoYfh8HLh5Nk1VDaRdO5rXEm5k7Vu5uGob\nAGjyQGS4hbBQC4UljWzYWglAz+4hzY0qM6OJjw0iv8hNfqGbLTsqm5MPhS6KS93+FTaOCbKa6JZ0\nfLZD95aVLrol2Qm2n/klFyIiIiIiIl2dyWQiOtxOdLid/mkxbR7zGQaVNW5/sqK4or55hoWznqOl\ntRwqal+KEx4SRGJMCI7WMyxafv6n5Uo7q04T9WOPPUZOTg4mk4l58+a1WfKrIxQ+/Buqcg4QMziZ\n7Cse5s1/HqCxobk5pS3IRGOTQXWtl5o6L+m9QumdFkpkhJXqWg+7vqhlzdoyqqo97Z43PMxCv95h\nx1e5aPnjiLf5Z1KIiIiIiIhI12I2mYiNDCY2MpiBPb6SsPAZVFS7WiUsWn46GzhUVMP+gup2zxcZ\nZmtXEnKsp4Xd1nm/+O4USYnNmzdz+PBhVqxYwf79+5k3bx4rVqzo0BjK/vU+tshgSn72G15emY+n\n8XhPhyaPQWx0EGYzVNU0kXuwntyDx5f1NJmae030GRLpTzwcS0JERnSKt1hERERERETOEGazifjo\nEOKjQxjcK7bNY16fj/IqF0UVbctBip315OZX8eXRqnbPFx1u+0qyovnvjugQbEGBTVh0iivm7Oxs\nLrvsMgD69OlDVVUVtbW1hIeHd1gMvgfnU2eL4Kl/G3g9bWc8GAZUVDZhs5lITQ5pXlozOZiUZDup\nycEkJwZjt2kJMhEREREREfl+WcxmHC2JBYhr85jH66O0svQ84xgAABCuSURBVIFiZwMlrfpYFFc0\nsC+vki/yKttsbwJiI+0tSYpQ0hzhjB2ajNXScde3nSIpUVZWxuDBg/23Y2NjKS0t7dCkxMPvHMs+\neQkNNdMjJYTu3ULaJB/iY22YVXIhIiIiIiIinZDVYiY5LozkuLB2jzV5vJQ4G9okKkpaSkI+P+zk\n88NOAHokRdArObLjYu6wVzoFhmF84+MxMaFYrad3ismKJSMpr3DTs3sYkRFnX0fTM903rWsrnYPG\nqHPT+HRuGh8RERH5vgVZLaQkhJOS0P7Lf3ejl5LKBlyNHnomdex5SadISjgcDsrKyvy3S0pKSEhI\n+Nrtnc76r33s20hIiMBm8ZCcYMHtclHqcp3W55fvJiEhgtLS9p1npfPQGHVuGp/O7UwbHyVQRERE\nzj52m4Xujo6rVGitUzRCGDNmDG+//TYAu3fvxuFwdGjphoiIiIiIiIh0vE4xUyIzM5PBgwdz3XXX\nYTKZeOihhwIdkoiIiIiIiIh8zzpFUgLg/vvvD3QIIiIiIiIiItKBOkX5hoiIiIiIiIh0PUpKiIiI\niIiIiEhAKCkhIiIiIiIiIgGhpISIiIiIiIiIBISSEiIiIiIiIiISEEpKiIiIiIiIiEhAKCkhIiIi\nIiIiIgGhpISIiIiIiIiIBISSEiIiIiIiIiISEEpKiIiIiIiIiEhAKCkhIiIiIiIiIgFhMgzDCHQQ\nIiIiIiIiItL1aKaEiIiIiIiIiASEkhIiIiIiIiIiEhBKSoiIiIiIiIhIQCgpISIiIiIiIiIBoaSE\niIiIiIiIiASEkhIiIiIiIiIiEhDWQAcQaI899hg5OTmYTCbmzZvH0KFDAx1Sl7Fp0ybuuece+vbt\nC0C/fv249dZbeeCBB/B6vSQkJPDb3/4Wm83G66+/zosvvojZbOaaa67h6quvpqmpiblz51JQUIDF\nYuHxxx+ne/fuAd6rM9++ffuYNWsWN910E1lZWRQWFn7nMdm7dy8LFiwAoH///jz88MOB3ckz2FfH\nZ+7cuezevZvo6GgAfvKTn3DRRRdpfALkySef5NNPP8Xj8XDHHXcwZMgQHT9nOZ1HBN5Xj7uJEycG\nOqQuyeVyMWXKFGbNmsW0adMCHU6X8/rrr/Pss89itVr52c9+xkUXXRTokLqcuro65syZQ1VVFU1N\nTfz0pz9l3LhxgQ7rzGB0YZs2bTJuv/12wzAMIzc317jmmmsCHFHXsnHjRmP27Nlt7ps7d67x1ltv\nGYZhGL/73e+MZcuWGXV1dcbEiRON6upqo6GhwbjiiisMp9NprFq1yliwYIFhGIaxbt0645577unw\nfTjb1NXVGVlZWcb8+fONpUuXGoZxesYkKyvLyMnJMQzDMO677z5j7dq1Adi7M9+JxmfOnDnGBx98\n0G47jU/Hy87ONm699VbDMAyjoqLCGD9+vI6fs5zOIwLvRMedBMb//M//GNOmTTNeffXVQIfS5VRU\nVBgTJ040ampqjOLiYmP+/PmBDqlLWrp0qbFo0SLDMAyjqKjI+MEPfhDgiM4cXbp8Izs7m8suuwyA\nPn36UFVVRW1tbYCj6to2bdrEpZdeCsDFF19MdnY2OTk5DBkyhIiICIKDg8nMzGTbtm1kZ2czYcIE\nAC644AK2bdsWyNDPCjabjSVLluBwOPz3fdcxaWxsJD8/3//t4bHnkFN3ovE5EY1PYIwYMYLf//73\nAERGRtLQ0KDj5yyn84jAO9Fx5/V6AxxV17N//35yc3P17XyAZGdnM3r0aMLDw3E4HDzyyCOBDqlL\niomJobKyEoDq6mpiYmICHNGZo0snJcrKytp8WGJjYyktLQ1gRF1Pbm4ud955J9dffz3r16+noaEB\nm80GQFxcHKWlpZSVlREbG+v/nWPj1Pp+s9mMyWSisbExIPtxtrBarQQHB7e577uOSVlZGZGRkf5t\njz2HnLoTjQ/ASy+9xMyZM7n33nupqKjQ+ASIxWIhNDQUgJUrV3LhhRfq+DnL6Twi8E503FkslgBH\n1fUsXLiQuXPnBjqMLuvo0aO4XC7uvPNOpk+fruR1gFxxxRUUFBQwYcIEsrKymDNnTqBDOmN0+Z4S\nrRmGEegQupSePXty9913M2nSJPLy8pg5c2abbze+bjxO9X45fU7HmGicTq+pU6cSHR3NwIED+etf\n/8rTTz9NRkZGm200Ph3rvffeY+XKlTz//PNtatt1/Jz9ND6B0/q4k471r3/9i+HDh6uvV4BVVlby\n9NNPU1BQwMyZM/nwww8xmUyBDqtLee211+jWrRvPPfcce/fuZd68eaxatSrQYZ0RuvRMCYfDQVlZ\nmf92SUkJCQkJAYyoa0lMTGTy5MmYTCbS0tKIj4+nqqoKl8sFQHFxMQ6H44TjdOz+Y99INTU1YRiG\n/xtJOX1CQ0O/05gkJCT4p7K1fg45PUaPHs3AgQMBuOSSS9i3b5/GJ4DWrVvHn//8Z5YsWUJERISO\nn7OcziM6h68ed9Kx1q5dy/vvv88111zDK6+8wjPPPMOGDRsCHVaXEhcXR0ZGBlarlbS0NMLCwqio\nqAh0WF3Otm3bGDt2LAADBgygpKRE5WQnqUsnJcaMGcPbb78NwO7du3E4HISHhwc4qq7j9ddf57nn\nngOgtLSU8vJypk2b5h+Td955h3HjxjFs2DB27txJdXU1dXV1bNu2jfPOO48xY8awZs0aAD788ENG\njRoVsH05m11wwQXfaUyCgoLo3bs3W7dubfMccnrMnj2bvLw8oLn/R9++fTU+AVJTU8OTTz7JX/7y\nF/9qKDp+zm46jwi8Ex130rH+93//l1dffZV//OMfXH311cyaNYsLLrgg0GF1KWPHjmXjxo34fD6c\nTif19fXqZxAAPXr0ICcnB4D8/HzCwsJUTnaSTEYXn2u4aNEitm7dislk4qGHHmLAgAGBDqnLqK2t\n5f7776e6upqmpibuvvtuBg4cyJw5c3C73XTr1o3HH3+coKAg1qxZw3PPPYfJZCIrK4sf/vCHeL1e\n5s+fz6FDh7DZbDzxxBMkJycHerfOaLt27WLhwoXk5+djtVpJTExk0aJFzJ079zuNSW5uLr/+9a/x\n+XwMGzaMBx98MNC7ekY60fhkZWXx17/+lZCQEEJDQ3n88ceJi4vT+ATAihUrWLx4Mb169fLf98QT\nTzB//nwdP2cxnUcE1omOu4ULF9KtW7cARtV1LV68mJSUFC0JGgDLly9n5cqVANx1113+JsvScerq\n6pg3bx7l5eV4PB7uueceRo8eHeiwzghdPikhIiIiIiIiIoHRpcs3RERERERERCRwlJQQERERERER\nkYBQUkJEREREREREAkJJCREREREREREJCCUlRERERERERCQglJQQEREREZHvzdGjRznnnHOYMWMG\nM2bM4LrrruMXv/gF1dXVJ/0cM2bMwOv1nvT2119/PZs2bfo24YpIB1NSQkR47bXXvvHxjz76iMrK\nym/cZsaMGWzYsOF0hiUiIiJnidjYWJYuXcrSpUtZvnw5DoeDP/3pTyf9+0uXLsVisXyPEYpIoFgD\nHYCIBJbX6+WZZ55h6tSpX7vNCy+8wIIFC4iOju7AyERERORsNWLECFasWMHevXtZuHAhHo+HpqYm\nfv3rXzNo0CBmzJjBgAED+Pzzz3nxxRcZNGgQu3fvprGxkV/96lcUFRXh8XiYOnUq06dPp6GhgXvv\nvRen00mPHj1wu90AFBcXc//99wPgcrm49tprueqqqwK56yLyFUpKiHRx8+bNIz8/n1tuuYXJkyez\nfPlyQkJCiIuL49FHH+X1119n69at3H///Tz++OMcPHiQZ599FpvNhtfr5cknnyQ1NfU/vs7Ro0e5\n66676NevH3379uW2227jscceY/fu3QCcf/75/PznPwfgmWeeYe3atVitVvr27cv8+fMpLi7mjjvu\nYMyYMWzdupWYmBh++MMf8tprr5Gfn8/vf/97BgwYwKJFi9i4cSM2m43ExEQWLlyIzWb7Xt9DERER\nOXler5d3332Xc889l1/+8pf88Y9/JC0tjb179zJv3jxWrVoFQGhoKC+99FKb3126dCmRkZH87ne/\nw+VyMXnyZMaNG8eGDRsIDg5mxYoVlJSUcOmllwKwevVqevfuzcMPP4zb7eaVV17p8P0VkW+m8g2R\nLm727NnExsby6KOPsnjxYl544QWWLl1KcnIyL7zwAtOnTychIYFFixaRnp5OdXU1Tz31FEuXLmX8\n+PEsW7bspF9r//79/PSnP+XOO+9k9erVHD16lL///e8sW7aM9evXs3nzZrZv384777zDsmXLePnl\nl3E6nbzxxhsAHDx4kOuvv55Vq1Zx8OBB8vLyeP7555kyZQqvvvoqVVVVLFu2jBUrVvDyyy8zYcIE\nysrKvq+3TkRERE5SRUWFv6fEzJkzcTgcXHnllRw8eJD/+q//YsaMGfzmN7+htrYWn88HQGZmZrvn\nycnJYcyYMQAEBwdzzjnnsHv3bvbt28e5554LgMPhoHfv3gCMGzeO7Oxs5s6dywcffMC1117bQXss\nIidLMyVEBIA9e/YwePBgwsPDARg5ciTLly9vt118fDxz5szBMAxKS0vJyMg46deIiorynyTk5OQw\nevRoTCYTFouF8847j507d2KxWBgxYgRBQUH+OHbu3MmIESOIiYmhV69eACQmJvpPVpKSkigoKCAq\nKopx48aRlZXFhAkTmDx5MklJSd/pfREREZHv7lhPidZqamoICgpqd/8xx84FWjOZTG1uG4aByWTC\nMAzM5uPftx5LbPTp04c333yTLVu2sGbNGl588cUTnt+ISOBopoSInNCx/+Rba2pq4uc//zmPPPII\nL730EjNmzDil52x9cvF1JxVfdz/QrsFV69uGYQDwhz/8gUcffRSArKwsPv/881OKUURERDpGREQE\nqampfPTRR0DzjMinn376G39n2LBhrFu3DoD6+np2797N4MGD6dOnD9u3bwegsLCQgwcPAvDvf/+b\nnTt3csEFF/DQQw9RWFiIx+P5HvdKRE6VkhIiXZzZbMbj8finP9bW1gKwYcMGhg0bBjQnEDweD3V1\ndZjNZlJSUnC73bz//vs0NjZ+q9cdPnw4GzZswDAMPB4PmzdvZtiwYQwfPpxNmzbR1NQEQHZ2tj+O\n/yQvL48XXniBPn36cMsttzBhwgT27t37reITERGR79/ChQv5y1/+wg033MDcuXP9pRlfZ8aMGdTV\n1XHDDTdw4403MmvWLFJTU5k6dSpOp5Pp06fz1FNPMWTIEADS09N54oknyMrKYubMmdx2221YrZos\nLtKZ6IgU6eIcDgfx8fHMmjWL22+/nZtvvhmbzUZSUhL33XcfAGPHjuXOO+9k4cKFTJkyhauuuopu\n3brxk5/8hAceeIDVq1ef8utefvnlbNu2jeuvvx6fz8dll13mrwW94ooruOGGGzCbzQwePJgpU6ZQ\nUFDwH58zMTGRPXv2cNVVVxEWFkZUVBR33333KccmIiIip09qaioff/zxCR8bNGgQL7/8crv7v1rS\n8cUXXwBgtVpZtGhRu+3Dw8N57rnnTvga//jHP041ZBHpQCbj2JxnEREREREREZEOpJkSInLa5OXl\nMW/evBM+Nm/ePAYOHNjBEYmIiIiISGemmRIiIiIiIiIiEhBqdCkiIiIiIiIiAaGkhIiIiIiIiIgE\nhJISIiIiIiIiIhIQSkqIiIiIiIiISEAoKSEiIiIiIiIiAfH/AWYPaEdPhYOVAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "808784d7-f6d7-40b5-8997-ddcaf3690332"
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\n",
+        "    batch_size=10,\n",
+        "    input_feature='population'\n",
+        ")"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.62\n",
+            "  period 02 : 204.67\n",
+            "  period 03 : 196.26\n",
+            "  period 04 : 189.25\n",
+            "  period 05 : 184.46\n",
+            "  period 06 : 180.42\n",
+            "  period 07 : 178.24\n",
+            "  period 08 : 176.77\n",
+            "  period 09 : 176.09\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         118.4    207.3\n",
+              "std           95.0    116.0\n",
+              "min            0.2     15.0\n",
+              "25%           65.4    119.4\n",
+              "50%           96.6    180.4\n",
+              "75%          142.5    265.0\n",
+              "max         2954.5    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>118.4</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>95.0</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>65.4</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>96.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>142.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2954.5</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 176.09\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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dEsv5AxPJL65i+bcHgh2OEEII0eb5bUlQ4T+WKjtlDSyzWW61YamykxAdGuCo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5Xuf/0fVLoGVsqrytFuWY7KnI87uTeucWmg9fM1tJo9IxsoEGqEoJgm60e4ZThTpCPfokUlKfQ1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\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ci1ISxxrZ7v0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SjdQQCduZ7BV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb
new file mode 100644
index 0000000..ac2f11f
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,1795 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "50d290a8-1dfa-4b32-9f77-3bf40cd2423c"
+      },
+      "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       2643.3           539.7   \n",
+              "std         2.1        2.0                12.6       2164.3           421.4   \n",
+              "min        32.5     -124.3                 1.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3148.2           652.2   \n",
+              "max        42.0     -114.5                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       1431.0       502.1            3.9               2.0  \n",
+              "std        1164.2       385.4            1.9               1.2  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         787.0       282.0            2.6               1.5  \n",
+              "50%        1170.0       410.0            3.6               1.9  \n",
+              "75%        1722.0       608.0            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>2643.3</td>\n",
+              "      <td>539.7</td>\n",
+              "      <td>1431.0</td>\n",
+              "      <td>502.1</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2164.3</td>\n",
+              "      <td>421.4</td>\n",
+              "      <td>1164.2</td>\n",
+              "      <td>385.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</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>1463.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>787.0</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>435.0</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>3148.2</td>\n",
+              "      <td>652.2</td>\n",
+              "      <td>1722.0</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>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.7       2644.5           538.7   \n",
+              "std         2.1        2.0                12.6       2217.2           421.7   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1460.8           298.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           431.0   \n",
+              "75%        37.7     -118.0                37.0       3160.2           637.2   \n",
+              "max        42.0     -114.3                52.0      30401.0          4957.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1426.1       499.0            3.8               2.0  \n",
+              "std        1107.8       382.5            1.8               1.1  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         794.8       282.0            2.6               1.5  \n",
+              "50%        1158.0       406.0            3.5               1.9  \n",
+              "75%        1716.0       597.0            4.7               2.3  \n",
+              "max       15037.0      4339.0           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>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2644.5</td>\n",
+              "      <td>538.7</td>\n",
+              "      <td>1426.1</td>\n",
+              "      <td>499.0</td>\n",
+              "      <td>3.8</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>2217.2</td>\n",
+              "      <td>421.7</td>\n",
+              "      <td>1107.8</td>\n",
+              "      <td>382.5</td>\n",
+              "      <td>1.8</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>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1460.8</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>794.8</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>1158.0</td>\n",
+              "      <td>406.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3160.2</td>\n",
+              "      <td>637.2</td>\n",
+              "      <td>1716.0</td>\n",
+              "      <td>597.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30401.0</td>\n",
+              "      <td>4957.0</td>\n",
+              "      <td>15037.0</td>\n",
+              "      <td>4339.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>41.3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                208.5\n",
+              "std                 116.7\n",
+              "min                  15.0\n",
+              "25%                 120.0\n",
+              "50%                 181.6\n",
+              "75%                 267.0\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>208.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>267.0</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                204.3\n",
+              "std                 114.2\n",
+              "min                  17.5\n",
+              "25%                 118.8\n",
+              "50%                 175.9\n",
+              "75%                 259.9\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>204.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>114.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>17.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>175.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>259.9</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",
+        "  \"\"\" \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": 775
+        },
+        "outputId": "10aa98c6-05a4-4966-973d-c352fbce19db"
+      },
+      "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": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.83\n",
+            "  period 01 : 160.72\n",
+            "  period 02 : 154.15\n",
+            "  period 03 : 145.55\n",
+            "  period 04 : 133.90\n",
+            "  period 05 : 119.77\n",
+            "  period 06 : 109.82\n",
+            "  period 07 : 107.20\n",
+            "  period 08 : 107.16\n",
+            "  period 09 : 105.96\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   105.96\n",
+            "Final RMSE (on validation data): 104.84\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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a8fPxNDbuTaa0vJo2Le2ZHhGIh5OVoUu+RnPNpqmTXIyXZGO8JJu6kbtR10Nz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\nAElFTkSuQmCC\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": "84f01ba8-f8db-4547-da16-cc272d8285dd"
+      },
+      "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",
+        "  pass\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=0.01),\n",
+        "    steps=3000,\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": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 95.11\n",
+            "  period 01 : 72.92\n",
+            "  period 02 : 70.61\n",
+            "  period 03 : 69.22\n",
+            "  period 04 : 68.36\n",
+            "  period 05 : 67.80\n",
+            "  period 06 : 67.26\n",
+            "  period 07 : 66.99\n",
+            "  period 08 : 66.76\n",
+            "  period 09 : 66.58\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   66.58\n",
+            "Final RMSE (on validation data): 66.24\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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R8TUFlT6yWW0kRyTisVXS3NpCflmdv0sSEREZ8hRUjkB6dBqGyYM5vIa9Ovwj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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "jFfc3saSxg6t"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Ax_IIQVRx4gr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n",
+        "\n",
+        "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "D-bJBXrJx-U_",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize_linear_scale(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+        "  processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+        "  processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+        "  processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+        "    steps=2000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MrwtdStNJ6ZQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Optimizer\n",
+        "\n",
+        "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+        "\n",
+        "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate.  This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training.  You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`.  Note that you may need to use a larger learning rate with Adagrad.\n",
+        "\n",
+        "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad.  To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "61GSlDvF7-7q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 911
+        },
+        "outputId": "6c7fdf23-82d2-4fff-e709-35b327396bc6"
+      },
+      "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=0.1),\n",
+        "    steps=1000,\n",
+        "    batch_size=200,\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)\n",
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.005),\n",
+        "    steps=1000,\n",
+        "    batch_size=200,\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)\n",
+        "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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 90.83\n",
+            "  period 01 : 73.71\n",
+            "  period 02 : 71.79\n",
+            "  period 03 : 71.12\n",
+            "  period 04 : 71.08\n",
+            "  period 05 : 70.81\n",
+            "  period 06 : 69.87\n",
+            "  period 07 : 69.62\n",
+            "  period 08 : 69.26\n",
+            "  period 09 : 69.04\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.04\n",
+            "Final RMSE (on validation data): 68.66\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 191.84\n",
+            "  period 01 : 118.44\n",
+            "  period 02 : 108.89\n",
+            "  period 03 : 93.84\n",
+            "  period 04 : 76.09\n",
+            "  period 05 : 70.92\n",
+            "  period 06 : 70.02\n",
+            "  period 07 : 69.49\n",
+            "  period 08 : 68.88\n",
+            "  period 09 : 68.46\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   68.46\n",
+            "Final RMSE (on validation data): 68.13\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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GpNCpgvZubQE4dOEoLTq0xLasiPhcS/kpnxBCCGGmpNCpgsAW7VCXWnC2+Awq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\nEEIIIdosCTpCCCGEaLMk6AghhBCizfr/TpORDiNYIaAAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "FSPZIiYgyh93"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "X1QcIeiKyni4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, let's try Adagrad."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ntn4jJxnypGZ",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "5JUsCdRRyso3"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's try Adam."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "lZB8k0upyuY8",
+        "colab": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": "2648a2c8-b77e-4099-c5b7-7c46552db27e"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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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": "5a2d54df-b95f-4a33-fec6-f4195a709f44"
+      },
+      "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.GradientDescentOptimizer(learning_rate=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\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 : 209.93\n",
+            "  period 01 : 129.36\n",
+            "  period 02 : 114.71\n",
+            "  period 03 : 113.16\n",
+            "  period 04 : 111.32\n",
+            "  period 05 : 109.01\n",
+            "  period 06 : 105.80\n",
+            "  period 07 : 101.93\n",
+            "  period 08 : 97.13\n",
+            "  period 09 : 92.01\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   92.01\n",
+            "Final RMSE (on validation data): 91.36\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ERNTjMMAQERFRj8MAQ0RERD0OAwwRERH1OAwwRERE1OMwwBAREfUye/d+7dDzXnzxORQV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\nwAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "GhFtWjQRzD2l"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "OMoIsUMmzK9b"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "These are only a few ways in which we could think about the data. Other transformations may work even better!\n",
+        "\n",
+        "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n",
+        "\n",
+        "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n",
+        "\n",
+        "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XDEYkPquzYCH",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "\n",
+        "  processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n",
+        "  \n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "\n",
+        "  processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n",
+        "\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n",
+        "    steps=1000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "b7atJTbzU9Ca"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Use only Latitude and Longitude Features\n",
+        "\n",
+        "**Train a NN model that uses only latitude and longitude as features.**\n",
+        "\n",
+        "Real estate people are fond of saying that location is the only important feature in housing price.\n",
+        "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n",
+        "\n",
+        "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n",
+        "\n",
+        "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5McjahpamOc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "5997e98a-18fd-46c8-f664-da96a45f1ce8"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Train the network using only latitude and longitude\n",
+        "#\n",
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.01),\n",
+        "    steps=1000,\n",
+        "    batch_size=100,\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": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 235.76\n",
+            "  period 01 : 230.20\n",
+            "  period 02 : 222.30\n",
+            "  period 03 : 212.15\n",
+            "  period 04 : 199.87\n",
+            "  period 05 : 185.50\n",
+            "  period 06 : 169.27\n",
+            "  period 07 : 151.87\n",
+            "  period 08 : 134.16\n",
+            "  period 09 : 119.30\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   119.30\n",
+            "Final RMSE (on validation data): 115.80\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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g7KocWuytE251Gh09/SLo8+P+TeG+YR2+IWVZVT3bDxaxLaWQ0soGAAL9PBgX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\nytTXzbNsQzoAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "P8BLQ7T71JWd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "1hwaFCE71OPZ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "It's a good idea to keep latitude and longitude normalized:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "djKtt4mz1ZEc",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10, 5, 5, 5],\n",
+        "    training_examples=lll_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=lll_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Dw2Mr9JZ1cRi"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb
new file mode 100644
index 0000000..2afa885
--- /dev/null
+++ b/intro_to_neural_nets.ipynb
@@ -0,0 +1,1185 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "4ad8ca41-b54a-4f27-c77b-dc9e73ecf16b"
+      },
+      "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       2634.2           537.4   \n",
+              "std         2.1        2.0                12.6       2152.5           419.8   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2129.0           430.5   \n",
+              "75%        37.7     -118.0                37.0       3157.2           646.0   \n",
+              "max        42.0     -114.5                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1423.7       499.5            3.9               2.0  \n",
+              "std        1136.2       382.3            1.9               1.2  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         786.0       281.8            2.6               1.5  \n",
+              "50%        1168.0       407.0            3.6               1.9  \n",
+              "75%        1715.0       603.0            4.8               2.3  \n",
+              "max       35682.0      5050.0           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>2634.2</td>\n",
+              "      <td>537.4</td>\n",
+              "      <td>1423.7</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>2152.5</td>\n",
+              "      <td>419.8</td>\n",
+              "      <td>1136.2</td>\n",
+              "      <td>382.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>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>1453.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>786.0</td>\n",
+              "      <td>281.8</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2129.0</td>\n",
+              "      <td>430.5</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>407.0</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>3157.2</td>\n",
+              "      <td>646.0</td>\n",
+              "      <td>1715.0</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>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5050.0</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       2666.4           544.3   \n",
+              "std         2.1        2.0                12.5       2244.5           425.6   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.7                18.0       1481.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2124.0           442.0   \n",
+              "75%        37.7     -118.0                37.0       3130.0           654.0   \n",
+              "max        41.8     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1443.8       505.4            3.9               2.0  \n",
+              "std        1175.4       389.8            1.9               1.1  \n",
+              "min           9.0         1.0            0.5               0.1  \n",
+              "25%         796.0       282.0            2.6               1.5  \n",
+              "50%        1164.5       413.5            3.5               1.9  \n",
+              "75%        1735.2       608.0            4.7               2.3  \n",
+              "max       28566.0      6082.0           15.0              34.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.8</td>\n",
+              "      <td>2666.4</td>\n",
+              "      <td>544.3</td>\n",
+              "      <td>1443.8</td>\n",
+              "      <td>505.4</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>12.5</td>\n",
+              "      <td>2244.5</td>\n",
+              "      <td>425.6</td>\n",
+              "      <td>1175.4</td>\n",
+              "      <td>389.8</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>9.0</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>-121.7</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1481.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>796.0</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>2124.0</td>\n",
+              "      <td>442.0</td>\n",
+              "      <td>1164.5</td>\n",
+              "      <td>413.5</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>3130.0</td>\n",
+              "      <td>654.0</td>\n",
+              "      <td>1735.2</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>41.8</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>34.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.4\n",
+              "std                 116.2\n",
+              "min                  15.0\n",
+              "25%                 118.9\n",
+              "50%                 181.1\n",
+              "75%                 265.2\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.2</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                207.0\n",
+              "std                 115.5\n",
+              "min                  15.0\n",
+              "25%                 121.0\n",
+              "50%                 177.9\n",
+              "75%                 264.7\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.0</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>121.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>177.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RWq0xecNKNeG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Building a Neural Network\n",
+        "\n",
+        "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+        "\n",
+        "Use **`hidden_units`** to define the structure of the NN.  The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it.  For example, consider the following assignment:\n",
+        "\n",
+        "`hidden_units=[3,10]`\n",
+        "\n",
+        "The preceding assignment specifies a neural net with two hidden layers:\n",
+        "\n",
+        "* The first hidden layer contains 3 nodes.\n",
+        "* The second hidden layer contains 10 nodes.\n",
+        "\n",
+        "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+        "\n",
+        "By default, all hidden layers will use ReLu activation and will be fully connected."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ni0S6zHcTb04",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zvCqgNdzpaFg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural net regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                             \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U52Ychv9KNeH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `DNNRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2QhdcCy-Y8QR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train a NN Model\n",
+        "\n",
+        "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+        "\n",
+        "Run the following block to train a NN model.  \n",
+        "\n",
+        "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good.  We'll aim to beat that.\n",
+        "\n",
+        "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+        "\n",
+        "Overfitting is a real potential hazard for NNs.  You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+        "\n",
+        "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+        "\n",
+        "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rXmtSW1yKNeK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "outputId": "f30c1bfa-f3d9-4ea5-f891-f51455bfd1ec"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 150.29\n",
+            "  period 01 : 140.03\n",
+            "  period 02 : 129.80\n",
+            "  period 03 : 125.39\n",
+            "  period 04 : 115.97\n",
+            "  period 05 : 110.90\n",
+            "  period 06 : 110.52\n",
+            "  period 07 : 110.97\n",
+            "  period 08 : 105.45\n",
+            "  period 09 : 103.23\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   103.23\n",
+            "Final RMSE (on validation data): 103.09\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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olS6Ip5oM4VZhDrNPfUfm7SzjMhtrDdOGB96Z1LvjHBeTskrZkxBCCFG9SIAx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\nAABJRU5ErkJggg==\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": "a00097f9-7ed1-423c-cdf5-26e185a512d0"
+      },
+      "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, \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)\n"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 102.71\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vvT2jDWjrKew",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FyDh7Qy6rQb0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+        "\n",
+        "Note that we don't have to randomize the test data, since we will use all records."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vhb0CtdvrWZx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+        "                                               test_targets[\"median_house_value\"], \n",
+        "                                               num_epochs=1, \n",
+        "                                               shuffle=False)\n",
+        "\n",
+        "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..76c7b88
--- /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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "a56ff890-b669-4c6f-b376-406ff9cf71fa"
+      },
+      "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": "284f87c9-7a35-44b9-afec-1991285344a6"
+      },
+      "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": "ba9c8019-4dd5-4aeb-cd5e-0f8640c79c61"
+      },
+      "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": "4ebee0c4-c126-4731-b4c8-9a37b93a5e57"
+      },
+      "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": "6c53f563-5fec-4260-b860-cbf009971d27"
+      },
+      "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": "e3d8f4e2-9407-48b3-b305-2fc121226571"
+      },
+      "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 0x7f2a937a7bd0>]],\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": "a6f7e64e-8695-496a-8a82-d6de0e3ad3b1"
+      },
+      "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": "d618718e-d7f8-4073-885b-4413adb9a84d"
+      },
+      "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": "db46bf98-e320-48ab-a607-667590a2f4c5"
+      },
+      "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": "e32ad0bf-06c1-48d7-8cf1-ae8fd4cd4ab5"
+      },
+      "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": "fb99946e-a3c3-48f7-c516-408420a0288b"
+      },
+      "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": "d3e8e8a5-7340-4451-8a88-4726bc8640b4"
+      },
+      "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": "1c6c5a19-e0c2-405d-ae35-4ec7ae412615"
+      },
+      "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": "6907aca1-8965-4ea7-8149-e22528384aea"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities['>50 sq.miles area and Named after a saint']= cities['Area square miles'].apply(lambda val: val>50) & cities['City name'].apply(lambda val: val.split(' ',1)[0]=='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>&gt;50 sq.miles area and Named after a saint</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",
+              "   >50 sq.miles area and Named after a saint  \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": "b468840e-e4d4-4c89-b7be-dd71960b7056"
+      },
+      "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": "6e3d437c-8225-41c6-8256-ae93f286961c"
+      },
+      "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": "850bfab4-212f-48e7-e3d6-8acaaa6fa0ea"
+      },
+      "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>&gt;50 sq.miles area and Named after a saint</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",
+              "   >50 sq.miles area and Named after a saint  \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": "9a51ca4a-1e9e-47ab-c7a7-dbde3a1c0511"
+      },
+      "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>&gt;50 sq.miles area and Named after a saint</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>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "\n",
+              "   >50 sq.miles area and Named after a saint  \n",
+              "2                                      False  \n",
+              "1                                       True  \n",
+              "0                                      False  "
+            ]
+          },
+          "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": "73e0346d-77a1-4e6a-81cd-609f521e5739"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>&gt;50 sq.miles area and Named after a saint</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",
+              "  >50 sq.miles area and Named after a saint  \n",
+              "0                                     False  \n",
+              "4                                       NaN  \n",
+              "5                                       NaN  \n",
+              "2                                     False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "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..ab97dfe
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1620 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "c7528455-a0f1-448f-a4d4-0edd92480738"
+      },
+      "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       35.7     -119.6                28.7       2630.6           536.8   \n",
+              "std         2.1        2.0                12.6       2143.8           416.7   \n",
+              "min        32.5     -124.3                 2.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           295.8   \n",
+              "50%        34.3     -118.5                29.0       2126.5           433.0   \n",
+              "75%        37.7     -118.0                37.0       3135.2           647.0   \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       1422.3       498.2            3.9               2.0  \n",
+              "std        1150.9       378.8            1.9               1.1  \n",
+              "min           6.0         2.0            0.5               0.0  \n",
+              "25%         791.0       281.0            2.6               1.5  \n",
+              "50%        1164.0       409.0            3.5               1.9  \n",
+              "75%        1717.2       603.0            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>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>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2630.6</td>\n",
+              "      <td>536.8</td>\n",
+              "      <td>1422.3</td>\n",
+              "      <td>498.2</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>2143.8</td>\n",
+              "      <td>416.7</td>\n",
+              "      <td>1150.9</td>\n",
+              "      <td>378.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</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>1463.0</td>\n",
+              "      <td>295.8</td>\n",
+              "      <td>791.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2126.5</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1164.0</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>3135.2</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1717.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.3       2675.0           545.7   \n",
+              "std         2.1        2.0                12.6       2264.2           432.7   \n",
+              "min        32.6     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.7                18.0       1459.8           300.0   \n",
+              "50%        34.2     -118.5                28.0       2134.5           437.0   \n",
+              "75%        37.7     -118.0                37.0       3180.0           653.0   \n",
+              "max        41.9     -114.7                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       1447.1       508.4            3.9               2.0  \n",
+              "std        1140.4       397.8            1.9               1.3  \n",
+              "min           3.0         1.0            0.5               0.1  \n",
+              "25%         786.8       283.0            2.6               1.5  \n",
+              "50%        1175.5       410.0            3.6               1.9  \n",
+              "75%        1733.5       610.0            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>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>28.3</td>\n",
+              "      <td>2675.0</td>\n",
+              "      <td>545.7</td>\n",
+              "      <td>1447.1</td>\n",
+              "      <td>508.4</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>2264.2</td>\n",
+              "      <td>432.7</td>\n",
+              "      <td>1140.4</td>\n",
+              "      <td>397.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>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>0.1</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>1459.8</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>786.8</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>28.0</td>\n",
+              "      <td>2134.5</td>\n",
+              "      <td>437.0</td>\n",
+              "      <td>1175.5</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>3180.0</td>\n",
+              "      <td>653.0</td>\n",
+              "      <td>1733.5</td>\n",
+              "      <td>610.0</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>-114.7</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": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "uon1LB3A31VN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## How Would Linear Regression Fare?\n",
+        "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n",
+        "\n",
+        "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "smmUYRDtWOV_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "B5OwSrr1yIKD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "SE2-hq8PIYHz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_regressor_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    \n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "TDBD8xeeIYH2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "54c2b19c-f2b2-40fa-b7f7-9a76af424edd"
+      },
+      "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": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.45\n",
+            "  period 01 : 0.45\n",
+            "  period 02 : 0.44\n",
+            "  period 03 : 0.44\n",
+            "  period 04 : 0.44\n",
+            "  period 05 : 0.44\n",
+            "  period 06 : 0.44\n",
+            "  period 07 : 0.44\n",
+            "  period 08 : 0.44\n",
+            "  period 09 : 0.44\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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CalKS4AghhBCtTFpaKtu3b2nQts888xxeXt43XL948dKmCqtJ3ZZTNQghhBDixpYuXUJs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\nChwhhBBCuBwpcIQQQgjhcv4/RWbJJfmCc8YAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JjBZ_q7aD9gh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Can We Calculate LogLoss for These Predictions?\n",
+        "\n",
+        "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n",
+        "\n",
+        "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability.  For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n",
+        "\n",
+        "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily.  Remember, `LogLoss` is defined as:\n",
+        "\n",
+        "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n",
+        "\n",
+        "\n",
+        "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n",
+        "\n",
+        "Given the predictions and the targets, can we calculate `LogLoss`?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dPpJUV862FYI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to display the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kXFQ5uig2RoP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "7e01b754-9b39-4e2c-e780-82eb51eab41b"
+      },
+      "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": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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vliT19vZedIVgOvaaS98xNtEjVIPBoNxu90Wv9fT0xGPMqJrMY2OfeOIJnTx5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\nFDUAAAajqAEAMNj/BwDkX1Fl8cLTAAAAAElFTkSuQmCC\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(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VM0wmnFUIYH9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "b5ac4cec-8aa4-43e2-c881-ee8c2020ad25"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.61\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.55\n",
+            "  period 08 : 0.54\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wX3RnDtdRhCIiIvVDxUwD8vsBLfF0c+KbTQlk5hZVeey4yl2B1Z0REZHGTcVM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\n8fzHO1m9MxGL2UTMiNY2FTS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HkzvDmg9kaLMBHM9KYOOZLexI2c2Xx5by9fHldAnsyMDw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\nJDNCCCGEmNP+f6qbdJ2hb8H2AAAAAElFTkSuQmCC\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": "a0eecd61-d0f3-4166-e0fe-5e13e0d697a0"
+      },
+      "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": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AUC on the validation set: 0.76\n",
+            "Accuracy on the validation set: 0.78\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": "ccd1b291-6a81-4b0b-b6e1-c4ed9f67fe68"
+      },
+      "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": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PLx3LP505hAAfN4wDg1SsSriqspYKVmSkUdBYhJ+bL3ekzOWKkCFqlyV6iYSw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\nD1lZWRQVFWGxWCgvL8fpdBIdHU1+fj4VFRU8//z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+            "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": "ee81b2b6-6c7c-4abb-acf5-420aa0ead4d2"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.49\n",
+            "  period 01 : 0.48\n",
+            "  period 02 : 0.47\n",
+            "  period 03 : 0.47\n",
+            "  period 04 : 0.47\n",
+            "  period 05 : 0.47\n",
+            "  period 06 : 0.47\n",
+            "  period 07 : 0.47\n",
+            "  period 08 : 0.47\n",
+            "  period 09 : 0.46\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.81\n",
+            "Accuracy on the validation set: 0.78\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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E2hQFHAfSraMPkSHe7D1sybDxSAC2pG+3R2kiIiJtigKOAzk/bGwyNz5sHBPU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\n/wc0p4t5Hj/wcwAAAABJRU5ErkJggg==\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": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000003,\n",
+        "    steps=20000,\n",
+        "    batch_size=500,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb
new file mode 100644
index 0000000..83fc9e6
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2462 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "471a96a7-fd24-4019-f15b-d18ef4b94efe"
+      },
+      "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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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4816</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
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+              "      <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",
+              "3917    0    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "8915    7    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "7457    4    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "1480    0    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "4816    3    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "3917    0    0    0    0    0    0    0  \n",
+              "8915    0    0    0    0    0    0    0  \n",
+              "7457    0    0    0    0    0    0    0  \n",
+              "1480    0    0    0    0    0    0    0  \n",
+              "4816    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": "76bbeab2-37eb-4b62-a52c-ad99b3190df2"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_dataframe.loc[:, 72:72]"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>72</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
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+              "      <th>8915</th>\n",
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+              "    <tr>\n",
+              "      <th>1480</th>\n",
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+              "    <tr>\n",
+              "      <th>4816</th>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>882</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5945</th>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>9112</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7798</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4088</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "3917   0\n",
+              "8915   0\n",
+              "7457   0\n",
+              "1480   0\n",
+              "4816   0\n",
+              "...   ..\n",
+              "882    0\n",
+              "5945   0\n",
+              "9112   0\n",
+              "7798   0\n",
+              "4088   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": "acedbcba-1a63-4f37-dbe4-14d3ca75105c"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
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+              "      <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",
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+              "      <td>0.0</td>\n",
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+              "      <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",
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+              "      <td>0.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.8    0.2    1.0    0.2    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 7500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4-Vgg-1zu8Ud",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "30ee07e5-64ce-4b1f-dd7e-1329402de6bd"
+      },
+      "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": [
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  </thead>\n",
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+              "      <td>2500.0</td>\n",
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+              "      <th>std</th>\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",
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+              "      <td>0.0</td>\n",
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+              "    <tr>\n",
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+              "      <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>0.7</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    0.7    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 2500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wrnAI1v6u8Uh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Show a random example and its corresponding label."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s-euVJVtu8Ui",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        },
+        "outputId": "87f50fc9-6951-4bfa-9d90-3b9afe6099f5"
+      },
+      "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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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ScmYX7xdZMXE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Build a Linear Model for MNIST\n",
+        "\n",
+        "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n",
+        "\n",
+        "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix).  The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n",
+        "\n",
+        "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cpoVC4TSdw5Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  \n",
+        "  # There are 784 pixels in each image.\n",
+        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMmL89yGeTfz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OeS47Bmn5Ms2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
+        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The training features.\n",
+        "    labels: The training labels.\n",
+        "    batch_size: Batch size to use during training.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns batches of training features and labels during\n",
+        "    training.\n",
+        "  \"\"\"\n",
+        "  def _input_fn(num_epochs=None, shuffle=True):\n",
+        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
+        "    # gets a good sampling of data, even when number of steps is small, we \n",
+        "    # shuffle all the data before creating the Dataset object\n",
+        "    idx = np.random.permutation(features.index)\n",
+        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
+        "    raw_targets = np.array(labels[idx])\n",
+        "   \n",
+        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8zoGWAoohrwS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_predict_input_fn(features, labels, batch_size):\n",
+        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The features to base predictions on.\n",
+        "    labels: The labels of the prediction examples.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns features and labels for predictions.\n",
+        "  \"\"\"\n",
+        "  def _input_fn():\n",
+        "    raw_features = {\"pixels\": features.values}\n",
+        "    raw_targets = np.array(labels)\n",
+        "    \n",
+        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size)\n",
+        "    \n",
+        "        \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "G6DjSLZMu8Um",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, and a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `LinearClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create a LinearClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(),\n",
+        "      n_classes=10,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ItHIUyv2u8Ur",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n",
+        "\n",
+        "Stop if you get anything above about 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yaiIhIQqu8Uv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1092
+        },
+        "outputId": "d38ff04a-1718-4550-8bca-8e61e95a651d"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.61\n",
+            "  period 01 : 4.12\n",
+            "  period 02 : 4.01\n",
+            "  period 03 : 3.77\n",
+            "  period 04 : 3.65\n",
+            "  period 05 : 3.70\n",
+            "  period 06 : 3.56\n",
+            "  period 07 : 3.41\n",
+            "  period 08 : 3.41\n",
+            "  period 09 : 3.40\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.90\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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u5ubG3LlP8sgjD3L69EnKys5d8fVHjhwmNrYnAI6OjrRv34GsrCzg0mlbzZV3FttTf/31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\nLiIi4iMU6iIiIj7i/wNkZ04onpae/gAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1JEREqsVEQkREEshZkcTHx+PkyZMQBAGRkZHw9vbWP/fHH3/g5ZdfRn19PQYNGoQVK1a0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\nScJEQkREkjCREBGRJP8PjtsDUKLesqYAAAAASUVORK5CYII=\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",
+        " \n",
+        "\n",
+        "  periods = 10\n",
+        "  \n",
+        "  steps_per_period = steps / periods  \n",
+        "  \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",
+        "  \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",
+        "\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  \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",
+        "  \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",
+        "    \n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "   \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",
+        "    \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",
+        "    \n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    \n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  \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",
+        "  \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",
+        " \n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  \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": "ftlxpTLWchp0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 983
+        },
+        "outputId": "4e97d2ff-ccdc-46ae-d4c9-b6f5b7f63d3e"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_nn_classification_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\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": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 3.76\n",
+            "  period 01 : 3.00\n",
+            "  period 02 : 2.76\n",
+            "  period 03 : 2.21\n",
+            "  period 04 : 2.09\n",
+            "  period 05 : 2.09\n",
+            "  period 06 : 2.07\n",
+            "  period 07 : 2.00\n",
+            "  period 08 : 2.03\n",
+            "  period 09 : 1.93\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.94\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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38fraD0gvzlS7RCGEaDQkvG+Qoig8MKQ1Lo46ftiQRGZeidol1RvOds70b9ab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\nBKPwFhERSTAKbxERkQSj8BYREUkw/w8KTT+i3u9sqQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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iBCZMmIDjx4/j0UcfxeDBgzF8+HAcPHhQjpJERKQQWXokSUlJ+OSTT1BaWorw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\nRJIwSIiISJL/BekANm+IQn/4AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "610a941c-da43-45d3-cb6c-b424b6d5e89e"
+      },
+      "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": 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>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": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PDuLd2Hcu8VL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "1cfdfca0-5fb8-4fba-b25f-9228a53750a7"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
+        "#\n",
+        "predict_test_input_fn = create_predict_input_fn(\n",
+        "    test_examples, test_targets, batch_size=100)\n",
+        "\n",
+        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+        "  \n",
+        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+        "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.95\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "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": "c8056575-6cfc-49ae-9f35-cf1652c412d8"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(classifier.get_variable_names())\n",
+        "\n",
+        "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n",
+        "\n",
+        "print(\"weights0 shape:\", weights0.shape)\n",
+        "\n",
+        "num_nodes = weights0.shape[1]\n",
+        "num_rows = int(math.ceil(num_nodes / 10.0))\n",
+        "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n",
+        "for coef, ax in zip(weights0.T, axes.ravel()):\n",
+        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
+        "    ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n",
+            "weights0 shape: (784, 100)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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xos+Hn+gn0urO/TD8M/B5Eyajv1DVJ1Adco7i30XbjiH3ihJ/58yL3DMk9+vo5XDqWexp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\nSshRisWeoSsHcr7qCyz39QjD3NbTfdRq1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HRA7qjHunFyM8+902+A/v30t6ilk2CrPydr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28kbMWTMjJD7Toq4k5HH5jeL9TWYGxvGhz3Btw/38KE/OUznH9r28k/J83DBmJFO218RukYzk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\n9yR/Dc9FrzRsugKmwDHLO5DlT+W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++I6ErFVKhJVSqiUXUgonIYPuKOJxmjYzxYjby7B3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\na1sXpAUX/ofLE0LvgkTGdxrGc/VBYh05xOVpHfW4rwEeSGF1tOP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lMVI+7W3QB78+yWUr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fWRZw26Vtt18Tm69iBqCMZMU/+FZM4IgiAIgiAIgiAIgiCMIPLljCAIgiAIgiAIgiAIwghy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G8jx86XXQc90iIWcxN+XuMw2n8Nn+xLlroo6a60FyNKWNd5ZyiarPOFS4P/UJZGEhgdw1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\n+A6kGXpb1asbIaOh1osRcznNt+Vsrbqd6G+GZCB0qc6ath6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EQRAEQRAEoRe5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+ckZDQ0NDQ0NDQ0NDQ0NDQ0PjGuKqsqYN22AT2T8sjF6TFm0vLId8541fX6W8E++B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\nwv4w1gX7jfR3eO2IOc9iv9lVj77uEObK4go+R72FyM2o1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gYaE2otnfYur8+SMAM1Jhyj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AIBAIBAKB4DZCvpwRCAQCgUAgEAgEAoFAILiNkC9nBAKBQCAQCAQCgUAgEAhuI27Zc2bi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M/Om/dzMDRTnsaYaSnl7zF8OKznO0mPOCcHbikcuhx5Vz7DmdBtEO85M+a5CVp84n30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\n",
+            "text/plain": [
+              "<Figure size 1440x1440 with 100 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kL8MEhNgrx9N",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits.  You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n",
+        "\n",
+        "It can be interesting to stop training at different numbers of iterations and see the effect.\n",
+        "\n",
+        "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n",
+        "\n",
+        "What differences do you see visually for the different levels of convergence?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb
new file mode 100644
index 0000000..3146dca
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1140 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "e539d684-3cd5-411a-b3e8-b05b7ebeaed4"
+      },
+      "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       2645.0           540.3   \n",
+              "std         2.1        2.0                12.6       2199.2           424.3   \n",
+              "min        32.5     -124.3                 2.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1458.0           295.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3152.2           651.2   \n",
+              "max        42.0     -114.5                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       1429.7       501.6            3.9               2.0  \n",
+              "std        1139.2       388.3            1.9               1.1  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         786.8       280.0            2.6               1.5  \n",
+              "50%        1163.5       408.0            3.5               1.9  \n",
+              "75%        1727.0       607.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2645.0</td>\n",
+              "      <td>540.3</td>\n",
+              "      <td>1429.7</td>\n",
+              "      <td>501.6</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>2199.2</td>\n",
+              "      <td>424.3</td>\n",
+              "      <td>1139.2</td>\n",
+              "      <td>388.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1458.0</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>786.8</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>2127.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1163.5</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>3152.2</td>\n",
+              "      <td>651.2</td>\n",
+              "      <td>1727.0</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>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2640.5           537.4   \n",
+              "std         2.1        2.0                12.5       2133.1           414.9   \n",
+              "min        32.5     -124.3                 1.0         15.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1470.0           300.0   \n",
+              "50%        34.2     -118.5                29.0       2130.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3150.0           643.0   \n",
+              "max        41.9     -114.3                52.0      25187.0          4819.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1429.3       500.4            3.9               2.0  \n",
+              "std        1168.4       375.3            1.9               1.3  \n",
+              "min           3.0         4.0            0.5               0.0  \n",
+              "25%         796.0       286.8            2.6               1.5  \n",
+              "50%        1174.5       410.0            3.6               1.9  \n",
+              "75%        1705.5       599.0            4.8               2.3  \n",
+              "max       35682.0      4769.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2640.5</td>\n",
+              "      <td>537.4</td>\n",
+              "      <td>1429.3</td>\n",
+              "      <td>500.4</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>12.5</td>\n",
+              "      <td>2133.1</td>\n",
+              "      <td>414.9</td>\n",
+              "      <td>1168.4</td>\n",
+              "      <td>375.3</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>15.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1470.0</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>796.0</td>\n",
+              "      <td>286.8</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>2130.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1174.5</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>3150.0</td>\n",
+              "      <td>643.0</td>\n",
+              "      <td>1705.5</td>\n",
+              "      <td>599.0</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>25187.0</td>\n",
+              "      <td>4819.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>4769.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "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": "f0cfb27e-beee-4376-d1ca-43d32223d026"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    regularization_strength=0.1,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.33\n",
+            "  period 01 : 0.29\n",
+            "  period 02 : 0.28\n",
+            "  period 03 : 0.27\n",
+            "  period 04 : 0.26\n",
+            "  period 05 : 0.26\n",
+            "  period 06 : 0.25\n",
+            "Model training finished.\n",
+            "Model size: 747\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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s27YNgISEBPz8/EyXi/R6PU888QRVVY2P+z9x4gTh4eFN7tMWODvaMWdiLABv\nfZlI9QW9yhU1uqPLLXg7evHV6W/JLs9RuxwhhBDCLBa7zNS7d2/i4uKYPn06iqKwcOFC1q1bh5ub\nG6NHj+ahhx5i1qxZ6HQ6unbtysiRI1EU5Tf7tDVRwZ5MGBjGxu+z+PDrU8ydGHf1nSzMSefIzJg7\nWXZ0OSsTV/FE33nYae3ULksIIYRoFsVoLQ8/uUaWvJ5pqWt/+gYDL3x0hIxz5cyZGMvAuIAW/x7X\nYnXKenad+Z6RIUO5I/KWazqGXGM2n/TMfNIz80nPzCc9M1+bu2dGXJlOq2HuxFgc7LV8+NUpCktr\n1C4JgEkR4/F18mHH6d2kl2apXY4QQgjRLBJmVOLn5czdo6KoqW1g+cZEGgzqr5PkoLVnZsw0AD5I\nWkVtQ53KFQkhhBBXJ2FGRYNuCODGaD/SzpSxeV+22uUAEOEZxs3BQyioKeKL9M1qlyOEEEJclYQZ\nFSmKwqyxXfFyc+CLPVmknytTuyQAbuk8Fn9nP3ad+Z5TxWlqlyOEEEI0ScKMylyd7PjdLbEYjUbe\n2pBITa3607XttXbMip2KgsKHyWuo0V9QuyQhhBDiiiTMWIGYUC/GDQghv7SGT7anql0OAGHuIYwJ\nHUHxhRI+T9uodjlCCCHEFUmYsRK3D+lMqL8be07k8kNyvtrlABAfPopAlwD2njtIQtEptcsRQggh\nLkvCjJXQaTXMvTUWe52G97ckU1yu/qUdO42OWbHT0SgaPk5eS3W9dUwhF0IIIX5JwowV6ejjwvRR\nXaiu1bNiYyIGg/rPMwx2C2R82ChKa8tYm7pB7XKEEEKI35AwY2WG9QikV5cOJJ8uZdvB02qXA8CY\n0BGEuAVx4PxhjhckqF2OEEIIcQkJM1ZGURTui4/Gw8Wedd9lkHW+XO2S0Gq0zIyZhk7R8vGpz6is\nq1K7JCGEEMJEwowVcnO254FbYmgwGHlzQyK1dQ1ql0SgawC3dB5LRV0lq1I+V7scIYQQwkTCjJXq\nFu7DmL7B5BVX8+kO65iuPTJkKOHuoRzJP87hvGNqlyOEEEIAEmas2uRhEXTydWXXj+c4klKgdjlo\nFA0zY6dip7FjVcrnlNfJarJCCCHUJ2HGitnpNPz+1ljsdBre25JMSUWt2iXh7+zLpIh4quqr+SR5\nHUaj+jOuhBBCtG8SZqxckK8rU0dEUllTzzubEjFYQXgY1ukmunh25nhhAgfPH1G7HCGEEO2chBkb\ncHPvILpH+JCQVcL2H3LULgeNouGemDux19qzJvULSmutY4FMIYQQ7ZOEGRugKAqzx8fg7mzH2l3p\nnM5T/16VDk4+3BF5CzX6C3zyCsRgAAAgAElEQVSUtFYuNwkhhFCNhBkb4eFiz/0TYtA3GFn+ZSJ1\n9epP1x4c2J8Y7ygSi0/xfe5BtcsRQgjRTkmYsSHdIzowsncnzhVWsWZnutrloCgKd0dPwVHryLrU\njRTVlKhdkhBCiHZIwoyNuXNEBIEdXPjmyBmOpxeqXQ5ejp5MibqVCw21fJi8BoPRoHZJQggh2hkJ\nMzbG3k7L3Imx6LQK72xKoqyqTu2SGBDQh24+MaSUpPF54lYJNEIIIVqVhBkbFOLvxpRhEZRX1/Pu\n5iTVb75VFIUZ0ZNxtXNh1ckvWXr4f2SXqz/rSgghRPsgYcZGjeobTFyYF8fTi9hx5Kza5eDh4M7f\n+j7CwOA+ZJaf5uVD/+GjpDVU1FWqXZoQQog2TsKMjdIoCvdPiMXVyY5VO9I4W6B+aPB29OLPN/2O\neb1+T0cXf77P/YFF+15iR85uGgzqz74SQgjRNkmYsWFebg7Mjo9G32DgzQ2J1OutIzBEeUXwRN95\n3Bk1CUVR+Cz1S5YcfI3kYutYMFMIIUTbImHGxvWK8mV4z0DOFFTy2a4Mtcsx0Wq0DO80iEUD/srg\nwP7kVRfw+o9vsfzESgpritUuTwghRBsiYaYNmHZzFwK8nfnqhxxOZhapXc4lXO1duCt6Mn/r+wid\nPcI4VnCS5w+8wsaMbdQ1qD8TSwghhO2TMNMGONhr+f2tcWg1Cm9vTKKi2vpCQrBbEPN7/4H7Yu/C\nRefMlqxveG7/KxzJP676bCwhhBC2TcJMGxEa4MYdQztTVlXHe1uSrTIgKIpC34BePDPgccaEjqCi\nroK3T37IsqNvcrYyV+3yhBBC2CgJM23I2P4hRId4cjS1kF3HzqldzhU56hyYFBHPU/0f44YOMaSW\nZvDPg/9idcp6quqr1S5PCCGEjZEw04ZoFIXf3RKLi6OOT7enkltUpXZJTfJz7sCD3Wfzxx734+vs\nw64z3/Ps/pfYfXa/PEVYCCFEs0mYaWO83R2ZNS6aOr2B5RsS0TdYfyiI84nmqX7zuS1iPHqDnk9P\nrePFH/5NWmmm2qUJIYSwARJm2qC+0X4MvqEj2XkVfL7beqZrN0Wn0TE6dDgLB/yV/gF9OFN5jteO\nvMG7CR9TWlumdnlCCCGsmISZNuquUV3w83Ri6/7TJGWXqF1Os3k4uDMrdhqP9XmIELcgDuX9yLP7\nX2Zb1g7qDXq1yxNCCGGFJMy0UU4OOubcGouiKKzYmEhlTb3aJZmls0coj9/4MHdHT8FeY8eGjK38\n48CrnChMtMqZWkIIIdQjYaYNiwj0YNLgMEoqanl/q3VO126KRtFwU2A/Fg74KyOCB1N8oYT/O/4e\n/zv2DnlV+WqXJ4QQwko0O8xUVjYuZFhYWMihQ4cwGKz/xlIBEwaG0aWTB4dPFbDnhG0+y8XZzokp\nXW5lQb8/09UrksTiUyw++Bqfp22iRn9B7fKEEEKoTLto0aJFV3vT888/T2lpKUFBQUydOpXc3Fz2\n79/PiBEjWqHEplVb8Gm3Li4OFj1+a1AUhZgQL/acyOV4ejF9Y/xwdbKz2PezZM/c7F3pF9CbINeO\nZJZnk1CUzP7cQ7jZuRLoGoCiKBb5vpbWFs6z1iY9M5/0zHzSM/NZsmcuLg5X3NaskZnExETuvPNO\ntmzZwu23386yZcvIzs5usQKFZXXwdGLm2K7U1jfYzHTtK1EUhZ5+N/D3/n/hlvAx1OgvsDJpFUsP\n/4/s8hy1yxNCCKGCZoWZn+61+Pbbb7n55psBqKuTtGpLBsQGMDDOn8zccjbszVK7nOtmr7UjPnwU\nT/f/C738upNZfpqXD/2Hj5LWUFFXqXZ5QgghWlGzwkx4eDjjx4+nqqqKmJgY1q9fj4eHh6VrEy3s\n7tFd6eDhyKZ9WaTklKpdTovwcfLid93uYV6vuXR08ef73B94dv9L7MjZTYOhQe3yhBBCtALF2Iwp\nLg0NDaSkpBAREYG9vT0JCQkEBwfj7u7eGjU2qaCgwmLH9vV1s+jx1ZB6ppQXPjqCt5sDz97fD2fH\nlr1/Rs2eNRga2H1uPxszvqJGX0OAsx93Rk0i2ruLKvU0V1s8zyxNemY+6Zn5pGfms2TPfH3drrit\nWSMzSUlJnD9/Hnt7e1577TVeeuklUlJSWqxA0Xq6dPJk4k1hFJXX8uFXbevPUKvRMrzTIBYN+CuD\nA/uTV13A6z++xVsnVlJUU6x2eUIIISykWWHmH//4B+Hh4Rw6dIgTJ07w9NNP8+9//9vStQkLmTgo\njIhAd/Yn5rEv4bza5bQ4V3sX7oqezF/7PkxnjzB+LDjJ8wdeYWPGV9Q1yL1eQgjR1jQrzDg4OBAW\nFsY333zD1KlTiYyMRKOR5+3ZKq1Gw5yJsTjYa/nwq1MUlNaoXZJFhLh1Yn7vP3Bf7F0465zZkrWd\n5/a/wpH84zb3AEEhhBBX1qxEUlNTw5YtW9i+fTuDBw+mtLSU8vJyS9cmLMjPy5l7RkdRU9vAWxsT\naWijD0FUFIW+Ab14ZsDjjAkdQUVdBW+f/JBlR9/kbKVtPkRQCCHEpZoVZubPn8+XX37J/PnzcXV1\n5YMPPuC+++6zcGnC0m7qFkDfaD/SzpSxaV/bfm6Qo86BSRHxPNV/Pt18YkgtzeCfB//F6pT1VNVX\nq12eEEKI69Cs2UwA1dXVZGZmoigK4eHhODk5Wbq2ZpHZTNen6kI9z7x9kLLKOp64pzeRQdc35d5W\nenayMInPUr8kv6YQFztnJnYex6DAfmiU1r98ais9sybSM/NJz8wnPTOfVc9m2r59O2PGjGHhwoX8\n/e9/Z+zYsezatavFChTqcXG0Y84tsRiNRpZvSKCmVq92Sa2iW4cYnuo/n9sixqM36Pn01Dpe+uHf\npJdmqV2aEEIIM+ma86YVK1awYcMGvL29AcjLy2PevHkMGzasyf2WLFnCsWPHUBSFBQsW0L17d9O2\n/fv3s3TpUjQaDeHh4SxevJiamhr+9re/UVZWRn19PQ899BBDhgy5jo8nmiM61Iv4AaFs3p/Nx1+n\n8MAtsWqX1Cp0Gh2jQ4fTL6A3X6Rv4cD5wyw98j9u9O/J7ZET8HSQB0MKIYQtaFaYsbOzMwUZAH9/\nf+zsmn7Y2sGDB8nOzmbVqlWkp6ezYMECVq1aZdr+zDPPsHLlSgICAnjkkUfYvXs3OTk5hIeH89hj\nj5GXl8e9997L1q1br/GjCXPcNiSchKxi9p48zw0RPvSL8Ve7pFbj4eDOrNhpDA4awJqU9RzK+5Hj\nhYnEh45kRMgQ7DTN+t9ECCGESpp1mcnFxYV33nmH5ORkkpOTWbFiBS4uLk3us2/fPkaNGgVAREQE\nZWVlVFb+vGbOunXrCAgIAMDb25uSkhK8vLwoLW18zH55eTleXl7X9KGE+XRaDb+/NQ57Ow0rt56i\nqOyC2iW1us4eoTx+48PcHT0Fe40dX2Rs4R8HXuVEYaJM5RZCCCvWrBuAi4qKWLZsGcePH29ctbhn\nTx5++OFLRmt+7emnn2bYsGGmQDNjxgwWL15MeHj4Je/Lz8/n7rvvZvXq1Xh5efHAAw9w+vRpysvL\nefPNN+nZs2eTten1Deh02uZ8VtEM2/Zn8581P9Itwod/PDgIrUZRuyRVVNVVs+bkRram7cJgNNCr\nYxz39pxCoHuA2qUJIYT4lWaNn/v4+PDcc89d8lp6enqTYebXLpeZioqKePDBB1m4cCFeXl588cUX\nBAYG8vbbb5OcnMyCBQtYt25dk8ctKbHctNr2eCd7r85e9I7y5UhKAR9sPMmEgWFm7d+WejYhOJ5e\nXr1Ym7qBo7kJHD+fzIjgwYwLG4mTzrHFvk9b6llrkZ6ZT3pmPumZ+ax6NtPlPPvss01u9/Pzo7Cw\n0PR1fn4+vr6+pq8rKyuZM2cOjz76KIMHDwbgyJEjpv+Ojo4mPz+fhgZZ+bg1KYrCffHReLras353\nJpm57fvhiIGuATzccw5zus3Ew8Gd7ad38dz+lzmQexiDsW0+aFAIIWzNNYeZq12dGjRoENu2bQMg\nISEBPz8/XF1dTdtfeOEF7r33XoYOHWp6LTQ0lGPHjgFw9uxZXFxc0GrlElJrc3Wy44FbYmkwNE7X\nrq1r34FSURR6+t3A0/3/woTw0dToa1iZtIqlh98guzxH7fKEEKLdu+ZpGorS9L0UvXv3Ji4ujunT\np6MoCgsXLmTdunW4ubkxePBg1q9fT3Z2NmvXrgXglltuYdq0aSxYsIB77rkHvV7PokWLrrU8cZ3i\nwrwZ2y+YbQdz+OSbVO6Lj1a7JNXZa+0YHz6a/gE38nn6Jo7mH+flQ/9hYMe+3BoxDjd716sfRAgh\nRItrMsz8FDQup6Cg4KoH/8tf/nLJ19HRP/9APHny5GX3WbZs2VWPK1rHHUMjSMwq4btj57ihsw99\nuvpefad2wMfJi991u4eUkjTWpGzg+9yDHC04zoTwMQwNGohWI6OJQgjRmpoMM4cPH77itqvNMhK2\nz06nYe6tcTz33g+8tyWJzoHueLk5qF2W1YjyiuSJvvPYfW4/GzO+Ym3qBvacO8CdXW4l2ruL2uUJ\nIUS70ey1mayVrM1keTuOnOHDr1KICfXisek90TRxibG99qyirpKNGdvYe+4gRoz09O3GHZG34ON0\n9Rl/7bVn10N6Zj7pmfmkZ+ZTazZTs+6ZmTFjxm/ukdFqtYSHh/PHP/4Rf//287TY9mhEryCOpxdx\nPL2Irw7mMK5/iNolWR03e1fuip7MoKD+rEn5gh8LTpJQlMyokOGMCR2OvdZe7RKFEKLNatZspptu\nuomAgADuvfdeZs+eTXBwMH369CE8PJwnn3zS0jUKlSmKwv3jY3B3tuOzXemczpPfVK4kxK0T83v/\nkXtjp+Osc2JL1nae2/8KR/KPy1OEhRDCQpoVZg4fPsyrr77KmDFjGDVqFC+88AIJCQncd9991NfX\nW7pGYQXcXey5f0LjdO03NyRQW9++p2s3RVEU+gX05pkBjzMmdAQVdRW8ffJD/n10OWcrc9UuTwgh\n2pxmhZmioiKKi4tNX1dUVHDu3DnKy8upqJDf0tuL7hE+jOzTidyialbvTFO7HKvnqHNkUkQ8T/Wf\nTzefGFJK03nhh2WsTvmC6nrLPblaCCHam2bdMzNr1izi4+MJCgpCURTOnDnD73//e3bu3Mm0adMs\nXaOwIlNHRJB8uoSdR85yQ2cfekZ2ULskq+fn7MsfeszmZGESn6V+ya4zezmUd5RbO4/jpsB+apcn\nhBA2r9mzmSorK8nKysJgMBASEoKnp6ela2sWmc3U+s7kV/Lc+4dwctDy3P398HD9ebq29KxpeoOe\nnTl72JK1ndqGOoJdA5nSfTwB2iBc7ZpeiV78TM4z80nPzCc9M59as5m0i5rxmN2qqiref/99Nm7c\nyKFDhygqKqJbt27odNf8AOEWU11dZ7Fju7g4WPT4tsrdxR5Hey2HUwo4W1jFgFh/02w36VnTNIqG\nCM8wBnS8kYr6SpKKU9ifc4Ttp3dxrOAkedUF6A163OzdsNPaqV2u1ZLzzHzSM/NJz8xnyZ65uFz5\nOWfNGpmZP38+/v7+9O/fH6PRyPfff09JSQmvvPJKixZ6LWRkRh0Go5F/rT7GycxiZozqwqgbgwHp\nmblyKs6RUZPOj2cSySjPRm/QA6CgEOwWSBevCKI8I4j0DMexBVfqtnVynplPemY+6Zn5rPo5M4WF\nhSxdutT09YgRI5g5c+b1VyZslkZRuH9CDM+8fZDVO9OJDvWik6+sTWSuYLdAenfuyjC/IdQ31JNZ\nnk1KSTopJelkledwuuIs35z+Do2iIcStE1FeEUR5RRDhESbPrhFCiIuaFWZqamqoqanByckJgOrq\nampray1amLB+nq4OzB4fzeufnWD5hgSevvdGtUuyaXZaO6K8IonyigSgtqGOjLIsUkrSSS1JJ7vi\nDFnlp/kqeydaRUuYe7Ap3IS7h8plKSFEu9WsMDNt2jTi4+Pp1q0bAAkJCcybN8+ihQnb0KuLL8N7\nBfHt0bOs/TaDR+7qrXZJbYaD1p4Y7yhivKMAuKC/QPrFcJNSkk5GWTbpZVlsyfoGnUZHuHvIxXAT\nSZh7MDqN+ve0CSFEa2jW33ZTpkxh0KBBJCQkoCgKTz/9NB988IGlaxM2YtrNkSRnl/D1oRxu6hlE\naAdntUtqkxx1jsT5RBPn07j6fHV9DellmZwqSWscvSnNILU0g02ZX2OnsSPCI8w0chPi1klW8xZC\ntFnN/tWtY8eOdOzY0fT18ePHLVKQsD0Odlp+f2sc/1h5iH+8c4CJN4UxfmAoOm2znskorpGznRM3\ndIjlhg6xAFTWV5FWkkFKaePITXJJKsklqUDjKE+EZzhRno3hJtgtCI0ifz5CiLbhmsehZZ0Z8Uuh\nAW48OrUH729JZv2eTI6kFvC7CbF08pObgluLq50LPf1uoKffDUDjSt4pJemklDbec5NYdIrEolMA\nOOkciTSFm0gCXQMk3AghbNY1h5lfr6ItRFyYN/95/Gb+s/ooe47n8ux7P3Dr4HDGDwhBq5EflK3N\nzd6VPv496OPfA4DS2jLTzcQpJemcKEziRGESAC52znTx7GyaCt7RxV/+HxdC2Iwmw8ywYcMu+xea\n0WikpKTEYkUJ2+XiZMf942O4sasv721J5vPvMjiaUsADE2IIkqnbqvJ08KBfQG/6BTTepF18ocR0\nM3FKSTo/Fpzkx4KTALjZudLFq3PjPTeeEfg5+0q4EUJYrSYfmnf27Nkmdw4KCmrxgswlD82zLr/s\nWdWFej7Znsr3J8+j0yrcNqQzY/sFyyjNr1jDeWY0Gim6UPyLcJNGWd3PNXnYu5tuJo7yisDH0VvV\ncGMNPbM10jPzSc/Mp9ZD85q9NpO1kjBjXS7Xs6OpBazceoqyqjo6B7rzwIQYOvrIOkQ/scbzzGg0\nkl9dYLqZOLUkg4r6StN2LwfPS8KNt6NXq9ZnjT2zdtIz80nPzCdh5hpJmLEuV+pZZU09H3+dwv7E\nPHRaDXcM7cyYvsFoNHLpwhbOM6PRSG5Vnulm4tSSDKr01abtHRy9ifKKaLznxisCTwcPi9ZjCz2z\nNtIz80nPzCdh5hpJmLEuV+vZ4VP5rNx2iorqeiKDPLh/QgwB3u37uTS2eJ4ZjAbOVZ43jdyklWZQ\no79g2u7v7Gu6mTjKKwI3+5a9X8oWe6Y26Zn5pGfmkzBzjSTMWJfm9Ky8uo6Pvkrhh+R87HUaJg+L\nYOSNndC00xtM28J5ZjAayKk4a5oKnl6aSW3DzyvndnTxN91MHOnVGVe767vM2BZ61tqkZ+aTnplP\nwsw1kjBjXczp2Q/J+Xyw7RSVNfVEdfJg9oQY/L3a3yhNWzzPGgwNnK44Y7qhOL0si3pDPdC4Inig\nawBRXhF09Yok0jMcJ52TWcdviz2zNOmZ+aRn5pMwc40kzFgXc3tWXlXHB9tOcTilAHs7DXcOj2RE\n76B2NUrTHs4zvUFPVnkOqSXpnCpJI7P8NHqDHmgMN8FuQZesCO6oc2zyeO2hZy1NemY+6Zn5JMxc\nIwkz1uVaemY0GjmYlM+HX52i6oKe6BBPZo+PwdfTvN/WbVV7PM/qG+rJLM82jdxklefQYGwAQKNo\nCHXrZLqZOMIjDHut/SX7t8eeXS/pmfmkZ+ZTK8zIsrpCdYqi0D/Wn+gQT1ZuO8XR1EKeefsgU0dE\nMKxX+xqlaS/stHZEeUUS5RUJQG1DHRkXVwRPLUknu+IMmeWn+Sp7J1pFS5hpRfAIwt1DVK5eCGFt\nZGSmCZLKzXe9PTMajexPyOOjr1OortUTE+rF7PHRdPBou6M0cp791gX9BdLLsjhVkkZqSTo5Fecw\n0vhXlU6jI8onnFCXECI8wwl3D8VR56ByxdZPzjPzSc/MJ5eZrpGEGevSUj0rqajl/a3JHE8vwtFe\ny7SbIxnaI7BNPlJfzrOrq66vIa305xXBz1WeN4UbjaIh2DWICM8wIj07E+EZdt2zpdoiOc/MJz0z\nn4SZayRhxrq0ZM+MRiN7T5znk29SqanV0y3cm/vio/F2b/rmUFsj55n5nD20HEw/SVppJmmlmZyu\nOGO65wYap4JHeIYT6RFOpGc4Xo6eKlZrHeQ8M5/0zHxyz4wQv6IoCoO7dyQ2zIv3tiZzMqOYp98+\nwPSRXRh8Q8c2OUojmsfF3pluHWLo1iEGgLqGOrLKT5vCTWZZNrlVeew5ux8AH0cv06hNpEe4LJwp\nRBsjIzNNkFRuPkv1zGg0svt4Lp9+k8qFuga6R/hw77hovNxs/14JOc/Md7WeNRgayKk8awo3GaVZ\nlyy/4Gbn2jhyc/GfINeOaJS2vQCqnGfmk56ZTy4zXSMJM9bF0j0rKrvAu1uSSMwqwdlBx4zRXRgY\nF2DTv2XLeWY+c3tmMBo4X5V/MdxkkF6WRWltmWm7o9aRzp6hRHqEE+EZTqh7MHaatjVwLeeZ+aRn\n5pMwc40kzFiX1uiZ0Whk14/nWLUzjdq6BnpGduDecV3xcLXNURo5z8zXErPmii6UNAab0kzSyjLJ\nry40bddpdIS5BxPp2ZlIj3DCPUKu+iA/ayfnmfmkZ+aTe2aEaCZFURjeK4hu4d68szmJH9MKSV1R\nyt2jo+gf62/TozSidSiKQgcnbzo4eTOg440AlNVWkF7WeFkqvTST9NIs0kozgcYZU51cA02XpSI8\nwnG1lxlTQlgLGZlpgqRy87V2zwxGI98ePcvqnWnU1RvoHeXLzLFd8XCxv/rOVkLOM/O1Rs+q62vI\nKMsivSyLtNIMsssvnTEV4OzXGGwuBhxvRy+L1nO95Dwzn/TMfDIyI8Q10CgKN/fudHGUJpkjKQWk\n5JRyz5go+sX4q12esGHOdk6/mjFVT/YvZkxllGez59wB9pw7AIC3oxcRHuF0uRhw/GXGlBCtRkZm\nmiCp3Hxq9sxgNPLN4TN89m06dXoDN0b7cc+YKNydrXuURs4z81lDzxoMDZypPGcKN+llmVTV/zxj\nytXO5ZKRm06ugarOmLKGntka6Zn5ZGRGiOukURRG3xhM984+vL05iUPJ+Zw6XcLMMV25MdpP7fJE\nG6PVaAl1DybUPZiRIUMxGA3kVReQVpphCjg/Fpzkx4KTADhqHQj3CG28qdgznFC3Tthp7VT+FEK0\nDTIy0wRJ5eazlp4ZDEa+PpTDuu8yqNcb6B/rz92jo3B1sr4fHtbSM1tiCz0zGo0UXyi5ZOQmr7rA\ntF2n0RHqFmy6qbizR6hFZ0zZQs+sjfTMfDIyI0QL0mgUxvYLoXuED+9sSuJAYh5J2SXcO7YrvaJ8\n1S5PtAOKouDj5I2Pkzf9O/YBoLyugvTSrMbp4KUZF28wzmRbNigoBLsFmpZhiPAMx83eVeVPIYRt\nkJGZJkgqN5819sxgMLLth9N8/l0m+gYDA+MCmDG6Cy6O1jFKY409s3ZtpWc1+hoyyrJN08Gzy3PQ\n/2LGlL+zH5E/LaDpEY6P07XPmGorPWtN0jPzyciMEBai0SjE9w+le0QH3tmUyL6E8yRmF3PvuGh6\nRnZQuzzRjjnpnIjziSbOJxqA+oZ6sspzTJelMsqy2HvuIHvPHQTAy8HTdFNxF89w/J39ZMaUEMjI\nTJMklZvP2nvWYDCw9cBp1u/OpMFgZNANAdw1sgvOKo7SWHvPrFF76dlPM6bSTffdZFFZX2Xa7mrn\ncvGyVBgRF2dMaTXayx6rvfSsJUnPzCcjM0K0Aq1Gw4SBYfSI6MDbm5LYe+I8iVkl3BcfzQ2dfdQu\nT4hL/HLG1M0hQzEajeRV55N68bJUWmkmxwpOcuzijCkHrT2dPcJMTykOcw+WGVOiXZCRmSZIKjef\nLfVM32Bgy/5sNuzNosFgZEj3jky7uQvOjq2b8W2pZ9ZCevazopqSi8swZJBWmkVedb5pm05pDEMR\nnuF07xSFS4M7HZx82vwK4S1FzjPzyUKT10jCjHWxxZ6dzqvg7U1J5ORX4u3uwOz4GOLCvVvt+9ti\nz9QmPbuyirpK0+KZaaWZnKk4h5Gf/5q30+gIcPYjwCWAQFd/Orr4E+gSgJejp4ScX5HzzHwSZq6R\nhBnrYqs90zcY2Ph9Fpv2ZdNgMDK8ZyB3jojEycHyozS22jM1Sc+ar0Z/gcyybEqNxaTln+Zc1XnO\nV+VRb9Bf8j4HrT0BF4PNTwGno6s/Hvbu7fYmYznPzCf3zAihIp1Ww21DOtOriy9vb0rk2x/PcSKj\nmPvHRxMT1nqjNEK0NCedI7E+XRt/yHRo/CFjMBoorCkmt+o8uVV5nKts/PeZinNkl+f8an+ni+HG\nn46m0ZwAeQaOsCoWHZlZsmQJx44dQ1EUFixYQPfu3U3b9u/fz9KlS9FoNISHh7N48WI0Gg0bNmxg\nxYoV6HQ6HnnkEYYPH97k95CRGevSFnpWrzfw5feZbN53GoPRyM29g5gyPAJHe8tk/7bQs9YmPTNf\nc3rWYGigoKaQc1V55Faeb/x3VR4FNYUYjIZL3utq52IaveloGs3xx9nO2ZIfo1XJeWa+Njcyc/Dg\nQbKzs1m1ahXp6eksWLCAVatWmbY/88wzrFy5koCAAB555BF2795N9+7d+e9//8tnn31GdXU1r7/+\n+lXDjBAtzU6n4Y6hERdHaZLYceQsJzKKuH98DF1Drv2hZUJYO61GS4CLPwEu/uD38y+f9Q315FUX\nNI7iXBzNya08T2ppBiml6Zccw8PenUDXxnDzU8jp6OJn0aUahLBYmNm3bx+jRo0CICIigrKyMior\nK3F1bRyaXLdunem/vb29KSkpYd++fQwcOBBXV1dcXV15/vnnLVWeEFcV3tGdhffdyPo9mWw9cJoX\nPz7KqBs7MXlYBA52l3+WhxBtkZ3Wjk5ugXRyC7zk9dqGOs5fHL35OeTkkVScQlJxyiXv9Xb0Ml2q\n6ujiT6BrAP7OftjL1HHRAiwWZgoLC4mLizN97e3tTUFBgSnA/PTv/Px89u7dy7x581izZg0XLlzg\nwQcfpLy8nIcffpiBA9tobX0AACAASURBVAdaqkQhrspOp+XO4ZH0jvLl7Y1JbD90huPpRTwwIYYu\nnTzVLk8IVTlo7U3PwfmlGn0NuVX55F68F+enoHOyKJmTRcmm9yko+Dr5NI7euP5847Gfcwd0Grml\nUzRfq50tl7s1p6ioiAcffJCFCxfi5dU4fF9aWsp//vMfzp07x6xZs9i5c2eTd9J7eTmj01nut+Sm\nrtGJy2uLPfP1daNXbEc+2prM+l1pvPDRESYNjeCe+JgWGaVpiz2zNOmZ+VqvZ26E4Ad0u+TVitpK\ncspyySk7R075OdN/HytM4Fhhgul9WkVDRzd/gj0CCfbo2Phv9474u/pe8QnHliLnmfnU6JnFwoyf\nnx+FhYWmr/Pz8/H1/Xm14srKSubMmcOjjz7K4MGDAfDx8aFXr17odDpCQkJwcXGhuLgYH58rP5m1\npKTaUh9Bbv66Bm29ZxMHhBDdyZ13NiWxflc6+07k8rsJMUQEeVzzMdt6zyxBemY+a+mZrxKAr2cA\nvT17A42/6JbXVf5mZlVu1XnOlOey7xeTq3QaHf7Ovj9PH784muPt6GWRZ+RYS89sSZu7AXjQoEG8\n/vrrTJ8+nYSEBPz8/EyXlgBeeOEF7r33XoYOHWp6bfDgwTzxxBPMmTOHsrIyqqurTSM2QliLLp08\nWXR/P9btymD7oRyWfHiYcf1CuG1IOHYWHCUUoi1SFAUPB7f/b+/eg6Ou7v6Bv797v292N3vJPSEX\nSLJAQFASQFTQPirVp1pLxGKfX1tnrNOpdqozDlZpx9YpTttxRMe2tn2q+LREESmON6qSihJABQIJ\nCSQhJCFkd7OXJBt2N9f9/bHLQohFAiS7m7xfM8xmrzl7Jtm8OedzzoFersUcY2Hs9nA4jJ6B3tgU\nVSTkONB1xoXO/q4xryETSc8VG0dXV6WrrUiR62fsHjkzzaQuzf7tb3+LL774AoIgYMOGDTh69Ci0\nWi2WLVuGxYsXY8GCBbHHrl69GmvWrMGWLVuwdetWAMCPfvQjrFy58qLfg0uzE8tM67Nj7T789d0G\ndPeEkGZS4YerS5CXppvQa8y0Prsa2GcTN136bDQ8Ck/Qh64zZ5eOR8KO84wLw+GRMY9VShTR1VRj\nC4+1Us0lhZzp0mdTiTsAXyaGmcQyE/tsYHAEW6tb8NGBUxAJAm5dko07luZBKrm0Ye+Z2GdXin02\ncdO9zyJ75HjOFRxHp6tc/2GPnAsDTpraCvUFe+RM9z6bDNNumoloppDLxLjvliIsnG3G/77bgHdq\n2nCo2Y0f3F6MXNvERmmI6PJE9sixwKa2YAHmxm4fGh2GK9B93sqqyGhOc08rmnpOjHkNvUwbCTjR\nM6uKwjmQDCigl+t4blWC48jMRTCVT9xM77PQ4DDe2NWCXQc7IRIE3F6eg28uzYVE/J8/CGd6n10O\n9tnEsc/GGhwZhCPgQlf/2H1yvCHfuMeKBTGMihSYFEaYlEakKowwKQ0wKY0wKYzQSNWszYniyAzR\nNKCQSbDuG7OxcLYZf3u3AW/vORkbpcm2coknUaKQiWXI1mYiW5s55vbgcCi2EeAZwY8OjwPukBee\noBeNviZgfNaBXCyLBh1DLPCYFEakRi8VEvkUvauZiyMzF8H/yUwc++yc4MAwqj5uxie1pyEWCfhm\nRS5uK88ZN0rDPps49tnEsc8m7sI+Cw0PwBvywRPywh30whPywhP0RS+9CI0MfOXraKTqcWHn7OiO\nQWGAdBptEMiRGaJpRimX4H9unYNFs8343/casf3TVhxo6sYPby9BpoUnDhMlG4VEjnSNDeka27j7\nwuEwzgwH4Al64Qn54Al6YyM6npAXnf2n0ebvGPc8AQL0ct15IzmGMSM7rNe5NAwzRJPMPsuEp39w\nHbZ83IRPD3fhl3/7HHcuy8OtS7IhFvFDimg6EAQBGqkaGql63PEOQGRJed+gPzKic8GojjvoxYne\nk2jpbR33PNbrXBqGGaIpoFJI8P3birFothl/e68R2z45gQPHu/GD1SXcLp1oBhAJIqTI9UiR61GQ\nkjfu/uHRYfhCvbEpq3OjOr4rqNcxzJjTylkzcxGcY5449tnXOxMawj8+bMKeOgckYgHfXJ6PhflG\nZJg59XSp+HM2ceyziUukPhsYGRwzouMOeS6pXkctVY0f1YmGncmo12HNDNEMoVZI8cPVJbhmthmv\nvn8Mb1U3461qIMeqRbndhutKrNCrZfFuJhElELlYdtn1Oqf7u9DuPzXuedOpXodhhihOFhSaUZpr\nxAnXGXywpxVHTnjR9lETXv+4GfZZRpSX2rCgMBWyq3AqNxFNX6zXYZghiiuZVIzlZRmYk6FD35lB\n7GtwoqbOgcMtHhxu8UAhE2PRHAsqSm0oyk6BKEE+OIgoeUxWvY5MLLtg6sqEmzUVAKZ+NIdhhihB\n6NQy3LwoCzcvysJp9xnU1DtQU+/Ap4e78OnhLph0ciwptaHCbkOaSR3v5hLRNCERSWBWmWBWmb7y\n/gvrdcaGHh9On3HEHts32oM7c1ZPVdNjWAB8EYlU/JUs2GcTd7E+Gw2Hcay9BzV1DnxxzIXQYORU\n4Lw0LcpLbbi2xAqdaubV1/DnbOLYZxPHPvt64XAYgeEgPEEvfAM9WDSrFEP+yRlBZgEwUZISCQKK\ncwwozjHgvluKcLCpGzV1TtS1etDa5UfVx82YO8uEcrsNZQUmSCWsryGiqSMIAtRSFdRSFbKRiRSF\nFt3+qQ+ADDNESUIuFWNJiQ1LSmzo7R/AvqNO7Kl34FCzG4ea3VDKJVg8x4IKuw2FmfqEKcwjIpps\nDDNESUivkeOWa7Nxy7XZONXdj5o6B/YedeKT2tP4pPY0UvUKlEfra6xGVbybS0Q0qRhmiJJcplmD\ne24swN0r8tHQ7kNNnQNfHuvG23tO4u09J5GfrkO53YZri63QKKXxbi4R0VXHMEM0TYhEAkpzjSjN\nNWLdLSM4cLwbe+odOHrSi5bTffjHh02Yl29Chd2GefmpkEqSYzMsIqKvwzBDNA3JZWKU220ot9vg\n80fra+q6cLDJjYNNbqgVEiwutqLCbkN+uo71NUSU1BhmiKY5g1aO/7ouG/91XTbanX7U1Duwt96J\n6oOdqD7YCYtBifLSSPCxpCjj3VwiogljmCGaQbKtWmRbtfj2DfloOOnDnnoHDhzrxj8/bcU/P21F\nQaYeFaU2LC62QK1gfQ0RJQeGGaIZSCwSwT7LBPssE4K3DEfqa+ocaGzzoflUL/7+4XHML0hFhd2G\nubNMkIhZX0NEiYthhmiGU8olWDo3DUvnpsHbF8Leo07sia6I+vJYNzRKKa4ttqDCnoa8NC3ra4go\n4TDMEFGMUafAbUtycOt12Wh39mNPnQP7jjrw8YFOfHygE1ajChWlVpSX2pDK+hoiShAMM0Q0jiAI\nyLFpkWPT4js35aO+1Ys9dQ4cbHLjrd2teGt3K4qyUlBht2HRbAtUCn6UEFH88BOIiC5KLBJhXn4q\n5uWnIhAaxpfHXKipd6CxvQfHO3rwf/86jrJofU1pnpH1NUQ05RhmiOiSqRQSLJ+fjuXz0+HuDWJv\nfaS+5vNGFz5vdEGrkuK6Yisq5tqQY2V9DRFNDYYZIrosqXolVlfk4vbyHJx0+KP1NU58+OUpfPjl\nKaSZVKiw21BeaoNRp4h3c4loGmOYIaIrIggC8tJ0yEvTYc1NBag74Y2c5t3kxpv/PoFt/z6B2dkp\nqLCn4ZrZZijl/NghoquLnypEdNVIxCKUFaairDAVgdAQPm90Rfavae9BY3sPXtt5DAuKzKiw21CS\na4BYxPoaIrpyDDNENClUCilWlGVgRVkGunuCqKl3xKai9h11QqeWYUlJ5HyoLIuG9TVEdNkYZoho\n0plTlLhjaR6+WZGLE6f7sKfegf1Hndj5eQd2ft6BDLMaFXYblpTYYNDK491cIkoyDDNENGUEQUB+\nhh75GXrcu7IQh1s8qKlz4FCzG2/sasHWXS0oyTWg3G7DwiIzFDJ+RBHR1+MnBRHFhUQswsIiMxYW\nmdEfjNTX1NQ5UH/Sh/qTPsilx7EwWl9TnGOASMRpKCL6agwzRBR3GqUUNy7IwI0LMuD0BVBTF6mv\nqamP/EvRyLCk1IaKUhsyLZp4N5eIEgzDDBElFKtBhf9ePgt3LstDc2dvZFO+Bhfe39eO9/e1I8ui\nwY2LslCYrkO6ScXCYSJimCGixCQIAgozU1CYmYK1qwpR2+zBnjoHjpzw4NV3GwAAlhQlygpTMb8g\nFYWZeh6lQDRDMcwQUcKTSsRYNMeCRXMs6A8OodV1BrsPdOBIqze2Ikoll2Bevgllhamw55l4+CXR\nDMLfdiJKKhqlFDctysLcnBQMDY/iWIcPh5rcqG12Y+9RJ/YedUIsElCUlRLZwK8gFeYUZbybTUST\niGGGiJKWVCKCPc8Ee54J991chA5XPw41R4JNQ5sPDW0+/OPDJmSY1SgriASbvHQdRKyzIZpWGGaI\naFoQBAHZVi2yrVrcsTQPPv8AalvcONQUCTbv1LThnZo26NQyzI9OR5XkGiGXiuPddCK6QgwzRDQt\nGbRy3FCWgRvKMjAwOIKjJ7042OzG4WY3dh/uwu7DXZBKRCjJMcSKiFM03H2YKBkxzBDRtCeXibGg\nyIwFRWaMhsNoPd2HQ82RUZvaFg9qWzwAjiEvTRuZjio0I9Os5rJvoiTBMENEM4rovCMV7l6RD1dP\nELVNbhxqduN4Rw9au/x4a3crTDo5ygrMKCtMxezsFC77JkpgDDNENKNZUpS4eXEWbl6chUBoCEdO\neHGo2Y3DLR58dOAUPjpwCgqZGPZZJiwoSMXcfBM0Smm8m01E52GYISKKUimkuK7EiutKrBgeGUVT\nRw8ONXtwsKkbXzS68EWjCyJBQEGmHmUFqVhQmAqrURXvZhPNeAwzRERfQSIWoTjXiOJcIypXFuC0\n+0ykzqbZjaaOHhzv6MHru5qRZlJhfnTZd0GGngdiEsUBwwwR0dcQBAEZZg0yzBrcXp6L3jODOBwN\nNvUnvbFzozRKaWQX4oJUlOYZoZTzI5ZoKvA3jYhogvRqGZbPT8fy+ekYHBpBQ5sPtc1uHGx2Y0/0\nxG+JWMCcHENssz6jThHvZhNNWwwzRERXQCYVY35BZJ+a74bDaHP4Y8cr1J3wou6EF6/tPI5sqya6\n7DsVOVYtl30TXUWTGmaeeeYZ1NbWQhAErF+/HvPmzYvdt3fvXvz+97+HSCRCXl4efv3rX0Mkiix9\nDIVCWL16NR566CHcddddk9lEIqKrRiQIyEvTIS9Nh29dPwue3lCszqaxzYd2Zz92fHYSBq08Wmdj\nQnGOAVIJdyEmuhKTFmb279+PtrY2VFVVoaWlBevXr0dVVVXs/qeeegqvvvoqbDYbfvKTn2D37t1Y\nsWIFAOCll16CXq+frKYREU0Jk16BlddkYuU1mQgODKO+1YuDTW4cbnGj+mAnqg92Qi4VozTPiPkF\nJszPT4VOLYt3s4mSzqSFmZqaGqxatQoAkJ+fj97eXvT390Oj0QAAtm3bFvvaaDTC5/MBAFpaWtDc\n3IwbbrhhsppGRDTllHIJFs2xYNEcC0ZGR9HS2YdD0c36DhzvxoHj3RAA5GfoY8crpJtUnI4iugST\nFmbcbjdKS0tj141GI7q7u2MB5uyly+XCZ599hocffhgAsHHjRjz55JPYvn37JX0fg0EFySQO0ZrN\n2kl77emKfTZx7LOJS/Y+s1n1WLowCwBwyuXH/non9h91oKHVg+bOXmytbkGaSY1rS224rtSG4jzj\nFe9CnOx9Fg/ss4mLR59NWQFwOBwed5vH48GDDz6IDRs2wGAwYPv27SgrK0NWVtYlv67PF7iazRzD\nbNaiu9s/aa8/HbHPJo59NnHTrc/kArDcbsVyuxX9wSEcjp72faTVi39+0oJ/ftIClVwSWfZdmAp7\nngkqxcQ+vqdbn00F9tnETWafXSwkTVqYsVgscLvdsesulwtmszl2vb+/Hw888AAeeeQRLFu2DABQ\nXV2Njo4OVFdXw+FwQCaTwWazoaKiYrKaSUSUUDRKKSrsaaiwp2FoeBTHOnyx6ai9R53Ye9QJsUhA\nUVYKygojy77NKcp4N5soriYtzCxduhSbNm1CZWUl6uvrYbFYYlNLAPCb3/wG3/ve93D99dfHbnvu\nuediX2/atAkZGRkMMkQ0Y0klItjzTLDnmXDfzUXocPXHTvtuaPOhoc2Hf3zYhAyzOrbsOy9NBxHr\nbGiGmbQws3DhQpSWlqKyshKCIGDDhg3Ytm0btFotli1bhu3bt6OtrQ1bt24FAKxevRpr1qyZrOYQ\nESU1QRCQbdUi26rFHUvz4PMPoDY6HXX0pA/v1LThnZo26NQyzI9OR5XkGiGXctk3TX9C+KuKWZLI\nZM5ncr504thnE8c+mzj22VgDgyM4etKLg82Rzfr8gSEAkZGdkhwDygpTUbEgE5LRUa6OmgD+nE3c\ntKuZISKiqSGXibGgyIwFRWaMjoZxoqsPtdHpqNoWD2pbPHjl/WNQyiXIsWqQY9NG/lm1sBpVnJai\npMcwQ0Q0jYhEAgoy9CjI0OPuFflw9QRR2+zGaU8Qx9q8ONbeg8b2ntjj5TIxciwa5Nh0yLFFLtOM\nKp7+TUmFYYaIaBqzpChx86Ks2PB/cGAYHa5+tDn8OOnwo83pR1NnL46f6o09RyYVIdsSGbk5O4qT\nnqqCWHRl+9wQTRaGGSKiGUQpl6AoKwVFWSmx2wYGRyIBx+nHSUcf2hz9OHG6D82d5wKOVCJCplmD\n3POmqDLM6iveyI/oamCYISKa4eQyMQoy9SjIPHcm3uDQCE51n0Gb04+2aMBpd/rR2tUXe4xELCDD\nrEGOVRsLOZlmNQ/OpCnHMENEROPIpGLMStdhVroOQAYAYGh4FKfdZ6KjN5Epqg7XGbQ5/PikNvI8\nsUhAeqp6zBRVlkXDJeI0qRhmiIjokkglolhAOWt4JBJwzoabNocfHa5+dLj68emRLgCAIADpJnVs\neirHpkW2VQOFjH+C6OrgTxIREV02iVgU28xvefS2kdFRdHkCkYATDTntzn50us9gT50DACAAsJlU\n50Zwoq8x0TOniACGGSIiusrEokixcKZZg6Vz0wAAo6NhOH2ByAqq6L92lx9dngD2HnXGnms1KC8Y\nwdFCo5TG661QkmCYISKiSScSCUgzqZFmUqO81AYAGA2H0e0LRldRRQOO04/9DS7sb3DFnpuqVyDH\nFi0yjoYcrUoWr7dCCYhhhoiI4kIkCLAaVbAaVbi22AoACIfDcPeGYtNTZ0POl8e68eWx7thzjTp5\nLNicDTl6jTxeb4XijGGGiIgShiAIMKcoYU5RYtEcC4BIwPH2DcTCTXv08mCTGweb3LHnpmhkY1ZR\n5dp0SNHIeB7VDMAwQ0RECU0QBJj0Cpj0CiwsMgOIBJye/sExq6janP7YWVRn6VTSc0c1WCOXJp2C\nAWeaYZghIqKkIwgCDFo5DFo5ygpTY7f3nokGHEcf2pz9aHP04cgJD46cOBdwNEpp9MBNXWwUx6xn\nwElmDDNERDRt6NUyzMs3YV6+KXabPzB4bvQmOoJTf9KH+pO+2GNUcsmYVVQ5Ni2MJk083gJdBoYZ\nIiKa1rQqGex5JtjzzgWcM6EhtDv8OHleyGlo86Gh7VzAkUpEMKcoYTUoYTWqYDOqYl/r1azFSSQM\nM0RENOOoFVIU5xpRnGuM3RYcGEZ7NNycdPrh7g2hs7sfp91nxj1fLhPDmqKMrsZSwmpQxQIP98WZ\negwzREREiJwoPjvbgNnZBgCA2ayFy9UHf2AIDm8ATl8ATm8wehmAwxtAu6t/3OuoFRJYDCrYzgs5\nZwOPUs4/u5OBvUpERPQfCIIAnVoGnVqGoqyUMfeNhsPo8Q/A6Q3A6QvC4Q3AFb288ITxs3RqWWyq\nympQRqeuVLAYlJDxMM7LxjBDRER0GUSCAKNOAaNOgeLcsfeNjI7C0xcNOt5zIzoObwDNnb1oOtU7\n7vWMOvm5kZzzAo85RQmJWDQ1bypJMcwQERFdZWKRCJYUJSwpSsydZRpz39DwKNy9kRGc86etnL7g\nuCJkIBKaUvWKsSEnOm1l0ikgErEQmWGGiIhoCkklotg5VRcaGByBqycYDTeBaK1O5PqREx4cueDx\nEnFkx+TYdJVRCVt0dGcm7X7MMENERJQg5DIxsiwaZFnG73ETCA2PGcU5F3iC6PIExr+WVAzLedNV\nVkNktZXFqIRWKZ1WQYdhhoiIKAmoFBLkpemQl6Ybc3s4HIY/OASXN3jeqqto4PEF0PEVK65Ucklk\nqio6onOuRkcFlSL5okHytZiIiIhiBEGATiWDTiVDQaZ+zH1nz7ByegNw+AJjAk+Hqx+tXf5xr6dT\nSWExqqLTVeeWl1sMSsgTdMUVwwwREdE0df4ZVnNyDGPuGx0Nw9MXOrd/znmB50RnH5q/YsWVQSuP\nLSm3RKetrMb4r7himCEiIpqBRKJI8bA5RQl73tj7hkdG0d0TPK8251yNTmN7Dxrbe8Y8XhCAVL0C\n/2+1HXMyx06DTQWGGSIiIhpDIr7IiquhEXT7gmNWW7m8AXT3huDzhwAwzBAREVECk0vFyLRokPkV\nK67MZi26u8fX4Uw2bilIRERESY1hhoiIiJIawwwRERElNYYZIiIiSmoMM0RERJTUGGaIiIgoqTHM\nEBERUVJjmCEiIqKkxjBDRERESY1hhoiIiJIawwwRERElNYYZIiIiSmoMM0RERJTUhHA4HI53I4iI\niIguF0dmiIiIKKkxzBAREVFSY5ghIiKipMYwQ0REREmNYYaIiIiSGsMMERERJTWGma/wzDPPYM2a\nNaisrMThw4fj3Zykcfz4caxatQqvvfZavJuSNJ599lmsWbMGd999N3bu3Bnv5iS0YDCIhx9+GN/9\n7ndxzz33YNeuXfFuUtIIhUJYtWoVtm3bFu+mJLx9+/ZhyZIlWLduHdatW4enn3463k1KCjt27MAd\nd9yBu+66C9XV1VP+/SVT/h0T3P79+9HW1oaqqiq0tLRg/fr1qKqqinezEl4gEMDTTz+N8vLyeDcl\naezduxdNTU2oqqqCz+fDt771Ldxyyy3xblbC2rVrF+x2Ox544AF0dnbi+9//Pm688cZ4NyspvPTS\nS9Dr9fFuRtK49tpr8fzzz8e7GUnD5/PhxRdfxJtvvolAIIBNmzbhhhtumNI2MMxcoKamBqtWrQIA\n5Ofno7e3F/39/dBoNHFuWWKTyWR4+eWX8fLLL8e7KUlj8eLFmDdvHgBAp9MhGAxiZGQEYrE4zi1L\nTLfddlvs666uLlit1ji2Jnm0tLSgubl5yv+40MxRU1OD8vJyaDQaaDSauIxmcZrpAm63GwaDIXbd\naDSiu7s7ji1KDhKJBAqFIt7NSCpisRgqlQoAsHXrVlx//fUMMpegsrISjz76KNavXx/vpiSFjRs3\n4vHHH493M5JKc3MzHnzwQdx777347LPP4t2chHfq1CmEQiE8+OCDWLt2LWpqaqa8DRyZ+Ro87YEm\n24cffoitW7fir3/9a7ybkhS2bNmChoYGPPbYY9ixYwcEQYh3kxLW9u3bUVZWhqysrHg3JWnk5ubi\nxz/+MW699VZ0dHTg/vvvx86dOyGTyeLdtITW09ODF154AadPn8b999+PXbt2TenvJsPMBSwWC9xu\nd+y6y+WC2WyOY4toOtu9ezf+8Ic/4M9//jO0Wm28m5PQ6urqYDKZkJaWhuLiYoyMjMDr9cJkMsW7\naQmruroaHR0dqK6uhsPhgEwmg81mQ0VFRbyblrCsVmtsSjM7OxupqalwOp0MhBdhMpmwYMECSCQS\nZGdnQ61WT/nvJqeZLrB06VJ88MEHAID6+npYLBbWy9Ck8Pv9ePbZZ/HHP/4RKSkp8W5Owvviiy9i\no1dutxuBQGDMlDCN99xzz+HNN9/E66+/jnvuuQcPPfQQg8zX2LFjB/7yl78AALq7u+HxeFif9TWW\nLVuGvXv3YnR0FD6fLy6/mxyZucDChQtRWlqKyspKCIKADRs2xLtJSaGurg4bN25EZ2cnJBIJPvjg\nA2zatIl/pC/i3Xffhc/nwyOPPBK7bePGjUhPT49jqxJXZWUlnnjiCaxduxahUAhPPfUURCL+f4yu\nrptuugmPPvooPvroIwwNDeEXv/gFp5i+htVqxTe+8Q185zvfAQD8/Oc/n/LfTSHMohAiIiJKYvxv\nDRERESU1hhkiIiJKagwzRERElNQYZoiIiCipMcwQERFRUmOYIaIpc+rUKdjt9tiJxJWVlfjZz36G\nvr6+S36NdevWYWRk5JIff++992Lfvn2X01wiShIMM0Q0pYxGIzZv3ozNmzdjy5YtsFgseOmlly75\n+Zs3b+YZVkQ0BjfNI6K4Wrx4MaqqqtDY2IiNGzdieHgYQ0NDeOqpp1BSUoJ169Zhzpw5aGhowCuv\nvIKSkhLU19djcHAQTz75JBwOB4aHh3HnnXdi7dq1CAaD+OlPfwqfz4ecnBwMDAwAAJxOJx599FEA\nQCgUwpo1a/Dtb387nm+diK4ShhkiipuRkRH861//wjXXXIPHHnsML774IrKzs9HY2Ij169dj27Zt\nAACVSoXXXnttzHM3b94MnU6H3/3udwiFQrjtttuwfPly7NmzBwqFAlVVVXC5XFi5ciUA4L333sOs\nWbPwy1/+EgMDA3jjjTem/P0S0eRgmCGiKeX1erFu3ToAwOjoKBYtWoS7774bzz//PJ544onY4/r7\n+zE6OgogcszIhWpra3HXXXcBABQKBex2O+rr63H8+HFcc801ACIHx86aNQsAsHz5cvz973/H448/\njhUrVmDNmjWT+j6JaOowzBDRlDpbM3M+v98PqVQ67vazpFLpuNsEQRhzPRwOQxAEhMPhMefCnA1E\n+fn5eOedd/D555/j/fffxyuvvIItW7Zc6dshogTAAmAiijutVovMzEz8+9//BgC0trbihRdeuOhz\n5s+fj927dwMASQUuOgAAAN1JREFUAoEA6uvrUVpaivz8fBw8eBAA0NXVhdbWVgDA22+/jSNHjqCi\nogIbNmxAV1cXhoeHJ/FdEdFU4cgMESWEjRs34le/+hX+9Kc/YXh4GI8//vhFH79u3To8+eSTuO++\n+zA4OIiHHnoImZmZuPPOO/Hxxx9j7dq1yMzMxNy5cwEABQUF2LBhA2QyGcLhMB544AFIJPwIJJoO\neGo2ERERJTVOMxEREVFSY5ghIiKipMYwQ0REREmNYYaIiIiSGsMMERERJTWGGSIiIkpqDDNERESU\n1BhmiIiIKKn9f+lipDRZzN1FAAAAAElFTkSuQmCC\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb
new file mode 100644
index 0000000..2cb9918
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1313 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "8113ee39-5127-4303-9f33-233ea698e8d2"
+      },
+      "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>12513</th>\n",
+              "      <td>-121.6</td>\n",
+              "      <td>39.1</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2991.0</td>\n",
+              "      <td>637.0</td>\n",
+              "      <td>1419.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>73.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11106</th>\n",
+              "      <td>-121.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2911.0</td>\n",
+              "      <td>445.0</td>\n",
+              "      <td>1170.0</td>\n",
+              "      <td>460.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>158.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5254</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>4164.0</td>\n",
+              "      <td>1127.0</td>\n",
+              "      <td>2934.0</td>\n",
+              "      <td>1014.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>218.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5828</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>1807.0</td>\n",
+              "      <td>429.0</td>\n",
+              "      <td>1699.0</td>\n",
+              "      <td>424.0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>126.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12057</th>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>38.6</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1878.0</td>\n",
+              "      <td>338.0</td>\n",
+              "      <td>710.0</td>\n",
+              "      <td>342.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>161.4</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>12873</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>37.2</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2349.0</td>\n",
+              "      <td>394.0</td>\n",
+              "      <td>1266.0</td>\n",
+              "      <td>383.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>233.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10594</th>\n",
+              "      <td>-120.5</td>\n",
+              "      <td>36.9</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1287.0</td>\n",
+              "      <td>310.0</td>\n",
+              "      <td>954.0</td>\n",
+              "      <td>269.0</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>63.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2121</th>\n",
+              "      <td>-117.3</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>6859.0</td>\n",
+              "      <td>1535.0</td>\n",
+              "      <td>3405.0</td>\n",
+              "      <td>1351.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>109.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10974</th>\n",
+              "      <td>-120.9</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1320.0</td>\n",
+              "      <td>255.0</td>\n",
+              "      <td>720.0</td>\n",
+              "      <td>232.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>76.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3715</th>\n",
+              "      <td>-117.9</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>2246.0</td>\n",
+              "      <td>446.0</td>\n",
+              "      <td>1837.0</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>164.5</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",
+              "12513     -121.6      39.1                27.0       2991.0           637.0   \n",
+              "11106     -121.0      37.7                29.0       2911.0           445.0   \n",
+              "5254      -118.1      34.1                28.0       4164.0          1127.0   \n",
+              "5828      -118.2      34.1                41.0       1807.0           429.0   \n",
+              "12057     -121.4      38.6                38.0       1878.0           338.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "12873     -121.8      37.2                25.0       2349.0           394.0   \n",
+              "10594     -120.5      36.9                20.0       1287.0           310.0   \n",
+              "2121      -117.3      33.6                23.0       6859.0          1535.0   \n",
+              "10974     -120.9      37.7                36.0       1320.0           255.0   \n",
+              "3715      -117.9      34.0                30.0       2246.0           446.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "12513      1419.0       606.0            1.9                73.5  \n",
+              "11106      1170.0       460.0            5.0               158.1  \n",
+              "5254       2934.0      1014.0            2.7               218.8  \n",
+              "5828       1699.0       424.0            2.2               126.0  \n",
+              "12057       710.0       342.0            3.8               161.4  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "12873      1266.0       383.0            5.0               233.1  \n",
+              "10594       954.0       269.0            1.3                63.0  \n",
+              "2121       3405.0      1351.0            2.5               109.2  \n",
+              "10974       720.0       232.0            2.7                76.3  \n",
+              "3715       1837.0       431.0            4.8               164.5  \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": 1074
+        },
+        "outputId": "a992f481-7ea1-489e-fc0f-c92d5296136f"
+      },
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#\n",
+        "# YOUR CODE HERE\n",
+        "#\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=0.1,\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 190.09\n",
+            "  period 01 : 155.41\n",
+            "  period 02 : 133.92\n",
+            "  period 03 : 131.46\n",
+            "  period 04 : 134.94\n",
+            "  period 05 : 132.44\n",
+            "  period 06 : 131.46\n",
+            "  period 07 : 131.34\n",
+            "  period 08 : 133.13\n",
+            "  period 09 : 129.46\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         204.2    207.3\n",
+              "std           86.3    116.0\n",
+              "min           59.4     15.0\n",
+              "25%          170.3    119.4\n",
+              "50%          201.3    180.4\n",
+              "75%          227.6    265.0\n",
+              "max         4132.0    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>204.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>86.3</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>59.4</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>170.3</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>201.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>227.6</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4132.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 129.46\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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JE+zZs4fWrVvTsmVLtm/fTk5ODna7nUWLFjn3CwoKci6QmJKS4lxbqTJxNW/enMzMTPbs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UKhYPz48ezYsaOt6xHaSH2PBEea6pGgN1qcDiTgbLhR7/yRmmVVJvakFmM025p8/PiBoagqvXiu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\nf56QaiA39i4XAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i5Ul3zf5QYvW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Leaz2oYMQcBf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZjQrZ8mcHFiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Identify Outliers\n",
+        "\n",
+        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
+        "\n",
+        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
+        "\n",
+        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P0BDOec4HbG_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "8ce24d6d-a8b9-4beb-ac93-a3c1fee0b6d5"
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fbb6529fa50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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g63UaFJoNogfkr3zJDltpQcray+PsFpRYjGj3Js4A7/AGczrPmmpJU74Cp9zW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\nkoH/D4P1IE8F9UCQAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jByCP8hDRZmM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s0tiX2gdRe-S",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMQD0Uq3RqTX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
+        "\n",
+        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "POTM8C_ER1Oc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "a6833bf2-156e-471a-8df9-7f1ea1ac428d"
+      },
+      "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": "IZtUiZo1h710",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 972
+        },
+        "outputId": "ff601385-8a39-43b5-8286-8c00eb8db81e"
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "california_housing_dataframe[\"rooms_per_person\"]= california_housing_dataframe[\"rooms_per_person\"].apply(lambda val: max(val,5))\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.1,\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 141.91\n",
+            "  period 01 : 120.64\n",
+            "  period 02 : 120.62\n",
+            "  period 03 : 120.56\n",
+            "  period 04 : 120.51\n",
+            "  period 05 : 120.47\n",
+            "  period 06 : 120.44\n",
+            "  period 07 : 120.55\n",
+            "  period 08 : 120.35\n",
+            "  period 09 : 120.48\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         195.9    207.3\n",
+              "std           29.7    116.0\n",
+              "min          194.5     15.0\n",
+              "25%          194.5    119.4\n",
+              "50%          194.5    180.4\n",
+              "75%          194.5    265.0\n",
+              "max         1989.6    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>195.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>29.7</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>194.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>194.5</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>194.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>194.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1989.6</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 120.48\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fbb65227e90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PXGwAwOuvv468vDwAg3f8iIgoMm6//XaUlZXhyy+/BCCeE7Zt2wav14uOjg68/fbbyMvL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YDL7YZMJvNd5KtVnswJlzv4uZlYPRwGq2kmERGJK+wrrbq6OixatAijR4/26ynx1ltv9fucTz/9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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 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": "2bedf2c4-4183-40a1-cccf-40886ef735c5"
+      },
+      "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": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZsiRM1Znjcn384x//qHnz5iknJ0fLly8PU3WD2/79+3XHHXdo/vz5mj9/vpYt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\n",
+            "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": "030dbc70-995f-4036-9bba-7e9c1ce09f07"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 184.56\n",
+            "  period 01 : 142.26\n",
+            "  period 02 : 119.51\n",
+            "  period 03 : 117.09\n",
+            "  period 04 : 116.50\n",
+            "  period 05 : 116.03\n",
+            "  period 06 : 117.09\n",
+            "  period 07 : 115.98\n",
+            "  period 08 : 116.77\n",
+            "  period 09 : 117.09\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         191.2    207.3\n",
+              "std            0.0    116.0\n",
+              "min          191.2     15.0\n",
+              "25%          191.2    119.4\n",
+              "50%          191.2    180.4\n",
+              "75%          191.2    265.0\n",
+              "max          191.2    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>191.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>191.2</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>191.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>191.2</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>191.2</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>191.2</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): 117.09\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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9AwBck5wUJbukffn0lQAABCaKEgDcjtU3AMA19JUAAAQ6ihIA3C7YHKRBybFO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+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gySE-UgfSony",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        },
+        "outputId": "e5f388d2-22e1-4a8b-ec46-8a2c0f9de6ab"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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FF7rdK0EGAMAAvGQNAIABCDIAAAYgyAAAGIAgAwBgAIIMAIABCDIAAAYgyAAA\nGIAgAwBggP8DN4eriCouGnkAAAAASUVORK5CYII=\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..48a2f60
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1543 @@
+{
+  "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/rupamcyrax799/Assignment-5/blob/rupamcyrax799/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": "966b81be-d3dc-4b9c-84cd-3191599c18c9"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>34.6</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>2655.7</td>\n",
+              "      <td>547.1</td>\n",
+              "      <td>1476.0</td>\n",
+              "      <td>505.4</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>12.1</td>\n",
+              "      <td>2258.1</td>\n",
+              "      <td>434.3</td>\n",
+              "      <td>1174.3</td>\n",
+              "      <td>391.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.8</td>\n",
+              "      <td>-118.9</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1451.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>815.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.0</td>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2113.5</td>\n",
+              "      <td>438.0</td>\n",
+              "      <td>1207.0</td>\n",
+              "      <td>411.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>34.4</td>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>3146.0</td>\n",
+              "      <td>653.0</td>\n",
+              "      <td>1777.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.8</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       34.6     -118.5                27.5       2655.7           547.1   \n",
+              "std         1.6        1.2                12.1       2258.1           434.3   \n",
+              "min        32.5     -121.4                 1.0          2.0             2.0   \n",
+              "25%        33.8     -118.9                17.0       1451.8           299.0   \n",
+              "50%        34.0     -118.2                28.0       2113.5           438.0   \n",
+              "75%        34.4     -117.8                36.0       3146.0           653.0   \n",
+              "max        41.8     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1476.0       505.4            3.8               1.9  \n",
+              "std        1174.3       391.7            1.9               1.3  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         815.0       283.0            2.5               1.4  \n",
+              "50%        1207.0       411.0            3.5               1.9  \n",
+              "75%        1777.0       606.0            4.6               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "1b01325a-a8cc-43a8-a73e-445b792dfd87"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>198.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>111.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>117.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>170.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>244.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                198.0\n",
+              "std                 111.9\n",
+              "min                  15.0\n",
+              "25%                 117.1\n",
+              "50%                 170.5\n",
+              "75%                 244.4\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5l1aA2xOHpCj",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fLYXLWAiHpCk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "e6678c19-b854-4641-d8fd-826cfc81d7a1"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>38.1</td>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>31.3</td>\n",
+              "      <td>2614.8</td>\n",
+              "      <td>521.1</td>\n",
+              "      <td>1318.1</td>\n",
+              "      <td>491.2</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>13.4</td>\n",
+              "      <td>1979.6</td>\n",
+              "      <td>388.5</td>\n",
+              "      <td>1073.7</td>\n",
+              "      <td>366.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>36.1</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>37.5</td>\n",
+              "      <td>-122.4</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1481.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>731.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>37.8</td>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>2164.0</td>\n",
+              "      <td>424.0</td>\n",
+              "      <td>1074.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>3161.2</td>\n",
+              "      <td>635.0</td>\n",
+              "      <td>1590.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>2.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>18.3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       38.1     -122.2                31.3       2614.8           521.1   \n",
+              "std         0.9        0.5                13.4       1979.6           388.5   \n",
+              "min        36.1     -124.3                 1.0          8.0             1.0   \n",
+              "25%        37.5     -122.4                20.0       1481.0           292.0   \n",
+              "50%        37.8     -122.1                31.0       2164.0           424.0   \n",
+              "75%        38.4     -121.9                42.0       3161.2           635.0   \n",
+              "max        42.0     -121.4                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1318.1       491.2            4.1               2.1  \n",
+              "std        1073.7       366.5            2.0               0.6  \n",
+              "min           8.0         1.0            0.5               0.1  \n",
+              "25%         731.0       278.0            2.7               1.7  \n",
+              "50%        1074.0       403.0            3.7               2.1  \n",
+              "75%        1590.2       603.0            5.1               2.4  \n",
+              "max       28566.0      6082.0           15.0              18.3  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "86720387-0482-4dfd-b197-1b137c9ec0ba"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>229.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>122.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>130.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>213.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>303.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                229.5\n",
+              "std                 122.5\n",
+              "min                  15.0\n",
+              "25%                 130.4\n",
+              "50%                 213.0\n",
+              "75%                 303.2\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z3TZV1pgfZ1n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Examine the Data\n",
+        "Okay, let's look at the data above. We have `9` input features that we can use.\n",
+        "\n",
+        "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense  will get you far.\n",
+        "\n",
+        "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4Xp9NhOCYSuz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gqeRmK57YWpy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's check our data against some baseline expectations:\n",
+        "\n",
+        "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n",
+        "\n",
+        "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n",
+        "\n",
+        "If you look closely, you may see some oddities:\n",
+        "\n",
+        "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n",
+        "\n",
+        "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n",
+        "\n",
+        "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n",
+        "\n",
+        "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fXliy7FYZZRm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Plot Latitude/Longitude vs. Median House Value"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aJIWKBdfsDjg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n",
+        "\n",
+        "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5_LD23bJ06TW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "outputId": "9977aaac-8f51-44aa-eaaa-114226f803a0"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(13, 8))\n",
+        "\n",
+        "ax = plt.subplot(1, 2, 1)\n",
+        "ax.set_title(\"Validation Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(validation_examples[\"longitude\"],\n",
+        "            validation_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
+        "\n",
+        "ax = plt.subplot(1,2,2)\n",
+        "ax.set_title(\"Training Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(training_examples[\"longitude\"],\n",
+        "            training_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
+        "_ = plt.plot()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M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\nYII=\n",
+            "text/plain": [
+              "<Figure size 936x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "32_DbjnfXJlC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n",
+        "\n",
+        "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n",
+        "\n",
+        "**Go back up and look at the data from Task 1 again.**\n",
+        "\n",
+        "Do you see any other differences in the distributions of features or targets between the training and validation data?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pECTKgw5ZvFK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "49NC4_KIZxk_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75<sup>th</sup> percentile value for total_rooms per city block?\n",
+        "\n",
+        "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n",
+        "\n",
+        "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "025Ky0Dq9ig0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3:  Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n",
+        "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JFsd2eWHAMdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n",
+        "\n",
+        "By the way, there's an important lesson here.\n",
+        "\n",
+        "**Debugging in ML is often *data debugging* rather than code debugging.**\n",
+        "\n",
+        "If the data is wrong, even the most advanced ML code can't save things."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dER2_43pWj1T",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BnEVbYJvW2wu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n",
+        "\n",
+        "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xCdqLpQyAos2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 4: Train and Evaluate a Model\n",
+        "\n",
+        "**Spend 5 minutes or so trying different hyperparameter settings.  Try to get the best validation performance you can.**\n",
+        "\n",
+        "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n",
+        "\n",
+        "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rzcIPGxxgG0t",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "CvrKoBmNgRCO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wEW5_XYtgZ-H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D0o2wnnzf8BD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n",
+        "\n",
+        "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n",
+        "\n",
+        "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n",
+        "\n",
+        "See how much better you can do now that we can use multiple features.\n",
+        "\n",
+        "Check the data using some of the methods we've looked at before.  These might include:\n",
+        "\n",
+        "   * Comparing distributions of predictions and actual target values\n",
+        "\n",
+        "   * Creating a scatter plot of predictions vs. target values\n",
+        "\n",
+        "   * Creating two scatter plots of validation data using `latitude` and `longitude`:\n",
+        "      * One plot mapping color to actual target `median_house_value`\n",
+        "      * A second plot mapping color to predicted `median_house_value` for side-by-side comparison."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UXt0_4ZTEf4V",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # 1. Create input functions.\n",
+        "  training_input_fn = 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",
+        "    # 2. 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": "zFFRmvUGh8wd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "8c7765f8-2e80-4b71-bcf6-c763c7728082"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=1000,\n",
+        "    batch_size=10,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 213.79\n",
+            "  period 01 : 201.18\n",
+            "  period 02 : 189.93\n",
+            "  period 03 : 181.73\n",
+            "  period 04 : 174.28\n",
+            "  period 05 : 168.82\n",
+            "  period 06 : 165.53\n",
+            "  period 07 : 163.37\n",
+            "  period 08 : 162.18\n",
+            "  period 09 : 161.36\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hSGaCpcsSQgjRQEkDI+qMjdaGCa3HMKndBCqqKlh0+Ct+OrOFKqXK0qUJIYRo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\nos7/B6lMgej/bLRPAAAAAElFTkSuQmCC\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": "74fe518a-9e93-431a-ae8f-7c9af158d81e"
+      },
+      "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": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 163.33\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": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_test_input_fn = lambda: my_input_fn(\n",
+        "      test_examples, \n",
+        "      test_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file