diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
new file mode 100644
index 0000000..64e016a
--- /dev/null
+++ b/feature_crosses.ipynb
@@ -0,0 +1,1647 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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": "99008194-8e3b-4b49-c47e-5aa7e2adabcb"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.5                28.5       2643.2           539.0   \n",
+              "std         2.1        2.0                12.5       2172.9           419.2   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1462.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2128.0           432.0   \n",
+              "75%        37.7     -118.0                37.0       3155.0           649.0   \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       1433.1       500.9            3.9               2.0  \n",
+              "std        1163.2       382.3            1.9               1.2  \n",
+              "min           6.0         2.0            0.5               0.1  \n",
+              "25%         793.0       282.0            2.6               1.5  \n",
+              "50%        1171.0       409.0            3.5               1.9  \n",
+              "75%        1722.0       607.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2643.2</td>\n",
+              "      <td>539.0</td>\n",
+              "      <td>1433.1</td>\n",
+              "      <td>500.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2172.9</td>\n",
+              "      <td>419.2</td>\n",
+              "      <td>1163.2</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>6.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>793.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2128.0</td>\n",
+              "      <td>432.0</td>\n",
+              "      <td>1171.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3155.0</td>\n",
+              "      <td>649.0</td>\n",
+              "      <td>1722.0</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>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.7     -119.6                28.7       2644.7           540.5   \n",
+              "std         2.1        2.0                12.8       2196.9           426.9   \n",
+              "min        32.5     -124.2                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           299.0   \n",
+              "50%        34.3     -118.5                29.0       2126.5           436.5   \n",
+              "75%        37.7     -118.0                37.0       3140.8           647.2   \n",
+              "max        42.0     -114.6                52.0      25957.0          4798.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1421.2       502.0            3.9               2.0  \n",
+              "std        1110.3       389.8            1.9               1.0  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         781.0       281.0            2.6               1.5  \n",
+              "50%        1155.0       410.0            3.5               2.0  \n",
+              "75%        1714.2       602.2            4.8               2.3  \n",
+              "max       11973.0      4490.0           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>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2644.7</td>\n",
+              "      <td>540.5</td>\n",
+              "      <td>1421.2</td>\n",
+              "      <td>502.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.8</td>\n",
+              "      <td>2196.9</td>\n",
+              "      <td>426.9</td>\n",
+              "      <td>1110.3</td>\n",
+              "      <td>389.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1463.0</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>781.0</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>436.5</td>\n",
+              "      <td>1155.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3140.8</td>\n",
+              "      <td>647.2</td>\n",
+              "      <td>1714.2</td>\n",
+              "      <td>602.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>25957.0</td>\n",
+              "      <td>4798.0</td>\n",
+              "      <td>11973.0</td>\n",
+              "      <td>4490.0</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                207.1\n",
+              "std                 115.8\n",
+              "min                  15.0\n",
+              "25%                 119.9\n",
+              "50%                 180.4\n",
+              "75%                 264.6\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "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                 116.5\n",
+              "min                  17.5\n",
+              "25%                 118.8\n",
+              "50%                 180.1\n",
+              "75%                 266.4\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>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>116.5</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>180.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "2d85ac56-db38-4fbf-fbaa-9bcd4f292a75"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(training_examples),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 286.37\n",
+            "  period 01 : 170.95\n",
+            "  period 02 : 146.20\n",
+            "  period 03 : 144.89\n",
+            "  period 04 : 144.01\n",
+            "  period 05 : 113.24\n",
+            "  period 06 : 134.92\n",
+            "  period 07 : 114.98\n",
+            "  period 08 : 158.75\n",
+            "  period 09 : 185.04\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncDgSYIQQQgjhcP4/3Vde9Ws96q8AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i4lGvqajDWlw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## One-Hot Encoding for Discrete Features\n",
+        "\n",
+        "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n",
+        "\n",
+        "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n",
+        "\n",
+        "| # | feature_value |\n",
+        "|---|---------------|\n",
+        "| 0 |             2 |\n",
+        "| 1 |             0 |\n",
+        "| 2 |             1 |\n",
+        "\n",
+        "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n",
+        "\n",
+        "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n",
+        "|---|-----------------|-----------------|-----------------|\n",
+        "| 0 |             0.0 |             0.0 |             1.0 |\n",
+        "| 1 |             1.0 |             0.0 |             0.0 |\n",
+        "| 2 |             0.0 |             1.0 |             0.0 |"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KnssXowblKm7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Bucketized (Binned) Features\n",
+        "\n",
+        "Bucketization is also known as binning.\n",
+        "\n",
+        "We can bucketize `population` into the following 3 buckets (for instance):\n",
+        "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n",
+        "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n",
+        "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n",
+        "\n",
+        "Given the preceding bucket definitions, the following `population` vector:\n",
+        "\n",
+        "    [[10001], [42004], [2500], [18000]]\n",
+        "\n",
+        "becomes the following bucketized feature vector:\n",
+        "\n",
+        "    [[1], [2], [0], [1]]\n",
+        "\n",
+        "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n",
+        "\n",
+        "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cc9qZrtRy-ED",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_boundaries(feature_values, num_buckets):\n",
+        "  boundaries = np.arange(1.0, num_buckets) / num_buckets\n",
+        "  quantiles = feature_values.quantile(boundaries)\n",
+        "  return [quantiles[q] for q in quantiles.keys()]\n",
+        "\n",
+        "# Divide households into 7 buckets.\n",
+        "households = tf.feature_column.numeric_column(\"households\")\n",
+        "bucketized_households = tf.feature_column.bucketized_column(\n",
+        "  households, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"households\"], 7))\n",
+        "\n",
+        "# Divide longitude into 10 buckets.\n",
+        "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "  longitude, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"longitude\"], 10))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U-pQDAa0MeN3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train the Model on Bucketized Feature Columns\n",
+        "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n",
+        "\n",
+        "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YFXV9lyMLedy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "\n",
+        "  #\n",
+        "  # YOUR CODE HERE: bucketize the following columns, following the example above:\n",
+        "  #\n",
+        " # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0FfUytOTNJhL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "f6a2594a-53de-4395-eace-617821c8e42f"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.5,\n",
+        "    steps=750,\n",
+        "    batch_size=150,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 123.36\n",
+            "  period 01 : 98.34\n",
+            "  period 02 : 87.43\n",
+            "  period 03 : 81.46\n",
+            "  period 04 : 77.71\n",
+            "  period 05 : 75.33\n",
+            "  period 06 : 73.66\n",
+            "  period 07 : 72.47\n",
+            "  period 08 : 71.60\n",
+            "  period 09 : 70.89\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZTDHHM61NPTw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JQHnUhL_NRwA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ro5civQ3Ngh_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RNgfYk6OO8Sy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "de6cfc4e-3645-4420-9cbd-4256502a97b5"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.20\n",
+            "  period 01 : 142.91\n",
+            "  period 02 : 126.48\n",
+            "  period 03 : 115.29\n",
+            "  period 04 : 107.41\n",
+            "  period 05 : 101.54\n",
+            "  period 06 : 97.08\n",
+            "  period 07 : 93.60\n",
+            "  period 08 : 90.70\n",
+            "  period 09 : 88.33\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4/w/qorym+YBlzIAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AFJ1qoZPlQcs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Feature Crosses\n",
+        "\n",
+        "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n",
+        "\n",
+        "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n",
+        "\n",
+        "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-Rk0c1oTYaVH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train the Model Using Feature Crosses\n",
+        "\n",
+        "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n",
+        "\n",
+        "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-eYiVEGeYhUi",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  \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": "d835ba78-db76-4b45-cc9b-606bcb311562"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.5,\n",
+        "    steps=750,\n",
+        "    batch_size=150,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 114.36\n",
+            "  period 01 : 88.97\n",
+            "  period 02 : 78.25\n",
+            "  period 03 : 72.79\n",
+            "  period 04 : 69.45\n",
+            "  period 05 : 67.46\n",
+            "  period 06 : 66.23\n",
+            "  period 07 : 65.33\n",
+            "  period 08 : 64.78\n",
+            "  period 09 : 64.32\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0i7vGo9PTaZl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3tAWu8qSTe2v",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-_vvNYIyTtPC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "e1a9c6a9-dfff-413c-f1ce-86834f723200"
+      },
+      "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 : 163.02\n",
+            "  period 01 : 134.77\n",
+            "  period 02 : 117.73\n",
+            "  period 03 : 106.38\n",
+            "  period 04 : 98.46\n",
+            "  period 05 : 92.71\n",
+            "  period 06 : 88.28\n",
+            "  period 07 : 84.80\n",
+            "  period 08 : 82.01\n",
+            "  period 09 : 79.70\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nChgRERHJOf8f01gN+pVqwB8AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ymlHJ-vrhLZw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Try Out More Synthetic Features\n",
+        "\n",
+        "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/feature_sets.ipynb b/feature_sets.ipynb
new file mode 100644
index 0000000..a2a0742
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1552 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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": "5b35fac4-8773-45fc-a5f1-56c4260a315f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2644.1           539.3   \n",
+              "std         2.1        2.0                12.6       2196.8           421.8   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1467.0           299.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3157.0           647.0   \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       1428.6       501.3            3.9               2.0  \n",
+              "std        1167.2       386.3            1.9               1.2  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         791.0       282.0            2.6               1.5  \n",
+              "50%        1168.0       409.0            3.6               1.9  \n",
+              "75%        1714.0       606.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        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>2644.1</td>\n",
+              "      <td>539.3</td>\n",
+              "      <td>1428.6</td>\n",
+              "      <td>501.3</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2196.8</td>\n",
+              "      <td>421.8</td>\n",
+              "      <td>1167.2</td>\n",
+              "      <td>386.3</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>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>1467.0</td>\n",
+              "      <td>299.0</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3157.0</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1714.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>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     -119.5                28.5       2642.6           539.6   \n",
+              "std         2.1        2.0                12.7       2139.2           420.9   \n",
+              "min        32.5     -124.3                 2.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1444.0           292.0   \n",
+              "50%        34.3     -118.5                28.0       2126.5           435.0   \n",
+              "75%        37.7     -118.0                37.0       3135.0           651.0   \n",
+              "max        42.0     -114.5                52.0      21113.0          4386.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1432.0       501.1            3.9               2.0  \n",
+              "std        1100.1       380.3            1.9               1.1  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         785.8       280.0            2.6               1.5  \n",
+              "50%        1165.0       410.0            3.5               1.9  \n",
+              "75%        1744.0       603.0            4.7               2.3  \n",
+              "max       12427.0      3933.0           15.0              29.4  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2642.6</td>\n",
+              "      <td>539.6</td>\n",
+              "      <td>1432.0</td>\n",
+              "      <td>501.1</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>12.7</td>\n",
+              "      <td>2139.2</td>\n",
+              "      <td>420.9</td>\n",
+              "      <td>1100.1</td>\n",
+              "      <td>380.3</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>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>1444.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>785.8</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>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2126.5</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1165.0</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>3135.0</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1744.0</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>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>21113.0</td>\n",
+              "      <td>4386.0</td>\n",
+              "      <td>12427.0</td>\n",
+              "      <td>3933.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>29.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                208.5\n",
+              "std                 116.9\n",
+              "min                  15.0\n",
+              "25%                 120.1\n",
+              "50%                 180.9\n",
+              "75%                 266.4\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>208.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                204.5\n",
+              "std                 113.7\n",
+              "min                  25.0\n",
+              "25%                 118.5\n",
+              "50%                 178.5\n",
+              "75%                 261.9\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>204.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>113.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>25.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>178.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>261.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "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": "ca371de1-aaaa-4930-8df1-5dea4fd6b324"
+      },
+      "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": 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",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    latitude  longitude  housing_median_age  total_rooms  \\\n",
+              "latitude                 1.0       -0.9                 0.0         -0.0   \n",
+              "longitude               -0.9        1.0                -0.1          0.0   \n",
+              "housing_median_age       0.0       -0.1                 1.0         -0.4   \n",
+              "total_rooms             -0.0        0.0                -0.4          1.0   \n",
+              "total_bedrooms          -0.1        0.1                -0.3          0.9   \n",
+              "population              -0.1        0.1                -0.3          0.9   \n",
+              "households              -0.1        0.1                -0.3          0.9   \n",
+              "median_income           -0.1       -0.0                -0.1          0.2   \n",
+              "rooms_per_person         0.1       -0.1                -0.1          0.1   \n",
+              "target                  -0.1       -0.0                 0.1          0.1   \n",
+              "\n",
+              "                    total_bedrooms  population  households  median_income  \\\n",
+              "latitude                      -0.1        -0.1        -0.1           -0.1   \n",
+              "longitude                      0.1         0.1         0.1           -0.0   \n",
+              "housing_median_age            -0.3        -0.3        -0.3           -0.1   \n",
+              "total_rooms                    0.9         0.9         0.9            0.2   \n",
+              "total_bedrooms                 1.0         0.9         1.0           -0.0   \n",
+              "population                     0.9         1.0         0.9            0.0   \n",
+              "households                     1.0         0.9         1.0            0.0   \n",
+              "median_income                 -0.0         0.0         0.0            1.0   \n",
+              "rooms_per_person               0.0        -0.1        -0.0            0.2   \n",
+              "target                         0.0        -0.0         0.1            0.7   \n",
+              "\n",
+              "                    rooms_per_person  target  \n",
+              "latitude                         0.1    -0.1  \n",
+              "longitude                       -0.1    -0.0  \n",
+              "housing_median_age              -0.1     0.1  \n",
+              "total_rooms                      0.1     0.1  \n",
+              "total_bedrooms                   0.0     0.0  \n",
+              "population                      -0.1    -0.0  \n",
+              "households                      -0.0     0.1  \n",
+              "median_income                    0.2     0.7  \n",
+              "rooms_per_person                 1.0     0.2  \n",
+              "target                           0.2     1.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "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": "IGINhMIJ5Wyt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DSgUxRIlH3pg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 639
+        },
+        "outputId": "39768ca2-5db9-4650-d2a5-295df09882d7"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# Your code here: add your features of choice as a list of quoted strings.\n",
+        "#\n",
+        "minimal_features = [\n",
+        "    \"median_income\",\n",
+        "    \"latitude\",\n",
+        "    \n",
+        "]\n",
+        "\n",
+        "assert minimal_features, \"You must select at least one feature!\"\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "#\n",
+        "# Don't forget to adjust these parameters.\n",
+        "#\n",
+        "train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=400,\n",
+        "    batch_size=20,\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": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 180.17\n",
+            "  period 01 : 134.54\n",
+            "  period 02 : 118.83\n",
+            "  period 03 : 117.06\n",
+            "  period 04 : 115.95\n",
+            "  period 05 : 115.14\n",
+            "  period 06 : 114.21\n",
+            "  period 07 : 113.19\n",
+            "  period 08 : 113.02\n",
+            "  period 09 : 111.56\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tensorflow.python.estimator.canned.linear.LinearRegressor at 0x7ff37e6eb890>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BAGoXFPZ5ZE3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "884a52eb-5dec-47fe-d217-7c2f1be448eb"
+      },
+      "cell_type": "code",
+      "source": [
+        "minimal_features = [\n",
+        "  \"median_income\",\n",
+        "  \"latitude\",\n",
+        "]\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 167.07\n",
+            "  period 01 : 128.24\n",
+            "  period 02 : 118.66\n",
+            "  period 03 : 117.54\n",
+            "  period 04 : 116.88\n",
+            "  period 05 : 116.15\n",
+            "  period 06 : 116.12\n",
+            "  period 07 : 115.31\n",
+            "  period 08 : 114.90\n",
+            "  period 09 : 115.87\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RidI9YhKOiY2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Make Better Use of Latitude\n",
+        "\n",
+        "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n",
+        "\n",
+        "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hfGUKj2IR_F1",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "36237ce0-6c9a-40d5-f555-96a1a1b0db0d"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7ff37ac28dd0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFKCAYAAADMuCxnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvXt4G/WZ9/2dGWlGliXbkg85OSEk\ntpNAzjGnQCAJTgNs2U0LJDQLLS2lu28L7+719Py0y1K2e7rY5Wn7Xn26z7K0sNCUdMOzudjubgMh\nBw5JgMSOnQQS2wmQxLFj2ZZlyZJG0ozePxQpkjwnjUan8e/zD8SSZkajmbl/9+l7U/F4PA4CgUAg\nEAglhS71ARAIBAKBQCAGmUAgEAiEsoAYZAKBQCAQygBikAkEAoFAKAOIQSYQCAQCoQwgBplAIBAI\nhDLAUsqdezz+Uu7edLhcdni9wVIfhukg57UwkPNaGMh5LQxGndfGRqfsa8RDNhEWC1PqQzAl5LwW\nBnJeCwM5r4WhGOeVGGQCgUAgEMoAYpAJBAKBQCgDiEEmEAgEAqEMIAaZQCAQCIQygBhkAoFAIBDK\nAGKQCQQCgUAoA4hBJhAIBAKhDCipMEix8Acj6D3vRZyiMKfeDs94CDbWAo8vjCMnhyCKAj5767VY\nMr8eo74Qjp3xAIhj0bw6+AJROO1WNNRVIcTHUOvgwFkZ8FEBHm8QoCg01lWBs07tUeOjAnwBHlWc\nBSE+BkEQcfjUIC56JjHTVYW2uW5c9EzgYOcARidjWDDTgYfvWgQhDnSd8eDIqUsIBGNgrRTi8Thm\nNzjR0lyDd05cRlwUcO+tC0BTNN7uHgRFixj0BMGL0uegmgXmz6pFkI/hmplOzGtywlHNYdHcOjjt\nLPiogIGRAM4PTqB/YBzjgShuuX4m2pfMyPhuye+UPA9yfzOaYuyj2KRfQ7XVbMb1VSgGRwL4ryOf\n4sKQP3FdgUKdg4XHF8T54TAoANUcwPOACKDWYcGSa91Y1OzCOz1D8IfC4FgLtty2AIuucad+EwDo\nuziOg10XcKx3LLW/BTOrMcNtRzQmYDIsYF5TNRgLjcXX1OOW2qqCfc98MOO1RqgMqHg8Hld6w3vv\nvYc/+7M/Q2trKwCgra0NX/3qV/Gd73wHgiCgsbERzzzzDFiWxWuvvYYXX3wRNE1j69ateOCBBxR3\nXmilrkgshr968SgGPMap1ridLOxVVni8QfDRxKmzsQxuXTYTD97ZCoamIYgidu7rR+eZYYz5I6AA\nKJ7kElNtYzAZFmRfv23ZDDy0eRF2HTiHrl4PxiZ4uGs4rGxtQBxAd99I6m+r2hqxbWMLGNqY4Evy\nXKbv1+h9qNHY6DT0WhVEEb95sw+HTgwiHMlcQbmdLFYvajL8+wXCEfz5T9+BWIALkbVQiMT0bfj2\nlTPx8GcWF+23VKIcrjUjMPp6JSQw6rwqKXVpMsi//vWv8bOf/Sz1t+9///u4/fbbcffdd+PZZ5/F\nzJkzsWXLFnzuc5/Drl27YLVacf/99+Pll19GXV2d7LYLfdH85S/fx4XhQEH3kU5HezO2d7Rhx95e\n7D16sWj7LQZzmxyaz2XyPBiB3Lk0ch9qGP2A03J9GP39vvbMfsSE8lwWFvO3VKIcrjUjIAa5MBTD\nIOta9r333nu48847AQAbNmzA4cOH0d3djWXLlsHpdMJms2H16tXo7OzUd8QG4A9GcLGIxhgAuno9\n8Acj6Or1FHW/xSCXc9nVOwI+Ku9xa4WPCrLn0qh9FBs+KqDzzLDq+4z8foMjgbI1xgBw9KOhkv+W\nZrzWCJWHphxyf38//vRP/xQ+nw+PP/44QqEQWJYFANTX18Pj8WBkZARutzv1GbfbDY9H2TC5XPaC\n6YNe6vMUPUw85ufhj4gY8/NF3nPhyeVcev1hMKwVjQ3Vee1zcGRS9lwatQ+tKK1qcyHxnSKq7zPy\n+7198nLe2ygk45Oxov6WUpTTtWYERl2vhEwKfV5VDfL8+fPx+OOP4+6778aFCxfwxS9+EYJwdbUo\nF/FWiYQDQEEnkjhZuui5W7eTg5Ol4XZyGJ0wl1HO5Vy6nDYIkWje4R0hKsieS6P2oQUjQ4CJ78Sq\nGmUjv1/LzPI2JHXVlqL9lnKUy7VmBCRkXRjKImQ9Y8YM3HPPPaAoCvPmzUNDQwN8Ph/C4TAA4PLl\ny2hqakJTUxNGRkZSnxseHkZTU1PeB68Xp51Fc5OjqPtc1dYIp53FqrbGou63GORyLle1NRhSncpZ\nGdlzadQ+ig1nZbB6kfp9YeT3m9XggIWmDNlWIWhfMrPkv6UZrzVC5aFqkF977TU8//zzAACPx4PR\n0VF8/vOfx549ewAAr7/+OtatW4cVK1bgxIkTmJiYwOTkJDo7O9He3l7Yo1fhB19cjTmNdkO36Xay\naG6qBme9eupsLIM718zBto0tAIBtG1vQ0d4Mt5MzdN+Fotqm/LC5bdkM/OCLq9HR3oz6GhtoCqiv\nseHONXOwcc2cjL91tDenzoMRJM9lIfdRbLZtbMHGNXNgY6eed7eTK8j3+7v/52ZDt5cOa9Fv7G9f\nObNsfkszXmuEykK1yjoQCOBb3/oWJiYmEI1G8fjjj2PJkiX47ne/C57nMXv2bPzt3/4trFYrfv/7\n3+P5558HRVF46KGH8Id/+IeKOy9WWEWqD9lqZfBfhz/F6U+8EAA4qxjcdP0s3L58Fv725U6EIlOL\nOOocLH70lRtTfbta+pDPDfjwD68c1xU6r6miMRGSaSzOg9uWzcCCWTWkD1kjhQoBFrsP2ajq/2ua\nqvG526/2Ie95/zz2d13S/HmWASIC0OiqwoqF9WXXVlTpfcgkZF0YyqLtqZCU8qKRezjdunQmDp0c\nkjSgNAX8zdduRpNLu9fNRwX88Lkjkrkp1kIjEpM3uDQS4gxG89P/9zY47WwBtmxOzPKAE0QRO97o\nRVffCHyBCNw1Nixf6MbtK2eDoSg0uuzgrAye/92HePfkkOQ2su8BpetbK5XWVlTumOV6LTfKIods\nBviogGFvMNW6oNTicPq8F3UOq+RrdQ4upUqkFaXclJIxBgpjjAHgxNlR0sYxzUiKXvScHYUvEEGt\ng0UVx6C7fwRP/+oofrqrB68ePAtBFPGFTW2wsdKPhux7YGwinHcBI2krIhASmFo6U055Z8OqORiT\neYh4/TxmuOwAolNeq66y6gphbVl3Ld7pGURYIgxeCv7lPz/Cv799riJViAj62LmvPyMiNB6IYDxw\ntdJ7dIJPvb69ow23LZ8tGUEK8jG8evBs6rrZeyz/ELjXH4YvwOcUeSIQzIipn8Q73kiEpUcneMRx\n9aGz9+gFuGukPd06Bwc+Jm04g+GorpV8IBgFXwBjTOVROJs8Fzv39Rt3QISyRCkilE3SW00WOGUX\nnoUjQuq64aMCevpHZLakHZfTlnPkiUAwI6Y0yIIo4qXXz+DgcelCk56zY1je0iD52vyZTkXv2RfI\nPTxX6+DASVTU5osR2X8SLjQ/vgAve01nk/RWGZrGfXcsRLVNOojW1TsCz3hI83YBYKZbepgEaSsi\nEBKY0iDv3NeP/Z0DskL6Xn8YHWuaUy0OFBKtSzaWQVffCORaNvNbyZendGHyAUwwL7UOTjYilE2d\ng0MgHMVFTwAeb1DW4I75wxjzhTVvFwD+5I+WZrQVNbmqSFsRgZCG6XLIWsJzLqcN7hobtne04b47\nFuLlPWcyqkrlPE+9K3lfgJ8y1adcIOFC88NZGSxvacD+zgHV944HePz4xWNXPkeDtdLgo1OvXQrA\nT3b1yBZ/ZWNjGcx021P3nC/AY+H8evh9oZy+C4FgZkxnkLWE57IN6+nzXsn30VTCOLtrbFjV1qB7\nJV/r4FBfU55ymiRcaG6ShY3dfdpyyOlRJSlDnP0+rQvNW5ddVePirAyaXHbYWAtIcw6BcBXTGeRk\neE7K+NEUcMfK2RmGVcmAxwF868GVWDCnNi+jlWx9KqeRjDaWwdpl5aOSNN0plBhFdnW1HjgrjWqb\nFd4ADwrIeaYyRQF/dNt8AJnfk0AgZGI6g6xk/O5YNQcPf2ZRxt+UDLjbacvbGCdJGr6u3hF4/WG4\nnAlRhu6zo5ILApeDxYrWBhw5dbkg7VLhiACaokjLUwnhowLGJsLYe/QCeq5cB8nWPCPa0XKprlbe\njojvPbQcwVAUz7xyPOfPx+PAjjf74bBZ0XlmGGP+CNxOFretbMa9t8wj1yCBcAXTGWRA3vjdvmIW\nLg77U4pEgLIBX95Sb5i3wtB0Rv4s6QkxMophaxY3YXtHG7ZtbMXJc6P4+b+fNOQ40unqHcF9dywk\nIesik94fn70QzO4HzodcqqvVeOv4ALZubNWdeuk8PYxI7KprPeaP4LW3zyEQ5PHQpkUKnyQQpg/M\nU0899VSpdh4Mqs+F1QNNUVi2oB53rJyNW66fiUA4iiOnLuPNYwPY33UJbx67AG+Ax/XXukFTFK6b\n70KIj8EX4BHihUTuGMBEgMfIRBjXzXeBzqfpNw0LQ6O6ygoLk/AKru47Aj4Sg7vGhpuva8Kda5rB\nWhlwVgb+UATvnpCWMswHPhLDbctmobpKWpmMkKC6mjP0Wn3lzT7sPXoRIV4+8uELRHDHytmp60QP\nFguNw6eGFPejFX8wgmUL3AjxMXwylHvmV5BJNQ+OBtHRPjev70nIxOjrlZDAqPNaXS2frjGlh5yE\nszLY3zWAA1nC9+GIiH3HBkBTFLZ3tKW8V0EQsb/rUipHNuaPGOatyJHuOY9NhLH32EX09I/gQNel\nVPhyyby6guxbrsK60sX1yxmtYWQj1KvUahdsLKM5HTI6wePJX36A+hoOc5scmAxFMR7gU9GnnrOj\nujzncCQxYKO5qbCD3wmESsDUBlnt4dd5xpMK2fJRAT1nRyXfV4zQbnLxkN6akgxfhiOxguwzu8Ja\nTmqUyGsah9YwslHtaJLpm5Z6dKxpRq2DxasHz2lqh0oyOsFjdILHhtVzsPmGualF20uvn8lpOxkY\nFH0iECodUxtktYdfUnmryWVXfG8xtHaVFg+HTgzBxtKG9TLXpxnadLIrco3MZxISKBURpmNUO1p2\n7UIVZ8kY87h1QwsOnRhUbHGSortvBFs3tICzMhBEEWFe36LRxjJorJNW8CIQphumNshqDz/WSsNx\nZQyh0nsLLZ6RnJssd5xiXHu/pxoUgD+7f/mUEKHSgoAUfxmHWhi5Ps+edzksDIW9xy5KDlqJ5GiM\nAWDMz+PlPWfwyD2LsXNfPw6fuqzruNL7kwmE6Y6pDbLaw4+Pitj99jls72hTfG+hxDOUqm0LBWul\n4a61Tfm70hg9Mo3HWJTCyO4aW0GuNbnohyCIqHOy8PpzL1Z59+QQWJbRNWCCtdC465b5uPeWeTl/\nlkAwK6Y2yEDi4RcTxCmFXUnSvT+pB6VR3opUoZQRog25H4eI3W9/PCUEvffoBdnPEHlNY5FrgSsU\nStGPw6cuIyIz3UwLx3tH4NWhhe6wW/HwPUuIdCaBkIbpDTJD09i4ulnWIKd7f4V4UMoVSt1z8zwc\nO52/aIMe0ovZACgWtAHG9mMTrpKUkCw0SvUR+YrOjE/yYK0UItHc5Lu8EzyGRoOotpCCLgIhiakN\nctIYdp4Zln2Py8lN8f7yfVDyUQGe8RAQj2P/8UuSldNvdV/SlbszAq+fh8cbBGtlUOvgVIvfOtY0\nF/HoCEajtZBMD24nB7+O3sw4gKefP4IVC+tJFT+BcAVTG2QtIeHJcBSvHjxryENBEEW88mYf3j0x\npOp5lMoYA4DVQuGnu3pSHvvylga4nCzGJPKINpZBrYMtwVESjKKQWupWC5OhwJULHm+ooFX8pJ+e\nUGmY1iBrFWAIR0TDHgo79/XjzWM6ezGLSCQWT3lLoxM89ncOYG6TQ9IghyOCZM6ZUFlk10fUOaQX\nYLkyNBbMextGV/FnF0vWOVisam3A9k1txBMnlDWmvTpz1fHt6h0BH9WfTzNKyL9UBMNRcFbpfF6+\n54ZQepL1ET9+7CY89eUb8GBHa6kPKUWyjsMokpGx5KJzPBDB/q5LePqFoxDE8pxLTiAAJjbIybyZ\nVvJ9KBgp5F8Kxvw8eJnCHKMfmITSIIgiXj14Fj/d1YNf/PupUh9OCiOr+JUWxheGA9ixt8+Q/RAI\nhcC0BjmZN9NKvg8FtQVAudeSuhwsOIv05VDnmFr4Rqg80j1HfVnfwrCy1bgqfl+AVyxeO06iPYQy\nxrQGGUjkzTram1FfYwNNJVSQ5jY5JN+bLf7BRwUMe4Oab161BcC6FTPhMtCo0RTA0MaZ+eoqFnxM\nOpxXXWXNaJHK5bwQyoNyTqkYuTiodXCoUyhCHJ/kSbSHULaYtqgLkBZgsDDUlYIPafGPfAYsbNvY\ngng8nlFlbWMZrF02E3eubsbb3caNUBTjAOJxNLk4DHv1P2BcDg4rW+vRrdCHPBmKIMjHsPvtc2Tw\nRIVidEqFs9K4ZdlMMBSFY6cvY3xS/wCU7r5RPLBeMMRL5qwMVrU2YL+M7oCbiNwQyhhTG+Qk2X3F\nUuIffFTAqC+IPe+fz7iZcxmwwNA0/njTIty/viXVh9zosqe2n2svKE0DajUo+RjjOgeLp75yA0J8\nTFY4BQC8/gh+80Yv3j15dUFBBk9UFkb1IlMAZrjt+P7Dq+GsSniii+fW4ee79eekjZZm3b6pDf0D\nE7gwHJjyWqFkcAkEI5i2rk3SSFsYCjv29uKHzx3B9//PERw8Li+xmUv4urnRgeYmZ+rmzzWnDSSM\nMVtAJaP2xU1w2lnV/Hedg8Xp817J10gFdmXAWRksnufKeztxJFqdntnRlfqbu2aqNnouGC3NytA0\nnnykHRtWz4HLwYG6kq7qaG82fGgHgWAk08JDVhIIyBYPicsktPJdxQuiCDEez3mMYlzugPIge6KQ\nmnDEkvluHD4pHW4ngycqhy9sasOx3mFDJodd9Ezihd9/hIc/swiuHLoZpCiE18rQNB7+zCJs3dBC\nxEEIFYOpDbJaPjiXQpd8V/E79/Vjnw7REKOdT5ah8L0/XgWHncWoL5x6UCnlvz9/+wKcOe8tyWhK\ngnHYOQtuWz7bMMWut44PgqaAM5/6dG9jbpMD969fYMjxSFEsvXACwQhMbZDlRs4BibxnLoUu+azi\ny6nCNSLE8YPn3gNNJ1TK6tMWKXL5bwBFH01JKAzJqMjR08MYD+Sv1HWgazCvz18YDmDnm/3YfOM8\n4sUSpj2mNchKRjAp1adU6EJTifC124CB8eUmGhJJa2/KXqQk89/ZFHI0JaF4JDsPNt8wF9/+xeFS\nHw4A4ODxSzjQdYlU7hOmPaY1yEpGMD3vKef5rVsxG3ffZMyqvZDTdoxCTU+42DN8zU4pBx8Ioohd\nB88VdZ9KiFfKJEjlPmG6Y1qDrGQE0/OeVz2/hBA9TSUeECfOjsBqoQ3xAAs1bcfKGJdj1lqcRXJy\n+ZFPn7tR7NzXj/c+vFyUfenB6GETBEKlYNq4kFKbUXreM+n5LV9YD+Dqan3MH8Heoxexc1+/Icez\nbWOLrEqYXow0jKQ4qzhky1cmvUKjrjM1+KigOB+8HCDa6YTpimkNMgDcv34B5jY5kFSYpCnpqk4+\nKqBHRqnKqD7bmBBHMBzN6TNWlR7kQCi37SlBirMKj1pdg9brLB/5Ul+AN2TsohFQMpc3WRwSpium\nDVkDwK4D5zLUesR4oqpz14FzGTkqrflmNZTygnoKu1a01OPo6RHZ132T+Rvk2morblgyIxWaJ0Pd\nC0e+15kR4e4qzpJKy5QaG8sgxE9dVJDFIWG6YlqDrKXKOilpGYmJcDmlB7ZrWa1reVAq5bTdTg4r\nWhvQ0z+CMT8Pl4NFdRWLcwMTivutq7bkpSEMACtbG7G9ow2CKGLH3l6iVV1AlK6B2moOVZzy7ajW\nxqeFEB8rC2MMIGWMbSyDSFQglfuEaY9pDbKaNzI2Ecb+roGUAeJY6RW5ltW63INSEON4+DOLACgX\ndq1elDCK/tuuxcXhAN47fRlvHVfv72StFgD5GeST58bARwW8evBs3g97gjJK14A3wOPpFz6QXQRp\nXWCqUevgUF9mFf/VNgv+50OrM/reCYTpiGkNslqV9d5jF7G/86pyVro6VS6rdaUH5cGuASAex33r\nFyIQjGLLumsBTO3lvX/9ggzvVC63ls3weFjbGxXw+sPweIOGPOwJ6qT3c49OZP5+SosgLeHuWgen\nmm6wMNQVT7x8DLLXz4O1MuQaI0x7TGuQlbyR1uYadPdJGyC51bpcblXpQSnGgf1dl3D41GXwESEV\nBv7RozciEIyktrVjb68mPe1C4HLaAIqS9ZjGJohWtZEkq/rvXTsff/nL9yXVsqQWQcoLTA573j+P\nnrOjiukGQRTx9AtHcdEzWZgvpxNSxEUgJDCtQQYyvZGxiXAqLP3eh8OyQ9GzV+tq+WEtoh9J71vK\nAyq1rObieXWorWZlh15wLEMelgUgxMfgk5GulCrwUlpg2m1WTSNDd+ztkxxJmC+chQYf0z+wghRx\nEQgJTF2tk/RGfvzYTVi7dCbCEQHhiCBrjIGpq3W1vlE9YxXTW1zGJsIly+exVgrvnhzCj371PqKx\nMqn0mSYojbyU8xi3bWxBR3sz6mtsoK+MFNyweg4mQ9KGPf0646MCjvfKV+zrwe3k0NHejJuum6Hr\n802uKjISkUBIw9QecjoffTqm6X2r2hoAAMPeIKo4i6bc6raNLRDEOA52DWiqYE33gF7/4Lzm72A0\nkWjiYJX6UiNXQvUkZK0PuVSHkscr5zFKyZf6AjwOdEpPEUu/znwBHuMGim20L27Eo39wHTgrg8HR\nSbzVk9uQiWUL3Xjyq7fA7wsZdkxJSOseoVKZFgZZixhCfY0NK1rrEY/H8cPnjmBsgketg5WdiJP+\nsEvOXkU8nhE6lCPpAfFRAUdOla+EIUDye3rR0gqnd2BHunypVolYh50Fl+MsbsXvFxNTxs5dY8u5\ncrv3/DgEwZhjSR1TGciSEgj5MC0MspoYQm21Fctb6kEBeDNtZrHSeDopQ7V9UxsYhk49YFkrk8of\np7N8oRuclcHFYT/4qLEPJaMh+T19aOkZNmJgh1ZPe/fb5wwzxgBw6pMxBPkY7JxFl1Y7HxXxz7tP\n4qGOVsOOyYg+bQKhlEyLZaOaGIJvMor9nQN498SQ5m1KGar0nPVfffUm3HT9DNjYqae45+woduzt\nRdioyRAGwVlpcGnHa2NpiPE4BFH9QZ6PnKPZyFUiM+nx6l34SOWWk7lZPirg4rDf8MLBSCyO37zR\nCyDxfTesmoNbluaWSz551hhZ2uQxGCFLSiCUEk0ecjgcxmc/+1l8/etfxy233ILvfOc7EAQBjY2N\neOaZZ8CyLF577TW8+OKLoGkaW7duxQMPPFDoY9eMw26VrSJOR8qblcLGMtiyboHs65yVwf6uARyU\nCV8nV+7+oHFa1EbQ5LJnVOGGIyL2HRsATVGyHgYJE05Fq0SmUblOKU/bwlDYua8fnWeGC6Zdffq8\nFy/tOY3jfR54A7lfyyPjIV31CVLnzSj5WwKhlGgyyL/4xS9QW1sLAPjZz36G7du34+6778azzz6L\nXbt2YcuWLfj5z3+OXbt2wWq14v7778emTZtQV1dX0IPXyu63PzY0XBeJCggEI7DLSB1qbWXquzAO\nzkqXPGxd52CxqrVBccCGnDgICRNORS2v67CzBZEpTc8tv/zGGew7Jl3sZRSjE7ymmgk5WCsDh92q\n+f1Kiz+tuXQCoZxRvfvPnj2L/v5+rF+/HgDw3nvv4c477wQAbNiwAYcPH0Z3dzeWLVsGp9MJm82G\n1atXo7Ozs6AHrpUgH8U7GitAbTLymdnUOTjFG1zrIIkxP48l15R20UJRwIqWBnS0z1X1MJIkw9P+\nYISECSVQG/25++1zBR3ByEcFHDqRW9WzHmiNinJyhCMCdr/9seb3K7Ugah23SiCUM6oe8t///d/j\nL/7iL7B7924AQCgUAsuyAID6+np4PB6MjIzA7XanPuN2u+HxqHuILpcdFkthb5Sf/KZTcyi648Z5\noCkK73ZfmiJrmM6qRU1oni1vSJ21VWh0VWHYq97S0eR2oIrzSU69KQbxOHDw+CVU21nZY26oq8LC\n+fWwMjR++R+ncOTkIDzjIbidNtnKWq8/DIa1orGhutBfoSg0Njpzev/jW1fBXsXiyMlBjIyH0FBX\nhZuXzsIfb16EJ/7xgORnes6O4k/uq4KNza/W8pPBCUMjQnIYMaRC63f2BXh0yiz+ktuQO+dfufd6\nMMxU3yMcicE7wcNVw+V9zsuNXK9XgjYKfV4Vr8Ldu3dj5cqVmDt3ruTrcRmNR7m/Z+P1BjW9Ty98\nVEDXGfW2IhvLYO2ymfijtdeAoWmM+SbxTo+0QbaxDD63bj48Hr/iNpcvrNdUdXrs9DBuvm5GXqE/\nIzhy4pKsR8tZGfh9oSkSn0qLFpfTBiESVT1PlUBjo1PX99hy63zcfePcjHznxxe88Mgs1EbGQzj7\nyWheuU5BFPHr//5I9+e1wrE07CyjK3ecjtp3Toapj532wCvTR52+DalzPjY2KblNs9Y96L1eCcoY\ndV6VjLqiQT5w4AAuXLiAAwcOYGhoCCzLwm63IxwOw2az4fLly2hqakJTUxNGRq6qAA0PD2PlypV5\nH3i++AI8vBoKWsIRATRFgaFp8FEBh0/KG/Fbrp8BO6ee91IaIpDO6EQYG9fMQYiP4ciHw6rbLRRK\nhT+ToSj8wQg6z2g/PhImTJCe1wW09w3rZee+/qL0tkeiIta0NeHQSe2dCVKofefsGgUt28g+5+nw\nUQEv7zmDd9OOm9Q9EMoFxeXgT37yE7z66qv47W9/iwceeABf//rXsXbtWuzZswcA8Prrr2PdunVY\nsWIFTpw4gYmJCUxOTqKzsxPt7e1F+QJKKMkTZpPMeQ6MBKCkV6C1tSO9Berpr9yAeoXj2HdsAPfc\nfI2m7ZaCMT+PF39/WtFo1znYKS03hKlYGAp2m/SCLt9FTC71EvnicrBgDFhvLZonn/rRWhyp5bwl\n533/4J8PZxjjdKZz3QOhPMg5cfLEE0/gu9/9Lnbu3InZs2djy5YtsFqt+OY3v4lHH30UFEXhG9/4\nBpzO0ucwchEsGLtSuBSYVPavZQhiAAAgAElEQVSog6Hc5g9zVgbNTU4sXVCPg8elw9Ld/aMQijni\nKUdYC41OBR3k+hobnnykHSE+RuQKVdi5r19ywMPcJkfei5gdb/RprpfIF9bK4O1u/d4xTQEca8Hh\nk0M4c94rGTJWK46sc7BoX9yk6bxp8bRJexSh1Gg2yE888UTq/3/1q19Nef2uu+7CXXfdZcxRGUjy\nZlXKQQFAXXWicrpKppUpybWza3PafzJf1d0nb9DG/DzeOq7fszGidUpJyUxtPvOqtgY47Sycdjav\nYzA7Sh5fMBxDTIhDovZIE0E+hmM5pBTyZWgsPw1qMZ4Q7AHkQ8aK4X0Hh6e+coOma06rp03aowil\npvIrGFRIho6f+soNqLHLG9uVV8JerJWBXF0HQyc8g1xIrszHVTxvvdhYY/qYZzdKV0PbOYvi9tcu\nnUnC0xrRIl6hl9+80VvyfvZ8yQ4ZK7UyrVncqHkBqLUNkdQ9EEqN6Q0ykPBS/+PQJ5gMS4eb7ZwF\n992xEEBiHKKcUqQoIqeHZjFmHWutaFejtbkWzU1TjXKQj0nKfwJAfQ2HhzcvMkVlajHQM3JRC3xU\nwOnz3pw/V+cor4iG1KJESRY0HSXpVofdmpqFLkV9DUfqHghlgbma72RQyx8F+Rie/JcjWL2oCTGF\nii53TW4PTa0rc73QFMBHjTHIPf2jCISkW1jkZiWvamskHkUO6Bm5qIVcrzOKAr61bSWamxx4+oUP\nJEPC9TUc5jU50NUvrd5WCKQWJWoDOJRamGJCHL4Ajz3vn5fNra9dOhMPb15ErmNCWWB6g6zVSx3z\nR7D36EVFta7klCatKOXAjMAIYYYkSscoiHHcfP0M9F3w5TQmkDCVbRtbEI/H8e6JoZSRSB/ioSfa\nkOt15nbasGBOreICoYqzoFtGSrVQKC1K5FqZ5KRbz5wfRzAcxdgEL1sDYWMZbN/URowxoWwwvUHO\n1XtQqlLtaJcWSJGDszJYvrC+5KIfWmAtNCIx+ejAPTfNQ+NddjL4PU8YmgZFURnXmZYhHkrkOv4w\nafgEUYQYj2cMXrGxDNy1HC56JlW2kh9zmxzgowJGxkO6F3hKi+30Sna5rI6aJj2BUGxMfyUa5aXa\nWAbuGlvOn+ton1sRBlnJGAPA/q4BbN/URlpC8kRtTKDcEA+p7aQvjq4K0Xgw5ufhcnJYPM8Fq4XC\nyXNeycjGzn39UwZQhCMCLnm0K+g11tngGZcXvpFiTqMdTz7SjjpXNc5+Mqp7gZdvSohUVRPKDdMb\nZD3D043EXWNDfQHD1sVif9clMAxNlIzyJN8xgXI50/vXL4AgxhETRMTjgHeCx6GTQ3A7WaxobUTH\nmma4a2zgrAz4qIDB0UBOymuyx6OkoiPD8FgQg6NB1Lmq81rg5bvYJlXVhHLD9AYZSOTtBDGOg10D\nuvOufETQLRqwaJ4rb4nBciAXD44gTb7SmXI50yOnhhBIE61JXuZj/gj2dw6AoSls29iSMfbRiBIE\nPbOWowLwl8+/j0ZXFZYvrNetIZ3rYpumEuFrdw2pgSCUJ9PCIDM0jYc/swiIx2XDx/U1NixvqUd3\nn0fyIcOx+c1utbEMRFFERKZiuRIgSkb5k0+ltVK4O6CiINfVOwJBEA1PnygJyigRBzDsDaXOg1wV\ntRrpmvHJsLzdZpFUQ7tj1RxsvmEuqYEglC3TwiAnuW/9QvBREac/9WI8wMPltGH5Qjc62uemwnkM\nTUk+LMMRAa8eOIuHNy/WtK9sT6ZYkoZGIPeQJTk3Y9i2sQViPI5DGZXWDOIqldb55EzH/GF0KajF\n6cWISv93egbReWYYXn8k58lLUm1RFoa6shgemZI7Jz3zhHJmWhhkqbzbLdfPxBc2tU2psNyybgHe\n6bkkOU/24PFLAEVhe0er4o1dDEGQQjKn0SHpYZCcmzEwNA16SqW1gDePDYBSqLTOJ2daV80pSseW\nknBESJ0LtclL2cVsSbLbopR6lwmEcmVaGGSpvNu7J4cwGY7ijze1QRDjqZs2EIyAlxnuLsaRyscp\nFTcVWhDESDgrhZiQ6DUGEj2xC5tr0Da3Fsf7RknfsUbkDIXce/VUWudToLiyrQE9/SMVU1yYfR70\nzDBWGsNIIJQjpjfISg+/4/2jON5/GADgclixZvEMbFm3QNULUStuysWTqXWw8AUKo3OthsvBYdlC\nF95Km9oTjog40HkJHe3N+PFjNxEPQwU9hiKfSmutw1KSuBws1lyZiERRmNLmVK5knwe5YrZgOFY0\npa1cFl0Egh5Mb5C1eqveQDR1w6t5IWoPzVw8Gc5aupzW+CSPnrNjkq919Y7g3rXzi3tAFYicoQDk\nh93XOjhwLCNZV8BaGcU8fTJneu/a+fjBc0dUi7lWtjVie0cbBFHEmfPjWr5S0VAqCEuvV1BaVB9S\nGN9oFGqLLmKoCUZheoOca96tq3cEP3r0Bnz0qRcDMmpFWoqbtm1sgSCIOHj8kmLhi8ebm6iCocSB\ncRnvfHQijCeffx8Tk7kX2kwX8hP5yK8aymlnUefgVA1yT/8o+A0CfruvT/Z6LhVK90V6vYLaolrL\nIigf5BZdYjwOmqJyio4QCEqY/qpRGuEmhdcfxpgvjDAv/6BbpkHTmqFpbL5xnqxsX5JSNkGp7ds3\nGUEcVx9AO/f1F+OwKga94xR9AV6yaBC42u+uBh8VUvOElRjzh3HJE8CxXuMrrAFgRn3u6nVKuJ1T\nJy8pTclKJ3t8oxEoeucnhrD36EWMXunpJvcJIV9Mb5CBhLe6YfUcyGjMZ+By2gCKUlyR+wK8phtf\n64OkUijEA6+S0TtOsdbBoV7mc1onimlNxcTjwM9e7cFEgeZxT06qLwq0QlHAn29dge0dbRkeptZF\ndb4zpaVQOs9yrYzkPiHoZVoY5KQwyPpVs1Xfu6qtAY11VYqGtKtvFD987gh27O2FIDc8Gbl75+VO\nIR54lYzS76s2uUjP59LJZbHnm5Qeq2kEAZkZ43pwO21orKuSfC05F9ntlP/OheiT17OoJvcJQS/T\nwiAn2b6pDetXz4ZUesflYFOhMs7KYPE8l+K2tIankg8SG1v5p5oIg0wl+fvW19hAUwnFNy3D7vV+\nLonZFnsAsLylXnYxkixm++uv3Yxbl87M6fN8VMCwN6jLa1U6z3KjWsl9QtCL6Yu6srHQNFyORJGX\n027F8pZ63HPTNSmlriRf2NSGY73Dsrm+JGrFOwxNY8u6BXi7uzLaTZQgwiBTkVKK0nKO9H4unfvX\nL8CZ8+OSIi6VSHefJ6W5rdRb/Mg9i1Fls6Cr14PRCT5Vrd11JpHrTQr36GlJk0JKnnNVWwPi8Tje\nlGgjI/cJQS/TyiBnV0v6g1G82zOEKtYypTrTzllw2/LZqq1LWvSdX95zBny0cjWsAeTkvU1H9IpQ\ncFfanPQY5X/bf9Y0xhhIDKrQUi2dXMwktbmT1drjk4lBGv0XfXjykXZdLWlK+8tePAmiCIqiJCU6\nCQQ9TBuDrKdFJXljdZ5JzJiVQik8JYgidrzRi/c/upzHkZcel5PDfXcsJK0cBpOPB8dHBbx7IrcJ\nYgwN6JiWWHS0TBXjowJ6zo5KvnZhOICX9pzGqY+9urcvRfaiy4goB4GQzrR4wgqiiJf2nJHtRZYr\nwkjecH/55RuwurVB8rNK4amd+/ozVvCVyniAJ0UqBSDpwelpm/GMh3IeWCKKQGOdsW1KhWBsQr0o\nyuMNKqvp9Y3mfL/rJWmoiTEm5Mu08JB37utXnEcs5+UG+Rh+80YvTp/3YmyCB2ehQdEAHxFVZ6pW\n+oCJdNxOjhSpGEx+oiKAaoO7BBxLIxIpXMW1UXCsvFqZIIr4zZt9OHRiUHEb/mCUTC0jVBymN8ha\nDGO2l5sMJWZPfeJjif+f5bbjB19aAzsnPx+5kgZMqLG8hRSpGE0+etYA0Oiyw8bSqkWH6YQjIsKl\nkU03jJ37+jXrcctFpkjRFaFcMX3IWs0wznLbcf/6BRl/S4YS5R52g2NB7DqgHFY0kyhId59Hteea\nkBt6RUWScFYGa5fNKsShlZxIVFqtjI8K6DwzrHu7NAVsWDWbFF0RyhbTG2Q1w5gwrudS/9Yaaj50\n8rJiX6OZ+kST1a/FkgTMp2+0UuCsDFbI1CWsaJXvx03nC3e2psQyKECyv74SkVuQ+AI8xvz6Xfw4\ngM03ziPFiYSyxfQha87KYHlLA/Z3yoe50nN2YxNhTYMoIlERnvEQmhsdsu/J7l+sc3CorrIiGI5W\nzFzadPRWp2rFqL7RSkFOylWLxCuQWeU74PHjb17qNOrQSopcSLnWwcHtZHUbZTfJHRPKHFMb5OQD\n/nivcpgrPWe391gOw99VCmvk2iL8wQi+/YtDiEQrKwSsJbeZD0b1jVYCfFTA8T7pgQ/H+0Zx/3pB\n88KHszLgI2LFV/PXpy3A5Gib68KRD/W1EZLcMaHcMbVBzn7Ay8FaGTjs1kRvY7+2qTgMnSis0UJ2\n/6JvMlJxxhi4ep4KQd5VxxVGvkVd6fBRARzHKM4XLnduvn4GvnTXYsnfOL2yWmsR29wmB4LhGBHs\nIFQUpjXIubQdhSMCdr/9MTrWNGuujLZa9IdQK7U4KnmejPJW0we7azFQehWtyhGlOd1a23KyQ/w0\njdLO88yDe9fOV+znV6qsZi0JwZOk6ImNZdA2txafu30hAsGIKa4XwvTAtAY517ajzjMehCKxRAJP\nw0MtEhV1h2/f6lbuoSxncvVW041u8jNSueLlLQ1wyeQH6xwc9nxwAT39I6bJLSeL/qQiOHabBRZG\nPZOcHQGqBBUuKeprbHDXSAuWaKmsjmQNnApHBLx5bAAhXsDDmxcRY0yoGExrkJU8ECnG/Dze7dEu\nRahXXICPCujuq1zBEK3hVKUCLalc8f7OAcxtckga5Ooqa0ZRnllyy9s2tkgOh7gwHMDOff2K300p\nAqRxTVk2KOV286msPnRyCB996sV117jwhU1tsHOmfdwRTEJluhcaKHTbkZ4CEUEU8fKeM3m1bpQa\nrQsROVnIHW/0yhqSYDiKDatmZ4wk3LBqNoJhaXWpSh8EHxPiur+bUgRIzhhzVq3128Xj5uuasGXd\ntbKvJyur9eL183j35BC+9fN3SC89oewx9ZJRzgPJB5oC7lg1R1eByM59/XhXQcKzEtCyEFEs0Oob\ngS8gvSDx+nlsvnEetm5szcgZH+i6JPP+wlZ9F5p8CruUIkBuJ4cVrQ3o7huB18/D5eSwelEjxHhc\ns8pVsTjy4TD6LvpkUxCclcHqRU2aijOVCEdEU0RVCObGtB4yoOyB6EWMA5tvmJtz7rJSta1tLA0K\nCW9V6whGJUPjC0RQJ+NhJ73vdLF+vYpWlSAuko9al1IEaGVbAxiaAnXFIU7+d+uGhbh56Yy8jrkQ\nqA3V2LLuWnCsMY+qSo+qEMyNqT1krSIfucLqKBKpVG3rb39hFapt1pwqVRW9txoblrfUSwq1SHnf\nSsVPUu+vJHGRXL9bNtnCM8n2nng8LtvP/aXNi3Hm03F4ZcaJlhK5gsFAMIqIhnYnLbnzSo+qEMyN\nqQ3y3qMXZF/Lp/Dl1QNn8ehnr8vpM7kWmZULv/yv0/irR2/K6TNqhiZhHLUPdt+2sQWCGMfx3hGM\nT/JwK7y/0sRF5IyqlkiElPAMAPzwuSOS7+/q9UAQRPgmy/MalDOWudw7q9sa0NkrryVAJj0RyhnT\nGmSlAeaAsjGuc7AIhqOIxKTfdfq8NyHGkIOnrGSkypkBzyT8wQic9twKa5QMjdJg9+w2qaTH29M/\nAm+AR52DxfKFbkmPtxLFRYwYcp8M8fNRAecGfLKGa3SCx36ZfHw5IGcstd477hobvvIH18Fdcw7v\n9AxKzosmal2Ecsa0BllviPjm65tQxVpw6MQQ5Mz22ASPcwM+LJhTm1M/7q1LZ+Lt7gHw0UpqSgE+\nHpzA8oXSgxDk0GJo0hXM5ELN2YVI44EI9nddAsPQUzxeI9Wvik22mlsupJ+7SovApKNkLLdtbIEY\nj+PQiSFJQ5v8PENT6FjTjHtuvga7DpzF6U+9GA/wOat1pS8MAZhGkIZQ3pjWIOsNER/vG5W94ZNQ\nFPAPrxzXlJ80w8PSUaX/MtFqaORCzTaZYh4pj9cI9atKRKtEbDkzu2HqGNR0GJoGTVGS96aNZXDr\nspkQ43H88LkjGQu6p796IwLBqGZjmn2/Jq4/CnxEKOt6BII5MO1VpbcPWc0YA4lK6/TeWqWxhOn9\nuJUIQwNzGp0ACle5rBRqltMuTnq86Sj95mYNVWqp3qcp4MbFTUU6In1cGgli55v9U66v5DXnD0Zk\nv2e1zQJRTERSsvved7/9capiXwvZ92s4IiIcETTf7wRCPpjWQwYy85hjE2FQeYrvU5T0gCe5/GSl\ntjqls27lbFgYCjv29hascllPekHO482nSKoS8YyHVBd78Tjw2bXXoOecevSnlBw8fgkHui7BXcNh\nZWsD4gC6+0YwOsHDabfCH5RuYRzz8+iSmZyVS+2A1vu1XOsRCJWPqQ1ydh7zv987j4PH9RW11NhZ\n+INyghbS+clKbXUCANZC4faVc2SlLo2sXFYKNdtYJqfiHCOKpCoBQRSxY28vDmoo0nLX2NDosuPm\n65twoKt8ddSTi+XRCR5vZgmYyBljAKir5uAN5F87oPV+Lfd6BELlYtqQdZL04ow71zTr3s6aRQ05\nizjUOjhZEYxy4talM9PkKjncunQmnn1iHbZ3tCEmxBUrl40IXyuFmm9dNhMd7c0ZcppaBErSxUXM\nyM59/djfeUlTxCe5eNnUPq/wB1YCqqsssvKaudQOKAm16N0mgZALpvWQpap2q3SIy7uvyA5u29gC\nhpEunpHz1jgrg5VtDZIiGOWExULjR4/eIFn8UqzKZbU2KbN7vLmgZQISkHntAglPWS7iUMlc9Eyi\nualaUiN++UK35utGa3uVWesRCKXHtAZZKswK5BY+pijgz7euQHOjA4D2/GS6V37v2mvK3iAnw/h3\nrp6DgZEAWAuDxrqqDOnKQlcuq4Wa82kLKnekRlQqoWUC0lf/YAnWLG6S2F5ltdxpZWQ8hA2rZqPn\n7Bi8/jDqHByqq6zoOTuayksvb2lAx5pmuGtsiu1VwNW6E45NvC8SFUxfj0AoPaoGORQK4Xvf+x5G\nR0fB8zy+/vWvY/HixfjOd74DQRDQ2NiIZ555BizL4rXXXsOLL74ImqaxdetWPPDAA8X4DlMwqpjK\n7bShsa4q9W81oyHllV8zw5H3cRSDg8cvZeTXbSyNW5fNwoN3tuYl75grZja82eiV+UxOQJIzyhSA\nRfPqpvw2vgAvW7Ve6YQjIjasbk4NJtnzwYUpIzv3dw5gf+cA6hXOs5z6GYnOEIqBqkHev38/li5d\nisceewwDAwP4yle+gtWrV2P79u24++678eyzz2LXrl3YsmULfv7zn2PXrl2wWq24//77sWnTJtTV\n1RXje2RgVDGVUihaymhIeeWV2u4Ujoh489gAKIqadpXLxUJvsZzaBKQ4gL/7decUo1Pr4FBfZvKt\ntdVW+CalC7aqOQaTvPbwuiCIqahOT7+8fKaW85x9j0+XRSKhtKgWdd1zzz147LHHAACDg4OYMWMG\n3nvvPdx5550AgA0bNuDw4cPo7u7GsmXL4HQ6YbPZsHr1anR2dhb26GXQWpwhRfZkIz4q4OKwHxc9\nAcUCJjO0OEnxdvcl8FEB2zva8OPHbsLffO1m/Pixm7C9o81QcYRKmM5kJGoyn2rnYdvGFmxYPVv2\ndameWc7KwG6z6jvgAlFdJX88HGvBrTlMp3qrJ1FBrnVBTiY/EcoNzTnkBx98EENDQ/inf/onfPnL\nXwbLJqoa6+vr4fF4MDIyArfbnXq/2+2Gx6NsoFwuOyyWwoSAbl0xB6+9fS6nzzTWVeF7X2oHa7Wg\nqc6GF/7zQ+w7dgH8lTBfFWfBnTfMxVf/cCkYhkY4EoN3goerhkNsgsdYGU7QyRc+KuLVtz7G/9i+\nBgCgv05dGkEQ8cv/OIUjJwfhGQ+hsa4KNy+dha/cez0YpnyaABqviKMYxeDIpOz14vWHwbBWNDZU\nS74ejsQwNDqJu29diMMnLysWafWcHcWf3JdIuwyNTiLEGzuONF8ujQRlXxsP8OBs2jXUT54bhbO2\nCs7aKjS6qjDsDSm+f3QiDFgYw3/bcsCM36kcKPR51WyQX3nlFXz00Uf49re/jXiaOkZcSilD4e/p\neL3yN2O+bL5hDvYdPY9AKKb5MzaWwd/86n2MTvBgaEDISreF+Bh+987HCIYioCkqI/e3vKUBLod8\nXq+Seef4RWxdnxBCyLUASY0de3szQq/D3hBee/scgqFI2Uxnamx0wuPxG7pNISrA7ZQvlhMi0Sn7\nFEQRr7zZh3cV9JyzGRkP4X/9+hjOnPdi7IqKVblBywj21Faz6Dx9WfN2PONh/OTXx/DIPYuxfGG9\nJjnR375+Gg9vXpzL4ZY9hbheCcadVyWjrmqQT548ifr6esyaNQtLliyBIAiorq5GOByGzWbD5cuX\n0dTUhKamJoyMXM3bDA8PY+XKlXkfvF7+7uWunIwxTQMXhgOpf2cb43QOnRjMKI5JFozMbXKY0iBH\nhYR39Vb3JXT1jWA8EEG9xqpVJSpxOpNRcFYGK1sbpghgAMDK1nrJ771jb1/OFfuslcGhk0O6j7MY\nyPVST/IxRKK5FaG9e3IIVTZLRt3D6ERY9v09Z8dyntxGIBQK1Zjg0aNH8ctf/hIAMDIygmAwiLVr\n12LPnj0AgNdffx3r1q3DihUrcOLECUxMTGBychKdnZ1ob28v7NHLMB7gM4yrFsQc7nu5StVgOIoN\nq2aDs5ZPqNUofrH7JPZ3XcJ4ILHgSC5CfvDce/jhc0ewY28vhFxOIrT1OJsZQSaKdPpTb8a5FEQR\nL+05jYNdetrnytEnnorDZgFNZf4tV2OcpKt3BDEhnqp7eOK+pbLvnQ7XGaFyULUcDz74IMbGxrB9\n+3Z87Wtfw5NPPoknnngCu3fvxvbt2zE+Po4tW7bAZrPhm9/8Jh599FF8+ctfxje+8Q04naXJY7z8\n+pmS7Nfr57FhdTPsXGWtthtrbarvGR6X9zL0iu4rFd+ZXQ2Jjwo4IuO5DowEseON3tS/d+7rx/4u\nbapcSVgLjbVLZ1ZMm1MgHMtLZz6d0YkwxibCEEQRrx48ix1v9Mm+1+zXGaGyUA1Z22w2/OM//uOU\nv//qV7+a8re77roLd911lzFHphM+KuDjwdLkT1xOGxCPYzxQXoUzSlAAnrhvGX78r0cRieX3RMw1\nzKykjGR2NSSPN6hoLDt7Pdi6sRUAdFXvV9sssFgo9TealL1HL4BhaKK6RagoTKfU5QvwGC9RtfOq\ntgY0uuy65jCXijiAH790DE1uOy4OT+a1LT1Smvn2OBtdZFY0KGVj6ZuMpkKpenrqxwMRnOgf03Vo\nZqC7f1TxFGfLiuZDxV6DhLLDdAZZSeqxULgcHNYsvirCoEUPt5yIREVcHJ6UrCyf1VCFSETUdD71\nhP/0TmfSq3JVLiSkSWnwMnlSt5NLnUs913NNtVV2AtJ0wKuwKM+WxNVLpV+DhPLDdFcNZ2WworWh\nqPussjEZN+G2jS3oaG/WLU5SKqQqy5MLDC3oCf8lBUEA5DSdacfevtQg+UocHs9ZGdy2fJbs66sX\nNYKzMoqTsG6+bgbmNEhHIxx2K+oc2nt4SwVNAc2N0v3WWpBrVWettKyHnC2Jq5ek0lqlXoOE8sN0\nBhlI5EWLyaWRIHbsvVo4kvT6/vz+5UU+EuO5ODyJzTfOS41ApKhERKC5sRpuJ5fTSMR0gnwU//K7\nD/GDfz6M7/+fI5ortdUqjitJfenBO1uxcfVsZDtTNAWIopg6F8kFXvYIyoc2t2HEJ11sNzQawrKF\nbsnXyokbl8zAD7+0BnObHKkqa5oCZrnV0x4WhpJtT+SjomyRmBF5Yy1Ka9NNfY6QP6YLWfNRAcf7\n5HVsC8Xx3hFs3dCScaM3uuyoq7ZgfFJ7P3Q58vKe0/izB1ZOCSvryZ0lw3zv9AxmiFto1XFOVhzL\nUUnD4xmaBk3TU1ruxDiwr/MS6CsLu+ywfhVnQYiP4fJoUDbkLYhx8BEBjipLTv34xYShgYc2L8Ku\nA+cy2hTFODA4FgTH0imVPCliQm5FiDSVWABsWbcg77yvWsveS3vOpMRYSCiboBXTGWSjBkvkyvgk\nP8UQcFYGzmobxidz64kuNz76dDwlnpD9/XI1fNkDFbJRqtTWohdeCW0sSWNQxVkU5xp39XoyzoWF\nobD32MVUztJpV759P/rEC9bKAGVqkFmrBYIgyv+mBrdQi3HgyIeX0dXnAUUl9ASSk5+2rFuAQDCi\n2UAr1apki7FoXWwSCKYzyEo3ipxEnxFwVgYOe6ZQPh8VEAxXTguUHJGYCI83iOYmZ16ehRaDquTh\nallslXMbS3YRUK2DTQmtSDHmz1zkZS9mJoLKhtYfioEqU2MMAGE+ho8vTcj+pnLef76kbzdpLN/p\nuQQ+Imr2ZpVa9uRWEmZXnyPkj+kMstKNQtMURA1hLqfdCn8wN0MajgjY/fbHGStgX6Byxy9mI8Tj\n2LG3N6eK0mzjrcWgKnm4aoutO1bNwZZ112LYGyzLFpRsg6pkjIHMSms908Rq7VbQDK1YcVxK4gBe\n3HMGHEuXXMAkuf9cvFmplr3F8+rwrozgSyWlUwilwXQGGZC+UZbMd+GdK+PZ1Pje9lV49rfdORvT\n7BVwFWcpqFdeTPZ3XsRb3drCcHLtIFvWXavawqPk4SotttatmAWGpvCXz79flnk7PQZ1VVtj6lzo\nScX4glHY2NJ/dyXKdbGgxZuVatkDgNPnvbJDQ8o9nUIoLaY0yOk3imc8hEg0hp//+0nNnxfikBX+\nVyJ7BRzijZMDLDU9Z6VFJqQeXNmeYLrxXt7SoDggIRIRIIiirBGVExKJx+Oy+yyHvF0uBtXGMrh1\n2cyMqnW9/fVJz8/C0Epv5bEAACAASURBVIgpTUwhZJCLN5tdSzFd1ecI+WNKgwwkvLR/O9A/ZTKT\nJuJxXfUk2SvgWgcHt9McIxnlwqvZDy61dpDH71umaJDf6hnEx0N+PPlIu6RRlvNKfvjcEdl9lkPe\nTotBrXOwePy+ZZjT4JhyvMo5S3WsDBCrkO6bcogq5ePN5qs+R5i+mNYg79zXj305erhAohUjUf2a\nu35w+go4mT9d0dKg2KZTKdTJFCBlP7jU2kEYCqhXMUwXhgPY8Uav4pzadK9k2BtUnRpV6rydFoM6\nMRmBw2aVXTxkP+jrHByqOAYDI+pzxUMVMmQCKL0xBvLzZvWqzxEIpjTIfFRQbCdRhsJf/+sxjE+q\ne7VJqcn6mqsrYKn86Sy3HYNj6g/NcsXtZGUXFtkPLiVPsLY6UaSkxdPr6hvB1o3yc2rTC8aU9llO\nebttG1sgCCIOHpee3KR2rNkP+v868ine6tZWF1FfY4N/MoxIBXjJbieHFa0N6Or1qBa+GYWNZRCJ\nCoZ6s3raAgnTG1MaZF+A1x0mFsS4JmOceG9CUegHX1oDO5doedqxt3dKLhMAbGVQSaoXq4XG9k2J\nPGxX3wh8gQjcNdIPLiVP0Bvg8fQLH2BFawPWr5qFt44PynpDvkBE0rOVKxiTy/mXU96OoemE109R\nkmH7xfPqNG2HszKodXCajTGQOA+DI5M49YlX82dKRXWVFds7WrHltmvxnf99CHys8PdNtc2C//nQ\najTmIN9KIBhNeZdg6oCPCojERLgcVvU3G8DgWBCvHjib2rd8JW3ljsIb9YWxY28fes6OwheIoM7B\nYXlLvWwFc7rU45RtTfDYd2wAFobBuhWzZffprkl4i9nyg3L6wXFAUl6yHPN22ztaM47VxjKwsTTe\nPTmkWUL0k8EJTftyOTh0tDdjy7pr0Xth3IjDLzgXhgPYua8fTjuL21bI633nQk218vNgzM8DFEWM\nMaGkME899dRTpdp5MGhcOEoQRbzyZh92vNGL/z5yHnFQOUvr6WU8wGP9qjkYmwjjd4c/lXyPKMbR\nvrgRgxryfeWGGAc+GfQjxCeMYjgiXPl3DMsW1E95P01RWLagHrdcPwNHTg1lSGQm8QUi+Mbnl+HE\nuVFMSEQk1i6dgY8+9WLHG7343aFPcfjUEC6PBdHdP5I6jnQmJqN4/PPLsHFNM25bNgv33HINVrU2\nIhoTMTYRhsVCwyI3iUCF6mrO0Gs1eX7uWDkbYxM8Ph70p67VEC/g3KUJ2XPLRwWMTYRx5rwXJz9W\n9nZrq6340aM3on1REy57Qzl3DZQSXyCCO1bOxvKF9RgPhHH+sn61O/qKKlftFaMsyIRluvs9GJkI\n47r5LtAq4zHLGaOvV0ICo85rdbV8Wso0IevsVhspI1AokuHVvUcvyL6HtTLou+gzWg2wpKhVMIf4\nGHwK1dmBYARPPtKOHW/0TgmFi/E43swK/WvVsG5y2SGIYs5CJqXgzHlpo5p9brND9U67+iSnG5bM\nSL0vGKosxbixiau/57HT+WnTJ+2vb1L5HIz5I2XVKkeYfpjCIOsRXTASd40NVZwFPWdHZd8TjghF\nXSQUA7UKZi3FVsm86taNgqY2JrmWmOyCKKVe6HJ52CpVpI9lndupspnKK/Uqls4I14+VqQCHHKyV\nRiQm4tMhH4K8sfKfNpaB3WaRPffl0ipHmH6Uj6uQB6UaKJFkVVsDQnyspMdQCtSqgpVm+WYXWyUr\nUtUkNpVG6gGJFih/MKI6Gq8cqHVwoGnp0ChNUXnJZoYiIoLhq4bMYqmsECwfFfHk8+/jRy8cM3zb\nkaiAL35mkezryYUmgVBsTOEhK3liNpYpmGdqYxND5rdtbEFMiOtSUqoEOCsFPjrVEqYbQbleSz0i\nCUq/p9vJYkVrI3r6R1PbW9laDzEexw+fO6I6tKFc+pKBhGGQy2cKYhyRKxO29C44P73sx9JrE3no\na2fW5HWsZoK1Mpg30ynbD19OrXKE6YUpDLJSq82q1gZcGA7gomfS0H2yFho3XT8jlZNkaHnJvEpn\n28ZWXBoNZhjVpQtcCIZjKSMol6PVI5LAWRnYbVbJh2V1FYuHP7MI/IarIe5XD57NyDcr9a6W08P2\n4rByodLF4QCWzHfrls08evoyWpvr4AvwiBShdahSCEcE/MehT7B4nktyEEQ5tcoRphemMMjAVE+M\ntTIA4jh86nJB9heJiTjYdQlWhk7lJLdtbIEYj+PQCenK4kpl0dw6rF/VjPvuWIixiTD2HruIwycz\nJUnVcrS5iCTwUQGTIWmjOuwNIsjHYOcsaHLZcw7nltPDtrnJofj6ex8NoW1eHTgro6oBLsX7H3lw\n6mMvxiZ41BWpDbBSONg1ADGOK8M3KMNFQQgEPZjGIKd7Yi/tOZMxILyQpBeAMDQNmqJMZYyBq20i\nnJXB/q4BRcNgREGML8DDKyPswkdF/OaNXjz62etS71UK57ocHHyTfFk+bFkrA4amZMPWb3UPwWJh\nQFMUuvtyL1pMLyT0BiqryrrQJE95clG5dulMPLx5Udks1gjTE9MY5HTkWkkKQXpOstTV3oVi8kpx\nkJbvZ0SOttbBwaUwlOP0eS/4K/lVpXBufY0NTz7SjhAfK0s9YV+AlzXGScwWbSlXzpyvDNEUgrkx\nRZV1Or4AX9TCKpcz0fI07A3CozDkoJK5PBZMaUerfT8jcrSclcHia9yyr3v9fKoKVq2S22lnU9Xb\n5Uatg1OdV0yMcXFILiSzleEIhGJiOg+51sHJTiYqBHabBU+/8EGqsImrYM1qOV74/Rn8x6FPsHxh\nvWpxkVE52u2bWtHZ65E0SNlGvxzH3aUPv1A6H9ECFVuxVhqRqLmuw0JS5+Cw54ML6OkfKWshGYK5\nMZ1B5qwMVrUWfuSh28mhusqKC2mVsmZseUqSVMpqbqoGZNrLki1gesg2YHbOituWz9I06L2cxt3J\nDb+QerB7xkMQCmAzaRq45foZOHhc+/CJ6Q4fjWXURpSjkAzB/JjOIAPA9k1t6B+YyDCWRuJycPj+\nQ6vxd7/ulHzdxjKIx+PgTeiheLxBbFg9J9UHXOfgsPgaF7Zvak1NvMoFJQOWq+dbDuPuclEIi8SU\nw6IsQyGiQ49dFAGKotDR3pw6d9U2Bv4QCcPKMRmWPjdEtYtQTExpkBmaxpOPtOOl18+gq3cE/qCx\nFaa+SR6DY0FZjzgSFfAXj7Rjz3vnceRDvXOZyxM+GseGVXOwdUOLId6okgG7746F6FjTjHvXzi/b\nwqx0lIrepB7srMqwi2gew1EOnRjC3/3pLamoQVefBzv3ndW9velKOQnJEMyPKQ1y0us6dW4MAY3G\nmLXQmsUTXE4bjp6RN7SslUFjnR1b1i0wnUEGgEg0Zog3qmTA3ukZROeZYXj9kQyvuZxR1KaeCOPc\ngA8L5tSmjHIVp3z7KSmOqRGJifjePx1G++ImbN/UijkNyj3PBGnKSUiGYH5MaZCzvS4t5KJktHyh\nW3WQxO63z2HLugWwmbDIi7Uac9koGbD0HtpKyecptWBRFPAPrxzPWFyozTSe11SdV3FiJCbi0Mkh\ndPZ6cP21Lt3bMQtWGsg1i1ROQjIE82O68sFC9gIzNLBxzRx0tM9Vbf/p6h3BqwfPms4Yc1YatdWs\nIa0hSQOmlXIaDCGFUguWGAfiuLq42LmvH3GVmbu3Lp+NjvZmcJb8btNwRMCxM/mNMDQD/+PBlair\nVh9bCSSmim1YNbvsozIEc2E6D7mQk58EMTGFx11jU23/GZsI41CP+apcG11VGW1eubSGZFdSK2mQ\nS1EJ+bz0QrSxiTAomXGRXb0j2HzDXMVtLZhdi98d+hQ80aE2hP+9+5TmepIblzTh4c2LC3xEBEIm\npjPItQ4OLEuDL5Bn2tXrQSQaU72xrVbaVFXW9TUc7LapbV57j15EKBzDQwqyg7lVUnOYDEclIwul\nyueFIzHFiVbppLdgnRvw4R9eOS75Pq8/DEGMo7mxWnLwSXNjNX76b92GD0UxKxQSEQgltBpjG8vg\nIRVjLNVnnv43ACVvwSNUHqYzyAAQK6BHMTrB461udZ1sSvXxUDlQAP7H1hV49rfdkq+/e3IIJz8e\nxeq2Rmzf1DbFW1ZrBcruIX714FlJr3l5S31RH27JhUTP2VF4vKGcIgKclcGCObWykZTk4uL7D6/G\nd39xGIHQ1dnF1TYLmlxV6OwlYWatLJpXi9PnfYZs67bls2CXKbiTWlyubG1AHEB3X0JUhGMTg23C\nERH1RGCEkAOmu0I83mBBxBa0Ul9jw61LZ0rOD65U4gA+HvQrpgJ8k1Hs77qEp184CkG8+gOotQIl\nc8LJqm3OymDbxhZ0tDej/kp+mb6Sau3u82DH3t6M7ReS5EJi2Buakv/VgpqsJ2dl8O9vfZxhjIGE\ndnghjLGZHTUjjLHbyaGjvXlK3jhdTjN5TYxO8Klr4s1jA9h3bCD1t0RBYuIazfWaIUxvzOchqxTK\nFBKrJdH/zFoZnD7vNY1yF0UBi+bVgWMZVW3lC8MB7Njbh4c/swiAck5fLiecDPsKgoj9XZdSOdgx\nf6Ro1da59hTLoSRuEuRjeKensIpy6ZRxPVzJuXXpzClpl2xv2OVkEeT1nUQiMELQgukMcq3GKspC\nEI2J8AV4NDc5ZYuVbrpuBvovjleUsWZoCg47C/UsXYLjvSPYuqFFdRqTUk6YjwqyrWXFeLjpWUhI\noSTr+Zs3The1Cl9LnnU6wlopWKwULEzmYj471SI3fUwLlVCQSCg9pgtZh/iY+pt0Qmtxvq946FfD\nrjbQVCKU3dHejK9+dolsGLNcEYQ4BkYCmo3H+KT2aUxyRlWLQSwkSi1ZeorL0kPyQGLBcbqIY0KB\nyjHGnDXxWNJ0vxlAJBrHwa7BjHSL0e2TRGCEoAXzecgODvUqLUl6URldCwpIPcSVPKNtG1tw5vx4\nwbS2jSYOIDAZ0Xxe3QZMY1LyrFkrc8VjLxxKLVlGiEUUsj2v0kl2JyTvNxpAMeII6ekWo38fIjBC\n0ILpPGQlj6zQxAHsOpCpF5ztGQGJ9ovL3sppZ6Ep4NrZtZrPq9w0ph8/dhP+5ms348eP3YTtHVOr\nsdNR+h2TSmiFJhnlaHJVZUQ5jBCLyFUUZTpTzBrN470j8AcjCPExsFZpF93GMqmCQykYmkK6THly\n2EyxihEJlQvz1FNPPVWqnQeDhZlZfN18F0J8DL5ABOFI4ULYUgyOTGLTDXNhkRgcIIgiXnmzD7/8\n3UeIxColgAg0Nzmw+cZ5GeeVj8TgruGuhOQp8DEhUWG+bCa2bWwBLVFcZ2FoVFdZJc+NFAvn1GBf\n5wBiEkMWfIEI7lg5W/O29EBTFJYtqMfn7mzD6oX1uOeWa7CqtVHyu+VCsl81EI7hk0G/4ntnue24\n/to6DHiCOe+n1sEiGhVQ62BVi/EICcIRAYdPDmLPBxdluzXWr5qDr917PY6cGpI8r6yFzhgMEhPi\n+HjQjxAfw7IF9YU69BTV1VzBnq3TGaPOa3W1/GLOdCFrIDNcfOz0MP7lPz8q2r75mAjPeAjNjY4p\n4gF6NLZLDUNT+PYXVlz5/6lheCAx1xfxOBqzIgH5EghGwcsYkmIWydhYiyH7karandvkwGQoIlsw\nFImJ+Ny6BXjvw9zzmQ9tasPcJgeqOAuefuGDiiokLCW+SWUBkZggIBCKwiejMy4nCEQqrQlqmNIg\nA1e9kEXz6opeXSqIInbs7c0QD1i+sF5xIEW5Eo/HEQwLcFRd/RtnZVBfa5NV3zJCACESi+H/+78n\nZH+3RB459/nLpUSqanfMH8HN1zXhvQ+HJb+r1x/GqE+fIZ0/0wmHnYUvwGN5SwP2dw7oPHJCOge6\nBkFRtKp8bjak0pqghukMspSSjt3GyA4gNxoby+Ct45ewv+tqf+noBJ/x70rCaqFThi/d489W08pn\nIpOUDOFf/2snBhRkIxN55I8li+bktllKlKp2ey/4FFvDmpsccDms8AZym+v9m719+GRoAl5/BC4n\nizkN1RgYqZzahWLAWRNjV+uqOXhzqNw/fHIIN13fhINdU/XqbTL9+qTSmqCG6QyylExjMXE7OXT3\nS6ss0TKDBsoZPiri/751DjRFaRJIyCUsJ6dxfc/N12DAo16BLjUz+f71C7DrwLmCee56UaraHQ/w\nuOX6mXj35FRJ1lVtDXDaWSyZX49DEq8r0dl39Toc80eAPPpoiw0FoH1JIz74qDCT2wCAs9C4ZekM\nbGqfB0eVNaewfjgiIBIV0dHePKV7IB6P481jU6MRpNKaoIapDHIhRy9q5dKofPFNpRnjJIdOZBav\nKAkkpIfl1LxUOY3rsYmwpnMlNTM5u52sXGYpqwmkfGFTG6pslowhG4vnubBl3QIAwPZNrejs9Uyb\n4ix3jQ12W2EfT3xMxP/f3pkHtlHeef87M9KMLEs+ZMuJj9w+AiSx4ySQmxwOAZZs0xdIwE0ohaXs\nS9nt7ktLKaUEWo5C9u1SurRQ2lCOzTZs2M0LXdpASAIJIYTETpyE2I6d20d8yYcsaSSP9P4hjyLJ\nM6ORNDozn38gtqx5NHrm+T3P7/j+9tZ3QENRqK0pD6vzGAA0ne/Hs9+dP8ZLw7ndIAgirDI/FRVA\npkF+8cUXceTIEYyMjODBBx/EzJkz8eijj4LjOJjNZmzevBk0TeP999/Hm2++CZIksW7dOtx5552x\nHn8AStQOjs/PwLWTTGOMUDiInYRNRgaVZfloaOn1LbpaDYXOvvAzaONJOPch16iDQa/F1l3NqGvq\nQt+QEyYjjeqKgoBTqtTm6VyndOaxFGIn63ATaoI790RLqLpmPaNBbU051i6Zgq0fn0bj+T4cONGJ\nxgsW3wl/8azClEsKjBSGJnHw5OW4XIufG7zBrGvqRt9Q6HWkb4hF36ADhXmZAXFhKQ2CcEm20ItK\nbAlpkA8ePIjTp09j27ZtsFgs+OY3v4kFCxagtrYWt9xyC375y19i+/btWLt2LV555RVs374dWq0W\nd9xxB1atWoWcnJx4fA4A0qcQuTgcHP7X0mng3B4cPN4ZUS9asdNddYUZtTXlYJd7HzI++zWdmF2e\nj//67Ax2+7nseA1qt8eDDatCa1z3D7EYb8pAZ5897OuL3Xu5CTVCbvRFlcVYs2Bi1C5vOQIpO/ad\nDXBN+5/wA3otDzngSVGPixzae+K3SfWfG7U15VizcDI2bTmEfpEsan92Hb6IdSvKBI0mr0EQCVIt\nS9WuUelLyDrkwsJCrFq1ClqtFjRN47XXXkNXVxeefPJJUBQFnU6HDz74AAUFBejt7cWaNWug0WjQ\n2NgIhmEwZcoU0fdWulZOQ5G43GeL6oTlcHLo7rfjwIlOcBH6mPOyGMy/dhyGbK7Ret3A+ly+Hre7\n344PD16IeKzxQqzsVkdTyM6kwTo532dcu2Qq/vDnrwVrhzt6baiZ663R1mhIfHGyE3aBWLQpi8FP\nvz0H+xo64AxzQ0QSwhn1piwGty6YHLJu+U+fnPb2eB4dl53l0HTBokgNKV/XfGNVERbPLAyoa2Zd\nHDr7bPjvz1phF/BIDFidWDa7GLPLzLixqgjjc/UBMWKVyKG1JNYumeqbGwNWFv/zxXlZf9vZZ8P+\nhnb8+cB5fHGyEz0DDlw7OTfqWnWheXimfVDWPFTrkGNDUtQhUxQFvd67y9u+fTuWLl2K/fv3g6a9\n0oV5eXno7u5GT08PTCaT7+9MJhO6u+MXz/XvXQtccRtHUvL0VWNXVGOZVZqPjTdVhHQ3/e79k1Fd\nJ16QBAFO4Di2eFbhGLfcpa4hUc1rh5NDt8WGkgIjGC0FvU4r6M1gXRwyGC3+9R8W4+2PmnC0uReD\nNqespLhis0FQknTY4cJ7n7ZKnjCU6vAUCv+TU/BJSOzj+Z/iNBSB023K9P5V8SYuvrunBbU1ZaBI\nEgY9DYYmZWm3C+UxANHlK8RjHqqu8OREdtbErl27sH37dmzZsgU33XST7+ceEb+Z2M/9yc3VQ6NR\nZjK8vuN4QHyNX7gT4dVbu6wUZrMRAFAi8poBK4v23tQoQeHcHtxYXYxTZ/vQ029Hfk4G5s8oxH1r\nrgNFkQGfcTjEiTbXlAmz2QiHc0RURc1qH8F/7z+H/317JX648Xo4nCNoOm/BT187IPq+piwGiyuL\n8e1br8GbH57Cx4cuBDQacTjd2HX4EvQZNB5YO1PwPTp6hkVjh5YhByhaC3N+puTnC5fgeStGfk4G\npk32nox++14DPj2ammV0ycqeujZk6LRYe2MpPvjifFRduBpae/Hg7RnQ0ZElpSkxD/n1JxiOc2PL\nBydx8EQHuvvtMAc9yyrSiN1XpZA1Y/bt24dXX30Vv//972E0GqHX6+FwOKDT6XD58mUUFBSgoKAA\nPT1XXGhdXV2oqqqSfF+LRZk4Eevi8Pmx5BE9aO8chH1Yevd56lwfUknadmVVEe5aXhqwq+7rG7uh\n0ITYiFn6hqHxeDBgZdHd7xB93RcNHVizYJLv/hlpEtmZtGBcL9fAYNO982DU0xgYsOOW6ydg/9FL\nsAusaZ8fa8ct108Q/F44FweTUTwTmnO60N0deTgkGNbFYf9ReUlaM6aa8Np7x3yJcpGitmAU5y8H\nzuHDA+eifp+efjtaz/VGHj+Och6azUbR32/d1RywAeyy2PH+vjOw2Z0JrUJIBaTua7jvI0bILdHQ\n0BBefPFFvPbaa74ErYULF2Lnzp0AgI8++ghLlixBZWUljh8/jsHBQQwPD6Ourg5z586NevBySLbO\nOa/+vxP48WsH8cTrB7F1V7OgqHxhfuqo9ehoyieLGdwoIxhriBjLk1u+whOvH8TOQxeQYxDv2GSx\nels48qpnP/vjV6JJNnOmm2H06/40YGVhETFaUq0bI20VGSkDVlaWcaVIoPliv7ckLBpjTAA3XFsQ\n8d/HC6M+MdWYSm1UsjMZZDCRf4ZYzcNQrnDWdXWU1CUzIWfNhx9+CIvFgn/6p3/y/ewXv/gFnnji\nCWzbtg1FRUVYu3YttFotHnnkEdx///0gCALf+973YDTG9njPo0R2tZLwWrh8TInj3Fh9/UTfyZJ1\ncdi+N/bdipRiwYxxvnGHijs1XegP+X68clmxOVPUyOpoCtkGRlL/myS8MeM7lk0N+Hmoml+pUiah\nTOhFlUVYs2BiyM8VLhmMRlZcnHMDl7qiD2+YjDpsWF2Bth5bUrf+nFhgxMlz8e0VHQliilwWK4un\n3ziE6ooCrF0yBVabK+xYbSQtS0Mhp8e4KuuZWEIa5PXr12P9+vVjfv7GG2+M+dnNN9+Mm2++WZmR\nhYFUjWcy8OnRduytb0eukUaGTovBYSeGbOHJICaaYG3u2eVmwcWmYqL8Mjeb3QWKhGhXHWcIoRe3\nx9vDdtsnLQEbnmh6GQvVkJYU5SjqquaxsyNxFYuZPjEHHOeBzZHcc+/CZeXvdSyoKs9HhpZCQ2sf\negcDwy98qd++Y+1wutxhly0pWcvME81GVSU+pI1S1/oVpbA7RgTlBxMNv+immnwhz4HjnQEdbPiT\n//6GDrBODjkGBlXl+aitKUNedgYMGRpY7aHbXkrVeTpdHC51WWWFIvgNj/+iF+0JI5oaUrlkGxjk\nxcizM//acTh9aQCWIQdoLQXAg89PdOLkuT5Z9bWJZEjG3Ek0FEngyxOXYcpiMGNqHg4cb4eQx5d/\nbiLNwFZyHkazUVWJD2nTD5kkCFw3xYQDx9ujypBUGYtYPTZfa+xwcjjXMYSjp3uwtLIQK+YU47Oj\n7SFriE1GBnqdRqQWWYc1iybj0KnLgr/3hx8dX6tptTlRVWYWrfkNl1jVdWooEp8f78TgsLLvzWhI\nPLZhDlbMKUHfIIuzHUMB31WyY8ygkr5fOJ+7aGc5nO8cku3piEcfb6n5OranuXQPc5UrJEUdcirB\naKmIRPhVlOFilxVbP27GxtXT8fL3l6J3wI6mC/04daEPnx8fK4NYXeFNXBHbsRv1dEShiE+PtgME\ngdqasricdCOFdXEYtitv6GeW5vmU4JouJH8sNpiiPAOaLqVnnXXfoPxYbSxqhWPhCldRjrQyyIBX\nhP/Lry9HrLKlBHxpSSp2d4qW+tM9WLeCG+2ZnIGFMzNww3XjkMFoBd3HnNuNxvMWtPcMw+0Zm6jl\n73oOjtOJ4fZ460opkkjqUo6+QYdk1rSWBER63UvSeM6CHzceRLZBuEws2bllwSSc+38nwKahp4sZ\nTVaUIh6ymcm8Ub2aSTuDTJEktBoCnDNxlnBJ5XjUzJmAD7+8EDeB/FhCawk4XfLu54DVOeYEILYr\n59xuPPtWHS759T3mE7W27z2D2prygL/tG3Rg1+GLaGjtQ9+gA0SIDY+S6lqxYNcR8ZM/SXiNcSSb\nOqvDG4NNRWMMAG982IjcTBqdTnkbsFiik6nYpSRiXdCAxHYsU4k9aSfNMmBlExpDNug00Goo/Gp7\nA748eRk6moKOTu3bvHDGeMyXWb9qyhLP1gyuY976cbNo+Q1fF8m6OHSNCsgU5mVi4+rpeOaBG/D8\ng/Nx4+xiybFI1RwnGtbFoUGkbzZwxQhfbR4WABgYdqLTknhjDAAzpkSnXx6Mc9QNLYZaK3x1k3Yn\n5FhmrsphxO0OaE7OJ9Gkqvt6QoEB31pVgY6eYRz8OrTGt9xsTdbFoV6iOULvoAPv7GxC4wXLGLcd\nb9hra8rgdrvx6dEOwfdI5lKOeIvZ5BoYDAyzyNLT6Fc4iSwdmHdtAbIytKOdtFjf86q0ZnioOanW\nCl/dpPbRTQBGS6GyLD9h1xc7naeCMV5SNR55WToQhHcBv7GqCH//jeswwnlGlbrEp4vJSKNmbons\nsqIBKyvpUmU0JD4/0Yne0YYLvNvujx82+k4J3vCEuPHX6zTQUMmZOZptYJATp81CroHBU/fNw3Pf\nnY8Hv3FdXK6Zapy5NACCIDCr1Hsi5p/XAYXd/qE2rHytsBCJ3GDynir1hB5b0u6EDADuVLB+oyST\ntjCj0eCZB27wxvRfdQAAIABJREFUi9X24rOjV+p783N0aOsW1h/X67Rj4ltSWaIhPRkidvTzE504\ndb7Pp4IkJRxyscuKbbtbkjLuxmgpVJXnY09d7DXYq0Yz1o16Gk51QRWE3/Dp6NjkG+RlyauDT7Za\nYbUvc3xJO4PMujjsOybswkxGTFkMHlo7Az9/60iih4L65m7cfuM07Klvw576K92E+MVKqjFXe88w\nhmxOGPW0rIdYauEpNOnR2SfeeIRXQbI5RkK6fZM5sau2pgwtlwZiKmM5ocCA2poy3795TwcbSfr2\nVYBUnTatISKqj87JpPHkvXMD9NaliIVsZqSoCWbxJe0M8sWuoYSWPIWLZYiFVkMmNO7N0zvI4s2/\nNqLpfJ/g70ckDlduD3Cpy4prJptkP8TBJU1GvRazy/KwbkUZNv3hUMj70XjeElLDPFFxNzk1pBRJ\n4sl752Lrx82oP92jSFY0QXhFK4wZWsyu8Pbl9j/JMFoKi2YVYveR5OmOlgpEs4kZGHbCzo7AqKdl\nz4tkqBWOV39wlSuknUH+8MD5RA8hLHKNOphz9UmjxR1NmRZDkxiyOUUf4rqmLiytLII5JwOMlgJF\nkqO1yB4cbe5Bv5XFybMW7Nh3FpVl+SGNRr+VxYLrxkvKpQbH3WLdmD1cFx9FkqhdVQ6Hk8MXCpTI\n8QpSQ3YXTp7pw7bdLWOuvW75NJw6b0FHjzLtT9MJkoRgW9RoPAq5RgYGvVZQD17K9ZvoWmE1wSz+\npJVBZl0czl8eTPQwwmJ2eT40FAGni4OGBEKoTSY1z7xVhxwJMYq+ISc2/eFQwGK0bXdLQByVP02v\nnFOMmrklqG/uFj0B5xp1WLeyFBe6rKJuXz7uFq9YWCQuvm27WxQxxsGIXXv73jOqMRYhFj3KK0vz\nsGPf2ZRz/arNKOJPWkXlvZm7ydvJZlxuBvKyGJCEN8mjZm4J/mbBRHz/V/vw2bGOlDbGPKHcrv4Z\n01s/bhY9TR893Yvbb5yGZx6Yj4Uzxgu+ZnZ5Pj74/JygMdbRVEDWN28og7O2t+1uCevzSRFJDanV\nzsY8scv/2qyLQ11T6PK1qw2SADJipBdwrLUX+xvaBX8Xbm1xPLOd490fXCXNTsjJ1hfZnwkFBjx5\n71yMcB4MWFkY9DR27DuDR145EJNdeSpQf7pHtKzE3yX2nVunQ6/T+CW5MJg+MRe3zp+EZ986LPj3\nekaD22+cBor0utGPNMY+FhaJi+/5t+tjnvPgf+0BKysp13m14vYA9hgJCkklHgrNC6GwSqKynZMp\nwexqIK0MMqOlMGNaHj6tF96NxgstBV8rNlpLYsGMcdiwyptcQ5FAQa4eW3c1J0XMOJH0W53INTCw\nCCgX+bvE+CSXtUumYOvHp9F4vg8HQrQS7Ley6Bt0YE99Gw43dom+TslYWLguviGbUzKbXCn8r51t\nYGAy0qpRThL8vxspo5uobOdkSTC7WkgblzXndmPrrmZ8cTzxnZ78vUlOlxtaigrYxUq5Nq8mSALI\n0Ak/3EIusR37zuLAiU70DTnhgbR7PNeow64jl7Dr8CXJ19FaCga9NqLxBxOui+9SlzUuNej+12a0\nFKor5MmgqsQe/+9GLKwiFdqJl5xmsOytSmxIG4PMT+ZQPXgTQfBDE2/ZRKWoVlgBze0B2gWSiyYU\nGMa4xMLdxMyaZpLUiuZxODns2HdW9vuGYv2KUqycUxwgMKGjSbg9HnBBsYmSAoNi1wWA7EwtllZ6\n1db88xSC7+X6FaWYP2OcotdWCQ1FEjAZGcHvRjL/4HRPyFBIMqOqfMknLVzWyX7iDHaLJnOsWwxG\nS+C+266Bad9Z1DV1B+j9MhoSIKIrDfHH5nBhhPPAv4d7qE0Mr9XMx7iWzy7GXpmhCyXjyBRJgiCI\nAIEJh9ON3UfaQBKB7SBpLQWKBDiF9pBZmQzuveXakKVdFEliw6pyHDl1GeoaGUhsNec9eOLbc2G1\nOQGCgDknw+c5k5rfoTxBiZTTlJpnqspX+KSFQR6wsklt3IIfGimVqmSFdXnw3qdnsH7FNDhHONQ3\n9WDI7s1oZ0fcyDXSmF2Wg8bzfegfHonqWr2D7Ji4rtQmJi9LhyfvnQs7O+JbHFgXJ3vTo2Qc2caO\nSGbU+hv+ASurmDEGvBsZ1sX53Iv8yURowdyx76xqjAWIZX4d5wYe/91BZNAk+q2uAAMV6SY93tnO\nrIvzSuseuYSGlh5JQ6uqfIVPWhjkDCa5P8as0rwxD83aJVNxvLUHl5OkzZwc9tS14auvL/v67fpj\nGXLi4NddKDFnRm2QSWLsdxpK45fXapbz+mCUPGX8x8fNog1GhDwlSiq08RuZvGyd5Mkk2T1KyUpB\nrg5dUT6vDifn854EG6hZpfK1zfP8vtN44H/aDZ6vQoZWVfmKjLTwG9jZ6AxArDlwvAPvfNwEzu32\nJZ89+fuDKWWMeYSMsT89A3YsrRwf4G4OF7dH+Dtdv6IUNXNLYDIyIACYjIxkh6ng14t1q1LqlGFj\nR3CkWbzGV6shAxLIpJLAIoHfyISquU7VHIZ4k2u4Eu9dOacY10zKjcl1+ByTmjklsv9m400VvrK+\nSAkntrt112nfnBLDP1dGTgmgyliS+2gpk2RXjGFdV2KIAFLKVR0uDqcbNXMm4PYbS/HkH77EwHD4\nQi0mIyP5nY7extFEqdA+Rv71mRlaFORqMWx3od/KKl5TKXU6Brzz4L8+O4MNqyp8P/Ov8+wbdESV\nde32eBfCUCeTVMxhiDc5BhqPb6wG5/Yg28DgvU9bRftuRwtvoExZOtkek19tb4g4Jsu53QH66XkS\n78O/9tOjofMx/D1A4ZYAxlrSNlVIC4OcKrutI41dIMnk7M+rKAQBOzuCwQiMMeBtFyj0UG7ddTrA\npddvdWJPXRtaLg3gyXvn+tyx3f12wOPBnqPtAa/vG/Tu2pdXF2P1vAmKPvysi0PjBUvI1x043ok7\nl5X6rutf59ndb8ezbx2OODnOZGQAgpAlTjJrWl5ARy+VQPqtTjz3dh2qyvNx+43TYuri5w1UOGEW\nf88HID8my7nd+NkfDweo20m9z7bdLbLnib+h1VAE9DqtoEH290ipiV+BpIVBfvOvjYkegiwsVqdY\nm9+kIEuvwZyKAiytKsLzbx+JqNWcjqZgymLw3t7WiMcRfI9C7dIvdlnxzsdN0JAkPj/eKdlCDwAa\nWnqxbnmpojtxuW5gh5NDt8WGkgLjmN8NDTujylSvrjDDnJMBrUibQFpL+RbMmrkTVIPsh1B2tcXq\nlTVtPGeJqTfB30DxHhMpMZtgwonJbt11WlT3Pfh9ws010Os00FDep3fb7hbB65SYMwM8UmriVyAp\nvwVhXRzaumPXT1ZJcg00TFnJ616/a2U5Nq6ejknjsiLOOL7+2gLs2HcWe+rbI3a/Hj3dGxDX4nfp\nUt7pL05cxidH2kIaY0D5GBbr4uAczeqWA+f3OficgideP4jNfzoa8RgWzhiPby6dgmffOixrI2XK\n0kEXI+3mVMQ92rJSiI4YqalRJLBiTnGAgeI9Jk/fdz1yDPL6J8udz6yLw9Fm8dr8vsHA9wk31+Bi\nlxXbdrdIGvL2nmFs3XUanNsdkfZ7upPyJ+QBK4shW3IndfFUleXD6XJLtgtMKB74HoJIE+UcLIeT\nZ4T7KcvFMuRAt8UGWkshg9HI2qU7wzhZKpVVHRyLk2vgPjvaho2rpwMYe0KIhLwsBhtXV+DZtw7j\nUvew6OtYJxeQ5e1JnbbhcYEv44sXnBsgCUIwbvvBgXNwOOU9g3Lns7f5jriBzTbQAe8TSa5BfXMP\nls4qFDXkbo+3WoMiCdTMKVHbOwaR8gY528AgS6/FgC15ujxpKQIjbk/AgmfQaXCspQd9Q05FxSCU\n5Hd//ho5e2lMn5gTcRbu0dM9Uaul0VoKv9regL5BFjkiWtfRoERWtY114dk3jwScnviELh1NSZ7U\nG1r7fBsfJWKTDENh664mSWMMeE/F2QYGnNuNt3c2KSbkohI5Qu5msU0aAQh6neTMZ87txs5DF0AQ\n4hux2WWB7xOJXoJlyAEQREhDXt/cgzULJ6vtHYNIeZ8Vo6UwqXBsPC6RaDXkmElvdYz4BP2T0Rjz\n9Fu99cSR5lM4R9zIzoxOG9rh5HwlO3KNsVhJE+CNDxISUpLhwLuYH/m3z0VdmRk0hXtWi8e/+N2/\nUuVH7d027DsW2uvCL9zbdrfgQLJ6aa4ygt3NUm5cD7wx2FDSqEKECvtMKDCgdtXYOcuXDubJDMfk\nGnUw52SELOezDDlgZ0fU9o5BpPwJGQBWz5uAhtbo3KRKYmNTP/YRzaZhanFO3IQncgw0ZpflgyQJ\nfHJEWFThxqoirL5+oiJZ1XJczBarE2/tbBY90fjv/nPj1HlpaeV4rF0yBZe6rWo/5CQi+CQYSnVw\n2D6Cp+6bF6BKFwopI08SwJLKImy4qVwwq5mPaS+dVYgnt3wV8lq8IV2/ohQc58anR4U3AfznVts7\nBpIWBjkWpUSx1bRNbxbPHIe8LAZfnbocUR1yOPRbnWho7UVlWT5WVBfhwInLPnexjqawcOZ43L2y\nTJESinCzTsWmz6xpJq/84OGL6B+OvTEuNmeC1mqw6Q+H0DfqeVBJDoJPgtkGBjkGWryt6DALOzsS\nVmxVyhPj8QC33DAx5PNhztWHbNvJaK80UaFI0psnQRCC6mP+n1tt73iFtDDIlhicMIrNBtHyABVp\nivMNqGvqAUmEbwQj2Qj1DrLYfaQNNXNL8K//sNhXh2xWuF1ctC5mkvCWhhxr6YlbyVGJOROlE7LT\nWowmldDRFJwuTvQkyGgpzC7LF50fpghiq1LJWXxegRSc2433Pm0N6fnzF0DiS5Zqa8pAkYTvBJxj\nYDB9Ui7WLpkS8Le8/vrVTloYZLnZiHK5saoId9eUYvveM6hv7kHvYOpJXCYKDUXgqTcOSSpWSRHN\nRohPkCkxK9vWkCdahSu3B7Da41cRsLRyPO6uqcATrx+M2zVVAiFG4xamLK8BXrtkCqw2l+RJsHZV\nOVraBgWfg0hiq6F04EO9X7iVAP6JarzLe+2SqfiPj5vReMGCL050oumC5aoWABEjLQyy261sltTK\n6mLQGo3PlfLmXxpx8OvLUb/v1eAGH+E8GOHkf0gtRYBze3wnhjuWTfVthPiYEq0hZdWCWoYc6O63\ng9aQMXF9SS1stMa7qCSiH3dJQSYqJuTg6OneMXG43gGHqludQJZWFmHlnJIAj42ekU56pEgST947\nF//+cTO+ONHpy4b376sdrhGLNFYbSSMSoZKlHfvOBJR7Xu0CIGKkhUG+ZpJJ2TckAmPSzRdDSyIG\nQ5IE3KPWl9GSMOdkhCxLuRrJ0Gnwg/VVAe7l4JgS5/bgkVf2gw1x6qa1FF569ygsQ86YSfCNXdgY\nTJ+Yi7tXleP5d46gLQHfsd3B4Y5lpbhjWemYOFy2gYlb4phKIMXmTFAUgV/957GwZSEpkoSGIgNK\n08T6asvBX6I1VKzWX1c6kjBNcKKa2vlJPmlhkKloWgsFoaMpmHMyAHi792z5n6/DXszGm/R44ttz\nvBPZ4xWn/9kfQ2coXo0M2VygtdSYBzI4pmTODr2hkWptpxRiCxvr4mB3JKYWvs/vRBIch2O0FDIz\nVIOcCDzwYLdf5n84c1LaiHVHbMSkYrVCutKzpuWFHaYJdoPL6fykxo+9pIXznlOwsLe63OyrNf3B\nK/tRJyE1J8aD37gOekaLErMBJQVG2NkR1W0oQqjOToB3cbKGEH5hNMJTOVYSfPzCxi88A1Y2YUaP\nALDz0AVwAqEb1sXBlqCNwtVOe7dwmEXOnJQqf+odZNHdbw9onRhOK0UxhNp27qlvl91vPtdAB9RF\n82PKYDSisrJinZ+i/SypSlqckM92DCn2XgdOdKKuuVuWJrIQtIbAeFPgbk9tdyeOXqcNudMfsLIY\nkCgPokiAFYndxmsHnm1goKPJsJLZSAIozNOjpMCAo6d7IlbOcnuAPfXtoChyzMlL7X2cfMiZk1SI\nUs5/3VaPfqsLuUYamRk0bA6X71S7qLIYaxaELmXyR+pELifURmtIVJVdiUlv3dUccNJOpc5PiWwF\nmRYGuWJijqLvF6kxBoDK0rFZi5FI0F0tDNtdYF2cz+0r9CBkGxjQWlLUYEk5SCKR4Iv8gQyvHt7t\nAdp6bGjrUaZ5gVA8Tt0MJh85htBeoc5e6TlhsXq9Hn1DzgDPTO8gi/f3nYHN7gwrVBPtxs054vaV\nagXr9fcOek/7EwoMsDlGRJPKEt35KRk2BGlhkPOyM2DI0MS1pESM9l7h3SQ/8eIhlpFK9FtZ9A06\nsKe+TfJB8HgiOz3OKs2TbVTFHsiH180OeJ2/wXa6OFzqsoJhpPWr44HQyUvdDCYfcubj4SiV7sJN\nllJq47ZXosubzTGCJ++dK6gylgyJX4neEABpYpABoHq6GZ/VdyR6GOjosWHI5oRRL9w6LZQr6moj\n18hg1+GLAUIIwQ/CgJVFuKXmvGzlsdPdoEhC1i5X7IHUZ9BYu2hygMHuHWQDmoQkw9eaY2RgtTvh\nHHH7EhMHrKxPhIEft0pi6eiz4YnXD4qevlgXh+Ot4eeu+CM3VOO/uVRi4yZV8MjrV/NjkpvNHY+w\nk5wNQTxIC4PMujicTBIta7cHuNRlxTWTA0uxlGizFyvG52ag02JPyLXLSrLR0Nor+Dv+Qcg2MCFl\n+4LhF4a+IaesXa7UA3nwRAduuX4C3vu0NeA79HeVJ0N9ed8gi2feqgPgjatrNSRYp9t30n/6/hvQ\n3W/HL94+IhpzV4kPYqcvzu3GOzubok4QDBWqEfIGVZXlY8WcYhw73Yu+UTEkJac1P6Zws7nFPouS\nsV45G4KSqK4gj7TIso5V4got0UFIipKCQKWoSIrr48mDa6/DhILYqFuFYkllUcgHgdFSqArRPSYU\ndU3dklmbUnOop9+Obostqb/DYDi3t26Vz5bddfgSduw7gwyaUo1xErG/oQM29koIa9vuFkX6pYdS\n4BLKqP5ktMb5mQduwPMPzsfSqqKoxyE0JrFsbr1OWDAl+LPwVTBPvH4QP37tIJ54/SC27moWrDKQ\nC++yFyKerSDTwiBL3cxIoDUEdDQFl8uNnEwaWpGSGiFI0itQ4U+yZ7ruqbuUsNIY14hb1oMQrRpb\n3xCLd3Y2iT60UnMoPycDIIik/g7lUN/c4y1BMQqHU1Tij8PJYevHpwFEtnHPNdCYUGBAXhbja8n4\nt0umSipwSV3ncGMXnC4OBbl6bLipHCXmzLDGIwajJWB1uNBvZUWvPWx3YXl1ccj2kkIGfdfhS9i2\nuyXscfElVgCSohVkWrislU5ccY54AHhPU+F243G7MSbekeyZrp/J6KUbC0gCmDjOIFkSoaEIvP1R\nEz47Gn1+wOcnOpGh0wi6rqXm0PwZhTDnZCT1dygHPoZ3zSSTIqcwFWVoPG/xuV/D2fTlZdF45oEF\nYyoUSopy0N0tXgoqdZ1+qxNPbfkKc6Z749ubvjMPb+9sjHqNYF0eHDxxGfWN3aIemn4ri9XzJmDd\n8rGKc1feR5nkLyG3eWVZPlbOKRaUoI0XaXFCBoC1S6ZARydefo0kMKaQnl/sVQIpys/En784Lyii\nX2LOxPoVpd7G6nVtisVopUQZrjRjD9yh37fmurT4DmkthWwDg7tXlUOT+EdFZRTLEOszQOF4+lx+\nhs1fqMbhHJEU1jDoaTC0+NJvsV45cVIkiXtvuRbLq4vlfyAJpMIlvEcsWHTHHzmxXjkInbJ3H2nD\nsN2bCf7cd+fjmQduQG2NcJ/oWJEWJ2QAsNpcYGNYdkIQwLic0MlP3o4+rjFZ1ncsm4qmC/1o67bC\n7UnfRhMayisvKUfkYlpxNg4cFz759gw4YHOMKB63lcrYFJPFpCgSnNsNt8cTtvhHcuGdcHpGg5xM\nBj0pfNpPJ2jtlWYo4Xj6Bm0jAXOZP/U1tPai22JHjoFBVXk+br9xakCHqR37zsiaw/7ZxUsrC2Fz\nuPDl112Rf9AQyHENS3kb5cZ6JRM4v76M+tPdWDyrEHetLJM3cAWRZZCbm5vx0EMP4d5778WGDRvQ\n0dGBRx99FBzHwWw2Y/PmzaBpGu+//z7efPNNkCSJdevW4c4774z1+H3E2i1sMuqw6b7rcaatH5v/\ndEzytTsPnce9t1wb8LPte88EnATT0RgDwAgH/HhDNX7/wde4bLFJfs5jLT2iC4PDyeFs+0DE3ycj\nIiQiR5QhWO/X4RzBGx824kASunk1BDAicy45nG7fRoN1Jb5mX2UsvHu0rqkbfUPSc5/WEAFzObiS\nw2JlsaeuDZ8dbYfb7fFlM4tVNQRjGXLgzZ2NONrcHZNNaI6BxuCwMyzXcLStJIHQOT2sy41PjrSB\niKCJR7SENMg2mw0///nPsWDBAt/PXn75ZdTW1uKWW27BL3/5S2zfvh1r167FK6+8gu3bt0Or1eKO\nO+7AqlWrkJOjrIqWGLEWQKgsy8N7n7airin0DnF/Qycoihptzk0mfZa10vzhf76W1S5xwCodn2cY\nDQgC8ISxecnL8nZfOts5iHYBBazMjNBSnTz8iePY6IkjGfnxxjnQakj0DbH41X82SJap8OGU7n47\nhuxXn05wsuIccftOuv5emjNtA9j8p6Pif+jXlU5qjeFGd8V8NrNcaC2FgyeibzsrBEkAs6aZsPr6\nSTBkaGFnRzDCeRCqTxDr4rB8djE4twcNLZHFeuUe3qJp4hEpIQ0yTdN4/fXX8frrr/t+9uWXX+Lp\np58GACxfvhxbtmzBlClTMHPmTBiNRgBAdXU16urqsGLFihgNfSzfXDoFB090wupQbvefN1rD6fF4\nZBt7twfYU9cGiiR8whapnqEbDkKGUAhTFgOr3SV4ktXRJD6tbwvLGFeX5+Ncx6DkSXbY7sSQzSmo\nFhRMMteO8+TnZPiy+kMtMm4P8Ic/n8TZjsF4DU9FBh4P8OeD53DL9ZNgytKBGe1+NrU4G7SGFO2x\n7XS50TfoQGFeZlhrjNxwmSeGbjy3x5tMerbDGqDDLSaWIla7XDN3gu+eyUXu4a1vNLYfz05UIQ2y\nRqOBRhP4MrvdDpr2xkjz8vLQ3d2Nnp4emExXxDBMJhO6u+N3KuTcbjz6ygEMs5Ht/HU0iUydFpYh\nFrSWgsfjAetyw+PxgOPcst08/gQIW6R4hm4smD3aWWtP3dhde162DgfDjFfJ6czVN+TEpi2HMGCV\n7pmcCK/Gd//2WmgpElt3nYYlhLuSZ9vuFjRdsKBvkAUtkajD03Am/N7eKpFDEF4DwK8nYuw/1on9\nxzp9B4A7lk3Fu7tbRI0xz293nMCm78xDtoFBjoGBRUZSk5id5fMjeIMdj3p1/zCelFSlkIqeWEMV\nOaxfUQrO7cHeujZRr5KcTnRKE3VSl0fkCCP2c39yc/XQKJDuyXFu/OP/3ROxMQa88bVH75mNz45c\nxN76Kz1M+4acYbl5/LEMOUDRWhTmZ6KyvAC7D1+MeHzpAEkC8HhPdfNnFOK+Ndfh9++fEHxt70B4\nmxdeKlMO/aOucn9pzAfWzgx4TUfPcMgYntJMLMpBQa4ev9khfE+E8PcGsCmbbJa+VJaacfS0/I0d\nPyfPtA/iTHtoT8al7mH8176zeOiOKiyYVYgPD5wL+TfmHB3mXTseh09dRpfFDpL0lmvyS3ai81sa\nWnvx4O0Z0NFe8+RwjogeiIJfKxeOc8OYyUDHULCL2I1FlcUoKQoMuZrNxrCuEy4RGWS9Xg+HwwGd\nTofLly+joKAABQUF6Om5ckLp6upCVVWV5PtYLMp0uXn7oyZcuDy2dCZcfv77g/5hmQAiyYrONerA\nOV3o7h7C/1oyBQca2lI4Qzd6tBSBORXjULuqDHpGi47Lg/iiQXizE26jhmjWkM+PteOW6ycEqgG5\nOJiM8fVqPPHqARTmZSArkw4ZX1dJfkgCuNA5ENHfngsjrPDJ4Yv424WT8c3Fk3G8pUewjNCfytJ8\n3LF0Kux2J7osdvBaOZG2/1Sa7n47Dh1rw9TibDBaCl0Wm2gOR0+/Ha3nesN2K2/d1SzqstbRFBbN\nHI81CyYG1HObzUbJ+m65SBn1iAqsFi5ciJ07dwIAPvroIyxZsgSVlZU4fvw4BgcHMTw8jLq6Osyd\nOzeyEYeBkq5FD8SNbiS7Rv+sPz2jweJZykrRKcX8awtAh6FGJocJBQYw2mA3sAcHTnRix76zAJJH\nwUyofjFRdccdvfakMMbJ0Cwj1RmXq4clQk3qcNYbp8uN7n47KJLEk/fOxa0LJyPH4A0p6mgKOpoa\nratnsGjGeKxdMhU2dgRfnEy+qgHA6+3a/KejPklMg55WVNZSymbkGGi88PcL8K1VFXGtP+YJeUI+\nceIEXnjhBbS1tUGj0WDnzp34l3/5Fzz22GPYtm0bioqKsHbtWmi1WjzyyCO4//77QRAEvve97/kS\nvGLJgJWNywJmMnqVXI6d7gnpyjQZGVRXmMdk/fH/TqauOxRJ4Nu3XIO1S1g89tpBRd5zdmkeso0M\n2rqFd+r7jrVj7ZIpSRNbz86kBR9q/vtqaO31uvb8vCTpWkfOk86fLR6QJPD9dbPw5O+/HFX+iy3c\naKcTiiTxv2+vxJoFk9BtsQEEAUOGBtv3nkHj+T4cONGJxgsW0BpKtreOBBDPszM/9/xjytGWOvkj\ndRAYHPYmfIp164s1IQ3yjBkz8Pbbb4/5+RtvvDHmZzfffDNuvvlmZUYmk3gt6tUVZl/ywJ66NsnX\nfu+bMzClKHvMz/1LGv74l0Z8+XVsSgrCQUMpexQiABj1NPZKxN1ZlxvP/PEwfv7ADSGzHXU0BdbJ\nyXJJj8vV4bLFEfaYJ403CD7UFEli/YpS0LQGnxy6EJDkohosFSk8HuCld4/FxRgDwGcNHdg4PguA\n1zi/92mrLyOZoQN7dYe7VpqyGfSEmdMRDgxNSuY/1Df34On75/n+P1pZSyXERWJFyktnMloKs0rz\nY/b+OpqkQSz9AAAgAElEQVTCijnFWL+iFKyLQ0NL6EzeUM0oGC2Fv7vtGsWNYSQ4XVfEIvIUaNBR\nZM7EocbQ2dGdFjv+/eNmjLjdkPIMZeo0mDu9QNa1L1scY9zkcrh1wSTR323b3YIPD5xTOySphAWt\nJdHZF7/a9aPNPRiyeT2FWz44GSALGW4+RjCxNMYA8I+3z4RRL9zpCQD6hhyw2lyorSnHMw/cELWs\npVQ4Kp6NJIRIeYMMADVzYtep0uHkQBIEKJKUFfOkSO8OTEpLFgBsjhFwXOKPWcGyfdFg0Glw363X\nyF4APm9ox966dkg1cuodZPGVDAPPQ4hl5YlAkoCO1gp+V7EqfaLUAG3U6JnkVv31xHn/ZrGyePIP\nX+LtnY04eCL6RizxxDXiwZBNvNtcTuaV8iNmVI99wMpKrq98F6dwdevj2UhCiOSe1TIxZemQY6B9\n5SxKs7+hQ3bM0+MBfvbHr0IWul/oHFK0+Xek+FenRRvj1mhIeMJYiSSeJx9SwghCOJycr4xDDhQB\nbPrDIcHvSumkM4IA8rN06B4I362uBOHIbCY7j9xVhS9OdmJfQ3tSlXuRBDBvuhlfnoq/Mt/AsCvi\nEs1EQZKAnqGQJ7GuVo2eWoXEQWaXm7F2yVRYbU5kGxhoKELwNcFrsJhufaJJC4PMaCnMLsuP2WR0\nODn8+8fNyNRpMRyib7DbcyVGI1XofjhJpDT9ZftGOA9q5pRg9bwJeOqNQxh2hOfqGrA6QWs1ijZg\nCMcYA94ErYEwWmbymwKh70rp/ISsDG3CjDGQPsaYJID8bB1q5pRgdmleSG35eOLxAASRGo7HcDau\nscLtBp57p160ymNCgQG1Nd4mD0LiILsOX8L+0U2ZKYuBXqeVJTbi364ynkpcoUgLgwwAtavK0dI2\nGLIGTwqpzNm6pu6I6/SC+3SyLg4nzoSv/BUL8rJ0MOhpbN3V7NtV5hiYsI0x4JVuNOdkYOHMQuw+\nIp34FisyGE1YBjkY/+8qHH10OSf5AQm3nEoYEFe8UNkG5bJhC3J06OqPbsOk1RA4falfoRHFlkQb\nY3/4Z4ciCbjdHmQbaMwuy0ftqvKQ/QD4zX/vICu6eeafa7kn6ESR+BEoBF+Dt7y6GLkRZslJZc5G\nUzQfXOeaLPW3gDeJYce+MwFJIHLk94TfywxGS+HulWWomVuCXAUXS7l09tkkk8RCEfxdrV9RKplw\noqNJ0BQR9kleJXLcbvjmqpJhqmiNMQA4RzwJL+MDgOqK/JSsJefcHtxw3Tg8/+ACbFw9XbHwEf9c\nC/VB3nX4Et74sFEyJh0v0sYgA16jvPGmCjz34HzMnzEu7L/PNdDQydADDoaAt/ZYRwvHIIJT6bMN\nDLIzE1PnlkETIHAliWHtkilRJy5RJIGVo5no3n97y4X0GeKGjIdRWJAEALyfMDKCvyubY0Qy4cTh\ndMOZBMl56Ugkz2I6YDLSUQv1nG0fSipXbDg0nR/rYeDDR5GSa9Qhg9GIrnUHTnT6hEi4BLoO0sZl\nHczpC+G7jeaMltcIuSjF4qJ5WQy+f8csmHP1eO/TVlnF64yWgkGvRX8UrtVImVKYjXtuno4MRgM7\nO4K+AUfEO0+vFKYZG1ZXQM8EGt+tu06jrXtY8u/zshjMKs0XrOsON5nLHy6KIuGykqyAf5+VoSes\nogxayhvT54V13B5PwkIfiaR8Yi4OnoxOo4BvTpIMceJw6R8e22Up2va6s8vzveudxFonlfMTL9LS\nIA9YxWMJYhAEsGbRZF85RX2zt0G4yeiNMYgtDrPLzSgp8CqSXclSFi9eZ10cui022NjENIg/db4f\nOw9dQENrry+GwkSYhJWVSePbt1wzJjuRdXE4IqNv9Kxped6SNY8HDa19sAw5RlsJehKm+X3w6y4c\nbenBwpmFuHtlmaS7OhWhKSJpT/QZDA2XzemrPli3fBpIgkB9cw96BxOXDBcpxgwthuzy8wZIEijK\nz0TT+T7FxpBqxhgATCLiHMHrK62lBEssJxQYYHOMjFmDRziPzD7IPXHvg8yTVgaZz5yjSAKMhgxL\nzMHjAbZ90oLv3DodnNsDjvPA47nStcp/cRAztv6p9LxsnTknAxRJ+lL265q60Behvq0SeICAbPRo\n4l19g8L9QgesLAaHpRei8SY9Glp7sbe+3dvbtDQfdtaFgyfDa7kYCxxON3YfaQNJELj9xmmJHo5i\n6GgKc8rN+FyiX3QiGRwVtrAMeU8qHo8H31pVgTULJ+OpLV9FnNuQKO5cNg1b/tIo+/VuN3CpS9qr\ndDUgJs4RXKpk0GuxY99ZwTV5hPOMKWeiSO8hIFQ1Dh9vToTLPy0Msn99Wu8gG1YrPn9OnevF0298\nhUt+rta+IWeAGyNU3RrnDpSt47P4Rtxu7BXo+5ss6GgKekaDfiuLXKMOM6bmwjXiwZGmLtGENoam\nBHeycoQvOvuudPrqHWSxp64NVJQhw0i/dzGONHZhzcLJoDVE3CQQY8niWYWYNS0vaQ1yMJ8f78Qd\ny0phZ0fQHwNjzGgJZOq0MdkgMxoS5twMxd9XjHTQVs8ZzawOJc7BaCmfsRRbkykSvtf4lzjVzJ0Q\n0iAnUj4zLQxycH1apPPSYnXBYhU+2fm7MaR2TmK1ctEam1jjdHF4fOMcUCSBXUcuoaGlB32jmYhi\nCPW85txuvPVRU0Rj4KJ0rym9HlmsTrz518aUN8Z5WVdODjbHSMos3g4nh+5+O8w5GaKuxmg+y5yK\ncdi4ugJv72wK6CutBPNnjkemLvbL66KZ43Dr/Mn4y8Hz2H88NTZawdAaEnqdBgNWJxpae0FRLWPK\nkHijyue+8P/ljbDQmiwkJDKrND/kBnvWNFPCREJS3iDHSt4wmN7B0G4MqbFEa2xiTa5RB3NOBt77\ntDVk8wweXgfbfyf69s4mHG9VLgaWaOqaQ2uXJzO5BgZP3jvX173GqKdRmJ8ZMuEuafB4JBN6IjXG\nWopA7aoyMFoK37l1OvQ6DepGF+5omVBgwIZV5egfYqNKTgzFeFMG7v+b6wAAN1wzLmUNsnPEDedo\n+VpwYlWw95PfgPHesDy/OuJgN7XQ4UjO2lYzd0IsPqYsUt4gx7Om99/+6zgera2CzcEJuqyTqb44\nXGaXext0hLO5MWV5NWaDHxqV5GFgmB3TTu7vv3Etfvr7rxI4KnnoaArm0c2eUMLkpHEG1J2ObMNU\nWpLjqwzgY5NLZxXiyS3R3ZdCUwYe2zAbP/vjYbR1W2PmiSAJ4PGN1QC8G+EMHZUyng851Dd34/Yb\np42pXOE/H/8xeQPedKEfNofrykl4Wh4aWsMXX8o1MDBl6RT4BJGR8gY5nj11L3UP459/fQBut0dQ\n4SVZ+vuGg/8OszfMEiheCGTrruaIyxFUYkuukYHTxYF1cb4NZKYuMTXw4bJw5ni/mOBY7WGni8PR\nlv0RGaG7V46NU5pz9WC0BFhX5FbN7uTw8zcOo9MS205PiysLkcFoAxT2SBLKx20SRO8giz/+pVG2\n6lmwXGakMsqzSvMSqm2d8gY52vq0cOFrXIVq1uI9lmhZOGM8Nq6u8E28cDYUJQWZWLNoMi51DcUl\nZKASGVa7C09u+QomI43qigKsX1GK9/a2JnpYkvB1yELJPf7xQkZLodhsiEgu1yXiRvZ2C4vcqkWr\nHCb36sdbevB0+2BAVrZQWExDEd6KkahGFTukTvXR9osP12OgZygcb+3BZ0fbEyapST311FNPxe1q\nQdhsymQ3Xjs5F3Z2BANWFnaWA0l4JzWjJeGOsQ9nwOrEjVVF0FAkhmxOZGgp0FoKVpsTdjbxUmxC\nkCSwbHYR7lldAa3myi5QQ5HoGXDgjAwxjMFhF/bWt+Hjw5eS9nOqXNlA2p0czrQPYtDmxMmzvbBH\n2SNXLhQhz8DkZTGYf914PHDbtbht4WTMLjODlNFKc9HMcTjW0ovBMEV2pk/IRUmBIeBnfYMO/PXQ\nxbDeJ1E4nO6QpYVA8ruwSwoMYX93cgn3o7s4j++5sLPe58XOjmDm1DwAQGYmo4jNyswUz+BO+RMy\nMNad5Z+BZ2dH8INXPo/ZxLQMOdAzYMPv3j/lixmRBGDOTlwcQopMhsBzDy4KiCn64x+rCyXGEG3j\nc5X4c/B4p2R9/sypJnxj8WT8dsfJqEMvJAHI0SAx6kg888B8URehf9lK8GtojQZP33c9LnUNhRf/\nFbD1dBK031O6dC+ZWVo5HrWryvH2X5tjUopnMjKoLMtHQ0sv+oYcIBD+BqW+uRtLZxX6chliTVoY\nZB5/dxZvcBgthWXVxTGT4MvOZPCbHSfR0XOlrtbtAS4rIFQfC4ZZz5gkH3/4zc2ahZOxacuhmPWY\nVkkM7IhbctH/zq3TkWPQKRJ6kbv4DTnccPrFuHnE+t8KuRHDnacFuWM3zMngyk+UMaZIIirJ2Ui4\ndf5k0BoNNqyuwKnzfYrXg1dXmFFbUw52uXdDt2PfWRwM0w3eO8jiyS1fIS+LwaLKYqxZMDGmLuwk\nr45VhrtXlmFp5fiQr5tQYMCy2YVhvbfFygYY41RAjnCHNwQQ/gOSgg1mrjqkll3nqAjM+hWlqJlb\ngrwsHUjC2/Aglghphot15tm2u2XMa1vb5GvXkwRQbDYG/Ix1cfj6vCXscStNlp7G8tlFyIuikUJk\n19VieXVx3PQSTEbGJ77BaClUVxQo9t46mkLN3BKft48/qG1YXRFx047eQRbv7zsjOPeU5KowyBRJ\n4tb5kyVf89N75uDp+67Ht1ZVjC5EDAgCyDXSqCrNQ7Y+8e4spWjrCV2DGkl3FZORxtzp+ZEOSyVO\n5IoY17ysK4sk7yl55oEb8Nx35+PZ7y7AhKCYq5IEa4ZL1fTXN/eMaZU3cVyW4GuFKMzXC5Ys9g8l\nvjqiqjwPG1dPxzMPzMfCGaEPEUoxMOzE6nkT8Mt/WAJzTuzDbdUVZt93wLnd8Hg8Ad3yotkYZNAU\nbr9xmu8ky7o4dFlsoEgCi2ZGd0+F5p6SXBUGGRg1MCILkcnIoMjsXWyuLETz8fx35+O57y7AXSvL\nMGiLfbw018BgxZxiFOXHNl7x5l9OhWwzxmgp6HXhNVbQZ2jxVWNqC2lcDVwzyST4c76MzR/+dMFo\nKfzknmqUmDMDfk+RxJifRQL//PFI1fQH96wGEJYRcbDcmEU12vZ+gLcjXF4WA5KAaCvWUKyeNxEA\nfIIlK+YUC74XoxVeujN1VET9wGktBYOehjFDi03fuR60jGCmISP8iGdeFhNwegW8npBPjrQF5KRw\nbq/HkvfQ5GUxmFBggMnIhPTC9VudGLCy4NxubN3VjCdeP4gfv3YQT7x+EGSU81Vo7ilJWsWQpWC0\nFGZMy8NnRzvG/M5/t+b/ej4erWR9sY4W7lCSY6Dx1H3zQGspPPyvn0V9HSksVlfINmOsi8OwXdhl\nnWvQoqq8AA0tvT6RhlmleThwPHTt342zC+FyedB43gLLkLQ0p4ry5BoY1K4qg16nkWyUIgSt0eBn\n99+AIZsTZ9sHYdRrUWQ2QEMR2La7BfsbOiJK9DNnj03WknrmhLSGzbl60BrAKaOJmmVI+fZ+ALB4\nVpFf4wMa//1ZKz4/3um7J6HKcLIztQGiFBRJYsOqCty5rNTXrCY7k4adHQlorNA36ED2qA507apy\n/Pf+c/jwwLmwxu5wctix7wxqa8qxY98ZWfeR0VKYN73A16kt16hDWUkWDn4t3CCGAPD9O2b5uuMB\n0p4Qm2MET947N0Aik++W96//2eBrMRlM7qg7XEip65MjbVg5pxhlE3JQ39Qt2gJXrDlRrHWurwqD\n7BwZwbNv1aGtO7Be0b82Uwql6osXzhiPDIbCJwIJZnOnF8Cop3G2fUAyuSJUSzeC8LYvqyw1ofni\nANp7hkUXAak2YwNWFhaRJIuBYRdWz5uAdctLfdmv3RabpCxddqYW864Z50vI4TNn/3roAvZGWMSv\nEj5V5fnQM1pZjVLEMOppzCoNDE3U1pRj7ZIp2PrxaTSet/ialFw3JQefHZPOoHW63AHCJYD0MyfU\nDYjRUlhcKS95U2xRXb+iFB6PJ8CIUqQ82dv5116Z27yh/9aqCtyxrBTd/XbA40G2gcEPf/O5qI5y\ntYCHgv9s/kaMT8gU+w6/u3YmnM4Rr7EOI7u4vrkHaxZOlq0r0DfEYvX1E7FuRZlvDABw+tKA4EbK\nlKUbk60cyhNiZ0fGbJxKCoyYUyG+HldXmEc/j/DnOHq6F888cAPWLS9F36ADH3110Xu4sF6R5hQ7\nhot1olKKq8IgP/tWnaB4gF6nld2IWk450OKZ49HQ2otB21iDmZfFYOPqCmgoAoREG8chgb/15+6V\nZXh3b4tgVqk5R4d/uH0WzDkZvklzpr0fz7xVJ/hefRL63HJOKAGi7iFqRr91UwVmTs3zxXX4v/3W\nqnJoKDLqtpS0BhjhvIvVgERd47zpZpy+NIABqxPZBvqqyiKfUGBAbU2Z79+hGqWEi57R4u9uuzag\nTKnbYgtpkAftLsF5KKe/uD93rywDSRCoa/L2MtdSgFC4T6q9X7ARNWXrsPXj0yGbT6yaWyKYfcto\nKZSMuuNZFyf6mJAEcPsy6YOBEELfIUUFloH+5dB5fFo/1jMYjGXIgUtdVtlqfXxiVvAY5Gyk/JtF\nhOMJ4RHaPOloCgtnjg+pOujfXrEwLxPfvnk63t7ZiD317b6NC9/hTkdTcLo45Bp1WFRZhDULJsq6\nN5GS9gZ5yOYcczLmae8ZxpDNKVoC5I9/rXPfoGO0I1LvmIUi2E3C4x+fkzqdTCmSTk65bqoJczsL\nBK+xYGaR7+HnGZ+XKbrLF2ufCIR/QsnOlL6Hv/nvEwEynfziFXxfd+w7i68aw++J/M/rqrC/oROn\nJJq752XpcN/fXAsAvsXgZ3/8KqWkTnlC1auOz8uAy+UZ486Mh+pQOBs1QLwhvZBcptTpZGy/XBo7\n9p0J2zXvb0QBYOPqCpw824sBCSEOrYzs3QErKyrL6fYAVpsTeka5JZn/HjQyv/Ncow4lBQbZ4Tmh\nnANAeiMlVMqm12kFryd1GhXaPJlzryTrhRPyYF2cqO51pk6DxzdUw5yrR0lRDrq7h0Lel2hIe4N8\nqUtc4N3t8f7+msnCSS5CMFoKhXmZ2HhTha++zX+hkLurFzudGPU0SsyZAT2ZeUrMmTDqadFr3Lfm\nOvT1Bf7de3tbI+40Fc4Jxc6GDjoJyY3y8Pd1w03laL5okVz8hDjS3BNSXMD/Aefv/fSJuVGLEhDw\nCmo0nIm8y5Upi8Ft8yfhrY+aZb1+WXUxvjp1GVb72PuuoQj8/P4bBJu0xxuvt4YU7akNADOmSre7\nC/ckL6dfbjgwWgrV5WZRfWQdTcoSjshgNKJxZJLw/l5pWBeHozIbcMwuz4dRT4cMz+loCotGT6JC\nSG2kgnXvewdZ9A6ymFBggM0xImvjFCwUE3wIAcI7UEi7zVnQWipuz0/aG+SSAoPkQxAsnxcOgu6i\nMHf1Qjzx7Tl49q06XOqygg9nlBQY8JN7qiWvQQXVCrAuDvUSDyM/scUWu3A+C5/FLsftLBS79t85\nh2uM/8+dlXhzZ6Po7/1P5sHcvaocR5q74HBG3iKP1pC4bdHkqAxy/xCL0pJs5Mk4nSyeNR61NWW4\nY9lU/Oi3XwQYZYNOgxceWgCKJAOatCcKRkvhuskmya5Mc8vNMR9DtPehdlU5Tl8aENwoL5xZKOsZ\nt7MjkocDKcGeSAnVgY6AN7brbwCFNuKzppmwtKoIFEkGhMSkCL7v4SZwBSNHKMbfFb58djE4t0fQ\nk+lPuAmEsSTtDbJRT4sK0BebDQEPgJREX7hEswjwcoBDNicudVlRUmAQfFBDXWPAykrGSHMyGVmT\nTc5n4Yv75SS++cdweMRc/aHHRiInixFddIQyO/3RMxosnlUUVcKek3NDR2ugo0lBw05rCBj1tKSh\nzTV6E17kJA/etmAyKJJEBk3i5e8vRe+AHU0X+lExMQd52RkRf45YsWF1hahBJglg4njh7yYa+Gxc\nEIRsAyIFRZLY9J152LrrtC87V6oJhhDZBkZ0w+VfA64kUsYmL4vB9++YFeDqBZQ5VAgRbgJXMEJZ\n0/y/+XAhn4vCH8LyRlsx1sydAFOWTjRpLpzwXCxJe4MMAD+5p9qXZc1rTRebr5w4w5HoiydGPR2W\nOz0YqQUA8GbcKjnZ/HfWfYMOECKeCaEYjtjOOVSpyOJZhTDnZIguOkKZnVLj5nfSM0tNaLk0ENBN\nRwyTUQdzTgYWzSwUzKBfUlkEgiAkDS3/4K9fUYpT5y1oEziJAUCugR6zcOdlZ2DhzOQzxDw5Bm8N\nqZxNcbRwbjf+45PTOHC8w7c54l2sd60si+p5pkgSG2+qCKguCOf5kV74heOx0RLqmmIbVf5vlfSw\nRHMSDSUUw3HugJACv2bwrRj5RDcxwk0gjBVXhUEOdeKU2nnJzcIORsnTdqRIPYzBGbdKELyz3vnV\nRcFSqOBsyzNtA6I7ZyljvHDGlUU2mh2u0IngvU9bZRlj/2vctbJsNIPem+VrMo51ldc3d6N38Ep5\nRfApa4TzwCERj68qi++OXSmENsWTC7PwaG2VotfZtrtlTOmTw8nhkyNtIAgi4ufZn2gMVSIW/mQx\nNtGcRKVO131DDsnQHCBd4gnEzisQLleFQeYROnGG2nlJfYlCJNtpO/jUGo+MW37Bqq0pA0UKl3j5\n3yfeQHlE4vxCRtlk9JaR8Z9BiUWHH7fUnKA1JDJ0GgwOO2EKukaoh1qsI5ncBBMAqJk7QfbnSSaE\nNsVTJ+UpmrXKujjUNYln6Nc3d4f9PCtNIhb+ZDE2QOTPqdTpOieTgSWEepZQmEwIpb0C4XJVGWQh\n5Ej0hfMFxeK0HQ2JfBjDybYUOwmLxf+D1dWU/JxSc2KEc+ORdZWgtZToNaQeaqGOZP5Ix/x0AUpO\nqUi0YRgpBqysZFJhn4BCV6JIxMKfaGMDRP6cSp2uq8rz0dDSEzJHI57JWZFy1WhZiyGlYRvulxiu\nIH488dckTvS1pe4TD695+5N7qgO6DuVl6cZo4UpdKxJCzQlzrj5m95JfeISId4JJqiGlVw8EdhhS\nSSyRPKfBHcj4taC2pkz0meFJlWfnqj8hK5lhp/RpO10J5ZYFAB2j8XkUxHbUsYrTJzrrMllifkoT\nnPmsNKEy/WOVOKUSH6RO1/yzwau0+WdZi5U8JiNXvUEGlFsAk6meLZmR06yjI0hFzd/dFo84fSKN\nYjLF/JRALPO55vqJ+MbCSYrmMqxfUQq3x4MDQZKKUkIWKqmFHP0HsRyNZIfweIRSaeJDrGXIwkWJ\nE1dwbJTH61qJbQzZbDYm3T0VQ+w++fPDu6oE443xvMesiwNFa8E5XSn1YCcTUt91rJ4LpeuQU4lU\nWgdSCaXuq9ksXmp21ceQ/VEi/igW50jU7pxvzp3I+LUQ61eUYmmleLNwMRW1eMfpGS2FwvzMq2pB\nVxI5mc+xmJuM1tsVqMRsUL87lZRBdVkrTLK4G8XcumuXTIHV5kq4K4ciSdx7y7U40z4kKEcoJhiR\ninH6ZKhJTxSplPmcbMRr3lzN8zPZUA1yjEh0iYFY+dX+hg6wTi7h9dE8vG63mIpaMKkUp0+2mvRE\nEErjXM18Hku85o06P5MP1SCnIVJuXT7RJdH10Txydbt5Ep0BHQ7JVpOeCNTM5/CJ17xR52fyoW6D\n0hA5ZUU8ia6P5uEFI+ToGidbnF6IZK5JjzfrV5RixZxi6OgrhldHU7ht8ZSk+s6SgXjNG3V+Jifq\nCTkNkVNWxJOscVcpkiVOL0UqxrpjBUWS2LCqAncuKw3IfI5Hw/dUI17zRp2fyYl6Qk5DpNSegkm2\nuGs4JFJ9LBRKKsClC2rmc2jiNW/U+ZmcqAY5TQl26/q7C/1JtrhruqBKYKpEQrzmjTo/kxPVZZ2m\nBLt1DXoaO/adSTs5xmQmXSUwVWJLvOaNOj+TD1WpK42QoySj1hyGT7QKPeo9F0ZVlJIm0nkT7n1V\n56c84qHUpfgJ+bnnnsOxY8dAEAQef/xxzJo1S+lLqERBouujr0bUe64SCfGaN+r8TB4UNciHDh3C\n+fPnsW3bNrS2tuLxxx/Htm3blLyEioqKiopKWqJoUtcXX3yBmpoaAMC0adMwMDAAq3Vsc3kVFRUV\nFRWVQBQ1yD09PcjNzfX922QyobtbuhG9ioqKioqKSoyzrEPli+Xm6qHRqEkESiKVMKASOep9jQ3q\nfY0N6n2NDbG+r4oa5IKCAvT09Pj+3dXVBbNZXKDCYrEpefmrHjVrNTao9zU2qPc1Nqj3NTakXD/k\nRYsWYefOnQCAkydPoqCgAAbD2J62KioqKioqKoEoekKurq7Gddddh7vuugsEQWDTpk1Kvr2KioqK\nikraongM+Qc/+IHSb6mioqKiopL2JFSpS0VFRUVFRcWL2lxCRUVFRUUlCVANsoqKioqKShKgGmQV\nFRUVFZUkQDXIKioqKioqSYBqkFVUVFRUVJIA1SCrqKioqKgkATHVslaJDXa7HY899hh6e3vBsiwe\neughLF++HACwb98+/N3f/R2ampoSPMrUQ+i+Ll68GI899hjOnz+PzMxMvPzyy8jOzk70UFMOoXtr\nMBjwy1/+EhqNBnq9Hi+++KJ6byPE4XDgtttuw0MPPYQFCxbg0UcfBcdxMJvN2Lx5M2iaTvQQU5Lg\n+/rjH/8YIyMj0Gg02Lx5s6Q0dCSoJ+QUZM+ePZgxYwbeeecdvPTSS/jFL34BAGBZFr/73e8UnyRX\nC0L39d1330Vubi62b9+OW2+9FYcPH070MFMSoXv7/PPP49lnn8Xbb7+N2bNnq73To+C3v/2tbzPz\n8ssvo7a2Flu3bsWkSZOwffv2BI8udfG/ry+99BLWrVuHd955B6tWrcIbb7yh+PXUE3IKcuutt/r+\nv6OjA+PGjQMAvPrqq6itrcXmzZsTNbSURui+7tmzB//4j/8IAFi/fn2ihpbyCN1brVaL/v5+AMDA\nwPgkrx0AAANeSURBVACmTp2aqOGlNK2trWhpacGyZcsAAF9++SWefvppAMDy5cuxZcsW1NbWJnCE\nqUnwfd20aRMYhgEA5Obm4uTJk4pfUzXIKcxdd92Fzs5OvPrqqzh79iwaGxvx/e9/XzXIUeJ/X//5\nn/8Zn332GTZv3oz8/Hxs2rQJOTk5iR5iyuJ/b7VaLTZs2ICsrCxkZ2fjkUceSfTwUpIXXngBP/3p\nT7Fjxw4A3vAA76LOy8tTe9JHSPB91ev1AACO47B161Z873vfU/yaqkFOYf70pz/h1KlT+OEPf4jC\nwkI88cQTiR5SWuB/X91uN6ZMmYKHH34Yv/nNb/Daa6/hRz/6UaKHmLL431uTyYR/+7d/w5w5c/DC\nCy9g69atuOeeexI9xJRix44dqKqqwoQJEwR/ryojR4bYfeU4Do8++ijmz5+PBQsWKH5d1SCnICdO\nnEBeXh4KCwtxzTXXYHh4GC0tLb7GHl1dXdiwYQPeeeedBI80tQi+rxzHgSRJzJs3DwCwePFi/PrX\nv07wKFMToXv75ZdfYs6cOQCAhQsX4oMPPkjwKFOPvXv34uLFi9i7dy86OztB0zT0ej0cDgd0Oh0u\nX76MgoKCRA8z5RC6r+PHj8eOHTswadIkPPzwwzG5rmqQU5DDhw+jra0NP/nJT9DT0wO3243du3eD\nJL05eitWrFCNcQQE31ebzYa77roL+/btw+23346TJ09iypQpiR5mSiJ0b8vKytDS0oLS0lIcP34c\nkyZNSvQwU46XXnrJ9/+//vWvUVxcjPr6euzcuRPf+MY38NFHH2HJkiUJHGFqInRfe3p6oNVqfTkl\nsUDt9pSCOBwO/OQnP0FHRwccDgcefvhhrFixwvf7FStWYPfu3QkcYWoidF8XLFiAH/3oR+ju7oZe\nr8cLL7yA/Pz8RA815RC6tzk5OXjxxReh1WqRnZ2N5557DllZWYkeasrCG47FixfjRz/6EViWRVFR\nEZ5//nlotdpEDy9l4e/ru+++C5ZlYTAYAADTpk3DU089pei1VIOsoqKioqKSBKh1yCoqKioqKkmA\napBVVFRUVFSSANUgq6ioqKioJAGqQVZRUVFRUUkCVIOsoqKioqKSBKgGWUVFRUVFJQlQDbKKioqK\nikoSoBpkFRUVFRWVJOD/A0tETZB6m7PqAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6N0p91k2iFCP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Try creating some synthetic features that do a better job with latitude.**\n",
+        "\n",
+        "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n",
+        "\n",
+        "Or you could break the space into 10 different buckets.  `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n",
+        "\n",
+        "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n",
+        "\n",
+        "What's the best validation performance you can get?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wduJ2B28yMFl",
+        "colab_type": "code",
+        "cellView": "form",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n",
+        "#\n",
+        "def 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": "pZa8miwu6_tQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PzABdyjq7IZU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n",
+        "\n",
+        "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xdVF8siZ7Lup",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def select_and_transform_features(source_df):\n",
+        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "  return selected_examples\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U4iAdY6t7Pkh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "2b7cd8e1-0c4f-4878-e96c-e344c883c465"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=10,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 228.71\n",
+            "  period 01 : 218.47\n",
+            "  period 02 : 208.31\n",
+            "  period 03 : 198.26\n",
+            "  period 04 : 188.32\n",
+            "  period 05 : 178.51\n",
+            "  period 06 : 168.86\n",
+            "  period 07 : 159.41\n",
+            "  period 08 : 150.18\n",
+            "  period 09 : 141.21\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xd8VFX+//HX1CSTZCZ1kkwC6STU\ngBBaKNIDokiRBYTFsm5RVlfdteyq393Vtev+1F3LWrGjLFUFFBEBlRJKaCmUQAIJaZPey/39Ec2C\nQJiBCdwkn+fj4eNhMuXcyfuecHLO596jURRFQQghhBCiA9Fe6QMQQgghhHCWDGCEEEII0eHIAEYI\nIYQQHY4MYIQQQgjR4cgARgghhBAdjgxghBBCCNHh6K/0AQihZnFxcXTv3h2dTgdAU1MTiYmJPPTQ\nQ5hMpot+308++YTZs2ef9f1ly5bx4IMP8uqrrzJmzJjW79fW1jJ8+HAmTpzIk08+edHtOio7O5vH\nH3+crKwsADw8PFi0aBHjx49v97ad8fLLL5OdnX3Wz2Tbtm3ceuuthIWFnfWatWvXXq7DuyQnTpxg\n3LhxREZGAqAoCgEBAfzlL3+hV69eTr3Xc889h81mY+7cuQ6/ZuXKlSxdupT33nvPqbaEuFxkACPE\nBbz33nsEBwcDUF9fz913381rr73G3XfffVHvV1hYyBtvvHHOAQxASEgIn3322RkDmG+++Qaz2XxR\n7V2MP/7xj0ybNo1XX30VgNTUVBYuXMiaNWsICQm5bMdxKUJCQjrMYOV8dDrdGZ/hiy++4I477mDd\nunUYjUaH3+fee+9tj8MT4oqSJSQhnGA0Ghk5ciRpaWkA1NXV8cgjjzBp0iQmT57Mk08+SVNTEwDp\n6enMmTOH5ORkpk2bxubNmwGYM2cOubm5JCcnU19ff1YbV111Fdu2baOmpqb1e1988QVJSUmtX9fX\n1/PYY48xadIkxo4d2zrQANi9ezczZswgOTmZKVOm8P333wMtf9GPGDGCd999l2uvvZaRI0fyxRdf\nnPNzZmZmkpCQ0Pp1QkIC69atax3I/etf/2L06NFcf/31/Oc//2Hs2LEAPPDAA7z88sutrzv96wsd\n1+OPP878+fMB2LlzJzNnzmTChAnMnj2bnJwcoGUm6g9/+ANjxoxh/vz5nDp16gKJnduyZctYtGgR\nCxcu5Omnn2bbtm3MmTOHu+66q/Uf+zVr1jB16lSSk5P55S9/SXZ2NgAvvfQSDz30ELNmzeKdd945\n433vuusu3nrrrdav09LSGDFiBM3Nzfzzn/9k0qRJTJo0iV/+8pfk5+c7fdxTpkyhtraWo0ePArBk\nyRKSk5MZO3Ys99xzD7W1tUDLz/2JJ57g2muvZc2aNWfkcL7zsrm5mb///e9cffXVzJo1i/T09NZ2\nt2/fzvTp05kyZQqTJ09mzZo1Th+7EC6nCCHOq0ePHkpeXl7r16WlpcqNN96ovPzyy4qiKMprr72m\n3HbbbUpDQ4NSU1OjzJw5U1mxYoXS1NSkTJ48WVm9erWiKIqyd+9eJTExUamoqFC2bt2qjB8//pzt\n/fe//1Xuv/9+5Y9//GPraysqKpRx48Ypn376qXL//fcriqIo//rXv5SFCxcqdXV1SlVVlXL99dcr\nGzZsUBRFUaZOnap89tlniqIoyvLly1vbysnJUXr16qW89957iqIoyhdffKFMmDDhnMfx+9//Xhkz\nZoyyePFi5fDhw2c8lpGRoQwaNEgpKChQGhoalN/97nfKmDFjFEVRlPvvv1/597//3frc079u67h6\n9+6tLFu2rPXzJiYmKlu2bFEURVFWr16tTJ8+XVEURXn//feVG2+8UWloaFDsdrsyZsyY1p/J6dr6\nGf/0c+7fv7+SlZXV+vy+ffsq33//vaIoinLy5Ell4MCByrFjxxRFUZQ333xTWbhwoaIoivLiiy8q\nI0aMUIqLi896388//1y58cYbW79+4YUXlEcffVTJzMxUJk6cqNTX1yuKoijvvvuusnz58vMe308/\nl549e571/cTEROXIkSPKjh07lGHDhimnTp1SFEVRHn74YeXJJ59UFKXl537ttdcqtbW1rV//+9//\nbvO83LhxozJx4kSlsrJSqampUWbNmqXMnz9fURRFmTFjhrJt2zZFURQlKytLueeee9o8diEuB5mB\nEeICFixYQHJyMuPGjWPcuHEMHTqU2267DYCNGzcye/Zs9Ho97u7uXHvttXz33XecOHGCoqIirrnm\nGgD69u2LzWZj3759DrV5zTXX8NlnnwGwfv16xowZg1b7v+76zTffMG/ePIxGIyaTiWnTpvHll18C\nsGLFCiZPngzAwIEDW2cvABobG5kxYwYAvXv3Jjc395ztP/PMM9x4442sXr2aqVOnMnbsWD766COg\nZXYkMTGRwMBA9Ho9U6dOdegztXVcDQ0NTJgwofX9g4KCWmecpk6dSnZ2Nrm5uaSkpDBhwgT0ej2+\nvr5nLLP9XF5eHsnJyWf8d3qtTEREBBEREa1fu7u7M2zYMAC+++47hgwZQnh4OAA33HAD27Zto7Gx\nEWiZkfLz8zurzauvvpqDBw9SWloKwFdffUVycjJmsxm73c7q1aspKytjwYIFXH/99Q793H6iKApL\nliwhKCiIiIgINmzYwJQpUwgKCgJg7ty5recAwLBhw3BzczvjPdo6L3fs2MHo0aPx9PTE3d29NSsA\nf39/VqxYwZEjR4iIiOC5555z6tiFaA9SAyPEBfxUA2O321uXP/T6lq5jt9uxWCytz7VYLBQXF2O3\n2/H29kaj0bQ+9tM/YgEBARdsMykpiYceeojS0lI+//xzbr/99taCWoCKigqeeOIJnn/+eaBlSalf\nv34ArF69mnfffZeqqiqam5tRTtvuTKfTtRYfa7Vampubz9m+m5sbt956K7feeivl5eWsXbuWxx9/\nnLCwMMrKys6ox/H397/g53HkuLy8vAAoLy8nJyeH5OTk1seNRiN2u52ysjK8vb1bv282m6mqqjpn\nexeqgTk9t59/XVJScsZn9Pb2RlEUSkpKzvnan5hMJoYPH87GjRsZOHAg5eXlDBw4EI1Gw0svvcRb\nb73Fo48+SmJiIn/7298uWE/U1NTU+nNQFIWYmBhefvlltFotFRUVfPXVV2zZsqX18YaGhvN+PqDN\n87KsrAyr1XrG93/y+OOP88orr3DzzTfj7u7OPffcc0Y+QlwJMoARwkF+fn4sWLCAZ555hldeeQWA\ngICA1r+2AUpLSwkICMDf35+ysjIURWn9x6K0tNThf+wNBgNjxoxhxYoVHD9+nAEDBpwxgLFardxy\nyy1nzUDk5+fz0EMP8emnn9KzZ0+OHTvGpEmTnPqcdrudtLS01hkQs9nM7Nmz2bx5M5mZmXh7e1NR\nUXHG83/y80FRWVmZ08dltVqJiopi2bJlZz1mNpvP27Yr+fv7s3v37tavy8rK0Gq1+Pr6XvC1kyZN\n4quvvqKkpIRJkya15j906FCGDh1KdXU1Tz31FM8+++wFZzJ+XsR7OqvVyvTp07n//vud+lznOy/b\n+tkGBATw8MMP8/DDD7NlyxZ+//vfM3LkSDw9PR1uWwhXkyUkIZxw8803s3v3brZv3w60LBksXbqU\npqYmqqurWblyJaNHjyYsLIzg4ODWItldu3ZRVFREv3790Ov1VFdXty5HnM8111zD66+/fs5Ll8eN\nG8enn35KU1MTiqLw8ssvs2nTJux2OyaTiaioKBobG1myZAnAeWcpzqW2tpY777yztbgT4Pjx46Sm\npjJo0CAGDBhASkoKdrudxsZGVqxY0fq8wMDA1uLPnJwcdu3aBeDUcSUkJFBYWEhqamrr+/zpT39C\nURT69+/Phg0baGpqwm63s2nTJoc/lzOSkpJISUlpXeb6+OOPSUpKap15a8uYMWPYvXs369evb12G\n2bJlC3/7299obm7GZDIRHx9/xizIxRg7dixffvll60Bj/fr1/Oc//2nzNW2dlwMGDGDLli3U1NRQ\nU1PTOnBqaGhgwYIFFBQUAC1Lj3q9/owlTSGuBJmBEcIJXl5e/PrXv+app55i6dKlLFiwgJycHK65\n5ho0Gg3JyclMnjwZjUbD888/z//93//xr3/9Cw8PD1544QVMJhNxcXFYLBaSkpJYvnw5NpvtnG0N\nHjwYjUbDlClTznps3rx5nDhxgmuuuQZFUejTpw8LFy7EZDIxatQoJk2ahL+/Pw888AC7du1iwYIF\nvPjiiw59RpvNxiuvvMKLL77IY489hqIoeHl58eCDD7ZemfSLX/yC6dOn4+vry8SJEzl06BAAs2fP\nZtGiRUycOJFevXq1zrLEx8c7fFzu7u68+OKLPProo1RVVWEwGLjrrrvQaDTMnj2blJQUxo8fj81m\nY/z48WfMGpzupxqYn3v66acv+DMIDg7mscce4/bbb6ehoYGwsDAeffRRh35+Xl5e9O7dm4yMDPr3\n7w9AYmIin3/+OZMmTcJoNOLn58fjjz8OwH333dd6JZEzevfuzW9/+1sWLFhAc3Mz/v7+/O1vf2vz\nNW2dl2PGjGHjxo0kJycTEBDA6NGjSUlJwWAwMGvWLG666SagZZbtoYcewsPDw6njFcLVNMrpC9FC\nCOGklJQU7rvvPjZs2HClD0UI0YXIHKAQQgghOhwZwAghhBCiw5ElJCGEEEJ0ODIDI4QQQogORwYw\nQgghhOhwOuRl1IWF575s0hV8fU2UlFS32/uLiyfZqJPkol6SjXpJNo4JDPQ+72MyA/Mzer3uSh+C\nOA/JRp0kF/WSbNRLsrl0MoARQgghRIcjAxghhBBCdDgygBFCCCFEhyMDGCGEEEJ0ODKAEUIIIUSH\nIwMYIYQQQnQ4MoARQgghRIcjAxghhBCik9m48WuHnvfCC8+Rm3vyvI8/8MA9rjokl5MBjBBCCNGJ\n5OXlsn79Ooeee9dd92KzhZ738SeffN5Vh+VyHXIrASGEEEKc2/PPP0Va2gFGjkxk4sTJ5OXl8v/+\n38s88cTfKSwsoKamhltu+TVJSSNZtOjX3HPPfXzzzddUVVWSnX2ckydPcOed9zJsWBLXXDOOzz//\nmkWLfk1i4hB27UqhtLSUp576JwEBAfz97w9z6lQeffv2Y8OG9Sxf/sVl+5wygBFCCCHayScbDrMj\nveCs7+t0GpqalIt6z8R4K7PHxpz38blzF7Bs2SdERkaTnX2Ml19+g5ISO4MHD2Xy5KmcPHmChx9+\ngKSkkWe8rqAgn2effZGtW79n5cr/MmxY0hmPe3p68sILr/DKKy+xadMGbLYw6uvr+M9/3uG77zbz\nyScfXdTnuVgygDlNUWkNx4uq6e7vgUajudKHI4QQQlySnj17A+DtbSYt7QCrVi1Do9FSXl521nP7\n9esPgNVqpbKy8qzHExIGtD5eVlbG8eNZ9O2bAMCwYUnodJd3fycZwJzmsx+OsSk1j57hvixMjsPq\na7rShySEEKIDmz025pyzJYGB3hQWVrR7+waDAYCvvlpLeXk5//73G5SXl/OrXy0467mnD0AU5ezZ\noZ8/rigKWm3L9zQazWX/w1+KeE9z/cgoEnsFkXa8hIff3M6arcdpam6+0oclhBBCOEyr1dLU1HTG\n90pLSwkJsaHVavn22w00NDRccjuhoWFkZBwEYPv2rWe12d5kAHMaHy83Hr5lCL+d1hsPo45PNx7h\n0XdSOHaq/EofmhBCCOGQ8PBIMjLSqar63zLQ1VeP5fvvN3PXXb/Dw8MDq9XK22+/fkntDB8+kqqq\nKn73u1tJTd2N2Wy51EN3ikY51zyRyrXntNtP03qVNQ188s1htuzNQ6OBiYnduH5EFG7Gy7vGJ/7n\nck25CudILuol2ahXZ8imvLyMXbtSuPrqcRQWFnDXXb/jww//69I2AgO9z/uY1MCch5eHgVum9GRY\nryAWr81g3fYcdmYU8svkOPpE+l/pwxNCCCGuKJPJkw0b1vPhh++hKM38/veX96Z3MgPzM+caFdc3\nNLHyuyzWbcuhWVEY1juYOeNi8DYZ2+04xNk6w18snZHkol6SjXpJNo6RGZhLZDTouOHqGIb0DOLt\nNen8cOAU+44WM3d8LEN7Bckl10IIIcRlJkW8Tuge5M1DvxzIL8bGUN/YxOurD/LPT1MpKq250ocm\nhBBCdCkygHGSTqtl0uDuPHrrEHpH+rH/qJ2H3tzGl9uzaW7ucKtxQgghRIckA5iLFOjjwT2zE7ht\nai+Meh0fbzjMP95LITtf1jSFEEKI9iYDmEug0WgY1ieYx24bwrDeQWTlVfD3d1JYuvEI9Q2X94Y+\nQgghhDNmzbqW6upq3nvvHfbv33vGY9XV1cyadW2br9+48WsAvvhiNd9++027Hef5SBGvC5hNRm67\ntjfDegezeG0GX2w9TkpGAQuT4+kZ7nulD08IIYQ4rwULbnL6NXl5uaxfv46rrx7HlCltD3Taiwxg\nXKhPlD+P/WoIyzcf5auUHJ75aDcj+oXwi7ExeLobrvThCSGE6AJuueVGHn/8OYKDgzl1Ko8HH7yX\nwEArNTU11NbWcvfdf6JXrz6tz//HP/7K1VePo3//AfzlL/dRX1/furEjwJdfrmHp0iXodFoiIqK5\n//6/8PzzT5GWdoC3336d5uZmfHx8mDnzF7z88gvs25dKY2MTM2fOJjn5GhYt+jWJiUPYtSuF0tJS\nnnrqnwQHB1/y55QBjIu5GXXMGRfLkF5BvLMmnS1789h7pJh542NJjLfKJddCCNGFLDv8GbsL9p31\nfZ1WQ9NFXvgxwNqXGTFTz/v4qFFj+O67TcycOZvNm79l1KgxREfHMmrU1ezcuYMPPljMP/7xzFmv\nW7duDVFR0dx55718/fWXrF+/DoCamhqee+4lvL29ueOO2zhy5DBz5y5g2bJPuPnm23jzzdcA2LNn\nF0ePHuGVV96ipqaGhQvnMGrU1QB4enrywguv8MorL7Fp0wZmz553UZ/9dFID004iQ8w8vHAQs66O\npqaukVdXHuCl/+7DXl57pQ9NCCFEJ9YygNkMwJYt3zJixGi+/fZrfve7W3nllZcoKys75+uOHTtK\nnz4JAAwYMLD1+2azmQcfvJdFi37N8eNZlJWVnvP16ekH6d//KgA8PDyIiIgiJycHgISEAQBYrVYq\nKyvP+XpnyQzMaSobqqgsKcULH5e8n16nZcrQcAbGBbJ4TTp7DheRll3CrNHRjLkqFK3MxgghRKc2\nI2bqOWdL2vNOvFFR0RQXF5Kff4qKigo2b95IQICVhx9+lPT0g/zrX//vnK9TFNBqW/5d+um2IA0N\nDTz//NO8886H+PsHcN99fzhvuxqNhtPv7d/Y2ND6fjrd//YRdNUGADIDc5qVh7/g/i8f5+0DH1JR\n75oRIkCQr4k/zR3AzVPi0Ws1fPBVJk+8v5OTha5rQwghhPjJsGEj+M9/XmbkyNGUlZUSGhoGwLff\nfkNjY+M5X9O9ezjp6WkA7NqVAkB1dRU6nQ5//wDy80+Rnp5GY2MjWq2WpqYzr7aNj+/N7t07f3xd\nNSdPniAsrHt7fUQZwJxuQvjVRPuFk5K/h0e3PssPeSkuGylqNBpG9rPx2G1DGdzTypGT5fz17R2s\n2HyUhsZml7QhhBBCAIwePab1KqHk5GtYsuQD7r77Dnr37kNxcTGff77qrNckJ1/DgQP7uOuu35GT\ncxyNRoPF4kNi4hB+9atf8vbbrzNv3gJefPF5wsMjychI58UXn2t9fUJCf+Li4rnjjtu4++47+O1v\nF+Hh4dFun1E2c/wZf39Plu5Zx6qja6lvqqeHbwxz42ZgNQW4tJ09h4t4b10GJRV1hPibWJgcT49u\nrlm66qxk8zN1klzUS7JRL8nGMW1t5qj761//+tfLdyiuUV1d327v7eXljlUfzODgARRWF5Fmz+T7\n3G1o0RJh7o5W45pJq2A/E6MSbNTVN7HvaDFb9uVRVlVPbJgPBr1MjJ2Lp6dbu2YvLo7kol6SjXpJ\nNo7x9HQ772MyA/Mzp4+KFUVhV8FePj20kor6SkK9QpgXP5MIs2vX9A6fLGPxmnROFlXh42Vk/sQ4\nruoR6NI2OgP5i0WdJBf1kmzUS7JxjMzAOOH0UbFGo8HmFczwkESqGqo5aM/gh9wdVDfUEGWJQK91\nzUVcfmZ3RiXY0Ok07M+ys/VgPicKKokN88HDTS4U+4n8xaJOkot6STbqJdk4pq0ZGBnA/My5TiqD\nzkC/wF7E+kRxtOwYB+zp7Di1G6spAKvJNTMlWq2GuO6+DIq3klNQyf4sO5v35uHpoad7kLfcAA/p\n8GoluaiXZKNeko1jZADjhLZOKn8PP5JsgwE4aM9gR/5u8qsKiPaJxE13/h+yM7xNRpL6huDj5cbB\nY3Z2ZhSSnl1KdKgZb5PRJW10VNLh1UlyUS/JRr0kG8fIAMYJFzqpdFodcX4xJAT2JqcilzR7Jj/k\n7sDL4EWYl80lMyUajYaIEDPD+4RQVFbL/iw7m1LzQAPRNnPrjYG6Gunw6iS5qJdko16SjWOuWBHv\n008/zc6dO2lsbOQ3v/kNffv25cEHH6SxsRG9Xs8zzzxDYGAgq1atYvHixWi1WmbPns0NN9zQ5vte\nriLeC2lWmtl08gdWHVlDXVM9sT5RzIuf6bJlpZ/szCjg/a8yKausJzTQk5smxxNts7i0jY5Ait7U\nSXJRL8lGvSQbx7RVxNtuA5itW7fy5ptv8vrrr1NSUsL06dMZMmQIo0ePZsqUKXzwwQecPHmSRYsW\nMX36dJYuXYrBYGDWrFm8//77+Pic/54oahnA/KSktpQlmcvZV5SGXqtncsR4xncf5bIiX4Dq2gaW\nbjzCxj25aICxA8OYMSqqSxX5SodXJ8lFvSQb9ZJsHHNFrkIKCQlhwoQJGAwGjEYjr732Gm+//TZx\ncXFotVpOnDhBZmYmFouF4uJirr32WvR6Penp6bi5uREZGXne976SS0jn4qF3Z6C1PyFewRwqOcK+\nooPsLTxAN+9QfN1dM1Ni0OtIiAkgvrsPh0+Ws+9oMVsPniLI10Swn8klbaidTLmqk+SiXpKNekk2\njmlrCand/nzX6XSYTC3/sC5dupRRo0a1ft3U1MSHH37IHXfcQVFREX5+fq2v8/Pzo7CwsM339vU1\nodfr2nzOpWhrxNeWSdYkkmL780HqCr4+uoXndv6bSbGjmdt3Gh4Gd5cd2+B+oXyyPpOlGw7xwtK9\njOofyq+u74Ovt2vaULOLzUa0L8lFvSQb9ZJsLk27rz+sX7+epUuX8tZbbwEtg5f77ruPoUOHMmzY\nMFavXn3G8x1Z0SopqW6XYwXXTOvNiLiOvpY+fJTxX9Ye2sjW7N3MiZtO34BeLjpKmDQojN7dfXhn\nbTqb9pxkZ3o+s8fGMKJvSKe95FqmXNVJclEvyUa9JBvHtDXIa9d71m/evJlXX32V119/HW/vloN4\n8MEHCQ8PZ9GiRQBYrVaKiopaX1NQUIDVam3Pw7osYn2jeDDxD0yOGE9FfSWv7n2HN/a/T1md607Y\nMKsXf54/kHnjY2lsVnj7i3Se/XgPBe04wBNCCCHUoN0GMBUVFTz99NO89tprrQW5q1atwmAwcOed\nd7Y+LyEhgX379lFeXk5VVRW7du1i0KBB7XVYl5VBZ2Bq1EQeSLyLSHM4uwv28ui2Z/nu5DaaFdfs\nQK3Vahg/qBuP3TqEftH+pB0v4eE3t7Nm63GammWXayGEEJ1Tu12FtGTJEl566aUzinFzc3Mxm814\neXkBEB0dzV//+lfWrl3Lm2++iUajYf78+Vx33XVtvrfarkJyRLPSzJaTW1l5ZA21TXXE+EQyL24m\nQZ6um21SFIUd6QV8+FUm5dUNdLd6sXByPJEhZpe1cSXJlKs6SS7qJdmol2TjmCtyGXV76ogDmJ+U\n1JbySeZK9hYdQK/RkRwxjgnhV7v0kuvKmgY+2XCYLfvy0GhgwqBuXD8yEndjx77kWjq8Okku6iXZ\nqJdk4xgZwDjhcp1Uewr28UnmCsrqKwjxDGJe/CyiLOEubSPtmJ3F6zIoKKnB3+zOgkk96Bcd4NI2\nLifp8OokuaiXZKNeko1jZDdqJ1yua/ODPYMYFjKYmsYaDtoz2JqXQkV9FdE+ERhcNBsT6OPBqAQb\nCrA/y84PB/LJK64itpsP7sb2uwy9vch9E9RJclEvyUa9JBvHXLGtBNpLZ5iBOd3h0iw+TP8v+dUF\n+LhZmN3jehICe7u0jRMFlbyzNp2jueWY3PTMHhvDyH4d65Jr+YtFnSQX9ZJs1EuycYzMwDjhSoyK\n/dx9GW4bjFaj5WBxBin5u8mtPEWMTyTuetfscm32NDKibwjeJiMHj9lJySgko4Ptci1/saiT5KJe\nko16STaOkd2onXClTiqdRksP32gGWPtyojKPNHsm3+dtx6T3IMzbdbtcR9nMDOsdTEFJDQeO2dmU\nmosCRIdaVL/LtXR4dZJc1EuyUS/JxjEygHHClT6pvIxeDA0ZiMXNm3T7YfYU7iOz5AiRlnC8jJ4u\nacPDTc/gnla6Wb1Izy4l9XAxOzML6W71xs+s3u0IrnQ24twkF/WSbNRLsnGMDGCcoIaTSqPREG7u\nxpCQq7DXlrTMxuRuoxmFSEs4Os2l339Qo9FgC/BkVD8bNXWN7DtazOa9eZRX1RMb5oNB3643ab4o\nashGnE1yUS/JRr0kG8dIEa8T1FhYlVq4nyUZKyirLyfYZGVe/CyifSJc2sahE6UsXptBblEVFi8j\n8yf04Koegaoq8lVjNkJyUTPJRr0kG8dIEa8T1DgqDva0MtyWSG1jHQftmfyQt4OK+sofL7k2uKQN\nf7M7oxJs6HUa9mfZ2XawgJyCSmLDLHi4qeMGeGrMRkguaibZqJdk4xhZQnKCWk8qg9ZAn4CexPvF\nklWezcHidLbl7cLfw49gF21HoNVqiOvuy6B4KycKqziQ1VLk627UExHsfcVnY9SaTVcnuaiXZKNe\nko1jZADjBLWfVL7uPiTZBqPX6EizZ5CSv4eTlXlE+0TgrndNAa63ycjwvsH4md05eKyEXZmFHMiy\nExVixux55S65Vns2XZXkol6SjXpJNo6RAYwTOsJJpdVoifWNYoC1Hyd/uuQ6dwceene6eYe67JLr\n8GBvkvqFUFJRy/4fZ2MaGpt2gVvJAAAgAElEQVSJCbWg013+It+OkE1XJLmol2SjXpKNY2QA44SO\ndFJ5GT0ZEjIQXzcL6SWH2FO4n4ySw0RauuNt9HJJG+5GHYPirUSGeJOZU0rqkWK2pxcQGuBJoI+H\nS9pwVEfKpiuRXNRLslEvycYxMoBxQkc7qTQaDd3NYQwJHth6yfV3udtpVppddsk1QJCfiVEJNhoa\nm9l3tJjv9p+iqKyGHt18MBouz75KHS2brkJyUS/JRr0kG8fIZdRO6OiXtu0tPMCSzBWU1pURZLIy\nN24Gsb5RLm0jK6+cxWvSyS6oxMvDwNzxsQztFdTuRb4dPZvOSnJRL8lGvSQbx8hl1E7o6KPiIE8r\nw22DqWuq42BxBltPpVBaW0aMTwQGnWsuufb1dmNkQggeRj0HsuzsSC/gaG45MWEWPN1d08a5dPRs\nOivJRb0kG/WSbBwjS0hO6AwnlUGrp7d/PD39enCsPIeD9paBjI+bhRBP18yUaDUaYsIsDOkVxKni\n6pYi3z256HVaIm3eaNthNqYzZNMZSS7qJdmol2TjGBnAOKEznVQ/XXJt1BpJs2eysyCVYxU5RFsi\nMBlcU4Dr6W5gaO8ggv1MpGWXsPtQEamHiggP9sbX2zU7abe21Ymy6UwkF/WSbNRLsnGMDGCc0NlO\nKq1GS7RPJAOt/TlVVfBjke829Fo94d7d0LpoX6Uwqxcj+9kor65nf5adzXtzqalrJCbMgt5Fl1x3\ntmw6C8lFvSQb9ZJsHCNFvE7ozIVViqKwI383/z20msqGKrp52ZgbP5NwczeXtpN2zM7idRkUlNTg\nb3ZnwaQe9IsOuOT37czZdGSSi3pJNuol2ThGinid0JlHxRqNhlCvEIbZEqmsr+KgPYPvc3dQ3VhD\nlCUCvdY1ex4F+ngwKsGGAuzPsvPDgXzyiquI7eaDu/HiL7nuzNl0ZJKLekk26iXZOEZmYJzQlUbF\nGfbDfJyxjIKaInzdfPhF3PX0Dejl0jZOFFTyztp0juaWY3LTM3tsDCP7hVxUIXFXyqYjkVzUS7JR\nL8nGMTID44SuNCoO8PAjyTYYNBrS7JnsyN9NXuUpon0icde7pgDX7GlkRN8QvE1GDh6zk5JRSEZ2\nKdGhZrxNzu2r1JWy6UgkF/WSbNRLsnGMFPE6oaudVDqtjjjfGBIC+3CyMrf1Tr4mg2v3VYqymRnW\nO5jC0prWfZUUIDrUglbrWBtdLZuOQnJRL8lGvSQbx8gAxgld9aTyNnoxNGQQFjczGa37Kh0iwuy6\nfZU83PQM6RVEWKAX6dklpB4uZmdmId2t3viZL7yTdlfNRu0kF/WSbNRLsnGMDGCc0JVPKo1GQ/hP\n+yrVlf64y/V2GpsbibKEo9O6Zs8jW4Ano/rZqKlrZN/RYjbvzaO8qp7YMB8M+vNfct2Vs1EzyUW9\nJBv1kmwcI0W8TpDCqv/ZV3SQJRkrKKkrJdDDn7lxM4nzi3FpG4dOlLJ4bQa5RVVYvIzMn9CDq3oE\nnnPpSrJRJ8lFvSQb9ZJsHCNFvE6QUfH/BJkCGW4bTGNzIweLM9h2aifFNXaiLZEYdc4V4J6Pv9md\nUQk29DoN+7PsbDtYQE5BJbFhFjzczrysW7JRJ8lFvSQb9ZJsHCNLSE6Qk+pMeq2eXv5x9PHvSXZ5\nDgftmWzNS8Fs9CbU6+Iuh/45rVZDXHdfBsVbOVlY1Vrk627UExHs3dqGZKNOkot6STbqJdk4RgYw\nTpCT6twsbmaGhSTirncn3Z7JrsK9HC07TpQlAk+DySVteJuMJPUNxt/sTtrxEnZmFnIgy05UiBmz\np1GyUSnJRb0kG/WSbBwjAxgnyEl1flqNlihLBIlBA8ivKWzdV0mDhkhzd5ftqxQe7E1S3xBKKmpb\nZ2MaGpvpGxtIXW2DCz6JcCXpM+ol2aiXZOMYKeJ1ghRWOUZRFHYVpPLpoVVU1Fdi8wxmXvxMIi3h\nLm1n75Ei3luXQXF5HSEBnswbH0vvCD+XtiEujfQZ9ZJs1EuycYwU8TpBRsWO0Wg02LyCGR6SSHVj\nNQftGfyQl0JFfSXRPhEYtAaXtBPkZ2JUgo2Gxmb2HSni+/2nKCipJrabD24G11zWLS6N9Bn1kmzU\nS7JxjMzAOEFGxRfncGkWH6X/l1PVBViMZmb3mEZCYB+XFPn+pKyuif/30S6On6rA071lX6URfV1T\nSCwunvQZ9ZJs1EuycYzMwDhBRsUXx8/dl+G2weg0WtLsmaQU7CGnMpdoSwQe+gvfZdcR3UIsDIzx\nx9PdwIHjJaSkX/y+SsJ1pM+ol2SjXpKNY6SI1wlyUl08nUZLrG80V1n7kVt1qrXI16gzEm4Ou+SZ\nEk9PN2pq6okOtTD8Z/sqNTUrRIea0WkvvZBYOEf6jHpJNuol2ThGlpCcINN6rqEoCj/kpbD88GdU\nN9YQ7t2NefEzCfO2XfR7niubXZmFfPBVJiUVdQT5mVg4KY74cN9LPXzhBOkz6iXZqJdk4xhZQnKC\njIpdQ6PR0M07lGEhiZTWlbXsq5S3nbqmOqItERe1r9K5sgnx92RUgo36hib2Hy3mu/2nKC6rJbab\nD0Yp8r0spM+ol2SjXpKNY2QGxgkyKm4fB4sz+DhjOcW1dvzd/ZgTN51e/nFOvceFssnKK2fxmnSy\nCyrx8jDwi7ExDO8TLEW+7Uz6jHpJNuol2ThGZmCcIKPi9hFoCiDJNphmpZmD9gy2n9pFQXUhMT6R\nuDm4r9KFsvH1dmNkQggeRj0HjtlJSS/k0IkyYkIteHm45rJucTbpM+ol2aiXZOMYKeJ1gpxU7Uen\n1RHvF0vfgF7kVJxsWVbK3Y6XwZMwL9sFZ0ocyUar0RATZmFo7yDyS2o4kGXn2z25gEJ0qAWtVmZj\nXE36jHpJNuol2ThGBjBOkJOq/VncvBlmS8TTYCK95BC7C/dxqPQokebueBk9z/s6Z7IxuRsY0iuI\n0EAvMrJL2HO4mJSMArpZvfC3uOaybtFC+ox6STbqJdk4RgYwTpCT6vLQaDREWrozOPgqCmuKWy+5\nVlCIsISjO8e+Ss5mo9FoCA1oKfKtqW9k/1E7W/blUVLxY5GvXop8XUH6jHpJNuol2ThGinidIIVV\nl5+iKKQW7ueTzJWU1ZcTZLIyL34mMT6RZzzvUrM5fLKMd9emc6KwCrPJwJzxsQzpGSRFvpdI+ox6\nSTbqJdk4Rop4nSCj4stPo9EQ7BnEcFsitY31pNkz+CFvB2V1ZURbIjDoWgpwLzUbP7M7IxNsuBl1\nHMiysz2tgKO55USHWfB0lyLfiyV9Rr0kG/WSbBwjS0hOkJPqyjFoDfQJiKenXw+Oledw0J7B1lMp\n+LpZCPEMckk2Wq2G2DAfBvcKIq+4urXIV6uBKJtZinwvgvQZ9ZJs1EuycYwsITlBpvXUoam5ia+z\nN/HFsa9oaG6kl38ctw9bgKbadXseKYrC9rQCPlqfSXl1A6GBnixMjicm1OKyNroC6TPqJdmol2Tj\nmLaWkGQA8zNyUqlLYXUxH2csI73kEG46I5MjxjO228iLupPv+VTVNvDpN0fYlJqLBrh6QCgzR0dh\nkmUlh0ifUS/JRr0kG8fIAMYJclKpj6IobD+1i+VHP6eirpJQrxDmxs0g0hLu0nYyc0p5d10GuUVV\nWDyNzJvQg0FxgVLkewHSZ9RLslEvycYxMoBxgpxU6uVu1vDmtk/4Pm8HGjSMDB3KddHJeOg9XNZG\nY1Mza7Zls/q7YzQ2NdMv2p/5E3oQ4OO6Njob6TPqJdmol2TjGLkKyQlSWKVevmZvok0xxPnGkFV2\nnAP2DLbm7cTXzYcQT9dcDq3Vaojr5sPgXlZyi6painxTc9HrtETavNHKbMxZpM+ol2SjXpKNY+Qq\nJCfISaVeP2Xj5+5Lkm0wBq2eNHsmOwtSOVaRQ5QlApPBNTMlXh4GhvcJxurrQdrxUnYfKiL1UBHh\nwd74ep+/Q3VF0mfUS7JRL8nGMTKAcYKcVOp1ejZajZYYnygGWhPIrypovZOvVqMlwtwd7Tnu5Oss\njUZDN6s3IxNsVFQ3sC/LzubUXCprGogNs2DQX3obnYH0GfWSbNRLsnGMXEbtBFmXVK/zZaMoCjvy\nd/PfQ6upbKjC5hnM3PiZRLm4yDf9eAnvrsvglL0aX2835o3vwcC4QJe20RFJn1EvyUa9JBvHSA2M\nE2RUrF7ny0aj0RDqFcJw22CqG6tbboCXl0JZfcUZd/K9VAE+HoxKsKHTatifVcy2g/lk51cQG2bB\nw03vkjY6Iukz6iXZqJdk45grNgPz9NNPs3PnThobG/nNb35D3759ue+++2hqaiIwMJBnnnkGo9HI\nqlWrWLx4MVqtltmzZ3PDDTe0+b4yA9M1OZrN4dIsPspYxqmqfLyNXtwQex1XWRNcejl0XnEV767N\nICOnFDejjhkjoxg3MKxL3slX+ox6STbqJdk45opcRr1161befPNNXn/9dUpKSpg+fTrDhg1j1KhR\nTJ48meeff57g4GCuv/56pk+fztKlSzEYDMyaNYv3338fHx+f8763DGC6JmeyaWxu5OvsTaw5tp6G\n5kZ6+vVgTtx0Ajz8XXY8iqKwZV8en2w4TFVtI+HB3tyUHE948Pk7XGckfUa9JBv1kmwcc0WWkEJC\nQpgwYQIGgwGj0chrr71GQUEBjzzyCDqdDnd3d1avXo3VaqW4uJhrr70WvV5Peno6bm5uREZGnve9\nZQmpa3Imm5Yi30gGWvuTX134vyJfXFvkGx7kzYh+IZRV1rM/y86m1Fxq6hqJCbOg13WNIl/pM+ol\n2aiXZOOYtpaQ2m3hXqfTYTKZAFi6dCmjRo1iy5YtGI0te9n4+/tTWFhIUVERfn5+ra/z8/OjsLCw\nzff29TWh17vuVvI/19aIT1xZzmYTiDd/7f4HvstOYfHuT1l5dA27ilP59aB5xAVEu+aYgD/f4k9q\nZiH//m8qX+7IYdehIn43ox+Dewe7pA21kz6jXpKNekk2l6bdKw/Xr1/P0qVLeeutt5g4cWLr98+3\ncuXIilZJSbXLju/nZFpPvS4lmzhTPA8NvpcVR9bwXe42Hv76WUbYhjAtejImg8klx2fzdef/Fg7i\nsx+OsWZrNo++tY2BcYHMG9+jU987RvqMekk26iXZOKatQV67znFv3ryZV199lddffx1vb29MJhO1\ntbUA5OfnY7VasVqtFBUVtb6moKAAq9XanocluiiTwcS8+Jncc9XthHgGsSV3G3/f9iwpp3Y7NHB2\nhNGgY8aoaP56cyIxYRZ2ZhTyl9e38vXOEzQ3d7g7FgghhGq12wCmoqKCp59+mtdee621IHf48OGs\nW7cOgC+//JKRI0eSkJDAvn37KC8vp6qqil27djFo0KD2OiwhiPaJ4IHEu5gWNZnaxjrePvgR/059\nk8LqYpe1ERroxQM3XsXC5Di0Gg0ffJXJ4+/vJDtf/uISQghXaLerkJYsWcJLL710RjHuk08+yUMP\nPURdXR02m40nnngCg8HA2rVrefPNN9FoNMyfP5/rrruuzfeWq5C6pvbIpqimmI8zlpNmz8Sg1TM5\nYjzjuo9Cr3Xd6mpZVT0ff32IbQfz0Wo0TBzcjWlJkbgZ26+O63KSPqNeko16STaOkd2onSAnlXq1\nVzaKorCrIJVPD62ior6SEM8g5sTNIMbn/FfCXYz9R4t5d10GRWW1BFjcuXFCDxJiAlzaxpUgfUa9\nJBv1kmwcI3fidYJc2qZe7ZWNRqPB5hXM8JDB1DTVcrD4xzv51pURZYnA6KI7+Vp9TYzqb6NZUTiQ\nZeeHA/mcKKwkNsynQ9/JV/qMekk26iXZOEY2c3SCnFTq1d7ZGHQG+gb0pKdfD46X57RuSWBxM2Pz\nDHbJnXz1Oi29I/y4qkcgOQWVHMiy821qLm56HZEhZpfeLfhykT6jXpKNekk2jpEBjBPkpFKvy5WN\nr7sPSbbBuOncSLNnsqsglaNlx4m0hOPpokuuzZ5GkvqF4Gd2J/14CbsOFZF6uJjwYO8Od8m19Bn1\nkmzUS7JxjAxgnCAnlXpdzmy0Gi3RPhEMChpAQXUhaSUtd/LVgGvv5BvszYi+IZRVtdzJd3NqLpXV\nDcSEWjDoO8adfKXPqJdko16SjWOu2GaO7UWKeLumK5VNS5HvXpYeWkV5fQXBJitz42e6vMg37XgJ\n767LIN9ejcXLyLzxPRgUF6j6ZSXpM+ol2aiXZOMYKeJ1goyK1etKZfNTkW+SbTC1jXUctGfyQ94O\nSmvLiPZxXZFvoI8HoxNs6LUaDmSVsD0tn6y8CqJDLXi6u6aN9iB9Rr0kG/WSbBwjS0hOkJNKva50\nNgatgT4BPenpF8fxipYi3x/ydri0yFen1RDX3ZfBPa3kFVe1FPnuyUWrgSibGa1WfbMxVzoXcX6S\njXpJNo6RAYwT5KRSL7Vk4+tuYXjIYNz17qTbM9lVsJcjZceItHTH0+Dpkja8PAwM6x1MsJ+JjOwS\n9hwuZmdmIWGBXvhb3F3ShquoJRdxNslGvSQbx8gAxglyUqmXmrLRarREWSJIDBpAQU0RafZMvsvd\nDgpEWLqjc1GRb5jVi5EJNmrqmth/tJgt+/IoLq8lNswHo0Edd/JVUy7iTJKNekk2jpEiXidIYZV6\nqTUbRVHYXbiPpZkrKfuxyHdO3AxifaNc2s6Rk2UsXpvBicJKvDwMzB4TQ1Jf1yxdXQq15iIkGzWT\nbBwjRbxOkFGxeqk1G41GQ4hnEMNtg6ltrG+5Ad6pFEpqS4nyicCoM7qkHT+zO6P6h+Bh1HPgmJ2U\njEIyskuJspnxNrmmjYuh1lyEZKNmko1jZAnJCXJSqZfas2kp8o2nl3/cGXfyNRu9CfUKcclMiVaj\nISbMwrDewRSW1nDgWEuRb2NTM9E2Czrd5b93jNpz6cokG/WSbBwjAxgnyEmlXh0lGx+3liJfD71H\ny518C/dy+MciXy8XFfma3PUM6RVEd6sXmSdKST1czPa0AoL9TVh9XXO3YEd1lFy6IslGvSQbx8gA\nxglyUqlXR8qmpcg3nMSgqyisKW4p8j25DQWFCEu4S4p8AUL8PRmVYKOhsZn9WXa+33+KvOIqYsIs\nuBsvzwaRHSmXrkayUS/JxjFSxOsEKaxSr46ajaIopBbu55PMlZTVlxNkCmRO3Ax6+Ea7tJ3s/Are\nXZfB0dxyPNx0zBwdzdX9Q9v93jEdNZeuQLJRL8nGMVLE6wQZFatXR81Go9EQ/GORb11TPQeLW4p8\ni2vsRFkicHNRka/Fy40RfUOweBo5eLyUXZmF7DtqJyLYGx+v9tsgsqPm0hVINuol2ThGlpCcICeV\nenX0bAxaPb394+ntH092xUkO2jP4Pnc7ngYTYV42lxT5ajQaIkPMjOgbTGllywaRm1JzqalrJLqd\nNojs6Ll0ZpKNekk2jpElJCfItJ56daZsmpVmNp34gdVH11LbVEekOZy58TMI9QpxaTv7s4p5f10m\nBaU1+Hq7MW98D67qEeDSe8d0plw6G8lGvSQbx8gSkhNkVKxenSkbjUZDhKU7Q0IGUlpX1non39qm\nWiLN4ei1rinAtfqaGJXQMruzP8vOtrR8svMriQ41Y3LRBpGdKZfORrJRL8nGMbKE5AQ5qdSrM2bj\nrnfnKms/IszdOVp6jAPF6ew4tRt/Dz+CPa0uaUOn09Iz3JfEeCu5RVXsz7LzbWouOl3LctOlFvl2\nxlw6C8lGvSQbx8gAxglyUqlXZ87GagogyTYEDZBmzyQlfzc5FSeJNIdjMni4pA1vk5HhfYIJ9PEg\n/Xgpew4VsftQId2CvPEzX/wGkZ05l45OslEvycYxMoBxgpxU6tXZs9FpdcT5xTDA2pe8qvwfl5W2\nodPoiDB3Q+uiDSK7B3kzMsFGVW0D+47a2bw3j9LKOmLDLBj1zm8Q2dlz6cgkG/WSbBwjRbxOkMIq\n9epK2SiKwvZTu1h2+DMqG6qweQYzJ24G0T4RLm0nM6eU99ZlcLKoCrPJwC/GxTK0V5BTRb5dKZeO\nRrJRL8nGMVLE6wQZFatXV8pGo9EQ5m1juG0w1Y01HLRn8EPeDkpry4jyCXfZBpH+FndGJdhwM+o4\nkGVnR3oBh06UER1qwcvDsSLfrpRLRyPZqJdk4xhZQnKCnFTq1RWzMeoM9A3oRU+/2DM2iPQyehHm\nqg0itRpiw3wY0iuIgpIaDmTZ+XbPSZoViLaZ0WnbXrrqirl0FJKNekk2jpEBjBPkpFKvrpyNr7sP\nSbbBuOvdSS85xO6CvWSWHiHC3B1vo5dL2vB0NzCkVxBhgV5k5LRsELkjvZBQfxOBPucvJO7Kuaid\nZKNeko1jZADjBDmp1KurZ9OyQWQEg4MHYK8pIc2eyZbcbTQ0NxBlCUendb4A9+c0Gg22gJYNIusb\nmtifVcx3+09RUFJNbJgPbsaz2+jquaiZZKNeko1jpIjXCVJYpV6SzZn2Fh7gk8yVlNSV4u/uy+we\n19MnoKdL2zh2qpzFazM4fqoCk5ueWWOiGZVgQ3va0pXkol6SjXpJNo6RIl4nyKhYvSSbMwV5WkkK\nHUKz0sxBeyY78neTW3mKKEs4HvqLv6/L6Xy83BjVz4aXh4GDx0vYmVHIgWN2okLMmD1bCoklF/WS\nbNRLsnGMLCE5QU4q9ZJszqbX6oj3i6V/YB9OVua13jvGoDPQ3TvMZfeOibJZGN4nBHt5LQeySvh2\nTy61DU3EhFowm90lF5WSPqNeko1jZAnJCTKtp16STdualWa25u1kxeHPqWqsJszLxpy4GURauru0\nnb1Hinj/y0yKymrxN7tz+w0JRAZ6urQN4RrSZ9RLsnFMW0tIMoD5GTmp1EuycUxlfRXLj3zO1rwU\nNGgYETqU66KSXbYlAUBdQxOrvzvGuu3ZNDUrDIgNYN74HvhbXLN0JVxD+ox6STaOkQGME+SkUi/J\nxjmHSo7yceZyTlXl423wYmbstQwK6u+Se8f85ERhJUu+OcKBo8UYDVqmJUUyIbEbet2lL12JSyd9\nRr0kG8dIEa8TZF1SvSQb5/h7+JJkG4yb1khaySF2FaRypOwYEZbueBlcs+Rj9jRy3egYPPRa0rNL\n2XO4iF2ZhYQGeBJgcd2Mj7g40mfUS7JxjBTxOkFOKvWSbJyn1WiJ9olkUNAACmuKWop8T26jWWkm\n0tzdJfeO8fR0I8DbyMgEGzV1jew/amfLvlMUldYQE2bBzXDpbYiLI31GvSQbx0gRrxNkWk+9JJtL\noygKqYX7+fTQKkrrygj08OcXcdPp6dfjkt7357kcOVnGe+syyC6oxNNdz8zR0Yzqf+a9Y8TlIX1G\nvSQbx7TLEtKxY8fw8fG52GO6JDID0zVJNpdGo9EQ7BlEkm0wjc2NpNkz2XZqJwXVhURZInDXn/8v\nnbb8PBc/szsjE0Ja7h1zrISdmYXsO2onItgbH6+La0NcHOkz6iXZOKatGZg2K+1uvvnmM75++eWX\nW///kUceucTDEkJcCe56d2bGXst9g+4kwtydlPw9/H3rM3x74nualWaXtKHTapkwqBv/uG0og3ta\nycor5++Ld/DhV5lU1za6pA0hRNfW5gCmsfHMXzRbt25t/f8OuPIkhDhNN28b9w68nTlxM9BoNHyS\nuYJnUv5FdsUJl7Xh6+3Gb6f14d5f9Mfq48H6nSf4yxtb2XYwX36HCCEuSZsDmJ9fbnn6LxxXXoop\nhLgytBotI0OH8sjQP5IYNIDsihM8veMlPs1cSU1jrcva6R3px99vHcz1IyOpqmnktVUHeG7JHk7Z\nq13WhhCia3HqZg0yaBGiczIbvbmp91x+3/82Ak3+bDzxHY9ufYZdBXtdNlNi0Ou4LimSx341mL5R\n/hw8VsIjb25j2aaj1Dc0uaQNIUTXoW/rwbKyMn744YfWr8vLy9m6dSuKolBeXt7uByeEuLzi/WL5\n8+B7WH98I2uPb+DN/e/Tyy+O2T2uJ9Dk75I2rL4m/nBDP3ZmFPLR14f47PtjbDt4ihsnxNEv2jVt\nCCE6vzYvo16wYEGbL37vvfdcfkCOkMuouybJ5vIqqC5iScZy0ksOYdDqSY4Yx7juozFoz/y751Jy\nqalrZNV3WXy14wTNisLAHoHMHR+Ln1m2JHAF6TPqJdk4RrYScIKcVOol2Vx+iqKwqyCVpYdWU15f\nQZDJypy46fTwjW59jityOVFQybtfZnD4RBluBh3TRkQyflCYbElwiaTPqJdk45i2BjBt/naorKzk\nnXfeaf36448/Ztq0adx5550UFRW57ACFEOqk0WgYGNSfR4b+kdFhwymoLuSF3a+x+ODHVNRXuqyd\nMKsXD9x4FTdPiceg1/LJN4f52zs7yMwpdVkbQojOpc0b2T3wwAPo9XqGDx9OVlYW9957L4899hhm\ns5mPPvqI5OTky3io/yM3suuaJJsrx6A10Ns/nt7+8WRXnCTNnsn3udsx6T2IC4qkprrhktvQaDSE\nB3kzMsFGVe1PWxLkUVxWS7RsSXBRpM+ol2TjmIu+kV1OTg733nsvAOvWrSM5OZnhw4czZ84cmYER\nogsKN3fjvkG/54bYaTQrzXyUsYxHvn6OExW5LmvDy8PATZPj+fOCgXSzerFlXx5/+c9Wvt1zkuaO\nt+IthGgnbQ5gTCZT6/9v376doUOHtn4tl1QL0TVpNVqu7pbEw0P/yFXWfmQWH+XJHS+wNHOVS+8d\nExNq4ZGbBjFnXCyNzQqL12bwxHs7yc6XugEhxAUGME1NTRQXF5Odnc3u3btJSkoCoKqqipqamsty\ngEIIdfJxs3Brn/n8ZfTvCfDw45sTW3h06zOk5O9x2b1jdFotExO78fhtQ0mMt3Ikt5y/vbODD9dn\nUlMnWxII0ZW1WQPj7+/PTTfdxHvvvccdd9zB8OHDqa2tZe7cucycOZN+/fpdxkP9H6mB6ZokG3WK\nCgpjgKU/eq2e9JJD7CxI5XDZMSLM3fAyerqkDQ83PYnxVqJDzRw5Wc6+o3a+25+Hn7cbtgBPmRE+\nD+kz6iXZOKatGpgLXgYaJ6IAACAASURBVEbd0NBAXV0dXl5erd/bsmULI0aMcN0ROkkuo+6aJBt1\nOj2XoppiPslcyYHidHQaHeO7jyY5YixGndFl7TU0NvHF1mw+/+E4jU3N9I70Y/6EHgT5mS784i5G\n+ox6STaOuej7wOTmtl2YZ7PZLv6oLoEMYLomyUadfp6LoijsLTrAp5mrKKkrxc/dl9k9ptE3oJdL\n280vqeaDLzPZn2VHr9MwZWg4U4aGY5SrlVpJn1EvycYxFz2AiY+PJzIyksDAQODszRzfffddFx6m\n42QA0zVJNup0vlzqmupZk7Wer3M20aw00zegJzfETsPfw89lbSuK0rolQUlFHVYfD26c2IO+UbIl\nAUifUTPJxjEXPYBZuXIlK1eupKqqimuuuYapU6fi5+e6Xz4XSwYwXZNko04XyiWvKp8lGcs5VHoU\ng9bw45YEo87akuBS1NQ1snJLFutTWrYkGBQXyJxxsiWB9Bn1kmwcc8lbCeTl5bF8+XJWr15NaGgo\n06ZNY8KECbi7X5lfDjKA6ZokG3VyJBdFUdiRv5tlhz+jor6SIFMgs3tcT7xfrEuPJaegknfXpXPk\nZDluRh3Xj4hk3MCuuyWB9Bn1kmwc49K9kD799FOeffZZmpqaSElJafO5mZmZ3H777dx0003Mnz+f\nHTt28Pzzz6PX6zGZTDz99NNYLBbeeOMN1q5di0ajYdGiRYwePbrN95UBTNck2aiTM7lUN9TwWdaX\nbDrxPQoKA60JzIidio+bxWXH06wobNmbx6ffHKaqtpGwQE8WTIojNszHZW10FNJn1EuyccwlD2DK\ny8tZtWoVy5Yto6mpiWnTpjF16lSsVut5X1NdXc1vfvMbIiIiiIuLY/78+cyYMYNnn32WqKgoXn31\nVbRaLZMnT+auu+7i448/prKyknnz5vH555+j052/EE8GMF2TZKNOF5NLdsUJPs5YzvHyHNx1bkyN\nmsSo0GHotK4rwK2ormfpxiNs3psHwIh+IdxwdTTeJtddEaV20mfUS7JxzEVv5rhlyxbuvvtuZs6c\nSV5eHk8++SQrV67klltuaXPwAmA0Gnn99dfPeJ6vry+lpS2bs5WVleHr68u2bdsYOXIkRqMRPz8/\nQkNDOXz4sDOfTwjRwXT3DuOPA+9gbtwMtBotSw+t4qmUFzladsxlbXibjNw8pSd/nj+QsEAvtuzN\n48//2cqm1FzZkkCITuCCVyFFRESQkJCAVnv2WOeJJ564YAMvvfQSvr6+zJ8/nyNHjjB//nzMZjMW\ni4UPP/yQN954Aw8PDxYuXAjAn/70J6ZNm9bmfWYaG5vQ6+VSSSE6g/LaCj7Yu4Jvsr4HYGzkcOYl\nTMfs5nWBVzquqamZ1Vuy/n979x0f1ZXfffwzoxn1Ue+9AqZ3RO/YNIMplo3BeaVsnsTZJLthnfXj\ndSFx1nlhZ7P78trZlk12H7xeY3rHpglk000XqIsiCdRRb1OeP8CsbYw8F640Z6Tf+z/k0T1nXt9z\nzI97zr2HDz6+Qmu7jQGJwbywbBjJMfotXQkhelaXjwF88Zh0XV0dwcHBX/lvpaWlmht74403ePfd\ndxk1ahRr167lgw8+uO8zzmzJqatr0dy2s+S2nrokGzXpkcuy5MWMCB7O+vwtHCw5yokb51iUNpfx\n0WMwGvTZgDtxYASPxQXw4cFCTudW8r3/PMys0XEsmpSMj5d+T0SpROaMuiQb5zz0EpLRaGT16tW8\n+uqrvPbaa0RGRjJ27Fjy8/P52c9+prkjeXl5jBo1CoAJEyZw6dIlIiIivnKydUVFxbcuTwkhep/U\noCR+OPofWJq+EKvDyge5m/jPz/+LG41lurUREuDNC4sH8/2nhxEW6M0np27wo98c51RupW7nNwkh\nekaXBcxPf/pTfve733Hy5ElefPFFXnvtNVatWsXx48fZsGGD5sbCwsLu7W+5ePEiiYmJZGRkkJWV\nRUdHBxUVFVRWVpKWlvZw30YI4dY8jB7MiJ/MaxkvMipiGCUN11l76h025G+j1arfAbJDUkL5178c\ny5MTk2hq7eQXWy/x04/OU9GNd3eFEPrqcg/MqlWrWLdu3b0/z5o1ix/+8IfMnj37Wy986dIl1q5d\nS1lZGSaTicjISL7//e/z1ltvYTabCQwM5M033yQgIIB169axY8cODAYD3/ve9xg/fnyX15ankPom\nyUZN3ZlLbm0B6/O3UNlSTYCnhSVpCxgdOVzXwxsralt4f18+OSW1mDyMzMtI6DVHEsicUZdk45yH\nfoz6+eef/8pxAV8vaFxFCpi+SbJRU3fn0mm3cuD6YfZePUCn3Uq/oFQy+y8myi9StzYcDgen86r4\n4/58bjd1EBbozYrZ/RieFqZbG64gc0Zdko1zHnoPzNfJkfVCiJ5mNpp4Imkmr4z7AYNDHyP/dhFv\nnvwZ24r20G7r0KUNg8HAmAER/Pg7GTw+Np66xnbe2XiBdzZeoOq2fktXQgj9dHkHZsiQIYSG/ulQ\ntJqaGkJDQ3E4HBgMBrKysnqij/eROzB9k2Sjpp7O5UJVDhsKtlPbVkewVxDL+y1iaNhAXf+BVVbV\nxPuf5JN34zZmk5H5GYnMzUjA7Gavb5A5oy7JxjkPvYRUVtb17v/Y2NiH79UjkAKmb5Js1OSKXNpt\nHey9eoAD149gc9gYHPoYy/stIkznk65PXK5g/cFC6ps7iAjyYcXsfgxNdZ+TrmXOqEuycY6uZyGp\nQAqYvkmyUZMrc7nVXMn6/K3k1xXeW2qamTC1W0+6HpEexrMz0wkL8tGtje4ic0Zdko1zuipgPNas\nWbOm57qij5YWfda9v4mfn1e3Xl88PMlGTa7Mxd/Tj3FRI4n0DafgdgkXqy9zpvI8kb7hhPvoc6fE\nbDIyOCWUkf3CKatqIudqHYfPleMAUqID8DCquzdQ5oy6JBvn+Pl5PfC/yR2Yr5GqWF2SjZpUyaXV\n2squ4n1klX7WbSddOxwOjudUsP5QIQ3NHUQE+/Dc7H4MSVFzWUmVbMT9JBvnyB0YDaQqVpdkoyZV\ncjEbzQwM7c+QsEGUNZVzuTafz8pPYDaaSLDE6XIkgcFgID7CnynDYuiw2rhUUsuxnApKK5tIjQnE\n11utIwlUyUbcT7JxTld3YKSA+RoZVOqSbNSkWi6BXhYyokcT4h1Efl0R56tzuFB9mRi/aEK8g3Rp\nw2wyMiQllBHpYZRWN5NTUsvhc2UYDJCs0LKSatmIP5FsnCMFjAYyqNQl2ahJxVwMBgPxlljGR4+h\npbOVy7V5HLt5itq2OlICE/Hy8NSlnUB/LyYNiSY8yIf8G7c5V1jDqdxKokJ8iAj21aWNR6FiNuIO\nycY5UsBoIINKXZKNmlTOxdPDk6HhA3kspB/XG0u5UpvP0fKT+Jp8iLPE6PLuGIPBQEKkhSnDYmjv\ntHOppIZjlyooq2oiNTbQpSddq5xNXyfZOEcKGA1kUKlLslGTO+QS7B3EhOix+Jn9yKsr5FzVJS7X\n5hFviSXQK0CXNswmD4am3l1WqmrmUkktWefKMBoNJEcHYHTBspI7ZNNXSTbOkaeQNJCd4eqSbNTk\nbrnUtzewuXAnpyvOYcDAlLjxLEh+HF+zfu91sTscHL14iw1ZhTS2dBIV4svKOf0YmKTfi/ac4W7Z\n9CWSjXPkRXYayKBSl2SjJnfNJa+2kPX5W6hoqcLi6c+StAWMiRyh65EEzW2dbDlSzKGzZTgcMGZA\nBJkz0ggJ8Natja64azZ9gWTjHHmMWgO5racuyUZN7ppLmE8IE2LG4Wk0k1tbwJnKCxTcLiYxIB6L\np78ubXiaPBiaGsbwtDBKK5u4VFLL4XPleHj0zLKSu2bTF0g2zpElJA2kKlaXZKOm3pBLTWstGwq2\nc7H6MkaDkelxk5iXPAtvk353SuwOB59duMmGrCKaWjuJDvVl5ex+PNaNy0q9IZveSrJxjiwhaSCD\nSl2SjZp6Uy4Xqy+zIX87NW21BHoGsCRtPqMih+u6rNTUemdZKetsGQ5g7GMRZM5IJ9jy4H9pPqze\nlE1vI9k4R5aQNJDbeuqSbNTUm3KJ9A1nYsw4PIwe5NUV8PndZaUES5x+y0pmD4alhTE0NZQblU13\nXoJ3vhyT0UhStEXXZaXelE1vI9k4R5aQNJCqWF2SjZp6ay7VrbVs/NKy0rS4icxLno2PzstK2efL\n2XS4mKbWTmLC/Fg5ux8DEoN1uX5vzaY3kGycI0tIGsigUpdko6bensul6itsKNhOdWsNAZ4Wnkqb\nr/vTSk2tnWw6XMSRu6dcZwyM5OkZaQT5P9qyUm/Pxp1JNs6RJSQN5LaeuiQbNfX2XCJ8w5kUMw6T\n0UxuXT5nKi+Qf7uIBEscAZ4P/p+rFp5mD4anhTEkJZTrFY33nlYye9xdVnrIYqm3Z+POJBvnyBKS\nBlIVq0uyUVNfyqWmtZZNBTs4X52D0WBkatwE5ifPxsek40vw7A6OnC9n0+EimtusxIX7sXJOf/rF\naz+Isi9l424kG+fIEpIGMqjUJdmoqS/mklOTx4b8rVS11mDx9Oep1PmMjRqp67JSY0vHnWWl8zcB\nGD8oiqenpxKoYVmpL2bjLiQb58gSkgZyW09dko2a+mIuEb5hTIzNwGw0kVtbyNmqC+TVFRJviSXA\nS59lJS+zB8PTwxmcEsL1W3degnfkfDmeZg+SopxbVuqL2bgLycY5soSkgVTF6pJs1NTXc6lprWNz\n4Q7OVV3CgOHustIcfc9Wsjs4fK6MTYeLaWm3Ehfuz8o5/b51WamvZ6MyycY5soSkgQwqdUk2apJc\n7rhck8eG/G1UtlZjMfuzOG0eY6NGYjQYdWujoaWDjVlFfHrhzrLSxMFRLJueRqCf5zd+XrJRl2Tj\nHFlC0kBu66lLslGT5HJHuG8YE2PvnK2UV1fA2aqL5NUVEG+JI1DHZaUR6eEMSg7h2q3Ge8tK3p4e\nJEb537esJNmoS7JxjiwhaSBVsbokGzVJLverbatjc8FOzlZdxICBKXHjWZD8uO7LSofOlrH5SDGt\n7VYSIvxZOac/aXGB9z4j2ahLsnGOLCFpIINKXZKNmiSXB7tSm8+G/G1UtFThb/Zjceo8xkWP0ndZ\nqbmDDVmFfHbxFgCThkSzbFoqAX6eko3CJBvnSAGjgQwqdUk2apJcuma1Wzl4I5s9Vw/QYesgOSCB\np/svJsESp2s7BaW3ef+TfG5UNuHrZWLJ1BSWzR5AbU2Tru0Ifci8cY4UMBrIoFKXZKMmycU5dW23\n2Vy4kzOVFzBgYHJsBgtTHsfX7KtbGza7nUNnytiSXUxru42U2EAyp6eSHqf9JXiie8m8cY4UMBrI\noFKXZKMmyUWb3NoCNuRv41ZLJf5mPxalziUjerSuy0r1zR1sOFTI0Ut3lpXGD4pk+fRHP1tJ6Efm\njXOkgNFABpW6JBs1SS7aWe1WDt34lN1X99Nh6yApIIHMfotJCNB3Wam6qZN3N5zlekUTXp4ePDkx\nidmj4zF56FcsiYcj88Y58hi1BvJom7okGzVJLtoZDUZSg5LIiB5FfXsDV2rzOVp+kvqORlICE/H0\nMOvSTmJsEKPTwgiyeJF//TbnCqo5lVtJZLAPEcH6LV0J7WTeOEceo9ZAqmJ1STZqklweXX5dIevz\nt3GruQI/sy+LUuYyPmbMIy8rfTmbptZOtmYXc+hsGQ4HjEgPI3NmOhFB+j3aLZwn88Y5soSkgQwq\ndUk2apJc9GGz2zhU+im7S/bRbusg0RJPZv/FJAbEP/Q1vymb6xWNfLAvn/zSekweRuaOS2De+ES8\nzB6P+hWEBjJvnCMFjAYyqNQl2ahJctHX7fZ6thTu4nTFOQwYmBAzlidTn8Df7Kf5Wg/KxuFwcOJK\nBRsOFVHX2E5IgBeZM9IZ3T9c1xO1xYPJvHGO7IHRQNYl1SXZqEly0Ze3yZsREUNID0rhWuMNLtfm\ncaz8FN4mb+ItMZoKjAdlYzAYiAv3Z+rwGAAuX63l5JVKCkrrSYqyEPCAs5WEfmTeOEf2wGggVbG6\nJBs1SS7dx2a3cbj0M3aV7KPN1k6CJY7M/otJCkhw6vedzaaitoU/HijgQlENRoOBGaNiWTwpGV9v\nfTYTi/vJvHGOLCFpIINKXZKNmiSX7lff3sCWwt2cqjiDAQPjo8ewKHUu/p5dLytpzeZ8YTV/PFBA\nZV0rFl8zS6emMmlo9H2HRIpHJ/PGOVLAaCCDSl2SjZokl55TUFfMR/lbKW++ha/Jh4UpTzApdtwD\nn1Z6mGw6rXY+OXWdnUev0d5pIznawnOz+5MSE6DHVxB3ybxxjhQwGsigUpdkoybJpWfZ7DaOlB1j\nZ/EntNnaiLfEktlvMcmBifd99lGyqW1oY0NWEScuVwB3DolcOi2VQNkfowuZN86RAkYDGVTqkmzU\nJLm4Rn17I9uKdnPi1ucA95aVLJ7+9z6jRzZ51+v4w74CSqua8PHyYNGkFGaMjJW3+T4imTfOkQJG\nAxlU6pJs1CS5uFbh7RI+yt9KWdNNfEw+LEx5nMmxGRgNRt2ysdntZJ0tZ2t2Mc1tVmLC/HhuVjqP\nJYXo8A36Jpk3zpECRgMZVOqSbNQkubiezW4ju+w4O0s+ptXaRpx/DJn9FzMubYiu2TS2dLD5SDFH\nzpXjAEb3D+fpGWmEBcrbfLWSeeMcKWA0kEGlLslGTZKLOho6Gtla+KdlpalJGTweO5tArwf/JfAw\nrt1q5A/78iksq8fTZGReRiJPjEvAU97m6zSZN86RAkYDGVTqkmzUJLmop7j+KuvztlLaVI63hxdz\nk2cxLW4iJqNJtzYcDgfHcm6x4VAR9c0dhAV688zMdEakh8nbfJ0g88Y5UsBoIINKXZKNmiQXNdkd\nds43nOeP57fRbG0h0jecZelPMjC0v67ttLZb2fHZVfadvoHN7mBQcggrZqUTHar96IO+ROaNc+Qo\nAQ3k9c7qkmzUJLmoyWAwMDS+H8ODhtNu6+BKbT4nK85wo7GMREs8fmZfXdoxm4wMSg5hzIAIKupa\nySmp5fC5clrbraTGBGI2ydNK30TmjXO6OkpACpivkUGlLslGTZKLuvz8vOhsczA4bADDwgdzq7mS\nK7X5fFp2nE67lcSAeN2WlSy+nowfFElipIXCsnouFtfy2cWbWHzNxEX4y7LS18i8cY6chaSB3NZT\nl2SjJslFXV/PxuFwcKbyApsLd3K7vZ4gr0CeSp3HqMjhuhYYHZ029p68zu5j1+iw2kmNDeC52f1I\nipK3+X5B5o1zZA+MBjKo1CXZqElyUdeDsmm3dbDv2iH2XT+M1W4lNTCJ5f0WE2+J0bX96vpWPjpY\nyOm8KgzA5GExLJ2agsVX3uYr88Y5UsBoIINKXZKNmiQXdX1bNtWttWwu3Mn5qksYMDAxdhwLkx//\n1kMitbp8tZYP9hdQXt2Mr5eJp6akMG1EDB7Gvrs/RuaNc6SA0UAGlbokGzVJLupyNpsrtflszN/O\nrZZKfE0+LEh5nEkx4/Aw6vdeF6vNzqEzZWz9tJjWdhtx4f48Nzud/gnBurXhTmTeOEcKGA1kUKlL\nslGT5KIuLdnY7DYOlx1lV/E+2mxtxPhFsbzfIvoFp+rap4bmDjYeLuLTCzcBGPtYBE9PTyMkwFvX\ndlQn88Y5UsBoIINKXZKNmiQXdT1MNo0dTWwv2sOxm6dx4GBkxFCeSptPiLe+d0qKyxv4w748Sm42\n4mk2snBCEnPGJPSZx65l3jjHZe+Byc/PJzMzE6PRyNChQ+ns7OSf//mf+c1vfsOuXbuYMWMG3t7e\nbN++nZdffpmNGzdiMBgYNGhQl9eVx6j7JslGTZKLuh4mGy8PT4aGD2JQ6ADKm27dfez6BA6Hg8SA\neN2WlYItXkweFkNogDf5N25zrrCGk5crCAvyISpEn3fUqEzmjXNc8h6YlpYWXnzxRYYMGUJYWBhD\nhw7lww8/pK2tjXfffZeOjg5u375NVFQUq1ev5oMPPmDZsmX86Ec/Yt68eXh7P/h2ohQwfZNkoybJ\nRV2Pkk2QVyAZ0aMJ9wmlqL6EizVXOF1xlmDvYCJ9w3V57NpgMJAYZWHqsBg6rQ5ySmo5frmCkpsN\nJEcH4O9jfuQ2VCXzxjkuKWAMBgMLFiwgLy8PHx8fhg4dyjvvvMPzzz9PZGQkgwcPJiUlhdOnT1NT\nU8PChQsxmUzk5ubi5eVFcnLyA68tBUzfJNmoSXJR16NmYzAYiLPEMDFmHHaHndzaAk5XnKO4/hoJ\nAXFYPP116afZ5MGQlFBG9Q/nVm3L3bf5ltHeaSclJgCTR+9bVpJ545yuChj9Tvb6+oVNJkymr16+\nrKyMI0eO8PbbbxMWFsbrr79OdXU1ISEh9z4TEhJCVVVVl9cODvbFZOq+U0+7WnMTriXZqElyUZc+\n2Vj46+hnWDBoGr87u4Fzty7z5smf8kT6NJYPmo+fpz5LPuHhFoY9FsXRCzf57Y5L7D5+jeOXK/jz\nhYOYOiK2173NV+bNo+m2AuabOBwOkpOT+e53v8t//dd/8atf/YqBAwfe95lvU1fX0l1dlI1VCpNs\n1CS5qEvvbMz48VeP/RmXIq6wsWAHu/MPcqTkBItS55IRPRqjQZ87Jf1iLPzrX4xl97Fr7DlxnZ/8\n4XN2HC7k2Vn9SIzqHX/py7xxTldFXo/elwsLC2PMmDEATJo0icLCQiIiIqiurr73mcrKSiIiInqy\nW0IIIZxkMBgYEjaQV8atZlHKXDrsnfwhdyNvn36X4vprurXjZfbgqSkp/Pg74xiRHkZ+aT3/+rtT\n/G7PFRqaZelF9HABM2XKFLKzswHIyckhOTmZYcOGcfHiRRoaGmhububMmTOMHj26J7slhBBCI7PR\nxJyk6bye8SJjIkdwvbGUn3z+Hv/v8nrq2xt0ayc8yIe/XzqU1ZnDiQnz48j5m/zfXx9j74nrWG12\n3doR7qfb3gNz6dIl1q5dS1lZGSaTicjISP7jP/6DH//4x1RVVeHr68vatWsJCwtj7969/Pa3v8Vg\nMLBy5UqefPLJLq8t74HpmyQbNUku6urJbApvl7Axfxs3msrx8vBkbtIspsdP0u20awCb3U7W2XK2\nZhfT3GYlItiHZ2akMywt1O32x8i8cY68yE4DGVTqkmzUJLmoq6ezsTvsHC0/yfbivTR3thDhG8ay\n9CcZFDpA13aaWjvZ9mkJh86UYXc4GJQUzDMz04kN1+epqJ4g88Y5UsBoIINKXZKNmiQXdbkqm5bO\nFnaW7CO77Bh2h53BoY+xNH0hEb5hurZTVt3MhwcKyCmpxWgwMH1ELIsmJ7vF+2Nk3jhHChgNZFCp\nS7JRk+SiLldnU9Z0k43528m/XYTJ4MGMhCk8njgDb9OD3+2hlcPh4HxRDesPFFBR14qft4nFk9U/\n7drV2bgLKWA0kEGlLslGTZKLulTIxuFwcLbqIpsLdlLXfptAzwAWp81jTOQIXfetWG12DnxeyvbP\nSmhttxET5sczM9MYnByqWxt6UiEbdyAFjAYyqNQl2ahJclGXStl02DrYdy2Lfdez6LRbSQlMZHm/\nRSRY4nRtp6G5gy3ZxRw5V44DGJ4WRuaMNCIVO19JpWxUJgWMBjKo1CXZqElyUZeK2dS01rK5cBfn\nqi5iwMCEmLEsTHlct2MJvnC9opE/7i8g78ZtPIwGZo2OY+GEZHy9e/T9rQ+kYjYqkgJGAxlU6pJs\n1CS5qEvlbHJrC9hYsJ2bzRX4mHxYkDyHybEZup12DXeWrz7Pq+KjQ4VU17dh8TWzZEoKk4fGYDS6\n9rFrlbNRiRQwGsigUpdkoybJRV2qZ2Oz2zhSdoxdJZ/Qam0j2i+S5emL6B+Spms7nVYbH5+8wa5j\n12jvtJEQ4c+zs9LpnxCsaztaqJ6NKqSA0UAGlbokGzVJLupyl2waO5rYUbyXo+WncOBgePgQlqTN\nJ9Qn5Nt/WYO6xnY2HS7i6KVbAIzuH87T09MIC/LRtR1nuEs2riYFjAYyqNQl2ahJclGXu2VzvaGU\nj/K3UdJwDbPRxOyEacxOnIanh6eu7RSXN/DH/fkUlTdg8jDyxLh45mUk4u3Zc/tj3C0bV5ECRgMZ\nVOqSbNQkuajLHbNxOBycqjjL1sJd1Hc0EuwVxJL0BYwIH6LrY9d2h4MTlyvYmFVEXWM7Qf6eLJuW\nSsagKIw9cCyBO2bjCl0VMB5r1qxZ03Nd0UdLS/edROrn59Wt1xcPT7JRk+SiLnfMxmAwEOsfzcSY\ncTgcDnLrCvi88jyFt0uIt8QS4Pngv9C0thMf4c+04bEYDQauXKvjdG4Vl0pqiQ3zIyTAW5d2HsQd\ns3EFP78Hv/RQ7sB8jVTF6pJs1CS5qKs3ZFPZUsWmgh1cqsnFgIGJseNYkDxH98euq+tb2ZhVxMkr\nlQCMHxTJsmlpBFv0e2vwl/WGbHqCLCFpIINKXZKNmiQXdfWmbHJqctlUsJOKlkq8PbyZmzyTaXET\ndT3tGiD/xm0+2J/P9YomPM1G5mUk8sTYBDzN+j3eDb0rm+4kBYwGMqjUJdmoSXJRV2/Lxma3kV12\nnF0ln9BibSXcJ5QlaQsYEjZQ3/0xdgefXrzJ5sNFNLR0EhrgzdMz0hjdP1y3dnpbNt1FChgNZFCp\nS7JRk+Sirt6aTXNnC7u+dNr1gOB0lqQvINY/Wtd2Wtut7Dx6lU9O3cBmd9AvLpBnZ/UjMerR9+H0\n1mz0JgWMBjKo1CXZqElyUVdvz+ZWcwWbCnZyuTavW/fHVNS18NHBQs4WVGMAJg2NZsnUVAL9Hv7x\n7t6ejV6kgNFABpW6JBs1SS7q6ivZfHl/jI/Jm7lJs5gaN0H3/TE5V2v5cH8BZdXNeHt6sHBiErNG\nxWM2GTVfq69k86ikgNFABpW6JBs1SS7q6kvZfHEswe6SfbRYW4nwCWNJ+gIGhz6m6/4Ym93O4XPl\nbDlSTHOblYhgHzJnpDE8LUxTO30pm0chBYwGMqjUJdmoSXJRV1/Mpqmzmd0l+8guO35vf8zS9IXE\n+Efp205rJ9s/OKcB7gAAFJBJREFULeHgmTLsDgcDk4J5ZmY6ceHOLV/1xWwehhQwGsigUpdkoybJ\nRV19OZubzRVsKtjBldp8DBiYFJvBguQ5+Hv66dpOWXUz6w8UcKmkFqPBwLQRMSyenIK/j7nL3+vL\n2WghBYwGMqjUJdmoSXJRV1/PxuFwkFOTy+bCnVS0VOFj8mZe0iym6Lw/xuFwcKGohg8PFlJR24Kf\nt4lFk5KZNiIWk8c374/p69k4SwoYDWRQqUuyUZPkoi7J5o4v9sfsKtlHq7WVCN8wlqTpvz/GarNz\n4PNStn9WQmu7jehQX56dmc7glND7PivZOEcKGA1kUKlLslGT5KIuyearmjqb2VW8j0/Lu3d/TENz\nB1uyizlyrhwHMCw1lMyZ6USF+N77jGTjHClgNJBBpS7JRk2Si7okm29W3nSLzYU7uVKbj9FgZFLM\nOOZ3w/6Y6xWNfHiggNzrt/EwGpg1Oo6FE5Lx9TZJNk6SAkYDGVTqkmzUJLmoS7J5sC/2x2wq3EFl\nSzU+Jh/mJc9iSux43ffHfJ5XxUeHCqmub8Pia+apKSksmdmf2pom3drpraSA0UAmvLokGzVJLuqS\nbL7dN+2PWZq2kEGhA3TdH9NptfHJqRvsPHqN9k4bSdEBLJ2SwqDkEN3a6I2kgNFAJry6JBs1SS7q\nkmyc19TRzK6SP+2PeSykH0vSFui+P6ausZ3Nh4s4mnMLhwOGpoayfHoasWH6Ll/1FlLAaCATXl2S\njZokF3VJNtqVN91iU8EOcusK7u6PyWB+ymz8zfoWGA3tNn656Ty5129jNBiYMjyGxZOSCXiE85V6\no64KGI81a9as6bmu6KOlpaPbru3n59Wt1xcPT7JRk+SiLslGO4unP2OjRpIQEMe1xhtcrs3js/KT\nmI1mEiyxGA3azz36JnHRgQxPCSEpKoCSW43klNSSda4MgKQoCx4PeH9MX+Pn5/XA/yZ3YL5G/sWi\nLslGTZKLuiSbR2O1W++er7SfVmsrkb7hLElboMv+mC9nY7XdOV9p26clNLV2EhrgxdKpqYwdGIlR\nx3047kiWkDSQCa8uyUZNkou6JBt93Nkf8wnZZcdx4OCxkH4sTV9ItF/kQ1/zm7Jpaetk57Fr7D99\nA6vNQXK0hcwZ6fSLD3rUr+C2pIDRQCa8uiQbNUku6pJs9PX1/TGTYzOYl/xw+2O6yqbqdiubDhdx\n8kolAKP6h7N8WioRwb7f+PneTAoYDWTCq0uyUZPkoi7JRn8Oh4NLNVfYXLCTytZqfE0+zEuezZTY\n8XgYPZy+jjPZFJbVs/5AAUXlDXgYDcwcFcfCiUn4eXd9UGRvIgWMBjLh1SXZqElyUZdk032sditH\nSo+y++p+Wq1tRPpGsDT9zv4YZzibjcPh4FRuJRuziqiub8PP28TCicnMGPnggyJ7EylgNJAJry7J\nRk2Si7okm+7X2NF05/0xd/fHDAzpz5L0Bd+6P0ZrNp3WOwdF7jh6ldZ2KxHBPiyflsbIfmG6vnBP\nNVLAaCATXl2SjZokF3VJNj3n/v0x45mfPBs/8zfvW3nYbBpbOtj+6VUOnS3D7nDQLz6IzBlpJEcH\nPOpXUJIUMBrIhFeXZKMmyUVdkk3P+qb9MfOT5zA5NuO+/TGPms3NmmY2HCriXGE1ABmDIlk6JZXQ\nQO9H+g6qkQJGA5nw6pJs1CS5qEuycQ2r3crh0qPsubs/Jso3giXpCxkU2v/eZ/TK5sq1OtYfLOB6\nRRNmk5E5Y+KZl5GIj5d+B1K6khQwGsiEV5dkoybJRV2SjWs1djSxs+QTPis7cWd/TGh/lqYtJMov\nQtds7A4Hxy7dYvORYuoa2wnwNbN4cgqTh0XjYXTvjb5SwGggE15dko2aJBd1STZqKGu6yaaCHeTV\nFWI0GJkSO57nRz9Fa4Nd13baO218fPI6e45fp73TRmyYH0/PSGNISqiu7fQkKWA0kAmvLslGTZKL\nuiQbdTgcDi5WX2Zz4U6qWmvwM/vweOJMpsRNwGzUd7nndlM7W44U8+mFmziAQckhZE5PIy7CX9d2\neoIUMBrIhFeXZKMmyUVdko16Ou++P+bjawdo7mwl1DuERalzGRkxVPfHoW9UNvHRwQJyrtZhMMDk\nodE8NTmFQP8HH5CoGilgNJAJry7JRk2Si7okG3X5BBh5//NtHC49is1hIzkggSXpC0gJTNK1HYfD\nwcXiWtYfLOBmTQteZg/mZSQwZ2wCXmbn3xzsKlLAaCATXl2SjZokF3VJNur6Ipuqlhq2Fe3mbNVF\nAIaHD2FR6lwifMN0bc9mt3Pk/E22ZhfT2NJJsMWLJVNSGD84SukTr6WA0UAmvLokGzVJLuqSbNT1\n9WyK66+yuWAnJQ3X8TB4MCVuPE8kzXyogyK70tpuZffxa3x88gZWm53ESAuZM9IYkBisazt6kQJG\nA5nw6pJs1CS5qEuyUdc3ZeNwODhbdZGthbupaavFx+TDE0kzmBo3UfeNvjX1bWw6UsTxnAoARqSH\nsXx6GlEhap14LQWMBjLh1SXZqElyUZdko66usum0W8kuPcqeqwdosX6x0fcJRkYM032jb3F5A+sP\nFlBQWo+H0cC0EbEsmpSMv48aJ15LAaOBTHh1STZqklzUJdmoy5lsmjtb2Hv1wL2NvkkBCSxJW0Bq\nUJKufXE4HJzJr2LDoSIqb7fi62ViwYQkZo6Kw2xy7YvwpIDRQCa8uiQbNUku6pJs1KUlm6qWGrYV\n7+Fs5QWg+zb6Wm12Dt498bq5zUp4kDfLpqUxun+4y068lgJGA5nw6pJs1CS5qEuyUdfDZFNcf+3u\nRt9rdzb6xo7niWT9N/o2tXay47OrHDxTis3uIC02kMwZaaTGBurajjOkgNFAJry6JBs1SS7qkmzU\n9bDZfLHRd1vhbqrbavExefNE0kymxk7A7KHvvpWKuhY2Hiri8/wqAMY+FsGyqamEBfno2k5XpIDR\nQCa8uiQbNUku6pJs1PWo2XTarWSXHWNPyf67G32DeTJ1LqO6YaNv3vU61h8s5OqtRkweRmaPjmP+\n+CR8vbv/xGspYDSQCa8uyUZNkou6JBt16ZVNS2cLe7600TcxIJ4laQtIC0rWoZd/Ync4OHG5gk2H\ni6htaMffx8yiSclMHR6DyaP7NvpKAaOBTHh1STZqklzUJdmoS+9sqltr2Fa0hzP3NvoOvrvRN1y3\nNgA6Om3sO32Dnceu0d5hIzrUl1Vz+nfbi/CkgNFAJry6JBs1SS7qkmzU1V3ZFNdfY0vhTorrr2E0\nGJkSO565SbPw99R3o299cwfbsos5fL6cqBBffvydDF2v/4WuCphufcA7Pz+fWbNm8f7773/l59nZ\n2fTv3//en7dv387SpUtZvnw5GzZs6M4uCSGEEL1WSmAi/zTyBf5q8CpCvIPJKv2MNcfXsu9aFp22\nTt3aCfTz5PknBvDmX2fwD0uH6nZdLbptB05LSwtvvPEG48eP/8rP29vb+fWvf014ePi9z7333nts\n3LgRs9nMsmXLmD17NkFBQd3VNSGEEKLXMhgMjIgYwpCwx8guO86ekv1sLdpNdtkx3Tf6Rga77uiB\nbrsD4+npyW9+8xsiIiK+8vNf/vKXrFixAk9PTwDOnz/PkCFDsFgseHt7M3LkSM6cOdNd3RJCCCH6\nBJPRxPT4SawZ/8/MjJ9CfXsD/5vzAW9//i6Ft0tc3b1H1m13YEwmEybTVy9fUlJCbm4u//iP/8jb\nb78NQHV1NSEhIfc+ExISQlVVVZfXDg72xWTy0L/Td3W15iZcS7JRk+SiLslGXT2XjYX/E/Msi5tm\n8ccL2zh643N+euYXjI0dzophi4mxRPZQP/TV/Q9xf8m///u/88orr3T5GWf2FNfVtejVpfvIpjd1\nSTZqklzUJdmoyxXZGPHmufRMJkRksLlwFyfLznG6/AKTY8czrxs2+urBZZt4v6yiooLi4mJ+8IMf\n8PTTT1NZWcnKlSuJiIigurr63ucqKyvvW3YSQgghhD6SAxP5p5F/y3cGryLUO5jDpZ/x+jH9N/p2\ntx67AxMZGcn+/fvv/XnGjBm8//77tLW18corr9DQ0ICHhwdnzpzh5Zdf7qluCSGEEH2OwWBgeMQQ\nBn9to++RsmMsSnmCkZHDMBpcexL1t+m2AubSpUusXbuWsrIyTCYTH3/8MT//+c/ve7rI29ub1atX\n85d/+ZcYDAb+7u/+DotF1myFEEKI7vbFRt9xUSPZe+0gh298xv9e/iMHb3zKU2nzSQ9OcXUXH0he\nZPc1smasLslGTZKLuiQbdamaTXVrLduL9vB55XkAhoUNYlHaPCJ1fqOvs7raA9Ojm3iFEEIIoa4w\nnxD+YvBzTK+fzJbCnZyvzuFizRUmx2YwN2kWFk9/V3fxHilghBBCCPEVyYEJfH/k33K+Ooethbs4\nXHqUEzfP8HjSdKbHTcLsYXZ1F6WAEUIIIcT9DAYDw8MHMzh0wL2NvtuK9nCk9BhPpj7B6MjhLt3o\nq/YWYyGEEEK41J/e6PtDZiVMpbGjkd9f/pC3T79LQV2Ry/olBYwQQgghvpWv2Yen0ubzWsaLjI4c\nzvXGUn529lesz9vikv7IEpIQQgghnBbqE8KfD1rB9PhJ7Cj6mObO7ns7flekgBFCCCGEZkkBCfz9\niO+4rH1ZQhJCCCGE25ECRgghhBBuRwoYIYQQQrgdKWCEEEII4XakgBFCCCGE25ECRgghhBBuRwoY\nIYQQQrgdKWCEEEII4XakgBFCCCGE25ECRgghhBBuRwoYIYQQQrgdKWCEEEII4XakgBFCCCGE2zE4\nHA6HqzshhBBCCKGF3IERQgghhNuRAkYIIYQQbkcKGCGEEEK4HSlghBBCCOF2pIARQgghhNuRAkYI\nIYQQbkcKmC958803yczM5JlnnuHChQuu7o74krfeeovMzEyWLl3KJ5984uruiC9pa2tj1qxZbN68\n2dVdEV+yfft2nnzySZYsWUJWVparuyOA5uZmvvvd77Jq1SqeeeYZsrOzXd0lt2ZydQdUcfLkSa5d\nu8b69espKiri5ZdfZv369a7ulgCOHz9OQUEB69evp66ujqeeeoo5c+a4ulvirl/84hcEBga6uhvi\nS+rq6njvvffYtGkTLS0t/PznP2fatGmu7laft2XLFpKTk1m9ejUVFRX82Z/9GXv37nV1t9yWFDB3\nHTt2jFmzZgGQmppKfX09TU1N+Pv7u7hnYsyYMQwdOhSAgIAAWltbsdlseHh4uLhnoqioiMLCQvnL\nUTHHjh1j/Pjx+Pv74+/vzxtvvOHqLgkgODiYvLw8ABoaGggODnZxj9ybLCHdVV1d/ZXBFBISQlVV\nlQt7JL7g4eGBr68vABs3bmTKlClSvChi7dq1vPTSS67uhvia0tJS2tra+Ju/+RtWrFjBsWPHXN0l\nAcyfP5/y8nJmz57NypUr+eEPf+jqLrk1uQPzAHLCgnr279/Pxo0b+Z//+R9Xd0UAW7duZfjw4cTH\nx7u6K+Ib3L59m3fffZfy8nKef/55Dh06hMFgcHW3+rRt27YRExPDb3/7W3Jzc3n55Zdl79gjkALm\nroiICKqrq+/9ubKykvDwcBf2SHxZdnY2v/zlL/nv//5vLBaLq7sjgKysLG7cuEFWVha3bt3C09OT\nqKgoJkyY4Oqu9XmhoaGMGDECk8lEQkICfn5+1NbWEhoa6uqu9Wlnzpxh0qRJAAwYMIDKykpZDn8E\nsoR018SJE/n4448ByMnJISIiQva/KKKxsZG33nqLX/3qVwQFBbm6O+Kun/3sZ2zatImPPvqI5cuX\n88ILL0jxoohJkyZx/Phx7HY7dXV1tLS0yH4LBSQmJnL+/HkAysrK8PPzk+LlEcgdmLtGjhzJoEGD\neOaZZzAYDLz++uuu7pK4a/fu3dTV1fG9733v3s/Wrl1LTEyMC3slhLoiIyN5/PHHefrppwF45ZVX\nMBrl36uulpmZycsvv8zKlSuxWq2sWbPG1V1yawaHbPYQQgghhJuRklwIIYQQbkcKGCGEEEK4HSlg\nhBBCCOF2pIARQgghhNuRAkYIIYQQbkcKGCFEtyotLWXw4MGsWrXq3im8q1evpqGhwelrrFq1CpvN\n5vTnn332WU6cOPEw3RVCuAkpYIQQ3S4kJIR169axbt06PvzwQyIiIvjFL37h9O+vW7dOXvglhPgK\neZGdEKLHjRkzhvXr15Obm8vatWuxWq10dnby2muvMXDgQFatWsWAAQO4cuUKv//97xk4cCA5OTl0\ndHTw6quvcuvWLaxWK4sWLWLFihW0trby/e9/n7q6OhITE2lvbwegoqKCH/zgBwC0tbWRmZnJsmXL\nXPnVhRA6kQJGCNGjbDYb+/btY9SoUbz44ou89957JCQk3He4na+vL++///5XfnfdunUEBATwk5/8\nhLa2NubNm8fkyZM5evQo3t7erF+/nsrKSmbOnAnAnj17SElJ4V/+5V9ob29nw4YNPf59hRDdQwoY\nIUS3q62tZdWqVQDY7XZGjx7N0qVLeeedd/jRj35073NNTU3Y7XbgzvEeX3f+/HmWLFkCgLe3N4MH\nDyYnJ4f8/HxGjRoF3DmYNSUlBYDJkyfzwQcf8NJLLzF16lQyMzO79XsKIXqOFDBCiG73xR6YL2ts\nbMRsNt/38y+Yzeb7fmYwGL7yZ4fDgcFgwOFwfOWsny+KoNTUVHbt2sWpU6fYu3cvv//97/nwww8f\n9esIIRQgm3iFEC5hsViIi4vj8OHDAJSUlPDuu+92+TvDhg0jOzsbgJaWFnJychg0aBCpqamcPXsW\ngJs3b1JSUgLAjh07uHjxIhMmTOD111/n5s2bWK3WbvxWQoieIndghBAus3btWv7t3/6NX//611it\nVl566aUuP79q1SpeffVVnnvuOTo6OnjhhReIi4tj0aJFHDx4kBUrVhAXF8eQIUMASEtL4/XXX8fT\n0xOHw8F3vvMdTCb5354QvYGcRi2EEEIItyNLSEIIIYRwO1LACCGEEMLtSAEjhBBCCLcjBYwQQggh\n3I4UMEIIIYRwO1LACCGEEMLtSAEjhBBCCLcjBYwQQggh3M7/B1c0s7AT4vwpAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb
new file mode 100644
index 0000000..c19b906
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,2006 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "first_steps_with_tensor_flow.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "ajVM7rkoYXeL",
+        "ci1ISxxrZ7v0"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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",
+        "outputId": "417de690-31da-4cd0-c22b-bdb7c6a9f81b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        }
+      },
+      "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": 0,
+      "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>534</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>32.6</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3098.0</td>\n",
+              "      <td>490.0</td>\n",
+              "      <td>1391.0</td>\n",
+              "      <td>484.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>170.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10266</th>\n",
+              "      <td>-120.0</td>\n",
+              "      <td>36.7</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1902.0</td>\n",
+              "      <td>370.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>358.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>70.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1199</th>\n",
+              "      <td>-117.1</td>\n",
+              "      <td>32.8</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>1877.0</td>\n",
+              "      <td>519.0</td>\n",
+              "      <td>898.0</td>\n",
+              "      <td>483.0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>112.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14816</th>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>1543.0</td>\n",
+              "      <td>307.0</td>\n",
+              "      <td>859.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>138.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8343</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1495.0</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>598.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>265.4</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>2065</th>\n",
+              "      <td>-117.3</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>152.0</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>1275.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>162.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5434</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>1276.0</td>\n",
+              "      <td>226.0</td>\n",
+              "      <td>545.0</td>\n",
+              "      <td>209.0</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>230.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7746</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>3039.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>1421.0</td>\n",
+              "      <td>564.0</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>345.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14113</th>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>2040.0</td>\n",
+              "      <td>294.0</td>\n",
+              "      <td>787.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>8.8</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>719</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>32.7</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>3669.0</td>\n",
+              "      <td>617.0</td>\n",
+              "      <td>1694.0</td>\n",
+              "      <td>585.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>133.9</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "534       -117.0      32.6                11.0       3098.0           490.0   \n",
+              "10266     -120.0      36.7                33.0       1902.0           370.0   \n",
+              "1199      -117.1      32.8                24.0       1877.0           519.0   \n",
+              "14816     -122.2      37.8                47.0       1543.0           307.0   \n",
+              "8343      -118.5      34.2                38.0       1495.0           300.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "2065      -117.3      34.0                 8.0        152.0            19.0   \n",
+              "5434      -118.2      34.2                43.0       1276.0           226.0   \n",
+              "7746      -118.4      33.9                27.0       3039.0           606.0   \n",
+              "14113     -122.1      37.4                30.0       2040.0           294.0   \n",
+              "719       -117.0      32.7                35.0       3669.0           617.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "534        1391.0       484.0            5.0               170.4  \n",
+              "10266      1168.0       358.0            2.7                70.8  \n",
+              "1199        898.0       483.0            2.2               112.5  \n",
+              "14816       859.0       292.0            3.0               138.8  \n",
+              "8343        598.0       280.0            3.5               265.4  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "2065       1275.0        20.0            1.6               162.5  \n",
+              "5434        545.0       209.0            4.2               230.7  \n",
+              "7746       1421.0       564.0            5.6               345.5  \n",
+              "14113       787.0       278.0            8.8               500.0  \n",
+              "719        1694.0       585.0            3.9               133.9  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "41162fed-96c6-45bc-a1e0-18295f762a5e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 0,
+      "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": 25
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "outputId": "108a7ad6-68b6-46e0-e5d7-17e497b89756",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "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": 0,
+      "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",
+        "outputId": "aa684357-3bbc-4430-dfff-f6ba87bd8e3b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        }
+      },
+      "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": 0,
+      "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",
+        "outputId": "e2cecb8b-0dbc-439d-9f2e-7e9652c072b1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "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": 0,
+      "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": 33
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "9c5a2d94-9a12-4a9c-afbc-2e4ea3380c2c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 361
+        }
+      },
+      "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": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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",
+        "outputId": "13318121-5496-455b-c150-2e1fd3d997c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.0001,\n",
+        "    steps=100,\n",
+        "    batch_size=1\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.42\n",
+            "  period 02 : 204.04\n",
+            "  period 03 : 194.62\n",
+            "  period 04 : 187.86\n",
+            "  period 05 : 180.53\n",
+            "  period 06 : 174.58\n",
+            "  period 07 : 171.74\n",
+            "  period 08 : 168.84\n",
+            "  period 09 : 167.79\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         113.7    207.3\n",
+              "std           93.7    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           62.9    119.4\n",
+              "50%           91.5    180.4\n",
+              "75%          135.5    265.0\n",
+              "max         1631.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>113.7</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>93.7</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>62.9</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>91.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>135.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1631.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 167.79\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "outputId": "228b7d07-c6f5-4e43-a5ae-1042cc1a276c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.42\n",
+            "  period 02 : 204.04\n",
+            "  period 03 : 195.33\n",
+            "  period 04 : 187.55\n",
+            "  period 05 : 180.80\n",
+            "  period 06 : 175.66\n",
+            "  period 07 : 172.08\n",
+            "  period 08 : 169.46\n",
+            "  period 09 : 167.62\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         114.7    207.3\n",
+              "std           94.6    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           63.5    119.4\n",
+              "50%           92.3    180.4\n",
+              "75%          136.8    265.0\n",
+              "max         1646.5    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>114.7</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>94.6</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>63.5</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>92.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>136.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1646.5</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): 167.62\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "outputId": "97580de8-d3f9-4bdf-b80e-fb8305a2d4e3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "train_model(\n",
+        "    learning_rate=0.0001,\n",
+        "    steps=1000,\n",
+        "    batch_size=1,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 188.58\n",
+            "  period 01 : 176.39\n",
+            "  period 02 : 177.76\n",
+            "  period 03 : 184.28\n",
+            "  period 04 : 190.54\n",
+            "  period 05 : 198.31\n",
+            "  period 06 : 201.80\n",
+            "  period 07 : 206.44\n",
+            "  period 08 : 208.39\n",
+            "  period 09 : 203.62\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         204.4    207.3\n",
+              "std          164.1    116.0\n",
+              "min            0.4     15.0\n",
+              "25%          113.0    119.4\n",
+              "50%          166.9    180.4\n",
+              "75%          246.1    265.0\n",
+              "max         5102.5    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>204.4</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>164.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.4</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>113.0</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>166.9</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>246.1</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>5102.5</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): 203.62\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ci1ISxxrZ7v0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SjdQQCduZ7BV",
+        "colab_type": "code",
+        "outputId": "f41b0390-7b2f-4a04-9ba4-a28e1579c71c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        }
+      },
+      "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": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.84\n",
+            "  period 02 : 204.86\n",
+            "  period 03 : 196.59\n",
+            "  period 04 : 189.52\n",
+            "  period 05 : 183.91\n",
+            "  period 06 : 180.02\n",
+            "  period 07 : 177.64\n",
+            "  period 08 : 176.42\n",
+            "  period 09 : 175.93\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         122.7    207.3\n",
+              "std           98.5    116.0\n",
+              "min            0.3     15.0\n",
+              "25%           67.8    119.4\n",
+              "50%          100.1    180.4\n",
+              "75%          147.7    265.0\n",
+              "max         3061.6    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>122.7</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>98.5</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>67.8</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>100.1</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>147.7</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3061.6</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): 175.93\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFEqIiIiIiIiIiCkUSoiIiIiIiIiIKf4/v4ZiRGSwEeAAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb
new file mode 100644
index 0000000..9e51658
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,2086 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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": "4656155d-b27e-4bc7-8dc3-c50d59567780"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.5                28.5       2665.0           543.8   \n",
+              "std         2.1        2.0                12.6       2205.3           427.0   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1469.0           299.0   \n",
+              "50%        34.2     -118.5                29.0       2142.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3164.2           652.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1440.5       505.5            3.9               2.0  \n",
+              "std        1145.5       391.0            1.9               1.3  \n",
+              "min           6.0         2.0            0.5               0.0  \n",
+              "25%         798.0       283.0            2.6               1.5  \n",
+              "50%        1170.0       410.0            3.6               1.9  \n",
+              "75%        1720.0       608.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2665.0</td>\n",
+              "      <td>543.8</td>\n",
+              "      <td>1440.5</td>\n",
+              "      <td>505.5</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>2205.3</td>\n",
+              "      <td>427.0</td>\n",
+              "      <td>1145.5</td>\n",
+              "      <td>391.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>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>1469.0</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>798.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2142.0</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>3164.2</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1720.0</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>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.7     -119.6                28.9       2592.5           528.9   \n",
+              "std         2.1        2.0                12.5       2117.2           407.8   \n",
+              "min        32.5     -124.2                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                19.0       1440.8           293.0   \n",
+              "50%        34.3     -118.5                29.0       2098.0           430.0   \n",
+              "75%        37.7     -118.0                37.0       3123.2           639.0   \n",
+              "max        41.9     -114.6                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1403.4       490.9            3.9               2.0  \n",
+              "std        1153.1       368.4            1.9               0.9  \n",
+              "min           3.0         1.0            0.5               0.2  \n",
+              "25%         770.8       277.0            2.6               1.5  \n",
+              "50%        1158.0       405.5            3.5               1.9  \n",
+              "75%        1722.0       598.0            4.7               2.3  \n",
+              "max       35682.0      5050.0           15.0              29.4  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.9</td>\n",
+              "      <td>2592.5</td>\n",
+              "      <td>528.9</td>\n",
+              "      <td>1403.4</td>\n",
+              "      <td>490.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2117.2</td>\n",
+              "      <td>407.8</td>\n",
+              "      <td>1153.1</td>\n",
+              "      <td>368.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>1440.8</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>770.8</td>\n",
+              "      <td>277.0</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>2098.0</td>\n",
+              "      <td>430.0</td>\n",
+              "      <td>1158.0</td>\n",
+              "      <td>405.5</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>3123.2</td>\n",
+              "      <td>639.0</td>\n",
+              "      <td>1722.0</td>\n",
+              "      <td>598.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</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>29.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.4\n",
+              "std                 115.5\n",
+              "min                  15.0\n",
+              "25%                 120.6\n",
+              "50%                 181.0\n",
+              "75%                 265.5\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>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>120.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.0</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                207.0\n",
+              "std                 117.2\n",
+              "min                  15.0\n",
+              "25%                 118.2\n",
+              "50%                 178.6\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>207.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>117.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>178.6</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": {
+        "colab_type": "text",
+        "id": "NqIbXxx222ea"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Train the Neural Network\n",
+        "\n",
+        "Next, we'll train the neural network."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "6k3xYlSg27VB",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "De9jwyy4wTUT",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural network model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "W-51R3yIKxk4",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    my_optimizer,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A tuple `(estimator, training_losses, validation_losses)`:\n",
+        "      estimator: the trained `DNNRegressor` object.\n",
+        "      training_losses: a `list` containing the training loss values taken during training.\n",
+        "      validation_losses: a `list` containing the validation loss values taken during training.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor, training_rmse, validation_rmse"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "KueReMZ9Kxk7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "outputId": "6b581946-469d-4bf1-c3d8-d28e69c5920f"
+      },
+      "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": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 138.36\n",
+            "  period 01 : 115.55\n",
+            "  period 02 : 109.27\n",
+            "  period 03 : 103.26\n",
+            "  period 04 : 100.72\n",
+            "  period 05 : 98.70\n",
+            "  period 06 : 101.12\n",
+            "  period 07 : 97.73\n",
+            "  period 08 : 98.06\n",
+            "  period 09 : 97.33\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   97.33\n",
+            "Final RMSE (on validation data): 97.41\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\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": "8c20f0a3-f9e4-469b-b7a8-e1a2b2e99894"
+      },
+      "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",
+        "  \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",
+        "\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=0.001),\n",
+        "    steps=5000,\n",
+        "    batch_size=150,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 222.10\n",
+            "  period 01 : 165.70\n",
+            "  period 02 : 117.27\n",
+            "  period 03 : 110.75\n",
+            "  period 04 : 102.85\n",
+            "  period 05 : 92.71\n",
+            "  period 06 : 82.69\n",
+            "  period 07 : 78.06\n",
+            "  period 08 : 75.93\n",
+            "  period 09 : 74.47\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   74.47\n",
+            "Final RMSE (on validation data): 72.34\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "d4bbc924-7b5d-471a-862d-bc4e292cedae"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize_linear_scale(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+        "  processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+        "  processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+        "  processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+        "    steps=2000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.65\n",
+            "  period 01 : 112.43\n",
+            "  period 02 : 100.99\n",
+            "  period 03 : 87.09\n",
+            "  period 04 : 78.58\n",
+            "  period 05 : 75.80\n",
+            "  period 06 : 74.17\n",
+            "  period 07 : 73.11\n",
+            "  period 08 : 72.27\n",
+            "  period 09 : 71.81\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   71.81\n",
+            "Final RMSE (on validation data): 69.43\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhwGGiIiI6g4DDBEREdWd/wcKsFLEqZYNFAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MrwtdStNJ6ZQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Optimizer\n",
+        "\n",
+        "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+        "\n",
+        "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate.  This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training.  You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`.  Note that you may need to use a larger learning rate with Adagrad.\n",
+        "\n",
+        "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad.  To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AD9Tj7lC5KVH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Adagrad**"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "61GSlDvF7-7q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "12475b2d-7703-4363-8c0c-c36a86c345db"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n",
+        "#\n",
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=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)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 75.22\n",
+            "  period 01 : 72.01\n",
+            "  period 02 : 71.35\n",
+            "  period 03 : 70.77\n",
+            "  period 04 : 69.58\n",
+            "  period 05 : 68.51\n",
+            "  period 06 : 68.81\n",
+            "  period 07 : 66.92\n",
+            "  period 08 : 66.62\n",
+            "  period 09 : 66.21\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   66.21\n",
+            "Final RMSE (on validation data): 63.75\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGACAYAAACDX0mmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEdP6/+YdT1qs641MAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aKKlyfQU5Oxh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "***Adam***"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3a24Kor-5Vzw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "c0f28315-3cd3-49a9-e2a2-86a2123d57f9"
+      },
+      "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=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)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 120.62\n",
+            "  period 01 : 100.35\n",
+            "  period 02 : 72.54\n",
+            "  period 03 : 70.12\n",
+            "  period 04 : 69.24\n",
+            "  period 05 : 69.51\n",
+            "  period 06 : 67.73\n",
+            "  period 07 : 66.92\n",
+            "  period 08 : 66.45\n",
+            "  period 09 : 66.02\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   66.02\n",
+            "Final RMSE (on validation data): 63.39\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnP8HElqUr7Pev4EAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jgjWHa0f6xWM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 376
+        },
+        "outputId": "1ded8ed6-105b-4ce3-fd44-4a75b745900c"
+      },
+      "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": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFnCAYAAABdOssgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkUi2MFLIJRKJRCLZwvx/b64WvOiQndkAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "FSPZIiYgyh93"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "X1QcIeiKyni4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, let's try Adagrad."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ntn4jJxnypGZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "47ee097f-f04f-406a-c063-bc265252a5e9"
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 83.46\n",
+            "  period 01 : 74.93\n",
+            "  period 02 : 74.39\n",
+            "  period 03 : 75.15\n",
+            "  period 04 : 71.40\n",
+            "  period 05 : 71.02\n",
+            "  period 06 : 70.06\n",
+            "  period 07 : 70.40\n",
+            "  period 08 : 67.93\n",
+            "  period 09 : 67.26\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   67.26\n",
+            "Final RMSE (on validation data): 64.68\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xd4VFX6wPHvlEz6pPcQEkIJJYEI\noQWkSYcfRUEUsLsriOuurOJacNeG7oqKBXStC3YQRRCsiILUQEhooYSSkN57m8z9/REYiCkkIZOZ\nkPfzPHlk5tx7zzs5M86bc849R6UoioIQQgghRDultnQAQgghhBBXQ5IZIYQQQrRrkswIIYQQol2T\nZEYIIYQQ7ZokM0IIIYRo1ySZEUIIIUS7prV0AEK0Fz169CAoKAiNRgNAdXU1UVFRPPHEEzg4OLT4\nul988QWzZ8+u8/z69ev5xz/+wVtvvcWoUaNMz5eXlzN06FDGjRvHCy+80OJ6myopKYnnn3+eM2fO\nAGBvb8+iRYu44YYbzF53c6xcuZKkpKQ6v5M9e/Zw9913ExgYWOec7777rq3Cuyrnz59nzJgxhISE\nAKAoCp6enjz++OP06tWrWddavnw5/v7+3HLLLU0+Z8OGDaxbt441a9Y0qy4h2ookM0I0w5o1a/D1\n9QWgsrKSv/3tb7z99tv87W9/a9H1srKyePfdd+tNZgD8/PzYtGlTrWTml19+Qa/Xt6i+lvj73//O\ntGnTeOuttwCIi4vj9ttvZ8uWLfj5+bVZHFfDz8+v3SQuDdFoNLVew+bNm7n//vv5/vvv0el0Tb7O\n4sWLzRGeEBYlw0xCtJBOp2P48OEcO3YMgIqKCpYuXcr48eOZOHEiL7zwAtXV1QAkJCQwZ84cJkyY\nwLRp09i+fTsAc+bMITU1lQkTJlBZWVmnjuuuu449e/ZQVlZmem7z5s1ER0ebHldWVvLss88yfvx4\nRo8ebUo6AGJjY5k5cyYTJkxg0qRJ7Ny5E6j5S3/YsGGsXr2aqVOnMnz4cDZv3lzv6zxx4gR9+/Y1\nPe7bty/ff/+9Kal74403GDFiBNOnT+e///0vo0ePBuDRRx9l5cqVpvMuf3yluJ5//nnmzZsHwP79\n+7nxxhsZO3Yss2fPJjk5GajpofrrX//KqFGjmDdvHunp6VdosfqtX7+eRYsWcfvtt/Pvf/+bPXv2\nMGfOHB588EHTF/+WLVuYMmUKEyZM4LbbbiMpKQmA119/nSeeeIKbbrqJDz/8sNZ1H3zwQd5//33T\n42PHjjFs2DCMRiOvvPIK48ePZ/z48dx2221kZGQ0O+5JkyZRXl7O6dOnAfj888+ZMGECo0eP5qGH\nHqK8vByo+b0vW7aMqVOnsmXLllrt0ND70mg08vTTTzNy5EhuuukmEhISTPXu3buXGTNmMGnSJCZO\nnMiWLVuaHbsQrU4RQjRJ9+7dlbS0NNPj/Px8Ze7cucrKlSsVRVGUt99+W7n33nuVqqoqpaysTLnx\nxhuVr7/+WqmurlYmTpyobNy4UVEURYmPj1eioqKUoqIiZffu3coNN9xQb31ffvmlsmTJEuXvf/+7\n6dyioiJlzJgxytq1a5UlS5YoiqIob7zxhnL77bcrFRUVSklJiTJ9+nRl69atiqIoypQpU5RNmzYp\niqIoX331lamu5ORkpVevXsqaNWsURVGUzZs3K2PHjq03jgceeEAZNWqU8r///U85depUrbLjx48r\nAwYMUDIzM5WqqiplwYIFyqhRoxRFUZQlS5Yob775punYyx83Flfv3r2V9evXm15vVFSUsmPHDkVR\nFGXjxo3KjBkzFEVRlI8++kiZO3euUlVVpeTm5iqjRo0y/U4u19jv+OLvuV+/fsqZM2dMx4eHhys7\nd+5UFEVRUlJSlP79+ytnz55VFEVR3nvvPeX2229XFEVRXnvtNWXYsGFKTk5Onet+++23yty5c02P\nV6xYoTzzzDPKiRMnlHHjximVlZWKoijK6tWrla+++qrB+C7+Xnr27Fnn+aioKCUxMVHZt2+fMmTI\nECU9PV1RFEV58sknlRdeeEFRlJrf+9SpU5Xy8nLT4zfffLPR9+W2bduUcePGKcXFxUpZWZly0003\nKfPmzVMURVFmzpyp7NmzR1EURTlz5ozy0EMPNRq7EG1BemaEaIb58+czYcIExowZw5gxYxg8eDD3\n3nsvANu2bWP27NlotVrs7OyYOnUqv//+O+fPnyc7O5vJkycDEB4ejr+/P4cOHWpSnZMnT2bTpk0A\n/PTTT4waNQq1+tJH95dffuHWW29Fp9Ph4ODAtGnT+OGHHwD4+uuvmThxIgD9+/c39WoAGAwGZs6c\nCUDv3r1JTU2tt/7//Oc/zJ07l40bNzJlyhRGjx7Np59+CtT0mkRFReHl5YVWq2XKlClNek2NxVVV\nVcXYsWNN1/fx8TH1RE2ZMoWkpCRSU1OJiYlh7NixaLVa3Nzcag3F/VFaWhoTJkyo9XP53Jrg4GCC\ng4NNj+3s7BgyZAgAv//+O4MGDaJz584AzJo1iz179mAwGICanip3d/c6dY4cOZKjR4+Sn58PwI8/\n/siECRPQ6/Xk5uayceNGCgoKmD9/PtOnT2/S7+0iRVH4/PPP8fHxITg4mK1btzJp0iR8fHwAuOWW\nW0zvAYAhQ4Zga2tb6xqNvS/37dvHiBEjcHR0xM7OztRWAB4eHnz99dckJiYSHBzM8uXLmxW7EOYg\nc2aEaIaLc2Zyc3NNQyRabc3HKDc3FxcXF9OxLi4u5OTkkJubi7OzMyqVylR28QvN09PzinVGR0fz\nxBNPkJ+fz7fffsvChQtNk3EBioqKWLZsGS+//DJQM+wUEREBwMaNG1m9ejUlJSUYjUaUy7Zi02g0\nponLarUao9FYb/22trbcfffd3H333RQWFvLdd9/x/PPPExgYSEFBQa35Ox4eHld8PU2Jy8nJCYDC\nwkKSk5OZMGGCqVyn05Gbm0tBQQHOzs6m5/V6PSUlJfXWd6U5M5e32x8f5+Xl1XqNzs7OKIpCXl5e\nvede5ODgwNChQ9m2bRv9+/ensLCQ/v37o1KpeP3113n//fd55plniIqK4l//+tcV5x9VV1ebfg+K\notC1a1dWrlyJWq2mqKiIH3/8kR07dpjKq6qqGnx9QKPvy4KCAry9vWs9f9Hzzz/PqlWruPPOO7Gz\ns+Ohhx6q1T5CWIIkM0K0gLu7O/Pnz+c///kPq1atAsDT09P0VzhAfn4+np6eeHh4UFBQgKIopi+O\n/Pz8Jn/x29jYMGrUKL7++mvOnTtHZGRkrWTG29ubu+66q07PREZGBk888QRr166lZ8+enD17lvHj\nxzfrdebm5nLs2DFTz4her2f27Nls376dEydO4OzsTFFRUa3jL/pjglRQUNDsuLy9venSpQvr16+v\nU6bX6xusuzV5eHgQGxtrelxQUIBarcbNze2K544fP54ff/yRvLw8xo8fb2r/wYMHM3jwYEpLS3nx\nxRd56aWXrtjD8ccJwJfz9vZmxowZLFmypFmvq6H3ZWO/W09PT5588kmefPJJduzYwQMPPMDw4cNx\ndHRsct1CtDYZZhKihe68805iY2PZu3cvUDOssG7dOqqrqyktLWXDhg2MGDGCwMBAfH19TRNsDxw4\nQHZ2NhEREWi1WkpLS01DFg2ZPHky77zzTr23Q48ZM4a1a9dSXV2NoiisXLmS3377jdzcXBwcHOjS\npQsGg4HPP/8coMHei/qUl5fzl7/8xTQxFODcuXPExcUxYMAAIiMjiYmJITc3F4PBwNdff206zsvL\nyzRxNDk5mQMHDgA0K66+ffuSlZVFXFyc6ToPP/wwiqLQr18/tm7dSnV1Nbm5ufz2229Nfl3NER0d\nTUxMjGko7LPPPiM6OtrUI9eYUaNGERsby08//WQaqtmxYwf/+te/MBqNODg4EBYWVqt3pCVGjx7N\nDz/8YEo6fvrpJ/773/82ek5j78vIyEh27NhBWVkZZWVlpiSqqqqK+fPnk5mZCdQMT2q12lrDnkJY\ngvTMCNFCTk5O/OlPf+LFF19k3bp1zJ8/n+TkZCZPnoxKpWLChAlMnDgRlUrFyy+/zFNPPcUbb7yB\nvb09K1aswMHBgR49euDi4kJ0dDRfffUV/v7+9dY1cOBAVCoVkyZNqlN26623cv78eSZPnoyiKPTp\n04fbb78dBwcHrr/+esaPH4+HhwePPvooBw4cYP78+bz22mtNeo3+/v6sWrWK1157jWeffRZFUXBy\ncuIf//iH6Q6nm2++mRkzZuDm5sa4ceM4efIkALNnz2bRokWMGzeOXr16mXpfwsLCmhyXnZ0dr732\nGs888wwlJSXY2Njw4IMPolKpmD17NjExMdxwww34+/tzww031OpNuNzFOTN/9O9///uKvwNfX1+e\nffZZFi5cSFVVFYGBgTzzzDNN+v05OTnRu3dvjh8/Tr9+/QCIiori22+/Zfz48eh0Otzd3Xn++ecB\neOSRR0x3JDVH7969ue+++5g/fz5GoxEPDw/+9a9/NXpOY+/LUaNGsW3bNiZMmICnpycjRowgJiYG\nGxsbbrrpJu644w6gpvftiSeewN7evlnxCtHaVMrlg9VCCHEVYmJieOSRR9i6daulQxFCdCDSNyiE\nEEKIdk2SGSGEEEK0azLMJIQQQoh2TXpmhBBCCNGuSTIjhBBCiHat3d+anZVV/62YrcHNzYG8vFKz\nXV+0nLSNdZJ2sV7SNtZJ2qXpvLycGyyTnplGaLUaS4cgGiBtY52kXayXtI11knZpHZLMCCGEEKJd\nk2RGCCGEEO2aJDNCCCGEaNckmRFCCCFEuybJjBBCCCHaNUlmhBBCCNGuSTIjhBBCiHZNkhkhhBDi\nGrZt289NOm7FiuWkpqY0WP7oow+1VkitTpIZIYQQ4hqVlpbKTz9936RjH3xwMf7+AQ2Wv/DCy60V\nVqtr99sZCCGEEKJ+L7/8IseOHWH48CjGjZtIWloqr766kmXLniYrK5OysjLuuutPREcPZ9GiP/HQ\nQ4/wyy8/U1JSTFLSOVJSzvOXvyxmyJBoJk8ew7ff/syiRX8iKmoQBw7EkJ+fz4svvoKnpydPP/0k\n6elphIdHsHXrT3z11eY2e52SzAghhBBt4Iutp9iXkFnrOY1GRXW10uJrRoV5M3t01wbLb7llPuvX\nf0FISChJSWdZufJd8vJyGThwMBMnTiEl5TxPPvko0dHDa52XmZnBSy+9xu7dO9mw4UuGDImuVe7o\n6MiKFatYtep1fvttK/7+gVRWVvDf/37I779v54svPm3xa2oJSWYakJhaQLkR7GQgTgghxDWgZ8/e\nADg76zl27AjffLMelUpNYWFBnWMjIvoB4O3tTXFxcZ3yvn0jTeUFBQWcO3eG8PC+AAwZEo1G07Z7\nTkky04C3vj6CVqvmuXsHoVapLB2OEEKIdm726K51elG8vJzJyipqk/ptbGwA+PHH7ygsLOTNN9+l\nsLCQe+6ZX+fYy5MRRanbc/THckVRUKtrnlOpVKja+HtT+h0a0LOzGxm5pRw/l2fpUIQQQogWUavV\nVFdX13ouPz8fPz9/1Go1v/66laqqqquuJyAgkOPHjwKwd+/uOnWamyQzDRgW4QfA9vg0C0cihBBC\ntEznziEcP55AScmloaKRI0ezc+d2HnxwAfb29nh7e/PBB+9cVT1Dhw6npKSEBQvuJi4uFr3e5WpD\nbxaVUl//UTtiru45RVF48r29ZOaV8coD0Tja2ZilHtEybdk1K5pO2sV6SdtYp2ulXQoLCzhwIIaR\nI8eQlZXJgw8u4JNPvmzVOry8nBsskzkzDVCpVNwwMIjVm4+x92gGo64LtHRIQgghhFVycHBk69af\n+OSTNSiKkQceaNsF9iSZacToAZ1Ys+UY2+PTJJkRQgghGqDVann66WUWq1/mzDTCw8We8C4enE0v\nIjmz7q1pQgghhLA8SWauYPiFicA7ZCKwEEIIYZUkmbmCvl09cXawYdeRdAzVRkuHI4QQQog/kGTm\nCrQaNUN6+1JcVsXBk9mWDkcIIYQQfyDJTBPImjNCCCGuZTfdNJXS0lLWrPmQw4fja5WVlpZy001T\nGz1/27afAdi8eSO//vqL2eJsiNzN1ASBXk6E+Ok5fCaH3MJy3PV2lg5JCCGEaHXz59/R7HPS0lL5\n6afvGTlyDJMmNZ70mIvZkpmSkhKWLFlCQUEBVVVV3H///Xh5efH000+jVqvR6/UsX74ce3t70znr\n169nxYoVBAUFATB06FAWLFhgrhCbZXiEH2fSCtl5OJ0pQ4MtHY4QQghxRXfdNZfnn1+Or68v6elp\n/OMfi/Hy8qasrIzy8nL+9reH6dWrj+n45577JyNHjqFfv0gef/wRKisrTZtOAvzwwxbWrfscjUZN\ncHAoS5Y8zssvv8ixY0f44IN3MBqNuLq6cuONN7Ny5QoOHYrDYKjmxhtnM2HCZBYt+hNRUYM4cCCG\n/Px8XnzxFXx9fa/6dZotmfnqq68ICQlh8eLFZGRkcPvtt+Pp6cmjjz5KREQEL774IuvXr2fu3Lm1\nzps0aRJLliwxV1gtNrCnD5/+fJId8WlMHtK5zTfREkII0b6tP7WJ2MxDtZ7TqFVUG1u+EH+kdzgz\nu05psPz660fx+++/ceONs9m+/Veuv34UoaHduP76kezfv4+PP/4fzz33nzrnff/9Frp0CeUvf1nM\nzz//wE8/fQ9AWVkZy5e/jrOzM/fffy+Jiae45Zb5rF//BXfeeS/vvfc2AAcPHuD06URWrXqfsrIy\nbr99DtdfPxIAR0dHVqxYxapVr/Pbb1uZPfvWFr/+i8w2Z8bNzY38/HwACgsLcXNz46233iIiIgIA\nd3d3U3l74GCnZUAPLzLzyziR3H7iFkII0XHVJDPbAdix41eGDRvBr7/+zIIFd7Nq1esUFBTUe97Z\ns6fp06cvAJGR/U3P6/V6/vGPxSxa9CfOnTtDQUH934cJCUfp1+86AOzt7QkO7kJycjIAfftGAuDt\n7U1xceus4Wa2npnJkyezfv16xo4dS2FhIW+//TZOTk5AzWSiDRs2sGLFijrn7d27l7vvvhuDwcCS\nJUvo1auXuUJs1O60GDoZvAnQBpmeGx7hz64jGeyIT6NHkJtF4hJCCNE+zew6pU4virn3ZurSJZSc\nnCwyMtIpKipi+/ZteHp68+STz5CQcJQ33ni13vMUBdTqmhEI44Weo6qqKl5++d98+OEneHh48sgj\nf22wXpVKxeU7PxoMVabraTSay+ppne0hzZbMbNiwAX9/f9577z0SEhJ47LHHWL9+PaWlpSxYsIC7\n7rqL0NDQWuf07dsXd3d3Ro4cSWxsLEuWLGHjxo2N1uPm5oBWq2n0mJb4fs/PlJ+u4K3/ewGtuub6\nHh5O+P5wnJgTWfzF2Q4H2XzSohrbdExYjrSL9ZK2sU7mbpcxY0azevU7jB8/lry8PMLCeuDl5cxH\nH+1EpVLw8nJGo1Hj6emEnZ0NLi729OrVnaSkRLy8prN79zY0GjX29ipsbLSEhYWQlpbGiRMJODra\nYGtri0ajwsvLGUdHW5yc7AgLC2PVqlV4eTlTUlJCenoq/fr1QqfT4ubmiJeXM05OdlRV2bbK6zdb\nMnPgwAGGDRsGQFhYGJmZmVRWVrJw4UKmTJnCzJkz65wTGhpqSnAiIyPJzc2lurq6Vhb3R3l5pWaJ\nP8y1B7+l7GTHiQP09ggzPT+klw9fbT/D5u2JjOgXYJa6xZVdKzvNXmukXayXtI11aot2iYqK5r77\n7uLDDz+lvLyMZ599im++2cSNN85mw4aNfPjhx1RXG8nOLqa8vIqCgjKGDbuBxx77O7feOo+IiH4Y\njQoGg5b+/QcybdoMunbtxpw583jmmed4/fW3OXToME8++U8cHZ2wsSmnc+cehIR0Y/bsORgMBu69\ndyElJdVUVhrIyyshK6uI4uJySkoqmvz6G0t6VEpr9fH8wfvvv092djaPPPIIKSkp3HXXXUyfPh1F\nUVi4cGG957zzzjv4+fkxZcoUTpw4weLFi6/YM2OuN8HpgnMs3/8mUT6R3NH7FtPzuYXlPLxyJ138\n9Tx+2wCz1C2uTP7HbJ2kXayXtI11knZpusaSGbP1zNx888089thjzJs3D4PBwD//+U8efvhhAgMD\n2bVrFwCDBg1i0aJFLFiwgFWrVjF16lQefvhhPvvsMwwGA88995y5wruiEH0QPo6exGUfoaK6EluN\nDgB3vR29Q9w5fCaXlOwSAjwdLRajEEIIIczYM9NWzJnR/pz+C+uPbuHOXrcwwDfS9PzeYxm8teEI\nEwYGMXt0V7PVLxomf81YJ2kX6yVtY52kXZqusZ4Z2c6gEcM6RwGwLyO21vOR3bxwtNOy83CabD4p\nhBBCWJgkM40I1PvRycmfo7knKK4sMT1vo63ZfLKwtIpDiTkWjFAIIYQQksxcwQDfSIyKkQOZtTfe\nks0nhRBCCOsgycwVDPDphwpVnaGmIB9nOvs4E5+YQ35xhYWiE0IIIYQkM1fgautCN9cunC44S05Z\nbq2yYRF+GBWFXYfTLRSdEEIIISSZaYKoC3cy7cs4WOv5Qb180GrUbI9Pa7UlmYUQQgjRPJLMNEE/\nr3C0Kg37MmJrJS1O9jZc192T9NxSElMKLRihEEII0XFJMtMEDjb29PHsSXpJBinFtSf8Do/wB2B7\nfKolQhNCCCE6PElmmmiAz8WhptoTgXsGu+Ght2VvQibllQZLhCaEEEJ0aJLMNFEfjzDstXbEZBzE\nqFxaKE+tUhEd7kdFZTUxCVkWjFAIIYTomCSZaSIbjQ39vMLJryjgVP6ZWmXDwi+uOSNDTUIIIURb\nk2SmGaIuDDXF/GGoydPVnp6d3Th5voD03FJLhCaEEEJ0WJLMNEM3ty646Jw5kHmIKmPt+THDL6wI\nvENWBBZCCCHalCQzzaBWqenv048yQxlHcxJqlV3X3Qt7Wy2/H06j2iibTwohhBBtRZKZZjItoJde\ne6hJZ6NhcC8fCoorOXw6t75ThRBCCGEGksw0UyenAHwcvDmUc4wyQ3mtsuF9ZahJCCGEaGuSzDST\nSqUiyicSg9HAwazDtco6+zgT6OXEwVPZFJZWWihCIYQQomORZKYFBvj0AyDmD0NNKpWK4RF+VBtl\n80khhBCirUgy0wJeDh6E6IM4nneKgoraezIN7u2DRq2SzSeFEEKINiLJTAsN8I1EQWF/Zlyt550d\ndER28yQ1u4QzaUUWik4IIYToOCSZaaH+3n1Rq9R17moCGHZh88kdsiKwEEIIYXaSzLSQs86JMLdu\nJBWdJ6O09p5MfULccXO2Zc+xDCqqqi0UoRBCCNExSDJzFRpac0atVhEd7ktZRTUHjsvmk0IIIYQ5\nSTJzFSI8e6NT2xCTEVtnsm+0bD4phBBCtAlJZq6CndaWCK/eZJXlcK4ouVaZj5sDPTq5kpCUT2ae\nbD4phBBCmIskM1fp4poz9U8EvrAi8CFZc0YIIYQwF0lmrlIv9x442jiwPyOOamPtyb4Denhjp9Pw\n+6E0jEZZc0YIIYQwB0lmrpJGreE6774UVRVzIi+xVpmtTsPAnj7kFVVw9KxsPimEEEKYgyQzrSDK\n58JdTRl1h5qGR1ycCCybTwohhBDmIMlMK+ji0hkPOzcOZh2isrr2BpNd/PX4ezoSezKL4rIqC0Uo\nhBBCXLvMlsyUlJSwaNEi5s+fz5w5c9i+fTsJCQnMmTOHOXPm8NRTT9U5p6qqisWLF3PLLbcwb948\nkpOT67my9VGpVPT36UdFdSWHso/WKRsW7oehWmH3EZkILIQQQrQ2syUzX331FSEhIaxZs4YVK1bw\n3HPP8dxzz/HYY4/x2WefUVxczK+//lrrnE2bNqHX6/n000+57777WL58ubnCa3WNDTUN6eMrm08K\nIYQQZmK2ZMbNzY38/HwACgsLcXV1JSUlhYiICABGjRrFrl27ap2za9cuxo4dC8DQoUM5cOCAucJr\ndf5OvgQ4+XEk5zjFVSW1ylwcdUSEepCcWUxSRrGFIhRCCCGuTWZLZiZPnkxqaipjx45l3rx5PPLI\nI+j1elO5h4cHWVm1l/rPzs7G3d29JjC1GpVKRWVl7Tko1izKJxKjYiQ281CdsuEXNp+UFYGFEEKI\n1qU114U3bNiAv78/7733HgkJCdx///04Ozubypsy3NKUY9zcHNBqNVcVa2O8vJyvfNAF4xyj2ZC4\nhbjceGb2G1urbLS7I2t+OM6eY5ncPzsSnY35Yu4omtM2ou1Iu1gvaRvrJO1y9cyWzBw4cIBhw4YB\nEBYWRkVFBQaDwVSekZGBt7d3rXO8vb3JysoiLCyMqqoqFEVBp9M1Wk+eGbcK8PJyJiurqBln2NDV\nNYRjWadISErCw96tVungXj5s2ZPEDzvPMKiXT+sG28E0v21EW5B2sV7SNtZJ2qXpGkv6zDbM1Llz\nZ+Li4gBISUnB0dGR0NBQYmJiAPjhhx8YPnx4rXOio6P57rvvAPjll18YNGiQucIzm4sTgfdnHqxT\nZtreQIaahBBCiFZjtmTm5ptvJiUlhXnz5rF48WL++c9/8thjj/Hyyy8zZ84cgoKCGDp0KAALFiwA\nYNKkSRiNRm655RY+/vhjFi9ebK7wzCbSOxyNSlPvXk1+Ho50DXTh6Nk8sgvKLBCdEEIIce1RKe38\nXmFzds+1tPvv7fj/EZ99hMcG/o0AJ79aZdvjUvlgSwLThoUwbVhIa4Xa4UjXrHWSdrFe0jbWSdql\n6SwyzNSRRfleWHOmnt6ZAWHe2Npo2BGfhrF955FCCCGEVZBkxgz6ePTETmNLTMZBjIqxVpm9rZao\nMG9yCstJOJdnoQiFEEKIa4ckM2ag09jQzyucvIp8Thecq1N+aSKwbD4phBBCXC1JZszk0lBT3VWM\nuwW64OPuQMzxLErKZfNJIYQQ4mpIMmMm3d1C0eucic08hMFoqFWmUqkYHuGHodrI3qMZFopQCCGE\nuDZIMmMmapWa/j59KTGUciz3RJ3yoX18UatqNp8UQgghRMtJMmNGpp2067mrydXJlvAu7pxNLyI5\nUzafFEIIIVpKkhkzCnIOxNvBk/jso5QbyuuUD5PNJ4UQQoirJsmMGalUKgb4RFJlrCIu60id8r5d\nPXB2sGH3kQyqDMZ6riCEEEIAJ5ASAAAgAElEQVSIK5FkxsxMQ00ZdYeatBo1Q3r7UlxWRdyp7LYO\nTQghhLgmSDJjZt4OnnTWdyIh9ySFlXWXrB5+Yc0ZmQgshBBCtIwkM20gyicSBYX9GXF1ygK8nOji\nr+fwmRxyC+vOqxFCCCFE4ySZaQPXefdFhareoSaoWRFYUWDn4fQ2jkwIIYRo/ySZaQMuts6EuXfj\nXGEymaVZdcoHhvmg06pl80khhBCiBSSZaSMXJwLHZBysU+Zgp6V/D28y88s4mZzf1qEJIYQQ7Zok\nM22kr1dvbNQ27MuIRamn90UmAgshhBAtI8lMG7HT2hHh2YvM0mySis7XKe8e5IqXqx0xCZmUVRjq\nuYIQQggh6iPJTBsa4NMPqH/NGbVKxbAIfyoNRvYek80nhRBCiKaSZKYN9fLogaPWgf0ZcRiVuiv+\nRvfxRQXskKEmIYQQoskkmWlDWrWWSO9wCiuLOJGXWKfcXW9H7y7uJKYWkpJdYoEIhRBCiPZHkpk2\nFuV7HVD/TtoAwy9sPrlDNp8UQgghmkSSmTbWxaUzbrauHMw6RGV1VZ3yfl09cbTTsvNwOoZq2XxS\nCCGEuBJJZtqYWqVmgE8/yqsrOJxzrE65jbZm88mi0iriE3MsEKEQQgjRvkgyYwFRvhcW0GtgqGnY\nhTVnZCKwEEIIcWWSzFhAgJMf/o6+HMlJoLSqtE55kI8znX2diU/MIb+4wgIRCiGEEO2HJDMWEuUT\niUGpJjbzUL3lwyP8MCoKu2TzSSGEEKJRksxYSP9GFtADGNTLB61Gzfb4tHq3PxBCCCFEDUlmLMTD\n3o1QlxBO5Z8hr7zu5pKOdjb07+FFem4pp1IKLBChEEII0T5IMmNBUb6RKCj17qQNlyYCy+aTQggh\nRMMkmbGgSO9w1Cp1g0NNPTu74aG3Zd+xTMorZfNJIYQQoj5ac1147dq1fPPNN6bHcXFx9O3b1/Q4\nMzOTGTNmcN9995mee/3119m4cSM+Pj4A/N///R+zZs0yV4gW52TjSG+PHhzKPkZqcTr+Tr61ytUq\nFdHhfnzz+1n2JWSaVgcWQgghxCVmS2ZmzZplSkT27t3Lli1beOqpp0zl99xzD9OmTatz3m233ca8\nefPMFZbVifKJ5FD2MWIyDvJ/ThPqlA+L8GPj72fZEZ8myYwQQghRjzYZZnrzzTdZuHCh6fHOnTsJ\nDg7Gz8+vLaq3auGevbDV6IjJiK33riVPF3t6Brtx8nwB6bl116QRQgghOjqzJzPx8fH4+fnh5eVl\nem716tXcdttt9R7/3Xffceedd/LnP/+Z5ORkc4dncTqNjn5e4eSU53G64Fy9x8iKwEIIIUTDzDbM\ndNG6deuYMWOG6XFGRgalpaUEBQXVOXbEiBEMHjyYqKgovv32W5599lnefvvtRq/v5uaAVqtp9bgv\n8vJyNtu1LxrTfSh70vdzuOAwg7uF1ykfN9SBj388ye6j6fxpZgQajczbhrZpG9F80i7WS9rGOkm7\nXD2zJzN79uzhiSeeMD3+9ddfGTx4cL3HRkREmP49evRoXnrppStePy/PfEMvXl7OZGUVme36F/mq\n/XG2ceL3pBimdJqIRl03ORvY05tfDqSwde85+nX1NHtM1q6t2kY0j7SL9ZK2sU7SLk3XWNJn1j/x\nMzIycHR0RKfTmZ47dOgQYWFh9R7/7LPPEhMTA9RMGu7WrZs5w7MaGrWG/j59Kakq5VjuiXqPGS5D\nTUIIIUS9zJrMZGVl4e7uXuc5Dw+PWo+XLl0K1NwB9dJLLzFv3jzeffddHn/8cXOGZ1Uu7qTd0Joz\nnX2cCfRyIu5UNoUllW0ZmhBCCGHVVEo73/jHnN1zbdn9pygK/9z9bworClk2bCl2Wts6x/wYk8yn\nP53k5tFdGT+w7pyjjkS6Zq2TtIv1kraxTtIuTWexYSbRdCqViiifSCqNVcRnH6n3mCG9fdFqVLL5\npBBCCHEZSWasyJWGmpzsbejXzYvU7BLOpEkmL4QQQoAkM1bFx8GLIOdAEnJPUlRZXO8xw02bT6a2\nZWhCCCGE1ZJkxspE+fTDqBjZnxlXb3nvYHfcnG3ZczSDiqrqNo5OCCGEsD6SzFiZ/j79UKEiJr3+\noSa1WkV0uC/lldXsP57ZxtEJIYQQ1keSGSvjYqunh1tXzhQmkV2WU+8xw8JlzRkhhBDiIklmrNCA\nixOB0w/WW+7t5kBYkCsJSflkmnEFZCGEEKI9kGTGCvXz6oNWrWVfAztpw2WbTx5Kb8vQhBBCCKsj\nyYwVstfaEe7Rk4zSTJKLU+o9pn8Pb+x0Gn4/lIbRKGvOCCGE6LgkmbFSF9eciWlgqMnWRsOgXj7k\nFVVw5GxuW4YmhBBCWBVJZqxUL48w7LX2xGQcxKgY6z1mmGnNGZkILIQQouOSZMZK2ai1XOcdTkFl\nISfzTtd7TBc/Pf6ejsSeyKKoVDafFEII0TFJMmPFBvg0vr2BSqViWLgf1UaF3Ucz2jI0IYQQwmpI\nMmPFurqG4GrrwsGsQ1RVV9V7zNA+vmjUKrbHyeaTQgghOiZJZqyYWqVmgE8/ygzlHMlJqPcYvaOO\nvl09OZ9VTFJG/fs5CSGEENcySWasXNQVhprg8onAsvmkEEKIjkeSGSsX4OSHr6MPh7OPUVpVVu8x\n4V3ccXHUsftIBpWy+aQQQogORpIZK6dSqYjyicSgVHMw63C9x2jUaoaG+1JaYeDAyaw2jlAIIYSw\nLElm2oEon37AFYaaZPNJIYQQHZQkM+2Ah707XVyCOZmXSH5FQb3H+Hk40jXQhWNn88jOr384Sggh\nhLgWSTLTTkT5RKKgEJNR//YGAMMj/FCA3w/L5pNCCCE6Dklm2onrvCNQq9TEpDc81BQV5o2tjYYd\n8WkYZc0ZIYQQHYQkM+2Ek86RXu7dSS5OJb2k/tV+7XRaonp6k1NYTsK5vDaOUAghhLAMSWbakUtr\nzjQ+1ASy+aQQQoiOQ5KZdiTcqzc6jY6Y9NgGty7oGuCCj7sD+49nUVJe/xYIQgghxLVEkpl2xFaj\no69nb7LLczlTmFTvMSqViuERfhiqjeyRzSeFEEJ0AJLMtDNRvjVDTTGNrDkztI8vapVKhpqEEEJ0\nCJLMtDNhbt1wsnFkf0Yc1cb6ty5wdbIlItSDc+lFJGUUtXGEQgghRNuSZKad0ag19PfpS3FVCQl5\nJxs87uLmkzsOSe+MEEKIa5skM+3QgIt3NTWy5kxEqAd6Bxt2H8mgymBsq9CEEEKINifJTDsUog/C\n086duOwjVFRX1nuMVqNmSB9fisuqOHgqu40jFE2lKEqDd6YJIYRoGq25Lrx27Vq++eYb0+PDhw/T\np08fSktLcXBwAGDJkiX06dPHdExVVRWPPvooqampaDQali1bRqdOncwVYrulUqkY4BvJd2d/5lDW\nEQZcmBT8R8PC/fh+bzLb41OJCvNu4yhFfcoqDCSmFHDyfAEnz+dzOq2QHp3ceODGcLQa+dtCCCFa\nwmzJzKxZs5g1axYAe/fuZcuWLZw6dYply5bRvXv3es/ZtGkTer2e5cuXs2PHDpYvX86rr75qrhDb\ntSifmmRmX0Zsg8lMgJcTXfz1HDmdS25hOe56uzaOUuQWlnPyfAGnLiQvyVnFXN4R4+xgw6HTOfxv\nSwJ3Te6JSqWyXLBCCNFOmS2Zudybb77JSy+9xEMPPdTocbt27WL69OkADB06lMcee6wtwmuXfB29\n6eQcwNHcExRXluCkc6z3uGERfpxOLeT3w+lMHRrctkF2MEZFITWrhJPn8zmZUsDJ5AJyCstN5VqN\nmm4BLnTr5Eq3QBdCA1zQqtX8+9MD/H44HU9Xe6YNC7HgKxBCiPbJ7MlMfHw8fn5+eHl5AfDaa6+R\nl5dHaGgojz32GHZ2l3oLsrOzcXd3B0CtVqNSqaisrESn0zV4fTc3B7Rajdni9/JyNtu1r9bILoNZ\nE/clJ8uOMy5gRL3HTB4eymc/n2LHoTSCA1wJ8nEm0NsJO9s2yWPNytJtU1FVzcmkPI6eyeXY2Zqf\nkrJLqy47O+gY1NuXXiHu9ArxIDTQBZt63qv/+vNQHn5tOxt2nCE4wJUbBga15ctodZZuF9EwaRvr\nJO1y9Vr8jXb27FmCg4OveNy6deuYMWMGALfddhs9evQgKCiIp556io8//pi77767wXObMjEyL6+0\nyTE3l5eXM1lZ1rtOS5hTGCpUbD21m0iX6xo8bnAvb36LS+OVTw8AoAI8XOzw93QkwNMR/ws/fh4O\n2OnaR5JjibYpKq28MFxUwMmUfM6mFVFtvPQe9Xazp19XD7oF1vS8+Lo71Bo2ym/kvfqXG8N5fs1+\n3lh7EK1KoXewu1lfi7lY+2emI5O2sU7SLk3XWNLX6DfXnXfeyQcffGB6vHLlShYuXAjA0qVLWb16\n9RUr37NnD0888QQAY8eONT0/evRoNm/eXOtYb29vsrKyCAsLo6qqCkVRGu2V6ehcbV3o5hbKibxT\n5JTl4mFf/xfgvHE9GNLbl9TsElKyS0jNLiE1p5T4xBziE3NqHeuhtyPAyxF/D0f8PB0I8HTCz8MB\n+2ugJ6c5FEUhM7+Mk8k1c11OpRSQlnMpGVGrVHT2daJboCtdA1zoFuiCi5Nti+vz83DkgRsjeOmz\nWFZ+dYh/zO1PoLdTa7wUIYS45jX6DWUwGGo93r17tymZaUqvSUZGBo6Ojuh0OhRF4c477+S1115D\nr9ezZ88eunXrVuv46OhovvvuO4YPH84vv/zCoEGDmvt6Opwon0hO5J1iX8ZBJgSPrvcYrUZNjyA3\negS51Xq+uKyqdoJz4af+JMcWP8+aJCfA1JPjiIPdtZHkGKqNJGcWczI5/0LPSwGFJZdue7fVaegd\n4k63QBe6BbjQxd8FW13rDm927+TKPVN68daGI7yyNo4nbhuAm3PLEyQhhOgoGv0m+uOdFZcnME25\n6yIrK8s0B0alUjF79mzuuOMO7O3t8fHx4YEHHgBgwYIFrFq1ikmTJrFz505uueUWdDodL7zwQrNf\nUEfTz6sPnx9fz76MWMZ3HtWsu2Gc7G3o3smV7p1caz1fXFZFWk7dJOfw6VwOn86tdaybs23t4SoP\nR/w9HXCws2mV12cuZRUGElMLTD0vp9MKqay6tLigq5OOgT29TT0vgd6OaNTmv3V6YE8fcgrKWbst\nkVfXxvHo3Os6XK+YEEI0V7P+L9nc20b79OnDu+++a3o8adIkJk2aVOe4VatWAZjWlhFN52BjTx/P\nnhzMOkxKcRqBzv5XfU0ne5sL8z5qJzml5VWkZpeSmlNSq0fnyJlcjpypneS4OukI8HSs6c25LNlx\ntFCSk1tYzqmUS8nLH2+RDvByNM116RbggoeLncVuk54wKIisgnK2xaaw6uvD/OWmCFmDRgghGtFo\nMlNQUMCuXbtMjwsLC9m9ezeKolBYWGj24ETTRPlEcjDrMPsyYlslmWmIg50NXQNd6BroUuv50nID\naZcnOBf+feRsHkfO5tU61sVRZ5pwfPnkYyf71ktyTLdIp9QkLk25RdpSSVZ9VCoVc8d2I7ewnPjE\nHNZ8f5w7JobJGjRCCNGARpMZvV7PypUrTY+dnZ158803Tf8W1qG3Rxj2WjtiMg4yLXQialXb/hXv\nYKclNKAmKbhcWYWBtJxSUrKLSbvQo5OSVcKxc3kcO1c7ydE76vD3qJlw7O/pYEpynB2uPAG8sqqa\nM2mFNT0vFxaoK624NN/Lyd6Gfl096dbJhW6BrnT2ccZGa909HRq1mvum9ebFj2PZHp+Gp6u9rBMk\nhBANUCntfGMYc97S1p5umfvo2Fp2pe3jwcg/090t1NLhNKq8sibJuTgX5+JwVXZBeZ1jnR1sLg1X\nXZh87OFiR2FFNfuPpnPyfP23SHcLdGnwFun2pKC4gmdX7yensJx7p/RiSB9fS4fUqPb0melopG2s\nk7RL07X41uzi4mLWrVvHHXfcAcBnn33Gp59+SufOnVm6dCmenp6tGqhouSifSHal7SMmI9bqkxk7\nnZYQPz0hfvpaz1dUVpOWeynBScuuSXiOJ+WTkJRf77Va+xZpa+PiZMtfZ/fl+TX7eX/zMVyddPRs\np2vQCCGEuTSazCxdupSAgAAAzpw5w8svv8yrr75KUlISzz33HK+88kqbBCmurJtbF1x0eg5kHmJW\n9+nYqNvfHTC2Og3BvnqCff+Q5FRVk55zaeJxVn4Z3Tq74+9qZ5ZbpK1NgKcjD8wMZ/nnB3njq8M8\nNu86ArxkDRohhLio0YkDycnJLF68GIDvv/+eCRMmMHToUObMmUN2dnabBCiaRq1SM8CnH2WGMo7m\nJFg6nFZla6Ohs68zQ3r7cuOIUO6b1oc5Y3vQM9j9mk9kLgrr7MZdk3tSVmHg1bVx5BdXWDokIYSw\nGo0mMw4ODqZ/7927l8GDB5set9c5CNeyAb79ANiXHmvhSIQ5DOnty8zru5BTWMGra+MorzRc+SQh\nhOgAGk1mqqurycnJISkpidjYWKKjowEoKSmhrKysTQIUTdfJKQAfB28O5RyjzFB3Mq1o/yYP6cz1\nff1JyijmrQ1HqDYar3ySEEJc4xpNZu69914mTZrE1KlTWbhwIS4uLpSXl3Prrbcyffr0topRNJFK\npSLKJxKD0cDBrMOWDkeYgUqlYv747vTp4k58Yg4f/XCiSVuLCCHEtazRZGbEiBHs2LGD33//nXvv\nvRcAOzs7Hn74YebOndsmAYrmibow1BQjQ03XLI1azYJpfQjyduLXg6ls3n3O0iEJIYRFNZrMpKam\nkpWVRWFhIampqaafLl26kJqa2lYximbwtPcgRN+Z43mnKKiQVZqvVfa2Wh6c1Rd3vS1f/nqa3UfT\nLR2SEEJYTKP3744ePZqQkBC8vLyAuhtNrl692rzRiRYZ4NuPM4Xn2J8Zx+hOwy0djjATN2db/jqr\nL8s+2s/73x7Dzcm2zs7oQgjRETTaM/Piiy/i5+dHRUUFN9xwAytWrGDNmjWsWbNGEhkr1t+7L2qV\nWu5q6gACvZy4f0Y4igKvf3mI1OwSS4ckhBBtrtFkZtq0abz//vu8+uqrFBcXM3fuXO655x42btxI\nebncLWOtnHVOhLl3I6noPBmlWZYOR5hZr2B37pgYRumFNWgKZA0aIUQH06Td9vz8/Fi4cCFbtmxh\n/PjxPPvsswwbNszcsYmrEOUTCciaMx1FdLgf04eFkF1Qzqvr4qmorLZ0SEII0WaalMwUFhby0Ucf\nMXPmTD766CP+/Oc/s3nzZnPHJq5ChGdvdGob9mXEyq27HcTU6GCGhftxLr2ItzYcljVohBAdRqMT\ngHfs2MGXX37J4cOHGTduHC+88ALdu3dvq9jEVbDT2hLh1ZuYjIOcK0omWB9k6ZCEmalUKm6b0IO8\nonLiEnP45KeTzBvbXVbrFkJc8xpNZu655x6Cg4O57rrryM3N5YMPPqhVvmzZMrMGJ65OlE8kMRkH\n2ZceK8lMB6HVqFk4I5xlHx3glwMpeLnYM2GQtL0Q4trWaDJz8Y6lvLw83Nxq3/J5/vx580UlWkVP\n9+442jiwPyOOmV2noFF3jE0ZOzp7Wy1/nRXBc2v288Uvp3DX2zKwp4+lwxJCCLNpdM6MWq1m8eLF\nPPnkkyxduhQfHx8GDhzIiRMnePXVV9sqRtFCGrWG67z7UlRVzPG8U5YOR/yBoijklOVRbmj9u4/c\n9Xb8dVZf7HQa3t10jBPJ+a1ehxBCWItGe2ZeeeUVPvzwQ0JDQ/n5559ZunQpRqMRFxcX1q5d21Yx\niqsQ5RPJ9pRdxGQcpJdHD0uH0+HlledzIi+RE3mJHM87RV5FPv6OvjzUfyH2WrtWrauTtxMLZ/Rh\nxdp4Xv8ynsfm98fPw7FV6xBCCGvQaDKjVqsJDQ0FYMyYMSxbtowlS5YwduzYNglOXL0uLp3xsHPj\nQGY8NhobQl2CCXUJxt3OTSaGtoGiyuILycspTuQnklmabSpz1DrQyTmA5KIUPjjyCfdF3IFa1aQb\nDJusT4gHt03owQebE3h1bRyPzx+A3lHXqnUIIYSlNZrM/PHLzs/PTxKZdkalUjGly3g+SfiSHSm7\n2ZGyGwAXnZ4urjWJTReXzgQ6+cucmlZQWlXGqfzTpp6X1JJLeybZaWzp49GTHm6hdHfrir+TL4qi\nsCr+A47kJPDVqW+5sdvUVo9peIQ/OQXlfPP7WVasi+eRWyOxtZG2FkJcOxpNZv5I/pJvnwb6Xsd1\n3hGcL07ldP5ZEgvOcbrgLLGZ8cRmxgOgU9sQrA8yJTghLkHYa+0tHLn1q6iuJDH/jCl5SS5KQaFm\nXR8btQ1hbt3ofiF5CXIOqJswquDuPnN5KeZNtiZvx9fBm+iAQa0e57QLC+rtPJzOf785wv0zwlGr\n5fMshLg2qJRGVlQLDw/Hw8PD9DgnJwcPDw8URUGlUrFt27a2iLFRWVlFZru2l5ezWa9vSYqikF2W\ny+mCsyQWnOV0wVnSSjJM5SpU+Dv50uVCz421DU1Zqm2qjAbOFpzj+IWho7OFyVQrNavtalQagvVB\npp6XYJcgbNRN+3shqzSH/+x/nTJDOQ/0u5fubqGtHruh2sgrX8Rx7FweY/oHcusN3Vq9Pa/lz0x7\nJ21jnaRdms7Ly7nBskaTmZSUlEYvHBAQ0PKoWokkM62ntKqU0wXnLvyc5WxhElVGg6ncRacn1DWY\nLhfm3QQ4+VlsaKqt2qbaWE1S0XlT8nK64Kzpd6JCRZBzIN3dQunh1pUursHYalo+H+Vk3mleP/gO\ndhpb/j5gEd4Onq31MkxKyw0s+3g/KVklzBndlXEDW3cNmo72mWlPpG2sk7RL07U4mWkPJJkxH4PR\nQHJRKqcv9NwkFpylqLLYVK7T6AjWBxHq0plQlxCCXYJa/Y6chpirbYyKkZTi9JoJu3mnOJV/hvLq\nS7dOBzj5mZKXrq4hrT4UtzN1Lx8nrMPHwZu/978fB5vWH+rLLSznmdUxFBZXsmB6HwaEebfatTv6\nZ8aaSdtYJ2mXppNkpoXkTVbbH4emEgvOkl7P0FTNpOKaH3c7V7MMTbVW2yiKQkZppqnn5WTeaUoM\npaZybwdPurt1pYdbV7q5dsFZ53TVdV7J+pOb+Dn5N3q6d2dBxJ1m6f06l17EC58cwGhUePiWSLoG\nuLTKdeUzY72kbayTtEvTSTLTQvImu7KSqlLOFJwzzbs5V5hca2jK1daFLi6da4amXIMJcGydoamr\naZvsslxO5J3ieN4pTuQlUlh56Tputq70cK9JXrq7heJq2zpf8s1hVIy8Hf8/DuccY0TgUGZ3n26W\neg6dzmHF2ngc7LQ8Pr8/Pu4OV31N+cxYL2kb6yTt0nSSzLSQvMma7/KhqcSCs5zOP0tRVe2hqRB9\nkGneTUuHpprTNvkVBaa7jU7mJZJTnmcqc9Y5mRKXHm5d8bBzt4pJzuWGcpbvX0lqSTo3d5/O9YFD\nzVLPrwdT+N93x/F2s+fx+f1xdri6NWjkM2O9pG2sk7RL01kkmVm7di3ffPON6fHhw4f59NNPefrp\np1Gr1ej1epYvX469/aU5AevXr2fFihUEBdVMShw6dCgLFixotB5JZqyboihkleVcNu/mXJ2hqQAn\nvwvJTWe6uNbcNXUljbVNcWUJJ/ITTYvVZZRmmcoctPamW6W7u4Xi6+BtFclLfXLKcvl3zOuUGsq4\nv+/dhLl3M0s9X/6ayLe7zhEaoOfhOZHormINGvnMWC9pG+sk7dJ0Fu+Z2bt3L1u2bOHkyZM88sgj\nRERE8OKLLxIYGMjcuXNNx61fv56TJ0+yZMmSJl9bkpn25/KhqcT8syQV1R2aujjvJtQ1GH9H3zpD\nU5e3TZmhjFP5Z0zDRinFaabjbDU6urp2MfW8BDj5tfoqu+aUmH+W12Lfxkaj4+H+9+Pj2HqTdS9S\nFIV3Nh5l99EM+vfwYsH0PqhbmODJZ8Z6SdtYJ2mXpmssmWnWonkt9eabb/LSSy9hb2+Pk1PNBEp3\nd3fy82Xzu47I0caBPp496ePZE7g4NJVyYd7NOU7nn2V/Zhz7M+OAmoQkRN+5Zu6NazCBTv6kpZ9n\n79lDHM87RVLh+csWqtNeGDbqSg+3UIKcA9v1ysahrsHcGnYTq499zlvxH/L3AYtwtLn6uS2XU6lU\n3DmpJ/nFFew/nsUXW08xZ4x5eoGEEMIczN4zEx8fzyeffMILL7xgeq60tJTZs2ezYsUK095PUNMz\n8/HHH+Pq6orBYGDJkiX06tWr0esbDNVote33y0rUpSgKGcVZJGQncjz7NAnZp0gpTK/3WI1KTVeP\nEPp496CPTw+6eYSg09i0ccTm90n813x97Ht6e3fn8RF/QWuGBK24tJJH3thOckYx907vw/8Nb/2F\n+4QQwhzMnswsXbqUyZMnM2hQzRLtpaWlLFiwgGnTpjFz5sxaxyYmJpKcnMzIkSOJjY1l6dKlbNy4\nsdHryzBTx1BcVcKZCwv6pRan0cUriE62nejiEoyd1tbS4ZmdUTHy7qE1xGUfIdp/ELf0mGmWuT7Z\nBWU8t3o/hSWV3D8znOu6ezXrfPnMWC9pG+sk7dJ0Fp0zM378eDZu3IhOp8NgMHDPPfcwefJkZs2a\ndcVzo6Oj+e2339BoGv4rVJKZjqkjtk25oYJXDqzifHEqN3X7P0Z1GmaWes6mF/LCxwdAgYdvjSTU\nv+m3p3fEdmkvpG2sk7RL0zWWzJh1JmRGRgaOjo7odDW3e77zzjsMHDiwwUTmnXfeYdOmTQCcOHEC\nd3f3RhMZIToSO60t90XcgbPOiS9PbuRIznGz1BPsq2fBtD5UVRt5bV08mXmlVz5JCCEsyKzJTFZW\nFu7u7qbHH3/8Mb/99hvz589n/vz5vPHGGwCm26+nTp3K559/zrx581i6dCnPPfecOcMTot1xs3Pl\nz+F3oFFreP/wx7U2B21Nfbt6Mm9cD4pKq3hlbTzFZVVmqUcIIVqDLJrXCOn+s14dvW1i0mP54Oin\neNi588iAB3DSOZqlnhTGEHkAACAASURBVLXbTrFldxJdA114eE4/bK4w2b6jt4s1k7axTtIuTWex\nYSYhhHkM8I1kYvAN5JTn8t9DqzFctk5Pa7pxRCgDe3pz6nwB7246hrF9/+0jhLhGSTIjRDs1KeQG\nIr0jSCw4w6fH12OOTla1SsXdk3vSPdCFfQmZrNuW2Op1CCHE1ZJkRoh2Sq1Sc1vP2QQ5B7A7LYaf\nk38zSz02Wg2LbozA192B7/YksfXAebPUI4QQLSXJjBDtmE6j488Rd+Ci0/P1qc0cyj5qlnqc7G34\n6+y+6B1s+PjHExw8mW2WeoQQoiUkmRGinXO1deG+iDvQqrV8cOSTWntTtSZvV3senNUXG42at745\nzJm0QrPUI4QQzSXJjBDXgCB9ILf1upmK6kpWxX1AYaV57o4I8dPz52m9qTIYWbE2jqz8MrPUI4QQ\nzSHJjBDXiOu8I5gSMp68inzeObSaqmrzrA0T2c2LW2/oTmFpFa+ujaOkXNagEUJYliQzQlxDJgSP\nZoBPP04XnOOT41+a5Q4ngDH9Axk/sBNpOaW8/uUhqgxGs9QjhBBNIcmMENcQlUrF3LBZBOuD2Jt+\ngB/PbTNbXbNGdWVADy9OJOfz/mZZg0YIYTmSzAhxjdFpbPhT+O242bqy4fQWDmYdNks9apWKe6f2\nomuAC3uOZvDVb6fNUo8QQlyJJDNCXINcbJ25L+IOdBod/zvyKclFKWapx0ar4YEbw/Fxs+fbXedY\n/8spsvPLMFTLsJMQou3I3kyNkD0zrJe0TdPEZR3mnUNrcLHV88iAB3Cx1Zulnsy8Up5dvd+0IaUK\ncHW2xV1vi7uzHR56O9z1thf+a4eHix2OdlpUKpVZ4hF1yWfGOkm7NF1jezNJMtMIeZNZL2mbpvvh\n3C9sSNxCZ30n/hp5HzqNjVnqScsp4eDpXM6nF5JTWEFuYTl5RRVUG+v/X4xOq65JbPS2F/5bk+hc\nSnpsr7ixpWg6+cxYJ2mXpmssmdG2YRxCCAsYGzSS9JJM9qTv56NjX3Bn71vN0iPi5+FIRJhvrf8x\nG40KBSWV5BSWk1tYTm5hhenfORcep+eW/n97dx4eVX33ffw9k5nJMkuWCdkICUkAgUDY7yqg4ora\nolVbwQWst7cWvdWrrUUpt+vj3frQ7WkLrnWpxQ0XVNRKtVUUFZU1QABZEkJC9nWybzPPHwkDQYgx\nMJmZ5PO6Lq4kk3Mm37m+Z5IPv/M7v3PC53REmL1BJ7or5HhHdxyh2K0WjBrdERn0FGZEBjiDwcDV\no6+kvKmSTWXZJFjjuCTtgn752UajgWh7KNH2UBgaedxtWto6Thh0qlzNFJY3cKDk+P9zNYV0Pv+R\nUZ3OkNMZfjo/D7Po15zIQKd3ucggYDaauHn8An63cRnv5n1AfEQcU+In+LssAELNISQ6rSQ6rcf9\nvsfjoa6x7QSjO52BZ/fBmhM+vzXMdNRprNBup7KcjjAibRZCjLoWQiSYKcyIDBJ2i42FWTfw+03L\nWbFrJbHhMaQ6hvm7rG9lMBhwWC04rBbSEo8/gbmt3U11fQtVtc3fCDpVdS2UVTdRUFZ/3H2NBgPR\ndkvXSM6RoBMbGcaIoZFEhPlmjpGInDoKMyKDSJItgf/MvJbHt/2NJ7b9jbum3UFU6PFP/wQTs8lI\nXFQ4cVHhx/2+x+OhsaWdytqjRnbquo/y7D9Uy77C2m77GQ0GRg2LJCsjlgkjnCTEROgKLJEApKuZ\neqBZ5oFLvTk5/z74Cav2vcMw+1B+MfkWLCGWU/K8wdyXDrebmrpWquo6R3dKKhvZnltJXvGR1xMX\nFU5WhpMJI2IZNSwKsyl4Tk8Fc28GMvWl93Rpdh/pIAtc6s3J8Xg8vLj7NT4v3sDEIeO5cdy1GA0n\n/4d5IPaltr6FbbmVbNtfyY68KlpaOwAItYSQOTyGCRlOsjKcRNpC/VxpzwZibwYC9aX3dGm2iHRj\nMBiYe9rllDdVsrV8O+/mfcCc9Nn+LisgRdpCOTMriTOzkmjvcPN1QQ3b9lWSvb+CzXvK2bynHIDh\nCXbvqE1qgl2XjIv0I43M9ECJOXCpN6dGfVsDv9u4nIqmSn4y9mqmJUw6qecbbH0pqWoke18F2/ZX\nsqegxrtAYKTVwvgMJxMynIwdHkN4qP//3zjYehMs1Jfe02mmPtJBFrjUm1OnpKGU3218hHZPOz+b\n9FPSIlP7/FyDuS+Nze3sPFDVGW5yK6lr7Ly1Q4jRwOiUKO8k4rjoCL/UN5h7E8jUl95TmOkjHWSB\nS705tXZWfs2j2c9gs1i5a+rtxIRF9+l51JdObo+HvGIX2fsq2ba/goOlRy4LT4iJYMIIJxMyYhmR\nHIkppH8mEas3gUl96T2FmT7SQRa41JtTb23BZ7y69y2G2hL5xeRbCTN99wmt6svxVbmaOycR76tk\nZ34VrW2ddxUPDzUxLi2GrAwn4zOcOCJOzVVlx6PeBCb1pfc0AVhEvtXZydMpaSxj3aH1/G3nS9w8\nfsEpucJJIMYRxqyJQ5k1cSht7R3sPlhD9r4KsvdVsmF3GRt2l2EA0pMcZI2IZUKGk2FxNq1pI9JL\nCjMiAnRe4fTjkZdS1ljO9oqdrN6/hh+OuMTfZQ04ZlMI49OdjE93cu0FHooqGsjeX8m2fRXsPVTL\n/iIXb3ySS7Q9tOuy71jGDI8m1Dz47iDu8XhoaG6npq6F6voWqutaqKlrwR5h5qyJSboNhXjpNFMP\nNPwXuNQb32lsa+R3m5ZT1ljB/DFXcXri1F7vq76cnPqmNnbkdZ6O2p5bSUNzO9C5wvHolGgmjOhc\n0yY28vgrHfck0HrT3uGmpr6FmrrWbkHl2M/b2t3H3X9UciQ/vWxc501Mg1ig9SWQac5MH+kgC1zq\njW+VNpbzu43Lae1o5Y5JNzMiKq1X+6kvp06H283+Qy6y93de+n2ovMH7vaFDrEzIiCUrw0nGUEev\nRij6qzeHbx3hDSRdoeTYz11dV3sdjwFwWC1E2UOJtnXedf3w51F2Cx9vLWLT1+XYI8zcPCeTzLQY\nn78uX9F7pvcUZvpIB1ngUm98b3fVXh7JfpoIUziLpt5ObPi3/8FQX3ynoraJbfsryd5Xya78ato7\nOkcsrGEmxqc7yRrhZFyaE1v48W+MeSp6097hpra+9bjh5OjPW08wmgJgMRuPE1COhJZoeygOq6XH\nq7w8Hg//3lTIyg/34XZ7mDNjOJfOSMNoDL45RnrP9J5fwsyrr77K6tWrvV/v2LGDl156iQceeACA\n0047jQcffLDbPm1tbSxevJiioiJCQkJ4+OGHGTas57v6KswMTupN/1h36Ate/noVidZ47pzy34Sb\nwnrcXn3pHy2tHezKr/aO2lTXtQCdN8YcMdTBhBGdozZJsVbvJOKeeuPxeGjqGk3pfpqntVtQqWto\n5UR/MAyA3Wo5Jqh0ja4cFVbCQ02nbGJzbpGLx97cQaWrmTGp0dx8aSaRVt9dEeYLes/0nt9HZr76\n6ivee+899u3bx6JFi8jKyuLOO+/k0ksv5eyzz/Zu98Ybb7Bt2zbuv/9+Pv30U1577TX+9Kc/9fjc\nCjODk3rTf17d8xZrCz8j0zmahVk/6fEKJ/Wl/3k8HgrK6r2TiHOLXN7AERsZRlbXJOLkRAd5BdXf\nDCxdXx++XPx4zCbjkRGUo0dTjgopkbaeR1N8pb6pjWfe3cXWfRVEWi0svCyT01L6tk6SP+g903t+\nDzPXX389Dz/8MNdddx0ffvghAO+88w47duxg8eLF3u3uuusufvjDHzJ9+nTcbjezZs3ik08+6fG5\nFWYGJ/Wm/3S4O3hs27PsqtrDucPO5MqRc064rfrif67GVrbvryR7fyU5eZU0tXT0uL09wtxjUImy\nh2INO3WjKb7g8Xj451cFvLZ2Px48XHFWOhefnhoU98fSe6b3/LrOzLZt20hMTCQkJASHw+F93Ol0\nUl5e3m3biooKYmI6z8sbjUYMBgOtra1YLME1bCgykIQYQ7hx3LX8fuMjfFiwjgRrHDOSvufvsuQE\nHBEWZoxPZMb4RNo73OwtrGVHXiWhoWbCQgzdgkqkLRSzKfgvbzYYDFz0vRQyhjp4/K0cXv84lz0F\ntdw0Z+wJ5xDJwOLzMPPaa69x+eWXf+Px3gwI9Wab6OgITCbfrb/QUxIU/1Jv+pOdJefcxv98sJSV\nX7/ByMQUMuNGHXdL9SWwJCZEctbUFH+X0S+GDLGTOTKOP764mc1fl/F//raBu+ZPY0yAX+2k98zJ\n83mY+fLLL7nnnnswGAzU1NR4Hy8tLSUuLq7btnFxcZSXlzN69Gja2trweDzfOipTXd3ok7pBw3+B\nTL3pfyGEcWPmfJZt/Su/X/cEv5x6G3ERsd22UV8C12Dqza0/zOTd9TbeXJfLrx79lB/NyuDCacMC\n8lTZYOrLyeop9Pl0fLG0tBSr1YrFYsFsNpOens7GjRsBeP/99znzzDO7bT9jxgzWrFkDwEcffcT3\nvqehbJFAMjI6nXmnXU5DeyOPb/sbjW1N/i5J5BuMBgNzpg/nl/MmYQ03s/LDfSxftZ2G5hOvbSPB\nzadhpry83DsHBmDJkiX88Y9/ZN68eaSkpDB9+nQAbrnlFgAuueQS3G43V199NS+88AJ33nmnL8sT\nkT6YnvQfnDfsLEoby3gm5wU63D1PMBXxlzGp0Tx4wzRGp0SxZW8FDz67gbxil7/LEh/Qonk90PBf\n4FJv/MvtcfPEtufYUbmLs5NncNWoywD1JZAN5t643R7e+jSPdz4/QEiIgbnnjuTcyUMD4rTTYO7L\nd+W300wiMjAZDUZuyLyaJGsCHxd+xieF6/1dksgJGY0GLj8rnZ9fNYEwi4kXPtjD42/l0NTS7u/S\n5BRRmBGRPgkzhbEw6yfYzFZe3fsWu6v2+rskkR6NS3fywA3TGJEcyYbdnVc7HSzVqMhAoDAjIn3m\nDI/h5vHXY8TAUzuep8hV4u+SRHoU4wjjrqsncfH3UiitbuLXKzbxSXZRr5YCkcAV8sDhmyUFqcbG\nVp89t9Ua6tPnl75TbwJHTFgUMWHRbCrbyvqCzeS5CihpKKW+rREjBsJNYT3eAkH6h94zRxiNBjLT\nYkhNsLNtXwUbdpdRXtPMuLSYfr8lg/rSe1Zr6Am/5/N1ZkRk4Pte4hRqW128n/8RW8q2seWo75kM\nIcRb40i0xpNoTSCp66MzPFohR/xq4ohY7r9hGo+9mcP6nBIOlLi49fLxDI21+rs0+Y50NVMPNMs8\ncKk3gSk21sbewkKKGkoobiilqL7zY0lDKa3u7mt8WIxmEqxxJFoTuoJOPEm2BKJDowLiKpOBRu+Z\nE2vvcPPKR/v418ZCLGYjC2afxvRxif3ys9WX3vPrvZlEZPAwGAxEh0URHRZFpnO093G3x01Vc3W3\ngFPcUEpRQykH6w51e46wkFASrPFdIzjxJNo6w06kxaGQIz5hCjFyzfmjGJUcxbPv7eKpd3axp6CG\na84fhcXsu9vlyKmjMCMiPmc0GIkNdxIb7mR87Fjv4x3uDiqaqyjuFnBKOFhXyAHXwW7PEW4K7xZw\nDp+uslts/f1yZICaOjqOlHgbj765g0+yi8ktquPWy8eREBPh79LkW+g0Uw80/Be41JvAdKr60u5u\np6yxwhtwirtOW5U1VuCh+68sm9nqPUV19LycCLP+AB1N75nea2vv4KV/72PtlkOEWkK44eLR/MeY\neJ/8LPWl93SaSUSCisloIsmWQJItodvjbR1tlDaWe0dwihtKKa4vYV9NHntrcrttG2mxd87HscWT\n1DUvJ8EaT7gprD9figQhsymEBbNPY1RyJM+t+ZrH38phT0ENc88didmkSeuBSGFGRIKGOcRMsj2J\nZHtSt8dbO1opaSg7MvG4oYTi+lJ2V+9ld3X3xfyiQ6O6BZzDISc0xNKfL0WCwOmZCaQm2Hn0zR18\nuPkQ+4tc3PrDcQyJCvd3aXIMnWbqgYb/Apd6E5gCrS9N7c2UeE9VHZl8XNva/WaDBgw4w6K9k40T\nrfEMtSWSaI0fMJePB1pvgklLWwcvvL+HT7cXEx5q4sbvj2HyqCGn5LnVl97r6TSTwkwPdJAFLvUm\nMAVLXxrbGik6ej5OfedoTn1bQ7ftokOjmBo/kSnxE0m2JQb11VTB0ptAtm5bES+8v4fWdjcXThvG\nj2ZlnPQie+pL7ynM9JEOssCl3gSmYO9LXWu99zRVvquAbeU5NHe0ABAfEce0rmATFxHr50q/u2Dv\nTaAoLKvn0Td3UFLVSEaSg4WXjcMZ2fd5WOpL7ynM9JEOssCl3gSmgdaXto42cip3s7F0K9srd9Hu\n7rzLcoo9uWvEZgJRoZF+rrJ3Blpv/KmppZ0V//yaL3aWYg0zcdOcsWRl9C3gqi+9pzDTRzrIApd6\nE5gGcl+a2pvZVp7DxtKt7K7ei9vjxoCBEVFpTImfyKS48djMgbsM/kDujT94PB4+3lrEi//aS3uH\nm0tOT+Xys9IIMX63007qS+8pzPSRDrLApd4EpsHSl7rWeraUbWdj6Vb21+YBnQsDjokZxdT4iWTF\njiUswC4BHyy96W/5JXU89uYOymqaGDUsip9emkm0/cQ3RDyW+tJ7CjN9pIMscKk3gWkw9qW6uYZN\nZdlsLN1KQdetGcxGM+NixzA1fiKZMadhDjH7ucrB2Zv+0tjczrPv7WLT1+XYI8zcfGkmmcNjerWv\n+tJ7CjN9pIMscKk3gWmw96W0oYyNZdlsKt1KaWM5AOGmMCbEjmNqwkRGRWUQYvTPvX4Ge298zePx\n8K9Nhbzy4T7cbg+XzkxjzvThGI09XwGnvvSewkwf6SALXOpNYFJfOnk8Hgrri9hYupVNpdlUt9QA\nYDfbmByfxdT4iaQ5Uvv1Um/1pn/kFrl47M0dVLqaGTs8mpvmZBJpPfGCjOpL7ynM9JEOssCl3gQm\n9eWb3B43ubX5bCzdypaybd61bGLCopkSN4Gp8RMZ2g9r2Kg3/ae+qY2n39lJ9v5KIm0WFl6ayWkp\n0cfdVn3pPYWZPtJBFrjUm8CkvvSsw93B7up9bCrdSnb5Du8aNgkRcd7F+Xy1ho1607/cHg///Oog\nr6/NxYOHK85K5+LTUzEeE1rVl95TmOkjHWSBS70JTOpL77UetYbNjn5Yw0a98Y89BTU8sTqH6roW\nsjKc/NcPxmILPzIhXH3pPYWZPtJBFrjUm8CkvvRNf6xho974j6uxlb++vZOcvCpiHKEsvGwcI4Z2\nBlX1pfcUZvpIB1ngUm8Ck/py8ny1hk0w98bj8dDQ3oirpY7aVhc2s41hx9w5PdC5PR7e/fwAb36a\nh9Fg4MezMrhg2jDi4hxB25f+pjDTR8H85h/o1JvApL6cWlXN1Wwq7bzUu6C+COj7GjaB2Bu3x01d\naz21rS5vUOn8WIerxUVtax21LS7qWuto93R02/ei4efx/bQLgu6u5rvyq3lidQ6uhlYmjYzlruv/\ng6b6Zn+XFRQUZvooEN/80km9CUzqi++UNpSxsXQrG8u2UtZYAXy3NWz6szdt7nZcLXW4Wl3dgsmx\nH+ta6/Fw4j9BIYYQHBY7jlA7kRYHjlA7DoudL4s3UdlcRaZzND8ZezUR5vB+eV2nSm19C0+szmH3\nwRriosOZelocwxPspCU6iHGEBvXd2X1JYaaP9Is5cKk3gUl98T2Px0NB/SHvGjY1LbXAt69hcyp6\n09ze0hlQugWVum+MrDS0N/b4PBajGUeog0iL3fvxcFg5+mOEOfy4Iy8NbY08m/Miu6r2EBcey81Z\n15NojT+p19bf3G4Pb36axz++yMftPvJn2B5hZniCg7REO8MTHAxPtBNl6/3tEQYyhZk+0i/mwKXe\nBCb1pX99lzVsTtQbj8dDY3sTtS0uXF2ndVyt3wwota0uWjpae6wn3BTeLaAcDiXdH3MQFnLyow9u\nj5vV+9fwwcG1hIZYWDBmLhPjxp/Uc/pDuC2MTTuKOFBSR16xiwPFdVS6up92iraHMjzB7h29SU2w\nY4848UJ8A5XCTB/pF3PgUm8Ck/riP4fXsNlYuoXs8h3e4HF4DZvk2DgKK8q/eeqntc57WfiJ2MxW\nIkMdxx09iQy147A4cFjsWPxwD6pNpdk8v+sVWt1tzE49lx+kXxhU82iO955xNbZyoLiOAyWd4Sav\nxEVtffcgGRsZxvBEB2kJdoYnOkiNtxMRZurP0vud38LM6tWreeqppzCZTNxxxx2sWrWK6upqAGpq\napg4cSIPPfSQd/tVq1bx5z//mZSUFACmT5/OLbfc0uPPUJgZnNSbwKS+BIbWjjZ2VO5iU+lWdlTu\nPm5YMRqMnfNRLHZvIOl22ifU4f2+v+4n1VuH6ot5cttzVDRXMdZ5GjeMvZoIc4S/y+qV3r5nquta\nOFDiIu+okFPf1NZtm/iYiCOnpxLspMbbCbUEdu++C7+EmerqaubNm8frr79OY2Mjy5Yt6xZcfvWr\nX3H11VeTlZXlfWzVqlXs3buXu+++u9c/R2FmcFJvApP6Enia2pvIqdhNmNWEodXiDSpWc0RQjWB8\nm6Pn0cSGO/np+OtJsiX4u6xv1df3jMfjobK2ufP0VFe4OVBSR1PLkeBqMEBSrNV7emp4goNhcVbM\npuAMOD2FGZ+NSa1fv54zzjgDm82GzWbrFmRyc3Opq6vrFmREROTUCzeFMzVh0oAPmlZzBLdO+E/e\nzv0n7+d/xO82LWf+mKuYHDcw/84YDAZio8KJjQpn6ug4oHMtm/Lqps65NyV1HCh2kV9az6HyBj7b\nXgJAiNFA8hAbwxOPzMFJirViCgnuYOuzMFNYWEhzczMLFy7E5XJx++23c8YZZwDw97//neuuu+64\n+3311VfceOONtLe3c/fddzN27FhflSgiIgOI0WDksoyLGWYfyopdr/D0jucpSD2HOemzB9Qo1IkY\nDQbiYyKIj4ng9MzOUSm320NxZcORCcYldRwsrSe/tI6Pu/YzhRhJibeR1nX11PAEO4lOK0Zj8Fwi\n7rPTTE8++SSbN29m+fLlFBUVsWDBAj766CPa2tq48sorefvtt7+xz/79+ykoKGDWrFls2bKF++67\n77jbHa29vQNTkA6ZiYiIbxysOcTvPnuC0vpyJiaM5Y4z/hOb5eRuCTFQtHe4yS92sa+whr0Fnf/y\ni110HHWJeJglhIzkKEYkRzFyWOe/hAAOOD4bmXE6nUyaNAmTyURKSgpWq5Wqqip27959wtNLGRkZ\nZGRkADBp0iSqqqro6OggJOTEYaW6uuf1DE7GQB+WDWbqTWBSXwLXYOtNOA5+Oem/eXbnS2wt2cld\n7/2Gm7OuZ6gt0d+ldeOvvjhCQ5ic4WRyhhOAtvYOCsoaukZvOkdwduZVkpNb6d0nPNTUeYl4ot07\niuN0hPXbIn9+mTMzc+ZMFi9ezE033URtbS2NjY1ER0ezfft2Ro8efdx9/vrXv5KYmMgPfvAD9uzZ\nQ0xMTI9BRkRE5EQizBHcknUD7+a+z5r8D/n9xuVcN+YqpsRP8HdpAcdsCiE9yUF6ksP7WHNrOwdL\n673zb/JK6tiVX82u/GrvNrZwc7dwMy4txi8TjH0WZuLj45k9ezZXXXUVAPfccw9Go5Hy8nLvpdeH\n3XLLLTz22GPMmTOHRYsW8fLLL9Pe3s6vf/1rX5UnIiKDgNFgZE7GRQyzD+W5XSt5JucFCuoOcWnG\nRYNiHs3JCLOYGDUsilHDoryPNTa3k981cpPXFXJ25FaxI7cKgPMmJ3PthaP6vVYtmteDwTYsG0zU\nm8CkvgQu9QaK6kv46/a/U9ZUwZiYUdyQeQ1WP69HMxD6UtfYSn5JHQXl9YxPd5I8xOaTn9PTaSbF\nUhERGRSSbAksmno7mc7R7Kraw9INf+FQfbG/ywp69ggL49KdXPy9VJ8FmW+jMCMiIoNGhDmchVk/\n4eLh51HZXMXvNy5nU+lWf5clJ0lhRkREBhWjwcgP0mdz0/gFGAwGnsl5kTf2vUuHu8PfpUkfKcyI\niMigNHHIOBZNvZ24iFj+dfBjHs1+xnvncQkuCjMiIjJoJVrjuWvq7YxzjmF39V5+u2EZhXVF/i5L\nviOFGRERGdTCTeH8NOt6Lh5+fuc8mk2PsLFki7/Lku9AYUZERAa9znk0F3Lz+OsJMRh5dudLrNr7\njubRBAmFGRERkS4ThmSyaOrtxEcM4d8Fn/BI9tPUt2oeTaBTmBERETlKgjWORVNvY3zsWL6u3sfS\njX+hQPNoAprCjIiIyDHCTeHcPH4Bl6RdQFVzNX/Y9AgbNI8mYCnMiIiIHIfRYOT7aRfw0/HXE2II\n4W87X+L1vW9rHk0AUpgRERHpQdaQTO6aehvxEXF8WLCO5dlPU9da7++y5CgKMyIiIt8ivmseTVZs\nJnuq97F0w184WFfo77Kki8KMiIhIL4Sbwrhp/Hx+kHYhNS21/HHTo3xVstnfZQkKMyIiIr1mNBi5\nOO18fpp1PSEGE8/tfJnX9q7WPBo/U5gRERH5jsbHjuWuabeTEBHHRwWfsmzrXzWPxo8UZkRERPog\nPmIIi6bexoQh49hbk9s5j8aleTT+oDAjIiLSR2GmMP5r3HXMSZ9NTUstf9j8KF8Wb/J3WYOOwoyI\niMhJMBqMXDT8PBZm/QSz0cTfd63k1T1vaR5NP1KYEREROQXGxY7hrqm3k2CNZ23hZ5pH048UZkRE\nRE6RuIghLJry30wcMp69Nbn83w1/Jt9V4O+yBjyFGRERkVPoyDyai6htcfHHzY+xvnijv8sa0BRm\nRERETjGDwcBFw8/llgk3YDaaeX7XK7yy503No/ERhRkREREfyXSO5q6pt5Nojefjws/585YncbXW\n+busAUdhRkRExIfiImL55ZTbmDRkPPtr81i64S8ccB30d1kDisKMiIiIj4WZQrlx3HVcln4xtS0u\n/t/mx1lftMHfZQ0YJn8XICIiMhgYDAYuHH4OQ+1JPJvzIs/vfpWytlLOGHI6Q8KdGAwGf5cYtAwe\nj8fj7yJORnm5opDLKQAAC6BJREFU7849Dhli9+nzS9+pN4FJfQlc6k1gKW+s5Mntz1HUUAKAzWwl\nLTKV9MhU0hyppDqSsYRY/FxlYBkyxH7C72lkRkREpJ8NiXDyy6m3saNuO9mFu8mtzWd7xU62V+wE\nOlcVTrYlkX444ESmEh0apdGbE1CYERER8YPQEAsXjZzFlKgpANS01JJbm09e17+CukMcrCtkbeFn\nAESFRpLmSPGGm2T7UMxG/RkHhRkREZGAEBUayeS4LCbHZQHQ1tFGQf0hb8DJrc1nS/l2tpRvB8Bk\nNJFiH9p5esrRGXAiQx3+fAl+49Mws3r1ap566ilMJhN33HEHa9asIScnh6ioKABuvPFGZs2a1W2f\n3/zmN2RnZ2MwGFiyZAlZWVm+LFFERCQgmUPMpEcOJz1yOAAej4eq5urOcOPqDDcHXAXk1ubz7659\nnGHRXXNvhpMWmcJQayIhxhC/vYb+4rMwU11dzSOPPMLrr79OY2Mjy5YtA+AXv/gF55xzznH3+eqr\nr8jPz2flypXs37+fJUuWsHLlSl+VKCIiEjQMBgPO8Bic4TFMS5gEQEtHK/ldgebw6amNpVvZWLoV\nAIvRTKpjmDfcpEWmYjNb/fkyfMJnYWb9+vWcccYZ2Gw2bDYbDz30EIsXL/7Wfc4//3wAMjIyqK2t\npb6+HpvN5qsyRUREglZoiIVR0RmMis4AOkdvypoquoWbfTV57K3J9e4TFxFLumO4d+5NgjUOoyG4\nl53zWZgpLCykubmZhQsX4nK5uP322wF4/vnnefbZZ3E6ndx7773ExMR496moqCAzM9P7dUxMDOXl\n5T2GmejoCEwm3w2h9XQpmPiXehOY1JfApd4EplPdlzgcjCPd+3VjaxN7q/L4uiKXvZW57KnM44uS\njXxR0nnzywhzOCOdwxnlTGdUbDojY9KIsISf0pp8zadzZmpqali+fDlFRUUsWLCAhx9+mKioKMaM\nGcOTTz7J8uXLue+++064f2+WwKmubjyVJXejdRkCl3oTmNSXwKXeBKb+6ktSyDCS4odxTvzZuD1u\nShrKyK09QF7tQXJdB8gu2UV2yS4ADBhItMaT1jVykx6ZSlx4rN8vC/fLOjNOp5NJkyZhMplISUnB\narUyatQonE4nAOeeey4PPPBAt33i4uKoqKjwfl1WVsaQIUN8VaKIiMigYzQYSbIlkGRLYObQ0wGo\nb23wTirOq80n31VAUUMJnxV9CYDVHEGa48iaN6mOYYQG0KJ+PgszM2fOZPHixdx0003U1tbS2NjI\nfffdx+LFixk2bBhffvklI0eO7LbPjBkzWLZsGfPmzSMnJ4e4uDjNlxEREfExm8XK+NixjI8dC0CH\nu4NDDcXd5t7sqNzFjsrO0RujwchQW6J3xeL0yFRiwqL9NnrjszATHx/P7NmzueqqqwC45557sFqt\n/OxnPyM8PJyIiAgefvhhAH7+85/z8MMPM3nyZDIzM5k3bx4Gg4H777/fV+WJiIjICYQYQ0ixJ5Ni\nT2ZW8gwAaltc3vVu8lz5HHQVUlB3iI/5HACHxc6VI+cwNX5iv9erezP1QOeYA5d6E5jUl8Cl3gSm\nYO5Lm7udwroji/odrDvEOcNmcs6wmT75ebo3k4iIiJxSZqPJO0nY34L7wnIREREZ9BRmREREJKgp\nzIiIiEhQU5gRERGRoKYwIyIiIkFNYUZERESCmsKMiIiIBDWFGREREQlqCjMiIiIS1BRmREREJKgp\nzIiIiEhQU5gRERGRoKYwIyIiIkHN4PF4PP4uQkRERKSvNDIjIiIiQU1hRkRERIKawoyIiIgENYUZ\nERERCWoKMyIiIhLUFGZEREQkqCnMHMdvfvMb5s6dy7x589i2bZu/y5Gj/Pa3v2Xu3LlceeWVvP/+\n+/4uR47R3NzM+eefz6pVq/xdihxl9erVXHrppVxxxRWsXbvW3+UI0NDQwG233cb8+fOZN28e69at\n83dJQc3k7wICzVdffUV+fj4rV65k//79LFmyhJUrV/q7LAG++OIL9u7dy8qVK6murubyyy/nwgsv\n9HdZcpTHHnuMyMhIf5chR6muruaRRx7h9ddfp7GxkWXLljFr1ix/lzXovfHGG6SlpXHnnXdSWlrK\n9ddfz5o1a/xdVtBSmDnG+vXrOf/88wHIyMigtraW+vp6bDabnyuTadOmkZWVBYDD4aCpqYmOjg5C\nQkL8XJkA7N+/n3379ukPZYBZv349Z5xxBjabDZvNxkMPPeTvkgSIjo7m66+/BsDlchEdHe3nioKb\nTjMdo6KiottBFRMTQ3l5uR8rksNCQkKIiIgA4LXXXuOss85SkAkgS5cuZfHixf4uQ45RWFhIc3Mz\nCxcu5JprrmH9+vX+LkmA73//+xQVFXHBBRdw3XXXcffdd/u7pKCmkZlvobs9BJ5//etfvPbaazzz\nzDP+LkW6vPnmm0ycOJFhw4b5uxQ5jpqaGpYvX05RURELFizgo48+wmAw+LusQe2tt94iKSmJp59+\nmt27d7NkyRLNNTsJCjPHiIuLo6Kiwvt1WVkZQ4YM8WNFcrR169bx+OOP89RTT2G32/1djnRZu3Yt\nBQUFrF27lpKSEiwWCwkJCUyfPt3fpQ16TqeTSZMmYTKZSElJwWq1UlVVhdPp9Hdpg9rmzZuZOXMm\nAKNHj6asrEynzU+CTjMdY8aMGfzzn/8EICcnh7i4OM2XCRB1dXX89re/5YknniAqKsrf5chR/vSn\nP/H666/zyiuv8OMf/5hbb71VQSZAzJw5ky+++AK32011dTWNjY2anxEAUlNTyc7OBuDQoUNYrVYF\nmZOgkZljTJ48mczMTObNm4fBYOD+++/3d0nS5R//+AfV1dX87Gc/8z62dOlSkpKS/FiVSGCLj49n\n9uzZXHXVVQDcc889GI36f6y/zZ07lyVLlnDdddfR3t7OAw884O+SgprBo0khIiIiEsQUz0VERCSo\nKcyIiIhIUFOYERERkaCmMCMiIiJBTWFGREREgprCjIj0m8LCQsaNG8f8+fO9dwu+8847cblcvX6O\n+fPn09HR0evtr776ar788su+lCsiQUJhRkT6VUxMDCtWrGDFihW8/PLLxMXF8dhjj/V6/xUrVmhx\nMRHpRovmiYhfTZs2jZUrV7J7926WLl1Ke3s7bW1t3HfffYwdO5b58+czevRodu3axXPPPcfYsWPJ\nycmhtbWVe++9l5KSEtrb27nsssu45ppraGpq4uc//znV1dWkpqbS0tICQGlpKb/85S8BaG5uZu7c\nufzoRz/y50sXkVNEYUZE/Kajo4MPPviAKVOmsGjRIh555BFSUlK+ceO9iIgInn/++W77rlixAofD\nwR/+8Aeam5u55JJLOPPMM/n8888JCwtj5cqVlJWVcd555wHw3nvvkZ6ezoMPPkhLSwuvvvpqv79e\nEfENhRkR6VdVVVXMnz8fALfbzdSpU7nyyiv5y1/+wv/8z/94t6uvr8ftdgOdtxk5VnZ2NldccQUA\nYWFhjBs3jpycHPbs2cOUKVOAzhvHpqenA3DmmWfy4osvsnjxYs4++2zmzp3r09cpIv1HYUZE+tXh\nOTNHq6urw2w2f+Pxw8xm8zceMxgM3b72eDwYDAY8Hk+3ew8dDkQZGRm8++67bNiwgTVr1vDcc8/x\n8ssvn+zLEZEAoAnAIuJ3drud5ORkPv74YwDy8vJYvnx5j/tMmDCBdevWAdDY2EhOTg6ZmZlkZGSw\nZcsWAIqLi8nLywPg7bffZvv27UyfPp3777+f4uJi2tvbffiqRKS/aGRGRALC0qVL+d///V+efPJJ\n2tvbWbx4cY/bz58/n3vvvZdrr72W1tZWbr31VpKTk7nsssv48MMPueaaa0hOTmb8+PEAjBgxgvvv\nvx+LxYLH4+Gmm27CZNKvQJGBQHfNFhERkaCm00wiIiIS1BRmREREJKgpzIiIiEhQU5gRERGRoKYw\nIyIiIkFNYUZERESCmsKMiIiIBDWFGREREQlq/x8gPajI3GJNugAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "5JUsCdRRyso3"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's try Adam."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "lZB8k0upyuY8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "bcdf477b-fc88-49f9-aa67-7bec7c9f071b"
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 199.75\n",
+            "  period 01 : 120.84\n",
+            "  period 02 : 114.46\n",
+            "  period 03 : 107.93\n",
+            "  period 04 : 98.11\n",
+            "  period 05 : 83.61\n",
+            "  period 06 : 74.83\n",
+            "  period 07 : 72.51\n",
+            "  period 08 : 72.47\n",
+            "  period 09 : 71.09\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   71.09\n",
+            "Final RMSE (on validation data): 69.07\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRCTq/D8wtnOPK5vgVgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "twYgC8FGyxm6"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's print a graph of loss metrics side by side."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8RHIUEfqyzW0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 376
+        },
+        "outputId": "bdf1633b-eccc-418e-bbc6-536076cf066e"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.ylabel(\"RMSE\")\n",
+        "plt.xlabel(\"Periods\")\n",
+        "plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "plt.plot(adagrad_training_losses, label='Adagrad training')\n",
+        "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n",
+        "plt.plot(adam_training_losses, label='Adam training')\n",
+        "plt.plot(adam_validation_losses, label='Adam validation')\n",
+        "_ = plt.legend()"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFnCAYAAABdOssgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xd4VGX2wPHvTCZtkknIZFIJkEhP\no68Ukd5hASkqRVdZ3VVRf5YVdUFFVARXV1EQFBGBRVFsoCgIiIIKgiCETkhPIL3OpM7c3x+BkRhI\nKFMScj7P4/Nk5t6598zJlZNb3veoFEVREEIIIUSjpHZ2AEIIIYS4elLIhRBCiEZMCrkQQgjRiEkh\nF0IIIRoxKeRCCCFEIyaFXAghhGjENM4OQIj6tG/fnpYtW+Li4gKA2WymR48ezJ49G61We9Xb/fjj\nj5k8eXKt9z/77DOeeuopli5dyoABA6zvl5WV0bt3b4YOHcrLL7981fu9XCkpKbz00kskJiYC4Onp\nycyZMxk8eLDd930llixZQkpKSq2c7NmzhxkzZhAWFlbrM99++62jwrsmaWlpDBo0iIiICAAURcFg\nMPDvf/+byMjIK9rWq6++SmhoKLfffvtlf+bLL79k/fr1rF69+or2JZoWKeSiUVi9ejXBwcEAVFRU\n8Mgjj7Bs2TIeeeSRq9pednY2y5cvv2ghBwgJCeGrr76qUci///57fHx8rmp/V+Pxxx9n7NixLF26\nFICDBw9y55138s033xASEuKwOK5FSEhIoynal+Li4lLjO2zatIkHHniAzZs34+bmdtnbeeyxx+wR\nnhByaV00Pm5ubvTt25djx44BUF5ezjPPPMOwYcMYMWIEL7/8MmazGYDjx49z2223MXz4cMaOHcvO\nnTsBuO2228jIyGD48OFUVFTU2kfXrl3Zs2cPpaWl1vc2bdpEnz59rK8rKip44YUXGDZsGAMHDrQW\nXIADBw5wyy23MHz4cEaOHMnPP/8MVJ/h3XTTTaxatYoxY8bQt29fNm3adNHvefLkSTp16mR93alT\nJzZv3mz9g+att96iX79+jBs3jnfeeYeBAwcC8OSTT7JkyRLr5y58XV9cL730EtOmTQPgt99+Y8KE\nCQwZMoTJkyeTmpoKVF+Z+L//+z8GDBjAtGnTOHv2bD2/sYv77LPPmDlzJnfeeScLFy5kz5493Hbb\nbTz88MPWovfNN98wevRohg8fzh133EFKSgoAb775JrNnz2bixImsXLmyxnYffvhhVqxYYX197Ngx\nbrrpJiwWC//9738ZNmwYw4YN44477iAzM/OK4x45ciRlZWUkJCQAsG7dOoYPH87AgQN59NFHKSsr\nA6rzPn/+fMaMGcM333xT4/dwqePSYrHw/PPP079/fyZOnMjx48et+/31118ZP348I0eOZMSIEXzz\nzTdXHLu4TilCNHDt2rVTzpw5Y31dUFCgTJ06VVmyZImiKIqybNky5Z577lEqKyuV0tJSZcKECcoX\nX3yhmM1mZcSIEcrGjRsVRVGUQ4cOKT169FCKi4uV3bt3K4MHD77o/j799FNl1qxZyuOPP279bHFx\nsTJo0CDlk08+UWbNmqUoiqK89dZbyp133qmUl5crRqNRGTdunLJ9+3ZFURRl9OjRyldffaUoiqJ8\n/vnn1n2lpqYqkZGRyurVqxVFUZRNmzYpQ4YMuWgcDz74oDJgwADlgw8+UOLj42ssO3HihNK9e3cl\nKytLqaysVO677z5lwIABiqIoyqxZs5TFixdb173wdV1xRUVFKZ999pn1+/bo0UPZtWuXoiiKsnHj\nRmX8+PGKoijKmjVrlKlTpyqVlZVKXl6eMmDAAGtOLlRXjs/nuXPnzkpiYqJ1/ZiYGOXnn39WFEVR\n0tPTlW7duilJSUmKoijKe++9p9x5552KoijKokWLlJtuuknJzc2ttd2vv/5amTp1qvX1G2+8ocyb\nN085efKkMnToUKWiokJRFEVZtWqV8vnnn18yvvN56dixY633e/TooZw+fVrZu3ev0qtXL+Xs2bOK\noijKnDlzlJdffllRlOq8jxkzRikrK7O+Xrx4cZ3H5Y4dO5ShQ4cqJSUlSmlpqTJx4kRl2rRpiqIo\nyi233KLs2bNHURRFSUxMVB599NE6YxdNh5yRi0Zh+vTpDB8+nEGDBjFo0CB69uzJPffcA8COHTuY\nPHkyGo0GDw8PxowZw08//URaWho5OTmMGjUKgJiYGEJDQ4mLi7usfY4aNYqvvvoKgK1btzJgwADU\n6j/+l/n++++ZMmUKbm5uaLVaxo4dy5YtWwD44osvGDFiBADdunWzns0CVFVVccsttwAQFRVFRkbG\nRff/yiuvMHXqVDZu3Mjo0aMZOHAgH374IVB9ttyjRw8CAgLQaDSMHj36sr5TXXFVVlYyZMgQ6/aD\ngoKsVyBGjx5NSkoKGRkZ7Nu3jyFDhqDRaPDz86tx++HPzpw5w/Dhw2v8d+G99PDwcMLDw62vPTw8\n6NWrFwA//fQTN954I61atQJg0qRJ7Nmzh6qqKqD6CoVer6+1z/79+3P06FEKCgoA+O677xg+fDg+\nPj7k5eWxceNGCgsLmT59OuPGjbusvJ2nKArr1q0jKCiI8PBwtm/fzsiRIwkKCgLg9ttvtx4DAL16\n9cLd3b3GNuo6Lvfu3Uu/fv3w8vLCw8PD+rsC8Pf354svvuD06dOEh4fz6quvXlHs4vol98hFo3D+\nHnleXp71srBGU3345uXl4evra13X19eX3Nxc8vLy0Ol0qFQq67Lz/5gbDIZ699mnTx9mz55NQUEB\nX3/9Nffff7/1wTOA4uJi5s+fz2uvvQZUX2qPjY0FYOPGjaxatQqj0YjFYkG5oKWBi4uL9SE9tVqN\nxWK56P7d3d2ZMWMGM2bMoKioiG+//ZaXXnqJsLAwCgsLa9yv9/f3r/f7XE5c3t7eABQVFZGamsrw\n4cOty93c3MjLy6OwsBCdTmd938fHB6PReNH91XeP/MLf259f5+fn1/iOOp0ORVHIz8+/6GfP02q1\n9O7dmx07dtCtWzeKioro1q0bKpWKN998kxUrVjBv3jx69OjB3Llz633ewGw2W/OgKApt2rRhyZIl\nqNVqiouL+e6779i1a5d1eWVl5SW/H1DncVlYWEhgYGCN98976aWXePvtt7nrrrvw8PDg0UcfrfH7\nEU2XFHLRqOj1eqZPn84rr7zC22+/DYDBYLCefQEUFBRgMBjw9/ensLAQRVGs/2gWFBRcdtFzdXVl\nwIABfPHFFyQnJ9OlS5cahTwwMJC777671hlpZmYms2fP5pNPPqFjx44kJSUxbNiwK/qeeXl5HDt2\nzHpG7OPjw+TJk9m5cycnT55Ep9NRXFxcY/3z/vzHQWFh4RXHFRgYyA033MBnn31Wa5mPj88l921L\n/v7+HDhwwPq6sLAQtVqNn59fvZ8dNmwY3333Hfn5+QwbNsz6++/Zsyc9e/bEZDKxYMEC/vOf/9R7\nZvvnh90uFBgYyPjx45k1a9YVfa9LHZd15dZgMDBnzhzmzJnDrl27ePDBB+nbty9eXl6XvW9xfZJL\n66LRueuuuzhw4AC//vorUH0pdf369ZjNZkwmE19++SX9+vUjLCyM4OBg68Nk+/fvJycnh9jYWDQa\nDSaTyXqZ9lJGjRrFu+++e9EhX4MGDeKTTz7BbDajKApLlizhxx9/JC8vD61Wyw033EBVVRXr1q0D\nuORZ68WUlZXx0EMPWR+CAkhOTubgwYN0796dLl26sG/fPvLy8qiqquKLL76wrhcQEGB9SCo1NZX9\n+/cDXFFcnTp1Ijs7m4MHD1q3869//QtFUejcuTPbt2/HbDaTl5fHjz/+eNnf60r06dOHffv2WS//\nf/TRR/Tp08d6JaYuAwYM4MCBA2zdutV6eXrXrl3MnTsXi8WCVqulQ4cONc6Kr8bAgQPZsmWLteBu\n3bqVd955p87P1HVcdunShV27dlFaWkppaan1D4jKykqmT59OVlYWUH1LRqPR1LjVI5ouOSMXjY63\ntzf33nsvCxYsYP369UyfPp3U1FRGjRqFSqVi+PDhjBgxApVKxWuvvcazzz7LW2+9haenJ2+88QZa\nrZb27dvj6+tLnz59+PzzzwkNDb3ovv7yl7+gUqkYOXJkrWVTpkwhLS2NUaNGoSgK0dHR3HnnnWi1\nWm6++WaGDRuGv78/Tz75JPv372f69OksWrTosr5jaGgob7/9NosWLeKFF15AURS8vb156qmnrE+y\n33rrrYwfPx4/Pz+GDh3KqVOnAJg8eTIzZ85k6NChREZGWs+6O3TocNlxeXh4sGjRIubNm4fRaMTV\n1ZWHH34YlUrF5MmT2bdvH4MHDyY0NJTBgwfXOIu80Pl75H+2cOHCenMQHBzMCy+8wP33309lZSVh\nYWHMmzfvsvLn7e1NVFQUJ06coHPnzgD06NGDr7/+mmHDhuHm5oZer+ell14C4IknnrA+eX4loqKi\n+Oc//8n06dOxWCz4+/szd+7cOj9T13E5YMAAduzYwfDhwzEYDPTr1499+/bh6urKxIkT+dvf/gZU\nX3WZPXs2np6eVxSvuD6pFEX6kQvR2O3bt48nnniC7du3OzsUIYSDyXUZIYQQohGTQi6EEEI0YnJp\nXQghhGjE5IxcCCGEaMSkkAshhBCNWKMcfpadffGhLlfLz09Lfr7JptsUFye5dgzJs2NInh1D8gwB\nAbpLLpMzckCjcXF2CE2G5NoxJM+OIXl2DMlz3aSQCyGEEI2YFHIhhBCiEZNCLoQQQjRiUsiFEEKI\nRkwKuRBCCNGISSEXQgghGjEp5EIIIUQjJoVcCCHENfnuu2/p1+9GCgoKLrr800/X8d57y+waQ0JC\nPDNn3lvr/e+/33rZ21i9eiWHDx+65PJnn32K8vKyq4rPnuxayBcuXMitt97KhAkT2LJlC2fOnGH6\n9OlMmTKFhx9+mIqKCgA2bNjAhAkTmDRpEp988ok9QxJCCGFj3323mebNw9ix4/KLpiNUVlaybt3a\ny15/+vS/ER0de8nlc+fOx93dwxah2ZTdpmjdvXs3p06dYt26deTn5zN+/Hh69erFlClTGDFiBK+9\n9hrr169n3LhxLF68mPXr1+Pq6srEiRMZMmQIzZo1s1doQgghbKSoqJBjx47w1FPPsHbtKsaNmwjA\nvn2/smjRq+j1/vj7GwgNbU5VVRUvvvgc2dlZlJaWcvfd99KnT1/27t1zbl0DLVu2olmzZnTp0o2P\nPlqDyWRizpx/s337Tnbs2IbFYqFXrz7cffe9ZGVlMmfOk7i6utKmTbtasS1a9BqnT8fzn/+8TGRk\nFLt3/0xOTjZz577ERx+t4ejRI1RUVDBu3ATGjBnHiy8+R//+gygsLODQod8pKMgnJSWZKVOmM3r0\nOCZOHMOqVev4738XYjAEcOLEMTIzz/LMMy/Qvn0HXn/9FeLiDhERcQMpKcnMnfsSISGhdv8d2K2Q\n9+jRg9jY6r9sfHx8KC0tZc+ePcydOxeAAQMGsGLFCiIiIoiJiUGnq55HtmvXruzfv5+BAwfaK7Qa\nykorOfRbGsEtfFCpVA7ZpxBC2NrH2+PZezzLptvs0SGQyQPb1LnO9u1b6d37Jm68sRcLFrxAdnYW\nAQGBLFv2FnPmzKNt23Y8/vhDhIY2p7i4iL/8pScjRowmPT2NOXOepE+fvrz99pvMmfM8rVu35YEH\n7qFHjxsBOH06ng8//Izmzf3Zvn0nS5YsR61WM3nyWG69dQrr13/EoEFDmTz5dtasWUl8/MkasU2Z\nMp2jRw/z+ONPsmnTRjIzz7J06QoqKioIDg7lwQcfpby8jMmTxzFmzLganz19Op6lS1eQlpbKs88+\nzejRNZdXVFTw2mtv8cUX6/n226/RaDQcOvQ7y5evJjExgbvvnmqD38DlsVshd3FxQavVArB+/Xpu\nvvlmdu3ahZubGwD+/v5kZ2eTk5ODXq+3fk6v15OdnW2vsGo5fiCVX35MYcSEaMLbGhy2XyGEuB5s\n3bqZO++cgYuLCwMGDGLbti3cdts0zpw5Q9u21WfJnTt3pby8HJ3Oh2PHjrBhw2eoVGqKigoByMw8\nQ7t2HQDo2bM3ZrMZgDZt2lprhoeHBzNn3ouLiwsFBQUUFRWRlJTIgAGDAejSpTu7d/9cZ6wdO0ai\nUqlwd3enqKiQf/7zbjQaDQUF+bXWjY6OxcXFhYCAQIzGklrLO3XqAkBAQBBHjx4hKSmRyMgY1Go1\nrVu3ITg45GrSeVXs3v1s69atrF+/nhUrVjB06FDr+4qiXHT9S71/IT8/rc0m0fcvSgEg9WAiPXpH\n2GSbom51dfERtiN5doyGkucHbu3i8H2ePXuWo0cPs3TpIpYtU1FWVoZOp+PBB+9Do3Gx5kardUOj\ngd27d1BRUcrHH6+joKCAiRMnEhCgQ6VSWdf19vagqqqKZs20eHtrCQjQkZ6ezvr1H/L555/j5eXF\n6NGj0eu9cHV1Qa/3JiBAh6+vB25umhq/j/JyLzQaNQEBOnQ6D3x8vAgI0PHrr78SF3eAjz5ai6ur\nK126dCEgQIeHhyu+vp5YLGXodJ4EBOgwGtWo1dXxubioMRi88fBwvWC/nri7a/Dx8cDLy926f43G\nBb3eyyHHh10L+c6dO1m6dCnLly9Hp9Oh1WopKyvDw8ODzMxMAgMDCQwMJCcnx/qZrKwsOnfuXOd2\nbdnOzq9VIJrfUjidWEVWVpFcXrezgACdzdvQitokz47R1PO8bt2njB8/iQcffASoPhG77bbx/P77\nMfR6A7/9FkeLFq3YtetnoqJiSE09i59fALm5Rr78ciNlZeVkZxfj56dn3744wsJasGPHj3Tp0o2C\nAhPl5ZVkZxeTn5+Pj48vJpOFAwf2kpaWTmZmAcHBzfnll30EBbVi+/YfqaioqvH7yM8vtW6juLgM\nk6mC7OxikpMz8PMzUFBQxq5dm6mqMpORkUdZWSWFhaU11jWZTJjNFrKzizGbLeTklFjXy84uprCw\nlLKySnQ6f37//RBZWUUkJyeRnp5OXp4Rd3fbHB9OaWNaXFzMwoULWbZsmfXBtd69e7N582YAtmzZ\nQt++fenUqRNxcXEUFRVhNBrZv38/3bt3t1dYtXi1bYehPJNSs4a8HKPD9iuEEI3d1q2bGTVqjPW1\nSqVixIjRbN26mXvvvZ/Zs2cxa9YjBAYGAdC//0B+/nknDz98H56engQGBvL+++9yzz338+9//4sn\nn3yUVq3CcXGpecW1Y8eOeHpque++u9m2bQtjx97Cq68uYNKk2/n66w08+uhMiotrF0yDwUBVVSWz\nZ8+q8X737jeSlpbCzJn3kp6eRu/eN/Gf/8y/plx06BBJixYtuffeO/n447WEh9+AWu2YEd4q5XKu\nZV+FdevW8eabbxIR8cfl6pdffpnZs2dTXl5OaGgo8+fPx9XVlW+//Zb33nsPlUrFtGnT+Otf/1rn\ntm39F/Bvb/2PX0ua06N7AN0HR9l026Kmpn4G4yiSZ8eQPNvGr7/upkWLloSEhLJw4Yt07tyNoUOH\nW5c3hjxXVFSwbdsWRowYTWlpKVOnTuTjj79Eo7HNhe+6zsjtVsjtyda/0JI9v7B6exkBOoWJMx3z\ntHxT1Rj+h7weSJ4dQ/JsG3v2/MLSpW+i1Xrh56fn2WdfwNXV1bq8seT5v/9dyOHDcajVKsaPn8TI\nkWPq/9BlqquQ2/1ht8YgtGc3fL75ghxVAOVllbh7uNb/ISGEEDZx4429uPHGXs4O45o98sgTTtmv\nTNEKuBv8CXIpRkFFSrzjhr4JIYQQ10oK+TktI6ofyEv8Pcm5gQghhBBXQAr5Oc27dcStykRaRull\njWUXQgghGgIp5Odo27bDv+ws5RYXss82/IcqhBBCCJBCbqXSaGhuqB67mHgoxcnRCCFE49GQ25he\nrpkz7yUhIZ5Nmzbyww/f11o+atSgOj9/vl3q7t0/8/nn6686jqshhfwCrWJaoVIsJJ20beMBIYS4\nnjXUNqZXY+TIMfTrN+CKPnNhu9SePXszfvxEe4R2STL87AJ+XWLw/WELeapgTMYKtF5uzg5JCCEa\ntIbcxvSppx7n1lunnGvaUsbUqZNYu/ZT5s9/vlYM5733XvVspGPHTmDu3NlkZWXSsWOkdfnevXtY\nvnwprq6u6HQ6nn/+5VrtUhMSTjNz5v/x8ccfsm3bFgD69u3HtGl/48UXn7toC9RrIYX8Aq56f4I0\nxRQQQsqpLDp0DnN2SEIIcVk+i/+KA1lxNt1ml8AYbmkzus51GnIb0379BvDTTzvp3Lkre/fuoUeP\nnhiNJReN4c/27t1NVVUVy5a9z5Ejh1m/fh1QPf34s8++QGhoc+bNe4Y9e36p1S4VICMjnW++2ci7\n764C4N5777R2avtzC1Qp5DbWsrWeE6nV98mlkAshRN0achvTPn1uZu3aVTzwwMPs3PkDgwYNvWQM\nf5aYmEhMTCwAUVHRuLu7A9CsWTMWLHgBs9lMRkY63br1uOjnT506QVRUjHWK1piYTtY/NP7cAvVa\nSSH/k5AuHfFISCT9rBaLxeKwSe+FEOJa3NJmdL1nz7aWlZXJ0aOHeeut11Gpzrcx9ea226bV+Lfz\n/JDe7777lqKiIhYvXk5RURF///v0Wtu8sAPl+Wla09PTWbfuf6xY8T+0Wi3Tp0+2blelUp/72VJr\nWzqdDoMhkJSUJA4fPsS//vX0ZcVwLmrrti/8DvPnz+OVV14nPDyC115bUEd2VDWGMldWVlq3d2FT\nGFsMd5Yq9Sfadu0xlJ+h0qImM73I2eEIIUSDtXXrZsaPn8QHH3zIypVr+fDDTykqKiI9PQ2DIYCU\nlCQUReHAgd8AKCgoICQkFLVazQ8/bKeyshIAvd6f5OQkzGYze/fuqbWf/Px8/Pz80Gq1nDhxnLNn\nz1JZWUnLlq04fvwoAPv377tojDff3J8PPlhhPTu+VAx/duG24+IOUlFRAYDRWEJQUDDFxcXs3/+b\ntUCfv4pwXrt27Tl8OI6qqiqqqqo4evQI7dq1v4os108K+Z+oNBpCzw9Di0t1cjRCCNFwNfQ2plBd\nyLdt28KAAYPqjOHPevbsQ0VFOTNn3su2bVsICAgE4JZbJnHffTNYuPBFpk69gzVrVqJSUatdakhI\nKH/963gefPBeHnjgHsaMGUtwcMi1JfwSpPsZtTvr5Gz/nk93W/DxUnP7w1c2DEHUrbF0MWrsJM+O\nIXm2jeuhjam9SfezK+TbKRa/778hVx1GSVEZ3j4ezg5JCCGuW4qi8PTTj1vbmJ4/exaXRwr5Rbj6\n+xPoWkwukHwqm6huLZwdkhBCXLeulzamziL3yC+hVWsDINO1CiGEaNikkF9CUNcotBWFZGSVY66q\nPaxBCCGEaAikkF+CR5u2GMrPYFbUnEm7eCMAIYQQwtmkkF+C2tWV5gHVkxEkxqU7ORohhBDi4qSQ\n16FFbAQulkqST+c4OxQhhGiwHNnGND7+FCkpyZe1bm5uDgsXvnjJ5c5oOWoPUsjr4BMbg96UQXGZ\nisJ8k7PDEUKIBsmRbUx/+GE7qamX9xCyv7+BJ5749yWXO6PlqD3I8LM6uBoCCNKUkA0kn8wm9sZW\nzg5JCCEaFFu0MZ058166du3O3r17UKvVjBgxik2bvkKtVvPGG29b93X6dDxffvkZP/ywHT8/P55/\nfg49e/bBz8+P3r378tprC9BoNKjVaubNexmj0cjs2bN4773V3HrrOMaOvYWfftpJRUUFb7yxhB07\ntpOQcJoJEybz4ovPERranPj4U7Rr154nn5xDfPwpXnzxWby9dXToEElBQT7//vdzTsr0pUkhr0eL\ntgYOp0Li4TQp5EKIBiv7k48o3rfXptvUde9BwKTb6lzHFm1Mofrs+e233+O+++6mqKiIJUuWc//9\nfychIZ7g4O4AtG7dhhtv7EX//oOIjIymqqqKnj1707Nnb/bu3c0jj/yLdu06sHz5UrZs+YY+fW62\nxmk2m2nZMpwpU+7g2WefYt+fcnXixDHmzn0JPz8948ePpLi4mPfff4e//e0e+vUbwJw5T+Lh0TAn\nB5NCXo+AzlF4x5/kbI4flRVmXN1c6v+QEEI0EbZoYwoQGRkFVBf0tm2rm4vo9XpKSkrq3P/5z/n5\n+fP2229SXl5GTk42Q4YMr7Xuhe1Djcaa223evAX+/tXzhxgMARiNJSQnJxEb2wmAm266mX37fr3i\n/DiCFPJ6eLZrj6HsB5Lc9aSn5BPexuDskIQQopaASbfVe/Zsa7ZsY3pho5QrafOp0VSPLnrjjf8w\ndeqd9OzZm7VrV1NaWvu5prq2++dGLYqi1GiTemF71YZGHnarh9rVleaB1QdK0hEZhiaEEOfZqo3p\nlVCpVLVahgIUFhbQvHkYFRUV7N79E1VVVdf8/Zo3D7O2Mt29++dr3p69SCG/DGGdWqMxl5N8Os8m\nTeCFEOJ6YKs2pleiU6cuvP76K7Uuc0+YcCtPPfU4c+bMYsKEW/nmm6/qvSxfnzvumMHixa/z6KMz\n8fPzq3GVoSGRNqbU3yKvMjubja9vJFN3A7fO6IE+wMum+29KpB2hY0ieHUPy7BjOyvPhw3F4eHjQ\npk1bVq9+H0VRuOOOux0eB0gb02vmGhBAoGsJmUDSqSz0ARHODkkIIYSdubm58vLL83B3d8fd3YPn\nnnvB2SFdlBTyy9SybSBxKQpJRzLo2lsKuRBCXO+qh7KtcnYY9bLrBf+TJ08yePBg1qxZA8DevXu5\n/fbbmT59Ov/4xz8oLKweerB8+XImTpzIpEmT+OGHH+wZ0lXz7xyNT3kOWbkVlJdd+0MUQgghhC3Y\nrZCbTCbmzZtHr15/NIufP38+L774IqtXr6ZLly6sW7eO1NRUNm3axNq1a1m2bBnz58+/6BOJzubZ\nrh2GsjMoqEhLynN2OEIIIQRgx0Lu5ubGu+++S2BgoPU9Pz8/66T6hYWF+Pn5sWfPHvr27Yubmxt6\nvZ7mzZsTHx9vr7CumtrVjbAgNwCSjmY4ORohhBCimt0KuUajqTWd3dNPP80DDzzAsGHD+O233xg/\nfjw5OTno9XrrOnq9nuzsbHuFdU1CY9vgVlVKSmK+DEMTQgjRIDj0Ybd58+bx1ltv0a1bNxYsWMDa\ntWtrrXM5BdLPT4tGY9upUus0+eGPAAAgAElEQVR6tP88Xf9e+G9byxlNW6rKLYS2aGbTGJqKy8m1\nuHaSZ8eQPMNXX33FrFmz2LlzZ40Ts/PWrFlDfn4+Dz744FXv40rzvGfPHv73v/+xaNEi7rvvPt5+\n++0ay+uL6fjx47i7uxMREcEjjzzC/PnzZa51gBMnTtCtWzcAevfuzcaNG+nZsyeJiYnWdTIzM2tc\njr+YfBu3FL3sMYouXgS5lnAG+P3XZFw9ZN71KyXjbh1D8uwYkudqn376BaGhzfn00y+t3c8uVFJS\nhtFYftW5upo8FxSYKC+vJDu7mOefX1jr8/XF9MUXX9GhQyTe3gaefvp5iosrKS6+8pnobKXBjCM3\nGAzEx8fTpk0b4uLiaNWqFT179uT999/nwQcfJD8/n6ysLNq0aePIsK5Ii3ZBHEyxkHwsgx433+Ds\ncIQQwqkc2cb01KmTvPnmayxatBSAFSveQafzITw8guXLl+Lq6opOp+P551+uEeOoUYP4+uttlx1T\ncHBIjXapzzzzFKtWraOkpJj585+nsrIStVrNk0/OQaVSXbQFqiPZrZAfPnyYBQsWkJ6ejkajYfPm\nzcydO5fZs2fj6uqKr68vL730Ej4+PkyePJlp06ahUql47rnnGuw0eAB+nWLwPXmE7PwgSk0VeGrd\nnB2SEELw8/bTJBzPsuk2b+gQSO+Bretcx5FtTNu2bUdOTjbFxcXodDp27fqRBQteIy7uEM8++wKh\noc2ZN+8Z9uz5Ba1WWyvWy41pxYo1Ndqlnrd8+VJGjx7LoEFD+f77raxY8Q4zZvzjoi1QdTrH3XKx\nWyGPjo5m9erVtd7/6KOPar03ffp0pk+fXuv9hsizfXsMZVsp8AwmNSGPdtHBzg5JCCGcxtFtTPv0\nuZk9e34mOroT7u5uBAQE0qxZMxYseAGz2UxGRjrduvW4aCG/0pj+7MSJY/zznzMB6Nq1OytXLgcu\n3gL1uijk1yu1qxthwe7El0PSsTNSyIUQDULvga3rPXu2NWe0Me3XbwCffvoxhYUF9Os3EID58+fx\nyiuvEx4ewWuvLbhkvFcaU20q6+cqK6usLU4v1gLVkRruNewGLCi2He6VJaQmFWCxWJwdjhBCOIUz\n2phGRcWQlJTAzz//RP/+gwEwGksICgqmuLiY/ft/u+R2rySmi7VL7dgxkv379wHw+++/0aFDxyuO\n3x6kkF8F75hYDKZ0KswqMjPkiVUhRNPkjDamKpWK6OhOGI0lBAdXXxG95ZZJ3HffDBYufJGpU+9g\nzZqV5Obm1PrslcR0sXapf//7P/n220089NA/2bTpK2bM+McV58wepI0pVze0Yc8zr7Jf240uN4bR\nc0DDfcq+oZHhOo4heXYMybNjSJ7rHn4mZ+RXqUX7EFSKmeTjZ50dihBCiCZMCvlV8u0UjV/pWfIK\nqygpLnd2OEIIIZooKeRXybNdBwxlZwBISch1cjRCCCGaKinkV0nt5kZYsCcAScfk8roQQgjnkEJ+\nDQI7tcOzopD0lCLMVTIMTQghhONJIb8GXtHVw9CqLHAm7dKzAQkhhBD2IoX8GrgFBRHkWj19YPKp\nhtlDXQghxPVNCvk1CusQitpSSfKJTGeHIoQQogmSQn6NfGJj0ZvOUFhipqig1NnhCCGEaGKkkF8j\nz/YdMJRXD0NLPi3D0IQQQjiWFPJrpHZzIyz03DA0meVNCCGEg0khtwFDTEe8y/M4k15CZaW5/g8I\nIYQQNiKF3Aa8YmLwN6VjtkBGcoGzwxFCCNGESCG3Abeg4D+GocXXbp0nhBBC2IsUchtp3jEMjbmC\n5JNZNMLOsEIIIRopKeQ24h0Ti96UTonJTH6uydnhCCGEaCKkkNuItn0HDGUZAKTIMDQhhBAOIoXc\nRtTu7oQ19wIg6USWk6MRQgjRVEghtyF9bCQ+ZdmcPVNCRXmVs8MRQgjRBEghtyGv6Fj8TekoCqQm\n5js7HCGEEE2AFHIbcg0KItitehhaigxDE0II4QBSyG1IpVIR3KEVrlWlJJ/KlmFoQggh7E4KuY15\nx1bP8lZabiEns8TZ4QghhLjOSSG3MW37jhjKqruhyTA0IYQQ9iaF3MbU7u40b+6FSrGQdFKGoQkh\nhLAvKeR24BcThW9ZFlmZJkpNFc4ORwghxHVMCrkdeMXE4G9MB2QYmhBCCPuSQm4HrsEhBLlJNzQh\nhBD2Z9dCfvLkSQYPHsyaNWsAqKys5LHHHmPixInceeedFBYWArBhwwYmTJjApEmT+OSTT+wZkkOo\nVCqCOkbgXmkk9XQOFosMQxNCCGEfdivkJpOJefPm0atXL+t7H3/8MX5+fqxfv56RI0eyb98+TCYT\nixcvZuXKlaxevZoPPviAgoICe4XlMN4xMfib0iivUMjKKHJ2OEIIIa5Tdivkbm5uvPvuuwQGBlrf\n+/777/nrX/8KwK233sqgQYM4ePAgMTEx6HQ6PDw86Nq1K/v377dXWA6j7RhpHYaWnCDD0IQQQtiH\nxm4b1mjQaGpuPj09nR9//JFXXnkFg8HAs88+S05ODnq93rqOXq8nOzu7zm37+WnRaFxsGm9AgM6m\n2wMd4RHNiDOaST+dS8CETjbefuNl+1yLi5E8O4bk2TEkz5dmt0J+MYqiEBERwcyZM1myZAnLli0j\nMjKy1jr1yc832TSugAAd2dnFNt0mgLZDB/x2nSUz04WkhBy8dO4230djY69ci5okz44heXYMyXPd\nf8g49Kl1g8FAjx49ALjpppuIj48nMDCQnJw/nuzOysqqcTm+MdNGx1qHoaUk5Dk5GiGEENcjhxby\nm2++mZ07dwJw5MgRIiIi6NSpE3FxcRQVFWE0Gtm/fz/du3d3ZFh24xYiw9CEEELYl90urR8+fJgF\nCxaQnp6ORqNh8+bN/Oc//+HFF19k/fr1aLVaFixYgIeHB4899hgzZsxApVLxwAMPoNNdH/dCVCoV\ngZGt8UwsIi1RhdlswcVFhu4LIYSwHZXSCHtt2vpeiT3vv5T8foDvP/qFtGaRjLmtE2HhfnbZT2Mh\n97ocQ/LsGJJnx5A8N6B75E2RtkNHAs53Q5NhaEIIIWxMCrmdqT08CAnzQW2pJPlU3cPqhBBCiCsl\nhdwBdDHR6EvPUJBfTlFBqbPDEUIIcR2RQu4AXjEXDEM7LcPQhBBC2I4UcgdwCwklyM0IQPJpGYYm\nhBDCdqSQO4BKpcIQ1Qav8nzSk/KpqjQ7OyQhhBDXCSnkDuIVHYvBlIbZAukpjb+7mxBCiIZBCrmD\naDt2xL8sA4CU0zIMTQghhG1IIXcQtYcnwWF+aMwVJJ3KuazmMEIIIUR9pJA7kC42Br0pnZLiCgry\nbNvBTQghRNMkhdyBvKJj8TdVD0NLjpdhaEIIIa6dFHIHcgv9YxhaigxDE0IIYQNSyB1IpVKhj2qP\nriyHM6mFVJRXOTskIYQQjZwUcgfzionBYErDokBaUr6zwxFCCNHISSF3MG3HSPzPdUNLlmFoQggh\nrpEUcgdTe3gS1EKPa1UpKfG5MgxNCCHENZFC7gTeMTH4m9IxmSrJzSpxdjhCCCEaMSnkTuAVc8Ew\nNOmGJoQQ4hpIIXcCt9DmBLqZQFHkPrkQQohrIoXcCVQqFX7RHfAtyyIrvYiy0kpnhySEEKKRkkLu\nJNpz3dAUIDVRLq8LIYS4OlLInUSGoQkhhLAFKeRO4uLpSUALA+5VRlJO52KxyDA0IYQQV04KuRN5\nx8Tib0yjvMxM1pkiZ4cjhBCiEZJC7kTV07VWD0NLkWFoQgghroIUcidyax5GgFspKsUi98mFEEJc\nFSnkTqRSqfCJ7ohf6VlyMkswlpQ7OyQhhBCNjBRyJ/OKrr5PDpCaIJfXhRBCXBkp5E6mjYzCv1yG\noQkhhLg6GmcH0NS5eHri3yIQz4oiUhPUmM0WXFzk7yshhBCXRypGA+AdW315vbLSwtm0QmeHI4QQ\nohGxayE/efIkgwcPZs2aNTXe37lzJ+3bt7e+3rBhAxMmTGDSpEl88skn9gypQfKKjrUOQ5NuaEII\nIa6E3Qq5yWRi3rx59OrVq8b75eXlvPPOOwQEBFjXW7x4MStXrmT16tV88MEHFBQU2CusBsktLAx/\nt1LUShUpcp9cCCHEFbBbIXdzc+Pdd98lMDCwxvtLly5lypQpuLm5AXDw4EFiYmLQ6XR4eHjQtWtX\n9u/fb6+wGqTqYWhR6E1nyM81UVRQ6uyQhBBCNBJ2e9hNo9Gg0dTcfGJiIsePH+fhhx/mlVdeASAn\nJwe9Xm9dR6/Xk52dXee2/fy0aDQuNo03IEBn0+1dKVXvG/E/tJEcrxbkZ5to3Taw/g81Us7OdVMh\neXYMybNjSJ4vzaFPrc+fP5/Zs2fXuY6i1N88JD/fZKuQgOoDJDu72KbbvFLmsAj8yzIAOPJ7Oq3a\n+js1HntpCLluCiTPjiF5dgzJc91/yFz1pfWkpKQrWj8zM5OEhAQef/xxJk+eTFZWFtOmTSMwMJCc\nnBzrellZWbUuxzcFLlov9K1C8KooIC05n6pKs7NDEkII0QjUWcjvuuuuGq+XLFli/fmZZ565oh0F\nBQWxdetWPv74Yz7++GMCAwNZs2YNnTp1Ii4ujqKiIoxGI/v376d79+5XtO3rhVdMLP7GVMxVChmp\nTeuBPyGEEFenzkJeVVVV4/Xu3butP9d3Cfzw4cNMnz6dzz//nFWrVjF9+vSLPo3u4eHBY489xowZ\nM7jrrrt44IEH0Oma5r0QbfQf3dCS42UYmhBCiPrVeY9cpVLVeH1h8f7zsj+Ljo5m9erVl1y+fft2\n68/Dhw9n+PDhdW6vKXBv0RK9WzkaSyUpCbkoSpt68yyEEKJpu6J75FJU7EulUqGLjkZvSqeooIyC\nPBmGJoQQom51npEXFhbyyy+/WF8XFRWxe/duFEWhqKjI7sE1RV4xMfgf2kKWdzgpp3Px89c6OyQh\nhBANWJ2F3MfHp8YDbjqdjsWLF1t/FranjYzCv+wDoLobWqe/tHByREIIIRqyOgt5Xfe4hX24aL1o\nFt4cXVkuZ1JVVJRX4eYuTeqEEEJcXJ33yEtKSli5cqX19UcffcTYsWN56KGHaoz9FraljY7B35iK\nxaKQnpzv7HCEEEI0YHUW8meeeYbc3OomHomJibz22mvMmjWL3r178+KLLzokwKbIKyYWg1G6oQkh\nhKhfnYU8NTWVxx57DIDNmzczfPhwevfuzW233SZn5Hbk3qIlfh4VuFrKSTmde1nT1gohhGia6izk\nWu0fT0z/+uuv9OzZ0/pahqLZj0qlwjsqBv+SNIwlFeRmGZ0dkhBCiAaqzkJuNpvJzc0lJSWFAwcO\n0KdPHwCMRiOlpTLG2Z68YmLxN6UBkJIgPcqFEEJcXJ2F/J577mHkyJGMGTOG+++/H19fX8rKypgy\nZQrjxo1zVIxNkjYyCv/SM6AoJJ+WQi6EEOLi6hzX1K9fP3bt2kV5eTne3t5A9dzo//rXv7jpppsc\nEmBT5eLlhS6iBb6l2WSmqygrrcTD09XZYQkhhGhg6izkGRkZ1p8vnMnthhtuICMjg9DQUPtFJvCK\njsHwwykKPQNJTcyjbWSQs0MSQgjRwNRZyAcOHEhERAQBAQFA7aYpq1atsm90TZxXTCz+m3Zw2r8b\nKaelkAshhKitzkK+YMECvvzyS4xGI6NGjWL06NHo9XpHxdbkubdoia9HFe7mUlIS8rBYFNRqGS0g\nhBDiD3U+7DZ27FhWrFjB66+/TklJCVOnTuXvf/87GzdupKyszFExNlkqtRrv6Bj8S1IpK60k+2yx\ns0MSQgjRwFxWG9OQkBDuv/9+vvnmG4YNG8YLL7wgD7s5iFf0H8PQ5Ol1IYQQf3ZZ3TiKiorYsGED\nn332GWazmX/84x+MHj3a3rEJqoeh6UvfQ4WFlNO5/KVvhLNDEkII0YDUWch37drFp59+yuHDhxk6\ndCgvv/wy7dq1c1RsAnDx9sY7oiXNTJlkn1VjKilH6+3u7LCEEEI0EHUW8r///e+Eh4fTtWtX8vLy\neP/992ssnz9/vl2DE9W8YmIx7DhOvjaElIQ8OsSGODskIYQQDUSdhfz88LL8/Hz8/PxqLEtLS7Nf\nVKIGr+hY/L/exikDUsiFEELUUGchV6vVPPLII5SXl6PX61m2bBmtWrVizZo1vPPOO9xyyy2OirNJ\nc2/ZEp2HgqfZSGqCC2azBReXy3pOUQghxHWuzkL+3//+l5UrV9K6dWu2bdvGM888g8ViwdfXl08+\n+cRRMTZ51mFop1JIc+lIZnoRoS2bOTssIYQQDUCdp3VqtZrWrVsDMGjQINLT07njjjt46623CAqS\nWcYcSRsTI8PQhBBC1FJnIf9zz/GQkBCGDBli14DExXlFRuNXlokasxRyIYQQVld0o/XPhV04jou3\nN14R4fiZzpCfY6K4UGbWE0IIUc898gMHDtC/f3/r69zcXPr374+iKKhUKnbs2GHn8MSFvKJjMHx/\nlFxtGCkJuUR1ae7skIQQQjhZnYX822+/dVQc4jJ4xcTi//V3ACSfzpNCLoQQou5C3ry5FIqGxL1l\nK7w91XhVFZGerKaqyoxG4+LssIQQQjiRDEZuRFRqNdroaPyLU6iqtPD5qgPs25VE9tniGr3ihRBC\nNB2X1TRFNBxe0bG0/PUDKlp0ICvHSE5WCXt3JeHt406r1v6Et/WneUs/XDTyN5oQQjQFUsgbGa+o\naNwtZXQ3/krgw0+QmphHUnwOKafzOHIggyMHMtC4qmkRoSe8jT+t2vjjqXVzdthCCCHsxK6F/OTJ\nk9x///387W9/Y9q0aZw5c4annnqKqqoqNBoNr7zyCgEBAWzYsIEPPvgAtVrN5MmTmTRpkj3DatRc\nvL3xiLiB0tPxaKrKaNMxkDYdA7FYLJxNKyLpVA5J8bkknswh8WQOAMHNfQhva6BVG3/8/LUyjFAI\nIa4jdivkJpOJefPm0atXL+t7r7/+OpMnT2bkyJH873//4/3332fmzJksXryY9evX4+rqysSJExky\nZAjNmskUpJfiFRNLWcJpEp54FG2HjnjFxOIVHUtoywBCWzaj18DWFOSZSIrPJelULpnphZxNL2L3\njgR8mnkQ3qa6qIe08JU524UQopFzee65556zx4ZVKhWjR4/mxIkTeHp6EhsbS58+fWjfvj1qtZq0\ntDROnjyJr68vubm5jBkzBo1Gw/Hjx3F3dyciIuKS2zaZKmwaq5eXu823aU/uzcPAbMZcXEzZ6XiM\ncYco2PYdRXt2U5mdiUqlwjs0kNBWejrGhhDVNRS9wQuVWkVutpEzqYWcPJxJ3G9p5GYZsVgseOvc\nHfIEfGPLdWMleXYMybNjSJ6rc3Apdjsj12g0aDQ1N6/VagEwm82sXbuWBx54gJycHPR6vXUdvV5P\ndnZ2ndv289PavOgEBOhsuj27CtARPPNeAMoys8jff4CC/QcoOBRHwdbvKNj6HWo3N3xjomjWpQsB\n3brQcmBbbhrYlqqq6ileTx7J5OTRTOKPZRF/LAuVWkWrG/S0iwyiXVQweoOX/cJvTLluxCTPjiF5\ndgzJ86U5/GE3s9nME088Qc+ePenVqxcbN26ssfxyhlHl55tsGlNAgI7s7GKbbtNh1J5ouvfG0L03\n+spKyuJPYTx8CGNcHPm/HSD/twMkLgfXgEC00TF4xcTg3b4j3fuG0+2mVuRlG6331c//t2XDUfz8\ntYS39adVGwNBoT6o1ba5r96oc92ISJ4dQ/LsGJLnuv+QcXghf+qpp2jVqhUzZ84EIDAwkJycHOvy\nrKwsOnfu7OiwrgtqV1e0HSPRdowkYNJtVOblYjwchykuDtOxIxR+v43C77eh0mjwbNcer+hYvKNj\n6Nq7Fd36hGMsKSf5dPV99fSkfA7sTuXA7lQ8PF1p1VpPeFsDLSL8cHWTwQ5CCNFQOPRf5A0bNuDq\n6spDDz1kfa9Tp07Mnj2boqIiXFxc2L9/P08//bQjw7puuer9aXZzf5rd3B+lqorS0/HVhf3wIUxH\nj2A6egQ+/hCNvz9e0bF4RcfQoWNHIjuFUllpJj0pn6T4XJLjczlxOJMThzNRu6ho3rIZ4W0NhLfx\nx9vHw9lfUwghmjSVYqcpwQ4fPsyCBQtIT09Ho9EQFBREbm4u7u7ueHt7A9C6dWuee+45vv32W957\n7z1UKhXTpk3jr3/9a53btvUllqZ42aaqIB/j4cMYzxV1i+nc7QoXFzzbtsMrOgavmFjcQqun6c0+\nW0zSqVyS4nPIzTJat2MI9KZVm+qJaAKCdfUObWuKuXYGybNjSJ4dQ/Jc96V1uxVye5JCbluK2UxZ\nQkL1vfXDcZQnJ1mXafz0aKOj8YqORdsxEhetluLCMpLjq4t6enIBFkv1IaT1drNOQhPWyg+Na+0H\nEpt6rh1F8uwYkmfHkDxLIa+XHCQ1VRUWYjpyGOPhOIxH4rAYz52Bu7jg2boNXtExaKNjcG/RksoK\nM6mJ+edml8ulrLQKAI1GTVi4X/VENK31aL2rh05Irh1D8uwYkmfHkDxLIa+XHCSXplgslCUmnLu3\nHkdZUiKcO2RcfH3xiqq+BK+NjELlqSUzvdD69HtB7h+jCwJDdIS3NdDlLy1RuSCzy9mZHNOOIXl2\nDMmzFPJ6yUFy+aqKizAdPYIx7hCmI4cxF5/Lm0qFxw2trbPMubdsSWHB+UvwuZxJLThf/9H5ehB+\n7r56SItmMrucHcgx7RiSZ8eQPEshr5ccJFdHsVgoT0nGGFd9b70s4fQfZ+s6n3P31mPwioymytWD\n5NN5nE0t5NSxTCrKzQC4ubvQ8gY9rdpUX4J393B15le6bsgx7RiSZ8eQPEshr5ccJLZhLimpPls/\nd2/dXFhYvUClwiMiAm1UDGE396LE28DZ9GLrRDTFhWXnVyOkRbNzZ+sGfP08nfhtGjc5ph1D8uwY\nkmcp5PWSg8T2FIuF8rRUTIfjMMYdovR0PFgsALjodNXj1mNi8YyMotDEufvqOWRl/PF78DNoq4t6\nGwOBNpxdrimQY9oxJM+OIXmWQl4vOUjsz2wyYTp6BPOpY+Tu++2Ps3W1uvpJ+JhYvGI6UdksgJSE\nPOvsclVV1cXfQ+tKeOvq++ph4Xpc3ezf4KUxk2PaMSTPjiF5lkJeLzlIHCcgQEdWZiHlqSnV99YP\nHaQsMcF6b13jpz9X1GNxbdOeM2dLrWfrpcZKAFxcVDQP97O2Y/XWXborUFMlx7RjSJ4dQ/Ishbxe\ncpA4zsVybS4uxngkzvrQ3Plx69Y54WNi0UbHUqB4kRSfQ9KpXPKy/5hdLiBYZ30K3j/QW4a2Ice0\no0ieHUPyLIW8XnKQOE59uVYsFsoSTlvP1stTU6zLXAODrGfrlcGtSEkqIjk+l4yUP2aX8/Zxtz4s\nF9qiGS6apjm0TY5px5A8O4bkWQp5veQgcZwrzXVlfj6mw4cwHjqE8egRlPJzT7i7uaHtGFl9Cb59\nNGcKIOlUDsmn86gor55dztXNhRYR+up2rK398fBsOkPb5Jh2DMmzY0iepZDXSw4Sx7mWXCtVVZSe\nOll9th53iIozGdZlbs3Dqp+Cj4qhwD2A5MQCkk7lUFTwx9C24DBf69l6M73WJt+noZJj2jEkz44h\neZZCXi85SBzHlrmuyM7CdK6om44fQ6msfhhO7emJNioabXQMlc3bkXq2nOT4XM6mF1k/20zvaW3F\nGtTc97ob2ibHtGNInh1D8iyFvF5ykDiOvXJtKS/HdOL4ubP1g1Tl5FiXuYdH4BUTi7ptFJmV3iTH\n55GalEdV5bmhbZ4aWrauHq/eIsIPN3eNzeNzNDmmHUPy7BiSZynk9ZKDxHEckWtFUag4cwZj3MHq\nyWhOnQRz9ZSwLjpddee2yBgKfVqSklpCcnwuxpIKANQuKpq3bGY9W/f28bBrrPYix7RjSJ4dQ/Is\nhbxecpA4jjNybS4ttTZ6McYdwlxYUL1ApcKzTVu00bGUNW9PepGG5PhccrJKrJ81BHnTsVMIHWKC\nL9pfvaGSY9oxJM+OIXmWQl4vOUgcx9m5VhSlejKaQ9Vn6xc2etH4+eEVEwttosl0MZCSVER6cvXQ\nNk+tKzHdw4juGtooGrs4O89NheTZMSTPUsjrJQeJ4zS0XJtLSqonozl0COOROCwl587GXVzQtuuA\nS4dYkjQtOHo0l4pyM65uLkR1CSW2exheDXhGuYaW5+uV5NkxJM9SyOslB4njNORcKxYLZYkJ1ffW\nDx2iPCW5eoGLC57de3O2RXeOnjJiMlagdlHRPjqYzje2aJBD2Rpynq8nkmfHkDxLIa+XHCSO05hy\nXVVQQMmB3yjYtpWKs2cAcGvTnryO/Tl+Vk1hfikAN7QPoEvPFgSG+Dgz3BoaU54bM8mzY0ie6y7k\njX+cjRB2omnWjGYDBuHbbwCmo0fI37oF0+E4vONPcKPBQEnnIZw0+ZFwIpuEE9mEhfvRpWcLmrfy\nk/nehRAOI4VciHqo1Gq8omPwio6hPCODgm3fUfTLT3hu/ZBO7u6UdxtEgks4aUn5pCXlExDsTZee\nLYloF3DdTTQjhGh45NI6ctnGka6XXJtLSijc+SMF27dSlZ8HKhWVkTeS7N+J5IxyAHz9POncswXt\no4Id3rzleslzQyd5dgzJs9wjr5ccJI5zveVaqaqi5MB+8rduoex0PACVYe1Ib9mbxBwVFouC1tuN\n2B5hRHUOddiscddbnhsqybNjSJ7lHrkQdqPSaND1+Au6Hn+hNCGBgm1bKN63l/C0k4T6GMhsP4jE\nEjW7v09g/8/JRHVtTmz3MLRebs4OXQhxnZAzcuSvPUdqCrmuzM+n8PttFPy4A0tJCZWunmR3GEii\nJYiycgsuLio6xIbQ+cYW+DTztEsMTSHPDYHk2TEkz3JpvV5ykDhOU8q1paKC4t2/kL91CxUZ6ZhV\nLmTf0Iskz7YYyxRUKmjdMZAuN7bEEORt0303pTw7k+TZMSTPcmldCKdQu7nhe3M/fPrejOnYUQq2\nbsHl0C4C+Ync4GiSDbaij00AACAASURBVF2IP5pF/NEsWtygp2vPloS08JWha0KIKyKFXAg7U6lU\neEVG4RUZRcXZsxRs/w6Xn3ZhOBtHnk8r0sJ6kZqQR2pCHkGhPnTp2ZLwtv5S0IUQl0UurSOXbRxJ\ncl3NbDL+MXwtN5cCjwDSWvYh09IMAD9/LZ1vbEHbqCBcXK586Jrk2TEkz44heZZ75PWSg8RxJNc1\nKWYzJb/vp2Drd5SeOkmJqy9poX8hwy0URVHhpXOn01/CiOwUgqvb5V9Akzw7huTZMSTPdRdyl+ee\ne+45e+345MmT3HrrrajVamJjYzlz5gz3338/69ev58cff2TQoEG4uLiwYcMGnn76adavX49KpSIq\nKqrO7ZpMFTaN08vL3ebbFBcnua5JpVbjHtoc35v64hXbGU15MT7HdhFScBKVqyv5+JKSkM+RAxlU\nVZrRB3jhehl90SXPjiF5dgzJc3UOLsVu002ZTCbmzZtHr169rO8tWrSIKVOmsHbtWlq1asX69esx\nmUwsXryYlStXsnr1aj744AMKCgrsFZYQDZZHeDghM+7lhgWvEjpiMB1K4uhz+iMi8n7HUl7Gvp+S\nWfP2bnZ9d4riwjJnhyuEaCDsVsjd3Nx49913CQwMtL63Z88eBg0aBMCAAQP45ZdfOHjwIDExMeh0\nOjw8POjatSv79++3V1hCNHiaZs0wjB1PxMJXCbtjGh20ufSJ/4i22XvQVJiI+y2dtcv2sO2rY+Rl\nG50drhDCyez21LpGo0Gjqbn50tJS3NyqZ7Ty9/cnOzubnJwc9Hq9dR29Xk92dnad2/bz06LR1H95\n8UrUdf/h/9u70+g4yjvf49+q6n3RvssLtsGAd7CdYCPhDFsyyQRISLBD8OTOzcmdXEgyyfEECAlL\nQu7MMRnunTDhkGRgZjhwSEzMsCWEQCCAwLKxscE7NsZ40WqtLanVSy33RS9qSS3JNlK3Wvp/zulT\n1dXVpacflfR7aunnEeNL6vr0lX/hs1jX/TXde/ZS+vwfmLH9SVp8czhespRDey0O7W1h/oJyLr38\nXGbOKRr0XqnnzJB6zgyp55Fl7etnI91jdzr33nV2Bse1LHIjReZIXZ+lynMo+V+3kHddK0WvvkzV\nmy/QqpZyrHgJh/bDof0tVM7I56JVs5g1t4iysjyp5wyQ/TkzpJ4nUYcwHo+HUCiEy+WipaWFsrIy\nysrKaGtrS67T2trKsmXLMlksIXKGo6yMsnVfpfjaL1LyVh2Vr7zMqVMKxwoX03QSmn63h6JSL5+5\nbhH5xRPT/asQYnLJ6NiKq1ev5k9/+hMAL730ErW1tSxdupQ9e/YQCATo6+tj586drFixIpPFEiLn\naG43hVdezZz/s5GFX1/HqrwGPnH8Wcp7jtDR2ssT/76N1198n2hEz3ZRhRATbMK+R7537142btxI\nQ0MDNpuN8vJy/uVf/oXbb7+dcDhMVVUV//zP/4zdbufFF1/kkUceQVEUbrrpJq655ppRty3fI89d\nUtcTJ3T8GF2v/JnGnQfYV7KaXmcReXkOrrhmIRUz8rNdvClJ9ufMkHqWDmHGJDtJ5khdT7xIaytt\nv3mcdxvtHCtYhKLAsk/OYuVlc86qlzgxMtmfM0PqefQgl79qIaYYR1kZS+69i0v/Zgkr2l7FGe1l\n17YTbH54G+2tvdkunhBinEmQCzEFKYpCfu0aLrpzA5cXHaeq+306OsNs/s/t7NryEaaZcyfihBAj\nkCAXYgqzFRQw+5ZbuPxLy7moux5bNMTWNz7imf/cSqCrP9vFE0KMAwlyIaYB//IVrPzRd7iyuoXS\n3o9oORVm06/q2bfj+Gn13SCEmLwkyIWYJjSvl9l/9z/46xtXsiT4Lug6b/z5Q37/X/UEe8PZLp4Q\n4ixJkAsxzXgXLGTVD/83V8/tojDYyMmWCL958E0O7z6R7aIJIc6CBLkQ05DqdDL7Kzdw7d9dwoXR\n99ENiz+/cIQXH32TcCia7eIJIc6ABLkQ05h7zlzW/ODrXH1+mLxwO0ebdH7z879wbJ8cnQuRKyTI\nhZjmFJuNOdd/juu/sYr5HCdk2njh+SO88uhr0sWrEDlAglwIAYCrqorLb72JqxaBN9rNoSb47f99\nmYb9x7JdNCHEKCTIhRBJiqoy7/OX8+W/X81cWyu9uHn+2SO88V8vo0fk2rkQk5EEuRBiGHdZKVdv\n+DJXLLPjNEPsa7bz5P1/pGXvB9kumhBiCAlyIURaiqIw/zOXsvabq5np7KZbyeOZ5z6i/pHfY0Tk\ne+dCTBYS5EKIUXmKC/ib713LmuU+NAzePeXjqfuep+29A9kumhACCXIhxGlacNUK1v39Kirc/bTb\nSnj69yfY/uvN6MFgtosmxLQmQS6EOG2+4jyu+85nWL2iEEtV2dFRwrM/e4a2HTuzXTQhpi0JciHE\nGVEUhaVXLuWGb3ySEneUVmcVz77Ywq5fPIbeE8h28YSYdiTIhRBnpaDEz/XfvpKVFxeja0629s7k\nhZ9tpr3uTRlRTYgMkiAXQpw1VVVYcfVivvQ/V1LgNmnwzOW51zrZ/f9+TbS9LdvFE2JakCAXQnxs\nxeV+bvjWp1i6tISQ3ceW8Hxevv93tL38MpZpZrt4QkxpEuRCiHGhaSqr/3oR1331InxuhWN5F/LC\nll72bfw54caGbBdPiClLglwIMa4qZxaw7uYaLlxYQp+zkDdZwhs/f5JTzz2LpcsgLEKMNwlyIcS4\nsztsfOrzi/jcDYtxuWx8UHgRL78TYv9PN9L/4ZFsF0+IKUWCXAgxYWbNLWbdN1cz97wiut3lvOX4\nBPUPPknLb57ADEs3r0KMBwlyIcSEcrntfPr6JVx5zYVoTgfvl67i9f0m79/zE/r27c128YTIebZs\nF0AIMT2ct6CcypkF/OX3Bzh5DN40Srng3zdz3pJ6Sm/4CprPl+0iCpGTpv0ReVQ3OXC0A92Qr8gI\nMdF8fid/s24ptVefBw4Xeys+xZYjGofvupuuv7yCIf22C3HGpv0Red3uRh5/6RA+t51LFpZTs7iS\nWeX+bBdLiClLURQWXVzNjHMKefX5A7Qwly53Bec++yblv3uS/OUryKu9DPd581EUJdvFFWLSU6wc\n7Evx1KmecdtWTzDMM7t2s/2dIL1BA4DZ5X5qllTyyQXl+Nz2cftZAkpL/eP6+xPp5Uo9m6bJrq0n\n2P7mUSwTXGY/1R37qQ4cwlNaSH7tZeStuhRbfn62i5pWrtRzrpN6jtXBSKZ9kL9xsp5Nh56mwJnP\nXOciuo+Vs/+DfkzLwqYpLDuvlJrFlSyaU4SqytHBxyV/kJmRa/Xc1RFkz46THNzTjB41UTGp6DnC\nzM59+IwefEuXkV+7Bs/CRSjq5LkimGv1nKukniXIR9UXDfJy4yu88dE2wkYEVVFZUHgh/uB5HNyv\n0tgWu2ZX4HOwelElNUsqqSjyjNvPn27kDzIzcrWewyGdg7ub2PNOAz3dIQCKjQ6qW3dR0ncSe2Eh\neZfWkF9Ti72kNMulzd16zjVSz5MoyPv6+rjtttvo7u4mGo1yyy23UFpayj333APA+eefz49//OMx\ntzPev9DSUj8nmk7xdvMu6hrqaexrBqDcU8oC/zJ6Tpbzzv4uguFYr1TnVudTs6SSlReU4XZO+9sM\nzoj8QWZGrtezaVoc+6CdPe+cpOFYFwBeLUp1+x4qOw5gs3Q8Fy4gv3YN3mUXodqzcwks1+s5V0g9\nT6Igf/zxx2lpaWHDhg20tLTwta99jdLSUr7//e+zZMkSNmzYwDXXXMOaNWtG3c5EBHlim5Zl8WH3\nMeoa6tnVuhvdMrCrdi4qXUKJfgEHD1rsP9qBBTjsKivOL6NmcSXzZxWgyo05Y5I/yMyYSvXc3trL\nnncaOLSvBUM3salQbTZReawebzSA6vORt+pS8msuw1ldndGyTaV6nsyknkcPcu2exOFwBpw4cYIP\nPviAyy+/nKamJurr62lububWW28FIBqNsnPnTmpqakbdTjAYGddyeb3O5DYVRaHIVcCyssXUVF+C\nz+6lNXiKw90fcrh/D/6KDq5cMYu5RZW0dUU4eLyLt/Y2s2VvM/1hneJ8Fx6X3CA3ktS6FhNnKtWz\nx+vgnPNKWHhRFU6XjY72fk5F3JwsuJD+GRdiCwZg73a6X3s11sGMAo6ychTbxJ8tm0r1PJlJPcfq\nYCQZv0b+9a9/nePHjxMIBHjooYf4yU9+wjPPPANAfX09mzdv5v777x91GxN5RJ6OaZkc6DhMXUM9\ne9sOYGHhtrn4ZMVyZqgL2f9+hB0HTxGOGijABbMLqVlSyfL5pTjs2riWNddJyzozpnI9G4bJR4fb\n2L3jJM0nAwDkeRRmR45RdKgOmxlFcbrwf+IT5NeuwTVn7oR9jW0q1/NkIvU8iU6tP/vss+zYsYN7\n772XgwcPcsstt+D3+5NBvmXLFp566qkxg1zXDWy27ARkW18Hf/7wTV798C26QrF/IgvL5rNm1qVE\n2kp4ZXsD+492AOBx2ahdVs2Vn5jF+bMK5TuxQoyzxhNdvF13lL3vNmAaFk6nxrkFIUoPv4HWegIA\nz+xZlF91BaVr1mDPkz4ixNST0SC/++67Wb16NZ/+9KcBqKmpQdM0Xn/9dQCefvppDh06xG233Tbq\ndjJ9RJ6OYRq817aPupP1HOqKjeaU5/CzuuoTnO9Zwp73g2zZ20xnT2xgiMpiDzVLKlm9sIJ838in\nSKY6aVlnxnSr52BvmH3vNrFvVwP9fVEAZpY7mNn7Pq69b6IYBorNhu+ii8mrXYPnggvH5Wts062e\ns0XqeRJdIz969ChHjx7l0ksvpaGhgT/+8Y/Mnj2b6upqqqqqeOCBB/j85z/PzJkzR93ORF4jP12q\nolLpLeeSyhUsL1uKpqgc7znJwY7DvH1qG77iINdeMp9Lzp2DaVkcaQiw98MOXt5+kqNNAew2lbJC\n97T7brpc68qM6VbPdoeN6lkFLF4+g4IiN72BME0t/ZyIFtJ97iV4zz8fT7Cd0PsH6KnfQqD+LcxQ\nCHtJKZrbfdY/d7rVc7ZIPU+ia+R9fX3ccccdtLe3o+s6//AP/0BpaSl33XUXpmmydOlSfvCDH4y5\nnclwRJ5OxIiwo+U96hrqOd5zEoASVxE11ZewpGgZ+w738ubuJj5qjv2s6dgtrLSsM2O617NlWbQ0\nBNjzzkmOHDyFZYHLbeO82W6q2/egv7MFKxIBRcG7eAn5tZfhXbz0jG+Qm+71nClSz5PoGvl4maxB\nnupY4AR1DVvZ0fIuUTOKTdG4qGwJtdWrcISLeWtvM/X7mukJxk4DTpduYeUPMjOkngf0BkLs29XI\n/ncbCfXrKArMObeIuc4OHO+9QfjohwBoeXnkra4hv+YyHBUVp7VtqefMkHqWIB/TRO4kwWiQbc07\nqWvYSkuwFYAqbwW11au4uHQZh47FjtJ3H2mfFt3Cyh9kZkg9D6dHDQ7vb2X3jpN0nOoDoLTCz4Vz\n3RQ17KJv6xbMvthy9/zzya+9DN/FK1CdI5/SlHrODKlnCfIxZWInsSyLw11HeKNhK++d2otpmTg1\nBysrLuay6lX4lGLq9zbz1p4mGtpi/0ymYrew8geZGVLPI7Msi8bjXezZ0cDRw20AuL12FiypYLbW\nSnRbHcED+wFQ3W78l6yKfY1t1uxh25J6zgypZwnyMWV6J+kOB9jSuJ23GrfRGY51Pzk3fza11atY\nVrKIhlMh6nY3sW1/C/1TrFtY+YPMDKnn0xPo6mfvzgYOvNdEJGygagrnXlDGhed6cBzcQfdbdRhd\nsb9R56zZ5Ndehv+Tl6B5vIDUc6ZIPUuQjylbO4lpmextO0Bdw1YOdBzCwsJr97CqciU1VZeQby9g\n5+FTvLW7if0fdSa7hV0+v4xVC8vJ9zmxaQqapmJTFWyaGn/E5ifjaflM1LVpWei6SUQ3ieomUd1I\nzkeiBlHDJBod4fUhzw3DpCjPRVWJl+oSL5XFHuxZ6sPgTMg/vjMTjei8v7eFPTtO0tXRD0BFdR6L\nLq6iPNpEz1t19L33Lpgmit2Ob8VK8mvXMGv1ctraerNc+qlP9mcJ8jFNhp2krb+dNxu2Ud+0nd5o\n7NT6hUXzqa1exaLiC+jqibJlbxNv7Wmmtav/tLapKAwKdpumoiUDP9YAsKfMJxoDmqZg19TYMm1g\nmU1Vsdniy9SU92mDGxEjNSw0VcGf76altWdwiEbNWLimCdqIbiRDOaKb8Xkj5f3x96QEsW6YE/Z7\nUhQoLXBTXeKlKv6YjAE/GfbpXGRZFieOdrJnx0mOfxjr2Mnrd7Lo4irmz/US2bmN7ro3iLa2AKA6\nHGj5BdgKCrAVFmLLj021ggJsBYXxRwGqw5HNj5XzZH+WIB/TZNpJoqbOrtbd1DVs5cPujwAocOZT\nU/VJVld9gjyHn0Mnutj9YTuRaOyIUTcsdMOMPyx008QYusyILzPjz3UzuZ5h5twugKKAw6Zht6nY\nbSoOm4o9/txhU7HbY40Uhz1lWXwdxyjvGbpNVVE41R2isa2PxrY+GuLT3v7osPKkC/iKIk9Wuumd\nTPt0rupsD7L3nQYO7mlCj5poNpX5C8tZtLwKT2cDgbfexGhpItTWjhHohlH+laoebyzskwEfD/74\nc62gEFteHoo2eRqDk4nszxLkY5qsO0lDbxN1DVt5u/md5FjpS0sWUlu9ivmF88aty1fTsoYFv2GY\n6OZAAyBqDG00WBjm4IZC8n1DlsUaDLHt+HxOjKiRNjRj09Rl8ZCNh7LdPhDCmqpkrctby7LoCUaT\noX46AV9V7KW6NB7yxbEj+IkM+Mm6T+eidGOkV80qYMmKGaxYdQ7t7b1YhoEeCGB0daJ3daJ3daF3\nxqfdA/NmsG/kH6QoaHn5w47uBzcAClG93mnX3bPszxLkY5rsO0lID7G9ZRd1DVtp6G0CoNhVRIEz\nH7tqw67ZY1PVPng+7bLYvEOzY0t5blft8WU2HKodVVEn5J/FeNW1ZVnolkHUiBAxo0SMKNHkNELE\niBIxo0QT08RrifVTXzMiREw9+Vo05b1RS6fIVUiVt5wqbyVVvti02F2Iqgzv4jPQFzm9gCce8CXx\ngC+Ohfx4Bfxk36dzkWlaHDvSzp4dA2Okq5qCz+/En+/Cn+8iLz5NPDw+56B7VcxwGL27Ox72nRiJ\nwE8Je72rEysaHakYKDZb/Cg+/dF9Yjra1+ZyjezPEuRjypWdxLIsjgaOU9dQz+5T+wkbYSwm5ten\noAxqADjUWMiPvCzeQFDjDQRtoOHgUG3YNDs2RcPrd9DWGSASD9vokBCOmJFBQZoatBEjElsv/tp4\nf3ZN0ZINmsRUVVTa+zsIGeFB6zpUO5XeCqp8FbGQ91VS6a0gz+FL2wA6m4BPnJ4/m4DPlX06V7Wf\n6mXfzka62oN0tPcl+3cfSlUVfHlpgr7AjT/fhdfnGLa/WJaFGQwmQz15hJ9ypG90d6F3d4M58v0g\nqts96Dq9Fg98zeWO7WgAKAPzyXIoQybK4NdT35v2uTJ4c0Pfn1yc/v1KmuUFxX56ogqa24PqdqN6\nPOPSV34ukSAfQ67+00sclepmlIihoyeOPM0ouqkTMeLTePAl5k9/WTR+pDowH/sZesY+o4KSEqwO\nHPEzDA51YFnsDINjYL3kOg7smg2H6hi0jcT6ibBObEtT0welZVl0hLpo7GuiqbeFhr4mmvpaaO5r\nxbCMQev67F4q48E+EPDluG2utNsO9EUGBXti/nQDvqLYgzNNwOfqPp1rEvUcjRr0BkL0dA9+BOLT\n0YLeP+QoPvWRLugTLNPECASGBH4i9AeO9M3eqXlXvepyoXo8qG4Pmice8G4Pqif+PL5MG7TOwHLV\nnls9aEqQj0H+6Z0Z0zLRzXgDIqXREI2HfHSUZQV+L5F+c1CQDpoOCl07mqJN2uuBhmnQ2t9GY28T\njX0tNPY209jXTHt/x7CzBcnT8/Fgr/ZVUuYpxa6m7xPgbAO+qsRDVYmXmVUFdHcFUVUFVVWwxaea\nqqCpKorCpK3XXHK6/zvOOug1BX/e2QV9ghmNYHR1JwPfDPWT2D2tgRkGzVijLx9YfGbvHxY3Y70/\nzm1X6GnvwgwGMYJBzP7+2Hx/EDPYj9kfHPVmw3QUuz0W+m4Pqsc9EPbDgj/9a4pj7LofTxLkY5Ag\nz5zpUNdhI0JzXwsNvc009TUnAz4QGfy5VUWlzFN62tffAQLBCI2n4gHf3pecHxrwp0OLB/tAwCfm\n1YF5TUFT0s3H11Hiy4dtS02z3RF+Rvxht6k47RoOh4bTnnjEl8WfT7a+EcZrfx7voM9LXqPPbNhM\nlLHq2TJNzHAYMxjE7I+HfTzwY2EfTAn+WPgb/bF1E40DDGPE7aelabEj/tSzAPHT/prbg/OcOeR9\n8pKP+ckHSJCPYTqEy2Qxneu6N9JHY0qwN8aDfqTr75W+cqq9FVT6KqjyVo54/R0GB3xzexA0hWAw\ngmHGvl5oxqeGGftmgWlaGPFvKwybN2PfSEh9T+p8NiXC3mlXk+HutGs4HYmwV1MaAfFljsHLHSnv\nSd2WTTvza66Z2p+jUYPelGAf+ugPjhz0Xr8Tr9+Jx+/E7XPg8Thw2uM3s6YemY90cJzyWvKllJWG\nrj+wjpVmfdIc1adMRnjNn+fCMEwcLhsutw2ny47TZUs+1I95vdyyLKxoNOWIP02DYOh8/+DGgRUZ\nPMyq6nYz7+cPjtu1fAnyMUzncMk0qevBEtffm/qaB4V8S18r+pDr7167h6rkDXaxaaW3Iu3194mq\nZ8uysCxGDHs9NfQNM/bVRvP0GgwR3SQcMYhEDcKDHrFOf8KRgWWRqBmfxjoHGg+aqqRtFDhGODvg\ndGj4fS4CPf3Jz2BaFqbJwPP4ZzZNc1CDaPjrA4/E87TrWwPPzZTnlmFhMy0cgANwouBMzoOd3D8q\nH43doeF02XC57DhSAt7lTg38wfMutw2H0zZuZywsXR8U8po/D3tx8bhsGyTIxyThkjlS16fHMA1O\n9bcNOz3flub6e6GzgOp4qCfCfXZFGd2dITRFw6basKnaiKfrc51pWfHwj4d7ZGhDIB78kaHL4u+J\npD4faDwktpXpf5BDL0eoqfNKmtfSLVNTLoOosfvONcNEiZpEwga9oSh9/VGCYX3Ez6cqCh6XHZ/H\nhs9tjz8csanHjt9tx+OyoapKyp3tg7cRy8ihd7GnuaN92PLB93D4fE5aW3oIh6KEQzqhfj05H3sM\nzEcjZ3aK3OFMHOUPD3uny4bTbcPptA9rHNgdmb1/R4J8DBIumSN1/fEkrr8PPT3fHRm7ThUUbGos\n2JMBr2ho8aC3KTY0VcMWfy0xr6kjr2uPT1Pn024z+f6R5mPTyXY917Isoro54tmBvHw3vT2hQcE5\nWuiOFsyJUM4k3TDp7o3Q2ROmoydEZ084Ph+mMxCioydMV294xPvIVEWhwO+g0O+k0O+iyO+k0O+k\nKM8Vm/qd5PscaB/z9PKZ/N8wDJNIWE8Ge6g/FvKRkE5oaPj364TDA/P6GZzdURSGneJ3ppwBKKvI\nY878krP9yMNIkI9BwiVzpK4nRm+kj6a+Zhr6mmnua8WyGfT1hzBMA93U0S0Dw9Rj3zaw9CHLY8t0\n08CwDExr4vqqH42qqDg1B07NiUtz4rTFpqnzTs2Jy+Ycvk6aZbYRvhEwnqbD/myYJoG+aCzoA/GQ\nj4d+LPBjYT/S/ROKAvleRyzo8+JB73fFw99JUZ6TAp9z1HsUMlXPhm4OHPWnHumPcgYg0TgwjcGf\n3+7Q+J/frRm3GzRHC/LcHQ9TCJHkc3g5zzGP8wrnAR/vH1/i64VGPNx1U8ewjLTzyQaCFX9tSKNA\nN9PND91u7HnEjBDSw4SNMD3RXtr624fdJ3AmbIqGMzXgR2oEpDYUhjQYEvNOzTFiPwPjwbIsTMuM\nPbAwLSN2vd0yMSwDy7IwLHPIchMrPjVTHwx+PvR1h+agyFVAobMQv8M75iUXTVWToUtV+nVM0yIQ\njB/ZB4YGfezI/nhLD0ebAmnfrwB5XsdAuKeEfqHfSciEnkA/Ni0+ZkJ8Ot7fYtBsKh6fE4/vzHrF\ns+IjLsZCPxbsbq8jY9+ykCAXQgyiKioOTQWy32GGbuqEjDBhPRybGmFCifnkskhyedgY/FpivjPc\nTdgIf6yzDXbVPjzsnXZC4eiwEB0xZNM8DMucsB4ax2JTNAqc+RS6CihyFVIYny90FcbDPh/XCJ0Z\npVJVhQJf7Mh6TmX6dUzLojc49Mg+JfQDYU6e6uOj5tNvgCZGc7THR2VMDIyUnNdiIzbahzQAbEOm\ng+YTr6W+b9B7lNhgS5qK3Rb7+YqiYLdr2O0aPn/mu8aVIBdCTFo21YZPteGzez/2tizLImrqgxsD\nRpiQHhpoABiRYY2AdA2J3kgvYSOChYWCgqrERsqLTbXkvKZosXsTFA1Vs6OixtdR46+rKPGpOsJD\nU1QUEuuM8DMUZdRtqAz8rJARojPURWeoi45wbHq468MR681tc8dDvSAW+PFpYXxZgTPvtM5WqIpC\nntdBntfBORUj/456+6MDR/M9YToCIQwUenpDRPXYQEzR+HDFieGLk/OGSX84QtSIDXmcqa9Lpms4\nzK7w8/fXLMzIfR8S5EKIaUFRYt39OjQ7fofvY2/PsixKSn20t40yolmOiJo63eFuOuIB3xnuSs53\nhLto629PDtg0lIJCvjNvUNgPDXyvzXNagaYoCn6PA7/HwazygWvCZ3upyDRjIzeOFPq6PjA/vHFg\nEdWN+HrWoPWS7x3yvsQ0FDEI9EWwGHYj/4SQIBdCiLOgxI+IpwK7aqPEXUyJO/33ni3Lol8PxQO+\nk85Q97D5jwIn+NA6lvb9DtVOYfzUfVHiaD71ubMAuzb+l3JUVcGpamnHI5hKJMiFEEKMSlEUPHY3\nHrubal/6i+CmZdIdDtAZ7qYz1Bk7og93xYI+1ElHuIuWYOuIP8Nn98ZDvpAiZwEFrvz4dfsCLHc5\ngXAkPvKiDZs6nIvELAAACQNJREFUfh25TAUS5EIIIT42VVGTp9LJn512nYgRGXRtfuh8U18Lx3sa\nTuvnxQI9PkxyfMTDRNAnhlW2qQNDLA8MrTzk9TTvSwzNbEsOzTzw+kR+e+FsSZALIYTICIfmoNxb\nRrm3LO3rlmXRG+0bFvC6FqE32J8cSTFqRokaKSMtmlHCkXBytMWJ7AtBVdRBAT8Q9PZkY8Gm2pjl\nr+azc66asHKkkiAXQggxKSiKgt/hw+/wMYsZyeVnerObYRrJUE8EvZ5oBBjRlAaBHnsYUXRz6PJE\nYyHRYBjyenw7sX4P+pKvJ5zsaeQz51yRkfsoJMiFEEJMKYlug8f+Bvz4siwr3mDQcWj2jN0MKUEu\nhBBCjANFUWLX1yfgDvzRTI3vTgghhBDTlAS5EEIIkcMkyIUQQogclvFr5M899xwPP/wwNpuN73zn\nO5x//vnceuutGIZBaWkpP/vZz3A4HJkulhBCCJGTMnpE3tnZyYMPPsgTTzzBL3/5S1555RUeeOAB\nbrzxRp544glmz57N5s2bM1kkIYQQIqdlNMjr6+tZtWoVPp+PsrIy7r33XrZt28YVV1wBwF/91V9R\nX1+fySIJIYQQOS2jp9ZPnjxJKBTim9/8JoFAgG9/+9v09/cnT6UXFxdz6tSpTBZJCCGEyGkZv0be\n1dXFL37xCxobG/nbv/1bLGtgvNjU+dEUFnqw2ca3v9vSUv/YK4lxIXWdGVLPmSH1nBlSzyPLaJAX\nFxdz0UUXYbPZmDVrFl6vF03TCIVCuFwuWlpaKCtL3wdvqs7O4LiW62zHuhVnTuo6M6SeM0PqOTOk\nnkdvyGT0GnlNTQ1bt27FNE06OzsJBoOsXr2aP/3pTwC89NJL1NbWZrJIQgghRE7L6BF5eXk5n/70\np7nhhhsA+NGPfsTixYu57bbb2LRpE1VVVVx33XWZLJIQQgiR0xTrdC9MCyGEEGLSkZ7dhBBCiBwm\nQS6EEELkMAlyIYQQIodJkAshhBA5TIJcCCGEyGES5EIIIUQOm/ZB/k//9E+sXbuWdevWsXv37mwX\nZ8q67777WLt2Lddffz0vvfRStoszpYVCIa688kr++7//O9tFmdKee+45rrnmGr74xS/y2muvZbs4\nU1JfXx/f+ta3WL9+PevWraOuri7bRZqUMt7X+mTy9ttvc+zYMTZt2sSRI0e444472LRpU7aLNeVs\n3bqVw4cPs2nTJjo7O/nCF77A1Vdfne1iTVkPPfQQ+fn52S7GlJYYkvmpp54iGAzyb//2b3zqU5/K\ndrGmnKeffpo5c+awYcMGWlpa+NrXvsaLL76Y7WJNOtM6yOvr67nyyisBmDdvHt3d3fT29uLz+bJc\nsqll5cqVLFmyBIC8vDz6+/sxDANNG9+BbwQcOXKEDz74QEJlgqUOyezz+bj33nuzXaQpqbCwkPff\nfx+AQCBAYWFhlks0OU3rU+ttbW2DdoyioiIZRnUCaJqGx+MBYPPmzVx22WUS4hNk48aN3H777dku\nxpSXOiTzjTfeSH19fbaLNCV97nOfo7GxkauuuoqbbrqJ2267LdtFmpSm9RH5UNJb7cT685//zObN\nm/mP//iPbBdlSnrmmWdYtmwZM2fOzHZRpoWhQzL/5S9/QVGUbBdrSnn22WepqqrikUce4eDBg9xx\nxx1y70ca0zrIy8rKaGtrSz5vbW2ltLQ0iyWauurq6vjlL3/Jww8/jN8v4wpPhNdee40TJ07w2muv\n0dzcjMPhoKKigtWrV2e7aFNOuiGZOzo6KC4uznbRppSdO3dSU1MDwAUXXEBra6tclktjWp9av/TS\nS5NDqO7bt4+ysjK5Pj4Benp6uO+++/jVr35FQUFBtoszZf3rv/4rTz31FE8++SRf/vKXufnmmyXE\nJ0i6IZnl+u34mz17Nu+99x4ADQ0NeL1eCfE0pvUR+cUXX8zChQtZt24diqJw9913Z7tIU9ILL7xA\nZ2cn3/3ud5PLNm7cSFVVVRZLJcTZSzcks6pO6+OiCbF27VruuOMObrrpJnRd55577sl2kSYlGcZU\nCCGEyGHShBRCCCFymAS5EEIIkcMkyIUQQogcJkEuhBBC5DAJciGEECKHSZALMQ2cPHmSRYsWsX79\n+uRIUhs2bCAQCJz2NtavX49hGKe9/le+8hW2bdt2NsUVQpwBCXIhpomioiIee+wxHnvsMX77299S\nVlbGQw89dNrvf+yxx6QzDiEmoWndIYwQ09nKlSvZtGkTBw8eZOPGjei6TjQa5a677mLBggWsX7+e\nCy64gAMHDvDoo4+yYMEC9u3bRyQS4c4776S5uRld17n22mu58cYb6e/v53vf+x6dnZ3Mnj2bcDgM\nQEtLC//4j/8IxMZKX7t2LV/60pey+dGFmFIkyIWYhgzD4OWXX2b58uV8//vf58EHH2TWrFnDBqbw\neDw8/vjjg9772GOPkZeXx/33308oFOKzn/0stbW1bNmyBZfLxaZNm2htbeWKK64A4I9//CNz587l\nxz/+MeFwmN/97ncZ/7xCTGUS5EJMEx0dHaxfvx4A0zRZsWIF119/PQ888AA//OEPk+v19vZimiYQ\n68Z4qPfee48vfvGLALhcLhYtWsS+ffs4dOgQy5cvB2IDEs2dOxeA2tpannjiCW6//XbWrFnD2rVr\nJ/RzCjHdSJALMU0krpGn6unpwW63D1ueYLfbhy0bOlSnZVkoioJlWYP6G080BubNm8cf/vAHtm/f\nzosvvsijjz7Kb3/724/7cYQQcXKzmxDTmN/vZ8aMGbz++usAHD16lF/84hejvmfp0qXU1dUBEAwG\n2bdvHwsXLmTevHns2rULgKamJo4ePQrA888/z549e1i9ejV33303TU1N6Lo+gZ9KiOlFjsiFmOY2\nbtzIT3/6U37961+j6zq33377qOuvX7+eO++8k69+9atEIhFuvvlmZsyYwbXXXsurr77KjTfeyIwZ\nM1i8eDEA5557LnfffTcOhwPLsvjGN76BzSb/eoQYLzL6mRBCCJHD5NS6EEIIkcMkyIUQQogcJkEu\nhBBC5DAJciGEECKHSZALIYQQOUyCXAghhMhhEuRCCCFEDpMgF0IIIXLY/wekzvJ+vZ7O4QAAAABJ\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "UySPl7CAQ28C"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Explore Alternate Normalization Methods\n",
+        "\n",
+        "**Try alternate normalizations for various features to further improve performance.**\n",
+        "\n",
+        "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n",
+        "\n",
+        "For example, many features have a median of `-0.8` or so, rather than `0.0`."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "QWmm_6CGKxlH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 715
+        },
+        "outputId": "301c15fb-eba0-4e10-99c8-3832263a7d23"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABB0AAAK6CAYAAAB1zCTyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3XtclHX+///nMDBLKKQo465ldlbL\nU65l4qFASaQTmSdYrYxts9B0I5VI0z62QSrePERra6KsrsY6WZHrgpm4ZSKb0bratpntt108D4ai\nAoE4vz+8OT9JQGac4eLwuP/FvGeu6/V+zchr3r64DiaHw+EQAAAAAACAh/kYPQEAAAAAANA80XQA\nAAAAAABeQdMBAAAAAAB4BU0HAAAAAADgFTQdAAAAAACAV9B0AAAAAAAAXkHTAV514MAB3XbbbYbF\nX7p0qV566SWXtsnPz1dERESNz7300ktaunSpJ6YGAHVqiPr5z3/+U3FxcV6N4Um33XabDhw4oI8+\n+kgvvvii0dMBgEarrvXsBbt379a///1vSdKaNWu0aNGiS8ZdERERofz8fNcni2bP1+gJAAAAY/Ts\n2VMrVqwwehoui4iIuOxiGgBQt3fffVe//OUv1bVrV40bN67GccATaDqgQdhsNmVkZKikpETTpk1T\nVFSUFi9erJycHElS79699fLLLysgIEDh4eGaN2+e+vbtK0nOx71799bs2bO1a9cunTt3Tl26dFFK\nSopat26tLVu2aPHixSotLVXnzp21YMECBQcHS5IqKir0/PPP6x//+Ifat2+vpUuXqkOHDjp06JBm\nzZqlAwcOyM/PT7/+9a8VHR1dbd7FxcVKSEjQ999/r5tvvln+/v76+c9/Lul8R/hPf/qTHA6HWrdu\nreTkZN1yyy0N+K4CaAm8WT+/+uorzZw5Ux999JGWLl2q4uJiHT16VP/+97/Vtm1bvfnmm7Jarfrq\nq6/029/+VpL00EMPKScnRzNnzlS/fv1qnffSpUtVVFSkI0eO6KuvvlL//v0VFRWlpUuX6tixY5o7\nd67CwsJUUVGhefPm6dNPP1VlZaVGjx6tiRMnSpL+9re/6dVXX5Wvr68effRR5743bNigrKwsrVq1\nSkVFRZoxY4YOHjyoiooKjR8/XhMmTHDm/5vf/EY2m01HjhzRAw88oMTExDrf77KyMr344ov6+uuv\nVVlZqWHDhmnGjBmSVOf7UNf3EAAYpbaatm7dOn3wwQfaunWrfvjhB50+fVpHjhxR9+7daxz/3e9+\nJ+l8bb/weO/evZoxY4bOnj2re+65p1pcaiIuxukV8Lpz586psrJSH374oV588UUtWrRIf/3rX/XJ\nJ59ow4YN+stf/qKSkhKtWrWqzv1s375dBw4cUHZ2tjZv3qybb75ZX375pQoLCzV9+nSlpqbq448/\nVr9+/TRnzhzndnl5eUpISNDWrVsVHBwsm80mSZo1a5buuusu5eTk6K233tKrr76qAwcOVIu5fPly\ntW3bVlu3btXLL7+s7du3S5JOnz6txYsXa/369crOzlZcXJy2bdvmybcNALxeP38qOztbSUlJ2rJl\ni9q1a6d3331X0vl6+cQTT2jz5s1q3bq1vv/++3rNf9u2bXrttdf04YcfKjs72znviRMnavny5ZLO\n19n9+/frww8/1MaNG5WTk6Pc3FxVVVXppZde0uzZs/XXv/5VPj4+qqqquiTG73//e1177bXKzs5W\nRkaGUlNTdfjwYefzn3/+uTIzM/Xuu+9qzZo1OnLkSJ1zXrdunc6cOaPs7Gy999572rBhg3bt2lXn\n+3C57yEAMEptNS0mJkY9e/bUtGnTnI1aSbWO12TOnDl67LHHlJOTozvuuMO5jqYm4qdoOsDrHA6H\n8wiC2267TUeOHNG2bdsUHR2tgIAAmc1mjRgxQp999lmd+wkODtZ3332njz76SGVlZZo6daoGDRqk\nTz75RHfddZduvfVWSdLYsWO1detW5+L0l7/8pa655hpJUteuXXX06FFVVlZqx44dio2NlSRdc801\n6tevn3bu3Fkt5q5duzR8+HBJ0rXXXqu77rpLkvSzn/1MJpNJNptNRUVFGj58uJ566ikPvWMAcJ63\n6+dP9e3bV9dcc41MJpO6deumw4cPq7y8XF999ZUeeOABSdKvfvUrORyOes3/jjvuULt27dS2bVuF\nhIRo8ODBkqRbb71Vx44dkyTl5uYqNjZWFotFAQEBevjhh7V582Z9//33qqio0MCBAyVJjzzySI0x\nZs6cqVmzZkmSOnXqpJCQkGoN5AcffFBms1kdOnRQu3btqjUkavLkk0/qzTfflMlk0tVXX61bbrlF\nBw4cqPN9uNz3EAAYpbaadqV+/PFH7dmzR1FRUZKkyMhIXXXVVZKoibgUp1fA68xms7MI+fj46Ny5\nc/rhhx909dVXO19z9dVX6/jx43Xup2fPnpo5c6ZWr16tGTNmKDw8XLNnz9apU6e0a9cuRUZGOl/b\nunVrnThxwvnzxXOpqqrSiRMn5HA4FBgY6HwuKChIP/zwgzp16uQcO3ny5CWvkSQ/Pz+tWrVKy5Yt\n09KlS9WlSxfNnj1bXbp0cectAoAaebt+/tTF9e5CvTx58qRMJlO1+teuXbt6zb9Vq1bV9hcQEFAt\nF0k6deqUkpOTtXDhQknnT4nr2bOnTp48Wa1+X5zzxfbs2eM8usHHx0d2u925b6nm74C6fP/990pJ\nSdF//vMf+fj46MiRIxoxYkSd70Nd30P1fa8AwBtqq2lX6qfr7IvrIzURP0XTAYZo3769s1hJ5wtX\n+/btJVVfjErn/+N/QWRkpCIjI3XixAklJSVpxYoV6ty5s0JDQ7VkyZJ6x2/btq18fHx08uRJ50K2\npkIYFBSkU6dOOR9f3JS47bbbtGTJElVUVOjtt9/W7Nmz9c4777jwLgCA6zxZP0NDQy8br3Xr1nI4\nHCorK9NVV12ls2fP6ocffvBYPlarVU8++aTCwsKqjX/33Xc6ffq083FtMadNm6bHH39cMTExMplM\nNR7B4Yr/+7//0+233660tDSZzWaNHTtWUt3vg9Vqdfl7CAAaQm01rb5q+165sH4+ffq0AgMDde7c\nOedz1ET8FKdXwBD33nuvsrKyVFZWprNnz8pmszkvQBMSEuK8Tc+mTZv0448/Sjp/Jd20tDRJUps2\nbXTjjTdKkgYOHKhdu3apsLBQ0vlbwL366qt1xvf19dXAgQOVmZkpSfrf//6nXbt2XbIA7927t7Zs\n2eJ8zRdffCFJ+uabb/Tcc8+poqJCFotF3bt3l8lkuuL3BQAux5P1sz5atWqlm266SX/9618lSZmZ\nmR6td0OGDNH69etVVVUlh8OhN998U5988omuu+46mc1m5+3XNmzYUGPc48ePO2vwe++9p7KyMpWW\nlro9n+PHj6tbt24ym8367LPP9N///lelpaV1vg/ufA8BQEOoraZJ59fDF/9x7YKLx61Wq/bt2+c8\n0u6TTz6RJPn7+6tr16766KOPJEl/+ctfnN851ET8FEc6wBCRkZH65ptvNGLECDkcDvXr10+PPfaY\nJOnZZ5/V7Nmz9ec//1nDhg3TzTffLOn8wjQpKUn33XefzGazOnfurJSUFLVp00Zz585VfHy8Kisr\n1apVKyUlJV12Dq+88opmzpypDRs2yM/PT6+++qp+8Ytf6H//+5/zNU8//bR++9vfKjw8XDfddJPu\nu+8+SefPR7722mv1wAMPyM/PT61atdLLL7/shXcKAKrzZP385ptv6hVz9uzZmjVrllasWKHo6Gh1\n6NDBY42H2NhYHThwQPfff78cDoe6d++uxx9/XH5+fpo7d66SkpJksVg0YsQI5+kZF5syZYri4+PV\npk0bjR07VmPGjNGsWbO0du1at+bzzDPPKDk5WW+++aaGDBmiSZMmacmSJerWrVut74PVanXrewgA\nvK2umjZ06FDNnz9fhYWF1U5Fu3h80qRJysrK0tChQ3XjjTcqMjLSeUrfnDlzlJSUpLfeekuDBw/W\nTTfdJEnURFzC5Kjv1aAAAECL5XA4nI2Gu+++W6tWrWqR93DnfQAAwDWcXgEAAOr03HPPOW9xmZeX\nJ4fDoeuvv97YSRmA9wEAANdxpAMAAKjTd999pxdffFEnT56Un5+fpk2bpmuvvVbx8fE1vv6mm25y\nXkOisfnuu+/cnndN78OF62kAAICa0XQAAAAAAABewekVAAAAAADAK5rM3Svs9ktv53I5bdsGqLjY\n/dtmXSkj47fk3Ft6/JacuzvxQ0ICvTiblqep1eqm9u+V+M0jdkuP705sarVnuVqrW/K/15YevyXn\nbnT8pph7bbW6WR/p4OtrbrHxW3LuLT1+S869McSH6/h9IX5Li93S4xudO1xn9GdG/Jb7+9qS4zen\n3Jt10wEAAAAAABiHpgMAAAAAAPAKmg4AAAAAAMAraDoAAAAAAACvoOkAAAAAAAC8gqYDAAAAAADw\nCpoOAAAAAADAK3yNnkBj82TKVpden54Y7qWZAACAK+Xq97rEdzvQVPD7DTQNNB0AAAAAg5SVlSkx\nMVHHjx/Xjz/+qGeffVZdu3bV9OnTVVVVpZCQEM2fP18Wi0VZWVnKyMiQj4+PRo8erVGjRqmyslKJ\niYk6dOiQzGazkpOT1alTJ6PTAgAnTq8AAAAADJKbm6vu3btrzZo1WrRokVJSUrRkyRLFxsZq7dq1\n6ty5s2w2m0pLS5WWlqZVq1Zp9erVysjI0IkTJ7Rx40YFBQVp3bp1mjhxolJTU41OCQCqoekAAAAA\nGCQqKkpPPfWUJOnw4cPq0KGD8vPzNWTIEElSWFiY8vLytHv3bvXo0UOBgYHy9/dXnz59VFBQoLy8\nPEVEREiSQkNDVVBQYFguAFATTq8AAAAADDZ27FgdOXJEy5Yt04QJE2SxWCRJ7dq1k91uV1FRkYKD\ng52vDw4OvmTcx8dHJpNJFRUVzu1r0rZtgHx9zS7NLyQk0I2sPMdT8d3dT3PJv6nFbunxm0vuNB0A\nAAAAg73zzjv6+uuvNW3aNDkcDuf4xT9fzNXxixUXl7o0t5CQQNntp1zaxpM8Gd+d/TSn/JtS7JYe\nvynmXluTgtMrAAAAAIPs3btXhw8fliR169ZNVVVVatWqlcrLyyVJR48eldVqldVqVVFRkXO7Y8eO\nOcftdrskqbKyUg6Ho86jHACgodF0AAAAAAyya9cupaenS5KKiopUWlqq0NBQ5eTkSJI2b96sQYMG\nqVevXtqzZ49KSkp05swZFRQUqG/fvhowYICys7Mlnb8oZb9+/QzLBQBqwukVAAAAgEHGjh2rl156\nSbGxsSovL9fLL7+s7t27a8aMGcrMzFTHjh0VHR0tPz8/JSQkKC4uTiaTSfHx8QoMDFRUVJR27Nih\nmJgYWSwWpaSkGJ0SAFRD0wEAAAAwiL+/f423uVy5cuUlY5GRkYqMjKw2ZjablZyc7LX5AcCV4vQK\nAAAAAADgFTQdAKAZ27dvn4YOHao1a9ZIOn8P+PHjxys2NlZTpkxRRUWFJCkrK0uPPvqoRo0apfXr\n10s6f0GyhIQExcTEaNy4cSosLDQsDwAAADRNbjUd8vPzdffdd2v8+PEaP3685s6dy0IWABqZ0tJS\nzZ07V/3793eOLVmyRLGxsVq7dq06d+4sm82m0tJSpaWladWqVVq9erUyMjJ04sQJbdy4UUFBQVq3\nbp0mTpxY4+G/AAAAQF3cvqbDXXfdpSVLljgfv/jii4qNjdXw4cO1cOFC2Ww2RUdHKy0tTTabTX5+\nfho5cqQiIiKUm5uroKAgpaamavv27UpNTdWiRYs8khAA4DyLxaLly5dr+fLlzrH8/Hy98sorkqSw\nsDClp6frhhtuUI8ePRQYeP7eyn369FFBQYHy8vIUHR0tSQoNDVVSUlLDJ9HMPZjwgcvbpCeGe2Em\nAAAA3uGxC0mykAWAxsXX11e+vtXLfFlZmfP+7e3atZPdbldRUZGCg4OdrwkODr5k3MfHRyaTSRUV\nFXXe/71t2wD5+ppdnmtISKDL23iKkbHd4en5Gp2/0fFr0lBzMjp3fu8AAA3B7abD/v37NXHiRJ08\neVKTJk3y+kIWAOBZDofDI+MXKy4udXkeISGBsttPubydJxgZ212enK/R+RsdvzYNMSejc29qv3c0\nKQCg6XKr6XD99ddr0qRJGj58uAoLC/XYY4+pqqrK+bw3FrKN9a9nl9t/S/4rAvH57Ftq/MYsICBA\n5eXl8vf319GjR2W1WmW1WlVUVOR8zbFjx9S7d29ZrVbZ7XZ17dpVlZWVcjgcNIcBAADgEreaDh06\ndFBUVJQk6brrrlP79u21Z88ery5kG+tfz+raf1P7KwLxm0f8lpy7O/FbWoMiNDRUOTk5evjhh7V5\n82YNGjRIvXr10syZM1VSUiKz2ayCggIlJSXp9OnTys7O1qBBg5Sbm6t+/foZPX0AAAA0MW41HbKy\nsmS32xUXFye73a7jx49rxIgRLGQBoBHZu3evXn/9dR08eFC+vr7KycnRggULlJiYqMzMTHXs2FHR\n0dHy8/NTQkKC4uLiZDKZFB8fr8DAQEVFRWnHjh2KiYmRxWJRSkqK0SkBDeLJlK0uvZ6LewIAUDu3\nmg7h4eF64YUX9PHHH6uyslJz5sxRt27dNGPGDBayANBIdO/eXatXr75kfOXKlZeMRUZGKjIystqY\n2WxWcnKy1+YHAACA5s+tpkPr1q21bNmyS8ZZyAIAAAAAgAt8jJ4AAAAAAABonmg6AAAAAAAAr6Dp\nAAAAAAAAvMKtazoAAABczNU7PkjSh6kPe2EmAACgMeFIBwAAAAAA4BU0HQAAAAAAgFfQdAAAAAAA\nAF5B0wEAAAAAAHgFF5IEAAAADDRv3jx98cUXOnv2rJ5++mlt3bpVX331ldq0aSNJiouL07333qus\nrCxlZGTIx8dHo0eP1qhRo1RZWanExEQdOnRIZrNZycnJ6tSpk8EZAcD/j6YDAAAAYJCdO3fq22+/\nVWZmpoqLi/XII4/o7rvv1vPPP6+wsDDn60pLS5WWliabzSY/Pz+NHDlSERERys3NVVBQkFJTU7V9\n+3alpqZq0aJFBmYEANVxegUAAABgkDvvvFOLFy+WJAUFBamsrExVVVWXvG737t3q0aOHAgMD5e/v\nrz59+qigoEB5eXmKiIiQJIWGhqqgoKBB5w8Al8ORDgAAAIBBzGazAgICJEk2m02DBw+W2WzWmjVr\ntHLlSrVr106zZs1SUVGRgoODndsFBwfLbrdXG/fx8ZHJZFJFRYUsFkutMdu2DZCvr9mleYaEBLqR\nned4Kr67+2ku+Te12C09fnPJnaYDAAAAYLAtW7bIZrMpPT1de/fuVZs2bdStWzf94Q9/0BtvvKE7\n7rij2usdDkeN+6lt/GLFxaUuzS0kJFB2+ymXtvEkT8Z3Zz/NKf+mFLulx2+KudfWpOD0CgAAAMBA\nn376qZYtW6bly5crMDBQ/fv3V7du3SRJ4eHh2rdvn6xWq4qKipzbHDt2TFarVVarVXa7XZJUWVkp\nh8NR51EOANDQaDoAAAAABjl16pTmzZunt956y3m3ismTJ6uwsFCSlJ+fr1tuuUW9evXSnj17VFJS\nojNnzqigoEB9+/bVgAEDlJ2dLUnKzc1Vv379DMsFAGrC6RUAAACAQTZt2qTi4mJNnTrVOTZixAhN\nnTpVV111lQICApScnCx/f38lJCQoLi5OJpNJ8fHxCgwMVFRUlHbs2KGYmBhZLBalpKQYmA0AXIqm\nAwAAAGCQMWPGaMyYMZeMP/LII5eMRUZGKjIystqY2WxWcnKy1+YHAFeK0ysAAAAAAIBXcKQDAAAA\n3PZkylaXXv9h6sNemgkAoDHiSAcAAAAAAOAVNB0AAAAAAIBXcHoFALQwZ86c0YwZM3Ty5ElVVlYq\nPj5eISEhmjNnjiSpS5cueuWVVyRJb7/9trKzs2UymTRp0iTdc889Bs4cAAAATQ1NBwBoYd577z3d\ncMMNSkhI0NGjR/X4448rJCRESUlJ6tmzpxISEvS3v/1NN954ozZt2qR33nlHp0+fVmxsrAYOHCiz\n2Wx0CmgmHkz4wKXXpyeGe2kmAADAW66o6VBeXq4HHnhAzz77rPr376/p06erqqpKISEhmj9/viwW\ni7KyspSRkSEfHx+NHj1ao0aNUmVlpRITE3Xo0CHnbX46derkqZwAAHVo27atvvnmG0lSSUmJ2rRp\no4MHD6pnz56SpLCwMOXl5clut2vQoEGyWCwKDg7WNddco/3796tLly5GTh8ucvUifxL/uQcAAJ5z\nRdd0+P3vf6+rr75akrRkyRLFxsZq7dq16ty5s2w2m0pLS5WWlqZVq1Zp9erVysjI0IkTJ7Rx40YF\nBQVp3bp1mjhxolJTUz2SDADg8u6//34dOnRIERERGjdunKZPn66goCDn8+3atZPdbldRUZGCg4Od\n48HBwbLb7UZMGQAAAE2U20c6fPfdd9q/f7/uvfdeSVJ+fr7zHOCwsDClp6frhhtuUI8ePRQYGChJ\n6tOnjwoKCpSXl6fo6GhJUmhoqJKSkq4wDQBAfX3wwQfq2LGjVqxYoX//+9+Kj4931mlJcjgcNW5X\n2/jF2rYNkK+v66dfhIQEXv5FXmJkbHc0xHwb63vS3OZldD783gEAGoLbTYfXX39ds2bN0vvvvy9J\nKisrk8VikXT5v5JdPO7j4yOTyaSKigrn9jVprAvZy+2/JX+hE5/PvqXGb+wKCgo0cOBASVLXrl31\n448/6uzZs87njx49KqvVKqvVqv/3//7fJeN1KS4udXk+ISGBsttPubydJxgZ210NMd/G+p40p3kZ\n/W/P6PiuxqauA0DT5VbT4f3331fv3r1rvQ6Dq38lq89fzxrrQrau/bfkhTTx+eybSvyWuJDt3Lmz\ndu/erWHDhungwYNq1aqVrrnmGu3atUt9+/bV5s2bNX78eF1//fVauXKlJk+erOLiYh07dkw333yz\n0dMHAABAE+JW02Hbtm0qLCzUtm3bdOTIEVksFgUEBKi8vFz+/v7V/kpWVFTk3O7YsWPq3bu3rFar\n7Ha7unbtqsrKSjkcjjqPcgAAeM6YMWOUlJSkcePG6ezZs5ozZ45CQkL08ssv69y5c+rVq5dCQ0Ml\nSaNHj9a4ceNkMpk0Z84c+fhc0aWAAAAA0MK41XRYtGiR8+elS5fqmmuu0ZdffqmcnBw9/PDD2rx5\nswYNGqRevXpp5syZKikpkdlsVkFBgZKSknT69GllZ2dr0KBBys3NVb9+/TyWEACgbq1atdLixYsv\nGV+7du0lY+PHj9f48eMbYloAgBbOnbvtAGj8ruiWmRebPHmyZsyYoczMTHXs2FHR0dHy8/NTQkKC\n4uLiZDKZnBcri4qK0o4dOxQTEyOLxaKUlBRPTQMAAAAAADQSV9x0mDx5svPnlStXXvJ8ZGSkIiMj\nq42ZzWYlJydfaWgAAAAAANCIeexIBwAA4H0cfgwAAJoSrggGAAAAAAC8gqYDAAAAAADwCk6vAAAA\nuALunPLyYerDXpgJAACND00HAAAAwEDz5s3TF198obNnz+rpp59Wjx49NH36dFVVVSkkJETz58+X\nxWJRVlaWMjIy5OPjo9GjR2vUqFGqrKxUYmKiDh065LxYe6dOnYxOCQCcaDoAAAAABtm5c6e+/fZb\nZWZmqri4WI888oj69++v2NhYDR8+XAsXLpTNZlN0dLTS0tJks9nk5+enkSNHKiIiQrm5uQoKClJq\naqq2b9+u1NRULVq0yOi0AMCJazoAAAAABrnzzju1ePFiSVJQUJDKysqUn5+vIUOGSJLCwsKUl5en\n3bt3q0ePHgoMDJS/v7/69OmjgoIC5eXlKSIiQpIUGhqqgoICw3IBgJpwpAMAAABgELPZrICAAEmS\nzWbT4MGDtX37dlksFklSu3btZLfbVVRUpODgYOd2wcHBl4z7+PjIZDKpoqLCuX1N2rYNkK+v2aV5\nhoQEuppao+RuHkbnb2T8lpy70fGbS+40HQAAAACDbdmyRTabTenp6brvvvuc4w6Ho8bXuzp+seLi\nUpfmFhISKLv9lEvbNFbu5GF0/kbGb8m5Gx2/KeZeW5OiWTcdHkz4wOgpAAAAAHX69NNPtWzZMr39\n9tsKDAxUQECAysvL5e/vr6NHj8pqtcpqtaqoqMi5zbFjx9S7d29ZrVbZ7XZ17dpVlZWVcjgcdR7l\nAAANjWs6AAAAAAY5deqU5s2bp7feektt2rSRdP7aDDk5OZKkzZs3a9CgQerVq5f27NmjkpISnTlz\nRgUFBerbt68GDBig7OxsSVJubq769etnWC4AUJNmfaQDAAAA0Jht2rRJxcXFmjp1qnMsJSVFM2fO\nVGZmpjp27Kjo6Gj5+fkpISFBcXFxMplMio+PV2BgoKKiorRjxw7FxMTIYrEoJSXFwGwA4FI0HQAA\nAACDjBkzRmPGjLlkfOXKlZeMRUZGKjIystqY2WxWcnKy1+YHAFeK0ysAAAAAAIBX0HQAAAAAAABe\nQdMBAAAAAAB4BU0HAAAAAADgFTQdAAAAAACAV9B0AAAAAAAAXkHTAQAAAAAAeIWv0RMAAADA5T2Z\nstWl16cnhntpJgAA1B9NBwBogbKysvT222/L19dXzz33nLp06aLp06erqqpKISEhmj9/viwWi7Ky\nspSRkSEfHx+NHj1ao0aNMnrqAAAAaELcajqUlZUpMTFRx48f148//qhnn31WXbt2rfeCtbKyUomJ\niTp06JDMZrOSk5PVqVMnT+cGAKhBcXGx0tLS9O6776q0tFRLly5VTk6OYmNjNXz4cC1cuFA2m03R\n0dFKS0uTzWaTn5+fRo4cqYiICLVp08boFAAAANBEuHVNh9zcXHXv3l1r1qzRokWLlJKSoiVLlig2\nNlZr165V586dZbPZVFpaqrS0NK1atUqrV69WRkaGTpw4oY0bNyooKEjr1q3TxIkTlZqa6um8AAC1\nyMvLU//+/dW6dWtZrVbNnTtX+fn5GjJkiCQpLCxMeXl52r17t3r06KHAwED5+/urT58+KigoMHj2\nAAAAaErcOtIhKirK+fPhw4fVoUMH5efn65VXXpF0fsGanp6uG264wblgleRcsObl5Sk6OlqSFBoa\nqqSkpCvNAwBQTwcOHFB5ebl1A+AoAAAgAElEQVQmTpyokpISTZ48WWVlZbJYLJKkdu3ayW63q6io\nSMHBwc7tgoODZbfb69x327YB8vU1uzynkJBAl7fxFCNjN1aN9T1prPNyl7fzudz++b0DADSEK7qm\nw9ixY3XkyBEtW7ZMEyZMqPeC9eJxHx8fmUwmVVRUOLevibsLWW/jC534jTF+S869McRvCk6cOKE3\n3nhDhw4d0mOPPSaHw+F87uKfL1bb+MWKi0tdnktISKDs9lMub+cJRsZuzBrre9JY5+Uub+dT1/6N\n/rfvamzqOgA0XVfUdHjnnXf09ddfa9q0aVe0YPXWQrYhNNYvdKMXE8Tns28q8VviQrZdu3a64447\n5Ovrq+uuu06tWrWS2WxWeXm5/P39dfToUVmtVlmtVhUVFTm3O3bsmHr37m3gzAEAANDUuHVNh717\n9+rw4cOSpG7duqmqqkqtWrVSeXm5JNW5YL0wfuEQ3crKSjkcjjqPcgAAeM7AgQO1c+dOnTt3TsXF\nxSotLVVoaKhycnIkSZs3b9agQYPUq1cv7dmzRyUlJTpz5owKCgrUt29fg2cPAACApsStpsOuXbuU\nnp4uSSoqKnJ5wTpgwABlZ2dLOn9Ryn79+nkoHQDA5XTo0EHDhg3T6NGj9dRTT2nmzJmaPHmy3n//\nfcXGxurEiROKjo6Wv7+/EhISFBcXpwkTJig+Pt55jR4AAACgPtw6vWLs2LF66aWXFBsbq/Lycr38\n8svq3r27ZsyYoczMTHXs2FHR0dHy8/NzLlhNJpNzwRoVFaUdO3YoJiZGFotFKSkpns4LAFCHsWPH\nauzYsdXGVq5cecnrIiMjFRkZ2VDTAgAAQDPjVtPB39+/xttc1nfBajablZyc7E5oAAAAAADQRLh1\negUAAAAAAMDl0HQAAAAADLRv3z4NHTpUa9askSQlJibqwQcf1Pjx4zV+/Hht27ZNkpSVlaVHH31U\no0aN0vr16yWdvyh7QkKCYmJiNG7cOBUWFhqVBgDU6IpumQkAAADAfaWlpZo7d6769+9fbfz5559X\nWFhYtdelpaXJZrPJz89PI0eOVEREhHJzcxUUFKTU1FRt375dqampWrRoUUOnAQC1oukAAACahCdT\ntho9BcDjLBaLli9fruXLl9f5ut27d6tHjx7Ouwj16dNHBQUFysvLU3R0tCQpNDRUSUlJXp8zALiC\npgMAAABgEF9fX/n6XrokX7NmjVauXKl27dpp1qxZKioqUnBwsPP54OBg2e32auM+Pj4ymUyqqKiQ\nxWKpNWbbtgHy9TW7NM+QkOZxy2R38zA6fyPjt+TcjY7fXHKn6QAAAAA0Ig8//LDatGmjbt266Q9/\n+IPeeOMN3XHHHdVe43A4aty2tvGLFReXujSfkJBA2e2nXNqmsXInD6PzNzJ+S87d6PhNMffamhQ0\nHQAAAIBG5OLrO4SHh2vOnDkaNmyYioqKnOPHjh1T7969ZbVaZbfb1bVrV1VWVsrhcNR5lAPgbe6c\nCpeeGO6FmaCx4O4VAAAAQCMyefJk510o8vPzdcstt6hXr17as2ePSkpKdObMGRUUFKhv374aMGCA\nsrOzJUm5ubnq16+fkVMHgEtwpAMAAABgkL179+r111/XwYMH5evrq5ycHI0bN05Tp07VVVddpYCA\nACUnJ8vf318JCQmKi4uTyWRSfHy8AgMDFRUVpR07digmJkYWi0UpKSlGpwQA1dB0AAAA1XCXCKDh\ndO/eXatXr75kfNiwYZeMRUZGKjIystqY2WxWcnKy1+YHAFeK0ysAAAAAAIBX0HQAAAAAAABeQdMB\nAAAAAAB4BU0HAAAAAADgFTQdAAAAAACAV9B0AAAAAAAAXkHTAQAAAAAAeIWv0RMAAKAxejJlq0uv\nT08M99JMAAAAmi6OdAAAAAAAAF7BkQ4AAAAAgEs8mPCB0VNAM8CRDgAAAAAAwCtoOgAAAAAAAK+g\n6QAALVB5ebmGDh2qDRs26PDhwxo/frxiY2M1ZcoUVVRUSJKysrL06KOPatSoUVq/fr3BMwYAAEBT\n5PY1HebNm6cvvvhCZ8+e1dNPP60ePXpo+vTpqqqqUkhIiObPny+LxaKsrCxlZGTIx8dHo0eP1qhR\no1RZWanExEQdOnRIZrNZycnJ6tSpkyfzAgDU4fe//72uvvpqSdKSJUsUGxur4cOHa+HChbLZbIqO\njlZaWppsNpv8/Pw0cuRIRUREqE2bNgbPHAAAAE2JW0c67Ny5U99++60yMzP19ttv67XXXnMuWteu\nXavOnTvLZrOptLRUaWlpWrVqlVavXq2MjAydOHFCGzduVFBQkNatW6eJEycqNTXV03kBAGrx3Xff\naf/+/br33nslSfn5+RoyZIgkKSwsTHl5edq9e7d69OihwMBA+fv7q0+fPiooKDBw1gAAAGiK3DrS\n4c4771TPnj0lSUFBQSorK1N+fr5eeeUVSecXrenp6brhhhuci1ZJzkVrXl6eoqOjJUmhoaFKSkry\nRC4AgHp4/fXXNWvWLL3//vuSpLKyMlksFklSu3btZLfbVVRUpODgYOc2wcHBstvtl91327YB8vU1\nuzynkJBAl7fxFE/FNjIHNE3e/jdzuf03h987AEDj51bTwWw2KyAgQJJks9k0ePBgbd++vd6L1ovH\nfXx8ZDKZVFFR4dy+Ju4uZL2NL3TiN8b4LTn3xhC/MXv//ffVu3fvWk9pczgcLo3/VHFxqctzCgkJ\nlN1+yuXtPMGTsY3KAU2Xt//N1LV/I3/vJNdzp64DQNPl9jUdJGnLli2y2WxKT0/Xfffd5xx3ddFa\nn8WsOwvZhtBYv9CNXkwQn8++qcRvaQvZbdu2qbCwUNu2bdORI0dksVgUEBCg8vJy+fv76+jRo7Ja\nrbJarSoqKnJud+zYMfXu3dvAmQMAAKApcvvuFZ9++qmWLVum5cuXKzAw0LlolVTnovXC+IXDdCsr\nK+VwOOo8ygEA4BmLFi3Su+++qz//+c8aNWqUnn32WYWGhionJ0eStHnzZg0aNEi9evXSnj17VFJS\nojNnzqigoEB9+/Y1ePYAAABoatxqOpw6dUrz5s3TW2+95bySuSuL1gEDBig7O1uSlJubq379+nko\nHQCAqyZPnqz3339fsbGxOnHihKKjo+Xv76+EhATFxcVpwoQJio+Pd16fBwDgWfv27dPQoUO1Zs0a\nSXLpVsaVlZVKSEhQTEyMxo0bp8LCQsPyAICauHV6xaZNm1RcXKypU6c6x1JSUjRz5kxlZmaqY8eO\nio6Olp+fn3PRajKZnIvWqKgo7dixQzExMbJYLEpJSfFYQgCA+pk8ebLz55UrV17yfGRkpCIjIxty\nSgDQ4pSWlmru3Lnq37+/c8yVWxnn5uYqKChIqamp2r59u1JTU7Vo0SIDMwKA6txqOowZM0Zjxoy5\nZLy+i1az2azk5GR3QgMAAADNhsVi0fLly7V8+XLnGHeFA9CcXNGFJCE9mbLV5W3SE8O9MBMAaBmo\nuwCaE19fX/n6Vl+Su3IrY3fuCgcADYmmAwAAANBIeeOucO7cir653O3J3TyMzt/o+N5WV35G525k\n/OaSO00HAAAAoBFx5VbGF+4K17Vr13rfFc7VW9EbfTtsT3InD6PzNzp+Q6gtP6NzNzJ+U8y9tiYF\nTQcAAIAG9mDCB0ZPAY3YhbvCPfzww9XuCjdz5kyVlJTIbDaroKBASUlJOn36tLKzszVo0CDuCgeg\nUaLpAAAAABhk7969ev3113Xw4EH5+voqJydHCxYsUGJiIneFA9As0HQAAAAADNK9e3etXr36knHu\nCgeguaDpAACAB7hzVw0AAIDmzsfoCQAAAAAAgOaJIx0AAACaIXeOvklPDPfCTAAALRlHOgAAAAAA\nAK/gSAcAAABI4tokAADP40gHAAAAAADgFTQdAAAAAACAV3B6BQAAAIBaPZjwgcvbcFFSABdwpAMA\nAAAAAPAKmg4AAAAAAMAraDoAAAAAAACvoOkAAAAAAAC8gqYDAAAAAADwCu5eAQBo9p5M2Wr0FAAA\nQC1c/Z7m7ihNC0c6AAAAAAAAr6DpAAAAAAAAvOKKmg779u3T0KFDtWbNGknS4cOHNX78eMXGxmrK\nlCmqqKiQJGVlZenRRx/VqFGjtH79eklSZWWlEhISFBMTo3HjxqmwsPAKUwEA1Ne8efM0ZswYPfro\no9q8ebNL9RsAAACoL7ebDqWlpZo7d6769+/vHFuyZIliY2O1du1ade7cWTabTaWlpUpLS9OqVau0\nevVqZWRk6MSJE9q4caOCgoK0bt06TZw4UampqR5JCABQt507d+rbb79VZmam3n77bb322msu1W8A\nAACgvtxuOlgsFi1fvlxWq9U5lp+fryFDhkiSwsLClJeXp927d6tHjx4KDAyUv7+/+vTpo4KCAuXl\n5SkiIkKSFBoaqoKCgitMBQBQH3feeacWL14sSQoKClJZWZlL9RsAAACoL7fvXuHr6ytf3+qbl5WV\nyWKxSJLatWsnu92uoqIiBQcHO18THBx8ybiPj49MJpMqKiqc2/9U27YB8vU1uzvdRiUkJLBZxSF+\n44vfknNvDPEbO7PZrICAAEmSzWbT4MGDtX379nrX77q4W6v5zICWhd/5uuXn52vKlCm65ZZbJEm3\n3nqrfv3rX2v69OmqqqpSSEiI5s+fL4vFoqysLGVkZMjHx0ejR4/WqFGjDJ49AFTntVtmOhwOj4xf\nUFxcesVzaizs9lNejxESEtggcYjf+OK35Nzdid+SF75btmyRzWZTenq67rvvPue4u3Vacq9WG/1v\nBkDDc/V3viXW6rvuuktLlixxPn7xxRcVGxur4cOHa+HChbLZbIqOjlZaWppsNpv8/Pw0cuRIRURE\nqE2bNgbOHACq8+jdKwICAlReXi5JOnr0qKxWq6xWq4qKipyvOXbsmHP8wl/MKisr5XA4aj3KAQDg\nWZ9++qmWLVum5cuXKzAw0KX6DQBoeJwGB6Cp8mjTITQ0VDk5OZKkzZs3a9CgQerVq5f27NmjkpIS\nnTlzRgUFBerbt68GDBig7OxsSVJubq769evnyakAAGpx6tQpzZs3T2+99Zbzr2Gu1G8AgPft379f\nEydOVExMjD777DOXTmMGgMbE7dMr9u7dq9dff10HDx6Ur6+vcnJytGDBAiUmJiozM1MdO3ZUdHS0\n/Pz8lJCQoLi4OJlMJsXHxyswMFBRUVHasWOHYmJiZLFYlJKS4sm8AAC12LRpk4qLizV16lTnWEpK\nimbOnFmv+g0A8K7rr79ekyZN0vDhw1VYWKjHHntMVVVVzuev5DQ4qWGuldZYT4lxd15G52N0/Mam\nId8PrtN25dxuOnTv3l2rV6++ZHzlypWXjEVGRioyMrLamNlsVnJysrvhAQBuGjNmjMaMGXPJeH3r\nNwDAuzp06KCoqChJ0nXXXaf27dtrz549Ki8vl7+/f52nwfXu3fuy+2+Ia6U11mv1uDMvo689ZHT8\nxqih3g+u0+aZ6+949PQKAAAAAFcmKytLK1askCTZ7XYdP35cI0aM4DQ4AE2S1+5eAQAAAMB14eHh\neuGFF/Txxx+rsrJSc+bMUbdu3TRjxgxOgwPQ5NB0AAAAABqR1q1ba9myZZeMcxocgKaI0ysAAAAA\nAIBXcKQDAAAAALQAT6ZsNXoKaIE40gEAAAAAAHgFRzoYwJ0OY3piuBdmAgAAAACA99B0AAAAAIAm\npiWfKsEfcZsWTq8AAAAAAABeQdMBAAAAAAB4BadXAAAAAPColnzoP4DqaDoAAAAAaBHcaYZ8mPqw\nF2YCtBycXgEAAAAAALyCpgMAAAAAAPAKmg4AAAAAAMAruKYDAMBQDyZ8YPQUAAColavfU+mJ4V6a\nCa4E1/MwDkc6AAAAAAAAr+BIBwAAAADwEHf+os7REWjOaDo0Ea4WLw4FAgAAAAAYjaYDAAAAABjI\nnaMj0Dy0hCNjuKYDAAAAAADwCo50aKbcuRp8U+uYAQAAAIC3cOcSzzC06fDaa69p9+7dMplMSkpK\nUs+ePY2cDgCgBtRqAGjcqNNA48BpMjUzrOnw97//Xf/973+VmZmp7777TklJScrMzDRqOgCAGlCr\nAaBxo04DaOwMazrk5eVp6NChkqSbbrpJJ0+e1OnTp9W6dWujptTiNURnjkOOgKaFWg0AjRt1Gmh5\nGuL/bZ68G6JhTYeioiLdfvvtzsfBwcGy2+0UyGaupR9yRNMFTQ21GgAaN+o0gMau0VxI0uFw1Pl8\nSEigy/v0ZHcGaErc+X0hPuqDWg3AE6jT3nO5Oi25/v5Tp4GWyVO12rBbZlqtVhUVFTkfHzt2TCEh\nIUZNBwBQA2o1ADRu1GkAjZ1hTYcBAwYoJydHkvTVV1/JarVyGBgANDLUagBo3KjTABo7w06v6NOn\nj26//XaNHTtWJpNJs2fPNmoqAIBaUKsBoHGjTgNo7EyO+pz4BQAAAAAA4CLDTq8AAAAAAADNG00H\nAAAAAADgFY3mlplX4u9//7umTJmi1157TWFhYZc8n5WVpYyMDPn4+Gj06NEaNWqUKisrlZiYqEOH\nDslsNis5OVmdOnVyKe7l9rF37169/vrrzsf79+9XWlqaPvvsM3344Yfq0KGDJOmhhx7SqFGjXM67\nPjncfvvt6tOnj/PxqlWrdO7cuSvOvb7xN23apPT0dPn4+Kh///767W9/qw0bNmjx4sW67rrrJEmh\noaF65pln6h33tdde0+7du2UymZSUlKSePXs6n9uxY4cWLlwos9mswYMHKz4+/rLbuKqufe3cuVML\nFy6Uj4+PbrjhBv3ud7/T559/rilTpuiWW26RJN16662aNWuWV+KHh4fr5z//ucxmsyRpwYIF6tCh\nQ4Pkf/ToUb3wwgvO1xUWFiohIUGVlZVX9Hn/1L59+/Tss8/qiSee0Lhx46o91xCfP9xjVJ2WqNXU\n6oav1dRp6nRTRa02plYbVaclY2s1a+oWVKsdTdx///tfx8SJEx3PPvusY+vWrZc8f+bMGcd9993n\nKCkpcZSVlTnuv/9+R3FxsWPDhg2OOXPmOBwOh+PTTz91TJkyxeXYruzj5MmTjl/96leOqqoqx5Il\nSxyrV692OZ478e+6664rmveVxC8tLXWEhYU5Tp065Th37pxj5MiRjm+//dbx7rvvOlJSUtyKmZ+f\n7/jNb37jcDgcjv379ztGjx5d7fnhw4c7Dh065KiqqnLExMQ4vv3228tu48n4ERERjsOHDzscDodj\n8uTJjm3btjl27tzpmDx5stsxXYkfFhbmOH36tEvbeDL+BZWVlY6xY8c6Tp8+fUWf90+dOXPGMW7c\nOMfMmTNr/B3y9ucP9xhZpx0OajW1umFrNXWaOt1UUauNq9VG1GmHw9hazZq6ZdXqJn96RUhIiN54\n4w0FBgbW+Pzu3bvVo0cPBQYGyt/fX3369FFBQYHy8vIUEREh6XyXqKCgwOXYruxjxYoVevzxx+Xj\n47m33N0cPJF7ffZz1VVXKSsrS61bt5bJZFKbNm104sQJt2JdHHPo0KGSpJtuukknT57U6dOnJZ3v\nAl599dX6xS9+IR8fH91zzz3Ky8urcxtPxpekDRs26Oc//7kkKTg4WMXFxW7n6k58T21zpft67733\nNGzYMLVq1cqtOLWxWCxavny5rFbrJc81xOcP9xhZpyVqNbW6YWs1dZo63VRRq42r1UbU6QtxjarV\nrKlbVq1u8k2Hq666ynnYS02KiooUHBzsfBwcHCy73V5t3MfHRyaTSRUVFS7Fru8+ysvLtX37dg0Z\nMsQ5lp2drQkTJujpp59WYWGhS3FdiV9RUaGEhASNHTtWK1eudGnenoh/4T7R33zzjQ4ePKhevXpJ\nOn/4XlxcnB5//HH961//cilm27ZtnY8vfJ6SZLfba/2sa9vGVZfb14V8jx07ps8++0z33HOPpPOH\nAE6cOFExMTH67LPP3Ipdn/iSNHv2bMXExGjBggVyOBwNmv8F69ev18iRI52P3f28f8rX11f+/v41\nPtcQnz/cY2Sd/un+qdXUasm7tZo6TZ1uqqjVxtVqI+r0hbhG1WrW1C2rVjepazqsX79e69evrzY2\nefJkDRo0qN77cNRyh9DaxuuKvXv37nrtY8uWLbr33nud3dh77rlHd999t+6880795S9/0auvvqq3\n3nrLK/GnT5+uhx56SCaTSePGjVPfvn0vec3lcr+S+JL0/fff64UXXlBqaqr8/PzUq1cvBQcH6957\n79WXX36pGTNm6MMPP7zsHGpSn7l7YhtX9nX8+HFNnDhRs2fPVtu2bXX99ddr0qRJGj58uAoLC/XY\nY49p8+bNslgsHo//3HPPadCgQbr66qsVHx+vnJyces3ZU/El6csvv9SNN97o/LLw5OftCZ7MH5cy\nsk7XFp9aTa02slZTp11HnfY+arVxtbqx1un6zN1T29R3P6ypm0+tblJNh1GjRrl8YRir1aqioiLn\n42PHjql3796yWq2y2+3q2rWrKisr5XA46vwHW1PsxMTEeu0jNzdXMTExzsc/vUjJggULLpuHu/Ev\njnv33Xdr3759Lud+JfGPHDmi+Ph4zZs3T926dZN0/nCcm266SZJ0xx136IcfflBVVVWd3fULavo8\nQ0JCanzu6NGjslqt8vPzq3UbV9UVX5JOnz6tp556SlOnTtXAgQMlSR06dFBUVJQk6brrrlP79u11\n9OhRty6ydLn40dHRzp8HDx7s/LwbKn9J2rZtm/r37+98fCWf95XMzRufPy7PyDpdW3xqNbW6IWs1\ndbr+c6NOG4dabVytbix1WjK2VrOmblm1usmfXnE5vXr10p49e1RSUqIzZ86ooKBAffv21YABA5Sd\nnS3pfPHq16+fy/uu7z727t2rrl27Oh+/+uqr2rVrl6Tzh8hcuAKrp+P/5z//UUJCghwOh86ePauC\nggLdcsstHsm9PvEl6aWXXtKcOXN0++23O8eWL1+ujRs3Sjp/1dTg4OB6/7IMGDDA2Wn86quvZLVa\nnd2/a6+9VqdPn9aBAwd09uxZ5ebmasCAAXVu407Ode0rJSVFjz/+uAYPHuwcy8rK0ooVKySdP1zp\n+PHjzissezL+qVOnFBcX5zwc7/PPP3d+3g2VvyTt2bOn2r/3K/m8XdEQnz+8w5t1WqJWU6sbtlZT\np2tHnW7aqNXeq9VG1OkLcY2q1aypW1atNjma+DFs27Zt04oVK/Sf//xHwcHBCgkJUXp6uv7whz/o\nzjvv1B133KHs7GytWLHCeSjUQw89pKqqKs2cOVPff/+9LBaLUlJS9Itf/MKl2LXt4+LYktS/f3/l\n5eU5t/vmm280e/Zs+fr6ymQy6dVXX1Xnzp1dzr0+8efPn6+dO3fKx8dH4eHheuaZZzySe33it2nT\nRtHR0dU60E888YRuv/12TZs2zVm0Xb3dzIIFC7Rr1y6ZTCbNnj1b//rXvxQYGKiIiAh9/vnnzg73\nfffdp7i4uBq3ufgX2FW1xR84cGC1z12SHnjgAd1///164YUXVFJSosrKSk2aNMl5Xpon40dERCgj\nI0Pvv/++fvazn+m2227TrFmzZDKZGiT/CxdAevDBB7Vy5Uq1b99e0vnO/JV83he7cLusgwcPytfX\nVx06dFB4eLiuvfbaBvv84Toj67REraZWN3ytpk5Tp5siarVxtdqoOi0ZW6tZU7ecWt3kmw4AAAAA\nAKBxavanVwAAAAAAAGPQdAAAAAAAAF5B0wEAAAAAAHgFTQcAAAAAAOAVNB0AAAAAAIBX0HQAAAAA\nAABeQdMBAAAAAAB4BU0HAAAAAADgFTQdAAAAAACAV9B0AAAAAAAAXkHTAQAAAAAAeAVNBwAAAAAA\n4BU0HQAAAAAAgFfQdAAAAAAAAF5B0wEAAAAAAHgFTQcAAAAAAOAVNB0AAAAAAIBX0HQAAAAAAABe\nQdMBAAAAAAB4BU0HAAAAAADgFTQdAAAAAACAV9B0AAAAAAAAXkHTAQAAAAAAeAVNBwAAAAAA4BU0\nHWCI/Px8RUREeHy/qampWrdunSTp008/1aFDh1zex2233aYDBw54emoAAABAkzF+/Hh98MEHl33d\nn//8Z+fPkZGRKioq8ua00ATRdECzkpCQoJiYGEnSqlWr3Go6AACqe+mll7R06VJJ3ltQ/vOf/1Rc\nXJzH9wsA8B673a63337b+Tg7O1vt27c3cEZojGg6wFA//vijXn75ZQ0bNkzDhw9XSkqKqqqqJEnh\n4eF65513NHLkSA0cOFApKSnO7ZYtW6b+/fvr0Ucf1Z/+9CeFh4dLkhITE/Xmm29q0aJF2rlzp6ZN\nm6ZNmzY5xy+4+PHf/vY3RUREaPjw4dWKpiRlZmYqMjJS4eHhev7551VeXu7ttwQAGjVvLSh79uyp\nFStWeHy/ANDc5Ofn68EHH1RKSoqGDRum8PBw/eMf/6hzXd2lSxf98Y9/1MMPP6z+/fs7jwzesGGD\nnnjiCee+f/r4go8//lgPPvighg0bphEjRujrr7+WJI0dO1aHDh1SZGSkKioq1KVLFx05ckSS9Mc/\n/lFRUVGKjIzUM888ox9++EHS+XX4kiVLNGHCBIWFhWnChAkq+//Yu/+4qMt8//+P4cccQgcVYixL\nK1tTTyrmMU2UEpVEOq1YgkLaL8tc0bQoZUnL1jYpwzVPtJYlcsO1WMmK3BbIwm6ZyGaza9ppU/ts\nZWgwKAgKBuJ8//DrHFlAYZxhgHneb7duN+d6zzWv6zWj11y95v2+3jU1LnzHxN1UdBC3ysjI4Oef\nf+Yvf/kL7777Lrt372br1q3241988QVZWVm88847bNy4kZ9//pkDBw7wxhtv8P7777Np0yZyc3Mb\nve7ChQvp2bMnK1euJCoqqtn49fX1PPXUUzzzzDP89a9/xcvLyz457969m5dffpmMjAw++eQTunbt\nyssvv+z8N0FExMl++uknxowZw7p165g4cSITJ07kH//4B7NnzyYsLIzf/va3AGzbto0777yT8ePH\n8+CDD9oXhOXl5Tz44DF6zisAACAASURBVIOMGzeO2bNnU1VVZX/t8xeUaWlpTJw4kQkTJvDII49Q\nWVkJwP/8z//wu9/9joSEBMaPH8/UqVMpLS294JjPv+zuQv0PHTrEPffcQ0REBHfffTdff/01AIcP\nH2bWrFlMnDiR//7v/+a9995r1XtxofdDRKS9+e677xgyZAh5eXn85je/YdmyZRddV//www+8//77\n/OlPf+L555+nvLy8RbFOnz5NUlISy5cvJy8vj3HjxvHCCy8A8Pzzz3PllVeSm5uL0Wi09/nHP/7B\nm2++SWZmJrm5ufTq1YvU1FT78dzcXP7whz/w0UcfcezYMT766CMnvTPSHqnoIG61fft2YmNj8fHx\nwc/PjzvvvJPPP//cfvzOO+/E29ubnj17EhQUxJEjR/jiiy8YMWIEZrOZ//iP/+Duu+92OP73339P\nbW0tY8aMAWDKlCn2Y5988glRUVH07NkTgLi4OPLz8x2OJSLSlsrLywkODiYvL4/+/fvz2GOPkZKS\nQk5ODlu3buXHH39k0aJFpKam8vHHHzNy5EiWLVsGwLp16+jRoweffPIJTz/9NDt27Gj0+vv27eNP\nf/oT77zzDvn5+dTW1rJx40b78dzcXJKTk9m2bRtBQUG88847rRp/c/2XLl3KHXfcwUcffcRvfvMb\nFi1aZG8fMWIEeXl5vPbaazz33HP2/Xla8l4cOnSo2fdDRKS98ff3Z9KkSQDcfvvtfPPNN+Tl5V1w\nXX1uzdy3b1+uu+46vvrqqxbF8vHxYefOnQwdOhSA4cOHc+jQoQv22b59OxMnTiQoKAiAmJiYBmO5\n7bbb6N69Oz4+Ptxwww0cOXKk5clLh+Pj7gGIZzt27BjdunWzP+7WrRtHjx61P+7atav9z97e3tTX\n11NZWdmgz7migCOOHz/eIMb5r1tVVcVHH31kX2zbbDbq6uocjiUi0pZOnz5NZGQkADfccAMAgYGB\nAAQHB5OTk8OIESPsx6ZPn87o0aOpr69n9+7dzJ49G4Crr76aESNGNHr9QYMGsX37dvsvWzfddFOD\nRejw4cO56qqrABg4cGCrF5RN9f/ll18oKipizZo1AIwfP55Ro0ZRV1fHzp07Wb16NQBXXXUVI0eO\nZNeuXdxyyy0XfS9KS0v59ttvm30/vL29WzV2ERFXCwgIwGAw2P8McOLEiQuuq//92Lmz01oiMzOT\nd999l9raWmpra+2xm3Ps2DHMZnOD8Z4/FpPJZP/zuTW+dF4qOohbXX755VRUVNgfV1RUXPRa4a5d\nu1JdXW1/fLFTdgG8vLw4c+aM/fHx48eBsxPuiRMn7O3nn0prNpuZMmUKixcvvngiIiLtjLe3N35+\nfsDZOdDf37/BMR8fH3bv3m3/n3E4O79WVFRw/PjxBgvCcwva89XU1LBixQqKioqAs/Pq2LFj7ccv\ndUHZVP+KigrOnDljP2YwGOjSpQtWqxWbzdZozOfm9Iu9F/X19VRVVTX7fpz7pU5EpL04f/18bl0b\nEBBwwXV1eXm5vZhbUVFBt27dOHbsWIP5ualChMViYd26dWzevJmrr76azz//nKVLl15wfI6s8aXz\n0uUV4lZjx44lOzub+vp6qquref/997ntttsu2GfIkCEUFRVx7Ngxamtr7dft/jsfHx/7dcjBwcH8\n85//BM5eD2yxWADo06cP3t7e9kXzli1b7JXbcePGkZ+fb1+0btu2jddff/3SkxYRaQfMZjOhoaHk\n5uba/9u1axdBQUEEBAQ02Mehqb0NMjIy+P7779myZQt5eXlMmzbN5WPu0aMHBoPBfh2yzWbjhx9+\noHv37nh5edkX3kCriwUXej9ERNqbU6dOsW3bNgDy8vIYNGgQEydOvOC6+i9/+Qtwdj+IH374gZCQ\nEMxmM//617/45ZdfqKmpaXKvtGPHjhEUFESvXr2oqanh3Xffpbq6GpvNho+PD9XV1Zw+fbpBn7Fj\nx/LRRx/Z5+u33377omt86bxUdBC3mjlzJldccQV33HEHd999N2PHjrVfn9acIUOGMGXKFKZMmcK9\n995LeHh4k8+bOHEijz/+OOnp6cTGxlJcXMztt99OamoqEydOBMDX15fly5eTnJzMpEmTMBgM9l/A\nbrzxRubMmcPMmTOZNGkSGzZsYPz48c59A0RE3MRoNLJ79277JRFfffUVzz33HABDhw61L2Z//PFH\nvvzyy0b9jx49St++fenSpQvFxcV8+umnDc5Cc9WYR48ezbvvvgvAZ599xuzZs/H19WXMmDFkZWXZ\nx7x7925CQ0Nb/Npjxoxp9v0QEWlvrrrqKr788ksmTpzIa6+9xjPPPHPRdXVgYCCTJ0/mnnvuYcmS\nJXTr1o2RI0cSEhLCxIkTefjhh5tc64aFhWE2m5kwYQIPPvgg9913HyaTiUcffZT+/fvTrVs3Ro8e\n3eBW9UOGDGH27Nncc889REZGUlVVxWOPPdYm7420PwabzWZz9yBEWstms9nPSNi+fTurV69u9owH\nERFP89NPP3H77bfzv//7vwC8+uqr/Pjjj/ZbD0dERPDcc89x4sQJXn75Zerq6ujSpQvJyckMGzaM\nsrIyHnvsMYqLi7n++usJDAzk6quvZv78+fTv359PP/2UkydP8uijj3L69Gn69+9PfHw88+fPJyEh\ngaqqKn7++Wd+//vfA2fvRnH+46YUFRWxZMkSPvroo0bPP//xzz//zBNPPMGRI0fo1q0bzz77LIMH\nD+bIkSMsWbKE4uJifH19mTdvHhMnTmzxezFy5Eg+/vjjJt8PEZH25Pz5sqXOzd1XXHGFC0cm0jQV\nHaTDOXbsGJMmTWLLli306tWLpKQkLrvsMu0yLiIiIiKdnooO0tE4tJFkTU0NSUlJHD16lF9++YW5\nc+cyYMAAFi1aRH19PcHBwaxcuRKj0UhOTg4ZGRl4eXkRGxtLTEwMdXV1JCUlcfjwYby9vVmxYgW9\ne/d2dm7SSQUGBrJw4ULuv/9+DAYDffv2td8yTURERERERNoPh850+PDDDykuLubhhx+muLiYBx98\nkGHDhnHrrbcyadIkVq1axRVXXEF0dDRTpkwhOzsbX19fpk6dysaNGykoKOCrr77imWeeYceOHWRn\nZ9tvcyUiIiKdU0JCAt99912Tx9LS0rj++uvbeEQi7nfy5EkWL17M8ePHqaurIyEhgeDgYPsZnP37\n9+fZZ58F4I033iA3NxeDwcC8efO47bbbqKqqIjExkaqqKvz9/UlNTaV79+5uzEhEpCGHznSIioqy\n//nIkSP07NmToqIi+4QYHh7O+vXrue666xg8eLD9FlbDhg3DYrFQWFhIdHQ0AKGhoSQnJ19qHiIi\nItLOpaWluXsIIu3Ou+++y3XXXUdiYiIlJSXcd999BAcHk5yczJAhQ0hMTOTTTz+lb9++fPjhh7z9\n9tucOHGC+Ph4xowZQ0ZGBiNGjOChhx4iKyuLdevW8eSTT7o7LRERO4eKDudMnz6dn3/+mbVr1/LA\nAw9gNBoBCAoKwmq1UlZWRmBgoP35gYGBjdq9vLwwGAzU1tba+zfFaq1q9lhzevTwp7zctTtpt9f4\nnpy7p8f35NwdiR8cbHLhaDxPR5urO9rfV8XvHLE9Pb4jsTvzXN2jRw++/fZbACorK+nevTvFxcUM\nGTIEOPtjXmFhIVarlbCwMIxGI4GBgVx11VUcPHiQwsJCnn/+eftz58yZc9GYrZ2rPfnvq7vje3Lu\n7o7vybk7Gr+5ufqSig5vv/0233zzDU8++STnX6XR3BUbrW0/X48e/vj4eLd6jO7+knJnfE/O3dPj\ne3Lu7SG+tI4jc3tniK34+uw9Nb67c29v7rjjDrZs2UJERASVlZX88Y9/5He/+539+Lkf87p3737R\nH/OCgoIoLS11+hjd/Zl5cnxPzt3d8T05d2fHd6josG/fPoKCgrjyyisZOHAg9fX1dOnShVOnTuHn\n50dJSQlmsxmz2UxZWZm9X2lpKUOHDsVsNmO1WhkwYAB1dXXYbLYLnuUAOFTlCQ42OfSrm7O4M74n\n5+7p8T05d0fiq0AhIiLu9P7779OrVy/efPNN/vnPf5KQkGC/NBla96NdS7dqc+THPHd/X3pyfE/O\n3d3xPTl3Z8Z3qOiwe/duiouLeeqppygrK6O6upqwsDDy8vKYPHky+fn5hIWFERISwpIlS6isrMTb\n2xuLxUJycjInTpwgNzeXsLAwCgoKGDlypFOSERERERHpSCwWC2PGjAFgwIAB/PLLL5w+fdp+/Pwf\n8/71r3812W61WjGZTPa2i3HklOmO9INCZ4rvybm7O74n5+5o/OaKFF6ODGD69OkcO3aM+Ph4Zs+e\nzdNPP838+fN57733iI+Pp6KigujoaPz8/EhMTGTWrFk88MAD9sptVFQUZ86cIS4ujj/96U8kJiY6\nMgwRERERkQ7tmmuuYc+ePQAUFxfTpUsXrr/+enbv3g1g/zHvlltuYfv27dTW1lJSUkJpaSm/+tWv\nGD16NLm5uQ2eKyLSnjh0poOfnx+pqamN2tPT0xu1RUZGEhkZ2aDN29ubFStWOBJaRERERKTTmDZt\nGsnJycyYMYPTp0+zbNkygoODefrppzlz5gwhISGEhoYCEBsby4wZMzAYDCxbtgwvLy9mzpzJk08+\nSXx8PAEBAaxcudLNGYmINHRJG0mKiIiIiIjjunTpwssvv9yofdOmTY3aZs6cycyZMxv1f/XVV102\nPhGRS+XQ5RUiIiIiIiIiIhejooOIiIiIiIiIuIQurxBx0IMpn7S6z/qkcS4YiYi4Qmv/jevft4h0\nVncmvt/qPpoTReQcnekgIiIiIiIiIi6hooOIiIiIiIiIuISKDiIiIiIiIiLiEio6iIiIiIiIiIhL\nqOggIiIiIiIiIi6hooOIiIiIiIiIuISKDiIiIiIiIiLiEio6iIiIiIiIiIhLqOggIiIiIiIiIi6h\nooOIiIiIiIiIuISPuwcgIiLSGTyY8kmr+3yQOtkFIxERERFpP1R0EBERERFxk82bN5OTk2N/vG/f\nPt566y2WLVsGQP/+/Xn22WcBeOONN8jNzcVgMDBv3jxuu+02qqqqSExMpKqqCn9/f1JTU+nevbs7\nUhERaZKKDiIindiLL77Il19+yenTp3nkkUcYPHgwixYtor6+nuDgYFauXInRaCQnJ4eMjAy8vLyI\njY0lJiaGuro6kpKSOHz4MN7e3qxYsYLevXu7OyWHOHIWgohIW4iJiSEmJgaAv/3tb/z1r3/l97//\nPcnJyQwZMoTExEQ+/fRT+vbty4cffsjbb7/NiRMniI+PZ8yYMWRkZDBixAgeeughsrKyWLduHU8+\n+aSbsxIR+T/a00FEpJPatWsXBw4cICsrizfeeIPnn3+eNWvWEB8fz6ZNm7jmmmvIzs6murqatLQ0\nNmzYQGZmJhkZGVRUVLB161YCAgJ46623mDNnDqmpqe5OSUSkU0tLS+Phhx+muLiYIUOGABAeHk5h\nYSFFRUWEhYVhNBoJDAzkqquu4uDBgxQWFhIREdHguSIi7YnOdBAR6aRuvvlm+6I1ICCAmpoaioqK\n7KfphoeHs379eq677joGDx6MyWQCYNiwYVgsFgoLC4mOjgYgNDSU5ORk9yQiIuIBvvrqK6688kq8\nvb0JCAiwtwcFBWG1WunevTuBgYH29sDAQKxWK2VlZfb2oKAgSktLLxqrRw9/fHy8nZ/EeYKDTe36\n9TpSfE/O3d3xPTl3Z8ZX0UFEpJPy9vbG398fgOzsbG699VZ27NiB0WgE/m8he/6CFZpeyHp5eWEw\nGKitrbX3FxER58nOzmbKlCmN2m02W5PPb6q9uef+u/Ly6tYNzgFWa5XTXis42OTU1+tI8T05d3fH\n9+TcHY3fXJFCRQcRkU5u27ZtZGdns379em6//XZ7e2sWshdqP5+jv565u5LvTu7O3ZPje3Lu7o7v\n7tzbo6KiIpYsWYLBYKCiosLeXlJSgtlsxmw2869//avJdqvVislksreJiLQnKjqIiHRin332GWvX\nruWNN97AZDLh7+/PqVOn8PPza7BgLSsrs/cpLS1l6NCh9oXsgAEDqKurw2azXfQsB0d+PXN3Jd/d\nOtqvGJ0lvifn7u74zvz1rLMoKSmhS5cu9jm2b9++7N69m+HDh5Ofn8/MmTO59tprSU9PZ/78+ZSX\nl1NaWsqvfvUrRo8eTW5uLnPnziU/P5+wsDA3ZyMi0pDDRYd/3xH9k08+4euvv7bfomfWrFmMHTu2\n0++ILiLSXlVVVfHiiy+yYcMG+9wcGhpKXl4ekydPti9OQ0JCWLJkCZWVlXh7e2OxWEhOTubEiRPk\n5uYSFhZGQUEBI0eOdHNGIiKdk9VqbXCZW3JyMk8//TRnzpwhJCSE0NBQAGJjY5kxYwYGg4Fly5bh\n5eXFzJkzefLJJ4mPjycgIICVK1e6Kw0RkSY5VHQ4f0f08vJypkyZwi233MLjjz9OeHi4/XnndkTP\nzs7G19eXqVOnEhERQUFBAQEBAaSmprJjxw5SU1NZvXq105ISERH48MMPKS8vZ+HChfa2lJQUlixZ\nQlZWFr169SI6OhpfX18SExOZNWsWBoOBhIQETCYTUVFR7Ny5k7i4OIxGIykpKW7MRkSk8xo0aBBv\nvPGG/fGvfvUrNm3a1Oh5M2fOZObMmQ3aunTpwquvvuryMYqIOMqhokNTO6LX19c3et6ePXu0I7qI\niJtMmzaNadOmNWpPT09v1BYZGUlkZGSDtnNnoomIiIiIOMqhokNTO6J7e3uzceNG0tPTCQoKYunS\npU7dEb2jbk7myZs0eXr8prTVmNydu6fHFxERERGRsy5pI8nzd0Tft28f3bt3Z+DAgbz++uu88sor\n3HTTTQ2efyk7onfEzck62iZNiu96bTEmd+fe0eKrQCEiIiIi4jpejnY8tyP6unXrMJlMjBo1ioED\nBwIwbtw49u/f3+SO6Off2gdo8Y7oIiIiIiIiItKxOFR0OLcj+muvvWbfEX3+/PkcOnQIOHuf4X79\n+hESEsLevXuprKzk5MmTWCwWhg8fbr+1D6Ad0UVEREREREQ6KYcur2hqR/S77rqLhQsXctlll+Hv\n78+KFSvw8/PTjugiIiIiIiIiHsqhokNzO6JPmTKlUZt2RBcRERERERHxTA7v6SAiIiIiIiIiciEq\nOoiIiIiIiIiIS6joICIiIiIiIiIuoaKDiIiIiIiIiLiEig4iIiIiIiIi4hIqOoiIiIiIiIiISzh0\ny0wREREREXGOnJwc3njjDXx8fHj00Ufp378/ixYtor6+nuDgYFauXInRaCQnJ4eMjAy8vLyIjY0l\nJiaGuro6kpKSOHz4sP229L1793Z3SiIidjrTQURERETETcrLy0lLS2PTpk2sXbuWjz/+mDVr1hAf\nH8+mTZu45ppryM7Oprq6mrS0NDZs2EBmZiYZGRlUVFSwdetWAgICeOutt5gzZw6pqanuTklEpAEV\nHURERERE3KSwsJBRo0bRtWtXzGYzy5cvp6ioiPHjxwMQHh5OYWEhe/bsYfDgwZhMJvz8/Bg2bBgW\ni4XCwkIiIiIACA0NxWKxuDMdEZFGdHmFiIiIiIib/PTTT5w6dYo5c+ZQWVnJ/PnzqampwWg0AhAU\nFITVaqWsrIzAwEB7v8DAwEbtXl5eGAwGamtr7f1FRNxNRQcRERERETeqqKjglVde4fDhw9x7773Y\nbDb7sfP/fL7Wtp+vRw9/fHy8HRtsCwUHm9r163Wk+J6cu7vje3LuzoyvooOIiIiIiJsEBQVx0003\n4ePjQ58+fejSpQve3t6cOnUKPz8/SkpKMJvNmM1mysrK7P1KS0sZOnQoZrMZq9XKgAEDqKurw2az\nXfQsh/LyalenhdVa5bTXCg42OfX1OlJ8T87d3fE9OXdH4zdXpNCeDiIiIiIibjJmzBh27drFmTNn\nKC8vp7q6mtDQUPLy8gDIz88nLCyMkJAQ9u7dS2VlJSdPnsRisTB8+HBGjx5Nbm4uAAUFBYwcOdKd\n6YiINKIzHURERERE3KRnz55MnDiR2NhYAJYsWcLgwYNZvHgxWVlZ9OrVi+joaHx9fUlMTGTWrFkY\nDAYSEhIwmUxERUWxc+dO4uLiMBqNpKSkuDkjEZGGVHQQEREREXGj6dOnM3369AZt6enpjZ4XGRlJ\nZGRkgzZvb29WrFjh0vGJiFwKXV4hIiIiIiIiIi6hooOIiIiIiIiIuISKDiIiIiIiIiLiEio6iIiI\niIiIiIhLqOggIiIiIiIiIi6hooOIiIiIiIiIuITDt8x88cUX+fLLLzl9+jSPPPIIgwcPZtGiRdTX\n1xMcHMzKlSsxGo3k5OSQkZGBl5cXsbGxxMTEUFdXR1JSEocPH7bf5qd3797OzEtERERERERE3Myh\nosOuXbs4cOAAWVlZlJeXM2XKFEaNGkV8fDyTJk1i1apVZGdnEx0dTVpaGtnZ2fj6+jJ16lQiIiIo\nKCggICCA1NRUduzYQWpqKqtXr3Z2biIiIiIiIiLiRg5dXnHzzTfz8ssvAxAQEEBNTQ1FRUWMHz8e\ngPDwcAoLC9mzZw+DBw/GZDLh5+fHsGHDsFgsFBYWEhERAUBoaCgWi8VJ6YiIiIiIiIhIe+HQmQ7e\n3t74+/sDkJ2dza233sqOHTswGo0ABAUFYbVaKSsrIzAw0N4vMDCwUbuXlxcGg4Ha2lp7/6b06OGP\nj493q8caHGxqdR9ncmd8T869PcRvSluNyd25e3r89mT//v3MnTuX+++/nxkzZpCUlMTXX39N9+7d\nAZg1axZjx47VpXAiIiIi4hIO7+kAsG3bNrKzs1m/fj233367vd1mszX5/Na2n6+8vLrV4wsONmG1\nVrW6n7O4M74n594e4jenLcbk7tw7WvzOXKCorq5m+fLljBo1qkH7448/Tnh4eIPn6VI4EREREXEF\nh+9e8dlnn7F27VrWrVuHyWTC39+fU6dOAVBSUoLZbMZsNlNWVmbvU1paam+3Wq0A1NXVYbPZLniW\ng4iItJ7RaGTdunWYzeYLPk+XwomIiIiIqzh0pkNVVRUvvvgiGzZssJ+iGxoaSl5eHpMnTyY/P5+w\nsDBCQkJYsmQJlZWVeHt7Y7FYSE5O5sSJE+Tm5hIWFkZBQQEjR450alIiIgI+Pj74+DSe5jdu3Eh6\nejpBQUEsXbrUqZfCSevcmfh+q/usTxrngpGIiIiIuIZDRYcPP/yQ8vJyFi5caG9LSUlhyZIlZGVl\n0atXL6Kjo/H19SUxMZFZs2ZhMBhISEjAZDIRFRXFzp07iYuLw2g0kpKS4rSERESkeZMnT6Z79+4M\nHDiQ119/nVdeeYWbbrqpwXMu5VK4jrr/Tkfi7PfK3e+99j7yzPjuzl1ERNqOQ0WHadOmMW3atEbt\n6enpjdoiIyOJjIxs0HZuQzIREWlb5+/vMG7cOJYtW8bEiRMbXQo3dOhQ+6VwAwYMaPGlcB1x/52O\nxpnvlbvfe+195JnxHYndmYsURUVFLFiwgH79+gFwww038NBDD7Fo0SLq6+sJDg5m5cqVGI1Gbfor\nIh2Sw3s6iIhIxzN//nwOHToEnF3o9uvXj5CQEPbu3UtlZSUnT57EYrEwfPhwRo8eTW5uLoAuhRMR\ncaERI0aQmZlJZmYmS5cuZc2aNcTHx7Np0yauueYasrOz7Zv+btiwgczMTDIyMqioqGDr1q0EBATw\n1ltvMWfOHFJTU92djohIA5d09woREWm/9u3bxwsvvEBxcTE+Pj7k5eUxY8YMFi5cyGWXXYa/vz8r\nVqzAz89Pl8KJiLQjRUVFPPvsswCEh4ezfv16rrvuOvumv0CDTX+jo6OBs3usJScnu23cIiJNUdFB\nRKSTGjRoEJmZmY3aJ06c2KhNl8KJiLjPwYMHmTNnDsePH2fevHnU1NTYL2cLCgpqtLkvXNqmv47u\nv9Ma2n+mc8T29PienLsz46vo0EE8mPJJq57/QepkF41ERERERJzl2muvZd68eUyaNIlDhw5x7733\nUl9fbz/e2s19W7LpryP777SW9p/p+LE9Pb4n5+5o/OaKFNrTQURERETETXr27ElUVBQGg4E+ffpw\n+eWXc/z4cU6dOgVASUkJZrMZs9ncaNPfc+1WqxWgxZv+ioi0JRUdRERERETcJCcnhzfffBMAq9XK\n0aNHueuuu8jLywMgPz+fsLAwbforIh2WLq8QEREREXGTcePG8cQTT/Dxxx9TV1fHsmXLGDhwIIsX\nLyYrK4tevXoRHR2Nr6+vNv0VkQ5JRQcRERERETfp2rUra9eubdSenp7eqE2b/opIR6TLK0RERERE\nRETEJVR0EBERERERERGXUNFBRERERERERFxCRQcRERERERERcQkVHURERERERETEJVR0EBERERER\nERGXUNFBRERERERERFxCRQcRERERERERcQkVHURERERERETEJVR0EBERERERERGXUNFBRERERERE\nRFxCRQcRERERERERcQkVHURERERERETEJS6p6LB//34mTJjAxo0bAUhKSuLOO+9k5syZzJw5k+3b\ntwOQk5PD3XffTUxMDJs3bwagrq6OxMRE4uLimDFjBocOHbq0TEREREREOqBTp04xYcIEtmzZwpEj\nR5g5cybx8fEsWLCA2tpaQOtpEem4HC46VFdXs3z5ckaNGtWg/fHHHyczM5PMzEzGjh1LdXU1aWlp\nbNiwgczMTDIyMqioqGDr1q0EBATw1ltvMWfOHFJTUy85GRERERGRjuaPf/wj3bp1A2DNmjXEx8ez\nadMmrrnmGrKzs7WeFpEOzeGig9FoZN26dZjN5gs+b8+ePQwePBiTyYSfnx/Dhg3DYrFQWFhIREQE\nAKGhoVgsFkeHIiIiIiLSIX333XccPHiQsWPHAlBUVMT48eMBCA8Pp7CwUOtpEenQfBzu6OODj0/j\n7hs3biQ9PZ2goCCWLl1KWVkZgYGB9uOBgYFYrdYG7V5eXhgMBmprazEajU3G69HDHx8f71aPMzjY\n1Oo+zuTO+J6cOhuwQwAAIABJREFUe3uI35S2GpO7c/f0+CIiIi31wgsvsHTpUt577z0Aampq7Ovh\noKCgRutmcHw9fY6j6+rWcPZ3sbu/27Wm98z4npy7M+M7XHRoyuTJk+nevTsDBw7k9ddf55VXXuGm\nm25q8BybzdZk3+bazykvr271eIKDTVitVa3u5yzuju/Jubs7fnPaYkzuzr2jxXf3ZC4iIp7rvffe\nY+jQofTu3bvJ461dN19sPX2OI+vq1nLmWqCjrS06S2xPj+/JuTsav7l1tVPvXjFq1CgGDhwIwLhx\n49i/fz9ms5mysjL7c0pLSzGbzZjNZqxWK3B2ExybzXbRqqyIiIiISGexfft2Pv74Y2JjY9m8eTOv\nvvoq/v7+nDp1CoCSkhL7ulnraRHpqJxadJg/f75919yioiL69etHSEgIe/fupbKykpMnT2KxWBg+\nfDijR48mNzcXgIKCAkaOHOnMoYiIiIiItGurV6/mnXfe4c9//jMxMTHMnTuX0NBQ8vLyAMjPzycs\nLEzraRHp0By+vGLfvn288MILFBcX4+PjQ15eHjNmzGDhwoVcdtll+Pv7s2LFCvz8/EhMTGTWrFkY\nDAYSEhIwmUxERUWxc+dO4uLiMBqNpKSkODMvEREREZEOZ/78+SxevJisrCx69epFdHQ0vr6+Wk+L\nSIflcNFh0KBBZGZmNmqfOHFio7bIyEgiIyMbtHl7e7NixQpHw4uIiIiIdBrz58+3/zk9Pb3Rca2n\nRaSjcupGkiIiIq72YMon7h6CW7U2//VJ41w0EhEREZGLc+qeDiIiIiIiIiIi56joICIiIiIiIiIu\noaKDiEgntn//fiZMmMDGjRsBOHLkCDNnziQ+Pp4FCxZQW1sLQE5ODnfffTcxMTFs3rwZOHv7tcTE\nROLi4pgxY4b97kQiIiIiIi2looOISCdVXV3N8uXLGTVqlL1tzZo1xMfHs2nTJq655hqys7Oprq4m\nLS2NDRs2kJmZSUZGBhUVFWzdupWAgADeeust5syZQ2pqqhuzEREREZGOSEUHEZFOymg0sm7dOsxm\ns72tqKiI8ePHAxAeHk5hYSF79uxh8ODBmEwm/Pz8GDZsGBaLhcLCQiIiIgAIDQ3FYrG4JQ8RERER\n6bh09woRkU7Kx8cHH5+G03xNTQ1GoxGAoKAgrFYrZWVlBAYG2p8TGBjYqN3LywuDwUBtba29f1N6\n9PDHx8e71WMNDja1uo+0zMXeW3e/9+6M78m5uzu+u3MXEZG2o6KDiIiHstlsTmk/X3l5davHERxs\nwmqtanU/aZkLvbfufu/dGd+Tc3d3fEdiq0ghItJx6fIKEREP4u/vz6lTpwAoKSnBbDZjNpspKyuz\nP6e0tNTebrVagbObStpstgue5SAiIiIi8u90poOIiAcJDQ0lLy+PyZMnk5+fT1hYGCEhISxZsoTK\nykq8vb2xWCwkJydz4sQJcnNzCQsLo6CggJEjR7p7+CIi0kE8mPJJq/usTxrngpGIiLup6CDSzrX2\nS/uD1MkuGol0NPv27eOFF16guLgYHx8f8vLyeOmll0hKSiIrK4tevXoRHR2Nr68viYmJzJo1C4PB\nQEJCAiaTiaioKHbu3ElcXBxGo5GUlBR3pyQiIiIiHYyKDiIindSgQYPIzMxs1J6ent6oLTIyksjI\nyAZt3t7erFixwmXjExEREZHOT0UHkf+fI6cBioiIiIiISPO0kaSIiIiIiIiIuITOdBARERERcZOa\nmhqSkpI4evQov/zyC3PnzmXAgAEsWrSI+vp6goODWblyJUajkZycHDIyMvDy8iI2NpaYmBjq6upI\nSkri8OHD9svievfu7e60RETsdKaDiIiIiIibFBQUMGjQIDZu3Mjq1atJSUlhzZo1xMfHs2nTJq65\n5hqys7Oprq4mLS2NDRs2kJmZSUZGBhUVFWzdupWAgADeeust5syZQ2pqqrtTEhFpQEUHERERERE3\niYqK4uGHHwbgyJEj9OzZk6KiIsaPHw9AeHg4hYWF7Nmzh8GDB2MymfDz82PYsGFYLBYKCwuJiIgA\nzt4W2WKxuC0XEZGm6PIKERERERE3mz59Oj///DNr167lgQcewGg0AhAUFITVaqWsrIzAwED78wMD\nAxu1e3l5YTAYqK2ttfdvSo8e/vj4eLs2IQcEB5scOtYW3Bnfk3N3d3xPzt2Z8VV0EBERERFxs7ff\nfptvvvmGJ598EpvNZm8//8/na237+crLqx0bpItZrVVNtgcHm5o91hbcGd+Tc3d3fE/O3dH4zRUp\ndHmFiIiIiIib7Nu3jyNHjgAwcOBA6uvr6dKlC6dOnQKgpKQEs9mM2WymrKzM3q+0tNTebrVaAair\nq8Nms13wLAcRkbamooOIiIiIiJvs3r2b9evXA1BWVkZ1dTWhoaHk5eUBkJ+fT1hYGCEhIezdu5fK\nykpOnjyJxWJh+PDhjB49mtzcXODsppQjR450Wy4iIk25pMsr9u/fz9y5c7n//vuZMWMGR44c0e19\nRERERERaaPr06Tz11FPEx8dz6tQpnn76aQYNGsTixYvJysqiV69eREdH4+vrS2JiIrNmzcJgMJCQ\nkIDJZCIqKoqdO3cSFxeH0WgkJSXF3SmJiDTgcNGhurqa5cuXM2rUKHvbudv7TJo0iVWrVpGdnU10\ndDRpaWlkZ2fj6+vL1KlTiYiIoKCggICAAFJTU9mxYwepqamsXr3aKUmJiIiIiHQEfn5+Td7mMj09\nvVFbZGQkkZGRDdrO/XgnItJeOXx5hdFoZN26dZjNZnubbu8jIiIiIiIiIuc4fKaDj48PPj4Nu9fU\n1Ljs9j6O3tqns9xmpKPFVvymtdWY3J27p8cXEREREZGzXHbLTGff3seRW/t0xNuMOJMn5+7u+M1p\nqzF58nvf2vgqUIiIiIiIuI5T717h7++v2/uIiIiIiIiICODkooNu7yMiIiIiIiIi5zh8ecW+fft4\n4YUXKC4uxsfHh7y8PF566SWSkpJ0ex8RERERERERcbzoMGjQIDIzMxu16/Y+IiIiIiIiIgJOvrxC\nREREREREROQcFR1ERERERERExCVcdstMERERcb8HUz5pdZ/1SeNcMBIRERHxRDrTQURERERERERc\nQkUHEREREREREXEJFR1ERERERERExCW0p4NIG3Lk2moRERHp3F588UW+/PJLTp8+zSOPPMLgwYNZ\ntGgR9fX1BAcHs3LlSoxGIzk5OWRkZODl5UVsbCwxMTHU1dWRlJTE4cOH7bek7927t7tTEhGxU9HB\nDfQ/niIiIiICsGvXLg4cOEBWVhbl5eVMmTKFUaNGER8fz6RJk1i1ahXZ2dlER0eTlpZGdnY2vr6+\nTJ06lYiICAoKCggICCA1NZUdO3aQmprK6tWr3Z2WiIidig6d1J2J77e6j3YrFxEREWlbN998M0OG\nDAEgICCAmpoaioqKePbZZwEIDw9n/fr1XHfddQwePBiTyQTAsGHDsFgsFBYWEh0dDUBoaCjJycnu\nSUREpBkqOoiIiIiIuIm3tzf+/v4AZGdnc+utt7Jjxw6MRiMAQUFBWK1WysrKCAwMtPcLDAxs1O7l\n5YXBYKC2ttbevyk9evjj4+PtwqwcExxscuhYW3BnfE/O3d3xPTl3Z8ZX0UFERERExM22bdtGdnY2\n69ev5/bbb7e322y2Jp/f2vbzlZdXOzZIF7Naq5psDw42NXusLbgzvifn7u74npy7o/GbK1Lo7hUi\nIiIiIm702WefsXbtWtatW4fJZMLf359Tp04BUFJSgtlsxmw2U1ZWZu9TWlpqb7darQDU1dVhs9ku\neJaDiEhbU9FBRMTDFBUVccsttzBz5kxmzpzJ8uXLOXLkCDNnziQ+Pp4FCxZQW1sLQE5ODnfffTcx\nMTFs3rzZzSMXEel8qqqqePHFF3nttdfo3r07cHZvhry8PADy8/MJCwsjJCSEvXv3UllZycmTJ7FY\nLAwfPpzRo0eTm5sLQEFBASNHjnRbLiIiTdHlFSIiHmjEiBGsWbPG/vi3v/1ti3dKP7coFhGRS/fh\nhx9SXl7OwoUL7W0pKSksWbKErKwsevXqRXR0NL6+viQmJjJr1iwMBgMJCQmYTCaioqLYuXMncXFx\nGI1GUlJS3JiNiEhjKjqIiEirdkofN053uhERcZZp06Yxbdq0Ru3p6emN2iIjI4mMjGzQ5u3tzYoV\nK1w2PhGRS6Wig9g9mPJJq/voNpsiHdPBgweZM2cOx48fZ968edTU1LR4p/QLcXRHdHfvziwNteXn\noV3JPTO+u3MXEZG2o6KDiIiHufbaa5k3bx6TJk3i0KFD3HvvvdTX19uPt/WO6O7enVkaa6vPQ7uS\ne2Z8Z+6ILiIi7Z82khQR8TA9e/YkKioKg8FAnz59uPzyyzl+/HiLd0oXEREREWkpFR1ERDxMTk4O\nb775JgBWq5WjR49y1113tXindBERERGRltLlFSIiHmbcuHE88cQTfPzxx9TV1bFs2TIGDhzI4sWL\nW7RTuoiIiIhIS6noICLiYbp27cratWsbtbd0p3QRERERkZZyatGhqKiIBQsW0K9fPwBuuOEGHnro\nIRYtWkR9fT3BwcGsXLkSo9FITk4OGRkZeHl5ERsbS0xMjDOHIiIiIiIiIiJu5vQzHUaMGMGaNWvs\nj3/7298SHx/PpEmTWLVqFdnZ2URHR5OWlkZ2dja+vr5MnTqViIgIunfv7uzhiIiISCvpFsoiIiLi\nLC7fSLKoqIjx48cDEB4eTmFhIXv27GHw4MGYTCb8/PwYNmwYFovF1UMRERERERERkTbk9DMdDh48\nyJw5czh+/Djz5s2jpqYGo9EIQFBQEFarlbKyMgIDA+19AgMDsVqtF3zdHj388fHxbvV43H1fZ3fH\nd7UL5efu3N0d353cnbunxxcRERERkbOcWnS49tprmTdvHpMmTeLQoUPce++91NfX24/bbLYm+zXX\nfr7y8upWjyc42ITVWtXqfs7i7vhtobn83J27I6cGdyae/Pe+tfFVoBARERERcR2nXl7Rs2dPoqKi\nMBgM9OnTh8svv5zjx49z6tQpAEpKSjCbzZjNZsrKyuz9SktLMZvNzhyKiIiIiIiIiLiZU4sOOTk5\nvPnmmwBYrVaOHj3KXXfdRV5eHgD5+fmEhYUREhLC3r17qays5OTJk1gsFoYPH+7MoYiIiIiIiIiI\nmzn18opx48bxxBNP8PHHH1NXV8eyZcsYOHAgixcvJisri169ehEdHY2vry+JiYnMmjULg8FAQkIC\nJpNOcRYRERERERHpTJxadOjatStr165t1J6ent6oLTIyksjISGeGFxERERHpcPbv38/cuXO5//77\nmTFjBkeOHGHRokXU19cTHBzMypUrMRqN5OTkkJGRgZeXF7GxscTExFBXV0dSUhKHDx/G29ubFStW\n0Lt3b3enJCJi5/S7V4g4m6dvCikiIiKdV3V1NcuXL2fUqFH2tjVr1hAfH8+kSZNYtWoV2dnZREdH\nk5aWRnZ2Nr6+vkydOpWIiAgKCgoICAggNTWVHTt2kJqayurVq92YkYhIQ07d00FERERERFrOaDSy\nbt26BpuqFxUVMX78eADCw8MpLCxkz549DB48GJPJhJ+fH8OGDcNisVBYWEhERAQAoaGhWCwWt+Qh\nItIcnekgIiIiIuImPj4++Pg0XJLX1NRgNBoBCAoKwmq1UlZWRmBgoP05gYGBjdq9vLwwGAzU1tba\n+zelRw9/fHy8XZDNpbnQbazdfYtrd8b35NzdHd+Tc3dmfBUdRERERETaKZvN5pT285WXV1/SmFzF\naq1qsj042NTssbbgzvienLu743ty7o7Gb65IocsrRERERETaEX9/f06dOgVASUkJZrMZs9lMWVmZ\n/TmlpaX2dqvVCkBdXR02m+2CZzmIiLQ1FR1ERERERNqR0NBQ8vLyAMjPzycsLIyQkBD27t1LZWUl\nJ0+exGKxMHz4cEaPHk1ubi4ABQUFjBw50p1DFxFpRJdXiIiIyCVz5E5DH6ROdsFIRDqWffv28cIL\nL1BcXIyPjw95eXm89NJLJCUlkZWVRa9evYiOjsbX15fExERmzZqFwWAgISEBk8lEVFQUO3fuJC4u\nDqPRSEpKirtTEhFpQEUHuSStXWSuTxrnopGIiIiIdDyDBg0iMzOzUXt6enqjtsjISCIjIxu0eXt7\ns2LFCpeNT0TkUunyChERERERERFxCZ3pICIibnVn4vvuHoKIiIiIuIiKDiIiIuIWrS046RI9kc5N\nl+2KdE66vEJEREREREREXEJnOkibcmR3cxEREREREemYdKaDiIiIiIiIiLiEig4iIiIiIiIi4hIq\nOoiIiIiIiIiIS6joICIiIiIiIiIuoaKDiIiIiIiIiLiEig4iIiIiIiIi4hKd+paZdya+3+o+65PG\nter5ugWktDdt8fdeRMQdHPnOdWR+a20czaEiIiLN69RFBxERERER6ZzaqhApIpfGrUWH559/nj17\n9mAwGEhOTmbIkCHuHI6Ix2qLM3b0Jd9xaa6Wjqwt5jdHYnyQOtkFIxFPpXlaRNoztxUd/va3v/HD\nDz+QlZXFd999R3JyMllZWe4ajp0ulxAR+T/tda4WkfajtWsnFVycS/O0iLR3bis6FBYWMmHCBACu\nv/56jh8/zokTJ+jatau7hiQi7Yx+PXQ/zdUiruHI/jttQWeldTyap1tHZ3eKtD23FR3Kysq48cYb\n7Y8DAwOxWq3NTpDBwaZWx9D/fIh0bI7+G3ZkvpCmaa4WkYtx5N+w5mnnae08Da1//zVPu5e7/714\ncnxPzt2Z8dvNLTNtNpu7hyAiIhehuVpEpH3TPC0i7Y3big5ms5mysjL749LSUoKDg901HBERaYLm\nahGR9k3ztIi0d24rOowePZq8vDwAvv76a8xms649ExFpZzRXi4i0b5qnRaS9c9ueDsOGDePGG29k\n+vTpGAwGnnnmGXcNRUREmqG5WkSkfdM8LSLtncGmC79ERERERERExAXazUaSIiIiIiIiItK5qOgg\nIiIiIiIiIi7htj0dnOlvf/sbCxYs4Pnnnyc8PLzR8ZycHDIyMvDy8iI2NpaYmBjq6upISkri8OHD\neHt7s2LFCnr37t3q2Bd7nX379vHCCy/YHx88eJC0tDQ+//xzPvjgA3r27AnAr3/9a2JiYpwaG+DG\nG29k2LBh9scbNmzgzJkzbZI7wIcffsj69evx8vJi1KhRPPbYY2zZsoWXX36ZPn36ABAaGspvfvOb\nFsd9/vnn2bNnDwaDgeTkZIYMGWI/tnPnTlatWoW3tze33norCQkJF+3TWhd6rV27drFq1Sq8vLy4\n7rrr+P3vf88XX3zBggUL6NevHwA33HADS5cudUn8cePGccUVV+Dt7Q3ASy+9RM+ePdsk/5KSEp54\n4gn78w4dOkRiYiJ1dXWX9Hn/u/379zN37lzuv/9+ZsyY0eBYW3z+4hhPnadbEh80V3e2uVrztObp\nzsCRedtZWjJv/eEPf6CoqAibzcaECRN4+OGH2yz2P//5T5KTkwEYP368/e9xW8U/5/HHH8doNJKS\nktKm8Zv6zrhUjnxnOFNrvzO8vJz7231L5sDU1FT+8Y9/kJmZ2Waxjxw5wuOPP05dXR3/+Z//ye9+\n9zvHgtg6uB9++ME2Z84c29y5c22ffPJJo+MnT5603X777bbKykpbTU2N7Y477rCVl5fbtmzZYlu2\nbJnNZrPZPvvsM9uCBQscit+a1zl+/LjtnnvusdXX19vWrFljy8zMdChma2KPGDHiksZ8KfGrq6tt\n4eHhtqqqKtuZM2dsU6dOtR04cMD2zjvv2FJSUhyKWVRUZJs9e7bNZrPZDh48aIuNjW1wfNKkSbbD\nhw/b6uvrbXFxcbYDBw5ctI8z40dERNiOHDlis9lstvnz59u2b99u27Vrl23+/PkOx2xN/PDwcNuJ\nEyda1ceZ8c+pq6uzTZ8+3XbixIlL+rz/3cmTJ20zZsywLVmypMl/P67+/MUxnjxPtzS+5urOM1dr\nntY83Rk4Om87y8XmrW+//dY2bdo0m81ms9XX19siIyNtpaWlbRLbZrPZpk6datu3b5+tvr7e9thj\nj9mqq6udErul8W02m23Hjh22u+++27Z48WKnxW5J/Oa+My6FI98ZzuTId0ZbxrfZbLYDBw7Ypk2b\nZpsxY0abxn700Udt+fn5NpvNZlu2bJmtuLjYoTgd/vKK4OBgXnnlFUwmU5PH9+zZw+DBgzGZTPj5\n+TFs2DAsFguFhYVEREQAZyv6FovFofiteZ0333yT++67z2mVMUdzaKvcL7vsMnJycujatSsGg4Hu\n3btTUVHhUKzzY06YMAGA66+/nuPHj3PixAng7C823bp148orr8TLy4vbbruNwsLCC/ZxZnyALVu2\ncMUVVwAQGBhIeXm5w7k6Et9ZfS71td59910mTpxIly5dHIrTHKPRyLp16zCbzY2OtcXnL47x5Hm6\ntfGd0a+1r6O52rlzteZpzdOdgaPztrNcbN4ymUz88ssv1NbW8ssvv+Dl5cVll13WJrHLysqorq7m\nxhtvxMvLi1WrVjktdkviA9TW1vLHP/7xks5IcjR+e/nOcKaOsL5PSUlxyhklrYl95swZvvzyS8aN\nGwfAM888Q69evRyK0+GLDpdddpn9FMWmlJWVERgYaH8cGBiI1Wpt0O7l5YXBYKC2trbV8Vv6OqdO\nnWLHjh2MHz/e3pabm8sDDzzAI488wqFDh1wSu7a2lsTERKZPn056enqrxuyM+OfuE/3tt99SXFxM\nSEgIcPaUvVmzZnHffffxv//7v62K2aNHD/vjc58ngNVqbfazbq5Pa13stc7lW1payueff85tt90G\nnD1de86cOcTFxfH55587FLsl8eHshBAXF8dLL72EzWZr0/zP2bx5M1OnTrU/dvTz/nc+Pj74+fk1\neawtPn9xjCfP0y2Nr7m688zVmqc1T3cGjs7bznKxeevKK68kMjKS8PBwwsPDmT59uv3ftatjFxcX\n061bN5KSkpg+fTobNmxwStyWxgd47bXXiIuLc1rOrY3f3HfGpcRs7XeGMzn6ndFW8bds2cKIESO4\n6qqrnBr3YrGPHTtGly5dWLFiBXFxcaSmpjocp0Pt6bB582Y2b97coG3+/PmEhYW1+DVszdwhtLn2\ni8Xfs2dPi15n27ZtjB071v7r2W233cYtt9zCzTffzF/+8heee+45XnvtNafHXrRoEb/+9a8xGAzM\nmDGD4cOHN3qOq3P//vvveeKJJ0hNTcXX15eQkBACAwMZO3Ysf//731m8eDEffPDBRcfQlJaM3Rl9\nWvNaR48eZc6cOTzzzDP06NGDa6+9lnnz5jFp0iQOHTrEvffeS35+Pkaj0enxH330UcLCwujWrRsJ\nCQnk5eW1aMzOig/w97//nb59+9onaGd+3s7gzPylMU+epy8lvuZq5/RpzWu11Vytebr1NE+3LVfO\n247Gv9i8dejQIT766CO2bdvG6dOnmT59OlFRUQQFBbk8ts1m46effiItLQ0/Pz+mTZvG6NGj7fvB\nuDr+999/z759+5g/fz5FRUWtjnmp8c8fx/nfGc7k7jmgJd8ZbRW/oqKCLVu2kJ6eTklJiUvj/nts\nm81GSUkJ9957L1dddRWzZ89m+/btjB07ttWv26GKDjExMa3epMZsNlNWVmZ/XFpaytChQzGbzVit\nVgYMGEBdXR02m+2iC4um4iclJbXodQoKCoiLi7M//vdNpV566SWXxD4/5i233ML+/fvbNPeff/6Z\nhIQEXnzxRQYOHAicPXXn+uuvB+Cmm27i2LFj1NfXX7Cifk5Tn2dwcHCTx0pKSjCbzfj6+jbbp7Uu\nFB/gxIkTPPzwwyxcuJAxY8YA0LNnT6KiogDo06cPl19+OSUlJQ5tCHex+NHR0fY/33rrrfbPu63y\nB9i+fTujRo2yP76Uz/tSxuaKz18uzpPn6UuJr7m688zVmqdbPjbN0+2DM+dtZ8W/2Ly1d+9eQkJC\n7Jc19O/fn/379zf4e+2q2EFBQfTr18/+P57/9V//xYEDBxwqOjgSf/v27Rw+fJjY2FhOnDjBsWPH\nWLdunUMbaTrzO+NSOPKd4UyOfGe0Vfxdu3Zx7Ngx7rnnHmpra/nxxx95/vnn7RuZujJ2jx496NWr\nl32T4VGjRnHgwAGHig4d/vKKiwkJCWHv3r1UVlZy8uRJLBYLw4cPZ/To0eTm5gJnF5ojR4506PVb\n+jr79u1jwIAB9sfPPfccu3fvBs6e0ujIRHWx2P/v//0/EhMTsdlsnD59GovFQr9+/do096eeeopl\ny5Zx44032tvWrVvH1q1bgbM7XAcGBrZ4YTN69Gj7r0Jff/01ZrPZ/kvN1VdfzYkTJ/jpp584ffo0\nBQUFjB49+oJ9HMn5Qq+VkpLCfffdx6233mpvy8nJ4c033wTOniJ29OhR+274zoxfVVXFrFmz7KfA\nffHFF/bPu63yh7MLgfP/rl/K590abfH5i2t05nm6JfE1V3euuVrzdPM0T3cezc3bznKxeatPnz7s\n27ePM2fOUFdXx/79+x36MceR2L179+bkyZNUVFRw5swZvvnmG/r27euU2C2Jf//99/PBBx/w5z//\n+f9r7+7joqrz//8/h6tlVUhBxjLNdSvX1mvzIlG8QFGkLEpRIcjUdr1eazEjVlM/uUkqrmmWZl6w\nluVK5pIVWBvuZiJJ9GVt262srfAiAQURkRjh/P7w56wEKAwMA8zjfrt1uzln5pzXeXH0xfQ67/f7\naMmSJRo+fHi9PbmjJvGlqn9n1DVmbX9n1Cdbfmc0VPzg4GC98847+stf/qLnn39e3bp1q7eGw/Vi\nu7m5qWPHjvr222+t73fu3NmmOCbD0eNX6ujAgQPasmWLvvnmG/n4+MjPz09bt27VSy+9pP79+6tP\nnz5KTk7Wli1brMNW7733XpWVlWnRokX69ttvrY+auemmm2odv7rjXB1futwZunrRky+++EJLliyR\nm5ubTCZcUo7xAAAgAElEQVSTli9frk6dOtV77FWrVunw4cNycXFRYGCgZs2a1WC5t27dWqGhoRXu\nFj788MPq1q2bHn/8cesX7No+Gmv16tXKyMiQyWTSkiVL9Pnnn8vLy0tBQUE6cuSI9W7k6NGjNX36\n9Cr3ufrLVm1VF3/IkCEVrrkk3XPPPbr77ru1YMECFRYWymKxaO7cuXWaC3at/BMSErR371797Gc/\n069//WstXrxYJpOpQfK/sujQuHHjtG3bNrVt21bS5W54Xa731a482vDEiRNyc3NTu3btFBgYqA4d\nOjTY9UftOXOdrml8anXzqtXUaep0U2dr3a4vNamb69at06FDhyRd/h+zhx9+uMFiZ2Vlafny5TKZ\nTAoICNC8efPqJXZN41+Rnp6uN998s14fmWnr74yr10OyhS2/M+pTbX9nTJo0qUHiX6nbknT8+HE9\n+eST9f7IzGvF/u677xQTEyPDMNSlSxctXbrUpsW2m3zTAQAAAAAANE7NfnoFAAAAAABwDJoOAAAA\nAADALmg6AAAAAAAAu6DpAAAAAAAA7IKmAwAAAAAAsAuaDgAAAAAAwC5oOgAAAAAAALug6QAAAAAA\nAOyCpgMAAAAAALALmg4AAAAAAMAuaDoAAAAAAAC7oOkAAAAAAADsgqYDAAAAAACwC5oOAAAAAADA\nLmg6AAAAAAAAu6DpAAAAAAAA7IKmAwAAAAAAsAuaDgAAAAAAwC5oOgAAAAAAALug6QAAAAAAAOyC\npgMAAAAAALALmg4AAAAAAMAuaDoAAAAAAAC7oOkAAAAAAADsgqYDAADN0F/+8pd6+czx48f161//\n+rqfCwwMVEZGRo3OrTp/+MMftH79+jodAwCao4au6UB9oumACo4fP64hQ4bomWeeUWRkpNLT03X/\n/fcrODhYYWFhOnr0qCSpvLxcf/rTnxQcHKzg4GDFxMSouLhYkhQVFaWXXnpJkyZN0l133aVXX31V\nL7zwgoKDgxUSEqLs7GxJ0rvvvqt77rlHY8eO1bhx45Senn7Nc0tPT9e4ceMUFxenMWPGKDAwUP/v\n//0/SVJpaamWL19u3b5x40brfoGBgXr++ec1ZswYnTx50u7Hry6vkydPavr06RozZozuuece7d27\nt8LP/M9//rPGjRungIAAvfPOO7W6bgBwtbKyMq1cubLOnwEAOB41HU0dTQdUUlBQoDvuuEObNm3S\n/PnztWjRIiUnJ+uRRx7RggULVF5ernfffVf/+Mc/tGfPHr399tsqLCzU9u3brcc4cuSIXn31Va1Y\nsUKrVq3SjTfeqOTkZN1222164403JEnLli3Tpk2b9O6772rJkiX64IMPrntuX3/9tXr27KmUlBTN\nmjVLS5culSRt3rxZx44d01tvvaV9+/YpJSVFqamp1v1Onz6tlJQUtW/f3u7Hry6vxYsXa8CAAUpJ\nSdGmTZu0fPlyHT9+XJKUn58vFxcXvfXWW4qNjdXatWuv+7MAgOpMnTpV58+fV3BwsD7++OMqG55X\nfyY7O1vffPONwsPDNXbsWAUFBWnfvn21jnv48GGFhoZq2LBh+tOf/mTd/v7772vcuHEaOXKkpk2b\nprNnz0q6XPumTZumwMBA/fa3v9X58+et+/y0oVtd41b6XxM7ODhYDz30kL7//ntJ0vr167VkyRLN\nmDFDQ4YM0eOPP67U1FQ98MADGjJkiLWOf/nll5o0aZLuvvtujR49Wq+88krtf+gAYCeOqOlRUVH6\n05/+pLFjxyozM1MFBQWaP3++xowZo5CQEL300kvWz1Z3k3LPnj363e9+p+joaA0fPlxTp05VRkaG\nJk+eLH9/f+3atUvS5e/RU6ZMUUhIiEaNGlXh9weaCQO4SnZ2ttGlSxfj/PnzxqFDh4z77ruvwvv9\n+/c3vv/+e2PBggXG9u3brdvfe+89IyIiwjAMw4iMjDReffVVwzAM4/jx40aXLl2MoqIiwzAMY/36\n9caTTz5pGIZhhISEGKtXrzaOHz9eo3M7fPiwceeddxrl5eWGYRhGQUGB0aVLF6O4uNgYP368kZKS\nYv3stm3bjJiYGMMwDGPEiBHG+++/32DHryqv0tJSo2vXrkZhYaF12+zZs43du3dbf+YXLlwwDMMw\nvvvuO6Nbt241+pkAQFWys7ONO+64wzAMw5g2bZqxceNGwzAu1+Q777zTyM7OrvAZwzCMGTNmGJs2\nbTIMwzA+/vhjo2fPnkZpaWmlz1VnxIgRxsyZM41Lly4ZeXl5Rv/+/Y1///vfxvfff2/06dPH+OKL\nLwzDMIyNGzca8+bNMwzDMJ599lnj97//vfWc+/TpY6xbt856vEWLFlmPX10eJ06cMO68807j22+/\nNQzDMLZs2WJMmTLFMAzDWLdunTF06FAjLy/POHv2rNG9e3dj6dKlhmEYxo4dO4zw8HDDMAxj3rx5\nxp49ewzDMIwzZ84Ys2bNMn788cda/cwBwF4cUdMjIyONadOmGWVlZYZhGMbixYuNxYsXG4ZhGPn5\n+cbw4cONI0eOGEVFRcbAgQONjIwMwzAMIzk52Rg9erRRVlZmvPHGG0bv3r2Nb775xvjxxx+NgIAA\nY8aMGcalS5eMDz74wBg6dKhhGIYRFxdnrF+/3jAMwyguLjYee+wx4/Tp0/Xxo0MjwUgHVOLq6qpW\nrVrp7Nmz8vb2rvCel5eXzpw5o7Nnz+qGG26wbr/hhht05swZ6+uWLVtaj3X1axcXF5WXl0uSXnzx\nReXl5emBBx5QaGioPv744+uem7e3t0wmk/XPklRYWKjz589rxYoV1ukef/7zn3Xx4sUK51cT9XH8\nqvIqKCiQYRjy8vKqEOvK3T5XV1e1aNGi0s8IAOrCYrHo0KFDioiIkCTdfPPNGjhwoA4fPlzpsy+8\n8IKmT58uSbrzzjv1448/Kjc3t1bxxo0bJ1dXV/n6+qp///769NNP9Y9//EMDBgxQly5dJEmTJ0/W\nBx98oLKyMmVkZGjs2LGSpA4dOmjAgAEVjjd8+PDr5vHRRx9p4MCB6tSpkyQpLCxM6enpunTpkiSp\nT58+8vX1VZs2beTn56ehQ4dKkrp06aKcnBxJkq+vr1JSUvSvf/1Lbdq00QsvvCAPD49a5Q4A9tbQ\nNX3YsGFycbn8v4t///vfrXFbt26toKAgffTRR/rnP/+pG2+8UXfeeackacyYMcrPz9eJEyckSbfd\ndps6d+4sDw8PderUSUOGDJGrq2ulGnzw4EFlZGTIw8NDa9askdlstuEnhMbKzdEngMbL19dXBQUF\n1teGYejcuXPy9fVV27ZtK7xXUFCgtm3b1ur4t9xyi1asWKHy8nLt3btX0dHR+vDDD6+5z9Uxz507\nJ+ly4TObzZo2bZpGjBhRq3Owx/Gryis1NVUuLi46d+6ctUFRUFAgX1/fOp0vAFzL9RqeV/vwww/1\n4osvKj8/XyaTSYZh1LoB6uPjY/2zl5eXCgsLZRiGMjIyFBwcbH2vVatWKigo0Llz5yqd29WurpfX\nyuPq/by8vGQYhvLz8yX9r+ktVd/gXbBggTZt2qRHH31UP/74o2bMmKEHH3ywVrkDgL01dE2/+qba\nT29Gent7Kycn55o3KaXqa7Crq6v1fB5++GGVl5dr2bJlysnJ0YMPPqh58+ZZbwSi6WOkA6rVs2dP\n5eXl6dNPP5Ukvf3227rxxhvVoUMHDR8+XElJSbp48aIuXbqkxMREDRs2rMbHPnv2rKZOnaqioiK5\nuLioV69eNSosJSUlev/99yVJKSkp6t69u372s59p5MiR2r17t8rKymQYhl544QX94x//qHXOdT1+\ndXm5ublpyJAh1rlr33//vTIyMuTv71/rcwSAmmrTpo214XlFVQ1Pi8WiRx99VLNmzVJKSoqSkpJs\n+rJ3dZwrTVaz2Sx/f38lJydb/zt8+LB8fX3l7e1dYR2Hqr44Xy+PnzbIz507JxcXF7Vp06bG592y\nZUv9/ve/13vvvafnn39e69at03//+9/apA4AdtfQNf1q1d1wvNZNyppyc3PTb3/7W7311lt6/fXX\nlZSUpEOHDtXpfNG40HRAtVq0aKG1a9fq6aefVnBwsHbu3Kk1a9bIZDIpODhYQ4cO1QMPPKB77rlH\nN954ox566KEaH9vHx0cBAQEaP368QkJC9Pvf/15//OMfr7vfzTffrE8++URjxozRpk2btGTJEklS\nRESE2rdvr7vvvlvBwcH6+uuvrcO8aqOux79WXsuWLVN6erqCg4M1Z84cLV++XDfddFOtzxEArsfd\n3V3l5eUqKSmptuF55TNFRUW6ePGiiouL1b17d0lSQkKC3N3drU8lqqm3335b5eXlOnPmjD755BP1\n69dPQ4YMUUZGhvXJRf/85z+1fPlySVLv3r2tjd7vv/9en3zySZXHvVbjdvDgwRWO//rrr2vw4MFy\nc6v5YM6ZM2fqq6++knR52kWrVq24wwag0XBUTb/a8OHDrXHPnj2r9957T8OHD7/mTcqaeuqpp/TR\nRx9JujxiuG3bttTgZsZkGIbh6JMAaiI9PV2LFi3Se++91ySPDwANpby8XFFRUfryyy+1YcMGbdq0\nSSdOnJC7u7vmzp2rMWPGVPjMpk2b9Le//U1JSUny9fXVrFmzlJycrKysLG3atEn33XefPv/882vG\nDAwMVHh4uN59912dPXtWYWFhmjNnjiTpb3/7m5577jlZLBa1bNlSsbGx6tu3r/Ly8vTYY4/pxIkT\nuvXWW+Xj46MOHTpo3rx5CgwM1MqVK9WvXz9J0qlTp7Ro0aJKeUiXR6Y9//zzslgs6tChg55++mnd\ndNNNWr9+vX744Qdr8zcoKEjLly/XwIEDlZGRoYULF+qDDz7QwYMHtXLlSlksFknShAkTrHOhAcDR\nHFHTo6KiNGHCBN13332SLo8iW7p0qf7973/LxcVFDz74oHUa2scff6y4uDgVFxfLx8dHS5cuVZcu\nXbRnzx4lJSVZn3D38MMP695779UDDzygH374QcOGDdMXX3yhzz//XE899ZSKiopkGIYCAwO1cOFC\nGg/NCE0HNBk0HQAAAACgaWEhSTQqc+bM0ddff13le1OmTGn0xwcAAAAA/A8jHQAAwHXt3btXGzdu\nrPK9+++/XzNmzGjgMwIA2IqajoZE0wEAAAAAANgFT68AAAAAAAB20WTWdMjNPX/9D/1EmzYtlJ9v\n+6Nh6sqR8Z05d2eP78y52xLfz8/LjmfjfJparW5qf1+J3zxiO3t8W2JTq+tXbWu1M/99dfb4zpy7\no+M3xdyrq9XNeqSDm5ur08Z35tydPb4z594Y4qP2+PdCfGeL7ezxHZ07as/R14z4zvvv1ZnjN6fc\nm3XTAQAAAAAAOA5NBwAAAAAAYBc0HQAAAAAAgF3QdAAAAAAAAHZB0wEAAAAAANhFk3lkJgAAANDc\npKena/78+br99tslSV26dNEjjzyihQsXqqysTH5+flq1apU8PDyUlJSkhIQEubi4aOLEiQoLC5PF\nYlFMTIxOnjwpV1dXrVixQh07dnRwVgDwP8266TAu+q+13mdrTKAdzgQAUF+mxX1Q632o7QAaswED\nBmjdunXW108++aQiIiI0duxYrVmzRomJiQoNDdWGDRuUmJgod3d3TZgwQUFBQUpNTZW3t7fi4+N1\n8OBBxcfHa+3atQ7M5jJqNYArmF4BAAAANCLp6ekaOXKkJGnEiBFKS0tTVlaWevToIS8vL3l6eqpv\n377KzMxUWlqagoKCJEn+/v7KzMx05KkDQCXNeqQDAAAA0NgdO3ZMM2fO1Llz5zR37lxdvHhRHh4e\nkiRfX1/l5uYqLy9PPj4+1n18fHwqbXdxcZHJZFJpaal1/6q0adNCbm6utTpHPz8vGzKrnWvFaIj4\n1+LM8Z05d0fHby6503QAAAAAHOQXv/iF5s6dq7Fjxyo7O1sPPfSQysrKrO8bhlHlfrXdfrX8/OJa\nnaOfn5dyc8/Xah9bVBejoeJXx5njO3Pujo7fFHOvrklB0wEAAABwkHbt2ikkJESSdMstt6ht27Y6\nevSoSkpK5OnpqdOnT8tsNstsNisvL8+6X05Ojnr37i2z2azc3Fx17dpVFotFhmFcc5SDLWxZJw0A\nrmBNBwAAAMBBkpKStGXLFklSbm6uzpw5owceeEApKSmSpP379ysgIEC9evXS0aNHVVhYqAsXLigz\nM1P9+vXT4MGDlZycLElKTU3VwIEDHZYLAFSFkQ4AAACAgwQGBmrBggX629/+JovFoqVLl+qOO+7Q\nE088oV27dql9+/YKDQ2Vu7u7oqOjNX36dJlMJs2ZM0deXl4KCQnRoUOHFB4eLg8PD8XFxTk6JQCo\ngKYDAAAA4CCtWrXSxo0bK23ftm1bpW3BwcEKDg6usM3V1VUrVqyw2/kBQF3RdACAZio9PV3z58/X\n7bffLknq0qWLHnnkES1cuFBlZWXy8/PTqlWr5OHhoaSkJCUkJMjFxUUTJ05UWFiYLBaLYmJidPLk\nSeuX2o4dOzo4KwAAADQlNB0AoBkbMGCA1q1bZ3395JNPKiIiQmPHjtWaNWuUmJio0NBQbdiwQYmJ\niXJ3d9eECRMUFBSk1NRUeXt7Kz4+XgcPHlR8fLzWrl3rwGwAAADQ1LCQJAA4kfT0dI0cOVKSNGLE\nCKWlpSkrK0s9evSQl5eXPD091bdvX2VmZiotLU1BQUGSJH9/f2VmZjry1AEAANAEMdIBAJqxY8eO\naebMmTp37pzmzp2rixcvWh+l5uvrq9zcXOXl5cnHx8e6j4+PT6XtLi4uMplMKi0trfdHsQEAAKD5\noukAAM3UL37xC82dO1djx45Vdna2HnroIZWVlVnfNwyjyv1qu/1qbdq0kJuba63P1c/Pq9b71Nfx\n7R37eojvuPjOnLuj4zs6dwBAw6HpAADNVLt27RQSEiJJuuWWW9S2bVsdPXpUJSUl8vT01OnTp2U2\nm2U2m5WXl2fdLycnR71795bZbFZubq66du0qi8UiwzCuO8ohP7+41ufp5+el3Nzztd6vNqo7fkPE\nvhbiOy6+M+fu6Pi2xKZJAQBNV43WdPjyyy81atQovfLKK5KkU6dOKSoqShEREZo/f75KS0slSUlJ\nSRo/frzCwsK0e/duSZLFYlF0dLTCw8MVGRmp7OxsSdJ//vMfTZ48WZMnT9aSJUvskRsAOLWkpCRt\n2bJFkpSbm6szZ87ogQceUEpKiiRp//79CggIUK9evXT06FEVFhbqwoULyszMVL9+/TR48GAlJydL\nklJTUzVw4ECH5QIAAICm6bpNh+LiYj399NMaNGiQddu6desUERGhnTt3qlOnTkpMTFRxcbE2bNig\n7du3a8eOHUpISFBBQYH27dsnb29vvfbaa5o5c6bi4+MlSX/84x8VGxur119/XUVFRfr73/9uvywB\nwAkFBgbqyJEjioiI0OzZs7V06VI99thj2rt3ryIiIlRQUKDQ0FB5enoqOjpa06dP19SpUzVnzhx5\neXkpJCRE5eXlCg8P16uvvqro6GhHpwQAAIAm5rrTKzw8PLR582Zt3rzZui09PV3Lli2TdHn1861b\nt6pz587W1c8lVVj9PDQ0VNLl1c9jY2NVWlqqEydOqGfPntZjpKWladiwYfWeIAA4q1atWmnjxo2V\ntm/btq3StuDgYAUHB1fY5urqqhUrVtjt/AAAAND8Xbfp4ObmJje3ih+r6+rneXl58vb2tn72yjEA\nAAAAAEDzUeeFJOtj9XN7roheW/W9UJEzrwxNfK69s8YHAAAAcJlNTYcWLVrUafVzPz8/FRQUWD97\n5RjXYsuK6Laoz5Wcm9rK0MRvHvGdOXdb4tOgAAAAAOynRk+v+Cl/f/86rX7u7u6uX/7yl8rIyKhw\nDAAAAAAA0Hxcd6TDZ599pmeffVYnTpyQm5ubUlJStHr1asXExGjXrl1q3769QkND5e7ubl393GQy\nVVj9/NChQwoPD5eHh4fi4uIkSbGxsXrqqadUXl6uXr16yd/f3+7JAgAAAACAhnPdpkP37t21Y8eO\nStvruvr5bbfdpp07d9bmXAEAAAAAQBNi0/QKAAAAAACA66HpAAAAAAAA7IKmAwAAAAAAsAuaDgAA\nAAAAwC5oOgAAAAAAALug6QAAAAAAAOyCpgMAAAAAALALmg4AAAAAAMAuaDoAAAAAAAC7oOkAAAAA\nAADsgqYDAAAA4EAlJSUaNWqU9uzZo1OnTikqKkoRERGaP3++SktLJUlJSUkaP368wsLCtHv3bkmS\nxWJRdHS0wsPDFRkZqezsbEemAQBVoukAAAAAONCLL76oG264QZK0bt06RUREaOfOnerUqZMSExNV\nXFysDRs2aPv27dqxY4cSEhJUUFCgffv2ydvbW6+99ppmzpyp+Ph4B2cCAJXRdAAAAAAc5Ouvv9ax\nY8c0fPhwSVJ6erpGjhwpSRoxYoTS0tKUlZWlHj16yMvLS56enurbt68yMzOVlpamoKAgSZK/v78y\nMzMdlQYAVMvN0ScAAAAAOKtnn31Wixcv1t69eyVJFy9elIeHhyTJ19dXubm5ysvLk4+Pj3UfHx+f\nSttdXFxkMplUWlpq3b86bdq0kJubq50ysp2fn5dN7zUEZ47vzLk7On5zyZ2mAwAAAOAAe/fuVe/e\nvdWxY8cq3zcMo162/1R+fnHNTrCB5eaer3K7n59Xte81BGeO78y5Ozp+U8y9uiYFTQcAAADAAQ4c\nOKDs7GwdOHBAP/zwgzw8PNSiRQuVlJTI09NTp0+fltlsltlsVl5ennW/nJwc9e7dW2azWbm5uera\ntassFosMw7juKAcAaGis6QAAAAA4wNq1a/XGG2/oL3/5i8LCwjR79mz5+/srJSVFkrR//34FBASo\nV69eOnr0qAoLC3XhwgVlZmaqX79+Gjx4sJKTkyVJqampGjhwoCPTAYAqMdIBAAAAaCTmzZunJ554\nQrt27VL79u0VGhoqd3d3RUdHa/r06TKZTJozZ468vLwUEhKiQ4cOKTw8XB4eHoqLi3P06QNAJTQd\nAKCZKykp0T333KPZs2dr0KBBWrhwocrKyuTn56dVq1bJw8NDSUlJSkhIkIuLiyZOnKiwsDBZLBbF\nxMTo5MmTcnV11YoVK6qddwwAqJt58+ZZ/7xt27ZK7wcHBys4OLjCtiu1GQAaM6ZXAEAzx/PfAQAA\n4Cg0HQCgGeP57wAAAHAkplcAQDPW0M9/t/XZ7/Z+DjXPfid+Y4vt7PEdnTsAoOHY1HS4cOGCnnji\nCZ07d04Wi0Vz5syRn5+fli5dKkn61a9+pWXLlkmSXn75ZSUnJ8tkMmnu3LkaNmyYzp8/r+joaJ0/\nf14tWrRQfHy8WrduXW9JAQAc8/x3W5793hDPoebZ78RvTLGdPX59PvsdAND42dR0ePPNN9W5c2dF\nR0fr9OnTmjJlivz8/BQbG6uePXsqOjpaf//73/XLX/5S77zzjl5//XUVFRUpIiJCQ4YMUUJCggYM\nGKBHHnlEu3bt0ubNm/X444/Xd24A4NR4/jsAAAAczaY1Hdq0aaOCggJJUmFhoVq3bq0TJ06oZ8+e\nkv43Tzg9PV0BAQHy8PCQj4+Pbr75Zh07dqzCPOErnwUA1C+e/w4AAABHs2mkw9133609e/YoKChI\nhYWFevHFF/V///d/1vevzBNu3br1decJ+/r6Kicnp45pAABqgue/AwAAoCHZ1HT461//qvbt22vL\nli36z3/+Y/2CekVt5gPXZI6wZPviZLVV33MGnXmRJuJz7Z01fmPE898BAADgCDY1HTIzMzVkyBBJ\nUteuXfXjjz/q0qVL1vevnif83//+t8rtubm58vLysm67HlsWJ7NFfS6q1NQWaSJ+84jvzLnbEp8G\nBQAAAGA/Nq3p0KlTJ2VlZUmSTpw4oZYtW+rWW29VRkaGpP/NE77rrrt04MABlZaW6vTp08rJydFt\nt91WYZ7wlc8CAAAAAIDmxaaRDpMmTVJsbKwiIyN16dIlLV26VH5+fnrqqadUXl6uXr16yd/fX5I0\nceJERUZGymQyaenSpXJxcVFUVJQef/xxRUREyNvbW6tWrarXpAAAAAAAgOPZ1HRo2bKlnnvuuUrb\nd+7cWWlbVFSUoqKiKu3/wgsv2BIaAAAAAAA0ETZNrwAAAAAAALgemg4AAAAAAMAuaDoAAAAAAAC7\nsGlNBwAA6su46L86+hQAAABgJ4x0AAAAAAAAdkHTAQAAAAAA2AVNBwAAAAAAYBc0HQAAAAAAgF3Q\ndAAAAAAAAHZB0wEAAAAAANgFTQcAAAAAAGAXNB0AAAAAAIBd0HQAAAAAAAB24eboEwAAAACc1cWL\nFxUTE6MzZ87oxx9/1OzZs9W1a1ctXLhQZWVl8vPz06pVq+Th4aGkpCQlJCTIxcVFEydOVFhYmCwW\ni2JiYnTy5Em5urpqxYoV6tixo6PTAgArRjoAAAAADpKamqru3bvrlVde0dq1axUXF6d169YpIiJC\nO3fuVKdOnZSYmKji4mJt2LBB27dv144dO5SQkKCCggLt27dP3t7eeu211zRz5kzFx8c7OiUAqICm\nAwAAAOAgISEh+s1vfiNJOnXqlNq1a6f09HSNHDlSkjRixAilpaUpKytLPXr0kJeXlzw9PdW3b19l\nZmYqLS1NQUFBkiR/f39lZmY6LBcAqArTKwAAAAAHmzx5sn744Qdt3LhRU6dOlYeHhyTJ19dXubm5\nysvLk4+Pj/XzPj4+lba7uLjIZDKptLTUuj8AOBpNBwAAAMDBXn/9df373//W448/LsMwrNuv/vPV\narv9am3atJCbm6ttJ2pHfn5eNr3XEJw5vjPn7uj4zSV3mg4AAACAg3z22Wfy9fXVTTfdpDvuuENl\nZWVq2bKlSkpK5OnpqdOnT8tsNstsNisvL8+6X05Ojnr37i2z2azc3Fx17dpVFotFhmFcd5RDfn6x\nvdOySW7u+Sq3+/l5VfteQ3Dm+M6cu6PjN8Xcq2tSsKYDADRTFy9e1Pz58xUZGamwsDClpqbq1KlT\nip2K+2YAABnUSURBVIqKUkREhObPn6/S0lJJUlJSksaPH6+wsDDt3r1bkmSxWBQdHa3w8HBFRkYq\nOzvbkekAQLOUkZGhrVu3SpLy8vJUXFwsf39/paSkSJL279+vgIAA9erVS0ePHlVhYaEuXLigzMxM\n9evXT4MHD1ZycrKky4tSDhw40GG5AEBVGOkAAM3UlRXRf/Ob3+jEiROaNm2a+vbtq4iICI0dO1Zr\n1qxRYmKiQkNDtWHDBiUmJsrd3V0TJkxQUFCQUlNT5e3trfj4eB08eFDx8fFau3ato9MCgGZl8uTJ\n+sMf/qCIiAiVlJToqaeeUvfu3fXEE09o165dat++vUJDQ+Xu7q7o6GhNnz5dJpNJc+bMkZeXl0JC\nQnTo0CGFh4fLw8NDcXFxjk4JACqg6QAAzVRISIj1z1eviL5s2TJJl1dE37p1qzp37mxdEV1ShRXR\nQ0NDJV1eET02NrbhkwCAZs7T07PKx1xu27at0rbg4GAFBwdX2Obq6qoVK1bY7fwAoK5sbjokJSXp\n5Zdflpubm373u9/pV7/6lRYuXKiysjL5+flp1apV8vDwUFJSkhISEuTi4qKJEycqLCxMFotFMTEx\nOnnypLVQduzYsT7zAgD8/xpyRXQWJ6s94rNAlzPGd3TuAICGY1PTIT8/Xxs2bNAbb7yh4uJirV+/\nXikpKQzZBYBGqCFXRGdxstohPgt0OWP8+lycDADQ+Nm0kGRaWpoGDRqkVq1ayWw26+mnn1Z6erpG\njhwp6fKQ3bS0NGVlZVmH7Hp6elYYshsUFCTp8pDdzMzM+ssIACDp8orop06dkqRKK6JLuuaK6Fe2\n5+bmSlKNV0QHAAAArmZT0+H48eMqKSnRzJkzFRERobS0NF28eLHOQ3YBAPWHFdEBAADgaDav6VBQ\nUKDnn39eJ0+e1EMPPWT3IbsNNU+4vofvOfN8SeJz7Z01fmPBiugAAABwNJuaDr6+vurTp4/c3Nx0\nyy23qGXLlnJ1dVVJSYk8PT2vOWS3d+/e1iG7Xbt2rfGQ3YaaJ1yf8xub2nxJ4jeP+M6cuy3xm3OD\nghXRAQAA4Gg2Ta8YMmSIDh8+rPLycuXn5zNkFwAAAAAAVGLTSId27dppzJgxmjhxoiRp0aJF6tGj\nB0N2AQAAAACAlc1rOkyePFmTJ0+usI0huwAAAAAA4AqbplcAAAAAAABcD00HAAAAAABgFzQdAAAA\nAACAXdB0AAAAAAAAdkHTAQAAAAAA2AVNBwAAAAAAYBc0HQAAAAAAgF3QdAAAAAAAAHZB0wEAAAAA\nANgFTQcAAAAAAGAXNB0AAAAAAIBd0HQAAAAAAAB2QdMBAAAAAADYBU0HAAAAAABgFzQdAAAAAACA\nXdB0AAAAAAAAduHm6BMAAAAAnNnKlSv1ySef6NKlS5oxY4Z69OihhQsXqqysTH5+flq1apU8PDyU\nlJSkhIQEubi4aOLEiQoLC5PFYlFMTIxOnjwpV1dXrVixQh07dnR0SgBgRdMBAAAAcJDDhw/rq6++\n0q5du5Sfn6/7779fgwYNUkREhMaOHas1a9YoMTFRoaGh2rBhgxITE+Xu7q4JEyYoKChIqamp8vb2\nVnx8vA4ePKj4+HitXbvW0WkBgBXTKwAAAAAH6d+/v5577jlJkre3ty5evKj09HSNHDlSkjRixAil\npaUpKytLPXr0kJeXlzw9PdW3b19lZmYqLS1NQUFBkiR/f39lZmY6LBcAqApNBwAAAMBBXF1d1aJF\nC0lSYmKihg4dqosXL8rDw0OS5Ovrq9zcXOXl5cnHx8e6n4+PT6XtLi4uMplMKi0tbfhEAKAaTK8A\ngGaMecIA0DS8//77SkxM1NatWzV69GjrdsMwqvx8bbdfrU2bFnJzc7XtRO3Iz8/LpvcagjPHd+bc\nHR2/ueRO0wEAminmCQNA0/Dhhx9q48aNevnll+Xl5aUWLVqopKREnp6eOn36tMxms8xms/Ly8qz7\n5OTkqHfv3jKbzcrNzVXXrl1lsVhkGIZ1lER18vOL7Z2STXJzz1e53c/Pq9r3GoIzx3fm3B0dvynm\nXl2Tok7TK0pKSjRq1Cjt2bNHp06dUlRUlCIiIjR//nzrsK6kpCSNHz9eYWFh2r17tyTJYrEoOjpa\n4eHhioyMVHZ2dl1OAwBQBeYJA0Djd/78ea1cuVKbNm1S69atJV2uuSkpKZKk/fv3KyAgQL169dLR\no0dVWFioCxcuKDMzU/369dPgwYOVnJwsSUpNTdXAgQMdlgsAVKVOIx1efPFF3XDDDZKkdevWcfcM\nABqRquYJHzx4sM7zhK91B40hu7VHfIatOmN8R+femLzzzjvKz8/Xo48+at0WFxenRYsWadeuXWrf\nvr1CQ0Pl7u6u6OhoTZ8+XSaTSXPmzJGXl5dCQkJ06NAhhYeHy8PDQ3FxcQ7MBgAqs7np8PXXX+vY\nsWMaPny4JCk9PV3Lli2TdPnu2datW9W5c2fr3TNJFe6ehYaGSrrcyY2Nja1jGgCA6jTkPGGG7NYO\n8Rm26ozx63PIbnMwadIkTZo0qdL2bdu2VdoWHBys4ODgCtuurLkDAI2VzdMrnn32WcXExFhfs8ou\nADQ+V+YJb968ucI8YUnXnCd8ZXtubq4k1XieMAAAAHA1m0Y67N27V7179652FfOmvMpufXfSnXno\nIvG59s4av7G4Mk94+/btleYJ33fffRXmCS9atEiFhYVydXVVZmamYmNjVVRUpOTkZAUEBDBPGAAA\nADaxqelw4MABZWdn68CBA/rhhx/k4eHRbFbZrc+hhk1t6CLxm0d8Z87dlvjNuUHBPGEAAAA4mk1N\nh6sXfVy/fr1uvvlmffrpp9w9A4BGhHnCAAAAcLQ6Pb3iavPmzdMTTzzB3TMAQKMzLe6DWu+zNSbQ\nDmcCAADgXOrcdJg3b571z9w9AwAAAAAAV9j89AoAAAAAAIBroekAAAAAAADsgqYDAAAAAACwC5oO\nAAAAAADALmg6AAAAAAAAu6DpAAAAAAAA7IKmAwAAAAAAsAuaDgAAAAAAwC5oOgAAAAAAALug6QAA\nAAAAAOyCpgMAAAAAALALmg4AAAAAAMAuaDoAAAAAAAC7oOkAAAAAAADsgqYDAAAAAACwC5oOAAAA\nAADALtwcfQIAAAAAMC3ug1p9fmtMoJ3OBEB9YqQDAAAAAACwC5oOAAAAAADALmg6AAAAAA705Zdf\natSoUXrllVckSadOnVJUVJQiIiI0f/58lZaWSpKSkpI0fvx4hYWFaffu3ZIki8Wi6OhohYeHKzIy\nUtnZ2Q7LAwCqQtMBAAAAcJDi4mI9/fTTGjRokHXbunXrFBERoZ07d6pTp05KTExUcXGxNmzYoO3b\nt2vHjh1KSEhQQUGB9u3bJ29vb7322muaOXOm4uPjHZgNAFRG0wEAmjHungFA4+bh4aHNmzfLbDZb\nt6Wnp2vkyJGSpBEjRigtLU1ZWVnq0aOHvLy85Onpqb59+yozM1NpaWkKCgqSJPn7+yszM9MheQBA\ndWxuOqxcuVKTJk3S+PHjtX//fr7IAkAjw90zAGj83Nzc5OnpWWHbxYsX5eHhIUny9fVVbm6u8vLy\n5OPjY/2Mj49Ppe0uLi4ymUzW7+EA0BjY9MjMw4cP66uvvtKuXbuUn5+v+++/X4MGDVJERITGjh2r\nNWvWKDExUaGhodqwYYMSExPl7u6uCRMmKCgoSKmpqfL29lZ8fLwOHjyo+Ph4rV27tr5zAwCnduXu\n2ebNm63b0tPTtWzZMkmX755t3bpVnTt3tt49k1Th7lloaKiky3fPYmNjGz4JAHByhmHUy/artWnT\nQm5urnU6r8bAz8+rWcZqbPGdOXdHx28uudvUdOjfv7969uwpSfL29tbFixf5IgsAjYybm5vc3CqW\n+fq4e3ZlfwCAfbRo0UIlJSXy9PTU6dOnZTabZTablZeXZ/1MTk6OevfuLbPZrNzcXHXt2lUWi0WG\nYVy3TufnF9s7hQaRm3u+QeL4+Xk1WKzGFt+Zc3d0/KaYe3VNCpuaDq6urmrRooUkKTExUUOHDtXB\ngwf5IgsATQh3z66toe4uNJe7GE0xvjPn7uj4js69sfP391dKSoruu+8+7d+/XwEBAerVq5cWLVqk\nwsJCubq6KjMzU7GxsSoqKlJycrICAgKUmpqqgQMHOvr0AaACm5oOV7z//vtKTEzU1q1bNXr0aOv2\npvxFtr5/CTrzL3Tic+2dNX5jxt2zmmuIuwtN8S5Gc4nvzLk7On593j1rDj777DM9++yzOnHihNzc\n3JSSkqLVq1crJiZGu3btUvv27RUaGip3d3dFR0dr+vTpMplMmjNnjry8vBQSEqJDhw4pPDxcHh4e\niouLc3RKAFCBzU2HDz/8UBs3btTLL78sLy+vZvNFtj5/ATe1X+jEbx7xnTl3W+I35y+yVeHuGQA0\nLt27d9eOHTsqbd+2bVulbcHBwQoODq6wzdXVVStWrLDb+QFAXdn09Irz589r5cqV2rRpk1q3bi3p\nf19kJVX4Inv06FEVFhbqwoULyszMVL9+/TR48GAlJydLEl9kAcBOPvvsM0VFRenNN9/Un//8Z0VF\nRWnu3Lnau3evIiIiVFBQoNDQUHl6elrvnk2dOrXC3bPy8nKFh4fr1VdfVXR0tKNTAgAAQBNj00iH\nd955R/n5+Xr00Uet2+Li4rRo0SKGgQFAI8HdMwAAADiaTU2HSZMmadKkSZW280UWAAAAAABcYdP0\nCgAAAAAAgOuh6QAAAAAAAOyCpgMAAAAAALALmg4AAAAAAMAuaDoAAAAAAAC7oOkAAAAAAADswqZH\nZgIA0NxNi/ugVp/fGhNopzMBAABouhjpAAAAAAAA7IKmAwAAAAAAsAuaDgAAAAAAwC5oOgAAAAAA\nALug6QAAAAAAAOyCpgMAAAAAALALHpn5EzwiDQAAAGj8avu9XeK7O+AIjHQAAAAAAAB2QdMBAAAA\nAADYBU0HAAAAAABgF6zpAABAPbBlbvFb8ffZ4UwAAAAaD0Y6AAAAAAAAu6DpAAAAAAAA7ILpFQAA\nAACcAlPhgIZH0wEAAAcZF/3XWu/DM+YBAEBTwvQKAAAAAABgFw4d6fDMM88oKytLJpNJsbGx6tmz\npyNPBwBQBWo1ADRu1Gn7qu2oNEakARU5rOnw8ccf67vvvtOuXbv09ddfKzY2Vrt27XLU6QAAqkCt\nbnxsmY9cW3xhBpoO6nTjY0udpu6iOXNY0yEtLU2jRo2SJN166606d+6cioqK1KpVK0edkk0oKgCa\ns+ZSq1E7DdHYsAW/P4HKqNPNQ2OtuyyiifrgsKZDXl6eunXrZn3t4+Oj3Nzcagukn59XrWM4+z8S\nW35mxG8e8Z0598YQvzmhVqM5c3StcOb4js69OaltnZZq//OnTjs3R/97deb4zSX3RrOQpGEYjj4F\nAMB1UKsBoHGjTgNobBzWdDCbzcrLy7O+zsnJkZ+fn6NOBwBQBWo1ADRu1GkAjZ3Dmg6DBw9WSkqK\nJOlf//qXzGYzc88AoJGhVgNA40adBtDYOWxNh759+6pbt26aPHmyTCaTlixZ4qhTAQBUg1oNAI0b\ndRpAY2cymPgFAAAAAADsoNEsJAkAAAAAAJoXmg4AAAAAAMAuHLamQ336+OOPNX/+fD3zzDMaMWJE\npfeTkpKUkJAgFxcXTZw4UWFhYbJYLIqJidHJkyfl6uqqFStWqGPHjrWOfb3jfPbZZ3r22Wetr48d\nO6YNGzboo48+0ltvvaV27dpJku69916FhYXVa2xJ6tatm/r27Wt9vX37dpWXlzdI7pL0zjvvaOvW\nrXJxcdGgQYP02GOPac+ePXruued0yy23SJL8/f01a9asGsd95plnlJWVJZPJpNjYWPXs2dP63qFD\nh7RmzRq5urpq6NChmjNnznX3qa1rHevw4cNas2aNXFxc1LlzZ/3xj3/UkSNHNH/+fN1+++2SpC5d\numjx4sV2iR8YGKgbb7xRrq6ukqTVq1erXbt2DZL/6dOntWDBAuvnsrOzFR0dLYvFUqfr/VNffvml\nZs+erYcffliRkZEV3muI6w/bOGudrkl8iVrd3Go1dZo63VRRqx1Tqx1VpyXH1mq+UztRrTaauO++\n+86YOXOmMXv2bOODDz6o9P6FCxeM0aNHG4WFhcbFixeNu+++28jPzzf27NljLF261DAMw/jwww+N\n+fPn2xS/Nsc5d+6c8eCDDxplZWXGunXrjB07dtgUszaxBwwYUKdzrkv84uJiY8SIEcb58+eN8vJy\nY8KECcZXX31lvPHGG0ZcXJxNMdPT043f/va3hmEYxrFjx4yJEydWeH/s2LHGyZMnjbKyMiM8PNz4\n6quvrrtPfcYPCgoyTp06ZRiGYcybN884cOCAcfjwYWPevHk2x6xN/BEjRhhFRUW12qc+419hsViM\nyZMnG0VFRXW63j914cIFIzIy0li0aFGV/37sff1hG2eu0zWNT61uPrWaOk2dbqqo1Y6r1Y6o04bh\n2FrNd2rnqtVNfnqFn5+fnn/+eXl5eVX5flZWlnr06CEvLy95enqqb9++yszMVFpamoKCgiRd7hJl\nZmbaFL82x9myZYumTJkiF5f6+bHbmkND5f7zn/9cSUlJatWqlUwmk1q3bq2CggKbYl0dc9SoUZKk\nW2+9VefOnVNRUZGky13AG264QTfddJNcXFw0bNgwpaWlXXOf+owvSXv27NGNN94oSfLx8VF+fr7N\nudoSv772qeux3nzzTY0ZM0YtW7a0KU51PDw8tHnzZpnN5krvNcT1h22cuU7XNn597Ffb41Cr67dW\nU6ep000VtdpxtdoRdfpKXEfVar5TO1etbvJNh5///OfWYS9VycvLk4+Pj/W1j4+PcnNzK2x3cXGR\nyWRSaWlprePX9DglJSU6ePCgRo4cad2WnJysqVOnasaMGcrOzrZL7NLSUkVHR2vy5Mnatm1brc65\nPuJfeU70F198oRMnTqhXr16SLg/fmz59uqZMmaLPP/+8VjHbtGljfX3lekpSbm5utde6un1q63rH\nupJvTk6OPvroIw0bNkzS5SGAM2fOVHh4uD766CObYtckviQtWbJE4eHhWr16tQzDaND8r9i9e7cm\nTJhgfW3r9f4pNzc3eXp6VvleQ1x/2MaZ63RN41Orm0+tpk5Tp5sqarXjarUj6vSVuI6q1Xyndq5a\n3aTWdNi9e7d2795dYdu8efMUEBBQ42MY1TwhtLrt14uflZVVo+O8//77Gj58uLUjO2zYMN11113q\n37+/3n77bS1fvlybNm2q99gLFy7UvffeK5PJpMjISPXr16/SZ+yd+7fffqsFCxYoPj5e7u7u6tWr\nl3x8fDR8+HB9+umneuKJJ/TWW29d9xyqUpNzr499anOsM2fOaObMmVqyZInatGmjX/ziF5o7d67G\njh2r7OxsPfTQQ9q/f788PDzqPf7vfvc7BQQE6IYbbtCcOXOUkpJSo3Our/iS9Omnn+qXv/yl9ZdF\nfV7v+lCf+aMyZ67TdYlPra6ffWpzrIaq1dTp2qNO2x+12nG1urHW6Zqce33tU9Pj8J26+dTqJtV0\nCAsLq/XCMGazWXl5edbXOTk56t27t8xms3Jzc9W1a1dZLBYZhnHdv7BVxY+JianRcVJTUxUeHm59\n/dOFSlavXm2X2FfHvOuuu/Tll182aO4//PCD5syZo5UrV+qOO+6QdHk4zq233ipJ6tOnj86ePauy\nsrJrdtevqOp6+vn5Vfne6dOnZTab5e7uXu0+tXWt+JJUVFSk3/zmN3r00Uc1ZMgQSVK7du0UEhIi\nSbrlllvUtm1bnT592qZFlq4XPzQ01PrnoUOHWq93Q+UvSQcOHNCgQYOsr+tyvetybva4/rg+Z67T\ndYlPrW4+tZo6XfNzo047DrXacbW6sdRpybG1mu/UzlWrm/z0iuvp1auXjh49qsLCQl24cEGZmZnq\n16+fBg8erOTkZEmXi9fAgQNtOn5Nj/PZZ5+pa9eu1tfLly9XRkaGpMvDZK6swlqfsb/55htFR0fL\nMAxdunRJmZmZuv322xs09z/84Q9aunSpunXrZt22efNm7du3T9LlVVN9fHxq/I9l8ODB1k7jv/71\nL5nNZmv3r0OHDioqKtLx48d16dIlpaamavDgwdfcx5acr3WsuLg4TZkyRUOHDrVuS0pK0pYtWyRd\nHq505swZ6wrL9Rn//Pnzmj59unU43pEjR6zXu6Hyl6SjR49W+Ltel+tdGw1x/WEfzblO1yQ+tbp5\n1WrqdPWo000btdp+tdoRdfpKXEfVar5TO1etNhlNfAzbgQMHtGXLFn3zzTfy8fGRn5+ftm7dqpde\nekn9+/dXnz59lJycrC1btliHQt17770qKyvTokWL9O2338rDw0NxcXG66aabah2/uuNcHV+SBg0a\npLS0NOt+X3zxhZYsWSI3NzeZTCYtX75cnTp1qvfYq1at0uHDh+Xi4qLAwEDNmjWrwXJv3bq1QkND\nK3SgH374YXXr1k2PP/64tWjX9nEzq1evVkZGhkwmk5YsWaLPP/9cXl5eCgoK0pEjR6wd7tGjR2v6\n9OlV7nP1P+Daqi7+kCFDKlxzSbrnnnt09913a8GCBSosLJTFYtHcuXOt89LqM35QUJASEhK0d+9e\n/exnP9Ovf/1rLV68WCaTqUHyv7IA0rhx47Rt2za1bdtW0uXOfF2u99WuPC7rxIkTcnNzU7t27RQY\nGKgOHTo02PVH7Tlzna5pfGp186rV1GnqdFNErXZcrXZUnZYcW6v5Tu08tbrJNx0AAAAAAEDj1Oyn\nVwAAAAAAAMeg6QAAAAAAAOyCpgMAAAAAALALmg4AAAAAAMAuaDoAAAAAAAC7oOkAAAAAAADsgqYD\nAAAAAACwi/8PuRsGK34/Y2cAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1296x864 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Xx9jgEMHKxlJ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We might be able to do better by choosing additional ways to transform these features.\n",
+        "\n",
+        "For example, a log scaling might help some features.  Or clipping extreme values may make the remainder of the scale more informative."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "baKZa6MEKxlK",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def log_normalize(series):\n",
+        "  return series.apply(lambda x:math.log(x+1.0))\n",
+        "\n",
+        "def clip(series, clip_to_min, clip_to_max):\n",
+        "  return series.apply(lambda x:(\n",
+        "    min(max(x, clip_to_min), clip_to_max)))\n",
+        "\n",
+        "def z_score_normalize(series):\n",
+        "  mean = series.mean()\n",
+        "  std_dv = series.std()\n",
+        "  return series.apply(lambda x:(x - mean) / std_dv)\n",
+        "\n",
+        "def binary_threshold(series, threshold):\n",
+        "  return series.apply(lambda x:(1 if x > threshold else 0))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-wCCq_ClKxlO"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n",
+        "\n",
+        "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8ToG-mLfMO9P",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "5534b7df-9cde-4df3-98b4-7274bb9ace3f"
+      },
+      "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=0.001),\n",
+        "    steps=5000,\n",
+        "    batch_size=175,\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": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 192.24\n",
+            "  period 01 : 113.76\n",
+            "  period 02 : 110.26\n",
+            "  period 03 : 104.78\n",
+            "  period 04 : 96.39\n",
+            "  period 05 : 87.06\n",
+            "  period 06 : 80.75\n",
+            "  period 07 : 78.11\n",
+            "  period 08 : 76.76\n",
+            "  period 09 : 75.70\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   75.70\n",
+            "Final RMSE (on validation data): 73.62\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xl4VOXd//H3TCb7OjPZE8gAyhJA\ndmWVHSJq3ZC6QNXa5ana+rS0Wn9VW4sbtbXVKlbtouLj87jvCy4IiguKLLIkhC0sSci+75k5vz8C\nI2EJCWQyJ8nndV1emJkzZ76T7xn5eN/3OcdiGIaBiIiISDdi9XcBIiIiIh2lACMiIiLdjgKMiIiI\ndDsKMCIiItLtKMCIiIhIt6MAIyIiIt2Ozd8FiJjZoEGD6Nu3LwEBAQC43W7GjRvH7bffTlhY2Cnv\n94UXXmDBggXHPP7KK69w22238Y9//IPp06d7H6+vr2fixInMmTOH+++//5Tft7327dvHvffey549\newAIDQ3lpptuYtasWT5/745YtmwZ+/btO+Z3snbtWq6//npSU1OPec17773XVeWdlgMHDjBz5kz6\n9esHgGEYxMbG8rvf/Y709PQO7esvf/kLycnJXHnlle1+zeuvv85LL73E8uXLO/ReIl1FAUbkJJYv\nX05iYiIAjY2N/PKXv+Txxx/nl7/85Sntr6ioiH/+85/HDTAASUlJvPXWW60CzMcff0xUVNQpvd+p\n+PWvf81FF13EP/7xDwA2bdrENddcw7vvvktSUlKX1XE6kpKSuk1YOZGAgIBWn+Gdd97hxhtvZMWK\nFQQFBbV7P4sXL/ZFeSJ+pSkkkQ4ICgpiypQpZGZmAtDQ0MCdd97J3LlzOe+887j//vtxu90AZGVl\nccUVV5CRkcFFF13Ep59+CsAVV1xBXl4eGRkZNDY2HvMeo0ePZu3atdTV1Xkfe+edd5g0aZL358bG\nRu6++27mzp3LjBkzvEEDYMOGDVx66aVkZGQwb948Pv/8c6Dl/+gnT57MM888w4UXXsiUKVN45513\njvs5s7OzGTFihPfnESNGsGLFCm+Qe+SRR5g6dSoXX3wxTzzxBDNmzADgt7/9LcuWLfO+7sifT1bX\nvffey8KFCwH45ptvuOyyy5g9ezYLFixg//79QMtI1H//938zffp0Fi5cyMGDB0/SseN75ZVXuOmm\nm7jmmmv405/+xNq1a7niiiu4+eabvX/Zv/vuu1xwwQVkZGTwgx/8gH379gHw97//ndtvv5358+fz\n1FNPtdrvzTffzL///W/vz5mZmUyePBmPx8Nf//pX5s6dy9y5c/nBD35AQUFBh+ueN28e9fX17N69\nG4Dnn3+ejIwMZsyYwa9+9Svq6+uBlt/7fffdx4UXXsi7777bqg8nOi49Hg9//OMfmTZtGvPnzycr\nK8v7vl999RWXXHIJ8+bN47zzzuPdd9/tcO0inc4QkRMaOHCgkZ+f7/25vLzcuPrqq41ly5YZhmEY\njz/+uPHjH//YaGpqMurq6ozLLrvMeO211wy3222cd955xptvvmkYhmF8++23xrhx44yqqirjyy+/\nNGbNmnXc93v55ZeNW2+91fj1r3/tfW1VVZUxc+ZM48UXXzRuvfVWwzAM45FHHjGuueYao6Ghwaip\nqTEuvvhiY+XKlYZhGMYFF1xgvPXWW4ZhGMarr77qfa/9+/cb6enpxvLlyw3DMIx33nnHmD179nHr\n+PnPf25Mnz7dePrpp42dO3e2em779u3G2LFjjcLCQqOpqcn42c9+ZkyfPt0wDMO49dZbjUcffdS7\n7ZE/t1XX0KFDjVdeecX7eceNG2esWbPGMAzDePPNN41LLrnEMAzDePbZZ42rr77aaGpqMkpLS43p\n06d7fydHaut3fPj3PHLkSGPPnj3e7YcPH258/vnnhmEYRm5urjFmzBgjJyfHMAzD+Ne//mVcc801\nhmEYxsMPP2xMnjzZKCkpOWa/b7/9tnH11Vd7f37ooYeMJUuWGNnZ2cacOXOMxsZGwzAM45lnnjFe\nffXVE9Z3+PcyZMiQYx4fN26csWvXLuPrr782JkyYYBw8eNAwDMO44447jPvvv98wjJbf+4UXXmjU\n19d7f3700UfbPC5XrVplzJkzx6iurjbq6uqM+fPnGwsXLjQMwzAuvfRSY+3atYZhGMaePXuMX/3q\nV23WLtIVNAIjchKLFi0iIyODmTNnMnPmTMaPH8+Pf/xjAFatWsWCBQuw2WyEhIRw4YUX8tlnn3Hg\nwAGKi4s5//zzARg+fDjJycls3ry5Xe95/vnn89ZbbwHw4YcfMn36dKzW776uH3/8MVdddRVBQUGE\nhYVx0UUX8f777wPw2muvcd555wEwZswY7+gFQHNzM5deeikAQ4cOJS8v77jv/8ADD3D11Vfz5ptv\ncsEFFzBjxgz+93//F2gZHRk3bhxxcXHYbDYuuOCCdn2mtupqampi9uzZ3v0nJCR4R5wuuOAC9u3b\nR15eHuvWrWP27NnYbDbsdnurabaj5efnk5GR0eqfI9fKuFwuXC6X9+eQkBAmTJgAwGeffcY555xD\nWloaAJdffjlr166lubkZaBmRcjgcx7zntGnT2LZtG+Xl5QB88MEHZGRkEBUVRWlpKW+++SYVFRUs\nWrSIiy++uF2/t8MMw+D5558nISEBl8vFypUrmTdvHgkJCQBceeWV3mMAYMKECQQHB7faR1vH5ddf\nf83UqVMJDw8nJCTE2ysAp9PJa6+9xq5du3C5XPzlL3/pUO0ivqA1MCIncXgNTGlpqXf6w2Zr+eqU\nlpYSHR3t3TY6OpqSkhJKS0uJjIzEYrF4nzv8l1hsbOxJ33PSpEncfvvtlJeX8/bbb3PDDTd4F9QC\nVFVVcd999/Hggw8CLVNKZ511FgBvvvkmzzzzDDU1NXg8HowjbncWEBDgXXxstVrxeDzHff/g4GCu\nv/56rr/+eiorK3nvvfe49957SU1NpaKiotV6HKfTedLP0566IiIiAKisrGT//v1kZGR4nw8KCqK0\ntJSKigoiIyO9j0dFRVFTU3Pc9zvZGpgj+3b0z2VlZa0+Y2RkJIZhUFZWdtzXHhYWFsbEiRNZtWoV\nY8aMobKykjFjxmCxWPj73//Ov//9b5YsWcK4ceO46667TrqeyO12e38PhmFwxhlnsGzZMqxWK1VV\nVXzwwQesWbPG+3xTU9MJPx/Q5nFZUVFBfHx8q8cPu/fee3nssce47rrrCAkJ4Ve/+lWr/oj4gwKM\nSDs5HA4WLVrEAw88wGOPPQZAbGys9/+2AcrLy4mNjcXpdFJRUYFhGN6/LMrLy9v9l31gYCDTp0/n\ntddeY+/evYwaNapVgImPj+eHP/zhMSMQBQUF3H777bz44osMGTKEnJwc5s6d26HPWVpaSmZmpncE\nJCoqigULFvDpp5+SnZ1NZGQkVVVVrbY/7OhQVFFR0eG64uPj6d+/P6+88soxz0VFRZ3wvTuT0+lk\nw4YN3p8rKiqwWq3Y7faTvnbu3Ll88MEHlJWVMXfuXG//x48fz/jx46mtrWXp0qX8+c9/PulIxtGL\neI8UHx/PJZdcwq233tqhz3Wi47Kt321sbCx33HEHd9xxB2vWrOHnP/85U6ZMITw8vN3vLdLZNIUk\n0gHXXXcdGzZs4KuvvgJapgxeeukl3G43tbW1vP7660ydOpXU1FQSExO9i2TXr19PcXExZ511Fjab\njdraWu90xImcf/75PPnkk8c9dXnmzJm8+OKLuN1uDMNg2bJlfPLJJ5SWlhIWFkb//v1pbm7m+eef\nBzjhKMXx1NfX84tf/MK7uBNg7969bNq0ibFjxzJq1CjWrVtHaWkpzc3NvPbaa97t4uLivIs/9+/f\nz/r16wE6VNeIESMoKipi06ZN3v385je/wTAMRo4cycqVK3G73ZSWlvLJJ5+0+3N1xKRJk1i3bp13\nmuv//u//mDRpknfkrS3Tp09nw4YNfPjhh95pmDVr1nDXXXfh8XgICwtj8ODBrUZBTsWMGTN4//33\nvUHjww8/5IknnmjzNW0dl6NGjWLNmjXU1dVRV1fnDU5NTU0sWrSIwsJCoGXq0WaztZrSFPEHjcCI\ndEBERAQ/+clPWLp0KS+99BKLFi1i//79nH/++VgsFjIyMjjvvPOwWCw8+OCD/P73v+eRRx4hNDSU\nhx56iLCwMAYNGkR0dDSTJk3i1VdfJTk5+bjvdfbZZ2OxWJg3b94xz1111VUcOHCA888/H8MwGDZs\nGNdccw1hYWGce+65zJ07F6fTyW9/+1vWr1/PokWLePjhh9v1GZOTk3nsscd4+OGHufvuuzEMg4iI\nCG677TbvmUnf//73ueSSS7Db7cyZM4cdO3YAsGDBAm666SbmzJlDenq6d5Rl8ODB7a4rJCSEhx9+\nmCVLllBTU0NgYCA333wzFouFBQsWsG7dOmbNmkVycjKzZs1qNWpwpMNrYI72pz/96aS/g8TERO6+\n+25uuOEGmpqaSE1NZcmSJe36/UVERDB06FC2b9/OyJEjARg3bhxvv/02c+fOJSgoCIfDwb333gvA\nLbfc4j2TqCOGDh3Kf/3Xf7Fo0SI8Hg9Op5O77rqrzde0dVxOnz6dVatWkZGRQWxsLFOnTmXdunUE\nBgYyf/58rr32WqBllO32228nNDS0Q/WKdDaLceREtIhIB61bt45bbrmFlStX+rsUEelFNAYoIiIi\n3Y4CjIiIiHQ7mkISERGRbkcjMCIiItLtKMCIiIhIt9MtT6MuKjr+aZOdwW4Po6ys1mf7l1On3piT\n+mJe6o15qTftExcXecLnNAJzFJstwN8lyAmoN+akvpiXemNe6s3pU4ARERGRbkcBRkRERLodBRgR\nERHpdhRgREREpNtRgBEREZFuRwFGREREuh0FGBEREel2FGBERER6mFWrPmrXdg899Bfy8nJP+Pxv\nf/urziqp0ynAiIiI9CD5+Xl8+OGKdm17882LSU5OOeHz99//YGeV1em65a0ERERE5PgefHApmZlb\nmTJlHHPmnEd+fh5/+9sy7rvvjxQVFVJXV8cPf/gTJk2awk03/YRf/eoWPv74I2pqqtm3by+5uQf4\nxS8WM2HCJM4/fyZvv/0RN930E8aNO4f169dRXl7O0qV/JTY2lj/+8Q4OHsxn+PCzWLnyQ1599Z0u\n+5wKMCIiIj7ywsqdfJ1VeMzjAQEW3G7jlPY5bnA8C2acccLnr7xyEa+88gL9+g1g374cli37J2Vl\npZx99njOO+8CcnMPcMcdv2XSpCmtXldYWMCf//wwX375Oa+//jITJkxq9Xx4eDgPPfQYjz32dz75\nZCXJyak0NjbwxBNP8dlnn/LCC/97Sp/nVCnAHKG4rpTCwnziLUn+LkVEROS0DRkyFIDIyCgyM7fy\nxhuvYLFYqaysOGbbs84aCUB8fDzV1dXHPD9ixCjv8xUVFezdu4fhw0cAMGHCJAICuvb+TgowR3h7\nz/usK9jI3RN/R3Twie+AKSIi0h4LZpxx3NGSuLhIioqqfP7+gYGBAHzwwXtUVlby6KP/pLKykh/9\naNEx2x4ZQAzj2NGho583DAOrteUxi8WCxWLp7PLbpEW8R4gNjsdjeNhetsPfpYiIiJwSq9WK2+1u\n9Vh5eTlJSclYrVZWr15JU1PTab9PSkoq27dvA+Crr7485j19TQHmCLu3tyTVLUXb/VyJiIjIqUlL\n68f27VnU1Hw3DTRt2gw+//xTbr75Z4SGhhIfH89//vPkab3PxIlTqKmp4Wc/u55NmzYQFRV9uqV3\niMU43jiRyflq2O31T3ezovZfhAcH86epd3T5cJi0rauGXKVj1BfzUm/Mqyf0prKygvXr1zFt2kyK\nigq5+eaf8dxzL3fqe8TFnXg5h9bAHGFoPyfvfOGk1nmQgtpCEsMT/F2SiIiIKYWFhbNy5Yc899xy\nDMPDz3/etRe9U4A5gispElttPDgPklm6QwFGRETkBGw2G3/8431+e3+tgTmCLcDKIMdAADYXah2M\niIiIWSnAHGXcmS489WHsqtyD29O1K6pFRESkfRRgjjLyzDg8FbE0G43kVO73dzkiIiJyHAowR+mT\nEElIY8val8zSbD9XIyIiIsejAHMUi8XCYMcZGAZ8W5jl73JERER8Yv78C6mtrWX58qfYsuXbVs/V\n1tYyf/6Fbb5+1aqPAHjnnTdZvfpjn9V5IjoL6TjOciXy7Z5o8iy51DXXE2oL8XdJIiIiPrFo0bUd\nfk1+fh4ffriCadNmMm9e20HHVxRgjiPd5cC9MRZrRAU7ynZxVtxQf5ckIiLSLj/84dXce+9fSExM\n5ODBfG67bTFxcfHU1dVRX1/PL3/5G9LTh3m3v+eePzBt2kxGjhzF7353C42Njd4bOwK8//67vPTS\n8wQEWHG5BnDrrb/jwQeXkpm5lf/850k8Hg8xMTFcdtn3WbbsITZv3kRzs5vLLltARsb53HTTTxg3\n7hzWr19HeXk5S5f+lcTExNP+nAowx2GPDMZOCtXsIrN0hwKMiIickld2vsWGws3HPB5gteD2nNqF\n8EfFD+fSMy444fPnnjudzz77hMsuW8Cnn67m3HOnM2DAmZx77jS++eZr/ud/nuaeex445nUrVrxL\n//4D+MUvFvPRR+/z4YcrAKirq+Mvf/k7kZGR3Hjjj9m1aydXXrmIV155geuu+zH/+tfjAGzcuJ7d\nu3fx2GP/pq6ujmuuuYJzz50GQHh4OA899BiPPfZ3PvlkJQsWXHVKn/1IWgNzAsMSBmC4A3Q9GBER\n6VZaAsynAKxZs5rJk6eyevVH/Oxn1/PYY3+noqLiuK/LydnNsGEjABg1aoz38aioKG67bTE33fQT\n9u7dQ0VF+XFfn5W1jZEjRwMQGhqKy9Wf/ftbzuYdMWIUAPHx8VRXVx/39R2lEZgTGOaK5bMtDsoC\niiirL8ceEuPvkkREpJu59IwLjjta4st7IfXvP4CSkiIKCg5SVVXFp5+uIjY2njvuWEJW1jYeeeRv\nx32dYYDV2nIPQM+h0aGmpiYefPBPPPXUczidsdxyy3+f8H0tFgtH3l2xubnJu7+AgIAj3qdzbsGo\nEZgTGNTXjqfSCUBW2U4/VyMiItJ+EyZM5oknljFlylQqKspJSUkFYPXqj2lubj7ua/r2TSMrKxOA\n9evXAVBbW0NAQABOZywFBQfJysqkubkZq9WK2936Yq+DBw9lw4ZvDr2ultzcA6Sm9vXVR1SAOZGw\nEBvJQWkAbC3SNJKIiHQfU6dO954llJFxPs8//z/88pc3MnToMEpKSnj77TeOeU1Gxvls3bqZm2/+\nGfv378VisRAdHcO4cefwox/9gP/850muumoRDz/8IGlp/di+PYuHH/6L9/UjRoxk0KDB3Hjjj/nl\nL2/kv/7rJkJDQ332GS1GZ43ldCFf3oL8yGG9l1fv4qO6pwgLCeCBqb/HYrH47H3l5HrC7ed7IvXF\nvNQb81Jv2icuLvKEz2kEpg3D+jlwVzqp89SSV3PQ3+WIiIjIIQowbeifHI21JhaArNIdfq5GRERE\nDlOAaUOgzUq/yP4AbNY6GBEREdNQgDmJs/qk4qmNYHfFHpo8x1+5LSIiIl1LAeYk0l0tp1O7aWZP\nxV5/lyMiIiIowJxUanwEQfUJgNbBiIiImIUCzElYLRYGOwdgeCxaByMiImISCjDtMNyVgKcmhrza\nPGqbav1djoiISK+nANMO6Wl2PBVOwCC7bJe/yxEREen1FGDaITYmlGhPMgDbSrP9XI2IiIj4NMBk\nZ2cza9Ysnn32WQC+/vprrrzyShYtWsRPf/pT7y29//nPfzJ//nwuv/xyVq9e7cuSTtmwxP4Y7gC2\nFinAiIiI+JvPAkxtbS1LlixhwoQJ3sfuu+8+7rnnHpYvX86oUaN4/vnn2b9/P++88w7PPfccjz/+\nOPfdd98xd7g0g6GuWDyVTsqbyiipK/V3OSIiIr2azwJMUFAQTz75JPHx8d7H7HY75eXlAFRUVGC3\n21m7di1TpkwhKCgIh8NBSkoKO3fu9FVZp2ywdx0MZJXpdGoRERF/8lmAsdlshISEtHrs//2//8eN\nN97I3Llz+eabb7jkkksoLi7G4XB4t3E4HBQVFfmqrFMWERpIQmBfALYVaxpJRETEn2xd+WZLlizh\nkUceYcyYMSxdupTnnnvumG0Mwzjpfuz2MGy2AF+UCJz49t3nnHkGb5etZnvZLpyx4VgtWgPd1dq6\ntbr4j/piXuqNeak3p6dLA8z27dsZM2YMABMnTuTNN99k/Pjx7Nmzx7tNQUFBq2mn4ykr8921WOLi\nIikqqjruc/0SIvHsdVIXnMuGPdvpG5nqszrkWG31RvxHfTEv9ca81Jv2aSvkdekQQmxsrHd9y+bN\nm0lLS2P8+PGsWrWKxsZGCgoKKCws5IwzzujKstrtzNRoqIoFdFsBERERf/LZCMyWLVtYunQpubm5\n2Gw2VqxYwV133cXtt99OYGAg0dHR3HvvvURFRbFgwQIWLlyIxWLhD3/4A1arOadmggID6BfZj/1s\nYmtxNnPSpvu7JBERkV7JYrRn0YnJ+HLY7WTDem9/kcNbJcuxhdXyl6l/JCgg0Ge1SGsacjUn9cW8\n1BvzUm/axzRTSD1BusuBu8KJBze7K3L8XY6IiEivpADTQWkJkQTWtSwy1joYERER/1CA6SCr1cJg\nxwAMj4XNRdv9XY6IiEivpABzCoa64vBU2zlYl091Y42/yxEREel1FGBOQbrL4b2twHbdVkBERKTL\nKcCcggR7KBHuJAAytQ5GRESkyynAnAKLxcLQhH4YzYFsLc5u1+0PREREpPMowJyioS4nnkoHlU0V\nFNWV+LscERGRXkUB5hQNOXQ9GNDp1CIiIl1NAeYURYcHEW/rA0BmSbafqxEREeldFGBOw/DUvnjq\nQ8kq24nH8Pi7HBERkV5DAeY0pLvseCqdNHoa2Ft5wN/liIiI9BoKMKdhYJ8YjKpYQNeDERER6UoK\nMKchJMhG3zAXhgFbi7UORkREpKsowJym4WlJGLVR5FTupcHd6O9yREREegUFmNOU7rLjrnDiwcPO\n8t3+LkdERKRXUIA5Tf2SogiojQd0PRgREZGuogBzmmwBVgba+2F4rFoHIyIi0kUUYDrBMFccnio7\nBXUFVDRU+bscERGRHk8BphOku+x4KnQ6tYiISFdRgOkEybHhhDYlAloHIyIi0hUUYDqBxWJhaGIa\nRlMg24qzMQzD3yWJiIj0aAownSQ9zYm70klVcxUFtYX+LkdERKRHU4DpJEeug8nUNJKIiIhPKcB0\nEkdUCE5rKqB1MCIiIr6mANOJhqWm4KkPI7tsF26P29/liIiI9FgKMJ0o3eXAUxFLo6eRnMr9/i5H\nRESkx1KA6USD+8bgqXQCkFWqq/KKiIj4igJMJwoLCaRPWBqGAdtKtA5GRETEVxRgOtmwtASMmmj2\nVu2jrrne3+WIiIj0SAownSw9zYG7IhYDgx1lu/xdjoiISI+kANPJBqREY62JAyCrbKefqxEREemZ\nFGA6WaDNyhn2NAx3ANuKt/u7HBERkR5JAcYHhrpi8VQ5KKovpqy+3N/liIiI9DgKMD7Qsg7m0OnU\nmkYSERHpdAowPtAnIYKQhgQAtuu2AiIiIp1OAcYHrBYLQxL7YjQGs60kG8Mw/F2SiIhIj6IA4yND\nXQ7clU5qmmvIqzno73JERER6FAUYH2m5L9Lh2wpoGklERKQzKcD4SFxMKDGWZAAydV8kERGRTqUA\n40NDU1Lw1Eawo2wPTZ5mf5cjIiLSY/g0wGRnZzNr1iyeffZZAJqamli8eDHz58/nmmuuoaKiAoA3\n3niDyy67jMsvv5wXX3zRlyV1qXSXHU+lk2ajiT0Ve/1djoiISI/hswBTW1vLkiVLmDBhgvexF154\nAbvdzksvvcS8efNYt24dtbW1PProozz11FMsX76cp59+mvLynnHxtyFpdtyVLetgdDq1iIhI5/FZ\ngAkKCuLJJ58kPj7e+9jHH3/M9773PQC+//3vM3PmTDZt2sTw4cOJjIwkJCSE0aNHs379el+V1aUi\nw4JIDumDYVjYpnUwIiIincbmsx3bbNhsrXefm5vLJ598wgMPPEBsbCy///3vKS4uxuFweLdxOBwU\nFRW1uW+7PQybLcAndQPExUV22r7OHtyHtwpi2G/JJTTaSkRQeKftuzfqzN5I51FfzEu9MS/15vT4\nLMAcj2EY9OvXj5tuuolly5bx+OOPk56efsw2J1NWVuurEomLi6SoqKrT9udKCMeT7cSILOOLHZsY\nGT+80/bd23R2b6RzqC/mpd6Yl3rTPm2FvC49Cyk2NpZx48YBMHnyZHbu3El8fDzFxcXebQoLC1tN\nO3V3Z6bGYKmOBSCzTOtgREREOkOXBphzzz2XTz/9FICtW7fSr18/RowYwebNm6msrKSmpob169cz\nduzYrizLp4IDA+gf0xfDHUBmiQKMiIhIZ/DZFNKWLVtYunQpubm52Gw2VqxYwZ///GfuueceXnrp\nJcLCwli6dCkhISEsXryY66+/HovFwo033khkZM+aFxzqimVPvpOSgEJK6kpxhjpO/iIRERE5IYvR\nDe806Mt5Q1/MS+7Oq+S+914myJXJVYMvY1LyOZ26/95Cc8bmpL6Yl3pjXupN+5hmDUxv5UqMJKg+\nAdB9kURERDqDAkwXsFotDEpIwdMQQmbJTjyGx98liYiIdGsKMF1kqMuJp9JJnbuWA9V5/i5HRESk\nW1OA6SKH74sEmkYSERE5XQowXSTREUakOwlQgBERETldCjBdxGKxMLRPEp7aSHaW76HR3eTvkkRE\nRLotBZgulO5y4K5w4jbc7K7I8Xc5IiIi3ZYCTBdKT9M6GBERkc6gANOFoiOCSQxOxfBYySzN9nc5\nIiIi3ZYCTBdL7xuHpzqGA9V5VDfW+LscERGRbkkBpouluxx4Klqmkbbr7tQiIiKnRAGmiw3qE4NR\nFQtAVulOP1cjIiLSPSnAdLHQYBuumFSM5kAyS7PphvfSFBER8TsFGD8YmubEU+mgrKGcoroSf5cj\nIiLS7SjA+MHh68GATqcWERE5FQowftA/OQpbXTwAWVrIKyIi0mEKMH5gC7AyKCEFT30o20t34jE8\n/i5JRESkW1GA8ZN0lwNPpZOO2CaiAAAgAElEQVR6dz17Kw/4uxwREZFuRQHGT9JddtwVLadT63ow\nIiIiHaMA4ycpseGEuxPBgEwt5BUREekQBRg/sVgsDE1NwFMbxe6KHBrcjf4uSUREpNtQgPGjIS47\n7gonHsPDzvLd/i5HRESk21CA8aP0NAeeysO3FdA0koiISHspwPiRMzqE2MBkDI9V62BEREQ6QAHG\nz4amxeKpspNfc5CKhip/lyMiItItKMD4WXqaA49OpxYREekQBRg/G5wWg6dK90USERHpCAUYPwsP\nCaRvVApGUyBZpTswDMPfJYmIiJieAowJDHU5cFc6qWispKC20N/liIiImJ4CjAmkp9m962B0NpKI\niMjJKcCYwBmp0Vhr4gAt5BUREWkPBRgTCLQFMDAhEU99GNmlu3F73P4uSURExNQUYEwi3dVyOnWD\np4Gcyv3+LkdERMTUFGBMIv3QQl6ArNJsP1cjIiJibgowJtEnIYKQhngwIKtsp7/LERERMTUFGJOw\nWiyk903AUxPNnoq91DXX+7skERER01KAMZF0lx13RSwGBjvKdvm7HBEREdNSgDGRdJcDz+F1MJpG\nEhEROSEFGBOJjwnFbk0Ed4AW8oqIiLRBAcZkhrqcuKscFNQWUVZf7u9yRERETMmnASY7O5tZs2bx\n7LPPtnr8008/ZdCgQd6f33jjDS677DIuv/xyXnzxRV+WZHrpLgfuCk0jiYiItMVnAaa2tpYlS5Yw\nYcKEVo83NDTwxBNPEBcX593u0Ucf5amnnmL58uU8/fTTlJf33pGHwWl27zqY7bovkoiIyHH5LMAE\nBQXx5JNPEh8f3+rxf/zjH1x11VUEBQUBsGnTJoYPH05kZCQhISGMHj2a9evX+6os04sKCyI1MhGj\nKZis0h0YhuHvkkREREzH5rMd22zYbK13v2fPHrKysrj55pt54IEHACguLsbhcHi3cTgcFBUVtblv\nuz0Mmy2g84s+JC4u0mf7bo+x6Um8tc9JVWAedUGVpMWk+rUeM/F3b+T41BfzUm/MS705PT4LMMdz\n3333cfvtt7e5TXtGHMrKajurpGPExUVSVFTls/23R7/4cDybnRCbxxe7NhHWN9qv9ZiFGXojx1Jf\nzEu9MS/1pn3aCnlddhZSQUEBu3fv5te//jULFiygsLCQhQsXEh8fT3FxsXe7wsLCY6adepszU2Ow\n1MQCkKV1MCIiIsc45QCTk5PToe0TEhL48MMPeeGFF3jhhReIj4/n2WefZcSIEWzevJnKykpqampY\nv349Y8eOPdWyeoTgoADOiE/AUxvBjvLdNHma/V2SiIiIqbQZYK677rpWPy9btsz773feeWebO96y\nZQuLFi3i1Vdf5ZlnnmHRokXHPbsoJCSExYsXc/3113Pddddx4403EhmpecEhh85GavI0sadir7/L\nERERMZU218A0N7f+P/8vv/ySG264ATj5WpVhw4axfPnyEz6/cuVK779nZGSQkZFx0mJ7k3SXg9c3\nO7El7mV76Q4G2gf4uyQRERHTaHMExmKxtPr5yNBy9HPSuVxJkQQ3xIFhIbNM62BERESO1KE1MAot\nXSfAamVwahzu6hj2VR6gtsl3Z16JiIh0N21OIVVUVPDFF194f66srOTLL7/EMAwqKyt9Xlxvl+5y\nsHmrEyOyjOyyXYyMH+7vkkREREyhzQATFRXVauFuZGQkjz76qPffxbfSXXY8XziBnWSW7VCAERER\nOaTNANPWIlzxvURHGFGWeBrcNt0XSURE5AhtroGprq7mqaee8v78f//3f1x00UX84he/aHXxOfEN\ni8XC0DQn7koHRXUllNSV+rskERERU2gzwNx5552UlJQALfcxevDBB7n11luZOHEi99xzT5cU2Nul\nuxy4K1ruTp2ls5FERESAkwSY/fv3s3jxYgBWrFhBRkYGEydO5IorrtAITBcZfOiCdqDbCoiIiBzW\nZoAJCwvz/vtXX33F+PHjvT/rlOquYY8MJjEiHqMxhO2lO/EYHn+XJCIi4ndtBhi3201JSQn79u1j\nw4YNTJo0CYCamhrq6uq6pECBoWkt00g1zbUcqM7zdzkiIiJ+12aA+fGPf8y8efO48MILueGGG4iO\njqa+vp6rrrqKiy++uKtq7PXSXQ5NI4mIiByhzdOop06dypo1a2hoaCAiIgJoufnib37zGyZPntwl\nBQoM6huDURkLwPbSncxJm+7nikRERPyrzQCTl/fddMWRV97t378/eXl5JCcn+64y8QoNttE/Po79\ntZHstOyh0d1EUECgv8sSERHxmzYDzIwZM+jXrx9xcXHAsTdzfOaZZ3xbnXilu+zk7HPSHJbD7ooc\nBjvO9HdJIiIiftNmgFm6dCmvv/46NTU1nH/++VxwwQU4HI6uqk2OkO5y8NYWJyTlkFW6QwFGRER6\ntTYDzEUXXcRFF11Efn4+r776KldffTUpKSlcdNFFzJ49m5CQkK6qs9frnxxFYH0sGFaySrOBef4u\nSURExG/aPAvpsKSkJG644Qbeffdd5s6dy913361FvF3MFmBlUGos7qoY9lfnUd1Y4++SRERE/KbN\nEZjDKisreeONN3jllVdwu9389Kc/5YILLvB1bXKUIWl2tmU6CYgqZXvZDsYkjPR3SSIiIn7RZoBZ\ns2YNL7/8Mlu2bGHOnDncf//9DBw4sKtqk6Okuxy8sDaWQHaQVbpTAUZERHqtNgPMj370I1wuF6NH\nj6a0tJT//Oc/rZ6/7777fFqctJYSF06E4aTZHUhW2Q4Mw9AtHUREpFdqM8AcPk26rKwMu93e6rkD\nBw74rio5LqvFQrrLyYYKB6UBBRTVlRAfFuvvskRERLpcm4t4rVYrixcv5o477uDOO+8kISGBs88+\nm+zsbP72t791VY1yhPQ0O+4K3VZARER6tzZHYP7617/y1FNPMWDAAD766CPuvPNOPB4P0dHRvPji\ni11Voxwh3eXAs7Jl1CWrbAfnpk7wc0UiIiJd76QjMAMGDABg5syZ5Obm8oMf/IBHHnmEhISELilQ\nWnNGhxAX5sBoCCW7dCcew+PvkkRERLpcmwHm6AWiSUlJzJ4926cFyckNdTlxVzipc9ezt1JrkURE\npPdp14XsDtMZL+aQ7rLjrjh0d+oyrYMREZHep801MBs2bGDatGnen0tKSpg2bZr39N1Vq1b5uDw5\nnkF97RhVDjBaFvJmuGb6uyQREZEu1WaAee+997qqDumAiNBA0mKd5NdGsdu6lwZ3I8EBQf4uS0RE\npMu0GWBSUlK6qg7poHSXgwMHnFjDK9lZvpuhzsH+LklERKTLdGgNjJhHusuOp/LQ6dS6HoyIiPQy\nCjDd1Jmp0QTUOcBjVYAREZFeRwGmmwq0BXBmigN3lZ28moNUNFT5uyQREZEuowDTjaW7HHh0OrWI\niPRCCjDdWLrLjrtS90USEZHeRwGmG+sbH0moxw7NQWwv24FhGP4uSUREpEsowHRjVquF9DQHzRUO\nyhsqKagt9HdJIiIiXUIBppsbcsQ6mExNI4mISC+hANPNtVwPpmUdjBbyiohIb6EA083Fx4TiCLFD\nQzg7ynbj9rj9XZKIiIjPKcB0cxaLhXSXneZyJ/XuBnIq9/u7JBEREZ/zaYDJzs5m1qxZPPvsswDk\n5+dz7bXXsnDhQq699lqKiooAeOONN7jsssu4/PLLefHFF31ZUo+U7nIccTp1tp+rERER8T2fBZja\n2lqWLFnChAkTvI/97W9/Y8GCBTz77LPMnj2b//znP9TW1vLoo4/y1FNPsXz5cp5++mnKy8t9VVaP\nNCTNjqfSAYaFrLKd/i5HRETE53wWYIKCgnjyySeJj4/3Pvb73/+euXPnAmC32ykvL2fTpk0MHz6c\nyMhIQkJCGD16NOvXr/dVWT1SVHgQfZx2PDXR5FTso6653t8liYiI+JTPAozNZiMkJKTVY2FhYQQE\nBOB2u3nuuee48MILKS4uxuFweLdxOBzeqSVpv3SXHXeFEw8edpTt8nc5IiIiPmXr6jd0u93ccsst\njB8/ngkTJvDmm2+2er49V5O128Ow2QJ8VSJxcZE+27evTBiRwgeZTkjZxd76vcyMG+/vknyiO/am\nN1BfzEu9MS/15vR0eYC57bbbSEtL46abbgIgPj6e4uJi7/OFhYWMHDmyzX2UldX6rL64uEiKirrf\nnZ0TIoOx1NqxeALYkLuNoj7d7zOcTHftTU+nvpiXemNe6k37tBXyuvQ06jfeeIPAwEB+8YtfeB8b\nMWIEmzdvprKykpqaGtavX8/YsWO7sqweITgogAHJdporHRTUFlJWr4XQIiLSc/lsBGbLli0sXbqU\n3NxcbDYbK1asoKSkhODgYBYtWgTAgAED+MMf/sDixYu5/vrrsVgs3HjjjURGaljtVKS77OzKdhIQ\nU0RW2U4mJCkIiohIz+SzADNs2DCWL1/erm0zMjLIyMjwVSm9RrrLwevrDt1WoHSHAoyIiPRYuhJv\nD9IvKZJgdzSW5mCyyna0a0G0iIhId6QA04MEWK0M7uugqdxJVWM1eTUH/V2SiIiITyjA9DBH3p06\nq1R3pxYRkZ5JAaaHSXc5cFcowIiISM+mANPDJDnDiAmOgvoIdpbvpsnT7O+SREREOl2XX8hOfMti\nsZDucvB1uRNC9vLk5mdwRfUhOSKJ5PAEYkOdWC3KrSIi0r0pwPRAQ9LsfLEqmbDEAraWZLG1JMv7\nXKA1kKTwBJIjEkkJT2wJNhGJRAXp2jsiItJ9KMD0QOkuB0ZNNH2KL+W677nIrc4nr+YgedUHD/2Z\nz76qA61eExEYTnJ4IskRh/4JTyIpPIEQW7CfPoWIiMiJKcD0QPbIYJKcYWTvLycyMIphsTEMix3i\nfd7tcVNYV0xedT551QfJPRRusst3kV3e+k7WzhDHEaM1LSM28aGxBFh9dzNNERGRk1GA6aHSXQ4+\n+uYAj722hX5JUaTEhZMSG05sTCgB1gCSwhNICk9gTMJ3r6lvbiC/poC8mpZgc3jEZnPxNjYXb/Nu\nZ7MEkBAe/92ITXgiKRFJxARHY7FY/PBpRUSkt1GA6aHGpyfw+ZZ8NuwoZsOO7+72HWSzkuQMJzk2\nnJS4Q3/GhuOMDiHEFky/6L70i+7r3d4wDCobq8k/NPV0eLQmv6aA3Op8KPjuPUNtoSSHJ3gXDLf8\nmUhYYGhXfnQREekFLEY3vN68L29B3pNuce7xGBRX1JFbXENecU3Ln0U15JXU0uz2tNo2ODCA5Niw\nQ4EmwhtsHFHBxx1V8RgeiutKyKsp8E5F5dUcpLC2GIPWh1RMcPShaaikQwuIk0gMjyfQ2rH83JN6\n05OoL+al3piXetM+cXEnPsFEAeYoveGg8ngMisrrOFBUQ15xtTfgHCytpdnd+nAICQogOfa7kZqW\nqagIYiKCjhtsGt1NHKwtOGLBcMs/FY2VrbazWqzEh8Z6FwwfnopyhtpPeJp3b+hNd6S+mJd6Y17q\nTfu0FWA0hdQLWa0WEhxhJDjCGDMozvu42+OhsKyO3KIjRmyKa9h7sIrdea0DSGiwjZQjgk3yoTU2\n0eFB9I1MpW9kaqvtq5tqyD9iwXD+oT8P1haynm+92wUFBJEUnvDdKd6H1tlEBkX49pciIiLdigKM\neAVYW9bHJDnDWz3e7PZQUFrbeiqquIbdeZXszK1otW14iO27UBMbTkpcBCmx4USFh3OmfQBn2gd4\ntzUMg9L68u8WDR8KNfurctlbub/VfiMDIxgcP4ABEf0Z4hhEbKjDd78IERExPU0hHUXDeu3X1Pxd\nsDky3BSW1XL0URURGthqpOZwwIkMCzpmv82eZgpri1stGs6tzqesody7TXxoLIMdA0l3DuTMmP6E\n2EJ8/XHlBPSdMS/1xrzUm/bRGpgO0EF1+pqa3eSXHDFic2hKqqi8jqMPtqiwQFLiIo4atQknPCTw\nmP26Q+v5bMd6Mkt3kF22k3p3AwABlgD6R6e1BBrHQFIjk3W7hC6k74x5qTfmpd60jwJMB+ig8p2G\nJjcHS2rJPbxwuKhlxKa4ov6YbaMjglqtsUmJjWD0sCQqy2uBlovx7ancR2bJdraVZrO/Ktd79lNE\nYDiDHWcyxDGQwY4ziQmO7tLP2dvoO2Ne6o15qTftowDTATqoul59Y3PLiE2rxcPVlFQ2tNou0GZl\nQHIU6S4HQ1x2XImRBFhbRlqqG2vIKttBZmk2mSXZrc56Sg5PZIhzIEMcAzkjuh+BAceO7sip03fG\nvNQb81Jv2kcBpgN0UJlHXUMzeSUtIzUHimrYlV/JntwK7zRUaHAAg/vaGZJmZ4jLQbIzDIvFgmEY\n5NcUtISZ0mx2lu+mydMMtNzM8syY/gxxnMkQ5yASw+J19eDTpO+Meak35qXetI8CTAfooDKvuLhI\ndu8tIWtfOZk5pWzbW0ZhWZ33+eiIINLT7C0jNGl2HFEtC3sb3U3sKt/DttLtZJXuIK/moPc1McHR\nDHG0jM4McpxBRGD4Me8rbdN3xrzUG/NSb9pHAaYDdFCZ1/F6U1xRR2ZOGZl7y9iWU0plbZP3uURH\nGENcdtLT7AxOs3sXBpc3VJBZ0jI6k1W2g5qmlnU1Fiz0jUol3TGQwY6B9Ivqq5tWtoO+M+al3piX\netM+CjAdoIPKvE7WG8MwyC2uYVtOGZk5pWTtL6eh0Q2AxQJpCZEtgcbl4MyUaIICA/AYHvZX5ZJZ\nms22kmz2VO7FY7TcZiEkIIRB9gGH1s/o2jMnou+Meak35qXetI8CTAfooDKvjvam2e0hJ7+KbXtL\n2ZZTxq7cCtyelsPdFmDlzNToQ+tnvlsQXNdcT3bZLu/6meK6ku/eP9TJEMcghjjOZKB9gK49c4i+\nM+al3piXetM+CjAdoIPKvE63Nw2NbrIPlJOZU8a2vaXsK6j2PhcabGNw3xjv+pmkQwuCi2pLvGHm\nyGvPWC1W+kenMcQxqNdfe0bfGfNSb8xLvWkfBZgO0EFlXp3dm6raRrL2lbMtp5TMnDIKy79bEBwT\nEcSQNAfpLrt3QbCuPXN8+s6Yl3pjXupN+yjAdIAOKvPydW+Ky+vYtrdlQXDmCRcEOxicFkN4SODJ\nrz3jGMgQZ8+/9oy+M+al3piXetM+CjAdoIPKvLqyN4ZhkFtUw7ZDp2tvP2pBsCsx0jtCc0ZKNIE2\naxvXnrFxRkx/0h0De+S1Z/SdMS/1xrzUm/ZRgOkAHVTm5c/eNLs97MmvPLR+5vgLglummxy4EiNp\nNprbvPbMsNghnJsygZSIJL98ns6k74x5qTfmpd60jwJMB+igMi8z9ebwguDD62f2FR5/QXC6y06i\nI4yKxkoyS3eQWbK91bVnzozpz7Q+kxnuHNJtrzljpr5Ia+qNeak37dNWgLF1YR0iPUZwUADD+zsZ\n3t8JQGVtI1l7v7ug3oYdxWzYUQy0LAhuObsphUtcw4keGsiW4kxWH/icrLId7CjfjT04hqmpE5mY\nfDbhgWH+/GgiIt2CRmCOolRsXt2pN0Xldd4wk7m3jKojFgQnOcMY3t/JlLOSsIbVsPrA56zNX0ej\np4lAayDjEkYxrc+kbjO91J360tuoN+al3rSPppA6QAeVeXXX3ngOLQg+fP+m7fvKaWhqWRB8Zmo0\n00amkH5GBOsK1/PJgc8pri9teS6mP9NSJzE8Nt3U00vdtS+9gXpjXupN+2gKScSPrBYLfeIj6BMf\nwZyz+9Ls9rBpZzGrNuSyNaeMHQcqCA+xMWl4Ej8ZcSOl7GfV/s9aTS+dmzqBicln62aTIiKHaATm\nKErF5tUTe1NYVsvqTXms+TbfO800uG8MU0emkJzi4bODX7L24Dc0uhsJtNoYlzCKqamTSI1M9nPl\n3+mJfekp1BvzUm/aR1NIHaCDyrx6cm+a3R7WZxexakMuWfvKAYgIDWTyWUmcM8zOrrqtrD5ieumM\nmH5MS53MWSaYXurJfenu1BvzUm/aRwGmA3RQmVdv6c3B0lpWb8zls80Hqa5rGZUZkmZn6sgkgpwl\nrMn7gszSbICW6aWUQ9NLQf6ZXuotfemO1BvzUm/aRwGmA3RQmVdv601Ts5tvthexamMe2ftbRmWi\nwoOYclYSQwYGsrnyG748NL1kO2J6qU8XTy/1tr50J+qNeak37aMA0wE6qMyrN/cmt7iG1Rtz+Xzz\nQWobmrEAQ/s5mDgilurQ3Xya9wXFdSUADIjux7Q+kxgRO7RLppd6c1/MTr0xL/WmfRRgOkAHlXmp\nN9DY5ObrrEJWb8xjZ24F0HKhvMnDk0jsV8360q+900sxwdGcmzKBScnn+HR6SX0xL/XGvNSb9vFb\ngMnOzuaGG27g2muvZeHCheTn53PLLbfgdruJi4vjgQceICgoiDfeeIOnn34aq9XKggULuPzyy9vc\nrwJM76TetHagsJrVG/P4fGs+dQ1uLBY4q7+TEcNCKLBmsvbgOhoOTS+NTRjJtNRJ9IlM6fQ61Bfz\nUm/MS71pH78EmNraWn7605/icrkYNGgQCxcu5LbbbuPcc8/lvPPO48EHHyQxMZGLL76YSy65hJde\neonAwEDmz5/Ps88+S0xMzAn3rQDTO6k3x9fQ6OarzAJWbcxjT34lAI6oYCYMdxKSeJCvi9dS5J1e\ncjE1dRIj44Z12vSS+mJe6o15qTft45cL2QUFBfHkk0/y5JNPeh9bu3Ytd911FwDTp0/n3//+N/36\n9WP48OFERrYUOXr0aNavX8+MGTN8VZpIjxIcFMCUEclMGZHMvoIqVm3M44utB3n7szysFgtnnZHB\n2EEN5DR/S2ZpNrsqcogJjmZKyngmJZ9DZFCEvz+CiEiH+SzA2Gw2bLbWu6+rqyMoKAgAp9NJUVER\nxcXFOBwO7zYOh4OioiJflSXSo/VNiOQHcwdx+bQBrM0sYPWGPDbuKGHjDoiNTmfK8DE0xexmQ8kG\n3ty9gndzPmJM/Aim9ZlE38hUf5cvItJufruVwIlmrtozo2W3h2Gz+e7siraGrMS/1Jv265tq5/LZ\ng9mxv4z3vtjL6g0HeH9NPQFWB2OHXU78gFK2VHzD2oMt/wyKHcB5Z07j7NRR2Do4vaS+mJd6Y17q\nzenp0gATFhZGfX09ISEhFBQUEB8fT3x8PMXFxd5tCgsLGTlyZJv7KSur9VmNmpc0L/Xm1MSE2Lhi\n+gC+NyGNL7cdZNWGPNZ+WwLfQlzMRM4e3kx5yHa2F+9ge/EuooOimJIygckp7ZteUl/MS70xL/Wm\nfdoKedYurIOJEyeyYsUKAN5//32mTJnCiBEj2Lx5M5WVldTU1LB+/XrGjh3blWWJ9AphITZmjE7l\nrh+O43eLxjBpeCIV1Y2s/rSZLR+dwcDqizkregwN7gbe2rOC2z+7h2e2Pc++ygP+Ll1E5Bg+Owtp\ny5YtLF26lNzcXGw2GwkJCfz5z3/mt7/9LQ0NDSQnJ3PfffcRGBjIe++9x7/+9S8sFgsLFy7ke9/7\nXpv71llIvZN60/lq6pv4YstBVm3MI6+4BoAEZyBpQyrIt26juL5ldLRfVBrT+kxiVNzwY85eUl/M\nS70xL/WmfXQhuw7QQWVe6o3vGIbBjgMVrN6Yy9dZRTS7PdgCLAwc2oTh2ENO7S6AQ9NL45mUcg5R\nQS3/YVFfzEu9MS/1pn0UYDpAB5V5qTddo7quic8357NqYx4HS1vWmyUkeogdUECuJ4sGdwM2SwCj\nE0YwLXUSYwekqy8mpe+Meak37aMA0wE6qMxLvelahmGwfV85qzbm8s32Itweg8AgD670CmoidlLW\n2HJxvIHO/kxLnsJZselYLBY/Vy1H0nfGvNSb9vHLhexEpHuzWCwMTrMzOM1OZW0jn23OZ/XGPHZs\ntAJjie9bTXhqLtklu8ku2U1KRBIZrpmMjBuG1dKl5weISC+kEZijKBWbl3rjfx7DIGtvGas25rEh\nu2VUJjiqlriBByi17sHAIDE8gfPSZjA6YYSCjJ/pO2Ne6k37aAqpA3RQmZd6Yy4V1Q2s2ZzPJ9/m\nU1RWhzWkhvjBuVQGtQSZ+LBY5qbNYFzCqE6775J0jL4z5qXetI8CTAfooDIv9cacHI5w3vtsNyu+\n2see/CoswbXYB+ynPmIvBh6cIQ7muqZzTuIYbFbNWnclfWfMS71pH62BERGfCQiwcvaQBMYNjmfH\ngQpWfLWPjdvCIKgvEWn7KLPv5bmsl3l3z0fMSZvGhKRxBAYE+rtsEenmFGBEpFNYLBYG9olhYJ8Y\nCkpreX/dfj77NpzGnDRCU3OoiNvP89mv8V7OSmanTWNS8tkEBQT5u2wR6aY0hXQUDeuZl3pjTm31\npbquiY835PLRNweobKwiKCmHwIT9eCzNRAZGMCttKpOTxxNiC+7iqnsHfWfMS71pH62B6QAdVOal\n3phTe/rS1Ozhy20Hef/r/eSWlWFLzCEocR+GtZnwwDBm9DmXqakTCbWFdFHVvYO+M+al3rSP1sCI\niF8F2qxMOSuZycOT2JpTyoqvEtm6wYUtcS81ift4c/d7fLhvFdP7TGF66iTCAsP8XbKImJwCjIh0\nGYvFwrB+Tob1c3KgsJoVX/fly28PYIndC0k5vLPnAz7a9wnTUicxo88UIoLC/V2yiJiUppCOomE9\n81JvzOl0+1Je3cDK9QdYuXEvjVF7sCXtwRLYSKA1kKmpE5nZ91zvjSOlY/SdMS/1pn00hSQiphUT\nEcyl5w7g/PEuPtsykBXr9lAauAMjaQ8f7lvNqv2fMSVlPLPSphITHO3vckXEJBRgRMQUgoMCmDE6\nlWkjU9i0czDvfbWH3Y1b8STt4eMDa1h94AsmJo9jrms6jhC7v8sVET9TgBERU7FaLYwaGMeogXHs\nyR/Mu1/tYWPeJgKSdrEm70s+y/uKcfGjOX/ALGJDHf4uV0T8RAFGREyrX1IUN1w0guKKgby/bh9r\n9n2NJ34nXxWu46uCbxjpHMFFA2cTHxbn71JFpIspwIiI6cVGh3LVzEFcXD+AVRsP8P7OtTTYs9hY\nupGNX2xiUFQ684fMJeOMiQ8AABXlSURBVDki0d+likgXUYARkW4jLMTGvPEu5ozry9dZBbyx5QvK\nw7ey3bKVe9ZuxRU6kO8PO4++USn+LlVEfEwBRkS6HVuAlQlDkxiffgnb903j5Y1fcMC6gRxLNkvX\nZZNo68cVQ+dxpjPN36WKiI/oOjBH0bn55qXemJNZ+pJfUsOL33xBVv1XWCLKAXDQh/mDMxiRfKaf\nq/MPs/RGjqXetI/uhdQBOqjMS70xJ7P1pbKmgZfXf8U35Z9hhJcCENGcxIUD5jB5wFA/V9e1zNYb\n+Y560z4KMB2gg8q81BtzMmtfmprdvLHxGz45uJrmsCIAghvjmJk6g4z0UQRYrX6u0PfM2htRb9pL\nV+IVkV4n0BbAZWPP5lJjHB9mfsuKvSupC87nncLnWbH3AybGTeGSkecQHKT/DIp0R/rmikiPZrFY\nmJ0+gtnpI/gqZzuvZb9PReh+Pq1+nU/fX83IyPEsGDOJ6Ihgf5cqIh2gACMivcbZrkGc7RrEtoIc\nXtj6LkVhe9jofo8Nqz7njMAxfH/0FFLiIvxdpoi0g9bAHEXzkual3phTd+5LTlku/7vlXQ40ZoMF\nPLUROBoGMTZpBGcP7EOyMwyLxeLvMk9Zd+5NT6fetI8W8XaADirzUm/+f3v3HiNVef9x/H3mftv7\nzuyyLFBgQSp30RoVay9af9FftNXWpdStfzRNGm3SGmqktIq2TRNMTJpWQ9vUJgbTsC1atWm91Fj6\nI3WxIrAoglxEkF12Zi/DXuZ++/0xs5dZFlwuy8zA55VMZubMmbPf8dlxPzzPc55TnC6FdjkxFGDz\ne69wKPwBGBkyaYP0QC2uyAyW1y3m6qZpzJtRicVcWhN/L4W2uVSpbSZHk3hFRM5gmsfHg9fdRzB6\nkrc6drK9Yyd9lQFild20pXfznx1ezG9OZ2HNAq6aV8/iOTW4HdZCly1yWVOAERHJqXJUcvvcL3H7\n3C/hD3fzzondbO/cSbDaD9V+3k+10/5+HZn/m8bc8jksb6pj2bxafFWuQpcuctnRENI46tYrXmqb\n4nSpt0smk6Ez1MWOrt28fWIX/YnsKr+ZpJVUXx2p3mnU2WewrKmW5U1e5jSUYzIVx7yZS71tSpna\nZnI0hCQico4Mw2C6ZxrTm6Zxx9z/4eOBT3jXv5sd/nYGfcex+I4TTOzhjc56Xnu/HmfKy7KmWpY1\n1bJwdjUOrTMjMiX0zRIRmSTDMJhdMZPZFTO5a97/cujkEXb4d7MrsIdw/VEs9UdJx5283VPPW69N\nwxwrZ8GsapY31bK0qZbqckehP4LIJUNDSOOoW694qW2Kk9oFUukU+4MH2eHfzZ7uvURTMQDM8TKi\ngTpSffVkoh5m1nmyvTPzaplVVzblp2irbYqX2mZyNIQkIjKFzCYzC2sWsLBmAfFUgg9697PDv5v3\ne/dhbTyEtfEQtkQVJ/w+Pnmnnpf/46TSY2NZrmfms7OqsFnNhf4YIiVFAUZE5AKyma0s8y1mmW8x\n0WSUPT0f8K5/Nx/0HcDSGMTS+CHulI9wl5etewfZursTm9XEws9Us6ypliVNtVS4bYX+GCJFTwFG\nRGSKOCwOPld/FZ+rv4pQIszu7vfY4W/nYPAwmekBXNM/oIIGYgEfuz6KsutgDwYwp6Gcpbmhpum1\n7pJeDVhkqijAiIhcBG6rixsaruWGhmvpjw2wM7CHd/3tHBk4Cr4O3HUmvKaZJHrq+eijJIc7B3jh\n/z6itsIxMm9mfgmuBiwyVTSJdxxNrCpeapvipHY5P72RPt4NtPOuv53jQ50AWE1WpllnQ7CBowcd\nRLNzgnHazSyeU8PSploWz6nB4zzzasBqm+KltpkcXQvpLOiXqnipbYqT2uXC6QoFsmvMBHYTCPcA\nYDfb+YxzHqb+6Rw9ZKe3Pw6AyTCY11jBsnnZNWfqqk9dDVhtU7zUNpOjAHMW9EtVvNQ2xUntcuFl\nMhmOD53ILZi3m2Asu/qv2+pivuezWEONHPvIxpHO0f/u02pc2XkzTbU0Ta/AZDLUNkVMbTM5RRNg\nQqEQDz/8MP39/SQSCR544AG8Xi+PPfYYAFdccQWPP/74px5HAebypLYpTmqXqZXOpPl44Bg7/O3s\nDLQzGB8CoMJWzqKqRTgjMzn2sZl9HweJJ9MAeJxWlsyt4XOLpuEwG9RWOKj02IvmEgei781kFU2A\nee655/D7/axZswa/3899992H1+vloYceYsmSJaxZs4Y77riDm2666YzHUYC5PKltipPa5eJJZ9Ic\nCB7mXX87u7vfI5yMAFDrqGaZdwkVidkcOwbth3rpH4rnvddsMqgpd1Bb6aC2wkFthTN7X+nEW+Gg\n3G3T2U4Xkb43k1M0C9lVVVXx4YcfAjAwMEBlZSUdHR0sWbIEgC9+8Yu0tbV9aoAREbkcmQwTC6rn\nsaB6Hs1XfJV9fQd4199Oe89e3vhkK7CV+qo6brl9KfWmJjKJMo4cP0lPf4Se/ig9JyN88HFwwmNb\nLSZqKxzUVDjwjgk32bDjwOO0KuBIUbnoc2C+853vcOzYMQYGBti4cSM/+9nPePHFFwFoa2tjy5Yt\nPPnkk2c8RjKZwmLRqpUiIgCxZJydJ97jP8d2sKvzfRLpJADTy+qZXl5PQ3kd08uy99X2GkJDEAhG\n8PeG8Acj+PtCBPrC+PvCDIYTE/4Mh81MXbULX7WLujE3X5WLuhr3p54RJXKhXdQemJdeeomGhgae\neeYZ9u/fzwMPPEBZ2Wj30GSzVDAYnqoS1a1XxNQ2xUntUhyaHPNpmj+fe+ZE2NP9ATsCuzkycJSO\nwS7oyN+3zOah3uWjzuWlrsHLdfN81Lk+Q7Wjilg8ne2t6Y/QczI6+rg/SiAY5mjXxG3ttFvwDvfg\nVDrH9eQ4dFXucfS9mZyiGULauXMnK1euBGDBggXEYjGSyeTI636/H5/PdzFLEhG5pDgtTq6dtoJr\np62gttbD4Y5O/OFu/OFA9j7UjT/czaGTRzh48qO891pMFnzO2mywcfuom+Fl/hVe6lwzcFgcZDIZ\nwrFkLthE6D45Gm56+qN0BcMcCwxNWJfHaR0ZjhodmhodotK1oORsXdQAM2vWLNrb27n11lvp6OjA\n7XYzffp0duzYwdVXX83rr79OS0vLxSxJROSSZRgGFfZyKuzlzK+am/daPJWgO9KTCzWBvJDTGeqC\n7vxjVdjKs6HG5c3ear1cM9NHlaMRk5FdHTiTyTAYTuT12vSczN5390c53h3i49P04JS7baftwaku\nd2C1aAViyXfRT6Net24dvb29JJNJfvCDH+D1enn00UdJp9MsXbqUH//4x596HJ2FdHlS2xQntUvx\nOpe2yWQy9McHcj01AbrC3QTC3XSFAiPr0YxlNVnxuWqpd/nwubzUu7z43F58Ti8Oiz1v33QmQ/9Q\nnN7+KN3jAk5Pf4S+gRip9Kl/kgyg3GPD47TisltwO6y4HBZcdkv23mHFPeb5yOsOC3aruSgnH+t7\nMzlFcxr1haIAc3lS2xQntUvxutBtE0vFCYR7xgxHBQiEs0NS8fSpk3+r7JW54ShvLtxke3Aq7RUT\nhop0OkNwMDbSe9N9MpILO1H6BqKEo0kisSRn80fLbDJwjgSbbNjJhiALzuGwM0HwGd5mNk1Nz4++\nN5NTNHNgRESkdNnNNmaUNTCjrCFvezqTpj82QFc4MDLHZjjk7A8eZH/wYN7+NrNtdChq5ObD56ql\nJjeMdMVpakhnMkRjSULRJOFoknA0kX0cyz2PZZ9HosP7JAjn9u8biJJMnd2/2e02c653xzoaguy5\nIOQYE4zsY59nQ5HNairK3p9LhQKMiIicF5NhospRSZWjks9Wz897LZqMEYh0jwxJ+XM9Nl0hP58M\ndpxyrGpHVV6oGe7BqbCVYxgGJsPIhYdzO207nkiNBJpwNJELQcncttHnoWiCyJig1DsQ4Xh36qx+\nltlk5Hp68oe4XA4rNZVO0skUDpsFh92M02bBYTNnn9vM2Zs9+1hXIJ+YAoyIiEwZh8XOzLJGZpY1\n5m1PZ9IEoydHAs3oZOIA+/oOsK/vQN7+NrONMqsHj9WN2+aizOrBbXXhsbpz29wjjz1WNy6rc2Ry\ncd5xrGZsVjOVHvspr32adDqT6+kZ7dWJ5MLOcA9QXjAa2SdBz8nIhPN7JsNiNiYMNnnbbBacYx6f\nbl+7zYzpEukVUoAREZGLzmSYqHFWU+Os5sqa/AGjSDI6Mrdm+Ayp7kgvQ4kQHaETJAeTpznqKAMD\nl9WZDTdjgo3H5s4PPmO2O8z2Mw75mEwGHqf1nBbty2QyxBPpkZ4eh9NOV2CAaDxFJJ4kGk/lbrnH\nsTGPx2zvHYgRjYc4n9mr9nFBxzlBGDpjWLKPvs9iLtwwmQKMiIgUFafFwazyGcwqn3HKa5lMhlgq\nTigRYigRYigRzj6ODzGUCDOUCOW/Fg8RCPeQmcTUX7NhxmN1jYSa8b06Hqsrb5vb6sZmnlyYMQwD\ne64HpKrMjtdbRq3n3IbBMpkM8WSaaCw/4EQmEYDGPo7EkgQHo8QT6XOqA7LDZMvne7n/q4vO+Rjn\nSgFGRERKhmEYOCx2HBY7Nc7qSb0nnUkTSUZHw018NPgMJoYIxfODTzDWn10LZxJsZttouLHmhxuP\nzZ3/ms2N2+LCbDq/RfsMw8BuNWO3mqk4ryNlpdJpYrlwc/oQdPow5Kt0XoAqzp4CjIiIXNJMhgm3\n1YXb6gK8k3pPKp0a7d3J3bLhJ8xQYij3PBt8huIhukKBCU8ln4jT4qTM7sJu2HFYHDgtTpwWR+7m\nHHc/frsDi+nC/uk2m0y4HKZznhhdKAowIiIi45hNZirsZVTYT78OyXjxVHw07OR6dYaDz2AiRCge\nGgk+sXSMQLyHWCp+1rVZTVZcFgcOizN378BlcebdTxR+hl+zm20TTnAuNQowIiIiF4DNbKPabKPa\nUfWp+w4vZJdKp4imYkSSUSLJyLj70z3O3ocSIXoivaQyZ3d6t4GRCzsTh5/x250T7GO9wL1A56Lw\nFYiIiFymzCYzbtPw8NbZy2QyJNLJCcLP2OBz+u29kSAdqRNn/XOtJstIoFlSu5CvNt12TvWfDwUY\nERGREmUYBjazFZvZSoW9/JyOkc6kiSZjZx1+IqkI4USEvmjwAn+qyVGAERERuYyZDBMuqxOXtTBn\nE52r0p/FIyIiIpcdBRgREREpOQowIiIiUnIUYERERKTkKMCIiIhIyVGAERERkZKjACMiIiIlRwFG\nRERESo4CjIiIiJQcBRgREREpOQowIiIiUnIUYERERKTkKMCIiIhIyTEymUym0EWIiIiInA31wIiI\niEjJUYARERGRkqMAIyIiIiVHAUZERERKjgKMiIiIlBwFGBERESk5CjBj/PKXv6S5uZlVq1axZ8+e\nQpcjYzzxxBM0Nzdz99138/rrrxe6HBkjGo1y880388ILLxS6FBnj5Zdf5o477uCuu+5i69athS5H\ngFAoxPe//31aWlpYtWoV27ZtK3RJJc1S6AKKxX//+1+OHj1Ka2srhw8fZt26dbS2tha6LAG2b9/O\nwYMHaW1tJRgM8rWvfY2vfOUrhS5LcjZu3EhFRUWhy5AxgsEgTz/9NM8//zzhcJjf/OY3fOELXyh0\nWZe9v/71r8yePZs1a9bg9/u57777ePXVVwtdVslSgMlpa2vj5ptvBmDu3Ln09/czNDSEx+MpcGVy\nzTXXsGTJEgDKy8uJRCKkUinMZnOBK5PDhw9z6NAh/XEsMm1tbVx33XV4PB48Hg8///nPC12SAFVV\nVXz44YcADAwMUFVVVeCKSpuGkHJ6enryfpmqq6vp7u4uYEUyzGw243K5ANiyZQuf//znFV6KxIYN\nG1i7dm2hy5Bxjh8/TjQa5Xvf+x6rV6+mra2t0CUJcPvtt9PZ2cktt9zCvffey8MPP1zokkqaemBO\nQ1dYKD5vvPEGW7Zs4Y9//GOhSxHgxRdfZNmyZcyYMaPQpcgETp48yVNPPUVnZyff/va3+de//oVh\nGIUu67L20ksv0dDQwDPPPMP+/ftZt26d5o6dBwWYHJ/PR09Pz8jzQCCA1+stYEUy1rZt2/jtb3/L\nH/7wB8rKygpdjgBbt27lk08+YevWrXR1dWGz2aivr+f6668vdGmXvZqaGpYvX47FYmHmzJm43W76\n+vqoqakpdGmXtZ07d7Jy5UoAFixYQCAQ0HD4edAQUs4NN9zAa6+9BsDevXvx+Xya/1IkBgcHeeKJ\nJ/jd735HZWVlocuRnF/96lc8//zz/PnPf+Yb3/gG999/v8JLkVi5ciXbt28nnU4TDAYJh8Oab1EE\nZs2aRXt7OwAdHR243W6Fl/OgHpicq666ioULF7Jq1SoMw2D9+vWFLkly/vGPfxAMBvnhD384sm3D\nhg00NDQUsCqR4lVXV8ett97KPffcA8BPf/pTTCb9e7XQmpubWbduHffeey/JZJLHHnus0CWVNCOj\nyR4iIiJSYhTJRUREpOQowIiIiEjJUYARERGRkqMAIyIiIiVHAUZERERKjgKMiEyp48ePs2jRIlpa\nWkauwrtmzRoGBgYmfYyWlhZSqdSk9//mN7/J22+/fS7likiJUIARkSlXXV3Npk2b2LRpE5s3b8bn\n87Fx48ZJv3/Tpk1a8EtE8mghOxG56K655hpaW1vZv38/GzZsIJlMkkgkePTRR7nyyitpaWlhwYIF\n7Nu3j2effZYrr7ySvXv3Eo/HeeSRR+jq6iKZTHLnnXeyevVqIpEIDz74IMFgkFmzZhGLxQDw+/38\n6Ec/AiAajdLc3MzXv/71Qn50EblAFGBE5KJKpVL885//ZMWKFTz00EM8/fTTzJw585SL27lcLp57\n7rm8927atIny8nKefPJJotEot912GzfeeCNvvfUWDoeD1tZWAoEAX/7ylwF45ZVXmDNnDo8//jix\nWIy//OUvF/3zisjUUIARkSnX19dHS0sLAOl0mquvvpq7776bX//61/zkJz8Z2W9oaIh0Og1kL+8x\nXnt7O3fddRcADoeDRYsWsXfvXg4cOMCKFSuA7IVZ58yZA8CNN97In/70J9auXctNN91Ec3PzlH5O\nEbl4FGBEZMoNz4EZa3BwEKvVesr2YVar9ZRthmHkPc9kMhiGQSaTybvWz3AImjt3Ln//+9955513\nePXVV3n22WfZvHnz+X4cESkCmsQrIgVRVlZGY2Mj//73vwE4cuQITz311Bnfs3TpUrZt2wZAOBxm\n7969LFy4kLlz57Jr1y4ATpw4wZEjRwD429/+xnvvvcf111/P+vXrOXHiBMlkcgo/lYhcLOqBEZGC\n2bBhA7/4xS/4/e9/TzKZZO3atWfcv6WlhUceeYRvfetbxONx7r//fhobG7nzzjt58803Wb16NY2N\njSxevBiApqYm1q9fj81mI5PJ8N3vfheLRf/bE7kU6GrUIiIiUnI0hCQiIiIlRwFGRERESo4CjIiI\niJQcBRgREREpOQowIiIiUnIUYERERKTkKMCIiIhIyVGAERERkZLz/69pOGkwNRZuAAAAAElFTkSu\nQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "GhFtWjQRzD2l"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "OMoIsUMmzK9b"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "These are only a few ways in which we could think about the data. Other transformations may work even better!\n",
+        "\n",
+        "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n",
+        "\n",
+        "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n",
+        "\n",
+        "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XDEYkPquzYCH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "8852a4f0-9bd7-45b3-f227-c6537f0d3253"
+      },
+      "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": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 93.68\n",
+            "  period 01 : 76.95\n",
+            "  period 02 : 73.97\n",
+            "  period 03 : 72.60\n",
+            "  period 04 : 72.51\n",
+            "  period 05 : 70.96\n",
+            "  period 06 : 72.50\n",
+            "  period 07 : 70.42\n",
+            "  period 08 : 69.98\n",
+            "  period 09 : 69.67\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.67\n",
+            "Final RMSE (on validation data): 67.19\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGACAYAAACDX0mmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xd8lfXd//HXOSc7ZJ4MCCETEvYQ\ncIDIkiHg7eLGgdRB7e+22lK1lWrFPdtqi3W0jmrVWq0WtNa9UHEgYYaRBDLIguxF9jnn+v0ROAUJ\nIYScnJPk/Xw8fJizrutz8rkO553r+l7fy2QYhoGIiIiIBzK7uwARERGR41FQEREREY+loCIiIiIe\nS0FFREREPJaCioiIiHgsBRURERHxWF7uLkDEE6SmphIXF4fFYgHAbrczefJk7rjjDgICArq83H/+\n858sWbLkmPvXrFnDbbfdxp///GdmzpzpvL+pqYkpU6Ywd+5cHn744S6vt7Py8/N58MEHyc3NBcDf\n358bb7yRc8891+XrPhlPPfUU+fn5x/xONmzYwPLly4mNjT3mNR988EFPlXdKCgsLmT17NomJiQAY\nhkFERAS/+c1vGDly5Ekt69FHHyUmJobLL7+80695++23efPNN3n55ZdPal0iPUVBReSQl19+mYED\nBwLQ0tLCTTfdxF/+8hduuummLi2vrKyM5557rt2gAjBo0CD+85//HBVUPv/8c4KDg7u0vq745S9/\nyQUXXMCf//xnALZt28ZVV13F+++/z6BBg3qsjlMxaNCgXhNKjsdisRz1Ht577z1uuOEGPvzwQ3x8\nfDq9nFtuucUV5Ym4lQ79iLTDx8eHadOmsXv3bgCam5u58847mTdvHueddx4PP/wwdrsdgIyMDC67\n7DLmz5/PBRdcwFdffQXAZZddRnFxMfPnz6elpeWYdZx22mls2LCBxsZG533vvfceU6dOdd5uaWnh\n/vvvZ968ecyaNcsZKAC2bNnCxRdfzPz581mwYAHffPMN0PYX+tlnn81LL73E+eefz7Rp03jvvffa\nfZ9ZWVmMGzfOeXvcuHF8+OGHzsD2xBNPMH36dC688EKeeeYZZs2aBcCvf/1rnnrqKefrjrx9oroe\nfPBBrrzySgA2bdrEJZdcwpw5c1iyZAkFBQVA256lX/ziF8ycOZMrr7ySAwcOnKBj7VuzZg033ngj\nV111Fb/97W/ZsGEDl112GStWrHB+qb///vssWrSI+fPn86Mf/Yj8/HwA/vSnP3HHHXewePFiXnzx\nxaOWu2LFCv761786b+/evZuzzz4bh8PBH/7wB+bNm8e8efP40Y9+RElJyUnXvWDBApqamsjJyQHg\n9ddfZ/78+cyaNYubb76ZpqYmoO33/tBDD3H++efz/vvvH9WH422XDoeDe++9lxkzZrB48WIyMjKc\n6/3++++56KKLWLBgAeeddx7vv//+Sdcu0u0METFSUlKM/fv3O29XV1cbS5cuNZ566inDMAzjL3/5\ni3HdddcZra2tRmNjo3HJJZcYb731lmG3243zzjvPeOeddwzDMIzt27cbkydPNurq6ozvvvvOOPfc\nc9td37/+9S9j5cqVxi9/+Uvna+vq6ozZs2cbb7zxhrFy5UrDMAzjiSeeMK666iqjubnZqK+vNy68\n8ELjs88+MwzDMBYtWmT85z//MQzDMNauXetcV0FBgTFy5Ejj5ZdfNgzDMN577z1jzpw57dbxs5/9\nzJg5c6bxt7/9zdi7d+9Rj2VmZhqTJk0ySktLjdbWVuP66683Zs6caRiGYaxcudJ48sknnc898nZH\ndY0aNcpYs2aN8/1OnjzZWL9+vWEYhvHOO+8YF110kWEYhvHKK68YS5cuNVpbW43Kykpj5syZzt/J\nkTr6HR/+PY8fP97Izc11Pn/MmDHGN998YxiGYRQVFRkTJ0408vLyDMMwjOeff9646qqrDMMwjMcf\nf9w4++yzjYqKimOW++677xpLly513l69erVx3333GVlZWcbcuXONlpYWwzAM46WXXjLWrl173PoO\n/15GjBhxzP2TJ082srOzjY0bNxpnnXWWceDAAcMwDGPVqlXGww8/bBhG2+/9/PPPN5qampy3n3zy\nyQ63y3Xr1hlz5841Dh48aDQ2NhqLFy82rrzySsMwDOPiiy82NmzYYBiGYeTm5ho333xzh7WL9ATt\nURE5ZNmyZcyfP5/Zs2cze/ZszjzzTK677joA1q1bx5IlS/Dy8sLPz4/zzz+fr7/+msLCQsrLy1m4\ncCEAY8aMISYmhvT09E6tc+HChfznP/8B4JNPPmHmzJmYzf/9WH7++edcccUV+Pj4EBAQwAUXXMBH\nH30EwFtvvcV5550HwMSJE517IwBsNhsXX3wxAKNGjaK4uLjd9f/ud79j6dKlvPPOOyxatIhZs2bx\nj3/8A2jb2zF58mQiIyPx8vJi0aJFnXpPHdXV2trKnDlznMuPjo527kFatGgR+fn5FBcXk5aWxpw5\nc/Dy8iIsLOyow2M/tH//fubPn3/Uf0eOZUlISCAhIcF528/Pj7POOguAr7/+mjPOOIP4+HgA/vd/\n/5cNGzZgs9mAtj1M4eHhx6xzxowZ7Nq1i+rqagA+/vhj5s+fT3BwMJWVlbzzzjvU1NSwbNkyLrzw\nwk793g4zDIPXX3+d6OhoEhIS+Oyzz1iwYAHR0dEAXH755c5tAOCss87C19f3qGV0tF1u3LiR6dOn\nExgYiJ+fn7NXAFarlbfeeovs7GwSEhJ49NFHT6p2EVfQGBWRQw6PUamsrHQetvDyavuIVFZWEhIS\n4nxuSEgIFRUVVFZWEhQUhMlkcj52+MsqIiLihOucOnUqd9xxB9XV1bz77rv89Kc/dQ5sBairq+Oh\nhx7iscceA9oOBY0dOxaAd955h5deeon6+nocDgfGEZftslgszkHAZrMZh8PR7vp9fX1Zvnw5y5cv\np7a2lg8++IAHH3yQ2NhYampqjhovY7VaT/h+OlPXgAEDAKitraWgoID58+c7H/fx8aGyspKamhqC\ngoKc9wcHB1NfX9/u+k40RuXIvv3wdlVV1VHvMSgoCMMwqKqqave1hwUEBDBlyhTWrVvHxIkTqa2t\nZeLEiZhMJv70pz/x17/+lfvuu4/Jkydzzz33nHC8j91ud/4eDMNg6NChPPXUU5jNZurq6vj4449Z\nv3698/HW1tbjvj+gw+2ypqaGqKioo+4/7MEHH+Tpp5/mmmuuwc/Pj5tvvvmo/oi4g4KKyA+Eh4ez\nbNkyfve73/H0008DEBER4fzrGaC6upqIiAisVis1NTUYhuH8Uqiuru70l7q3tzczZ87krbfeYt++\nfUyYMOGooBIVFcW11157zB6FkpIS7rjjDt544w1GjBhBXl4e8+bNO6n3WVlZye7du517NIKDg1my\nZAlfffUVWVlZBAUFUVdXd9TzD/th+KmpqTnpuqKiokhKSmLNmjXHPBYcHHzcdXcnq9XKli1bnLdr\namowm82EhYWd8LXz5s3j448/pqqqinnz5jn7f+aZZ3LmmWfS0NDAI488wu9///sT7pn44WDaI0VF\nRXHRRRexcuXKk3pfx9suO/rdRkREsGrVKlatWsX69ev52c9+xrRp0wgMDOz0ukW6mw79iLTjmmuu\nYcuWLXz//fdA267+N998E7vdTkNDA2+//TbTp08nNjaWgQMHOgerbt68mfLycsaOHYuXlxcNDQ3O\nwwjHs3DhQp599tl2TwmePXs2b7zxBna7HcMweOqpp/jyyy+prKwkICCApKQkbDYbr7/+OsBx9zq0\np6mpiZ///OfOQZYA+/btY9u2bUyaNIkJEyaQlpZGZWUlNpuNt956y/m8yMhI5yDMgoICNm/eDHBS\ndY0bN46ysjK2bdvmXM6vfvUrDMNg/PjxfPbZZ9jtdiorK/nyyy87/b5OxtSpU0lLS3MennrttdeY\nOnWqc09aR2bOnMmWLVv45JNPnIdP1q9fzz333IPD4SAgIIDhw4cftVejK2bNmsVHH33kDBSffPIJ\nzzzzTIev6Wi7nDBhAuvXr6exsZHGxkZnQGptbWXZsmWUlpYCbYcMvby8jjoUKeIO2qMi0o4BAwbw\nk5/8hEceeYQ333yTZcuWUVBQwMKFCzGZTMyfP5/zzjsPk8nEY489xl133cUTTzyBv78/q1evJiAg\ngNTUVEJCQpg6dSpr164lJiam3XWdfvrpmEwmFixYcMxjV1xxBYWFhSxcuBDDMBg9ejRXXXUVAQEB\nnHPOOcybNw+r1cqvf/1rNm/ezLJly3j88cc79R5jYmJ4+umnefzxx7n//vsxDIMBAwZw2223Oc8E\nuvTSS7nooosICwtj7ty57NmzB4AlS5Zw4403MnfuXEaOHOncazJ8+PBO1+Xn58fjjz/OfffdR319\nPd7e3qxYsQKTycSSJUtIS0vj3HPPJSYmhnPPPfeovQBHOjxG5Yd++9vfnvB3MHDgQO6//35++tOf\n0traSmxsLPfdd1+nfn8DBgxg1KhRZGZmMn78eAAmT57Mu+++y7x58/Dx8SE8PJwHH3wQgFtvvdV5\n5s7JGDVqFP/3f//HsmXLcDgcWK1W7rnnng5f09F2OXPmTNatW8f8+fOJiIhg+vTppKWl4e3tzeLF\ni7n66quBtr1md9xxB/7+/idVr0h3MxlHHkAWETmOtLQ0br31Vj777DN3lyIi/Yj26YmIiIjHUlAR\nERERj6VDPyIiIuKxtEdFREREPJaCioiIiHgsjz49uays/dMRu0tYWABVVQ0uXYecPPXFc6k3nkl9\n8VzqTedFRga1e3+/3qPi5WVxdwnSDvXFc6k3nkl98Vzqzanr10FFREREPJvLDv04HA7uuusu9uzZ\ng7e3N3fffTfPPvssO3fuJDQ0FIDly5czY8YMV5UgIiIivZzLgsqnn35KXV0dr732Gvn5+TzwwAOE\nhYVx8803d3jJdhEREZHDXHboJy8vz3k5+ri4OIqLi7Hb7a5anYiIiPRBLpvw7YsvvuBvf/sbzz77\nLPv27ePiiy9m0qRJGIZBa2srVquVVatWER4eftxl2Gx2DUQSERHpx1w6M+0f/vAHNmzYQGpqKunp\n6Vx99dUMGzaMESNG8Mwzz3DgwAHuvPPO477e1acnR0YGuXwdcvLUF8+l3ngm9cVzqTedd7zTk106\nj8pNN93k/Pncc89l0aJFmM1tR5tmzZrF3Xff7crVi4iISC/nsjEqGRkZ3HbbbQB8+eWXjBw5khUr\nVlBQUADAhg0bGDZsmKtWLyIi0i+sW/dpp563evWjFBcXHffxX//65u4qqVu5bI9KSkoKhmGwePFi\nfH19+f3vf8++ffv4xS9+gb+/PwEBATz00EOuWr2IiEift39/MZ988iEzZsw+4XNXrLilw8cffvix\n7iqrW3n01ZM1RqV/Ul88l3rjmdQXz+Xq3vzqVyvYvXsnNTU1zJ17Hvv3F/PHPz7FQw/dS1lZKY2N\njVx77U+YOnUaN974E26++VY+//xT6usPkp+/j6KiQn7+81s466ypLFw4m3ff/ZQbb/wJkyefwebN\naVRXV/PII38gIiKCe+9dxYED+xkzZiyfffYJa9e+163vxS1jVERERPqDf362l40Zpcfcb7GYsNu7\ntj9g8vAolswa2uFzLr98GWvW/JPExGTy8/N46qnnqKqq5PTTz+S88xZRVFTIqlW/ZurUaUe9rrS0\nhN///nG+++4b3n77X5x11tSjHg8MDGT16qd5+uk/8eWXnxETE0tLSzPPPPMiX3/9Ff/85z+69J66\nol8GFbvDzrbyncwMm+zuUkRERLrFiBGjAAgKCmb37p38+99rMJnM1NbWHPPcsWPHAxAVFcXBgweP\neXzcuAnOx2tqati3L5cxY8YBcNZZU7FYem7qkH4ZVPZU5/D8jldosTRypvUMd5cjIiK93JJZQ9vd\n+9GTh+W8vb0B+PjjD6itreXJJ5+jtraWH/942THPPTJotDcC5IePG4aB2dx2n8lkwmQydXf5x9Uv\nL0o4KDAagPSS3W6uREREpOvMZvMxs75XV1czaFAMZrOZL774jNbW1lNez+DBsWRm7gLg+++/69GZ\n5vtlUAnxDSY6IJLd5dnYHZrWX0REeqf4+EQyMzOor//v4ZsZM2bxzTdfsWLF9fj7+xMVFcULLzx7\nSuuZMmUa9fX1XH/9crZt20JwcMiplt5p/fasn39krmF90XfcMvEGkkLiXbYeOXk6g8FzqTeeSX3x\nXH2lN7W1NWzenMaMGbMpKytlxYrrefXVf3XrOnTWzw+khCazvug7sqr2KqiIiIh0ICAgkM8++4RX\nX30Zw3Dws5/13ORw/TaoJAYlApBVlc38hBNPlCMiItJfeXl5ce+97pmktV+OUdl3oI6VT2zC6htF\nTk0erQ6bu0sSERGRdvTLoOLtZcbuMLA0RNDqsJFXk+/ukkRERKQd/TKoDLIGYA32pbwwAICs6mw3\nVyQiIiLt6ZdBxWQyMTrJSn15MAB7qhRUREREPFG/DCoAoxOtYPchCCu5NftosZ/6hDgiIiKeZvHi\n82loaODll19kx47tRz3W0NDA4sXnd/j6des+BeC9997hiy8+d1mdx9Nvz/oZER+GxWzCXmfFFlRB\nbs0+UsM7vviTiIhIb7Vs2dUn/Zr9+4v55JMPmTFjNgsWdBxoXKXfBpUAPy+GJ4STsb8En6C2cSoK\nKiIi0ltce+1SHnzwUQYOHMiBA/u57bZbiIyMorGxkaamJm666VeMHDna+fwHHribGTNmM378BH7z\nm1tpaWlxXpwQ4KOP3ufNN1/HYjGTkJDMypW/4bHHHmH37p288MKzOBwOQkNDueSSS3nqqdWkp2/D\nZrNzySVLmD9/ITfe+BMmTz6DzZvTqK6u5pFH/sDAgQNP+X3226ACMHF4FDs/PIAJE1kapyIiIl20\nZu9/2FKafsz9FrMJu6NrE8BPiBrDxUMXHffxc86Zyddff8kllyzhq6++4JxzZpKcPIxzzpnBpk0b\n+fvf/8YDD/zumNd9+OH7JCUl8/Of38Knn37EJ598CEBjYyOPPvongoKCuOGG68jO3svlly9jzZp/\ncs011/H8838BYOvWzeTkZPP003+lsbGRq666jHPOmQFAYGAgq1c/zdNP/4kvv/yMJUuu6NJ7P1K/\nHaMCMHF4NNi98beHs6+2gGZ7i7tLEhER6ZS2oPIVAOvXf8HZZ0/niy8+5frrl/P003+ipqam3dfl\n5eUwevQ4ACZMmOi8Pzg4mNtuu4Ubb/wJ+/blUlNT3e7rMzJ2MX78aQD4+/uTkJBEQUEBAOPGTQAg\nKiqKgwcPtvv6k9Wv96gkxgQTEuhDU2UojsgKcqrzGGFNcXdZIiLSy1w8dFG7ez9cea2fpKRkKirK\nKCk5QF1dHV99tY6IiChWrbqPjIxdPPHEH9t9nWGA2WwCwHFob09rayuPPfZbXnzxVazWCG699RfH\nXa/JZOLIqwTabK3O5VksliPW0z2XEuzXe1RMJhOjE8NprAwFNJ+KiIj0LmeddTbPPPMU06ZNp6am\nmsGDYwH44ovPsdnan3U9Li6ejIzdAGzenAZAQ0M9FosFqzWCkpIDZGTsxmazYTabsdvtR71++PBR\nbNmy6dDrGigqKiQ2Ns5Vb7F/BxWA0UlWHHVhGqciIiK9zvTpM51n5cyfv5DXX/87N910A6NGjaai\nooJ33/33Ma+ZP38hO3ems2LF9RQU7MNkMhESEsrkyWfw4x//iBdeeJYrrljG448/Rnx8IpmZGTz+\n+KPO148bN57U1OHccMN13HTTDfzf/92Iv7+/y96jyeiufTMu4OpLY0dGBpGbX8mKx78ieNxGbL5V\n/G7a3fh5+bl0vdKxvnJZ9L5IvfFM6ovnUm86LzIyqN37+/0elQH+3iQNCqahPASH4WBvda67SxIR\nEZFD+n1QgbbDP/bacAAd/hEREfEgCirA6KRwHAfDMBlmDagVERHxIAoqQOLAYAJ9fKExjMK6Yhpa\nG9xdkoiIiKCgArSdTz4qMZyWqlAMDPZonIqIiIhHUFA5ZEySFcehcSp7NE5FRETEIyioHDI6MRzH\nwVCNUxEREfEgCiqHhAzwJS4qBHtdKEUH93Owpd7dJYmIiPR7CipHGHPEacp7qnPcXI2IiIgoqBxh\ndGI4jloroPlUREREPIGCyhGSB4fgawsHh0XjVERERDyAgsoRvCxmRsZFYK8L5UB9CbUtuj6DiIiI\nOymo/MDopHCdpiwiIuIhFFR+YHSiFbvGqYiIiHgEBZUfsIb4MdB/EIbdQqaCioiIiFspqLRjTGIE\njrowyhrLqW6ucXc5IiIi/ZaCSjvaptPX4R8RERF3U1BpR8qQEMwNEYCCioiIiDspqLTD28tCamQc\nhs2LjIq97i5HRESk31JQOY6xSZE46sKpaqmiorHK3eWIiIj0SwoqxzE6Kdx53R/NUisiIuIeCirH\nER0WQKgRA0BmpQ7/iIiIuIOCSgfGxiZgtHqzu2IvhmG4uxwREZF+R0GlA2OTInDUhXPQVkt5Y6W7\nyxEREel3vFy1YIfDwV133cWePXvw9vbm7rvvJiAggFtvvRW73U5kZCS/+93v8PHxcVUJp2x4XBis\nt0J4CVnVe4kMsLq7JBERkX7FZXtUPv30U+rq6njttdd44IEH+O1vf8vjjz/OFVdcwauvvkp8fDxv\nvvmmq1bfLXx9LMQFJgCws2yPe4sRERHph1wWVPLy8hg7diwAcXFxFBcXs2HDBmbPng3AzJkz+fbb\nb121+m4zYUgiRqsPWVXZGqciIiLSw1wWVFJSUli/fj12u52cnBwKCgooKipyHuqxWq2UlZW5avXd\nZmyyFXttOI2OekobPL9eERGRvsRlY1SmT5/O5s2bWbp0KampqSQlJZGVleV8vDN7J8LCAvDysriq\nRAAiI4M6fDwiYgABH0fTwgEKWwsZHZns0nqkzYn6Iu6j3ngm9cVzqTenxmVBBeCmm25y/nzuuecS\nHR1NU1MTfn5+lJSUEBUV1eHrq6oaXFkekZFBlJXVnfB5KeFD2cE21u/ZxsTQ01xak3S+L9Lz1BvP\npL54LvWm844X6Fx26CcjI4PbbrsNgC+//JKRI0cyZcoUPvzwQwA++ugjpk2b5qrVd6vJCQkYLb7k\n1eVpnIqIiEgPctkelZSUFAzDYPHixfj6+vL73/8ei8XCypUref3114mJieHCCy901eq71cgEK470\ncFqs+9lfX0LMgIHuLklERKRfcFlQMZvNPPzww8fc/8ILL7hqlS4T4OdFhCWWKvazozRLQUVERKSH\naGbaThoblQLAlv0Zbq5ERESk/1BQ6aTTkxNxNPtR1FSAw3C4uxwREZF+QUGlk+IGBmFpiMRuaqaw\nbr+7yxEREekXFFQ6yWwyMSQgHoCNBbvcXI2IiEj/oKByEibFDgdgh677IyIi0iMUVE7C6ckJOJr8\nKbMVaZyKiIhID1BQOQlBAT4E2gZimFvZU5Hv7nJERET6PAWVk5QckgjAt3k73VyJiIhI36egcpKm\nJIwGIKs6282ViIiI9H0KKidpzJDB0BxIjbEfm93m7nJERET6NAWVk2Q2mwg3DQaLnc2F2qsiIiLi\nSgoqXTDcOhSA7ws0TkVERMSVFFS6YPrQMQDkHcxzbyEiIiJ9nIJKF8SGWbG0BNNgKaO+qdnd5YiI\niPRZCipdNNBnCCaLnfXZu91dioiISJ+loNJF4wamArBlf6abKxEREem7FFS66OykURgGFDftc3cp\nIiIifZaCSheF+AXhZw/D5ldJUUWNu8sRERHpkxRUTkFcYDwms4Ov9u5ydykiIiJ9koLKKZgUOwKA\nnWV73FyJiIhI36SgcgpOixkOBlQ4imi1OdxdjoiISJ+joHIKArz9GUAEBFSzK7/M3eWIiIj0OQoq\npyg5NAmT2eDbPI1TERER6W4KKqfojCFt41T2VOW4uRIREZG+R0HlFKWGJ4NhosH7AJW1Te4uR0RE\npE9RUDlFfl5+hHlFYQqsZUvOAXeXIyIi0qcoqHSDkdZhmEwGGwsy3F2KiIhIn6Kg0g3GD2q77k9B\nfR42u05TFhER6S4KKt0gOTQRk2HGEVBOTnGtu8sRERHpMxRUuoGvxYco35i2cSrZxe4uR0REpM9Q\nUOkmY6OHYTLBtgOaTl9ERKS7KKh0k5ERwwCocBRSU9/i5mpERET6BgWVbpIYHI8ZC+agSnbmVri7\nHBERkT5BQaWbeFu8iQ2MxRxYx9YcjVMRERHpDgoq3WhMZAoAuyuycTgMN1cjIiLS+ymodKOU8KEA\ntPiVknegzs3ViIiI9H4KKt0oPngIFpMX5qBKduRonIqIiMipUlDpRt5mL5KC4zEHHGTrviJ3lyMi\nItLrKah0s+HWttOUC+r3cbCx1c3ViIiI9G4KKt0sJSwZAHNQJbvyKt1cjYiISO+moNLN4oNi8TZ5\nYw6uJF3jVERERE6Jgko3s5gtDA1LxOxfT3p+MYah05RFRES6SkHFBVLD2k5Trvc6QEHpQTdXIyIi\n0nspqLjAkeNUduRqnIqIiEhXKai4QOyAGPwsfpiDNZ+KiIjIqVBQcQGL2cKwsETMfg3sKTlAY7PN\n3SWJiIj0Sl6uWnB9fT0rV66kpqaG1tZWbrjhBp555hkaGhoICAgAYOXKlYwePdpVJbhVSmgy6eW7\nYUAFu/dVcVpKpLtLEhER6XVcFlTWrl1LYmIit9xyCyUlJVx11VVERkby0EMPkZKS4qrVeoxhhwbU\nHj78o6AiIiJy8lx26CcsLIzq6moAamtrCQsLc9WqPNLgAQMJ8ArAK6SS7TkVOk1ZRESkC0yGC79B\nly9fTn5+PrW1tfzlL3/h0UcfJSQkhKqqKpKTk7n99tvx8/M77uttNjteXhZXledyv1//F74v2krT\ntnN4csUihkQHubskERGRXsVlh37efvttYmJieP7558nIyOD222/n+uuvJzU1lbi4OO666y7+/ve/\ns3z58uMuo6qqwVXlARAZGURZWZ3Llh8fEM/3bMUcVMmXmwqYO3mIy9bVl7i6L9J16o1nUl88l3rT\neZGR7f8x77JDP5s3b+bss88GYPjw4ZSWljJr1izi4uIAmDVrFllZWa5avUdwzqcSXKHTlEVERLrA\nZUElPj6ebdu2AVBUVERAQAAEd7hIAAAgAElEQVTLly+ntrYWgA0bNjBs2DBXrd4jDAqMZoB3IN6h\nVWTkV9Hcand3SSIiIr2Kyw79XHrppdx+++1ceeWV2Gw27rnnHqqqqrj66qvx9/cnOjqan/3sZ65a\nvUcwmUwMC0tmS+t27N4HycyvZmyy1d1liYiI9BouCyqBgYGsXr36mPsXLFjgqlV6pJTQZLaUbscc\n1Hb4R0FFRESk8zQzrYsdHqfiHVpFuq77IyIiclIUVFwsOiCSYJ8gvEKqKKmsp7S60d0liYiI9BoK\nKi5mMplICUvGbm7C5FfPTp39IyIi0mkKKj0gJfS/pymn5+jwj4iISGcpqPSAlEPX/QmIqGH3vipa\nbQ43VyQiItI7KKj0gAj/cMJ8QyGwguZWG3sLq91dkoiISK+goNIDDo9TsZmaMfkf1Nk/IiIinaSg\n0kOGOU9TrtR0+iIiIp2koNJDDg+oDYqqo7Csnqq6ZjdXJCIi4vkUVHqI1T8Mq184Lb6lgEG69qqI\niIickIJKD0oJS8ZGC6aAWh3+ERER6QQFlR50eDr94KhaduZVYXfoNGUREZGOKKj0oMNBJSCihsZm\nGznFtW6uSERExLMpqPSgUN8QovwjqLeUAg7NUisiInICCio9bFhYMq1GC15BdRqnIiIicgIKKj3s\n8OGfyNh68g7UUVvf4uaKREREPJeCSg8bdmg+Fe+QKgB2apZaERGR41JQ6WEhvkEMDIii2jgAJgfp\nuTr8IyIicjwKKm6QEjaUVqOV4Ih6duRU4jAMd5ckIiLikRRU3OC/41QaONjYyr4DdW6uSERExDMp\nqLjBsNAkAIzAtsM+OvtHRESkfQoqbjDAJ5DBAwZRZivGZNZ8KiIiIsejoOImKaHJ2Bw2YuNbyS6u\nob6p1d0liYiIeBwFFTcZdmicSsjAOgwDduVVubkiERERz6Og4ibDQhMxYaLZtxSAdI1TEREROYaC\nipsEeAcQGxTD/sYiAgNM7MipwNBpyiIiIkdRUHGjlNBk7IadhGQb1QdbKCqrd3dJIiIiHqXLQSUv\nL68by+ifDs+nEmCtAXT4R0RE5Ic6DCrXXHPNUbefeuop58933nmnayrqR5JDEzGbzNSa9wMKKiIi\nIj/UYVCx2WxH3f7uu++cP2s8xanz9/JjSNBgCuuLGDLInz2FNTQ22078QhERkX6iw6BiMpmOun1k\nOPnhY9I1KaHJOAwHsfHN2B0GGfk6TVlEROSwkxqjonDS/Q6PU7GEtM1Ou0Oz1IqIiDh5dfRgTU0N\n3377rfN2bW0t3333HYZhUFtb6/Li+oOkkATMJjNltkL8fSNIP3SaskKhiIjICYJKcHDwUQNog4KC\nePLJJ50/y6nz8/IlITiO3Jp9DE+YypbMGg5UNjDIGuju0kRERNyuw6Dy8ssv91Qd/VpKWDI5NXlE\nxjZCZtvhHwUVERGRE4xROXjwIC+++KLz9muvvcYFF1zAz3/+c8rLy11dW7+REto2TsUe0PY7Tc/V\nacoiIiJwgqBy5513UlHR9qWZm5vLY489xsqVK5kyZQoPPPBAjxTYHySGxONlslBQn0dsZCCZ+dW0\ntNrdXZaIiIjbdRhUCgoKuOWWWwD48MMPmT9/PlOmTOGyyy7THpVu5GPxJjEknsKD+0lNDKTV5iCr\noNrdZYmIiLhdh0ElICDA+fP333/PmWee6byts1K617CwZAwMQqMPArBds9SKiIh0HFTsdjsVFRXk\n5+ezZcsWpk6dCkB9fT2NjY09UmB/cXicykHLAXy9LZpPRUREhBOc9XPdddexYMECmpqauPHGGwkJ\nCaGpqYkrrriCJUuW9FSN/UJCSBzeZi/21uQwIv5ctu4tp6y6kchQf3eXJiIi4jYdBpXp06ezfv16\nmpubGTBgAAB+fn786le/4uyzz+6RAvsLb7MXSSEJZFbtZVGCH1v3wo7cSmZOGOzu0kRERNymw0M/\nxcXFlJWVUVtbS3FxsfO/pKQkiouLe6rGfuPwdPp+1rZZf3donIqIiPRzHe5RmTVrFomJiURGRgLH\nXpTwpZdecm11/czhoFLSUkB0+CB27avCZnfgZTmpSzKJiIj0GR0GlUceeYS3336b+vp6Fi5cyKJF\niwgPD++p2vqd+KAh+Fh8yKrKZkziKD7ZVMiewhpGxIe5uzQRERG36DCoXHDBBVxwwQXs37+ftWvX\nsnTpUgYPHswFF1zAnDlz8PPzO+5r6+vrWblyJTU1NbS2tnLDDTcQGRnJ3XffDUBqair33HNPt76Z\n3s5itpAcksDuyizmxPnCprbDPwoqIiLSX3XqmMKgQYP46U9/yvvvv8+8efO4//77TziYdu3atSQm\nJvLyyy+zevVqHnjgAR544AFuv/12XnvtNQ4ePMgXX3zRLW+iLzl8+IcB5XhZzKTrNGUREenHOhVU\namtreeWVV7j44ot55ZVX+H//7//x3nvvdfiasLAwqqurna8PDQ2lqKiIsWPHAjBz5ky+/fbbUyy/\n7zkcVHLrckmNC6Ww7CBVdc1urkpERMQ9Ojz0s379ev71r3+xY8cO5s6dy8MPP0xKSkqnFrxw4ULW\nrFnDnDlzqK2t5emnn+bee+91Pm61WikrKzu16vugIQMG42fxI6sqmylJZ7Azt5IduRVMGxvj7tJE\nRER6XIdB5cc//jEJCQmcdtppVFZW8sILLxz1+EMPPXTc17799tvExMTw/PPPk5GRwQ033EBQUJDz\n8SPPIDqesLAAvLwsJ3zeqYiMDDrxk3rYyOhhbC5OZ+yZQbz2KewpquXi2anuLqtHeWJfpI1645nU\nF8+l3pyaDoPK4dOPq6qqCAs7ekBnYWFhhwvevHmzcxzL8OHDaW5uxmazOR8vKSkhKiqqw2VUVTV0\n+PipiowMoqyszqXr6IqEgHg2k86+mmyswX5szijlQEkNFnP/OE3ZU/si6o2nUl88l3rTeccLdB1+\n85nNZm655RZWrVrFnXfeSXR0NKeffjpZWVn88Y9/7HCF8fHxbNu2DYCioiICAwNJTk4mLS0NgI8+\n+ohp06Z15b30eYfHqWRVZzMmKZyGZhu5xdrQRUSk/+lwj8of/vAHXnzxRZKTk/n000+58847cTgc\nhISE8MYbb3S44EsvvZTbb7+dK6+8EpvNxt13301kZKRzGePGjWPKlCnd+mb6isEDBhHg5c+eqmwu\nTJrJuq3FpOdUMDQ2xN2liYiI9KgOg4rZbCY5ue2v+9mzZ/PQQw+xcuVK5syZc8IFBwYGsnr16mPu\nf/XVV7tYav9hNpkZFprEtvKdREeDxWxiR24FF52T5O7SREREelSHh35MJtNRtwcNGtSpkCKnbtih\nwz/59XkMiw0hb38dtQ0tbq5KRESkZ53U6MwfBhdxnSPHqYxOsmIAu3I1+ZuIiPQvHR762bJlCzNm\nzHDerqioYMaMGRiGgclkYt26dS4ur/8aFBjNAO9AsqqymZG8gDeB9JwKzhw10N2liYiI9JgOg8oH\nH3zQU3XIDxwep7KlLB2/oGZCAn3YkVuJwzAwa8+WiIj0Ex0GlcGDB/dUHdKOlLBktpSls6c6m9FJ\n4XydfoD8kjoSBga7uzQREZEe0T9mEOulnONUqrIZk2QF0EUKRUSkX1FQ8WDRAVEE+wSRVZ3NiPgw\nTCbYkVPh7rJERER6jIKKBzOZTAwLTaKu5SD1RhVJMcFkF9XS0NTq7tJERER6hIKKhzvq8E+iFYdh\nsCuvys1ViYiI9AwFFQ+XEjYUaAsqo53jVHT4R0RE+gcFFQ8X6W8l1DeEPdU5xEUHMsDfmx25lRiG\n4e7SREREXE5BxcOZTCZSwpI52FpPSWMpoxLDqaprpqi83t2liYiIuJyCSi+QEnrkacrhAOzQacoi\nItIPKKj0AocH1O6pymZUosapiIhI/6Gg0gtY/cOx+oWxpzqHoAAv4qOD2FNYTVOLzd2liYiIuJSC\nSi8xLCyZBlsjRQf3MzopHJvdIGNftbvLEhERcSkFlV7i6HEqhw7/5Orwj4iI9G0KKr3EkRO/JcUE\n4+9rIT27Qqcpi4hIn6ag0kuE+YUS6W9lb3UuJpPByIRwymuaKK1qdHdpIiIiLqOg0oukhCXTZG+i\n4GDREVdT1uEfERHpuxRUepEjx6mMTmybTyVd86mIiEgfpqDSiww7YpxKeLAfgyMCycyvoqXV7ubK\nREREXENBpRcJ8Q0mOiCK7Jo87A47o5PCabE5yCrUacoiItI3Kaj0MilhybTYW9hXV+C8mrKm0xcR\nkb5KQaWXOfI05ZTYUHy8zRpQKyIifZaCSi9z5IBaby8zI+LC2F/RQHmNTlMWEZG+R0GllxngE0hM\n4EByavJoddh0+EdERPo0BZVeKCUsmVaHjbyafMYktZ2m/OHGAvIO1Lq5MhERke6loNILOcepVGcT\nFRbA7ImxlFQ2cN/f0njt0z26qrKIiPQZCiq90LDQJEyY2FOVDcDSOSn88rLxRIb689HGAlY9t4Ft\ne8vdXKWIiMipU1DphQK8A4gdMIjcmn202FsBGJkQzr3Xns7Cs+KpPtjC6je38/RbO6g52OzmakVE\nRLpOQaWXGhaWjM2wk1uzz3mfj7eFS6Ync9c1k0keHMzGjFJuf3YD67YW4dBVlkVEpBdSUOmljhyn\n8kOxkQO47cqJLJubAhi89EEmD/99M0Xl9T1cpYiIyKlRUOmlhoYmYsJEVtWxQQXAbDIx87RY7v/x\nmUxMjWRvYQ13//V71n6ZQ6tN1wYSEZHeQUGll/L38icuKJa82nya7S3HfV5YkC83XDSGn10yhuBA\nH975Jo87/7qRjH1VPVitiIhI1yio9GIpYck4DAfZ1bknfO6EYZHc/+MzOHdSLKWVDfz2H1v467u7\nOdjY2gOVioiIdI2CSi827Ijr/nSGv68XV5ybwm9+NIkhUQNYn76f3zz7Hd/uPIChwbYiIuKBFFR6\nseSQBMwmc7sDajuSFBPMqqsm8b8zk2lusfPsO7t47J/bKK3W9YJERMSzKKj0Yn5evsQHDaGgrohG\nW9NJvdbLYua8M+K578dnMDoxnJ25ldz53Abe/24fNrvDRRWLiIicHAWVXi71JMaptCcy1J+blozj\nJ/8zEl8fC2+sy+beF9PIKdZ1g0RExP0UVHq5w+NUMqv2dnkZJpOJM0cO5IHrzmTa2EEUlh3kgZfS\n+PvHWTQ267pBIiLiPgoqvVxSSAJ+Fj8+L1jPh3mf4TC6fthmgL831ywYwcorJhAdHsCnmwq547kN\nbMkq68aKRUREOk9BpZfzsXhzw/jlhPgG8++cD/jL9hepb204pWWmxoVxz7Wn8z9TE6itb+FPa9J5\nYk06VXW6bpCIiPQsBZU+ICkknl9PXsHwsGHsqMjg4Y2r2VdbcErL9PYyc+G0JO659nRSYkPYnFXG\nb579jk83FeJw6FRmERHpGZa77777bncXcTwNDcefcbU7BAb6unwdPcXX4sPkgRMwAenlu9mwP41A\n70DigmIxmUxdXm5QgA9TxgwiLMiX3XlVbM4qY2duJUmDggkO9Om+N3CEvtSXvka98Uzqi+dSbzov\nMNC33fsVVPrQBmQymUgJSyYxOJ70it1sKUuntLGcEeEpeJm9Tmm5CQODmTpmIFV1zezIreTLbcW0\n2hwMHRyCxdK9O+b6Wl/6EvXGM6kvnku96bzjBRWT4aIpSd944w3+/e9/O2/v2LGD0aNH09DQQEBA\nAAArV65k9OjRx11GWVmdK0pziowMcvk63KWqqZrnd7xCbm0+AwOi+PGYZQwKjO6WZW/PLuflD7Oo\nqG0iKtSfZfNTGZUQ3i3Lhr7dl95OvfFM6ovnUm86LzIyqN37XRZUjvT999/z/vvvs3fvXlatWkVK\nSkqnXqegcmpsDhtvZb/H5wXr8TF7c8XwxUweOKFblt3cYuet9Tl8tLEAw4CzRg3k0tlDCQ449cNB\nfb0vvZl645nUF8+l3nTe8YJKjwymffLJJ/npT3/aE6uSI3iZvVg87H9YPvpKzCYzL+76B69lrqXV\ncepzo/j6WLh01jDuvGoy8QOD+HbnAe54dgNfp+/XdYNERKTbuHyPyvbt23n11Vd5+OGHWbZsGSEh\nIVRVVZGcnMztt9+On5/fcV9rs9nx8rK4srx+o7iuhMe+fpb8miKSw+K5aep1RAVau2XZdruDd9bn\n8vcPdtPUYmfs0AhuWDyOmMgB3bJ8ERHpv1weVO68804WLlzIGWecwccff0xqaipxcXHcddddxMXF\nsXz58uO+Vod+uleLvYXXM9/iuwNpBHj586ORlzImYmS3Lb+8ppFXPspie3YFXhYz509N4Lwz4vA6\nycG2/a0vvYl645nUF8+l3nSe2w79bNiwgQkT2sZFzJkzh7i4OABmzZpFVlaWq1cvR/Cx+LBs5BKW\nDv9fWh2t/Hn7i7yd/T52h71blh8R4s+KxWO5/sLRBPp5sfbLHO55YSN7C2u6ZfkiItL/uDSolJSU\nEBgYiI+PD4ZhcPXVV1Nb23axuw0bNjBs2DBXrl6OY0rMZG6ZeCMR/lY+2vc5f9r6LDXN3ZP4TSYT\nk4dH8cB1ZzBjfAxF5fU8+MomXvowk4am1m5Zh4iI9B8uDSplZWWEh7edtmoymViyZAlXX301S5cu\n5cCBAyxdutSVq5cODAmK4deTf864yNHsqc7h4Y1/ZE9VdrctP8DPmx/NH86vl55GTEQg67YU8Zvn\nNpCWUarBtiIi0mk9cnpyV2mMiusZhsFnBV/xVvZ7GIbB/yTP59y46ZhN3ZdhbXYH73+3j3e+2YfN\n7mD80AiunJtCeHD7A6nVF8+l3ngm9cVzqTedd7wxKpqZtp/PGGgymUgKiSclLJldFZlsK99J4cEi\nRoan4m3x7pZ1mM0mUuPCmDwiiqKyg+zIreSLrcX4eltIHBR8zBT/6ovnUm88k/riudSbzjvezLS6\nKKEAMDQ0kdtO/wWpYUNJL9/NwxtXk19b2K3rGBgewK8un8C1C0bgZTHxj0/3cP9Laew7oL82RESk\nfdqjoqTrdPjChmCQXr6b7/anMcBnAHFBg0/pwoZHMplMxEUHcfaYQVTXt1036Ktt+2lqsTN0cAhe\nFrP64sHUG8+kvngu9abztEdFOsVsMrMoaR7Xj7sWX4svr2Wu4W+7XqfZ3r0ftOBAH35y/ihuvnQc\n4cG+fPB9Pque30B6TkW3rkdERHo37VFR0m1XVEAEE6PHkVuTz67KDLaV7yQ1bCgDfAK7dz1hAZwz\nPgaHYbAjp5Jvdx4gp6gGu91B6ABffLw1M7En0WfGM6kvnku96bwev3pyd9BZP+5nc9hYu/dd1hV+\njY/Fh6XDFzMperxL1pVfUsffPsgkd3/bXDsmEwwdHMLYZCtjkyOIjQzstkNQ0jX6zHgm9cVzqTed\n59arJ3eVgorn2FSyjb9nvEGzvYVzBk/h4mGL8DZ7dft6DMOgrsXBF2n5bM+pIKeolsMbaFiQL2OS\nrIxLtjIiIQw/n+5fv3RMnxnPpL54LvWm8xRU2qEN6OSU1Jfy3I5XKK4/QHzQEJaPvhKrf1i3r+fI\nvtQ1tLAjt5L07ArScyqob2q78rOXxUTqkFDGJEcwNtnKwPCAbq9DjqXPjGdSXzyXetN5Cirt0AZ0\n8lrsLbyWuZYNBzYR4OXPVSMvY3TEiG5dx/H64nAY5BTXsj2nnO17K8gvPeh8LCrMn7FJVsYOtZI6\nJBRvXXXbJfSZ8Uzqi+dSbzpPQaUd2oC6xjAMvin+nn/ueRubw8b8+FksTJrbbbPZdrYvVXXNpOdU\nsD27gp15lTS3tF1c0cfbzMj4cMYmWxmTZMUa0v4MuHLy9JnxTOqL51JvOk9BpR3agE5NQV0Rz6W/\nTHlTJSlhQ7lm1OUE+7S/oZ2MrvTFZneQVVDN9kOHiPZXNDgfi40MZEyylbFJVobGhmAx66z8rtJn\nxjOpL55Lvek8BZV2aAM6dQ2tjby8+59sL99JiE8Q146+kqGhiae0zO7oS2l1I+nZbXtbdu+rwmZ3\nABDg68WoxP/ubQkO9Dml9fQ3+sx4JvXFc6k3naeg0g5tQN3DMAw+LfiSt7PfB+B/ktoubNjVU4m7\nuy/NrXYy9lWxPbuC7dnlVNQ2A2ACEgYFHzr92Ur8wCDMOv25Q/rMeCb1xXOpN52noNIObUDda291\nLn/d8Qo1LXWMjRjFshFLCPD2P+nluLIvhmFQXNHA9uxy0rMr2FNYg93R9hEIDvBmTJKVMclWRieG\nE+DXPRdl7Ev0mfFM6ovnUm86T0GlHdqAul9tSx0v7PwHWVV7sfqF8+MxVxIXFHtSy+jJvjQ02diV\nV9m2tyWngtr6thkkzSYTQ2NDGJfcFlwGR2iyOdBnxlOpL55Lvek8BZV2aANyDYfh4N3cj/kg71O8\nzF7877D/YWrMGZ3+ondXXxyGQX5J3aFDRBXkFv93sjlrsG/bnC1JVkbEh+Hr0z9Pf9ZnxjOpL55L\nvek8BZV2aANyrZ0VGfxt52vU2xo4feBpXJZ6Mb6WEw9e9ZS+1Da0sDOnkm3Z5ezMrTxisjkzw+NC\nGZPcNktuVFj/mWzOU3ojR1NfPJd603kKKu3QBuR6FY1VPL/zFfbVFjAoMJrrRi8jOjCqw9d4Yl/s\nDgfZRbXOeVsKjphsLjo8wHmIKCU2FG+vvnv6syf2RtQXT6bedJ6CSju0AfWMVoeNtXv/wxeF3+B7\n6MKGEzu4sGFv6EtlbZMztOzKq6K5tW2yOV8fCyPjw5wXUgwLav9qoL1Vb+hNf6S+eC71pvMUVNqh\nDahnbSrZyt8z3qTZ3sL02KlcPHQhXu1c2LC39aXV5iCrsJr07Aq2ZVdQUvnfyeZiIgIZ4OeFxWLG\nYja1/Xf4Z8uh22YzFosJr0P/P/J5Xj98jfN15qN/tpgOPffYZbT3s9eh15nNJzdAuLf1pr9QXzyX\netN5Cirt0AbU8w7Ul/LcjpfZX19CQnAcy0cvJdzv6Asb9va+lFQ1OCebyyqspqXV4e6SjssE7QSf\nHwaeQ0HIYmJYXDij4kMZHheqGX49QE19C1uyyiipaSJ1cAhjh1o1F5CH6e3/nvUkBZV2aANyj2Z7\nC//IWMPGks0EegVw1ajLGWVNdT7e1/piGAYOw8BuN7A7Dv1nd2B3GNiO+Pm/jzva/dl21P0GNvvh\n2519/RHrOubnHy7Hcej5/12Ozdb2PgAG+HszYVgEk4ZHMSI+DC+LQktPqaprZnNWGWkZpWQVVnPk\nv+DRYf6cO2kIZ48Z1G/PTPM0fe3fM1dSUGmHNiD3MQyDr4s38EbW29gNB/MSZrEwcQ5mk1l98VAO\nh0FpXQufbMhjU2YZNYfmnAn082L8sAgmpUYxMiG8Tw8mdpfymkY2Z5aRllnG3qIa5/1DB4cwKTWS\nscOjee+rHL7bdQCb3SDQz4tzxscw+7RYwoN1UU530r9nnaeg0g5tQO6XX1vIczteoaKpktSwoVwz\n6gqSBg9SXzzU4c+MwzDYW1hDWmYpmzLLqKpruyyBv6+F8UPbQsvopHC8vfRXfVeVVDWwKbOMTZml\n5O5v+zyYTJA6JJSJqVGclhLpHKx9uC819S18vrmQz7cUUdfQisVsYtLwKOZOHkLioGB3vp1+S98z\nnaeg0g5tQJ6hobWBl3b/k/TyXYT4BPOLqcuJMg1yd1nSjvY+Mw7DILe4lo0ZpWzKLHVeS8nXx8K4\nZCuTh0cxOsmKr7dCy4nsr6gnLaMt/OUfOgXebDIxIr4tnExIiSSknQtp/rAvrTY73+4s4eONBRSV\n1wMwLDaEuZOHMGFY5EkPopau0/dM5ymotEMbkOcwDINP8r/g3zkf4DAcxAQOZGL0OCZGjScywOru\n8uSQE31mDMMg70AdaRmlpGWWUlbdBICPt5mxyRFthymSrfj5HHu2V39kGAZFZfXOPVOHQ4XFbGJU\nYjgTUyKZkBLJAP+Orzt1vL4YhsGuvCo+3JjPjpxKACJC/JgzaQhnjx2Ev6/64Gr6nuk8BZV2aAPy\nPDk1eXx14Bs279+BzdE2E2x80JC20BI9jlDfEDdX2L+dzGfGMAzySw6SlllKWkYpJVWNAHh7mRmT\nZGVSaiTjhkb0uy/Lo34vmWXO09m9LGbGJIUzKTWKcUOtJ3VRzM70pbi8no/TCvhmxwFabQ78fS1M\nGxvDuRNjiQg9+YuHSufoe6bzFFTaoQ3IM0VGBpG/v5RtZTvZVLKNjKo9OAwHJkwkhyYwKXo84yPH\nEOQzwN2l9jtd/cwcuedgY0Yp+yv+++U8OjGcScMjGT80os9esdowDHL217Ipo4y0zFLKa47Y05Rk\nZdLwKMYkWbsc2k6mL3UNLazbWsxnmwqpqW/BZIKJqW3jWIYO1h8C3U3fM52noNIObUCe6Yd9qWs5\nyNaydDaVbGNvdS4GBmaTmdSwoUyKHs+4yFH4e+kvwp7QXZ+ZovJ6Nh06PFRY9oPDHamRTBh24sMd\nnu54A479fCyMG9p2GKy7xu50pS+tNgff724bx3J4PExSTDBzJw9hYmqk5snpJvqe6TwFlXZoA/JM\nHfWlqqmaLaXbSSvZxr66AgC8zF6Msg5nYtQ4xkSMwKcTFz6UrnHFZ2Z/RT2bMtv2NOSXtH1hWswm\nhseHMSm1bYxGcEDv6Knd4SCroC2cbM4qo+Zg2yncAb7/PYV7VGJYt58NdSp9MQyDzPxqPtpYwLa9\n5Ri0XS189sQhnDNuUJ/dy9VT9D3TeQoq7dAG5Jk625eyhgo2lW5lU8k2iusPAOBj8WFsxEgmRY9n\nRHhKu1P0S9e5+jNTeuiU3LQfnJI7PC6MScPbTslt76wXd7LZHWTkV5GWUcaWPWXUNbQCbZPinZYS\nwcRU10+K1119Kals4OO0Atan76el1YGvt4Wzxw5izqTYfnWV8O6k75nOU1BphzYgz9SVvhQfPMCm\nkq2klWylvKnt7AZ/L38mRI5mYvR4UsKSMZu0K/tU9eRnpry6kbRD84hkF9cCbVP+pwwJdYYWd130\nsdXmYFdeJWmZpWzdU3/d1PMAABtrSURBVE59U9vA7+BAHyamRDIxNZLUHrzMQHf3pb6plS+3FvPJ\npkKq6poxAeOHRTB38hBShoRi0jT9nabvmc5TUGmHNiDPdKq7sfPrCkkr2crm0u1UN7fN4hnkM4DT\nosYyKXo8CcFxCi1d5K7PTGVtk3NPy97CGgzaQktybAiTUqOYlBrp8hlYW1rt7MhtCyfb9pbT2Nx2\nxeywIF8mpkQyaXgUQweHuGWOElf1xWZ3kJZZyscbC5x7uOKjg5g7eQiTR0Tp0gmdoO+ZzlNQaYc2\nIM/UXX1xGA6yq/NIK93KltLt1Le2nWkS5hvKpOjxTIweR+yAGP11eBI84TNz+Fo3mzJLySz477Vu\nkmOCmXgotHTX6bZNLTbScypJyyhle3YFza1t4cQa7Mek4ZFMTI0iKSbY7RcCdHVfDMNgb1ENH20s\nYHNWGYYBoQN8mD0xlunjB/f6gc+u5Amfmd5CQaUd2oA8kyv6YnfYyajay6aSrWwr20mTve300OiA\nSCZGjWNi9HgGBkZ16zr7Ik/7zBy+evDGjFIy86udF01MGBjEpOFRTEyNJPokx1Y0NtvYtrectMwy\n0nMqaLW1Xf06Ksy/be/N8Ejio4M8KuD2ZF/Kqhv5JK2Qr7YX09Rix8fLzJQxbeNYBlkDe6SG3sTT\nPjOeTEGlHdqAPJOr+9Jqb2VnZSabSraSXr6bVkfb4MfYATHO2XCt/mEuW39v5smfmdqGFrbuKSct\no5Td/7+9ew+K6rz7AP7dK8vustxkQbkpGDVyExCTeAm2Ynzfpm/y1jTRWmlm3plOO5n+0YzNxLGN\nJtNOO2YmM502mbR9287ktdPGJiZp7lWjKPUGCgIS7zdEhQW577LXc94/dlkXWJAa4DzLfj8zzNk9\nnF2e5Xd2+fKcc57nejd8kv+jLctqRukif0/LWH9I7U5P8LHN17rg9fkfO2eWKXhYJyPFJFQ4CaVE\nXRxOL6obb2H/yVbc6fMH/8LcZDxWlokHsxOF/V1NN5HfM6JhUAmDO5CYprMuTq8TTZ1ncbL9NM52\nXYBP9nftz7NkozS1CCXWIsTHhH/zRKNIec8MDAaCx3kbmq92BUNLeooJZQutKF1kRZxRh/oLHTh1\nvmNYsMm0mlG60H9YJ31WZPQQKFkXnySh/kIn9tbeCM7snJFixmNlmXhocWrUz6YdKe8ZETCohMEd\nSExK1cXucQQHlrvQfRkyZKigwgOJuViaWoQlKQUw6aL7Es1IfM84nB40XLqDk+dtaLrSBa9PGrXN\n3LQ4lC5MwdKFVqQmRV6NRanLlVt92FvbgpPnOiDJMiwmPb5enI7VJekRMxbOZBOlNpGAQSUM7kBi\nEqEuva5+1Nsaccp2Gld6rwMANCoNHkxagNLUIhTOyoNBq8ylsUoSoTZfxaDLi4bLnTh1rgN2pwdF\n82ehdMHknXyrFNHqcqfXiS/qWnHo9C0MurzQatRYnp+KtUszkZ4SXVNfiFYbkTGohMEdSEyi1eXO\nYBfqbI042X4arQO3AAA6tQ75sx7EUmsR8pIXQaeJjqseRKsN+YlaF6fbiyNNbdhXewO2Hv+klHnz\nkvBYWSby5yVFxXksotZGRAwqYXAHEpPIdWmz2/wDy9lOw+boBAAYNDEoSslHaWoRFiU+AI16codH\nF4nItYlmotdFkmQ0XPKfx3L+Rg8A/4nKa5dm4JG8NOgnYb4jUYleG5EwqITBHUhMkVAXWZbROnA7\nOBput8v/4WvSGVGcUoClqUuQmzBvxg0sFwm1iUaRVJfrbf3YW9uCmrM2+CQZ5lgdVhenY01JOuLN\nM+9waiTVRmkMKmFwBxJTpNVFkiVc62vByfYG1Nka0O/2T6wXr7dgiTUf8XoLdGotdBodtGqd/7Za\nG3J7xDqNNrheo9II1T0eabWJFpFYl+5+Fw7UtaKq/ibsTi80ahUeXpyKtWWZyEqdOVfaRWJtlMKg\nEgZ3IDFFcl18kg8Xe67gVPtp1HecwaB38Cs9nwoqaNXaEUEmXNi5G3hGhSFN6PdCg5H/e1r18O/r\n1f5ApVVrRvUIRXJtZrJIrovL48OxM23YW3sDbV3+0aMtRh0sJj3iTfrAMmbEff/SbNQpPirwvURy\nbaYbg0oY3IHENFPq4pW8uN7XCqfPBa/kgUfywiN5/bd9ofe98AS+75W8cEuewDYjv+8Ztb0kj77U\ndjJpVZq7wUejw/zkbBQlFiBv1oPQcWZqYcyE94wkyzhz5Q6q6m+hrcuBXrsbgy7vuI9Rq1SIM+kQ\nb9TDYtaPsYxBvEkPk0GrSO/kTKjNdJn2oPLOO+/gww8/DN4/c+YM/va3v+Hll18GACxcuBCvvPLK\nuM/BoBKdWJeJ80k+eGVfIPiMDjKhtz0+T+C+f93dUDR83dC2w+5LXji9TvS6/bMYx2pjUWItQFlq\nCXIT5s64c3EihSRLuNrbgi65A/MMOZgVm6x0kyaVx+tDr92NPrsHvXYX+uxu9Aa++kYsXW7fuM+l\nUatgGdEjE7oMvR0bM3mhhp9nE6doj0pNTQ0+++wzXLp0CS+88AIKCwuxZcsWPPHEEygvLx/zcQwq\n0Yl1EZdD14u9Z4+gtr0+ODN1YkwCytKKsSytBLNNqQq3cOYbCid1tgac7jgTrIMKKiyxFqAi61HM\ntWQp3Mrp53L70OsIBJcBN/ocbvQOuNDn8ASWgfV2N9ze8XsitRo14k06WEwxwQATLtBYTHoY9OOf\nR8bPs4lTNKg8++yz+NWvfoXNmzfjwIEDAICPP/4YZ86cwdatW8d8HINKdGJdxDVUG0mWcKnnCmra\n6lFvawpO8phpnoOytBKUphYhISZe4dbOHJIs4UrvddTbGoeFE6M2FoUpeVg8Oxf7LlTjRmCcn/kJ\n81CRVY685EXs7RpBlmU43b5RPTJDt++ud6HX7gk7knEovVbtDy5mPSxGPeLNMbAYdYGlHplz4uFx\numE06GAyaGf0pdhf1VhBZcoPMjc2NmL27NnQaDSwWCzB9cnJyejo6Bj3sYmJRmi1U1vUsX4xpCzW\nRVxDtUm1FmPFgmK4vW6cut2E6ms1qL99Bu9d+hjvX/4EBdZFWJW9DMsyliBWZ1C41ZFHkiVc6LyC\nozdO4URrPboH/eHEpDdi9bxHsDyzFPnWhdBq/B/j6+aX44ztPD46tw+n277EpZ6rSLek4b8WVmBV\n9rKoGZRwMsmyDLvTi55+J7r7Xejpc6F7wImefhd6+l3+dYHvXbvdH5wvajw6rRqmWB3MQ19GPUwG\nHczGofv+pX8b/wnDQ9tP5iGpSDLlPSrbt2/H448/jrlz5+IHP/gBPvjgAwDA0aNHsWfPHrz22mtj\nPpY9KtGJdRHXvWoz4LajztaI2va64NQDOrUORSl5KEstxoNJC2b0gHhf1VDPSZ2tEadtTcFzgoza\nWBSl5KPYWoiFibnQjjiReWRdbg7cxhcth1HbXg9JlmDRx2F1xgqsSn8Yxiifr2qqSLIMh9Pr740Z\ncPkPQw244VOp0NnlgN3pgcPphd3phcPpgcPlhcPpnVC4GaJWqWA0aGE0aGEyaGGM0QZ7aoaWsQYt\nTAbdqG2MMVqo1WKHHMUO/axbtw4fffQRVCoV1q5di6qqKgDA+++/jwsXLuDFF18c87EMKtGJdRHX\nv1ObDscdnGyvR017XXAUX7POhNLUIpSllmCuJTMq/zsc6W44aQiEE//v16Q1ojAlDyXWQixMnD9u\nwBurLt3OHhxs/ReO3DwBp88FvUaPFXOW4WsZq5Acmzhlr4nuGu89I8syXB4fHE5vIMSMDjNDt+2B\nbfzr/Nt57nGuzUixMaHh5W6gMRpCA09g/YhttJqpP4SoyKGf9vZ2mEwm6PX+WTNzcnJw8uRJLF26\nFHv37kVlZeVU/ngiUlCKMRn/Oa8C/zF3DVr6W1HTVoeT7adxqPUoDrUeRUpsMsrSSlCWWgyrcZbS\nzZ1Wkizhcs811Hc0jgony2eXBXpOxg8nE5FoSMD6+d/Ef85dgyO3anDwxr9w8Ma/cKj1KEqshajI\nKkdmXPpkvCS6DyqVCga9Fga9FkmWe28/ksfruxtghgWduz02Q+uCAcjlQXvP4D2vkhpJr1NjVnws\nnn+6CMnx03sod0qDSkdHB5KSkoL3t23bhu3bt0OSJBQVFWH58uVT+eOJSAAqlQrZlkxkWzKxfv43\nca77Imra6tDQ0YxPr+7Dp1f3YZ4lC2VpJSixFiJOPzNn1x0KJ3W2RpzuaELfsHCyDCXWQixIzJ2S\nQ2Ox2lhUZJVjdcYKnGpvwP6WQzgZmP5hYeJ8rMkqx+KkBezhijA6rQYJZg0S7mPqAa9PwqDLO6wH\nxx7osbnbg+MZFoTUakCJXYQDvvEQg3BYF3FNZm2cXicaOppR216Pc10XIUOGWqXG4qSFWJZWjIJZ\ni6HX6CflZynFH06uos7WNDyc6IwompU/aeHk362LLMs423UB+1sO4Xz3JQDAHFMaKrLKUZpaNOoc\nGLp//DybOI5MGwZ3IDGxLuKaqtr0uvpxynYatW11aOm/CcA/K/WSlAKUpRVjQWJuxFxm6790+2rw\nUuLQcLIkcELsgoTJ7Tn5KnVp6W/FFy2HUWdrhCRLSIiJx+qMFViZ/hBitbGT1sZoxc+ziWNQCYM7\nkJhYF3FNR23a7O2obatHTXs9upzdAPwTPC5NW4JlqSVIN88W7hBFaDip72gKTkw5FE5KrEV4ICFn\nyq54moy63BnsxsHWahy5VQO3zw2DJgYr0h/C1zJWItGQMEktjT78PJs4BpUwuAOJiXUR13TWZuhq\nmNq2OtTZGuEITPA4x5SGsrRilKUWK/oHdGjQu6HDOkPhxKwzoSjFf1hnKsNJqMmsi8PjQPXN46hq\nPYI+dz/UKjWWpi5BRVY50s2zJ+VnRBN+nk0cg0oY3IHExLqIS6naeCQvvrxzDjVt9TjT+SW8sv+K\nhQcScrAsrQRLUgpg1E39YYph4cTWhH6PcuEk1FTUxSN5UdtWjy9aDqHNYQMAPJi0ABVZ5ViYOF+4\nXi1R8fNs4hhUwuAOJCbWRVwi1MbhGUR9RyNq2+pxsecKAECr1qIg+UGUpRVjcfKiSZ3Z2Sf5cKnn\nKuo6GtFgOzMsnAydc6JEOAk1lXWRZAnNd87hi5bDwd93pnkO1mSVo8RayAH87kGE90ykYFAJgzuQ\nmFgXcYlWmy5nN062ncaJ9jq02dsB+EdxLbEWoiytBDnx2fd1Em5oODlta8KAxw7gbjgpsRZhfsI8\nYf5IT1ddrvW1YH/LYZy2NUGGjMSYBHw9axWWzy6DQctpEsIR7T0jMgaVMLgDiYl1EZeotZFlGa0D\nt1EbGFRuaOj5ZEMiylKLUZZWgjSTddzn8Ek+XOy5ErxaZyicxOnMKLLmoySlUKhwEmq669LhuIOD\nrdU4eqsWHsmDWG0sVqU/jNUZKxAfcx8jl81gor5nRMSgEgZ3IDGxLuKKhNpIsoQL3ZdR21aP+o5G\nuHxuAEBWXLp/ZmfrEsTH+D8Qh8JJna0RDSPCyRJrAUqsBZifkCP8pdFK1WXAY0d16zFUtR7BgMcO\nrUqDsrQSrMl6FLNNqdPeHhFFwntGFAwqYXAHEhPrIq5Iq43b50ZT55eoaavHl13nIckSVFBhUdID\nSIxJQGNnc5hw4u85ET2chFK6Lm6fByfaTuFAy2HYBv3zOuUnL0JFVjnmJ+RE9Ym3StcmkjCohMEd\nSEysi7giuTb97gH/zM5tdbja1wIAiNObUZxSgOIIDCehRKmLJEto6vwS+1sOBWfPzo7LxJqsR7Ek\nJV/Iw2ZTTZTaRAIGlTC4A4mJdRHXTKmNzdEJu8eObEtmxIaTUCLW5UrvNexvOYzGjmbIkJFsSMLX\ns1bhkdlliInwqRH+HSLWRlQMKmFwBxIT6yIu1kZMItel3dGBAy2HcbztFLySFyatEasyHsHqjBUz\ndgLKUCLXRjQMKmFwBxIT6yIu1kZMkVCXfvcADrUeweHWY7B7HdCqtXgorRRrsh5FqjFF6eZNmUio\njSjGCiqcIpOIiKZcnN6Mb+asw9rsr+H47ZM40HIYR26dwNFbNSiYtRgVWeXITZirdDNJQAwqREQ0\nbWI0epRnLMeq9IdxuuMM9rccQmNnMxo7mzHPko2K7HLkJS2ETqNTuqkkCAYVIiKadmqVGiXWQhSn\nFOBSz1XsbzmEM3fO4n+b/g+Af+bphJh4xMdYkBgTj/iYeCTEWJAQEx9cb9Iao/rS52jBoEJERIpR\nqVR4IDEHDyTmoM3ejsM3j+G23YZeVy86B+/g5sDtMR+rU2uHBZj4kCCTEFgfr7dE5WXRMwmDChER\nCSHNlIpnFvz3sHWDXid6Xb3ocfWh29UbvN0TcvtyzzXICH9diAoqmPWmkF6ZQIAJLIfWx3KuImEx\nqBARkbBitQbEag1IG2dIfp/kQ6+7LyTA+Jd3v/pw296Olv6bYz5HjEYf6JUZfogptLfGoo+bEePu\nRBoGFSIiimgatQZJhkQkGRLH3EaWZTi8g8Hg0uPqQY+rb0QPTR/aHR1jPodapYZFHzeqV2ZkqNFH\n0YB204FBhYiIZjyVSgWTzgiTzoh08+wxt/P4PMN6Z0b30PThRv9NXAtMwxBOrDY2GFqsliRofDqY\ndCaYdEaYA8u7Xybo1PxTPB7+doiIiAJ0Gh1mxSZjVmzymNtIsgS7xzHq8NLwUOM/3HS2694/U6/R\n3w0w2rsBZqxgY9IZYdDERM0VTwwqRERE/wa1So04vRlxejMy49LH3M7lc0NnlnCjvQMDHgfsHjvs\nw5aO4P0BjwPtdhvckmdCbdCoNKMDTCDkmPWmUYFn6CsSz7FhUCEiIpoCMRo9UsxxUA9O/Ioij88D\nu3d4gBkr2Ng9dvQGem4mQgUVYrWGUQFmeK/N6MCj9OB7DCpERESC0Gl0SND4T86dKEmW4PAMjg42\n4wSeLmcPfLJvQs+vV/vPsUmOTcT/5G1GfEz4OXmmCoMKERFRBFOr1DDrTTDrTRj7Iu7hZFmGy+e6\nZ49NaODpdvbAI7mn9LWEw6BCREQUZVQqFQxaAwxaA2bFJindnHFF3lk1REREFDUYVIiIiEhYDCpE\nREQkLAYVIiIiEhaDChEREQmLQYWIiIiExaBCREREwmJQISIiImExqBAREZGwGFSIiIhIWAwqRERE\nJCwGFSIiIhIWgwoREREJSyXLsqx0I4iIiIjCYY8KERERCYtBhYiIiITFoEJERETCYlAhIiIiYTGo\nEBERkbAYVIiIiEhYURlUfvnLX2LDhg3YuHEjGhsblW4OhXj11VexYcMGPPXUU9i7d6/SzaEQTqcT\nFRUVeO+995RuCoX48MMP8cQTT2D9+vWoqqpSujkUYLfb8aMf/QiVlZXYuHEjqqurlW5SxNIq3YDp\nVlNTg+vXr2P37t24fPkytm3bht27dyvdLAJw/PhxXLx4Ebt370Z3dze+9a1v4bHHHlO6WRTw5ptv\nIj4+XulmUIju7m688cYb2LNnDxwOB377299i9erVSjeLALz//vuYN28etmzZgvb2djz77LP4/PPP\nlW5WRIq6oHLs2DFUVFQAAHJzc9Hb24uBgQGYzWaFW0ZlZWUoLCwEAFgsFgwODsLn80Gj0SjcMrp8\n+TIuXbrEP4KCOXbsGB555BGYzWaYzWb8/Oc/V7pJFJCYmIjz588DAPr6+pCYmKhwiyJX1B366ezs\nHLbDJCUloaOjQ8EW0RCNRgOj0QgAePfdd/Hoo48ypAhi586d2Lp1q9LNoBFaW1vhdDrxwx/+EJs2\nbcKxY8eUbhIFPP7447h16xbWrl2LzZs348UXX1S6SREr6npURuIMAuLZv38/3n33Xfz5z39WuikE\n4IMPPsCSJUuQmZmpdFMojJ6eHrz++uu4desWvve97+HgwYNQqVRKNyvq/eMf/8CcOXPwpz/9CefO\nncO2bdt4ftd9irqgYrVa0dnZGbxvs9mQkpKiYIsoVHV1NX73u9/hj3/8I+Li4pRuDgGoqqrCjRs3\nUFVVhba2Nuj1eqSlpWH58uVKNy3qJScno7i4GFqtFllZWTCZTOjq6kJycrLSTYt6dXV1WLlyJQBg\n0aJFsNlsPJR9n6Lu0M+KFSvwz3/+EwDQ3NwMq9XK81ME0d/fj1dffRW///3vkZCQoHRzKODXv/41\n9uzZg7///e94+umn8dxzzzGkCGLlypU4fvw4JElCd3c3HA4Hz4UQRHZ2NhoaGgAAN2/ehMlkYki5\nT1HXo1JSUoK8vDxs3LgRKpUKO3bsULpJFPDpp5+iu7sbP/7xj4Prdu7ciTlz5ijYKiJxpaamYt26\ndXjmmWcAAD/72c+gVkfd/59C2rBhA7Zt24bNmzfD6/Xi5ZdfVrpJEUsl8yQNIiIiEhSjNxEREQmL\nQYWIiIiExaBCREREwmJQISIiImExqBAREZGwGFSIaNK0trYiPz8flZWVwVljt2zZgr6+vgk/R2Vl\nJXw+34S3/853voMTJ07cT3OJKAIwqBDRpEpKSsKuXbuwa9cuvP3227BarXjzzTcn/Phdu3ZxYCwi\nCoq6Ad+IaHqVlZVh9+7dOHfuHHbu3Amv1wuPx4Pt27dj8eLFqKysxKJFi3D27Fm89dZbWLx4MZqb\nm+F2u/HSSy+hra0NXq8XTz75JDZt2oTBwUE8//zz6O7uRnZ2NlwuFwCgvb0dP/nJTwAATqcTGzZs\nwLe//W0lXzoRTQIGFSKaMj6fD/v27UNpaSleeOEFvPHGG8jKyho1SZvRaMRf/vKXYY/dtWsXLBYL\nXnvtNTidTnzjG9/AqlWrcPToURgMBuzevRs2mw1r1qwBAHz22WfIycnBK6+8ApfLhXfeeWfaXy8R\nTT4GFSKaVF1dXaisrAQASJKEpUuX4qmnnsJvfvMb/PSnPw1uNzAwAEmSAPinthipoaEB69evBwAY\nDAbk5+ejubkZFy5cQGlpKQD/JKM5OTkAgFWrVuGvf/0rtm7divLycmzYsGFKXycRTQ8GFSKaVEPn\nqITq7++HTqcbtX6ITqcbtU6lUg27L8syVCoVZFkeNp/NUNjJzc3FJ598gtraWnz++ed466238Pbb\nb3/Vl0NECuPJtEQ05eLi4pCRkYFDhw4BAK5evYrXX3993McUFRWhuroaAOBwONDc3Iy8vDzk5uai\nvr4eAHD79m1cvXoVAPDRRx+hqakJy5cvx44dO3D79m14vd4pfFVENB3Yo0JE02Lnzp34xS9+gT/8\n4Q/wer3YunXruNtXVlbipZdewne/+1243W4899xzyMjIwJNPPokDBw5g06ZNyMjIQEFBAQBg/vz5\n2LFjB/R6PWRZxve//31otfyII4p0nD2ZiIiIhMVDP0RERCQsBhUiIiISFoMKERERCYtBhYiIiITF\noEJERETCYlAhIiIiYTGoEBERkbAYVIiIiEhY/w+G0J6kVviB6AAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "b7atJTbzU9Ca"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Use only Latitude and Longitude Features\n",
+        "\n",
+        "**Train a NN model that uses only latitude and longitude as features.**\n",
+        "\n",
+        "Real estate people are fond of saying that location is the only important feature in housing price.\n",
+        "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n",
+        "\n",
+        "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n",
+        "\n",
+        "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5McjahpamOc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "29db9a2e-46ab-4f9c-ec6a-f3b3beb386f3"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Train the network using only latitude and longitude\n",
+        "#\n",
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n",
+        "    steps=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": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 104.08\n",
+            "  period 01 : 100.24\n",
+            "  period 02 : 98.44\n",
+            "  period 03 : 97.93\n",
+            "  period 04 : 97.32\n",
+            "  period 05 : 96.92\n",
+            "  period 06 : 96.35\n",
+            "  period 07 : 95.86\n",
+            "  period 08 : 95.20\n",
+            "  period 09 : 94.59\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   94.59\n",
+            "Final RMSE (on validation data): 96.02\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqan8NAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "P8BLQ7T71JWd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "1hwaFCE71OPZ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "It's a good idea to keep latitude and longitude normalized:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "djKtt4mz1ZEc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "1029f313-74b8-4d18-beff-554db3f121f4"
+      },
+      "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": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 152.55\n",
+            "  period 01 : 108.84\n",
+            "  period 02 : 105.74\n",
+            "  period 03 : 104.04\n",
+            "  period 04 : 103.03\n",
+            "  period 05 : 101.58\n",
+            "  period 06 : 100.93\n",
+            "  period 07 : 100.30\n",
+            "  period 08 : 99.91\n",
+            "  period 09 : 99.54\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   99.54\n",
+            "Final RMSE (on validation data): 101.22\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDBEREVkOAwwRERFZDgMMERERWc7/A1g7Rf61zOELAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Dw2Mr9JZ1cRi"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb
new file mode 100644
index 0000000..d357bcc
--- /dev/null
+++ b/intro_to_neural_nets.ipynb
@@ -0,0 +1,1234 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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": [
+        "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",
+        "outputId": "7f48cb20-4829-450c-efdd-12f57c136976",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2644.7           539.5   \n",
+              "std         2.1        2.0                12.6       2200.5           424.6   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1464.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2130.0           432.5   \n",
+              "75%        37.7     -118.0                37.0       3148.0           650.2   \n",
+              "max        42.0     -114.6                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       1426.3       501.3            3.9               2.0  \n",
+              "std        1124.6       386.6            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         789.0       282.0            2.6               1.5  \n",
+              "50%        1166.0       408.0            3.6               1.9  \n",
+              "75%        1717.0       606.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2644.7</td>\n",
+              "      <td>539.5</td>\n",
+              "      <td>1426.3</td>\n",
+              "      <td>501.3</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2200.5</td>\n",
+              "      <td>424.6</td>\n",
+              "      <td>1124.6</td>\n",
+              "      <td>386.6</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1464.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>789.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2130.0</td>\n",
+              "      <td>432.5</td>\n",
+              "      <td>1166.0</td>\n",
+              "      <td>408.0</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>3148.0</td>\n",
+              "      <td>650.2</td>\n",
+              "      <td>1717.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</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": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2641.2           539.1   \n",
+              "std         2.1        2.0                12.5       2130.1           414.0   \n",
+              "min        32.5     -124.3                 1.0         12.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1454.0           295.0   \n",
+              "50%        34.3     -118.5                29.0       2121.0           436.0   \n",
+              "75%        37.7     -118.0                37.0       3161.5           644.5   \n",
+              "max        42.0     -114.3                52.0      25187.0          4952.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1437.3       501.0            3.9               2.0  \n",
+              "std        1201.9       379.6            1.9               1.3  \n",
+              "min           8.0         4.0            0.5               0.2  \n",
+              "25%         791.0       282.0            2.6               1.5  \n",
+              "50%        1168.0       411.0            3.5               1.9  \n",
+              "75%        1739.2       603.0            4.8               2.3  \n",
+              "max       35682.0      4769.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2641.2</td>\n",
+              "      <td>539.1</td>\n",
+              "      <td>1437.3</td>\n",
+              "      <td>501.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2130.1</td>\n",
+              "      <td>414.0</td>\n",
+              "      <td>1201.9</td>\n",
+              "      <td>379.6</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.2</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>1454.0</td>\n",
+              "      <td>295.0</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>29.0</td>\n",
+              "      <td>2121.0</td>\n",
+              "      <td>436.0</td>\n",
+              "      <td>1168.0</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>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3161.5</td>\n",
+              "      <td>644.5</td>\n",
+              "      <td>1739.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>25187.0</td>\n",
+              "      <td>4952.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>4769.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.3\n",
+              "std                 116.3\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 180.6\n",
+              "75%                 264.9\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.3</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>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                207.3\n",
+              "std                 115.2\n",
+              "min                  15.0\n",
+              "25%                 121.3\n",
+              "50%                 179.8\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>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>121.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "d9b40f28-36cb-4830-c3d5-fb84fbadbfc3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.0005,\n",
+        "    steps=8000,\n",
+        "    batch_size=10,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\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 : 159.59\n",
+            "  period 01 : 157.34\n",
+            "  period 02 : 143.38\n",
+            "  period 03 : 131.98\n",
+            "  period 04 : 124.51\n",
+            "  period 05 : 116.19\n",
+            "  period 06 : 111.78\n",
+            "  period 07 : 112.24\n",
+            "  period 08 : 109.54\n",
+            "  period 09 : 115.84\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   115.84\n",
+            "Final RMSE (on validation data): 113.84\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj2UjfaUAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "O2q5RRCKqYaU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "j2Yd5VfrqcC3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IjkpSqmxqnSM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "e0c1b056-d3d0-4026-a836-c56248f657f9"
+      },
+      "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": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 171.86\n",
+            "  period 01 : 163.10\n",
+            "  period 02 : 153.56\n",
+            "  period 03 : 143.47\n",
+            "  period 04 : 133.18\n",
+            "  period 05 : 124.22\n",
+            "  period 06 : 116.54\n",
+            "  period 07 : 114.28\n",
+            "  period 08 : 113.75\n",
+            "  period 09 : 109.51\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   109.51\n",
+            "Final RMSE (on validation data): 107.57\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c6diezCSeH4Y",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Evaluate on Test Data\n",
+        "\n",
+        "**Confirm that your validation performance results hold up on test data.**\n",
+        "\n",
+        "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n",
+        "\n",
+        "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "92c1910e-e7b5-40c3-a9b1-89bf47d750a5"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "# YOUR CODE HERE\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+        "                                               test_targets[\"median_house_value\"], \n",
+        "                                               num_epochs=1, \n",
+        "                                               shuffle=False)\n",
+        "\n",
+        "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 106.10\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vvT2jDWjrKew",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FyDh7Qy6rQb0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+        "\n",
+        "Note that we don't have to randomize the test data, since we will use all records."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vhb0CtdvrWZx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "56007efd-9772-4b85-a012-d263b933ff6c"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 106.10\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..3d815bd
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1902 @@
+{
+  "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
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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",
+        "outputId": "854c43be-8390-44f1-f7e9-48ef41753f44",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import pandas as pd\n",
+        "pd.__version__"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "u'0.22.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "27029eee-0764-49de-99fe-d4043d30e0c8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+      ],
+      "execution_count": 0,
+      "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": 25
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "f8bfa9fb-7a9a-4cb2-bf85-02fa6cc80c6e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        }
+      },
+      "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": 0,
+      "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": 26
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "54e18e33-1534-46ee-ba9e-0de4972e7a1f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "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": 0,
+      "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": 27
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "ac70743a-513f-47e7-82b8-e4f5b9f0c063",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.head()"
+      ],
+      "execution_count": 0,
+      "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": 28
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "ea641293-935f-447b-bf42-b1b587218d8f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 396
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.hist('housing_median_age')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6233cd6d10>]],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFZCAYAAABXM2zhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f623782ffd0>"
+            ]
+          },
+          "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",
+        "outputId": "27a0712d-6e0f-4c81-8805-b1d548afecf8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "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": 0,
+      "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": 30
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "V5L6xacLoxyv",
+        "outputId": "dc1ee199-4e57-4c98-cccd-fe3361529f78",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities['City name'][1]))\n",
+        "cities['City name'][1]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<type 'str'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'San Jose'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "gcYX1tBPugZl",
+        "outputId": "dffc47b0-e905-4dcb-8ea5-bb804660197e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 128
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities[0:2]))\n",
+        "cities[0:2]"
+      ],
+      "execution_count": 0,
+      "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": 32
+        }
+      ]
+    },
+    {
+      "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-",
+        "outputId": "b6309584-2070-459d-b3f1-970ff5e98859",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "population / 1000."
+      ],
+      "execution_count": 0,
+      "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": 33
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "cda50b5d-af0f-46d4-f548-9503112ac069",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.log(population)"
+      ],
+      "execution_count": 0,
+      "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": 34
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "4d16f4e0-d9e1-4b83-e367-172355f4636c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "population.apply(lambda val: val > 1000000)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "d54fb78d-ce81-42ac-f1fc-3e988ce6c6e8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        }
+      },
+      "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": 0,
+      "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": 36
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "eed92a63-935d-4ad6-b90b-3dde0f0102c3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities['Named after a saint and big'] = (cities['City name'].apply(lambda x: 'San' in x)) & (cities['Area square miles'] > 50)\n",
+        "cities"
+      ],
+      "execution_count": 0,
+      "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>Named after a saint and big</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",
+              "   Named after a saint and big  \n",
+              "0                        False  \n",
+              "1                         True  \n",
+              "2                        False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "022cf6b9-f207-4abb-c019-184b5897f0f4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        }
+      },
+      "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": [
+        {
+          "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>Named after a saint and big</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <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",
+              "      <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",
+              "      <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",
+              "   Named after a saint and big  Is wide and has saint name  \n",
+              "0                        False                       False  \n",
+              "1                         True                        True  \n",
+              "2                        False                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "f8125d84-a487-4315-d1e8-f11e6302f8ca",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names.index"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "F_qPe2TBjfWd",
+        "outputId": "90771bb8-3bcc-444a-bd99-5af2a437365b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.index"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "cbc50d0b-54bc-45a5-9459-9a5bbeaca0d5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([2, 0, 1])"
+      ],
+      "execution_count": 0,
+      "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>Named after a saint and big</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "      <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",
+              "      <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",
+              "      <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",
+              "   Named after a saint and big  Is wide and has saint name  \n",
+              "2                        False                       False  \n",
+              "0                        False                       False  \n",
+              "1                         True                        True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "0f13cfab-a238-422f-d18a-7137c190c9e7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex(np.random.permutation(cities.index))"
+      ],
+      "execution_count": 0,
+      "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>Named after a saint and big</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <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",
+              "      <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",
+              "      <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",
+              "   Named after a saint and big  Is wide and has saint name  \n",
+              "0                        False                       False  \n",
+              "1                         True                        True  \n",
+              "2                        False                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "a2152d7a-b55b-4f8c-bd3f-f8e53ce0022f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities.reindex([0, 1, 2, 3, 4])"
+      ],
+      "execution_count": 0,
+      "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>Named after a saint and big</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469.0</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785.0</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</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",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NaN</td>\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>4</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</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",
+              "1       San Jose   1015785.0             176.53         5754.177760   \n",
+              "2     Sacramento    485199.0              97.92         4955.055147   \n",
+              "3            NaN         NaN                NaN                 NaN   \n",
+              "4            NaN         NaN                NaN                 NaN   \n",
+              "\n",
+              "  Named after a saint and big Is wide and has saint name  \n",
+              "0                       False                      False  \n",
+              "1                        True                       True  \n",
+              "2                       False                      False  \n",
+              "3                         NaN                        NaN  \n",
+              "4                         NaN                        NaN  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "c8d5fb3f-09ba-4543-e222-9ee624558495",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "execution_count": 0,
+      "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>Named after a saint and big</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469.0</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <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",
+              "      <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",
+              "      <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",
+              "      <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",
+              "  Named after a saint and big Is wide and has saint name  \n",
+              "0                       False                      False  \n",
+              "4                         NaN                        NaN  \n",
+              "5                         NaN                        NaN  \n",
+              "2                       False                      False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "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..eba3289
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1731 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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": [
+        "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",
+        "outputId": "b7f7b4dc-9535-42b3-b5b3-228481eefb53",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2642.0           538.9   \n",
+              "std         2.1        2.0                12.6       2154.8           418.7   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2143.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3157.0           649.0   \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       1427.2       500.7            3.9               2.0  \n",
+              "std        1156.0       383.3            1.9               1.2  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         793.0       282.0            2.6               1.5  \n",
+              "50%        1170.0       410.0            3.6               1.9  \n",
+              "75%        1717.0       604.0            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>28.6</td>\n",
+              "      <td>2642.0</td>\n",
+              "      <td>538.9</td>\n",
+              "      <td>1427.2</td>\n",
+              "      <td>500.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2154.8</td>\n",
+              "      <td>418.7</td>\n",
+              "      <td>1156.0</td>\n",
+              "      <td>383.3</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>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>1463.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>793.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2143.0</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>3157.0</td>\n",
+              "      <td>649.0</td>\n",
+              "      <td>1717.0</td>\n",
+              "      <td>604.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.5       2647.7           540.5   \n",
+              "std         2.1        2.0                12.5       2239.3           428.2   \n",
+              "min        32.5     -124.3                 2.0         18.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1453.8           296.8   \n",
+              "50%        34.2     -118.5                29.0       2106.5           431.0   \n",
+              "75%        37.7     -118.0                37.0       3142.5           648.0   \n",
+              "max        41.9     -114.6                52.0      30401.0          4957.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1435.2       502.6            3.9               2.0  \n",
+              "std        1128.1       387.4            1.9               1.1  \n",
+              "min           3.0         4.0            0.5               0.0  \n",
+              "25%         779.8       280.0            2.5               1.5  \n",
+              "50%        1159.0       407.0            3.5               1.9  \n",
+              "75%        1737.0       608.0            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>-119.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2647.7</td>\n",
+              "      <td>540.5</td>\n",
+              "      <td>1435.2</td>\n",
+              "      <td>502.6</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>12.5</td>\n",
+              "      <td>2239.3</td>\n",
+              "      <td>428.2</td>\n",
+              "      <td>1128.1</td>\n",
+              "      <td>387.4</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>2.0</td>\n",
+              "      <td>18.0</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>1453.8</td>\n",
+              "      <td>296.8</td>\n",
+              "      <td>779.8</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2106.5</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>1159.0</td>\n",
+              "      <td>407.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3142.5</td>\n",
+              "      <td>648.0</td>\n",
+              "      <td>1737.0</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>41.9</td>\n",
+              "      <td>-114.6</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_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "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",
+        "outputId": "a6810e15-2164-44a1-a50c-dba899b22b27",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_linear_regressor_model(\n",
+        "    learning_rate=0.000001,\n",
+        "    steps=200,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.45\n",
+            "  period 01 : 0.45\n",
+            "  period 02 : 0.46\n",
+            "  period 03 : 0.45\n",
+            "  period 04 : 0.45\n",
+            "  period 05 : 0.45\n",
+            "  period 06 : 0.45\n",
+            "  period 07 : 0.44\n",
+            "  period 08 : 0.44\n",
+            "  period 09 : 0.44\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGACAYAAACgBBhzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiLyOiw4RERE5HVYcIiIiMjr/H+pLvVAAwYSxwAAAABJRU5ErkJggg==\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": "l17K_1E58k5s",
+        "colab_type": "code",
+        "outputId": "78adbdec-a494-4474-ee51-674894219471",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        }
+      },
+      "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": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFKCAYAAAAwrQetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKnAvdAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dPpJUV862FYI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to display the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kXFQ5uig2RoP",
+        "colab_type": "code",
+        "outputId": "7f35fc01-378a-4aa4-af91-68e916927f9b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                  validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "\n",
+        "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "\n",
+        "_ = plt.hist(validation_predictions)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFKCAYAAAAwrQetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKnAvdAAAAABJRU5ErkJggg==\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",
+        "outputId": "9f13bf2d-eead-414a-977f-78752e0529e0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.60\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.58\n",
+            "  period 03 : 0.57\n",
+            "  period 04 : 0.56\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.54\n",
+            "  period 08 : 0.54\n",
+            "  period 09 : 0.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEL4NQkzQgghhPBr/x8ZHlwQppGSAwAAAABJRU5ErkJggg==\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",
+        "outputId": "c8da47ff-c601-4d84-c06b-f0adb69fc34c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 642
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.61\n",
+            "  period 01 : 0.59\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.55\n",
+            "  period 07 : 0.53\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3XlclWX6+PHPc85hPSwCgiBuiCCC\nomJmpqLhBpnlloCJS4tT2TLZb2bSdLBSqxmzaXGaxhaX0igjMxPJ3TQTt1xARFFQQfZ9Xw6/P5z4\nZini8nAOcL1fr3m95izPc13nXB29vJ/7fm6lrq6uDiGEEEKIZkpj7ASEEEIIIW6HNDNCCCGEaNak\nmRFCCCFEsybNjBBCCCGaNWlmhBBCCNGsSTMjhBBCiGZNmhkhWrju3buTkZFxR8516dIlfH1978i5\njCEiIoLBgwcTHBzM6NGjuf/++1m1atVNn+f48eM89thjN32cr68vly5duunjhBAN0xk7ASGEaEp/\n+ctfeOihhwDIzs4mNDQUDw8PAgMDG30Of39/Pv74Y7VSFELcJBmZEaKVqqys5O9//zujR48mJCSE\nN954g9raWgB+/PFHhg4dSkhICFFRUQQEBNxwRKGgoIDnn3++fsTjv//9b/1rb7/9NqNHj2b06NFM\nmzaNzMzMBp//1e7duxk7duxVzz300EPs2bOHuLg4xo8fz/33309ISAgxMTE3/R04OzsTHBzMvn37\nADh79ixTp05l9OjRjB07lhMnTgBw4MABwsLCeP7553nxxRc5cOAAI0eOvOH3uHv3bkaOHElISAgf\nffRRfdzS0lJmz55NSEgIw4cPZ/78+VRXV990/kKIK6SZEaKVWrVqFRkZGXz//fd88803HDp0iE2b\nNlFbW8tLL73Eq6++SkxMDCkpKZSXl9/wfMuWLcPe3p7Y2FjWrl3LunXrOHToEGfOnGHLli1s2rSJ\n2NhYRo4cyf79+6/7/G8NHDiQjIwMLl68CMDFixfJyMjg3nvv5c0332Tu3Lls3ryZDz74gG3btt3S\n91BTU4O5uTkGg4HZs2fz0EMPERsby8KFC3n66aepqakBICEhgbCwMN56661Gf48vv/wykZGRxMTE\noNFo6pucDRs2YGdnR0xMDLGxsWi1Ws6ePXtL+QshpJkRotXatWsXkydPRqfTYWlpydixY9m3bx8p\nKSlUVVUxdOhQ4Mo8E4PBcMPz7d69mylTpgDQpk0bRo4cyb59+7CzsyMvL4/vvvuOwsJCIiIiGDdu\n3HWf/y1zc3Puu+8+duzYAcC2bdsYMWIEOp0OJycnNmzYQHJyMl26dPlDk9EYFy9eZMuWLYwcOZJz\n586Rm5vLpEmTAOjXrx+Ojo4cPXoUAEtLSwYOHHjT3+PgwYMBGD9+fP0xv5537969GAwGXnnlFXr0\n6HHT+QshrpBmRohWKi8vD3t7+/rH9vb25ObmUlhYiJ2dXf3zLi4ujT7fb4+zs7MjNzeXdu3a8d57\n77FlyxaGDRvGrFmzuHz58nWf/73Ro0df1czcf//9ACxZsgQrKytmzpzJqFGj2LJlS6Py/Oc//1k/\nAXjOnDm89NJL+Pv7U1RUREVFBSEhIQQHBxMcHExubi4FBQX138/1Pvf1vkcbG5urnv9VSEgIM2bM\n4J133mHgwIG88sorVFVVNSp/IcQfSTMjRCvVtm3b+r+o4cqcl7Zt22JjY0NZWVn98zk5Obd1PoB7\n7rmH//73v+zbtw83NzeWLl3a4PO/NWTIEBITE0lJSSElJYV77rmnPt6CBQvYs2cPf//735k7dy6l\npaU3zPMvf/kLW7ZsITY2lq+++qq+OXJxcUGv17Nly5b6/+3du7d+bszNfm57e3tKSkrqn8/Ly7vq\nuLCwML766is2b95MfHw8GzZsuGHuQohrk2ZGiFZq2LBhrF+/ntraWsrKyvj2228ZOnQoXbp0oaam\nhgMHDgCwbt06FEVp1PmioqKAK39xb926lWHDhrF3715eeeUVDAYD1tbW+Pj4oCjKdZ//PXNzcwYP\nHsw///lPhg8fjlarpbq6moiICLKysgDw8/NDp9Oh0dz6H2nu7u64urrWj/Dk5eUxZ86cqxq7633u\na32PnTp1QqvV1n+P0dHR9Z9v+fLlrF+/HoB27drRoUOHRn3HQohrk6XZQrQCERERaLXa+seLFi0i\nIiKCixcvMmbMGBRFITg4mJCQEBRFYeHChcydOxdbW1tmzpyJRqNBURTq6uqora0lODj4qvOvWLGC\nP//5zyxcuJDg4GA0Gg2zZs3C39+fyspKvv/+e0aPHo25uTmOjo4sWbIEFxeXaz5/LaNHj+bZZ59l\n5cqVAJiZmTFp0iRmzJgBgEajYf78+VhZWbF161Z27NjB66+/flPfkaIoLFu2jIULF/Kvf/0LjUbD\nzJkzsba2vuF3e73v8bXXXmPevHmYm5szYcKE+nM99NBDzJ07lxUrVqAoCr17965fLi6EuHlKXV1d\nnbGTEEKYrrKyMvr27cuhQ4ewtbU1djpCCPEHcplJCPEHEydOZPPmzQBs3rwZT09PaWSEECZLRmaE\nEH9w6NAhXn31VSorK9Hr9SxcuBB/f39jpyWEENekajOzZMkSjh07hqIozJs376o/DC9fvsycOXOo\nrq7G19eXV199FYCkpCSefvppZsyYwdSpU9VKTQghhBAthGqXmeLi4khNTSUqKorFixezePHiq15/\n4403ePTRR1m/fj1arZb09HTKysp47bXXrnljKiGEEEKIa1Gtmdm/fz8jRowAwNPTk8LCwvp7LhgM\nBg4fPkxQUBAAkZGRtG/fHnNzc1asWNHom3QJIYQQQqjWzOTk5ODg4FD/2NHRkezsbODK/Rv0ej2v\nv/464eHh9bch//V24Dejpqb2ziUthBBCiGanye4z89upOXV1dWRmZjJt2jTc3d2ZNWsWu3btYtiw\nYTd93vz8hm9odTucnW3Jzi5W7fzi1kltTJPUxXRJbUyT1KXxnJ2vv6JStZEZFxeXq26DnpWVhbOz\nMwAODg60b9++/g6ZAwcO5MyZM2qlIoQQQogWTLVmZtCgQcTGxgIQHx+Pi4tL/aZrOp2Ojh07kpKS\nUv+6h4eHWqkIIYQQogVT7TJTQEAAfn5+hIWFoSgKkZGRREdHY2try8iRI5k3bx4vvfQSdXV1eHt7\nExQUxMmTJ3nzzTdJS0tDp9MRGxvLe++9R5s2bdRKUwghhBDNXLO/aZ6a1xrlWqbpktqYJqmL6ZLa\nmCapS+MZZc6MEEIIIURTkGZGCCGEEM2aNDNCCCGEaNakmRFCCCFasF27tjfqfe+88xbp6WnXff2l\nl+bcqZTuOGlmhBBCiBbq8uV0tm2LbdR7n3/+Rdq3d7/u62+8sexOpXXHNdkdgIUQQgjRtJYte5NT\np+IZMqQ/o0aFcPlyOv/61795/fVXyc7Oory8nEcfncWgQUN45plZzJnzV3bu3E5paQkXLqSSlnaJ\n5557kYEDBzFmzHC+/347zzwzi/79B3DkyCEKCgp48823adu2La++uoCMjMv06uXPjh3b+OabzU32\nOaWZEUIIIZrAlzvOcjAx66rntFqF2tpbv0NKfx8XJgd1u+7r4eERREd/iYeHJxcupPDvf39Efn4e\nd999DyEhD5CWdokFC15i0KAhVx2XlZXJ0qXv8vPPP/Htt18zcOCgq17X6/W8884HfPDBe+zZs4P2\n7TtQVVXJf/+7kn37fuTLL9fd8me6FdLMXEdyWiHltXVYaRVjpyKEEELcth49/ACwtbXj1Kl4Nm6M\nRlE0FBUV/uG9/v59gCtbE5WUlPzh9d69+9a/XlhYSGrqeXr16g3AwIGD0Gq1an2Ma5Jm5jpWbEqg\ntKKG+dP60c7B2tjpCCGEaOYmB3X7wyhKU940z8zMDICtW7dQVFTE8uUfUVRUxOOPR/zhvb9tRq51\nb93fv15XV4dGc+U5RVFQlKYdCJAJwNcx9t4ulJZXszz6BJVVtcZORwghhLhpGo2G2tqr/w4rKCjA\nza09Go2G3bt3UF1dfdtx3N07cPp0AgBxcT//IabapJm5jkG93BgzyINL2aWs2pJ4zc5UCCGEMGWd\nO3tw+nQipaX/d6lo2LAgfvrpR55//imsrKxwcXHh009X3Face+8dQmlpKU899RjHjh3Fzs7+dlO/\nKbI3UwPaOOj567t7OJtWSPhwL0b276haLHFzZD8T0yR1MV1SG9PUUupSVFTIkSOHGDZsONnZWTz/\n/FOsXfv1HY3R0N5MMmemAWY6DU+N68mrKw8SteMsndrZ0L2Tg7HTEkIIIUyKtbWeHTu2sXbtGurq\nDDz7bNPeYE9GZhrwa8ecdLGAf647it5SR+TMu3GwtVAtpmiclvKvmZZG6mK6pDamSerSeLJr9m3y\n7tiG0KBuFJVV8+9vTlBdYzB2SkIIIYT4H2lmGml4vw4M9GtHcnoR67afMXY6QgghhPgfaWYaSVEU\npgX70NHFhl1H0/jxWLqxUxJCCCEE0szcFAszLbMn9EJvqWPND0mcv1xk7JSEEEKIVk+amZvk0saK\nWQ/6UVtr4N/fnKC4rMrYKQkhhBC3ZdKksZSVlbFmzUpOnjx+1WtlZWVMmjS2weN37doOwObN37F7\n907V8rweaWZuQa+uTowL7EpuUSX/+TaeWoNMCBZCCNH8RUTMoGdP/5s65vLldLZtiwXg/vvHMnTo\nfWqk1iC5z8wtGjOwMymXizh6JofoPed4eNj1dy0VQgghjOHRRx9hyZK3cHV1JSPjMnPnvoizswvl\n5eVUVFTwwgt/wde3Z/37Fy9eyLBhw+nTpy8vv/xXqqqq6jedBPjhhxjWr49Cq9XQpYsnf/vbyyxb\n9ianTsXz6acrMBgMtGnThokTQ/n3v9/hxIlj1NTUMnHiZIKDx/DMM7Po338AR44coqCggDfffBtX\nV9fb/pzSzNwijaLw2BhfXlt9iJifL+DhasddPi7GTksIIYSJij67iaNZJ656TqtRqDXc+u3e+rr0\nYkK3B677emDgfezbt4eJEyfz44+7CQy8D09PLwIDh3H48EE+/3wVixf/8w/HxcbG0LWrJ8899yLb\nt/9QP/JSXl7OW2+9h62tLbNnP0Fy8lnCwyOIjv6SmTOf4OOPPwTgl1+OcO5cMh988Anl5eVMnx5G\nYOAwAPR6Pe+88wEffPAee/bsYPLkKbf8+X8ll5lug7WljmfG98TCTMvHm0+RllNq7JSEEEKIelea\nmR8B2Lt3N4MHD2X37u089dRjfPDBexQWFl7zuJSUc/Ts2RuAvn371T9vZ2fH3Lkv8swzs0hNPU9h\nYcE1j09MTKBPnwAArKys6NKlKxcvXgSgd+++ALi4uFBSUnLN42+WjMzcJndnGx4d04MPNpzk/egT\nLJh2F9aW8rUKIYS42oRuD/xhFEXtOwB37epJbm42mZkZFBcX8+OPu2jb1oUFC14jMTGB99//1zWP\nq6sDjUYBwPC/kaPq6mqWLfsHK1euxcmpLX/965+vG1dRFH67v0BNTXX9+bRa7W/i3JlNCGRk5g7o\n7+NC8IBOZOaV8fH3CRia9w4RQgghWpCBAwfz3//+myFDhlJYWIC7ewcAdu/eSU1NzTWP6dSpM4mJ\npwA4cuQQAGVlpWi1Wpyc2pKZmUFi4ilqamrQaDTU1tZedbyPjx9Hjx7+33FlpKVdokOHTmp9RGlm\n7pSJQ7vSo7MDR8/ksHl/qrHTEUIIIQAYOvQ+tm2LZdiw4QQHjyEq6nNeeGE2fn49yc3N5fvvN/7h\nmODgMcTHn+D555/i4sVUFEXB3r4N/fsP4PHHp/HppyuYMiWCd99dRufOHpw+nci7775Vf3zv3n3o\n3t2H2bOf4IUXZvPkk89gZWWl2meUjSYbcLPDf0VlVby68iD5RZW8MLk3Pbs6qZZbayebs5kmqYvp\nktqYJqlL48lGk03Eztqc2eN7odVq+HBjPNkF5cZOSQghhGjxVG1mlixZQmhoKGFhYRw/fvUdBS9f\nvkx4eDiTJk3i73//e6OOaQ483OyYOsqb0ooalkefoLK69sYHCSGEEOKWqdbMxMXFkZqaSlRUFIsX\nL2bx4sVXvf7GG2/w6KOPsn79erRaLenp6Tc8prkI7N2eoX3acyGrhNVbTt+x2dpCCCGE+CPVmpn9\n+/czYsQIADw9PSksLKxfT24wGDh8+DBBQUEAREZG0r59+waPaW6mjPDGw82O/fEZ7DiSZux0hBBC\niBZLtWYmJycHBweH+seOjo5kZ2cDkJeXh16v5/XXXyc8PJy33nrrhsc0N2Y6DbPH98TW2owvtp/h\nzKVr31hICCGEELenye7u9ttLLXV1dWRmZjJt2jTc3d2ZNWsWu3btavCY63FwsEan097wfbeqodnT\njTl27vS7mf/hT/zn23j+NWcYjnaWdzC71u12aiPUI3UxXVIb0yR1uX2qNTMuLi7k5OTUP87KysLZ\n2RkABwcH2rdvT6dOV26gM3DgQM6cOdPgMdeTn1+mQvZX3Iklc672Fkwe5skXO87y2sc/89fwvui0\nsojsdslyRtMkdTFdUhvTJHVpPKMszR40aBCxsVc2poqPj8fFxQUbGxsAdDodHTt2JCUlpf51Dw+P\nBo9pzkb278jdPVw4e6mQqO1njZ2OEEII0aKoNjITEBCAn58fYWFhKIpCZGQk0dHR2NraMnLkSObN\nm8dLL71EXV0d3t7eBAUFodFo/nBMS6AoCjNDepCWU8r2I5fo4mbLoF5uxk5LCCGEaBHkDsANuNPD\nf5n5Zby68hA1tQbmTe1HZ1e5TnqrZGjWNEldTJfUxjRJXRpP7gBsIto5WPPEWF+qawws/+YEJeXV\nxk5JCCGEaPakmWlifbq15cFBXcgprODDjfH1W6sLIYQQ4tZIM2MEDw72wN/TifjzeWzYe87Y6Qgh\nhBDNmjQzRqBRFJ4Y64tLGys2/ZTKkaTmeWNAIYQQwhRIM2MkekszZk/ohbmZho82JXA5t9TYKQkh\nhBDNkjQzRtTRxYYZIT5UVNXyfvQJyitrjJ2SEEII0exIM2Nk9/i6Mqp/Ry7nlvHJ5lOyw7YQQghx\nk6SZMQGThnni3bENh09ns+XABWOnI4QQQjQr0syYAJ1Ww1PjetLGxpz1u5OJT8kzdkpCCCFEsyHN\njImw15sze3wvNIrCh9/Gk1NYbuyUhBBCiGZBmhkT4uluzyMjvSkpr2b5Nyeprqk1dkpCCCGEyZNm\nxsQM7dOewf5upGYUsyY2SSYECyGEEDcgzYyJURSFiFHedHG1Ze+Jy+z+Jd3YKQkhhBAmTZoZE2Sm\n0zJ7fC9srMz4fGsSyWmFxk5JCCGEMFnSzFzHxyc/478HPzfaZR4ne0uefMgPQ10dy785QWFplVHy\nEEIIIUydNDPXUVVbxbZze9l6YZfRcvDt4sikoZ4UlFTxwYaT1NQajJaLEEIIYaqkmbmOR3o8jKNV\nGzYmbyEx74zR8gge0Il+3Z1JuljAVzuTjZaHEEIIYaqkmbkOO3Nb5tz7BBpFw6fxa8mryDdKHoqi\n8Oj9PXBzsmbroYv8HJ9hlDyEEEIIUyXNTAO823ZlkteDlFSXsuLEGqprq42Sh5WFjmcm9MLSXMvK\nmEQuZpUYJQ8hhBDCFEkzcwND3O9hgGs/LhRf4sukb42Wh5uTnscf8KWqxsD70ccprTBOYyWEEEKY\nGmlmbkBRFMK6T6CjTXt+uhzHvvQDRsslwNuZB+7tTHZBBSu+S8AgN9QTQgghpJlpDHOtGY/3moa1\nzoovT28gteii0XIZN7grPT0cOZ6cy8a9542WhxBCCGEqpJlppLZWjsz0m0JtnYEVJ9ZQXGWceSsa\njcKsB/1oa2/Jxn0p/HI2xyh5CCGEEKZCmpmb4OvUnTEeo8ivLODT+LUY6oxz3xcbKzNmj++FmU7D\niu8SyMwrM0oeQgghhCmQZuYmje5yH73a9uB0/lm+OxdrtDw6u9oyPbg75ZU1vP/NCSqqaoyWixBC\nCGFM0szcJI2iYVqPMJytnPghdSe/ZJ80Wi739nRjeEAH0rJLWRmTKDtsCyGEaJWkmbkF1mZWzOo1\nHXONGWsSosgozTJaLqHDu9Gtgz1xp7L44aDxJiYLIYQQxiLNzC1qb+PKIz0epqK2khUnVlNRU2GU\nPHRaDU+P64m93pyvdiZzKtU4dyoWQgghjEXVZmbJkiWEhoYSFhbG8ePHr3otKCiIKVOmEBERQURE\nBJmZmRgMBhYsWEBYWBgREREkJ5v2XkR3tevDfR0Hk1GWxWenvjLaZZ42NhY8Pb4nigL/+fYkeUXG\naayEEEIIY1CtmYmLiyM1NZWoqCgWL17M4sWL//CeFStWsGbNGtasWUO7du3Yvn07xcXFfPHFFyxe\nvJh//OMfaqV3x4z3HEO3Nh4czT7B9ot7jJaHV4c2hA33orismuXfnKS6RnbYFkII0Tqo1szs37+f\nESNGAODp6UlhYSElJQ3fmyUlJQV/f38AOnXqRHp6OrW1tWqleEdoNVoe9ZuKvbktG85uJin/rNFy\nCQpwZ6CfK+cvF7F2W5LR8hBCCCGakmrNTE5ODg4ODvWPHR0dyc7Ovuo9kZGRhIeHs3TpUurq6vD2\n9mbv3r3U1tZy7tw5Ll68SH6+6c8Bsbew5fFeESiKwscnPye/osAoeSiKwrTg7nRysWH3L+nsOZZu\nlDyEEEKIpqRrqkC/n0/y3HPPMWTIEOzt7Zk9ezaxsbEEBwdz5MgRHnnkEbp3707Xrl1vOA/FwcEa\nnU6rWt7OzraNfF8vZtQ9zCdHoliZuJZXguZgpjVTLa+GLHj8Hl54ezef/ZBEL28XvDs53PigZqix\ntRFNS+piuqQ2pknqcvtUa2ZcXFzIyfm/W+1nZWXh7Oxc/3jcuHH1/z8wMJCkpCSCg4N54YUX6p8f\nMWIETk5ODcbJz1fv7rfOzrZkZxc3+v0B9gGcaHeGg5lH+OCnzwn3mahabg3RArPG+vL2l8dY9MkB\nImf0x05vbpRc1HKztRFNQ+piuqQ2pknq0ngNNX2qXWYaNGgQsbFX7pAbHx+Pi4sLNjY2ABQXF/PY\nY49RVVUFwMGDB/Hy8iIxMZG5c+cCsGfPHnx9fdFoms/qcUVRmOIzAXcbN/amH2B/+kGj5dKzqxPj\nA7uSX1zJf749Sa1BJgQLIYRomVQbmQkICMDPz4+wsDAURSEyMpLo6GhsbW0ZOXIkgYGBhIaGYmFh\nga+vL8HBwdTV1VFXV8ekSZOwsLBg6dKlaqWnGnOtOU/0nMabh97li6RvcLdxo5NdB6Pkcv/Azpy/\nXMTRMzms35VMaJCXUfIQQggh1KTUNfN74Ks5PHc7w38nc07xn+MrcbBsw9/6P4eNmf4OZ9c45ZU1\nvLrqEJl5Zfy/sD74dnE0Sh53mgzNmiapi+mS2pgmqUvjGeUyU2vXs20PQjxGkFeRz6cnjbfDtpWF\njicf9EOjKKzecprKatNe6i6EEELcLGlmVBTSZTg9nXxIzD/DpnM/GC2Pzq62jOrfkayCcjbuPW+0\nPIQQQgg1SDOjIo2iYbpvGG0tHYlN3cGx7Hij5fLQEA+c21gSG3eR1AwZ0hRCCNFySDOjMmsza2b5\nT8dMY8bqhCgyy7JvfJAKLMy0TBvtg6GujpUxibK6SQghRIshzUwTcLdxY4rPRCpqK/63w3alUfLw\n83BkUE9XUjOL2XrwklFyEEIIIe40aWaayN2uAQztMIjLpZmsTVxvtB22Q4d7YWttxoYfz5FVUG6U\nHIQQQog7SZqZJjSh2xi62nfhcNYxdl780Sg52FiZET7Ci6oaA6u3JBqtqRJCCCHuFGlmmpBOo+Px\nnlOxM7flm+TNnMlPNkoeA3q0w9/TiYSUfH46mWGUHIQQQog7RZqZJmZvYcdjPacC8PHJzymoLGzy\nHBRFIWJUdyzMtHyx/QxFpVVNnoMQQghxp0gzYwTd2ngwodsDFFeX8NGJNdQYapo8Byd7SyYM7Upp\nRQ3rtp9p8vhCCCHEnSLNjJEM6zCIu9r14XzRBb4+851Rchge0IGu7e04kJDJ8eScGx8ghBBCmCBp\nZozkyg7bk2ivd2VP2n4OXD7c5DloNAozgn3QahTWxJ6moqrpR4iEEEKI2yXNjBFZaM15otc0rHSW\nrDv9NReL05o8hw4uNoTc04ncokqi95xr8vhCCCHE7ZJmxshcrNsy3TeMakMNK06sobS6rMlzGHtv\nF9o5WrP90CWS05t+QrIQQghxO6SZMQG92voS0mU4uRV5rIxf1+Q7bJvptMwI7k4dsComkZpa2epA\nCCFE8yHNjIm432Mkvo7dScg7zebzW5s8fvdODgzt055L2aXEHLjQ5PGFEEKIWyXNjInQKBpm+IXj\nZOlITMp2TuQkNHkODw/zxN7GnO/2nedybmmTxxdCCCFuhTQzJkRvZs0TvSIw0+hYlfAFWWVNu1za\n2tKMqSO9qamtY9WW0xhkqwMhhBDNgDQzJqajrTvh3SdSXnNlh+3K2qa9O2+/7i4EeDuTdLGAPcfS\nmzS2EEIIcSukmTFBA9z6Eeg+kPTSDKPssP3ISG+sLLR8tTOZ/OLKJo0thBBC3CxpZkzURK+xeNh1\n5lDmL+y6tK9JYzvYWvDwsG6UV9awdmtSk8YWQgghbpY0MyZKp9HxeK+p2JrZEH12E2cLzjdp/MA+\n7fHuYM/hpGwOn85u0thCCCHEzZBmxoS1sbDnsZ6PAPDxyc8orCxqstgaRWF6iA86rcJnW09TViFb\nHQghhDBN0syYOC8HT8Z73k9RVTEfnfysSXfYdnPSM/beLhSWVLF+d3KTxRVCCCFuhjQzzcB9HYfQ\nz6U35wpTiD77fZPGDrmnM+7OenYdTSPpYkGTxhZCCCEaQ5qZZuDXHbbd9O3YfWkfcRlHmiy2Tqth\nRrAPCrAyJpHqmtomiy2EEEI0hjQzzYSlzoJZvaZhqbVkbeLXXCpuunvAeLrbE9SvAxl5ZWz6KbXJ\n4gohhBCNIc1MM+Ji7cw031CqDdWsOLGasibcYXtCYFcc7SzY/HMql7JLmiyuEEIIcSPSzDQzvZ39\nGN05iJyKPFYmfNFkO2xbWeiIGNWdWkMdK2MSMRhkqwMhhBCmQafmyZcsWcKxY8dQFIV58+bh7+9f\n/1pQUBCurq5otVoAli5dio0sbR+zAAAgAElEQVSNDX/7298oLCykurqa2bNnM2TIEDVTbJYe6DqK\nC8WXiM9NJCZlO2M8RjZJ3N7d2nJ3DxfiTmWx48glRtzVsUniCiGEEA1RrZmJi4sjNTWVqKgokpOT\nmTdvHlFRUVe9Z8WKFej1+vrHn332GR4eHrz44otkZmYyffp0tmzZolaKzdavO2y/efBdYs5vo7Nt\nB3q27dEkscNHeBN/Po+v95yjr5czTvaWTRJXCCGEuB7VLjPt37+fESNGAODp6UlhYSElJQ3PtXBw\ncKCg4Mry36KiIhwcHNRKr9mzMdPzRK8ItBotKxO+ILsst0ni2uvNCQ3yorKqljU/nG7yfaOEEEKI\n31NtZCYnJwc/P7/6x46OjmRnZ2NjY1P/XGRkJGlpafTr148XX3yRMWPGEB0dzciRIykqKuLDDz+8\nYRwHB2t0Oq0qnwHA2dlWtXPfLmfnHjxBOB8cXMOniZ+zaPhfsNCZqx53XJANh89kc+xMDolpRQT2\n7aB6zGsx5dq0ZlIX0yW1MU1Sl9un6pyZ3/r9v+Cfe+45hgwZgr29PbNnzyY2NpbKykrat2/Pxx9/\nTGJiIvPmzSM6OrrB8+bnq7eix9nZluzsYtXOfyf0tO3F4PYD2Jt+gHf3rmK6byiKoqgeNzyoG6fO\n5/Gf6ON0dLLGxspM9Zi/1Rxq0xpJXUyX1MY0SV0ar6GmT7XLTC4uLuTk5NQ/zsrKwtnZuf7xuHHj\ncHJyQqfTERgYSFJSEkeOHGHw4MEA+Pj4kJWVRW2t3KTtRiZ5P0QXu04czDzCnrT9TRLTxcGah4Z4\nUFxWTdSOM00SUwghhLgW1ZqZQYMGERsbC0B8fDwuLi71l5iKi4t57LHHqKqqAuDgwYN4eXnRuXNn\njh07BkBaWhp6vb5+tZO4PjONjsd7TsXGTM/6Mxs5V5jSJHFH9e9Ip3Y27DuRQXxKXpPEFEIIIX5P\ntWYmICAAPz8/wsLCWLRoEZGRkURHR7N161ZsbW0JDAwkNDSUsLAwHB0dCQ4OJjQ0lLS0NKZOncqL\nL77IwoUL1UqvxXGwbMNjPR+hrq6Oj06sobBS/WFLrUbDzJAeaBSF1VsSqayWUTQhhBBNT6lr5stR\n1LzW2ByvZW67sJtvzn6Pp70Hz/edhVaj/sjWlzvOsiXuAiEDOvHwfd1UjwfNszatgdTFdEltTJPU\npfGMMmdGGMfwjoH0dfEnufA83yQ3zQ7bDw3xwLmNJbFxF0nNkB+lEEKIpiXNTAujKApTfSbhau3C\nzot7OZRxVPWYFmZapo32wVB3ZauDWkPTbLEghBBCgDQzLZKlzvJ/O2xb8HnietJKLqse08/DkUE9\nXUnNLGbrwUuqxxNCCCF+Jc1MC9VO70KEbyhV9Ttsl6seM3S4F7bWZmz48RxZBerHE0IIIUCamRat\nj3NPRnW+j+zyXFafUn+HbRsrM8JHeFFVY2D1lkTZ6kAIIUSTkGamhRvbdTQ+Dl6cyDnF7ks/qR5v\nQI92+Hs6kZCSz08nM1SPJ4QQQkgz08JpFA3TfMOwMdOzIXmz6vNnFEUhYlR3LMy0fLH9DEWlVarG\nE0IIIaSZaQXsLWyZ2uNhagw1rIxfR3VttarxnOwtmTC0K6UVNazbLlsdCCGEUJc0M61Er7a+DHEf\nSHppBt8mx6geb3hAB7q2t+NAQibHk3NufIAQQghxi6SZaUUmdBtDO2sXdl7aS0LuaVVjaTQKM4J9\n0GoUVseepryyRtV4QgghWi9pZloRc605M/3C0Spa1pz6kuKqElXjdXCxIeSezuQVVfLNnnOqxhJC\nCNF6STPTynS0dWds19EUVRXzeeJ61ZdPj723M66O1mw/fInk9EJVYwkhhGidpJlphYZ3CsTboRsn\nchLYm35A1VhmOi3Tg7tTB6yMSaSmVrY6EEIIcWdJM9MKaRQN03pMxlpnxddnviOjNEvVeN07OTC0\nT3vSskuJOXBB1VhCCCFaH2lmWikHyzZM8ZlEtaGalQnrqDGoO0H34WGe2NuY892+81zOLVU1lhBC\niNZFmplWrK9LLwa69edicRqbzv2gaixrSzOmjvSmpraOVVtOY5CtDoQQQtwh0sy0cpO8HqStlRPb\nLuwmKf+sqrH6dXchwNuZpIsF7DmWrmosIYQQrYc0M62cpc6CGb7hKIrCqoQoSqvLVI33yEhvrCy0\nfLXzLPnFlarGEkII0TpIMyPwsO/E/V1GUlBZyLrT0aou13awteDhYd0or6xl7dYk1eIIIYRoPaSZ\nEQCM7nIfnvZdOJp1nAMZh1WNFdinPd4d7DmclM3h09mqxhJCCNHySTMjgCvLtaf7hmGpteTLpA1k\nl+WqGEtheogPOq3CZ1tPU1ah7saXQgghWjZpZkQ9JytHQruPo7K2ipUJ66g11KoWy81Jz9h7u1BY\nUsX6XcmqxRFCCNHySTMjrnK3awB3tetDStEFYlK2qxor5J7OuDvr2fVLOkkXC1SNJYQQouWSZkb8\nQaj3eBwtHdiSsp3kghTV4ui0GmYE+6BwZauD6hr1RoKEEEK0XNLMiD+wNrNium8YAKsS1lFeU6Fa\nLE93e4L6dSAjr4xNP6WqFkcIIUTL1ehmpqSkBICcnBwOHTqEwSAbBrZk3dp4MLrzfeRW5PNl0gZV\nY00I7IqjnQWbf07lUlaJqrGEEEK0PI1qZl577TViYmIoKCggLCyMNWvWsHDhQpVTE8Z2v8dIOtt2\nJC7jCIcyjqoWx8pCR8So7tQa6li5JRGDQbY6EEII0XiNamYSEhJ4+OGHiYmJYfz48bzzzjukpsol\ngZZOq9Eywy8Mc605XyR9Q255vmqxendry909XDiXXsSOI5dUiyOEEKLl0TXmTb/eEXbXrl38+c9/\nBqCqquqGxy1ZsoRjx46hKArz5s3D39+//rWgoCBcXV3RarUALF26lD179rBx48b695w8eZKjR9Ub\nERA35mLtzMNeD/F54lesPvUFz/f9ExpFnalWU0Z4E38+j693n6OvlzNO9paqxBFCCNGyNKqZ8fDw\n4P7778fR0ZEePXqwYcMG7O3tGzwmLi6O1NRUoqKiSE5OZt68eURFRV31nhUrVqDX6+sfP/zwwzz8\n8MP1x8fExNzs5xEqGOh2F/G5p/gl+yRbU3cxukuQKnHs9OaEBnnxyeZTrPnhNM9P8kdRFFViCSGE\naDka1cwsWrSIpKQkPD09AfDy8iIoqOG/0Pbv38+IESMA8PT0pLCwkJKSEmxsbBqV2PLly1m6dGmj\n3ivUpSgK4T4TOV94gU3nf8DH0YvOdh1ViTWolyv74zM4npxL3KksBvi2UyWOEEKIlqNR1wtOnTpF\nRkYG5ubmvP322/zjH/8gKanhTQJzcnJwcHCof+zo6Eh29tX78ERGRhIeHs7SpUuv2tzw+PHjuLm5\n4ezsfDOfRajIxkzPNN9QDHUGVsavo6JGnR2vFUVhenB3zHUa1m5LoqRctjoQQgjRsEaPzLzxxhsc\nOnSIEydOsGDBAl599VVWr17d6EC/34n5ueeeY8iQIdjb2zN79mxiY2MJDg4GYP369YwfP75R53Vw\nsEan0zY6j5vl7Gyr2rmbG2fnAFLKR/Dd6W18f2kLT/afqlIcWx4J9uHTTQl8+1MKfw4LuO77hOmR\nupguqY1pkrrcvkY1MxYWFnTp0oWoqCgmT55Mt27d0GgaHtRxcXEhJyen/nFWVtZVIy3jxo2r//+B\ngYEkJSXVNzMHDhxg/vz5jfoA+flljXrfrXB2tiU7u1i18zdHw92COJqWwI5z+/DUe9LHuacqce71\ndWH7wQtsP3iRPp5O+HVxvOp1qY1pkrqYLqmNaZK6NF5DTV+jLjOVl5cTExPDtm3bGDx4MAUFBRQV\nFTV4zKBBg4iNjQUgPj4eFxeX+vkyxcXFPPbYY/Urog4ePIiXlxcAmZmZ6PV6zM3NG5OaaGJmGh0z\n/cIx0+hYe2o9BZWFqsTRajTMDOmBRlFYvSWRymrZ6kAIIcS1NaqZmTNnDt999x1z5szBxsaGNWvW\nMGPGjAaPCQgIwM/Pj7CwMBYtWkRkZCTR0dFs3boVW1tbAgMDCQ0NJSwsDEdHx/pRmezsbBwdHRs8\ntzAuV307JnR7gNKaMtYkfImhTp27QXd2tWVU/45kF1Tw7d7zqsQQQgjR/Cl1v5/Mch1lZWWcP38e\nRVHw8PDAyspK7dwaRc3hORn+u766ujr+c/xTTuYmMqHbAwzvFKhKnMrqWv7+8QFyCytZMP0uOrte\nGWaU2pgmqYvpktqYJqlL4932ZaZt27YxatQoIiMjmT9/PqNHj2b37t13LEHR/CiKwtQek7E1s2Fj\ncgyXitNViWNhpmVasA+Gujo+jTlFrewJJoQQ4nca1cx89NFHbNy4kfXr1xMdHc1XX33FBx98oHZu\nwsTZmtsQ4TuZmrpaPk1YR1WtOsuo/bo4MqinKxcyS9h6ULY6EEIIcbVGNTNmZmZXzWNp164dZmZm\nqiUlmg8/Jx+GdriXjNJMNiR/r1qc0OFe2FqbseHHc2SpuIJNCCFE89OoZkav1/PJJ5+QmJhIYmIi\nH3300VXbEIjWbZznGFz17dh96SdO5pxSJYaNlRnhI7yoqjGwasvpP9y3SAghROvVqGZm8eLFpKSk\n8NJLLzF37lzS0tJYsmSJ2rmJZsJca8ZM33B0ipbPTn1FUZU6k9kG9GiHv6cTp1LzWb7+GGUVNarE\nEUII0bw0ejXT7yUnJ9fv1WRMsprJdOy4sIevz27Cz8mHp/xnqrJJZF5RBcu+PEZ6TiltbMyZOqo7\nAd6y7YWpkN+M6ZLamCapS+Pd9mqma3nllVdu9VDRQg3rOBgfBy/icxP5MW2/KjEc7SyJnNGfKaN9\nKCmv5v3oE7wffYL8YnX2ihJCCGH6brmZkTkL4vc0ioYI38noddZEn93E5dJMVeKY6TSEj+rOwpl3\n49XBniNJ2cz/6Gd2Hk3DIP9dCiFEq3PLzYwalxBE89fGwp4pPSZRbajh0/i1VBvUm9fSvq2evz0S\nwLTR3QFYE3uaNz8/QnpOqWoxhRBCmJ4GN5pcv379dV/Lzs6+48mIlqGPc08Gtb+bfelxfJe8hQle\nD6gWS6MoDOvrTu9ubVm7NYnDSdks/DSOMQO7cP89nTHT3XK/LoQQoplosJk5fPjwdV/r06fPHU9G\ntBwTvR7kTME5tl/cg69Td3wcvVSN52BrwewJvTiSlM1nP5zm273niTuVyYwQH7w6tFE1thBCCOO6\n5dVMpkJWM5mu1KKLLD28HFszG+YNeAEbszt3b6KGalNWUcPXe5LZdSSNOmBYX3cmDfXE2rLB3l3c\nAfKbMV1SG9MkdWm8hlYzNepP9ylTpvxhjoxWq8XDw4Onn36adu3a3V6GokXqbNeRBzxGsfHcFtYm\nfs0TPSOaZK6VtaWOiFHdGejrysotiew6msYvZ7J5ZGR3+nWXZdxCCNHSaBcuXLjwRm+6fPkyNTU1\nTJw4kYCAAHJzc/H29sbV1ZVPPvmEhx56qAlSvbaysirVzq3XW6h6/tagq31nzhQkcyovCQfLNnS0\ndb8j521MbRztLBni3x6dRuHk+TwOJGRyMasErw5tsLKQURo1yG/GdEltTJPUpfH0eovrvtao2ZGH\nDx/mrbfeYtSoUYwYMYI33niD+Ph4ZsyYQXW1OpsLipZBo2iY7huGlc6Sr5K+JausaSeOm+k0PDjY\ng1cevRtvWcYthBAtUqOamdzcXPLy8uofFxcXk56eTlFREcXFcq1PNMzR0oGw7hOoMlSzMv4Lag21\nTZ6Dm5Oev8oybiGEaJEaNdY+bdo0QkJCcHd3R1EULl26xJ/+9Cd27txJaGio2jmKFuCudn2Iz00k\nLuMIm89vZaxncJPncNUy7m1JHD4ty7iFEKIlaPRqppKSElJSUjAYDHTq1Ik2bUxjuausZmo+ymsq\neD3uX+RV5PN83z/h5dD1ls91J2pzNCmbz7YmkV9ciZuTtSzjvgPkN2O6pDamSerSeA2tZmrUBODS\n0lJWrVrFpk2bOHToELm5ufTs2ROdzviTKGUCcPNhptHR2a4D+y8f4nT+We5xuwszrdktnetO1MbN\nSU9g7/ZUVNVw8lwePx6/TGFpFV4d2sgozS2S34zpktqYJqlL4932BOAFCxZQUlJCWFgYkydPJicn\nh/nz59+xBEXr0dW+CyFdhpNfWUBU0jfGTgcrCx1TR3Vn7tR+tG+rZ9fRNOZ/9DOHT8sdroUQorlo\n1NBKTk4Oy5Ytq3983333ERERoVpSomUL7jKcU3lJHMr8BT8nH+52DTB2SnTrYM/Cmf3Z/HMqm35K\nYfk3JwjwduaRkd442F7/XwNCCCGMr1EjM+Xl5ZSXl9c/Lisro7KyUrWkRMum1WiZ7huOhdacqNMb\nyCnPu/FBTUCn1fDgIFnGLYQQzU2jmpnQ0FBCQkJ45plneOaZZxgzZgxTpkxROzfRgjlbOzHZexwV\ntRWsSjDOcu3rqV/GHdwdUFgTe5o3Pj9CmizjFkIIk9SoCcC+vr6MHj0aJycnevTowdNPP82uXbu4\n9957myDFhskE4ObL3caNjLIsEvJOo1W0N7W6Se3aKIpCF1c7BvVyJbewgpPn89jzSzoGQx2e7vZo\nNepvy9AcyW/GdEltTJPUpfEamgDc6OVIbm5uuLm51T8+fvz47WUlWj1FUQjvPoFzhalsTtmGj6MX\nHvadjZ3WVdrYWPD0+F4cPZPNZz8ksXFfCgcTs5ge7IN3R1nGLYQQpuCW15828822hYmwNrNmum8o\ndXV1rEz4goqaCmOndE19vZxZ9PgAggLcycgt443Pj7A69jRlFTXGTk0IIVq9W25mmmL3Y9E6eDt0\nY0SnoeSU5/LVmY3GTue6fruM212WcQshhMlo8DLT0KFDr9m01NXVkZ+fr1pSovV5oOsoEvPP8PPl\nQ/g5+RDg4m/slK6rWwd7ImUZtxBCmIwGm5m1a9c2VR6ildNpdMz0Def1g++wLvFrPOw64WBpunNS\nfl3G3d/HhVUxiRxJyuZUah6ThnoytK87Ghm5FEKIJtPovZluxZIlSzh27BiKojBv3jz8/f/vX9tB\nQUG4urqi1WoBWLp0Ke3atWPjxo189NFH6HQ6nnvuOYYNG9ZgDNmbqWX5Me1nvjgdjXcbT57t+wQa\n5dpXQk2pNoa6On48ls6XO5Mpr6yhWwd7pgf74N5Wb+zUmpwp1UVcTWpjmqQujdfQ3kyqba4UFxdH\namoqUVFRJCcnM2/ePKKioq56z4oVK9Dr/+8P/Pz8fJYvX87XX39NWVkZ77333g2bGdGyDG4/gPjc\nRE7kJLD9wh5Gdh5m7JRuSKMoDO3zv924tyZx6HQ2Cz+JY8zAzowZ2EX2eRJCCJWp9qfs/v37GTFi\nBACenp4UFhZSUlJyw2MGDhyIjY0NLi4uvPbaa2qlJ0yUoig84jMJO3NbvjsXy4XiS8ZOqdF+Xcb9\n7MRe2OnN2bgvhYWfxpF0scDYqQkhRIum2shMTk4Ofn5+9Y8dHR3Jzs7Gxsam/rnIyEjS0tLo168f\nL774IpcuXaKiooInn3ySoqIinn32WQYOHNhgHAcHa3Q6rVofo8FhLaEOZ2x55p4ZLNnzHmsSo3hz\n1DwsdOZ/fJ+J1maUsy2DAzqyevMpNv90njc+P0LIwC5MH+OL3urWdglvTky1LkJqY6qkLrdPtWbm\n934/Nee5555jyJAh2NvbM3v2bGJjYwEoKCjg/fffJz09nWnTprFz584Gl4Hn55eplrNcyzQed11H\n7uswmJ2X9vLhz+sI7z7hqtebQ20mDvGgd1dHVsUkErM/hZ9OpDN1pDf9ursYOzXVNIe6tFZSG9Mk\ndWm8hpo+1S4zubi4kJOTU/84KysLZ2fn+sfjxo3DyckJnU5HYGAgSUlJODk50bdvX3Q6HZ06dUKv\n15OXZxqbEIqm95BnCO31ruxN+5kTOQnGTueWdHO/sox7/BAPSsurWf7NSd77+jj5xbJRqxBC3Cmq\nNTODBg2qH22Jj4/HxcWl/hJTcXExjz32GFVVV/ajOHjwIF5eXgwePJiff/4Zg8FAfn4+ZWVlODg4\nqJWiMHFmWjNm+IWj0+j47NRXFFY2z3+96LQaxv66G3fHNhw9k3NlN+4jl2Q3biGEuANUu8wUEBCA\nn58fYWFhKIpCZGQk0dHR2NraMnLkSAIDAwkNDcXCwgJfX1+Cg4NRFIXRo0czefJkAObPn49GIytB\nWjN3GzfGed7P+jMbWXMqiqd7P3rd5dqmzs1Jz1+n9K1fxr3mhyT2x2cy834f3Jxa3zJuIYS4U1S9\nz0xTkPvMtHx1dXX8+9gnJOSdZpLXg9zXcXCzr01BSWX9Mm4Lcy1PPuhH725tjZ3WbWvudWnJpDam\nSerSeEaZMyPEnaIoClN7TMbGTM+G5M2klVw2dkq37ddl3H960A+DoY531x8nNu6CbOAqhBC3QJoZ\n0SzYW9jyiM8kagw1rIxfR1VttbFTuiMG+LbjpUcCsLMxJ2rHWVZtSaSm1mDstIQQolmRZkY0G/7O\nfgx2v4f00gwWbP9nixihAfBws2PBtLvo1M6GPccusyzqF0rKW0azJoQQTUGaGdGsTOw2lnvc7uJ8\n/kXePPguW1K2U2uoNXZat83RzpK5j/Sjn7cziRcKWLTqEJdzS42dlhBCNAvSzIhmxVxrRkSPybw0\n5GlszKz57lwsSw8vJ70kw9ip3TYLcy1Pje/JmIGdySooZ9Hqw5w8n2vstIQQwuRpFy5cuNDYSdyO\nsrIq1c6t11uoen5x67q5dqS3vT9FVcUk5J1mf3ocWo2WLnadmu3Sbbgy2dm3iyMubaw4nJTF/pOZ\n6K3M6NreztipNYr8ZkyX1MY0SV0aT6+3uO5rzfdPfdHqWZtZM803lD/1mo61mTXfJsew7MgHZJRm\nGTu12zawpyt/nRKAjZWOz7cmseaH09QaZGKwEEJcizQzotnzd/bj5QFzuKtdH1KKLvD6wX+x7cJu\nDHXN+y//bu72zJ9+Fx2c9ew8ksbbXx6jrEImBgshxO9JMyNaBBszPTP9pvBEr2lYaS355uz3LDv8\nAZll2cZO7ba0tbdi7tR+9PZ0IiEln0WrD5Op4uaqQgjRHEkzI1qUPs49mT/gRfq59OZ8USqvx73N\njgt7mvUojZWFjmcn+hN8dycy8spYtOoQian5xk5LCCFMhjQzosWxMdfzaM9HeKznVCy0Fnx9dhP/\nOvIfsspybnywidJoFCYHdWNmiA8VVbW8FfULe46lGzstIYQwCdLMiBYrwMWf+QNepI9zL5ILU1gS\n9zY7L+5t1qM0Q3q35/+F9cHSXMvKmES+2H4Gg0G2QBBCtG7SzIgWzdbchsd7TuVRvymYa81Yf2Yj\n7xz9kJzy5nv/lu6dHFgw/S7cnKz54eBF3v36OOWVNcZOSwghjEaaGdHiKYpCv3Z9mD/gRXq39eNs\nwXkWx73N7ks/NdtRGhcHa16OuAs/D0eOJ+eyZM1hsgvKjZ2WEEIYhTQzotWwM7fliV7TmOEbjk7R\n8mXSBt47uoLc8jxjp3ZLrC11/Plhf4b360BaTimvrTrEmUsFxk5LCCGanDQzolVRFIX+rn2ZP+BF\nerXtQVJBMovjlvFj2s/U1TW/uSdajYZHRnoTMcqbsooa/rnuKPtOtIwNOIUQorGkmRGtkr2FHX/q\nNYNpPULRKFq+OB3N+798RG5581zyfF9AB14I7Y25TsvH359i/a5kDM2wORNCiFshzYxotRRFYYBb\nP+YPmIOfkw+J+WdYEreMfekHmuUojV8XR16e1o92DlZs/jmV5dEnqKiSicFCiJZPmhnR6rWxsOcp\n/5lM7TEZUFib+DXLj31MfkXzm3/i5qTn5Wl34dOpDUfP5PDGZ0fIK6owdlpCCKEqaWaE4MoozUC3\nu5g/YA49HL05lZfEogPL2J9+sNmN0thYmTEntA+BvdtzIauE11Yd4lx6kbHTEkII1UgzI8RvOFi2\nYXbvx5jiMxGo47PEr/jg+KcUVBYaO7WbotNqmB7cnfDhXhSVVfHm2iMcSMg0dlpCCKEKaWaE+B1F\nURjUfgAvD5iDj4MX8bmJLDqwjAOXDzerURpFURjZvyPPT+qNVqPw4cZ4Nvx4TiYGCyFaHGlmhLgO\nR0sHnunzOGHdJ2Coq2X1qSg+PLGSwsrmdcnG39OJlyP60dbeko37Uvjw23gqq2uNnZYQQtwx0swI\n0QBFURjifg8v3z0H7zaenMg5xaIDbxGXcaRZjdK4O9swf/pdeHWw52BiFv9Ye4T84kpjpyWEEHeE\nNDNCNIKTlSPP9n2CUO9x1BhqWJXwBStOrKaoqtjYqTWanbU5/y+sL4N6unL+cjGLVh8iNaP55C+E\nENcjzYwQjaRRNAR2uJeXB8zBq01XjuXEs+jAWxzO/KXZjNKY6TQ8OqYHDw/zpKC4ktc/O8zh01nG\nTksIIW6LduHChQuNncTtKCurUu3cer2FqucXt86YtbE2s+Zu1wBszPTE557mcNYx0ksz8XbwxEJr\nbpScboaiKHh1aEMnFxuOJOWwPz4TrUbBq4M9iqLc1rnlN2N6SsqrWbf9DGWVtbg6WBk7HfE78ptp\nPL3e4rqvSTPTAPmPzHQZuzaKotDFvhMBLr25VJzGqbwkfr58CCcrR9z07YyW181wc9Lj7+nE8XO5\nHEnKIbugHH9PJ7SaWx+wNXZdxNWS0wpZ+sVRElMLiEvIoIurLa6O1sZOS/yG/GYar6FmRqlTcXx8\nyZIlHDt2DEVRmDdvHv7+/vWvBQUF4erqilarBWDp0qWkpKTw/PPP4+XlBYC3tzcLFixoMEZ2tnrX\n/J2dbVU9v7h1plQbQ52BXZf2sTE5hmpDDf1cejPZexw25npjp9YohSWVvBd9gnPpRXi62/HMBH/s\n9bc2wmRKdWnN6urq2HroEl/tPIvBUMd9Ae7sPX4ZrVbh5Yi7aN+2efy32RrIb6bxnJ1tr/uaTq2g\ncXFxpKamEhUVRXJyMuR7EkUAACAASURBVPPmzSMqKuqq96xYsQK9/v9+VCkpKdx99928++67aqUl\nxB2nUTQEdRyCn5MPaxK+5HDWMZLykwnzmUAf557GTu+G7G0s+NuUvnyyOZEDCZksWnWQ5yb1pqOL\njbFTE7egrKKGTzef4nBSNnbWZsx60A/fLo4E9HBl6eeHee/r48yffhd6SzNjpyrEHaPaBOD9+/cz\nYsQIADw9PSksLKSkpEStcEIYXTtrZ+b0e4rx3cZQXlvBihOr+TR+LSXVpcZO7YbMdFpmjfVl/BAP\ncosqWfLZYX45k2PstMRNSs0o5tWVBzmclI13xzYsfPRufLs4AjA0oAMh93QiM7+c/3wbT63BYORs\nhbhzVGtmcnJycHBwqH/s6OhIdnb2Ve+JjIwkPDycpUuX1q8GOXv2LE8++STh4eHs27dPrfSEUIVG\n0TCi01Dm9v8zne06cijzFxYfWMbx7Hhjp3ZDiqIwdtD/b+/O46Ou78SPv+ZMMkfu+ySEIyThSjjk\nisiheGzFqyCI7ta1vxZdf3bVX12sZXfb9Vf92a1bbXWr7RZxuwRR8agVRQWpCSRcIRdJIEBC7snk\nnlxz/P4IhkOIIWQy30nez8fDR5xkknkP7+/3k3c+ZyI/XJ2Gy+nipbeP8fGBSq9ZqTWeuVwu9hyp\n5t+2HqKhpYtbFyTw5L2zCDRdPMfgrswkZiSFUHTKyltfnPRQtEKMPLcNM13q0gbx0UcfZcmSJQQE\nBPDwww+za9cuZs+ezSOPPMLNN99MVVUV999/P5988gl6/ZXH74OCDGi1GrfFPdgYnfAsJecmLMzM\nL+J/zAelu9le+CH/WbCFzIT5/G36PYqfS3NLmJnJE4L5+R9y2f7FCZo7e/nhXTPRaYf2t4+S8zIW\ndfXY+e2OfPYcPtt/yOi6ucxNibzscyMi/Nn0d/N54tdf8kleFSlJoSyfGz/KEYtLyT1z7dxWzISH\nh2OxnO+mbmhoICwsbODx6tWrB/4/MzOTsrIyVq1axS233AJAfHw8oaGh1NfXExcXd8XXaW62uSH6\nfjIxS7m8JTeLQheSOGciW0u28+WZA+TXlrAu+S7SQqd5OrRBBfpqeXpDBr/ecYxPcys5U9vGw3ek\nYTYMPjHYW/IyVlRbOvntuwXUNtmYGO3PD25PJTTA77I5uDA3D69O42dbDvLyW0cx6tQkxQSMduji\nHLlnhm6wos9tw0yLFi1i165dABQVFREeHo7J1D+hsL29nQcffJDe3v7laHl5eUyePJn333+f3//+\n9wA0NjbS1NRERIR3LHMV4kqiTZE8kfEwfzNxFR19nbxy7L/YWrwdW1+Xp0MbVJDZh6fuSydjahhl\nVS38/I2DVFuUP/9nvMgurOVnW/KobbKxck4cT61PJzRgaPvIRAQb+MHqVBxOFy+/UyBHWwiv59al\n2S+88AIHDx5EpVKxefNmiouLMZvNrFy5ki1btrBz5058fHxISUnhmWeeobOzkyeeeIK2tjb6+vp4\n5JFHuP766wd9DVmaPT55a26qO2rZWrKdqvZqAn0CWJd8N6khUz0d1qCcLhc7953iw+zT+Plo+OHt\naaRNDLnsc701L96kt8/Bn3aX82V+DX4+Gv7u5mnMSQ7/1u+7XG4+ya1k2+cnmBBp5qn16eh17huy\nF5cn98zQDdYz49ZiZjRIMTM+eXNuHE4Hn5z5go9O78bpcnJd5BxWT7oFs17ZS6Fziur4r4+O43A6\nuXf5ZJZnxH5jx2Bvzos3qLfa+O3OQqoaOogPN/HDO9KICBraJniXy43L5eIPH5XwVUEd16VG8NBt\nKde8C7S4OnLPDN1gxYzsADwI2ZlRubw5N2qVmslBE5kemsLptkqKraVk1+Tip/Ulzhyj2F8mceEm\npk0IIr/cwsHSRtptfaQmBqNWn4/Xm/OidAePN/DiW/lY23q4flY0G1enETDIjqiXulxuVCoV0yeG\nUHLaSkGFFR+9hsmxgSMduhiE3DNDJ8cZDJNcZMo1FnIT4GNmYdRcjDojpc0nONpYSFHTceLM0QT6\nKHNCZrC/L3OSwyk508Kxk02crGll5qRQ9OdWFI6FvCiN3eFk22flZH1xArVaxfdumcZtCyeg0Vzd\nlMcr5UajVjEjKYTckgYOlzaSGGUmQo48GDVyzwydFDPDJBeZco2V3KhVahID4rkuag6tvW2UWMvI\nrsmjtaeNiQET0GuUt0urwVfHdakR1Fg6KaiwcrjMwvTEYEx+ujGTF6WwtHbxq+3HOFzWSHSokcfX\nzh7YBO9qDZYbX72WKXGBZBfVcaS8kfQpYd+6ck2MDLlnhk6KmWGSi0y5xlpufLU+zA6fzuTARE63\nVVFsLSOnNg+jzkiMKUpxQ086rZq5yeH02p3kn7Cwv6iOxEgzCTGBYyovnnT0hIVfbc+noaWLBakR\nPHrXDILMQx9WutS33TNBZh/CAnw5UNxA0SkrC9IiB3rchPuMtbbMnaSYGSa5yJRrrOYmxC+YRdHz\n8NX4cLz5BEcbCyhtLifeHIu/j7I21lKpVKQmBhPs78Oh0kZyiuoxG3REBRsUV3x5E4fTydt7T/Lf\nn5ThcsGGm6ZwR+ZEtEPctPBKhnLPxIab6LU7OHqiiar6DuanREgu3WystmXuIMXMMMlFplxjOTdq\nlZqkwAnMj0ynubuFEmsZX9UcwNbXRWJAPDq1soaeEiLMJMcHcaTcQnZBLTlFdWjUKqJDjWivcl7H\neNfc3sOvdxzjQHED4UF+PL5mFjMnhY5IQTHUe2ZafBCn69opqLDS0+cgLfHyy/DFyBjLbdlIk2Jm\nmOQiU67xkBs/rS/pETNJ9I/ndFslRdbj7K89hL/eTLQxUlF/MYcE+DJvWgQanYbiU1aOnrCw92gN\nvXYnMaFGfGT/km9VdNrKL7OOUttkI2NqGI/dPZPQwKFtgjcUQ71nVCoVM5JCOVLeyNETTYQG+BIf\noaxewbFkPLRlI0WKmWGSi0y5xlNuwgyhLIqZj1al5XhzGYcbjlHeUkGCf5yi9qYx+GpZOjeeOZND\n0WlVVNS0UXjKyueHztLa0UtUiAGjr7J6lZTA6XTxwVen+eNHx7E7XKxdPpk1yyaN+AZ2V3PP6LRq\nUhODySms43BZIykTggn29x3ReES/8dSWXSspZoZJLjLlGm+50Zzbm2ZOxGyaupsosZbz15oD9Dh6\nSPRPQKsetTNjB2U0+uDoczAtIZhlGTH4G/Scbeyg6HQznx06S21TJ6GBvt84zXm8auvs5TfvFrDv\nWC0h/j489t2ZzJka7pZet6u9Z0x+OuIjTWQX1pF/oon5KRH4+SjjOhtLxltbdi2kmBkmuciUa7zm\nxqDzY07EbOLNMVS0nqao6Ti5dYcJ8g0k0uCeX4JX48K8aDX9BxguS48lMsRAQ3MXJWea2Xu0hrKq\nFgKMesIC/Twes6eUVbXwwrYjVDV0MiMphH9cM4tIN+7vMpx7JjzIgJ9ew6GyRsrPtrAgNfKq97cR\ngxuvbdlwSDEzTHKRKdd4z02EIYxF0fNRqVQct5ZxqCGfU22VTPCPw6gzeiyuy+VFrVYRF25i6axo\nJsUG0NLRS8mZZnKK6jlSbsFXryEqxHDRTsJjmcvl4uPcSl7/oISePid3LU1i/Y1T3D6vaLj3zMRo\nf5pauzlWYaWprZv0KWHjtgB1h/Hell0NKWaGSS4y5ZLcgEatYWrQJDIiZtJgs/Sveqo+gN3lINE/\nHo169CfdDpYXlUpFeJCBRdOjmDUplK4eO8crmzlU2kh2YS2gIiZsbK+A6uzu49X3ivj8cDX+Jj3/\n++4ZLEgdncncw71n+o88CKb4dLMceeAG0pYNnRQzwyQXmXJJbs4z6ozMjZhNlCmSk62nKWwq4WD9\nEUL9QogwhI1uLEPMS6DJhznJ4SxMi8TlgvKzreSfbGLPkWq6euzEhJnw1Y+tFVCnatt4YdtRKmrb\nmJYQxONrZxMTNnoTuK/lntGo1cxICuFAcT1HyiwkRvnLkQcjRNqyoRusmJFTswchp5kql+Tm8rrt\nPfzl9G4+r9qH0+Vkeug07p58O6F+w9sC/2oNNy/ttl6+OFzN7kNn6ejqQ6tRs2h6JDfNi3frPJLR\n4HK5+PxwNVmfl+NwuPibRRP4zqLEUR9WG4l75lRtG//3zcPotCp+cv8cokI8N6Q5VkhbNnRyavYw\nScWsXJKby9OqtUwLnsKssDTqOuspsZbzVc1+XC6YEBCPRuXeIZzh5sVHp2FqfBDLMmIJNvtQbemg\n+HQznx86S1VDByH+vl65NLirx87rH5bwcW4lBl8dj9w1ncyZnjkZfSTumSCzD6GB5448ON3MwtQI\ndHLkwTWRtmzoZJhpmOQiUy7JzeDMehPzIzMIM4RS3lJBYVMJh+qPEm4II8wQ6rbXvda8aDVqEqP8\nWZ4eS2yYCUtr/wqofcdqKT5txeynJzzYO1ZAVTV08MK2I5SdbWVSbABP3jubBA9uPjdS90xcuIne\nPgdHT1iolCMPrpm0ZUMnxcwwyUWmXJKbb6dSqYgxRbEoeh69jj5KrGXk1h+mtqOOxIAE/LQj39Mx\nUnlRqfqPQ8icGU1yfBDtXX2UnGnmQEk9eccb0OvURIcY0Sh0BdS+/BpefreANlsfq+bH8/e3pXh8\nw8CRvGemJciRByNF2rKhk2JmmOQiUy7JzdDp1DpSQ5KZHppKdUctJc1l/LXmABqVmgn+cahHcOhp\npPOiUqkIDfTjutRIMqaE0dPnoKyqhcNlFvYdq8HldBETakJ3jYcwjpSePgdbPj7O+1+dxlen4Qer\nU1mREaeIZecjmZuvjzw4XNZIvhx5cE2kLRs6mQA8TDIxS7kkN8PjdDk5UHuInSc/oqOvk0hjBGum\nrGZKUNKI/PzRyIu1rZtPD1ax52gNPb0OfPUals6KYeXcOILMnttZuLapk9/uLKS6sZMJkWZ+uDqN\nsBE8W+lauSM3dVYbP9tykD67kx+vn01SdMCI/vzxQNqyoZMJwMMkFbNySW6GR6VSEWeOYWH0PLoc\n3ZQ0lbG/7iCNNguJARPw1V5bMTAaefHz0ZKWGMKy2TH4+WiprO+g6LSVzw6dpbGli4ggP/yNerfG\ncKn9xXX8ekcBze09LE+P5Qe3p2E2jG4M38YduTH56UiIMJFdVEf+ySbmT5MjD66WtGVDJ8NMwyQX\nmXJJbq6NXqNjeug0UkOSqWqv6d9wryYXvUZHvDlm2ENPo5kXnVbDlLhAlmfEEhrgS22TjZIzzXxx\npJpTtW0EmX0I8fd16+TUPruDP31aztt7K9Bq1Tx0Wwo3X5egyLk87spNeJABX72GQ6WNlJ9tZWFa\nBBq1Mob9vIG0ZUMnxcwwyUWmXJKbkRHoE8DC6LkE+Jgpaz5JvqWIY5ZiYkxRBPle/S6vnsiLRq0i\nIdLMDekxJESasbb3UHKmma8K6iiosGL01RIZbBjxoqahpYtfZeVz9ISF2DAjT947m6nxQSP6GiPJ\nnblJivbH0tpNQUUTTa09pE8JlRVOQyRt2dBJMTNMcpEpl+Rm5KhUKhL841gQNZeOvk5KrGXk1OZh\n7W5mYkACPpqhD5d4Mi8qlYqoECNLZkSTOiGYjq4+jp9pJu94A/uL69Fo1MSEGkfkoMTDZY38ans+\nltZuFs+I4uE7pyv+JHB35ubiIw+a8NVrmRQr82eGQtqyoZNiZpjkIlMuyc3I89HomRmWSnLQZCrb\nz1JiLSO7Jhc/rS9x5qFt9KaUvAT7+zI/JYJ508KxO5yUVbVwtNzC3vwa7HYnMWEm9MM42NHucLL9\nixP8z2flqFXwwKpkbl+c6BXnSbk7NxceeXC4vFGOPBgipdwz3kBWMw2TzDJXLsmNezmcDr6szuHD\nil10O3qIN8eyduodJPjHDfp9Ss1LS0cPnx06y+eH+89+0uvUZM6I5sa5cYQOccWRta2bV94r5GR1\nG5HBBjbekUbsKJ6tdK1GKzfnjzxQ85P7M+TIg2+h1HtGiWQ10zBJxaxckhv3UqvUJAbEc13UHFp7\n28710uTR2tvOxIAE9JrLbwCn1Lz46rWkTAhmWXoM/gYdlQ39xyV8dqiaOquNsEA/AgYZJiqoaOLf\ns/Kpt3Yxb1o4j949gxAvO15htHJz/siDejnyYAiUes8okQwzDZNcZMoluRkdvlofZodPZ3JgIqfb\nqii2lpJTm4dJZyTGFPWNoSel50WnVZMUE8DyjFgigv2ob7ZRfKaZPUdrOHG2hQCTD2EB51dAOZ0u\n3t13iq0fl+J0uVi/cgp3L03yyl/Oo5mbuHATPX0O8k9YqGzoYP40OfLgSpR+zyiJFDPDJBeZcklu\nRleIXzCLoufhq/HhuLWcI40FlDaXE2+Oxd/nfNevt+RFrVYRF25m6ewYkmICaO3opfhMMzmFdRwt\nt+Dro8Hoq+Pld46RXVhHaIAv/7hmFrMnh3ntL+XRzs20hCBO1bZTWGGlt89JauLonNzubbzlnlEC\nmTMzTDKWqVySG89p7m5hR/kHHG0sQIWKpbGLuHXijfhpfb06L6dq2/j4QCUHSxtwuUAFuIDZk0N5\n8NZpGDx8ttK18kRubN12fv7GQeqsNv7+tmksTIsa1df3Bt58z4y2webMuLWYefbZZ8nPz0elUrFp\n0yZmzJgx8LVly5YRGRmJRtPfXfvCCy8QEREBQHd3N7fddhsbN27kzjvvHPQ1pJgZnyQ3nlfcVMr2\nsp00djXhrzdz56TbuDltCRZLh6dDuyYNLV18klvJ4bJGbpoXz41z47y2N+ZCnrpnLjzy4Kn16UyM\n9h/1GJRM2rKhG6yYcdu+07m5uZw5c4asrCxOnjzJpk2byMrKuug5r732GkbjN2e6v/LKKwQEyB4F\nQihZSshUnp73j+yu3MuuM5/zx+L/4UDjQZbHXE9y0GSvLQDCA/2478ap3HfjVE+HMiZEBhv44e2p\n/OqtfF565xg/fWCuR8/QEmOT2zZHyMnJYcWKFQAkJSXR2tpKR8e3/8V28uRJTpw4wdKlS90VmhBi\nhOg0Om5OXMFP5j/B9NBplDSW8/LR1/lF3n+QV3cEh9Ph6RCFAqRNDOGepZNo7ejl5XcK6LPLdSFG\nltt6ZiwWC6mpqQOPg4ODaWxsxGQ6vy/D5s2bqa6uJiMjg8cffxyVSsVzzz3HM888w86dO4f0OkFB\nBrRuXFkwWLeW8CzJjXKEYeaZ+EepsJ7h/eOfknP2MH8s/h/+fHoXt05dzrLEhfjqvGsp81jkyXvm\nvltTsLT38PnBKrZ9cZIf3Zvutb13I03asms3asebXjo159FHH2XJkiUEBATw8MMPs2vXLrq7u5k1\naxZxcYNvzHWh5mbbSIc6QMYylUtyo0wTwxJYP3kNN8Wu4LPKfeTU5vHHI2+xveBDMmMXsjR2EWa9\n92w0Nxa4XC4q288yOSaW3nbPFg9rlk7kdE0rXxw6S5i/L6vmx3s0HiWQtmzoPDJnJjw8HIvFMvC4\noaGBsLCwgcerV68e+P/MzEzKysqoqKigqqqKPXv2UFdXh16vJzIykoULF7orTCGEG4T6hbBm6mpu\nTVzJ3upsvjybzcenP2N35V6ui8xgeXwm4Yawb/9BYtjaezs4UHeI7Jpc6m2N+BeY+F7KeiYHJXks\nJp1Ww8N3TOdft+Tx1p4TxIQZmT4xxGPxiLHDbXNmFi1axK5duwAoKioiPDx8YIipvb2dBx98kN7e\n/rX1eXl5TJ48mRdffJG3336b7du3c88997Bx40YpZITwYia9kVsTV/Kzhf/EmimrCdT789eaA/zr\n/hd4reANTrVWejrEMcXpclLSVMbrBVt5+qt/490Tf6apy0pqSDKdvTZ+ffQ1vqj66zd6ykdTkNmH\nf7hzBhq1mlffK6K2qdNjsYixw209M+np6aSmprJ27VpUKhWbN2/mnXfewWw2s3LlSjIzM1mzZg0+\nPj6kpKSwatUqd4UihPAwvUZPZuxCFsdcx9HGQj49s4ejjYUcbSwkKSCRlQnXkxqSjFql/AMblai5\nu4Wc2jxyag9i7W4GIMoYwaLo+cyNnI1JZ8RCHS/s+x07yt+nsv0s906964rHUrjbxGh//u7mZF77\nsJiX3i7gJ/dneP0+PsKzZNO8QchYpnJJbpRpqHlxuVyUt1TwaeUeiptKAYg0RrAiLpM5kbPRqUdt\nOp/XcjgdFDSVkF2TS3FTKS5c6DV65oTPYmH0PCb4X7w/TliYmbKqKl4r2MqZ9irizDF8f/r9BPsG\neew9bP/iBB8fqCRtYjCP3T0TtXr8TQiWtmzoPLZp3miQYmZ8ktwo03DyUt1Ry2eVX5JXfwSny0mA\n3p8b4hazOGY+ftqhnWg9njTYGsmuyWN/3UHae/u3u5jgH8/C6LlkhM/EV3v5VWNf56bP0ce2snfZ\nX3sQk87Ig2nrmRI0aTTfwgCn08V/7DhGQUUTq+bH890bPBOHJ0lbNnRSzAyTXGTKJblRpmvJS3N3\nC59X7eOrmgP0OHrx1fiwOOY6bohbTKDP+N5Es9fRx9HGArJrcilvqQDAoPVjXmQ6C6PnEWP69mMC\nLsyNy+ViX/V+3ip/D4A7Jt3KDbGLPbJU2tbdx8/fOESd1cZDt6WwIC1y1GPwJGnLhk6KmWGSi0y5\nJDfKNBJ5sfV18dfq/Xxx9q+09bajUWmYGzGb5fGZRJvG1y+6s+01ZNfmklt3hC57FwBTApNYFD2P\nmWFp6K5izsvlcnOi5RSvF26lvbeDuRGzWZd8F3qNfkTfw1DUNnXy8zcOjcsjD6QtGzopZoZJLjLl\nktwo00jmpc9pJ6/uMLsr91JvawQgLSSZFfFLmRSYOGY3XOu2d3Ow/ijZNXmcaa8CwF9v5rqoOSyI\nmku4IXRYP/dKuWnpaeW1gq2cbqskzhTNQ9PvJ8Rv9E+4Lqho4sW38gkw6nlmHB15IG3Z0EkxM0xy\nkSmX5EaZ3JEXp8tJgaWE3ZV7qWg9DUCCfxwr45cyMyx1TKyAcrlcnGqrJLsml0MN+fQ6elGhIjUk\nmYXR80gLSUajvradzgfLTZ/TzvbSnWTX5mLUGXgw9T6mBo/+/JWPD1Sy/YsTJEb589T62ejcuLu7\nUkhbNnRSzAyTXGTKJblRJnfnpaL1NLvP7OWYpRgXLsL8Qlgen8n8yDkeW2Z8LTp6O8mtP0x2TS61\nnfUAhPgGsSBqHtdFZRDkGzhirzWU3Py1ej/by97D6XJyx6RbWRa3ZFR7wFwuF69/WEJOUR0L0yJ5\n8NZpY7YH7mvSlg2dFDPDJBeZcklulGm08lLX2cBnlV+SW3cIu8uBSWdkaexiMmMXYNQZ3P7618Lp\nclLWfJLsmlzyGwuxuxxoVBpmhqWyKHo+U4KS3NLbNNTcVLSe5rWCrbT1tjMnYhbrk+8e1Xk0fXYH\nv/jvI5yqbWPNskncNG9sH3kgbdnQSTEzTHKRKZfkRplGOy+tPW3sOfsV+6pz6LJ3o1frWBg9j2Vx\nSzwy72MwLT2t7K89SHZNHk3dVgAiDeEsip7HvMgMTHqjW1//anLT0tPK6wVvcqrtDDGmKL4//QFC\nR/Hfs7m9h3/dkkdbZy8/umcmaWP4yANpy4ZOiplhkotMuSQ3yuSpvHTbu8muyeWzqn209LSiVqlJ\nD5/BivjriTPHjHo8X3M4HRQ1HSe7NpdCy/H+je3UOtIjZrIoeh6J/gmjNoxytbnpc9rZUfYef605\ngFFr4Htp60kOnuzGCC9WUdPGL/77MDqtmmcemENksLJ73IZL2rKhk2JmmOQiUy7JjTJ5Oi8Op4OD\n9UfZXbmXms46AJKDJrMi4XqSgyaPWuHQaGsipzaP/bV5tPb2/3vEm2NZGD2PORGz8LvCxnbuNNzc\nfFV9gKyynThdTlZPuoXlcZmj9u+YXVjL6x+WEBls4Cf3z8HgO/Z2hvb0PeNNpJgZJrnIlEtyo0xK\nyYvL5aLYWsbuyr2UNZ8AINYUzYr460kPn3HNK4Mup8/RR76liK9qcgde00/ry9yI/o3t4szRI/6a\nV+NaclPReobXC96gtbedjPCZrJ92Dz6jNI9m++cn+Di3kukTQ/jfd88Yc0ceKOWeGY4+u5M6q41q\nSwc1lk7qrV0smh7JjKThbR/wbaSYGSZvvsjGOsmNMikxL2faqthduZcjDQW4cBHsG8SyuCUsiJqL\nr/ba9zKp6agjuyaX3LrDdNptAEwKTGRR9HxmhU1XzCqra81Na08brxe+SUXr6XPzaO4n1M/9c1mc\nThcv7sinsMLKzfPjuWeMHXmgxHvmUnaHk3qrjWpLJzWWTqotnVQ3dtLQ3IXzkhLijsyJ/M3CCW6J\nQ4qZYfKGi2y8ktwok5LzYulq4rPKfeTU5tHn7MOg9SMzdiFLYxdh1puu6md123s43JBPdk0up9oq\nATDrTP0b20XPJcIQ5o63cE1GIjd2p50d5R+wrzoHg9aP76WuZ1rIlBGK8Mps3X387I1D1I/BIw+U\ndM/YHU4amrvOFyznipd6qw2H8+JSweCjJTrMSEyokejQ/o8xoUYCTO7b7FCKmWFS0kUmLia5USZv\nyEtHbyd7q7PZe/YrOvtsaNVarovMYHl8JuGDFCEul4sz7VVk1+RysP4oPec2tpsWMoVFUfOYHpri\nluGrkTKSucmuySWr9F0cLie3J93Mivjr3T6P5sIjD/7pvnQSo8bGkQeeuGecThcNLV1UN3ZSY+kY\nKFzqmr5ZtPj5aAaKlehQ00DxEmjSj/oeQFLMDJM3NMzjleRGmbwpL72OXvbXHuSzyi+xdFtRoWJm\nWCor4peSGHB+b5POPht5dUf4qubAwKTiIJ9AFkTPZUHUHIJ9gzz1Fq7KSOfmVGslrxdupaWnlfTw\nGdw37btun0dz7GQT/7EjH7VKRaBJT4DJhwDjhR/1BBj1BJ577G/Uo9Uoe4dod94zTqeLxtYuaho7\nLxoiqm2yYXc4L3quj15DdMgFPS3nel2CzD6K2bhQiplh8qaGebyR3CiTN+bF4XRwtLGQ3ZV7qGyv\nBiApIJHrouZQFvoX8wAAEWpJREFU2lzO0cZC7E47apWamaGpLIyeR3LwZK87RsEduWntaef3hVs5\n2XqaaGMk35/+AGEG986jyS6s5bND1bR19tDS0fuNnoRLmfx0A0VOgNGHAJOeQKMef5OewHOPA4w+\n+PloPPJLeyTy4nS5aGrtPl+wNHYM9LT02i8uWvQ69fmi5YJhohB/X8UULVcixcwweWPDPF5IbpTJ\nm/PicrkobznJp2f2UmwtHfh8hCGMhdHzmB+ZcdVza5TEXbmxO+28Xf4hX1Zn46f14+9S15EaMnXE\nX+dyXC4Xnd12Wjt6aOnspa2jl5bOHlo7emnt7KW1o+fcx15sPfZBf5Zeq8b/gl6dgQLo3ONAkw/+\nRj3+Rh0a9cgVsleTF5fLRVNb90APy9c9LrVNNnr6HBc9V6dVExViuGBOi4mYMCMhAb6oFV60XIkU\nM8PkzQ3zWCe5UaaxkpfqjloKLCVMCkwkKWCC4v9iHQp35yanJo9tZe/icDr4zsRVrExYqqh/t94+\nB22dvbScK25aB4qe/o8tnb20nfvapSt0LqQCzAbdJUNb53p8LimEfPXfvi/O5fLicrlobu+hxtLJ\n2cbzw0M1TZ309F5ctGg1lxYt/T0uYQF+Y3IZ+5WMvR2IhBDiGsWYoogxRXk6DK+yIHou0aZIflfw\nBu9V/IXK9rPcN+27I7L8fSTodRpCA/0IDfQb9HlOl4uOrr5LCp5eWjp6+ouhc48bW7qoaugY9Gf5\n6DX9vTpGPf4mHwIvKX4CjHocajXHT1nPDRF1DAwVdfVcXLRo1CoiL9PTEhboO6I9Rd5KihkhhBAj\nIsE/jh/PfZTfF77JkcYC6mwNfH/6A4Qb3LOJmjuoVSr8DXr8DXriGHxYsafX0V/wDPT29Bc9rZf0\n/pS3tDLUMRCNWkVEsIHUxPPLnaNDjYQH+Sl+MrMnyTDTIMZKl/lYJLlRJsmLco1mbhxOB2+f+JC9\nZ786N4/mXlJDkkfltZXI6XTRbvu62Ll4Lo8DMPtqB1YPRQQbpGi5AhlmEkIIMWo0ag3fnXI78eYY\n/qf0HV7J/y9um3gjNyUsU9Q8mtGiVqv659iYfIiPuPhr8gfAyJDyTwghhFtcFzWHx9M3EugTwAcV\nu3i9cCvd9m5PhyXGIClmhBBCuE28fyw/nvsokwMncrSxkP936Dc02Bo9HZYYY6SYEUII4VZmvYl/\nmPUQN8Qtpq6znucPvkShpcTTYYkxRIoZIYQQbqdRa7h78nd4IGUtdqedV4/9kb+c2o3T5fz2bxaK\n53A6qGw/S5e9yyOvLxOAhRBCjJp5kelEGsP53bE3+PDUJ1S1V7MhZQ1+Wl9PhyauUnN3CyXWMoqb\nSjneXE6XvZvMmIWsmbp61GORYkYIIcSoijf3z6P5Q9GfyLcUUXfwZf7X9PuJMIZ7OjQxiD6nnYqW\n0xRbSyluKh04eBUgxDeIORGzuSFusUdic2sx8+yzz5Kfn49KpWLTpk3MmDFj4GvLli0jMjISjUYD\nwAsvvIC/vz9PPfUUTU1N9PT0sHHjRm644QZ3hiiEEMIDzHoTj8x8kJ0nP+Lzqn08f/Bl/jZ1LdND\nUzwdmriApctKcVMpxdZSSptP0OvoBUCn1pISPJWUkP7/wv1CPbrs3m3FTG5uLmfOnCErK4uTJ0+y\nadMmsrKyLnrOa6+9htFoHHj80UcfkZaWxkMPPUR1dTXf+973pJgRQogxSqPWcNfkvyHeHMt/H9/B\nq8f+yC2JK7l5wnKvO5V8rOh19FHeUkFx03GKraU02CwDX4swhPUXL8FTmRQ4Eb1G58FIL+a2YiYn\nJ4cVK1YAkJSURGtrKx0dHZhMV94e+pZbbhn4/9raWiIiIq74XCGEEGPD3MjZ/fNoCt7go1OfUtVe\nzQMpa/DTDn6Okrh2LpeLBlsjReeGjk60VNDn7D9h3EejZ0ZoKikhU5gWPJVQv2APR3tlbitmLBYL\nqampA4+Dg4NpbGy8qJjZvHkz1dXVZGRk8Pjjjw90Ua1du5a6ujpeffXVb32doCADWq1m5N/AOYNt\nnyw8S3KjTJIX5VJybsLCknk+ZhP/kfM6BfXF/PuR3/Lk4h8Q4x/p6dDcbrTz0tXXTWFDKUdrizha\nV0xjZ9PA1xICYpgZlcqsyBSSQ5PQarxjau2oRXnpEVCPPvooS5YsISAggIcffphdu3axatUqALZt\n20ZJSQlPPvkk77///qDjcM3NNrfFLNtMK5fkRpkkL8rlLbl5aNrf8p7PX/is8kv+6ZNfcH/KWmaG\npX77N3qp0ciLy+WiprOuf+5LUyknW0/jcPWfyu2n9WN2+Ixz81+mEOgTMPB9zVbPLLO+Eo+czRQe\nHo7Fcn6sraGhgbCwsIHHq1efX7qVmZlJWVkZsbGxhISEEBUVxbRp03A4HFitVkJCQtwVphBCCAXR\nqDXcOek24s2xvFnyFr8r2MLNE1ZwS+IKmUdzFWx9No43nxgoYFp72wa+Fm+OJfXcxN0EcxwatftG\nN0aL24qZRYsW8dJLL7F27VqKiooIDw8fGGJqb2/nscce45VXXkGv15OXl8dNN93EwYMHqa6u5umn\nn8ZisWCz2QgKCnJXiEIIIRRqTsQsIg3982j+cno3Ve3V/G3qWplHcwVOl5Oq9uqBlUenWitx0T8i\nYtIZmRuRfm7uyxTM+ivPXfVWbitm0tPTSU1NZe3atahUKjZv3sw777yD2Wxm5cqVZGZmsmbNGnx8\nfEhJSWHVqlX09PTw9NNPs27dOrq7u/npT3+KWi2VuBBCjEex5mj+z9x/4L8K/0RhUwnPH3yJ709/\ngCijLA4BaO/tGNi0rsRaRkdfJwAqVCQGJAwMHcWZY8Z8r5bKdelkFi/jzrFGbxljHo8kN8okeVEu\nb86Nw+ngg4pdfFq5Bx+Nnjsm3UakIRyDzg+jzoBBa1DUMuGrcTV5cTgdnG6rGti0rqq9eqD3JUDv\nP7DnS3LQJAw6gzvD9giPzJkRQgghRoJGrWH1pFuIM8fwZsl2tpW+843naNVajFo//HQGjFo/DDoD\nBu3Xxc75xwadAaPO76LPKbnXoqWnleKmMoqtpRy3lg+cfaRRaZgcOHGggIk2Rnp00zpPk2JGCCGE\nV8iImEmsOZpCSwk2exe2Phs2exed5z7a+my097RT39kw0GMxFH5aXwxaQ39Pj9aAn85v0ILIqDNg\n0BnQq3UjXkDYnXYqWk9T3FRGUdPxyxwZMIuU4ClMCUrCV86zGiDFjBBCCK8RYQgjIj5s0Oc4XU66\n7T0DBU6n3YatrwvbuY+ddhtdfV10XlIQ1dsaB7brHwqNSoNB54dBe3Fvz/mCyHDu698siC5cQdTU\nZaXYWkpRUyllzSfoudyRAcFTCDeEjevel8FIMSOEEGJMUavU/UWEzg+uctdau9N+vgi6oAC6tAfo\nfEFko7Ovk8YuC06Xc8iv46vxwU/rh1aruWjTughD2EABo7QjA5RMihkhhBDiHK1ai7/ejL/+6nbl\ndblcdDt6rtADdL5nqPNcYfR1j1CPvYfpoSmkhkxV/JEBSibFjBBCCHGNVCoVflpf/LS+hDD0/dG8\neZWZkih3CrcQQgghxBBIMSOEEEIIrybFjBBCCCG8mhQzQgghhPBqUswIIYQQwqtJMSOEEEIIrybF\njBBCCCG8mhQzQgghhPBqUswIIYQQwqtJMSOEEEIIrybFjBBCCCG8mhQzQgghhPBqUswIIYQQwqup\nXC6Xy9NBCCGEEEIMl/TMCCGEEMKrSTEjhBBCCK8mxYwQQgghvJoUM0IIIYTwalLMCCGEEMKrSTEj\nhBBCCK8mxcxlPPvss6xZs4a1a9dy7NgxT4cjLvD888+zZs0a7rrrLj755BNPhyMu0d3dzYoVK3jn\nnXc8HYq4wPvvv893vvMd7rzzTvbs2ePpcATQ2dnJI488woYNG1i7di379u3zdEheTevpAJQmNzeX\nM2fOkJWVxcmTJ9m0aRNZWVmeDksA+/fvp7y8nKysLJqbm7njjju48cYbPR2WuMArr7xCQECAp8MQ\nF2hubuY3v/kNb7/9NjabjZdeeomlS5d6Oqxx79133yUxMZHHH3+c+vp6HnjgAT7++GNPh+W1pJi5\nRE5ODitWrAAgKSmJ1tZWOjo6MJlMHo5MzJ07lxkzZgDg7+9PV1cXDocDjUbj4cgEwMmTJzlx4oT8\nolSYnJwcFixYgMlkwmQy8bOf/czTIQkgKCiI0tJSANra2ggKCvJwRN5NhpkuYbFYLrqogoODaWxs\n9GBE4msajQaDwQDAjh07yMzMlEJGQZ577jmeeuopT4chLnH27Fm6u7v5wQ9+wLp168jJyfF0SAK4\n9dZbqampYeXKldx33338+Mc/9nRIXk16Zr6FnPagPLt372bHjh384Q9/8HQo4pydO3cya9Ys4uLi\nPB2KuIyWlhZefvllampquP/++/niiy9QqVSeDmtce++994iOjub3v/89x48fZ9OmTTLX7BpIMXOJ\n8PBwLBbLwOOGhgbCwsI8GJG40L59+3j11Vd5/fXXMZvNng5HnLNnzx6qqqrYs2cPdXV16PV6IiMj\nWbhwoadDG/dCQkKYPXs2Wq2W+Ph4jEYjVquVkJAQT4c2rh0+fJjFixcDkJycTENDgwybXwMZZrrE\nokWL2LVrFwBFRUWEh4fLfBmFaG9v5/nnn+c///M/CQwM9HQ44gIvvvgib7/9Ntu3b+eee+5h48aN\nUsgoxOLFi9m/fz9Op5Pm5mZsNpvMz1CAhIQE8vPzAaiursZoNEohcw2kZ+YS6enppKamsnbtWlQq\nFZs3b/Z0SOKcjz76iObmZh577LGBzz333HNER0d7MCohlC0iIoKbbrqJ7373uwD85Cc/Qa2Wv2M9\nbc2aNWzatIn77rsPu93OP//zP3s6JK+mcsmkECGEEEJ4MSnPhRBCCOHVpJgRQgghhFeTYkYIIYQQ\nXk2KGSGEEEJ4NSlmhBBCCOHVpJgRQoyas2fPkpaWxoYNGwZOC3788cdpa2sb8s/YsGEDDodjyM+/\n9957OXDgwHDCFUJ4CSlmhBCjKjg4mK1bt7J161a2bdtGeHg4r7zyypC/f+vWrbK5mBDiIrJpnhDC\no+bOnUtWVhbHjx/nueeew26309fXx09/+lNSUlLYsGEDycnJlJSUsGXLFlJSUigqKqK3t5dnnnmG\nuro67HY7t99+O+vWraOrq4sf/ehHNDc3k5CQQE9PDwD19fU88cQTAHR3d7NmzRruvvtuT751IcQI\nkWJGCOExDoeDTz/9lIyMDJ588kl+85vfEB8f/42D9wwGA2+++eZF37t161b8/f355S9/SXd3N7fc\ncgtLliwhOzsbX19fsrKyaGhoYPny5QD85S9/YeLEifzLv/wLPT09vPXWW6P+foUQ7iHFjBBiVFmt\nVjZs2ACA0+lkzpw53HXXXfz617/m6aefHnheR0cHTqcT6D9m5FL5+fnceeedAPj6+pKWlkZRURFl\nZWVkZGQA/QfHTpw4EYAlS5bwpz/9iaeeeorrr7+eNWvWuPV9CiFGjxQzQohR9fWcmQu1t7ej0+m+\n8fmv6XS6b3xOpVJd9NjlcqFSqXC5XBedPfR1QZSUlMSf//xn8vLy+Pjjj9myZQvbtm271rcjhFAA\nmQAshPA4s9lMbGwse/fuBeDUqVO8/PLLg37PzJkz2bdvHwA2m42ioiJSU1NJSkriyJEjANTW1nLq\n1CkAPvjgAwoKCli4cCGbN2+mtrYWu93uxnclhBgt0jMjhFCE5557jp///Of87ne/w26389RTTw36\n/A0bNvDMM8+wfv16ent72bhxI7Gxsdx+++18/vnnrFu3jtjYWKZPnw7ApEmT2Lx5M3q9HpfLxUMP\nPYRWK02gEGOBnJothBBCCK8mw0xCCCGE8GpSzAghhBDCq0kxI4QQQgivJsWMEEIIIbyaFDNCCCGE\n8GpSzAghhBDCq0kxI4QQQgivJsWMEEIIIbza/wfc9UY10tBX5wAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-Xo83_aR6s_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n",
+        "\n",
+        "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n",
+        "\n",
+        "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DKSQ87VVIYIA",
+        "colab_type": "code",
+        "outputId": "8c314a19-5f4f-4dd8-ec0b-4f311350fd03",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AUC on the validation set: 0.77\n",
+            "Accuracy on the validation set: 0.79\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",
+        "outputId": "610ed7b6-098d-42a5-fa48-b5f2beb5ca2c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "# Get just the probabilities for the positive class.\n",
+        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
+        "\n",
+        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
+        "    validation_targets, validation_probabilities)\n",
+        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
+        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
+        "_ = plt.legend(loc=2)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAFKCAYAAAAqkecjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSCIsYSIiIomwhImIiCTCEiYiIpLI/wCcyPcnndTu5AAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PIdhwfgzIYII",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+        "\n",
+        "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+        "\n",
+        "**Verify if all metrics improve at the same time.**"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XKIqjsqcCaxO",
+        "colab_type": "code",
+        "outputId": "bf380be1-c224-4d52-9268-fe3f8d6de2d4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000001,\n",
+        "    steps=10000,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.56\n",
+            "  period 01 : 0.54\n",
+            "  period 02 : 0.53\n",
+            "  period 03 : 0.53\n",
+            "  period 04 : 0.52\n",
+            "  period 05 : 0.52\n",
+            "  period 06 : 0.52\n",
+            "  period 07 : 0.52\n",
+            "  period 08 : 0.52\n",
+            "  period 09 : 0.52\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.76\n",
+            "Accuracy on the validation set: 0.78\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUJVjgAAAABJRU5ErkJggg==\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",
+        "outputId": "046810e7-37d9-4af7-fb8f-f129c2b4abcf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000003,\n",
+        "    steps=20000,\n",
+        "    batch_size=500,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.50\n",
+            "  period 01 : 0.49\n",
+            "  period 02 : 0.48\n",
+            "  period 03 : 0.48\n",
+            "  period 04 : 0.48\n",
+            "  period 05 : 0.47\n",
+            "  period 06 : 0.47\n",
+            "  period 07 : 0.47\n",
+            "  period 08 : 0.47\n",
+            "  period 09 : 0.47\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.82\n",
+            "Accuracy on the validation set: 0.80\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1hhmiIiISNYYZoiIiEjW/h9QPW0hinBUZAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb
new file mode 100644
index 0000000..de9b74f
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2951 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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",
+        "outputId": "0b874a11-a541-479a-f5ba-db48e6af6e80",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 233
+        }
+      },
+      "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": 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>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>7535</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>765</th>\n",
+              "      <td>9</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1796</th>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5585</th>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6565</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 785 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      0    1    2    3    4    5    6    7    8    9   ...   775  776  777  \\\n",
+              "7535    0    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "765     9    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "1796    6    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "5585    4    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "6565    0    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "7535    0    0    0    0    0    0    0  \n",
+              "765     0    0    0    0    0    0    0  \n",
+              "1796    0    0    0    0    0    0    0  \n",
+              "5585    0    0    0    0    0    0    0  \n",
+              "6565    0    0    0    0    0    0    0  \n",
+              "\n",
+              "[5 rows x 785 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "803f2f76-0202-4c2e-da6a-0cef7b8cc603",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_dataframe.loc[:, 72:72]"
+      ],
+      "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>72</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>7535</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>765</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1796</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5585</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6565</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3745</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4367</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8996</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>996</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3907</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "7535   0\n",
+              "765    0\n",
+              "1796   0\n",
+              "5585   0\n",
+              "6565   0\n",
+              "...   ..\n",
+              "3745   0\n",
+              "4367   0\n",
+              "8996   0\n",
+              "996    0\n",
+              "3907   0\n",
+              "\n",
+              "[10000 rows x 1 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "c49b5a10-dcb6-463b-cd53-4b9f2c1d6e06",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.8    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 7500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4-Vgg-1zu8Ud",
+        "colab_type": "code",
+        "outputId": "818aa3b9-5289-42b2-8615-b05ae7da6710",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets, validation_examples = parse_labels_and_features(mnist_dataframe[7500:10000])\n",
+        "validation_examples.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>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.5</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",
+              "      <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    ...      0.5    0.1    0.0    0.2    1.0    0.2    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 2500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "21446ae2-b8ce-4844-c7d1-0260b60f1695",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        }
+      },
+      "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": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFXCAYAAAAro2x+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\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",
+        "outputId": "3c431845-30bf-41e2-c253-0252cca15bc9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1092
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_linear_classification_model(\n",
+        "             learning_rate=0.025,\n",
+        "             steps=500,\n",
+        "             batch_size=20,\n",
+        "             training_examples=training_examples,\n",
+        "             training_targets=training_targets,\n",
+        "             validation_examples=validation_examples,\n",
+        "             validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\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 : 5.83\n",
+            "  period 01 : 4.78\n",
+            "  period 02 : 4.38\n",
+            "  period 03 : 4.45\n",
+            "  period 04 : 4.13\n",
+            "  period 05 : 4.13\n",
+            "  period 06 : 4.01\n",
+            "  period 07 : 3.98\n",
+            "  period 08 : 3.94\n",
+            "  period 09 : 3.81\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.89\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAFnCAYAAACM3c9QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\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",
+        "outputId": "4ea56bb8-7378-43ad-aff2-c7e4f573e9a4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        }
+      },
+      "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": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.32\n",
+            "  period 01 : 4.06\n",
+            "  period 02 : 4.05\n",
+            "  period 03 : 3.81\n",
+            "  period 04 : 3.59\n",
+            "  period 05 : 3.52\n",
+            "  period 06 : 3.73\n",
+            "  period 07 : 3.50\n",
+            "  period 08 : 3.66\n",
+            "  period 09 : 3.54\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.90\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nm85MAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAFnCAYAAACM3c9QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mk095OfpPdOx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Replace the Linear Classifier with a Neural Network\n",
+        "\n",
+        "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n",
+        "\n",
+        "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rm8P_Ttwu8U4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n",
+        "#\n",
+        "def train_nn_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, as well as a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `DNNClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  # Caution: input pipelines are reset with each call to train. \n",
+        "  # If the number of steps is small, your model may never see most of the data.  \n",
+        "  # So with multiple `.train` calls like this you may want to control the length \n",
+        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  # Create a DNNClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.DNNClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      n_classes=10,\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "TOfmiSvqu8U9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q9dOARLkZ-tp",
+        "colab_type": "code",
+        "outputId": "e1601360-1e50-4a40-836d-939171763920",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_nn_classification_model(\n",
+        "    learning_rate=0.1,\n",
+        "    steps=2000,\n",
+        "    batch_size=60,\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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 2.71\n",
+            "  period 01 : 2.11\n",
+            "  period 02 : 1.96\n",
+            "  period 03 : 1.67\n",
+            "  period 04 : 1.89\n",
+            "  period 05 : 1.67\n",
+            "  period 06 : 1.52\n",
+            "  period 07 : 1.78\n",
+            "  period 08 : 1.59\n",
+            "  period 09 : 1.55\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.96\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAFnCAYAAACM3c9QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRJL8H/CUKkxnE8B0AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "evlB5ubzu8VJ",
+        "colab_type": "code",
+        "outputId": "96344b80-74d1-4044-cd5e-a3c08c52c850",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        }
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PDuLd2Hcu8VL",
+        "colab_type": "code",
+        "outputId": "be951376-c1b2-4430-fd42-3e85ccd36052",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
+        "#\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": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.96\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",
+        "outputId": "0e8bfef7-4f74-426e-972b-5568316a37a4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        }
+      },
+      "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": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.96\n",
+            "  period 01 : 3.45\n",
+            "  period 02 : 2.85\n",
+            "  period 03 : 2.67\n",
+            "  period 04 : 2.56\n",
+            "  period 05 : 2.10\n",
+            "  period 06 : 2.03\n",
+            "  period 07 : 2.38\n",
+            "  period 08 : 1.80\n",
+            "  period 09 : 2.16\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.94\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFnCAYAAACcvYGMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niZIKqUQAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAFnCAYAAACM3c9QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qXvrOgtUR-zD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we verify the accuracy on the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scQNpDePSFjt",
+        "colab_type": "code",
+        "outputId": "2914be0d-640d-4955-a388-24e2b8f8c55b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        }
+      },
+      "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": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EVaWpWKvSHmu",
+        "colab_type": "code",
+        "outputId": "86088e7b-ee0d-4a5e-fd71-e71481094ec5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "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": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.95\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WX2mQBAEcisO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Visualize the weights of the first hidden layer.\n",
+        "\n",
+        "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
+        "\n",
+        "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
+        "\n",
+        "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eUC0Z8nbafgG",
+        "colab_type": "code",
+        "cellView": "both",
+        "outputId": "afbe5dd9-9da6-49f8-d9c0-157f5b47fac1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1172
+        }
+      },
+      "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": 22,
+      "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": "iVBORw0KGgoAAAANSUhEUgAABGcAAARNCAYAAAD/4C04AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI/bjjGEYhmEYhmEYhmEYxjzyvwHRwrgCeNmsMwAAAABJRU5ErkJggg==\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?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OOBHHOjggiSM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1172
+        },
+        "outputId": "914b2876-3d17-4b02-df60-07389a33af21"
+      },
+      "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": 24,
+      "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": "iVBORw0KGgoAAAANSUhEUgAABGcAAARNCAYAAAD/4C04AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI/bjjGEYhmEYhmEYhmEYxjzyvwHRwrgCeNmsMwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1440x1440 with 100 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PLycs-sDgigs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ 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..d2a23f1
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1180 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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": [
+        "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",
+        "outputId": "16558baf-40c2-402c-b0af-727a5dbadca0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2659.2           542.7   \n",
+              "std         2.1        2.0                12.6       2165.5           420.0   \n",
+              "min        32.5     -124.3                 2.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1464.8           298.0   \n",
+              "50%        34.2     -118.5                29.0       2139.5           437.0   \n",
+              "75%        37.7     -118.0                37.0       3177.5           657.0   \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       1436.3       503.9            3.9               2.0  \n",
+              "std        1124.3       383.3            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         790.8       283.0            2.6               1.5  \n",
+              "50%        1173.5       411.0            3.5               1.9  \n",
+              "75%        1734.2       610.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2659.2</td>\n",
+              "      <td>542.7</td>\n",
+              "      <td>1436.3</td>\n",
+              "      <td>503.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2165.5</td>\n",
+              "      <td>420.0</td>\n",
+              "      <td>1124.3</td>\n",
+              "      <td>383.3</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>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>1464.8</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>790.8</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2139.5</td>\n",
+              "      <td>437.0</td>\n",
+              "      <td>1173.5</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>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3177.5</td>\n",
+              "      <td>657.0</td>\n",
+              "      <td>1734.2</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>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2606.3           531.5   \n",
+              "std         2.1        2.0                12.6       2214.1           425.0   \n",
+              "min        32.5     -124.3                 1.0         26.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1454.8           293.0   \n",
+              "50%        34.2     -118.5                29.0       2104.0           428.0   \n",
+              "75%        37.7     -118.0                37.0       3088.5           634.0   \n",
+              "max        42.0     -114.5                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1413.5       494.7            3.9               2.0  \n",
+              "std        1202.4       387.5            1.9               1.4  \n",
+              "min          13.0         5.0            0.5               0.1  \n",
+              "25%         787.8       277.0            2.5               1.5  \n",
+              "50%        1153.5       405.0            3.5               1.9  \n",
+              "75%        1690.0       591.0            4.7               2.3  \n",
+              "max       35682.0      5189.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2606.3</td>\n",
+              "      <td>531.5</td>\n",
+              "      <td>1413.5</td>\n",
+              "      <td>494.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2214.1</td>\n",
+              "      <td>425.0</td>\n",
+              "      <td>1202.4</td>\n",
+              "      <td>387.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.4</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>26.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1454.8</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>787.8</td>\n",
+              "      <td>277.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2104.0</td>\n",
+              "      <td>428.0</td>\n",
+              "      <td>1153.5</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>3088.5</td>\n",
+              "      <td>634.0</td>\n",
+              "      <td>1690.0</td>\n",
+              "      <td>591.0</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>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gHkniRI1Ijna",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bLzK72jkNJPf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_buckets(feature_values, num_buckets):\n",
+        "  quantiles = feature_values.quantile(\n",
+        "    [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n",
+        "  return [quantiles[q] for q in quantiles.keys()]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "al2YQpKyIjnd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"households\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"longitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"latitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"housing_median_age\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"housing_median_age\"], 10))\n",
+        "  bucketized_total_rooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_rooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n",
+        "  bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_bedrooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n",
+        "  bucketized_population = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"population\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"median_income\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"rooms_per_person\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"rooms_per_person\"], 10))\n",
+        "\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "    set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n",
+        "\n",
+        "  feature_columns = set([\n",
+        "    long_x_lat,\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_total_rooms,\n",
+        "    bucketized_total_bedrooms,\n",
+        "    bucketized_population,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hSBwMrsrE21n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Calculate the Model Size\n",
+        "\n",
+        "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "e6GfTI0CFhB8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def model_size(estimator):\n",
+        "  variables = estimator.get_variable_names()\n",
+        "  size = 0\n",
+        "  for variable in variables:\n",
+        "    if not any(x in variable \n",
+        "               for x in ['global_step',\n",
+        "                         'centered_bias_weight',\n",
+        "                         'bias_weight',\n",
+        "                         'Ftrl']\n",
+        "              ):\n",
+        "      size += np.count_nonzero(estimator.get_variable_value(variable))\n",
+        "  return size"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XabdAaj67GfF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Reduce the Model Size\n",
+        "\n",
+        "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n",
+        "\n",
+        "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n",
+        "\n",
+        "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G79hGRe7qqej",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1: Find a good regularization coefficient.\n",
+        "\n",
+        "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n",
+        "\n",
+        "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n",
+        "\n",
+        "Again, the model will train on the entire data set, so expect it to run slower than normal."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1Fcdm0hpIjnl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    regularization_strength,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    regularization_strength: A `float` that indicates the strength of the L1\n",
+        "       regularization. A value of `0.0` means no regularization.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 7\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on validation data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9H1CKHSzIjno",
+        "colab_type": "code",
+        "outputId": "ccde5a8e-bd81-4868-d6c0-a7152bf8b382",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 707
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    # TWEAK THE REGULARIZATION VALUE BELOW\n",
+        "    regularization_strength=1.0,\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": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.34\n",
+            "  period 01 : 0.30\n",
+            "  period 02 : 0.28\n",
+            "  period 03 : 0.27\n",
+            "  period 04 : 0.26\n",
+            "  period 05 : 0.26\n",
+            "  period 06 : 0.26\n",
+            "Model training finished.\n",
+            "('Model size:', 535)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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",
+        "outputId": "e80d242b-b3c6-4ce5-91f6-56cdfb61eeba",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 588
+        }
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\n",
+            "  period 01 : 0.28\n",
+            "  period 02 : 0.27\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.26\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.25\n",
+            "Model training finished.\n",
+            "('Model size:', 753)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGACAYAAABY5OOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb
new file mode 100644
index 0000000..a0ec742
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1329 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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",
+        "outputId": "4bba32cb-b7d5-4270-891c-6c122cf728fa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 439
+        }
+      },
+      "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": 0,
+      "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>5507</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.9</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>1507.0</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>761.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>87.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12641</th>\n",
+              "      <td>-121.7</td>\n",
+              "      <td>38.1</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1910.0</td>\n",
+              "      <td>326.0</td>\n",
+              "      <td>1001.0</td>\n",
+              "      <td>345.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>115.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8328</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>2217.0</td>\n",
+              "      <td>422.0</td>\n",
+              "      <td>1064.0</td>\n",
+              "      <td>427.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>208.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11261</th>\n",
+              "      <td>-121.1</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1853.0</td>\n",
+              "      <td>331.0</td>\n",
+              "      <td>958.0</td>\n",
+              "      <td>340.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>149.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6839</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>1718.0</td>\n",
+              "      <td>354.0</td>\n",
+              "      <td>1026.0</td>\n",
+              "      <td>312.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>128.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14165</th>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>1606.0</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>1117.0</td>\n",
+              "      <td>268.0</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>247.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8808</th>\n",
+              "      <td>-118.7</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>1793.0</td>\n",
+              "      <td>339.0</td>\n",
+              "      <td>1016.0</td>\n",
+              "      <td>326.0</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>240.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3458</th>\n",
+              "      <td>-117.9</td>\n",
+              "      <td>33.7</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>4365.0</td>\n",
+              "      <td>804.0</td>\n",
+              "      <td>2663.0</td>\n",
+              "      <td>753.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>233.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8587</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.3</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>8850.0</td>\n",
+              "      <td>2139.0</td>\n",
+              "      <td>4717.0</td>\n",
+              "      <td>1979.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>254.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4989</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1981.0</td>\n",
+              "      <td>472.0</td>\n",
+              "      <td>1231.0</td>\n",
+              "      <td>457.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>153.7</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",
+              "5507      -118.2      34.9                 9.0       1507.0           293.0   \n",
+              "12641     -121.7      38.1                22.0       1910.0           326.0   \n",
+              "8328      -118.5      34.2                32.0       2217.0           422.0   \n",
+              "11261     -121.1      37.8                35.0       1853.0           331.0   \n",
+              "6839      -118.3      34.0                52.0       1718.0           354.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "14165     -122.1      37.6                16.0       1606.0           240.0   \n",
+              "8808      -118.7      34.2                27.0       1793.0           339.0   \n",
+              "3458      -117.9      33.7                24.0       4365.0           804.0   \n",
+              "8587      -118.5      34.3                21.0       8850.0          2139.0   \n",
+              "4989      -118.1      33.9                22.0       1981.0           472.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "5507        761.0       278.0            3.0                87.9  \n",
+              "12641      1001.0       345.0            4.8               115.8  \n",
+              "8328       1064.0       427.0            3.7               208.6  \n",
+              "11261       958.0       340.0            3.4               149.0  \n",
+              "6839       1026.0       312.0            2.0               128.0  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "14165      1117.0       268.0            6.1               247.0  \n",
+              "8808       1016.0       326.0            4.9               240.3  \n",
+              "3458       2663.0       753.0            4.6               233.3  \n",
+              "8587       4717.0      1979.0            3.8               254.2  \n",
+              "4989       1231.0       457.0            4.1               153.7  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "a4120507-327c-4554-8adc-ab8ee41fc010",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 975
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE\n",
+        "#\n",
+        "california_housing_dataframe[\"rooms_per_person\"] = ( california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.00005,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.51\n",
+            "  period 01 : 237.49\n",
+            "  period 02 : 237.46\n",
+            "  period 03 : 237.44\n",
+            "  period 04 : 237.41\n",
+            "  period 05 : 237.39\n",
+            "  period 06 : 237.36\n",
+            "  period 07 : 237.34\n",
+            "  period 08 : 237.31\n",
+            "  period 09 : 237.29\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.3    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.1     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.3    265.0\n",
+              "max            6.2    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.29\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\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",
+        "outputId": "47394a84-c715-4c63-a1dc-93247f6afb4a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 975
+        }
+      },
+      "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": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.82\n",
+            "  period 01 : 190.43\n",
+            "  period 02 : 171.87\n",
+            "  period 03 : 155.25\n",
+            "  period 04 : 142.22\n",
+            "  period 05 : 134.26\n",
+            "  period 06 : 131.23\n",
+            "  period 07 : 130.28\n",
+            "  period 08 : 130.06\n",
+            "  period 09 : 129.98\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         184.6    207.3\n",
+              "std           83.2    116.0\n",
+              "min           44.9     15.0\n",
+              "25%          151.9    119.4\n",
+              "50%          181.8    180.4\n",
+              "75%          207.1    265.0\n",
+              "max         3973.7    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>184.6</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>83.2</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>44.9</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>151.9</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.8</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>207.1</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3973.7</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 129.98\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZjQrZ8mcHFiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Identify Outliers\n",
+        "\n",
+        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
+        "\n",
+        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
+        "\n",
+        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P0BDOec4HbG_",
+        "colab_type": "code",
+        "outputId": "f4369b75-99d5-41aa-966a-5bb457f90587",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 420
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.title('Identifying Outliers')\n",
+        "plt.xlabel('Predictions')\n",
+        "plt.ylabel('Targets')\n",
+        "plt.scatter(calibration_data['predictions'], calibration_data['targets'])"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fe06f3dc450>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbAAAAGCCAYAAACB/JaMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBZiIiGQlBZiIiGSl/wAxEH/GrZIK9wAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jByCP8hDRZmM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s0tiX2gdRe-S",
+        "colab_type": "code",
+        "outputId": "eb837e51-75db-4262-c960-bb6439f91819",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        }
+      },
+      "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": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fe06daa7a90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAFlCAYAAACp5uxjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kMQD0Uq3RqTX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
+        "\n",
+        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "POTM8C_ER1Oc",
+        "colab_type": "code",
+        "outputId": "8be65ad3-4492-4399-fa94-c619c64153a5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAFKCAYAAAA5TzK7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfIzt3Nflkr4AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "outputId": "10e95c22-090e-4e6e-b45b-0d4abf5f20d1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFKCAYAAADScRzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQg0AgGGEGgAAw/4P1DJKJgyt6msAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WvgxW0bUSC-c",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8YGNjXPaSMPV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9YyARz6gSR7Q",
+        "colab_type": "code",
+        "outputId": "c19ab4b2-9d82-4a83-a62a-9f9400bb9cca",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        }
+      },
+      "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",
+        "plt.subplot(1, 2, 2)\n",
+        "\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAFKCAYAAAAubaJyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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",
+        "outputId": "a70c01b1-d32f-4f63-dde1-55ee26675278",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        }
+      },
+      "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": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.81\n",
+            "  period 01 : 189.03\n",
+            "  period 02 : 167.01\n",
+            "  period 03 : 147.29\n",
+            "  period 04 : 131.52\n",
+            "  period 05 : 119.92\n",
+            "  period 06 : 114.46\n",
+            "  period 07 : 110.91\n",
+            "  period 08 : 108.81\n",
+            "  period 09 : 108.92\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         189.4    207.3\n",
+              "std           48.6    116.0\n",
+              "min           47.0     15.0\n",
+              "25%          158.4    119.4\n",
+              "50%          189.4    180.4\n",
+              "75%          215.9    265.0\n",
+              "max          416.0    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>189.4</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>48.6</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>47.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>158.4</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>189.4</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>215.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>416.0</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): 108.92\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiksT/BzGaXwcK/SKkAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gySE-UgfSony",
+        "colab_type": "code",
+        "outputId": "1d168b66-d89a-4341-84af-6bff4400d4f9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFKCAYAAADMuCxnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\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..d8e8305
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1573 @@
+{
+  "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/NehaAgarwal2598/Assignment-5/blob/NehaAgarwal2598/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",
+        "outputId": "c52b1ee1-c695-40e3-bc39-41fea92d0c60",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2649.5</td>\n",
+              "      <td>540.6</td>\n",
+              "      <td>1430.6</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>2188.5</td>\n",
+              "      <td>424.6</td>\n",
+              "      <td>1167.4</td>\n",
+              "      <td>386.3</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>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>296.8</td>\n",
+              "      <td>790.0</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>29.0</td>\n",
+              "      <td>2124.0</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>3150.0</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1720.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.7       2649.5           540.6   \n",
+              "std         2.1        2.0                12.6       2188.5           424.6   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1465.0           296.8   \n",
+              "50%        34.3     -118.5                29.0       2124.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3150.0           651.0   \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       1430.6       502.1            3.9               2.0  \n",
+              "std        1167.4       386.3            1.9               1.3  \n",
+              "min           6.0         2.0            0.5               0.1  \n",
+              "25%         790.0       282.0            2.6               1.5  \n",
+              "50%        1164.0       409.0            3.5               1.9  \n",
+              "75%        1720.0       606.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "outputId": "8f229a2d-470f-473e-9bac-57ccb550def2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.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>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>207.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.7</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                207.9\n",
+              "std                 116.4\n",
+              "min                  15.0\n",
+              "25%                 119.3\n",
+              "50%                 180.9\n",
+              "75%                 266.7\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "4b4b1b0e-f2db-442d-e562-e66fcf67e8c5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_examples.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>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>2629.6</td>\n",
+              "      <td>536.6</td>\n",
+              "      <td>1427.2</td>\n",
+              "      <td>499.2</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>2159.5</td>\n",
+              "      <td>414.0</td>\n",
+              "      <td>1099.6</td>\n",
+              "      <td>380.2</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>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>-121.7</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1451.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>788.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2139.5</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1177.5</td>\n",
+              "      <td>408.5</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>3153.0</td>\n",
+              "      <td>645.2</td>\n",
+              "      <td>1722.0</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>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>16122.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>19.1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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       2629.6           536.6   \n",
+              "std         2.1        2.0                12.6       2159.5           414.0   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.7                18.0       1451.0           297.0   \n",
+              "50%        34.2     -118.5                28.0       2139.5           435.0   \n",
+              "75%        37.7     -118.0                37.0       3153.0           645.2   \n",
+              "max        42.0     -114.6                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1427.2       499.2            3.9               2.0  \n",
+              "std        1099.6       380.2            1.9               0.9  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         788.0       280.0            2.6               1.5  \n",
+              "50%        1177.5       408.5            3.5               1.9  \n",
+              "75%        1722.0       603.0            4.7               2.3  \n",
+              "max       16122.0      5189.0           15.0              19.1  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "outputId": "b5f27cff-fe2a-4124-820a-35db0d8738ed",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>205.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>114.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>178.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>261.5</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                205.9\n",
+              "std                 114.9\n",
+              "min                  15.0\n",
+              "25%                 120.0\n",
+              "50%                 178.4\n",
+              "75%                 261.5\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "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",
+        "outputId": "b9ef4dab-3db0-4edf-dccf-71dc6a7881f1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        }
+      },
+      "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": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAHhCAYAAAA2xLK+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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(training_examples, training_targets[\"median_house_value\"], batch_size=batch_size)\n",
+        "    predict_training_input_fn = lambda: my_input_fn(training_examples, training_targets[\"median_house_value\"], shuffle=False, num_epochs=1)\n",
+        "    predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value\"], shuffle=False, num_epochs=1)\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 = [_['predictions'][0] for _ in training_predictions]\n",
+        "        validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "        validation_predictions = [_['predictions'][0] for _ 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",
+        "outputId": "e94c1d1e-3f53-4911-d3dc-fd8d5b06356d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.0001,\n",
+        "    steps=100,\n",
+        "    batch_size=10,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 224.55\n",
+            "  period 01 : 211.79\n",
+            "  period 02 : 200.20\n",
+            "  period 03 : 189.96\n",
+            "  period 04 : 181.33\n",
+            "  period 05 : 175.72\n",
+            "  period 06 : 170.54\n",
+            "  period 07 : 167.95\n",
+            "  period 08 : 167.45\n",
+            "  period 09 : 166.74\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBUYIIYQQAef/A4mmRWtzrzjDAAAAAElFTkSuQmCC\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",
+        "outputId": "65f1e397-2022-454e-e523-699525fe9923",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        }
+      },
+      "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": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 218.05\n",
+            "  period 01 : 200.21\n",
+            "  period 02 : 185.87\n",
+            "  period 03 : 175.64\n",
+            "  period 04 : 170.03\n",
+            "  period 05 : 167.37\n",
+            "  period 06 : 166.83\n",
+            "  period 07 : 166.95\n",
+            "  period 08 : 167.76\n",
+            "  period 09 : 168.27\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP1Ke1z5Lw2+wAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "65sin-E5NmHN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 5: Evaluate on Test Data\n",
+        "\n",
+        "**In the cell below, load in the test data set and evaluate your model on it.**\n",
+        "\n",
+        "We've done a lot of iteration on our validation data.  Let's make sure we haven't overfit to the pecularities of that particular sample.\n",
+        "\n",
+        "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n",
+        "\n",
+        "How does your test performance compare to the validation performance?  What does this say about the generalization performance of your model?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "da0e615d-1649-4a95-a4bc-54c629268a6b"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 161.66\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yTghc_5HkJDW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_xSYTarykO8U",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "3ce6305a-ea9f-4298-ec0a-685d85c3f02f"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 161.66\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file