diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
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--- /dev/null
+++ b/feature_crosses.ipynb
@@ -0,0 +1,1371 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/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": 1209
+        },
+        "outputId": "e871121f-9522-4b79-ba93-cda4ad476367"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.5       2651.6           540.0   \n",
+              "std         2.1        2.0                12.6       2225.8           428.0   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1457.8           295.0   \n",
+              "50%        34.3     -118.5                28.0       2124.5           432.5   \n",
+              "75%        37.7     -118.0                37.0       3165.0           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       1429.1       501.5            3.9               2.0  \n",
+              "std        1178.2       390.3            1.9               1.2  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         786.0       281.0            2.6               1.5  \n",
+              "50%        1163.0       408.0            3.5               1.9  \n",
+              "75%        1725.0       608.0            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2651.6</td>\n",
+              "      <td>540.0</td>\n",
+              "      <td>1429.1</td>\n",
+              "      <td>501.5</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>2225.8</td>\n",
+              "      <td>428.0</td>\n",
+              "      <td>1178.2</td>\n",
+              "      <td>390.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1457.8</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>786.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2124.5</td>\n",
+              "      <td>432.5</td>\n",
+              "      <td>1163.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3165.0</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1725.0</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>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                28.8       2624.6           537.9   \n",
+              "std         2.1        2.0                12.5       2065.8           405.4   \n",
+              "min        32.5     -124.3                 2.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1472.8           300.0   \n",
+              "50%        34.2     -118.5                29.0       2144.0           437.0   \n",
+              "75%        37.7     -118.0                37.0       3120.0           641.0   \n",
+              "max        41.9     -114.5                52.0      21086.0          4095.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1430.7       500.5            3.9               2.0  \n",
+              "std        1071.5       370.3            1.9               1.1  \n",
+              "min           8.0         1.0            0.5               0.2  \n",
+              "25%         794.0       283.0            2.5               1.5  \n",
+              "50%        1177.0       411.5            3.6               1.9  \n",
+              "75%        1705.8       600.2            4.8               2.3  \n",
+              "max       11272.0      3832.0           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>28.8</td>\n",
+              "      <td>2624.6</td>\n",
+              "      <td>537.9</td>\n",
+              "      <td>1430.7</td>\n",
+              "      <td>500.5</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>12.5</td>\n",
+              "      <td>2065.8</td>\n",
+              "      <td>405.4</td>\n",
+              "      <td>1071.5</td>\n",
+              "      <td>370.3</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>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.2</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>1472.8</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>794.0</td>\n",
+              "      <td>283.0</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>2144.0</td>\n",
+              "      <td>437.0</td>\n",
+              "      <td>1177.0</td>\n",
+              "      <td>411.5</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3120.0</td>\n",
+              "      <td>641.0</td>\n",
+              "      <td>1705.8</td>\n",
+              "      <td>600.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>21086.0</td>\n",
+              "      <td>4095.0</td>\n",
+              "      <td>11272.0</td>\n",
+              "      <td>3832.0</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                207.3\n",
+              "std                 116.2\n",
+              "min                  15.0\n",
+              "25%                 119.4\n",
+              "50%                 179.8\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>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.8</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                207.3\n",
+              "std                 115.4\n",
+              "min                  22.5\n",
+              "25%                 119.6\n",
+              "50%                 181.3\n",
+              "75%                 266.6\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>22.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oJlrB4rJ_2Ma",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NBxoAfp2AcB6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hweDyy31LBsV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## FTRL Optimization Algorithm\n",
+        "\n",
+        "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S0SBf1X1IK_O",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "1Cdr02tLIK_Q",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 635
+        },
+        "outputId": "8f746ec1-76ea-43d7-cb12-fd07bf48bcaa"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(training_examples),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 284.65\n",
+            "  period 01 : 194.52\n",
+            "  period 02 : 126.89\n",
+            "  period 03 : 159.19\n",
+            "  period 04 : 144.13\n",
+            "  period 05 : 150.61\n",
+            "  period 06 : 184.45\n",
+            "  period 07 : 206.16\n",
+            "  period 08 : 217.48\n",
+            "  period 09 : 183.77\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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mpONEh0gCYy2XaxthG9Iu9kvaxn5J2zROQ0me1UpIJSUlzJkzhyVLllgG5Pbv\n35/NmzcDsHv3btq3b094eDipqakUFxdTWlpKUlIS0dHR1grrmnX27oCrgyvJprNlJF9PF4L8dOw9\nVkBZRY2NIxRCCCGuf1brgVm3bh0FBQU88cQTlmOzZ8/m1VdfJT4+Hq1Wy+zZs3FxcWHGjBlMmTIF\nlUrF1KlT0enst1vNQe1AuD6UhJOJHC3OINgzCIDITnqOZZew60geN3X1s3GUQgghxPVNpbTAFdis\n2e3WmG69Xbl7WZTyHoPb9GN8yBgAMnJO8c9l27ipq5G/3tbdavHdyKTL1T5Ju9gvaRv7JW3TODYp\nIV3POvuE4OrgQnJOqmXQcWuDG3pPF1IP51FjrrVxhEIIIcT1TRKYq+CodiBM342CykKOlWQAoFKp\niAwxUF5pZl96gY0jFEIIIa5vksBcpajTeyMlnbM3kmzuKIQQQjQPSWCuUhfvEFw0zuw4p4zUsbUn\n7q6OJKeZqG15Q4uEEEKIFkMSmKvkqHEkTN+NvIoC0kuOA6BRqwnv4EvhqSqOnZTBWUIIIYS1SAJz\nDSKNYQAk55zdGymykwGApAP2uR2CEEIIcT2QBOYadPXpjLPGieScFEsZKbS9D04OanbI5o5CCCGE\n1UgCcw2cNI509+1KbkU+x09lAuDsqKFbOx9O5JaSnW+9LQ+EEEKIG5kkMNfoYrORIs/MRpJeGCGE\nEMIqJIG5Rt18O+OkdqxXRgrvqEelguQ0GQcjhBBCWIMkMNfISeNEd31XTOV5nDiVBYCH1omQQE8O\nHi+iuLTKxhEKIYQQ1x9JYJpA5OkyUrKp/mwkBdhxUMpIQgghRFOTBKYJhPp2wfG8MlJkSN04GJmN\nJIQQQjQ9SWCagLPGiVDfLmSXmcgqzQbA6K0l0ODG7qP5VFaZbRyhEEIIcX2RBKaJRJ1e1K7ebKQQ\nA9U1tew6km+rsIQQQojrkiQwTSTUtyuOaof642BCzkynltlIQgghRFOSBKaJuDg40823CydLsy1l\npHatdHjrnNl5MBdzba2NIxRCCCGuH5LANKFIw5m9kerKSCqVisgQPaUVNaRlFNkyNCGEEOK6IglM\nE+qu74qD2qH+5o4hpzd3lDKSEEII0WQkgWlCrg4udPXpRGbpSbJLcwDo3NYLV2cHkg/kWqZYCyGE\nEOLaSALTxKLOW9TOQaMmvIMvecUVZOScsmVoQgghxHVDEpgmFqbvioNKU286dUSIbO4ohBBCNCVJ\nYJqYq4MrXXw6ceJUFjlldeNewoJ9cdCoSD4g42CEEEKIpiAJjBVEGs/MRqorI7k6O9AlyJv0nFPk\nFpXbMjQhhBDiuiAJjBX00Hdei+QAAAAgAElEQVRDo9LUW9Qu6vRsJCkjCSGEENdOEhgr0Dpq6ezT\nkYySE+SW5wFnx8HI5o5CCCHEtZMExkoiDadnI50uI3m5OxMc4MH+9EJOlVfbMjQhhBCixZMExkrC\nDaGoVerzNnfUU6sopBySXhghhBDiWlg1gZkzZw533HEH48eP54cffrAc37x5M507d7a8XrNmDePH\nj2fixIl89tln1gyp2bg5auns3ZH0kuPkldftRh3VScbBCCGEEE3BwVo3TkhIIC0tjVWrVlFQUMDY\nsWMZNmwYlZWVvPPOOxgMdb/My8rKWLBgAfHx8Tg6OjJhwgSGDh2Kl5eXtUJrNpHGMPbmHyDZlMqt\nbQfg7+uGn4+WXYfzqao24+SosXWIQgghRItktR6YmJgY5s2bB4CHhwfl5eWYzWYWL17M3XffjZOT\nEwA7d+4kLCwMnU6Hi4sLUVFRJCUlWSusZhWu745apa63N1JUiJ7KajN7jhXYMDIhhBCiZbNaD4xG\no0Gr1QIQHx9P//79SU9PZ9++fUyfPp3XXnsNgNzcXHx8fCzX+fj4YDI1vOCbt7cWBwfr9V4YDLqm\nuQ86Qo2dSM3eh0pbjd7Nh0ExQXy7NZ19GUUM7d2+SZ5zI2mqthFNS9rFfknb2C9pm2tjtQTmjPXr\n1xMfH8+yZcuYMWMGM2fObPD9jdnwsKCgrKnCu4DBoMNkKmmy+4V6dSM1ex8/7fudwW3746N1wMPN\niYTUTCYNCEatVjXZs653Td02omlIu9gvaRv7JW3TOA0leVYdxLt582YWL17Mu+++S1lZGYcPH+bp\np59m0qRJ5OTkcO+992I0GsnNPTuoNScnB6PRaM2wmlWEoTsqVCSdLiOp1SoiOuopLqvmUGaRjaMT\nQgghWiarJTAlJSXMmTOHJUuW4OXlhZ+fH+vXr+fTTz/l008/xWg08sEHHxAeHk5qairFxcWUlpaS\nlJREdHS0tcJqdjond0K8gjlSfIyCikKgbjo1yGwkIYQQ4mpZrYS0bt06CgoKeOKJJyzHZs+eTUBA\nQL33ubi4MGPGDKZMmYJKpWLq1KnodNdXXTDS2IMDhYfYYdrFoDZ96dbOG2dHDckHTEwc2AGVSspI\nQgghxJVQKY0ZdGJnrFk3tEZdsqiyhH/8+jLBnkE81fNRABZ+mUrifhMv/+VmAvRuTfq865XUjO2T\ntIv9kraxX9I2jWOzMTCijqezjo5e7TlcdIzCyrpxL5GWzR0bnnElhBBCiAtJAtNMIoxhKCjsMO0C\noEdHX9QqFUkHZByMEEIIcaUkgWkmZ2YjJZ/eG8nNxZHObb04klVMQUmljaMTQgghWhZJYJqJl7Mn\nwZ5BHCo8SlFlXd3zzGykHQelF0YIIYS4EpLANKNIYw8UFHaa6taEsYyDOSDjYIQQQogrIQlMM4ow\ndAew7I3k6+lCWz939h4roLyyxpahCSGEEC2KJDDNyNvFi2DPINIKD1NSdQqAqBAD5lqF1MN5No5O\nCCGEaDkkgWlmkYb6s5EiO9WVkZKkjCSEEEI0miQwzSzCGAZgmY3U2uCG3tOF1MN51JhrbRmaEEII\n0WJIAtPMfFy8aefR1lJGUqlURIYYKK80sy+9wNbhCSGEEC2CJDA2EGkMo1apJcW0G4CoTrK5oxBC\nCHElJIGxgUjD6TLS6enUHVt74ubiwI60XGpb3tZUQgghRLOTBMYGfF19CNK1YX/BQU5Vl6JRq4no\nqKegpJJjJ2VzLyGEEOJyJIGxkbNlpD11rzvJ5o5CCCFEY0kCYyORZ2YjmepmI4W288HRQU2ybO4o\nhBBCXJYkMDaid/WljS6Q/fkHKasuw9lJQ2g7H07klpJdUGbr8IQQQgi7JgmMDUUZemBWzKTkni4j\nnd7cUXphhBBCiIZJAmND5y9qFx6iR6WScTBCCCHE5UgCY0NGrZ7W7gHszU+jvKYcD60TIYGeHDxR\nRHFpla3DE0IIIeyWJDA2FmkMqysjnZ6NFBFiQFFg50EpIwkhhBCXIgmMjUUaewBnF7WLlFV5hRBC\niMuSBMbG/LQGAtxasTf/AOU1Ffh5awk0uLH7aD6VVWZbhyeEEELYJUlg7ECUsQc1tTXsyt0L1M1G\nqq6pZdeRfBtHJoQQQtgnSWDsQOR5s5EiQ2RVXiGEEKIhksDYgVZufvi7+bE7fz8VNRW0a6XDW+fM\nzoO5mGtrbR2eEEIIYXckgbETkYawujJS3j5UKhURIXpKK2pIyyiydWhCCCGE3ZEExk5YZiPl1M1G\nijpdRkqSMpIQQghxAQdr3nzOnDls376dmpoaHn74YcLCwnjuueeoqanBwcGB1157DYPBwJo1a1i+\nfDlqtZpJkyYxceJEa4Zll/zd/PDTGtmdt49KcxWd23rh6qxhR1oudw0JQaVS2TpEIYQQwm5YrQcm\nISGBtLQ0Vq1axdKlS5k1axZvvvkmkyZN4oMPPmDo0KG89957lJWVsWDBAt5//31WrlzJ8uXLKSws\ntFZYdkulUhFpDKO6tprdeftw0Kjp0UFPblEFGTmnbB2eEEIIYVeslsDExMQwb948ADw8PCgvL+ef\n//wnw4cPB8Db25vCwkJ27txJWFgYOp0OFxcXoqKiSEpKslZYdi3qdBkpyTIbqW5Rux2yqJ0QQghR\nj9USGI1Gg1arBSA+Pp7+/fuj1WrRaDSYzWY++ugjxowZQ25uLj4+PpbrfHx8MJluzHEfAW6tMLrq\n2Z27lypzFWHBvmjUKhkHI4QQQpzHqmNgANavX098fDzLli0DwGw288wzz9CrVy969+7N2rVr671f\nUZTL3tPbW4uDg8Yq8QIYDDqr3fty+rSL5su935FRfYxebaII72QgaV8OikaD0Udrs7jshS3bRlya\ntIv9kraxX9I218aqCczmzZtZvHgxS5cuRaera6jnnnuOoKAgpk2bBoDRaCQ392yJJCcnh4iIiAbv\nW1BQZrWYDQYdJlOJ1e5/OZ3dOwPf8cvBbXRwCaF7kDdJ+3L4aetRbo1uY7O47IGt20ZcnLSL/ZK2\nsV/SNo3TUJJntRJSSUkJc+bMYcmSJXh5eQGwZs0aHB0defzxxy3vCw8PJzU1leLiYkpLS0lKSiI6\nOtpaYdm91u4B6F19Sc3bS5W5mvCOsrmjEEIIcT6r9cCsW7eOgoICnnjiCcuxzMxMPDw8iIuLA6BD\nhw7861//YsaMGUyZMgWVSsXUqVMtvTU3IpVKRaQhjB/TN7I3fz/hhu4EB3iwP72QU+XVuLs62jpE\nIYQQV6mkrIrktFz6RLbGegMhbgwqpTGDTuyMNbvd7KFbL734OLMT5xPtF8H9oXfzze9H+fyXwzw4\nuhu9u7eyaWy2ZA9tIy4k7WK/pG3sg6IopB0vYuOOEyTuy6HGrGDwduUf9/bEw83J1uHZNZuUkMTV\na6MLxNfFm125e6k2V1s2d5TZSEII0XKUVlTzY2IGz/9vG69+mETC7mx8PV2J7mLEVFDO21+mUl0j\n+91dLavPQhJXrm5Rux6sT/+FvfkHCNN3w89Hy67D+VTXmHG04gwsIYQQV09RFA5nFrMx+QTb9uVQ\nXVOLRq3ipq5GBkYE0rlt3ZjQ5S6ObNpxghXf7+OBkV1ltfWrIAmMnYo0hrE+/ReSTan0MIQSGaLn\nu63p7DlaYBnYK4QQwj6UV9aQsPskPydnctxUt3q60cuVAREB9Anzv6BU9PidkWRkF/Nr6kkC9e7E\n3tzWFmG3aJLA2KkgXRu8nb1IMe2huraGqBAD321NJznNJAmMEELYiaMn63pbtu7JobLajFqlomdn\nAwMjAunazhv1JXpWnB01TBvXg5dXJPLZzwdp5aslQv5tvyKSwNipM3sjbcjYzP78NLoFdMHDzYkd\nabnUDldQq6W7UQghbKGiqoate7LZuCOTYyfrBkn7ergwsncQ/Xr44+Xu3Kj7eOuceWx8GK9+kMSS\nNbv5R1xPWhvcrRn6dUUSGDsWZezBhozNJOWk0F3flYiOvmzamcXhzGI6tva0dXhCCHFDSc8u4Zcd\nmfy++yQVVWZUKojoqGdgZADd2/te1R+W7Vp5MGV0Nxat3sX8+BRmTo7GQyszkxpDEhg7FuTRBi9n\nT1Jy91BTW0NkiIFNO7NISjNJAiOEEM2gqtrMH/ty2Jh8gkOZxUBdz8mwmDb0Dw/Ax8Ol0fcqriph\n28kkduTs4taQPkR41q06H9PFSGbf9ny15QgLv0jl6bsicdDIJOHLkQTGjqlVaiKNYfycsYX9BYfo\n1q4jzo4akg+YmDiwg4xaF0IIKzmRW8ovySf4bddJyiprUAFhwb4MjAigR0dfNOrGJRg1tTXsztvH\n71mJ7M7bR61SN2166fZ0Hu7hRJi+GwBj+rTjRG4piftyWPH9fu4f0UX+jb8MSWDsXKShBz9nbCE5\nJ4VQ3850D/Zh+34TWXllBOjdbB2eEEJcN6pratm+v6635cDxIgA83JwYFRXEgPAA9F6ujb7XiVNZ\n/J71B3+cTOZUdSkAbdwD6OUfg5+bgSWpy3lv90c83XMaAe6tUKtUTBnVFVNhOVtSsgjUuzH8JpmZ\n1BBJYOxce8+2eDp5kGLajbnzOKJCDGzfbyI5zSQJjBBCNIGT+WX8suMEv6ae5FR5NQDd2nkzMCKQ\niBB9o8s5pdVl/JGdTEJWIhklJwBwd3RjUJu+9GoVTWtdgOW9U2+azJu/L2Vxynv8LfoxdE7uODtq\neHx8D/69/A8+/fkg/r5aenSQmUmXctUJzNGjR2nXrl0ThiIuRq1SE2EM45fjv3Kg4BBhHdqjVqlI\nTstlVO92tg5PCCFapBpzLUkHTPyyI5O9xwoAcHd1JPbmtgyICMDPW9uo+9QqtezNP8DvWYmkmnZT\no5hRq9SE6bvSyz+G7r5dcFBf+Kv2lrY9STt5jG+O/Mg7qSt4PPIhHNUOeOuceXx8D179MInFX9XN\nTAqUmUkX1WACc//99/Pee+9ZXi9cuJBHH30UgBdeeIEVK1ZYNzoB1M1G+uX4rySbUujapROd23qx\n91gBBSWVeOsaN11PCCEE5BSWs2lHJltSMikuq+tt6dzGiwGRAfTsZMTRoXG9LdmlOfyelci2k0kU\nVdUN7m3l5kdv/2hi/KLwdL78psQj2t3KydIctufs5ON9nxPXdRIqlYr2/h48MLIrS9bsZl58Cs9P\njkYnM5Mu0GACU1NTU+91QkKCJYFpgXtAtljBnkF4OOnYadrNHZ3GEhmiZ++xAnYczGVQZKCtwxNC\nCLtmrq1lR1oev+w4we4j+SiAm4sDQ6PbMCAioNHl+PKaCpKyd/J7ViJHio8B4OrgSr/A3vTy70mQ\nrs0VDbxVqVTc23USueX5bD25HX83P4YGDQTg5m5+ZOaWsva3oyz4chdP3xkhM5PO02ACc35DnJu0\nyOjo5qNWqYkwhLHpxG+kFR4mIqQ1H61PIznNJAmMEEJcQl5RBZt2ZrI5JZPCU1UAdAz0ZGBkANGd\njTg5Xn5fuVqllrSCw/yelcgOUyrVtdWoUNHVpxO9/KMJ14fiqHG86hidNI483GMycxLf4qtD32LU\nGgg3hAJwW7/2ZOaVsn2/iQ9+2M/kWJmZdK4rGgMjX5ztRBrrEpjknBTu6hJCWz939h4toLyyBldn\nGYsthBAAtbUKKYfz+CX5BCmH81AUcHXWMDgqkIERgbQ2Nm48SW55PglZiWw9uZ38iroxMgZXX3r5\nR3Nzq554u3g1Wcyezh78tcefmbt9Ie/v+ZgZUY/SWheAWqXiL6O6YSrczqadWQTo3RkW06bJntvS\nNfibr6ioiN9//93yuri4mISEBBRFobi42OrBibM6erVH5+jODtMu7ug8lqgQA+nZp0g9nMdNXf1s\nHZ4QQthUQUklm1My2bwzk7ziSgDa++sYGBHITV39cHa6fG9LpbmKHTmp/J71B2mFhwFw0jjRyz+a\n3v4xdPBsZ7U/5NvoApnc7U7e3bWSxSnv87fox/B01uHsVDcz6aXliazakEYrHy09OvhaJYaWpsEE\nxsPDg4ULF1pe63Q6FixYYPn/ovmoVWrCjd3ZciKBg4WHiQjxY/WWIySn5UoCI4S4IdUqCnuO5LNx\nR2bdPnGKgrOThoERAQyICCSo1eV/TymKwuGiYyRk/UFSTgoV5rrkJ8QrmF7+0UQYwnBxaJ7JEhHG\nMP4UHMuaw9/xbupypkc+jKPGER8PF6aND2P2h8ksWbOLf8RFyzIaXCaBWblyZXPFIRohytCDLScS\nSM5JZVKnDug9XUg5lEuNuVYGdwkhbhhFpVVsSclk085MTIUVALQ1ujMwMpCbu/k1qqxeUFHI1pNJ\nbM1KJKc8FwBvZy8GtenLza2iMWht08sxLGgQWaU5/JGdxIf74pnc7U5UKhUdAjx5YGQX3lm7x7Jn\nkrvr1Y+9uR402MqnTp0iPj6eP//5zwB88sknfPzxxwQFBfHCCy+g18sCO82po1d73B3dSDalMrHT\nbUSGGPgxMYP96YWEtvexdXhCCGE1iqKw71gBG3dkknTAhLlWwclBTd8e/gyMCKS9v+6y5Z1qczUp\nubv5PSuRfflpKCg4qh2I9ougt38Mnbw7oFbZ9o9BlUrFPV3Gk1uexx/ZybRy8yO23WAAeoW2IjOv\nlK9/O8bCL1N56o4be2ZSgwnMCy+8QGBg3SyXI0eOMHfuXN58803S09P5z3/+wxtvvNEsQYo6GrWG\ncEN3fs3cyqHCo0SG6PkxMYOkNJMkMEKI61bq4Tw+Wp9Gdn4ZAIF6NwZGBtI71A+tS8O9EIqikF5y\nnISsRBKzd1BWUw5Ae4+29PKPJsoYjtax8VsENAdHjSMP9biPOX+8xdrD3+GnNRBpDAPg9n7BZOaW\nkXTAxEc/HiBueOcbdoJNgwlMRkYGc+fOBeD7778nNjaWW265hVtuuYVvvvmmWQIU9UUaw/g1cyvJ\nphTGd/wTbi4O7EjL5d6hnW7YH2IhxPWpVlFYs+UIa389ilqtoneoHwMjA+kY6HnZf++Kq0r442Td\nsv6ZpScB8HDSMbTtQHr596SVm32PHfRw0vFI+P38d/sClu/5BF9Xb9rqWtfNTBrdlVc+KGfjjkwC\n9G7cGn1jzkxqMIHRas8upbxt2zYmTJhgeS2/LG2jk1cH3By17MhJZULInwjvqOe3XSc5erKE9v4e\ntg5PCCGaxKnyat5Zu5tdh/Px9XDh0bHdL/tvnLnWzK68vfV2ftaoNEQYwujtH01Xn05o1JefjWQv\nAt39ub/bXbyTuoIlKcv5W/Q0vJw9cXFyqJuZtCKRj39Ko5Wvlu7tb7yZSQ0Wz8xmM3l5eaSnp5Oc\nnEyfPn0AKC0tpby8vFkCFPVp1BrC9aEUVZVwuOgYkSEGAJLTTDaOTAghmsaRrGJefG8buw7n0z3Y\nh3/eH9Ng8nLiVBafp63l/359mXdSV5Cau4dAt1ZMDLmNWX1n8mBYHN31XVtU8nJGD0Mot3UYQWFl\nEUtSllNlrluQz9fThWnjwtCoVSxavZusvFIbR9r8GuyBefDBBxk5ciQVFRVMmzYNT09PKioquPvu\nu5k0aVJzxSjOE2nswW9Zf7AjJ5Ux7Ufh6KAm+UAu4/p3sHVoQghx1RRF4ZcdmXy0/gBms8Ltfdsz\nuk871Bfp8S+tLiMxewcJWX+QfnrnZzdHLYNa96WXf/2dn1u6W9sO4GRpDgknE1m591MeCL0HlUpF\nx0BP7h/RlXe/3sO8+BRm3ndjzUxqMIEZMGAAW7ZsobKyEnf3utULXVxc+Nvf/kbfvn2bJUBxoc7e\nHdE6uJJsSmVcyGhC2/mw42Au2QVljd5BVQgh7ElltZmV3+/nt10ncXNx4OHxoXQPrl8WudTOz919\nu9LbP5ru+q4X3fm5pVOpVNzZZRym8lySclJo5ebHqPZDAejdvRUncktZl3CMRat38eSk8BtmZlKD\nLZ2ZmWn5/+euvBscHExmZiYBAddPhtuSaNQaehhCSchK5GhxBpEhenYczCX5QC6xN7e1dXhCCHFF\nsgvKWPDFLo6bTtHeX8cjt3dH73l2ZtCp6lJ+St/E1qztZ3d+1hrp5R/NTa16Nmrn55bOUe3Ag2H3\n8VriW6w78iOttAZ6+kUAMG5AMFl5pSSn5fLx+jTihne2cbTNo8EEZvDgwbRv3x6DoW6cxfmbOa5Y\nscK60YlLijSEkZCVSHJOCkNDhqP6DnakmSSBEUK0KMkHTCz9Zg/llWYGRgZy15AQHB3O9iAcKjzK\nst0fUlhZhKuDC30De9HbP/qKd36+Huic3Plrj/t5ffsCVu79FL2rL0EebVCrVDw4phuzVibxc/IJ\nAvRuDOnZ2tbhWl2DCczs2bP56quvKC0tZdSoUYwePRofH1lvxB508QnB1cGF5JxUxnUcTcdAT9JO\nFFFcWoWHm5OtwxNCiAaZa2v5YtNhvk1Ix8lBzZRRXekT5m85X6vU8lP6JtYc/g5FURgTPJzBbfrj\ndA07P18PAtxbcX/o3SxOeZ8lp/dM8nbxqpuZNCGMl5cn8vH6uj2Trvf1wTT/+te//nWpk126dOG2\n226jb9++pKSk8Morr7Bx40ZUKhVBQUE4ODRca5wzZw7z58/nk08+wdvbG61Wy6OPPkp8fDybNm1i\nyJAhaDQa1qxZw//93/8RHx+PSqUiNDS0wfuWlVVd1YdtDDc3Z6vev6moVWpOluZwsOgI3Xy74Ki4\nsftIPv6+2kbt/9EStZS2udFIu9gve22botIq3vo8hYTd2Ri9XZlxZ2S98S6nqktZtutDNp34HQ8n\nHX/tcT83+/dskbOILuVa2saoNeCicSbZtIu0wsPEtIrCQa1B6+JIx0AvftudRfKBXKI6G1r8oF43\nt0vvQ9WokT7+/v48+uijfPvttwwfPpyXX375soN4ExISSEtLY9WqVSxdupRZs2Yxf/587r77bj76\n6COCgoKIj4+nrKyMBQsW8P7777Ny5UqWL19OYWHhlX3CG9SZlRmTTSlEdqrb1iE5LdeWIQkhRIPS\njhfy4nvb2JdeSGSInhcmR9PG6G45f7joKK9se5Ndefvo6tOJ5256ghDvYBtGbJ8GtelHn4CbyCg5\nwYo9q6hVagHo2NqTybFdKKusYV58CqUV1TaO1HoalcAUFxfzwQcfMG7cOD744AMefvhh1q1b1+A1\nMTExzJs3D6jb1bq8vJytW7cyZMgQAAYNGsTvv//Ozp07CQsLQ6fT4eLiQlRUFElJSdf4sW4MXXw6\n1WXhOakYvVwJ1Lux+2g+lVVmW4cmhBD1KIrCj39kMOejZIpKq5g4sAPTxoVZtgKoVWr58dhG3kha\nTFFlMWOCY3k0/AF0Tu6XufONSaVSManT7YR4BbPDlMo3R360nOsT5s+Im9uSnV/GotW7qDHX2jBS\n62mwBrRlyxY+//xzdu3axbBhw3j11Vfp1KlTo26s0WgsK/nGx8fTv39/tmzZgpNT3fgMX19fTCYT\nubm59cbV+Pj4YDI1vCibt7cWBwfrdSUaDC2nBBPdOpwtx7ZR4lBAn4hAPl1/gIz8MnqHXZ8zxFpS\n29xIpF3slz20TXllDW99uoPNO07g5e7MM3HRhHU8uxlwSeUpFmz9gKSsXXi7eDK99xS6GUNsGHHz\naIq2+fvAR/i/9XP47uhPdGrVlr5BNwHw8IQI8kqq2LbnJF/9doy/jutxzc+yNw0mMH/5y19o164d\nUVFR5Ofn895779U7/8orr1z2AevXryc+Pp5ly5YxbNgwy/FzZzSd61LHz1VQUHbZ91wtg0GHyVRi\ntfs3tW4eXdnCNjbsTyA8sK6s90tiBh2vw3EwLa1tbhTSLvbLHtomM7eUBV+mkpVXRsdATx65vTve\nOmdLXIeLjrFs14cUVBbS1acTk7vdiU7lbvO4ra0p2+ah0Mn8d/vbLNy2EqdqLe09gwCYPLwTJ0wl\nfPPrEXzcHBkU1fJmJjWU5DWYwJyZJl1QUIC3t3e9c8ePH7/sgzdv3szixYtZunQpOp0OrVZLRUUF\nLi4uZGdnYzQaMRqN5OaeHbeRk5NDRETEZe8t6nT16YSzxonknBTG9IrFW+fMjoO5mGtr0ahvjMWM\nhBD2advebN5bt4/KajNDo9swcVAHyyJrtUotGzI289Whby2zjIYFDUKtkn+3rlQrNyNTQu9lYcoy\nlqQu55nox/Bx8cbV2YHp43vw7+WJfPhj3cykru2un5lJDf6kqNVqZsyYwfPPP88LL7yAn58fN910\nEwcOHODNN99s8MYlJSXMmTOHJUuW4OXlBcAtt9zC999/D8APP/xAv379CA8PJzU1leLiYkpLS0lK\nSiI6OrqJPt71z0njSJi+G7kV+ZwozSQiRE9pRQ0HjxfZOjQhxA2qxlzLR+sPsPir3QD89bZQ7ro1\nxJK8nKouZUnKcr48+A06RzemRz5EbLshkrxcg66+nRgfMoaSqlMsTnmfippKAPRerkwbF4ZKBQtX\n7yI733oVjObWYA/MG2+8wfvvv0+HDh346aefeOGFF6itrcXT05PPPvuswRuvW7eOgoICnnjiCcux\nV199lZkzZ7Jq1SoCAgK4/fbbcXR0ZMaMGUyZMgWVSsXUqVPR6a6/8oc1RRrCSMzeQXJOKpEhN/Fz\n0gmSDuTSua335S8WQogmVFBSyaKvdnHweBH+vlqmjg0jQO9mOX9uyaiLdwh/Dr1LBuo2kQGBt3Cy\nNIfNJ35n+Z5PeDAsDrVKTac2XtwX25n31u07vWdST8vg6ZZMpTQw6CQuLo6VK1daXt966608++yz\nDB06tFmCuxRr1kbtoWZ8parM1Ty75UU8nXT8I+ZpnnhrC24ujsz+a+/raqXKltg2NwJpF/vV3G2z\n91gBS77aRXFZNTd1NfLnEV1wcar7O1lRFH7K2GQpGY0OHnZDl4ys1TbmWjMLdv6P/QUHGdp2ILd3\nHGk5t2pDGt9vyyC0vQ9PTOzRIoYZNDQGpsHoz//l5+/vb/PkRVzISeNId98umMrzyC7PpkcHPblF\nFRw33Xjbqwshmp+iKCxs3KQAACAASURBVKxLOMZ/P0mmtKKGu24N4eE/hVqSl9LqMpakvs+XB7/B\n3dGNx6VkZDUatYa/dL8Xo1bPj+kbSchKtJybOLAjPTr4svtIPqt+OmjDKJvGFf30XE9/zV9vIo11\nU+SSc1KIDDm9qN2BhqejCyHE/7N33/FR19ni/1/TkknPpDdSIAkhPaFIkC5FsUuxgYJlLeiu+/Xe\nvXe93qt7va7L7v7utTdclaIrCKJYFkQh9J6QBukhpPdeJzPz+yMYZVUMJfnMhPN8PHw8dGbymTOe\nyczJ+7zLpers7uPVT7LYlFqEm5Md/3ZXMnMnfH9OUUlLKS8ceZGs+lNEGSL4/aQniDSMUTjqkc1R\n58jD8Stw0DrwYe5mCptLAFCrVTx0UwyBXk58c7yc1BMVCkd6ac5bwKSnpzNz5syBf7777xkzZjBz\n5sxhClEMRoxnFDq1jrS6TGLDPNCoVaQVSAEjhBg6ZbXt/Peao6QX1BMV7M6zKyYRHuQG9I/KfHNm\nN/+b9gbNPS3cEDaflYn342oncxyHg6+jNw/ELsWChdVZa2noagTAwV7L44vicXbQ8cHX+ZwqbVI4\n0ot33km827ZtG644xCWy19gR6xlFel0WzX0NjAsxkF3SSENLN55ueqXDE0KMMAeyq1i7LY/ePjML\nJodw6/SwgTkVHcZO1p3aQFb9KVztXFgRc5eMuiggyiOCJZE381HeFt7MfJ//N/5RHLR6fNwdWHlr\nLH/96ASvb8ni6Xsn4GtwVDrcC3bewxxdXV3P+49S5DDHn2bBQnpdFs46J0KcQ8koasDVSUfkKHel\nQ7ssbDk3I5nkxXoNRW6MfWY+2JHPJ3uKsdNpeOTmGK4ZH4T6By2jl9NXU9pWTpQhgseSHiDA2e+y\nxjASDNfvTYjrKDqMnWQ3nKKyvYrxvgmoVCq83Bxwd7bnaG4tJ083khLjh05rfXOSLvkwR2EbYjzH\noVNrSa/LYtI4H+x0anafqMRs/uXdjYUQ4pfUt3TxwvrjpKZXEOTtzH8tn0BSpDfwUy2jedIyshIL\nw29gnEck2Q25fFr4/TmG0xMCmDdxFFUNnby5NRuT2bbOTJICZgTRa+2J9oyiuqOGFlMjk6P9qG/p\nJqu4QenQhBA2Lru4gT+8d5TT1W1MifXjP+4ZP9B2+PEqowe5LmyOrDKyEhq1hvtj78bP0Ydvy/Zw\noPLIwH1LZoUTO9qD7OJGNu4sUjDKCyfvrhEm2TsO6F+NNDs5EIBd6bY901wIoRyzxcLWfSX838YM\neowm7rl2LPdfPw57Xf+Buj9cZTTWEH52lVG4wlGLf+agdeDh+BU4aR35KG8LBU39xYpareLhm2Lx\n93Rkx7Ey9mRUKhzp4EkBM8LEeo1Dq9aSXptFsK8L4YFuZBU1UNvcpXRoQggb095l5KWPM/l0Xwke\nrnp+v3Q8MxMDUalU/RvTndkz0DK6PmwujyU+IC0jK+bt6MmDccv6VyZlr6Ous3903lGv5TeL4nHS\na1m3PY+8M7axMkkKmBFGr9UT7TGWyo5qqjtqmZUciAXYLaMwQogLcLq6lT+8d5Ss4gZiR3vwzIqJ\nhPn3L97obxmt4ZPCL3DSOfJ44oMsCJsrLSMbEGEYw51jb6PD2Mmbme/R1df/x62Pof/YB4DXtmTb\nxB+98m4bgZJ8vmsjZTFhrA/ODjr2ZlZh7DMpHJkQwtpZLBZ2n6jgj+uO09jazS1Tw3hicQLODv1n\n55S0nOFPR18iq/5kf8to4m8Z6yEtI1syJWASs0dNo7qzlr9lf4DJ3P/dEBVi4O55kbR3GXl5UyZd\nPX0KR3p+UsCMQHFe49CqNKTXZaLTqpmeEEB7l5GjubVKhyaEsGI9RhPvfnWKNdvysNdpeGJJAjdN\nDUN9tmW088we/jftdZq6mwdaRm720jKyRbeGX0+MZxSnGvPZUvjlwO0zEwOZMz6IyvoO3tqaY9Wr\nWKWAGYEctA5EeURS0V5FbWcdMxMDUAE706SNJIT4abVNnfxx3XH2Z1UT6ufCMysmEjfaE4DOsy2j\nzdIyGjHUKjUrYu7C38mXXeX72FtxaOC+268JJzbMg8yiBj5Otd4zk+TdN0KN900A4GDVMbzcHYgf\n40lxZSunq1sVjkwIYW3SC+r4w/vHKKttZ2ZiAL9fOh4vNwegv2X0wtmWUaS0jEYUB62eh+NX4Kxz\nYmP+p+Q19hcrGrWah2+Owc/Dke1HytibaZ0rk6SAGaESveNw0jlyoPIIRpOR2eODANglozBCiLNM\nZjObdxfxyuYs+kxm7r9+HPdcG4VOq+5vGZXt5f/S3qCpu5kFYXN5XFpGI46XgwcPxt2DChXvZK+j\ntrP/DD1HvW5gZdLabXnklzUrHOmPSQEzQtlpdEzxn0S7sYPjtRnEhHng7a7n8MkaOrqNSocnhFBY\na0cv/7shgy8PluLj7sB/LBvP1XH+QH/L6O2stWwu+BxHnQOPJT7A9dIyGrHC3cO4M2ohnX1dvJH5\nHp3GTgB8PRx59JZYAF79JIs6K1uZJO/GEWxaYAoqVOwu348KmJUURG+fmf1Z1UqHJoRQUGFFC394\n/yinSptIivDiv5ZPINi3f2TldGt/yyizPodI9zH8fuJvifKIUDhiMdRS/CcwN3gmtZ31vJO9fmBl\n0rhQD+6ae3Zl0mbrWpkkBcwI5ulgIN47hjNtFZS0nmFqvD9ajZpdaeWYLdY7s1wIMTQsFgvfHCtj\n1QdpNLf3sGjmGFbeFoejXjfQMvrf42dbRqFzeDzpQWkZXUFuGnMtcV7R5DUVsqlg68Dts5ICuSY5\niIq6Dt62opVJUsCMcDODpgCwu3w/zg46rhrnQ01TF6dKbWOnRSHE5dHd28dbW3P48JsCnPRa/uWO\nJBZMDkGtUtFp7GT1dy0j7dmW0eh50jK6wqhVapZH30mgsz97Kg6yu/zAwH13zAknJtRARlEDm3Zb\nx5lJ8u4c4SLcx+Dv5EtabSYtPa3MSpbJvEJcaaoaOviftcc5cqqW8EA3nlkxiXEhBqC/ZfSnoy+R\n8V3LaNIT0jK6gum19jwcvxwXnTObCrZyqiEfOLsy6ZZYfD0c2Xb4DPsyqxSOVAqYEU+lUjEjaApm\ni5l9lYcJ83chxM+F9II6Glu7lQ5PCDHEjubW8t9rjlFZ38HcCaP43V1JGFzssVgs7Crbx/8ef4PG\n7mauG2gZuSodslCYh97Ar+LvRa1S87ec9VR39G+C6nR2ZZKjvZa123MpKFd2ZZIUMFeAib7JOGj1\n7Ks4hMliYnZSIBYLpJ6wzrX9QohL12cy885n2bzxaTZY4OGbY7hzTgRajXqgZbSpYOtAy+gGaRmJ\nHxjtFsLdUYvo6uvmjcz3aDd2AODn4cgjt8ZiNvevTKpXcGWSvFuvAHqtPSn+E2ntbeNEbRaTon1x\n0mvZk1FJn8msdHhCiMvMZDbzyuYsPttThL+nI/957wQmjfMFoLS1bKBlFOE+WlpG4mdN8kvm2pDZ\n1Hc18E7WOvrM/SuQYkI9uHNOBG2dyq5MkgLmCjE9cAoqVKSWH8Bep+HqOH9aO3pJy69TOjQhxGW2\n4dtCsoobSB7rw9P3TCDAy2mgZfT/HX99oGX066RfSctInNf1o+eR6B1LQXMxG/M/xXJ2Bes144OY\nlRRIeV0H67bnKRKbFDBXCG9HT2I8x1LSWsqZ1nJmJQUCcj6SECNN6okKvjleTqC3E/92zwQc7LV0\nGrtYnb2OTQVbcdDqWZl4v7SMxKCoVWruib6DUc4B7K88Qmr5/oH77pwTwfhIb3qMJmViU+RZhSKm\nB10NwO7yA/h6OBIT5kF+WTPlde0KRyaEuBxyS5v44Ot8nB10/HphPI563fcto7rsgZbROI9IpUMV\nNsReY8dD8ctxtXNhc8Hn5DTkAqDVqFl5WxyPL4xXJC4pYK4g4zwi8HHw4ljtCdp625l9dhRmV7qM\nwghh62qbu3htSxYAK2+NxctNzz/yd51tGTVxXeg1PJ74IO72bgpHKmyRQe/OQ/H3olVreDf7Ayrb\nld/RfUgLmPz8fObMmcP69esBOHr0KHfeeSfLli3joYceoqWlBYB33nmHRYsWsXjxYnbv3j2UIV3R\n1Co104Om0Gfu40DlEeLDPfFwtedAdrVVbQ8thLgwXT19vLwpk47uPpbNH0vkKHfWn/qY99I3/qBl\nNB+NWqN0qMKGhboGs2zcErpNPbyZ+T7tvR2KxjNkBUxnZyfPPfccKSkpA7e98MILPP/886xbt46k\npCQ2bNhAWVkZX331FR9++CFvvfUWL7zwAiaTMv20K8Fk/wnYa+zYW3EIsDAjMZCeXhMHc5SvpoUQ\nF85stvDW1hwq6zuYMyGI6QkBfFWyg0PVxxjjESItI3FZjfdNZEHoHBq6G3k7a+3AyiQlDFkBY2dn\nx+rVq/Hx8Rm4zWAw0Nzcv/FNS0sLBoOBw4cPM23aNOzs7PDw8CAwMJDCwsKhCuuK56DVc5XfeJp6\nmsmqP8n0hAA0ahW70ioGZpcLIWzHpt1FZBY1EBPmwe2zwzlSncZXp7/BS+/B76etlJaRuOyuC5tD\nsk88RS0l/D3vE8W+O4asgNFqtej1+nNue+qpp1i5ciXz58/n+PHj3HrrrdTX1+Ph4THwGA8PD+rq\nZGnvUJpx9nyk1PL9uDnZMSHKh4r6DvLLlN1VUQhxYfZnVbHt8Jn+zcVujuF06xk+OPUxDlo9jySs\nwFUvBzGKy0+tUrNs3BKCXYI4VHWMnWV7FYlDO5xP9txzz/Hqq68yfvx4Vq1axYcffvijxwymkjMY\nHNFqh66X6+09sn/pvb1diDsdRVZNLl26Vm6dFcHhkzUcOFnL1PHBSod3XiM9N7ZK8jL8TpU0smZb\nHs4OOv7wqxQ0Dl2s/mYtZiw8efWviPMLByQ31szWc/PUrJU8tWMVR+vSuGP89cP+/MNawOTl5TF+\n/HgApkyZwueff87kyZMpKSkZeExNTc05baef0tTUOWQxenu7UFfXNmTXtxZTfK4iqyaXLVk7uHPs\nbQR5O3Egs5LCknrcnO2VDu8nXSm5sTWSl+FX39LF/6w5htls4aGbYzAaO/nToddo62nnjrG34a8J\noq6uTXJjxUZGbjT8+4QnMJqNQ/ZazlfkDesyai8vr4H5LVlZWYSEhDB58mRSU1Pp7e2lpqaG2tpa\nwsPDhzOsK1Ks1zg89QaOVqfR1dfFrOQgTGYLezLkfCQhrFl3bx+vbM6itdPInXMiiAp242/Z66nu\nrGX2qGlMC5ysdIjiCuKkc1RsntWQjcBkZ2ezatUqKioq0Gq1bN++nT/84Q88/fTT6HQ63Nzc+OMf\n/4irqytLlixh6dKlqFQqnn32WdRq2Z5mqKlVaqYFpvBp0VccrDrGlOgpfLyrkNQTlSxICUEjORDC\n6pgtFt754hRlte3MTApkVlIAG/K3kNtUQJxXNLeGD/8wvhBKUVlscOnJUA67jYxhvcHpMHbyH/uf\nx83OhWdSfseHOwrYmVbBylvjGD/WW+nwfuRKyo0tkbwMn0/2FPPFgdNEBbvz/25PZE/FPjYXfkGQ\ncwC/TX4Evfbc9q/kxnpJbgbHalpIwro46RyZ6JtEfXcjOQ25A+cj7UovVzgyIcQ/O3Symi8OnMbb\nXc+jt8ZxqimXTwq/xM3OhYfjl/+oeBFipJMC5go3c9T35yMFejszdpQ7J083UdWg7A6LQojvlVS1\n8t5XuTjYa/j1ogSa+up4N+dDdGotD8evwKB3VzpEIYadFDBXuEBnf8LdwzjVmE9NRy2zxwcBkJou\nk3mFsAZNbT28vDmTPpOZh26Kxcmljzcz38NoMnJvzJ0EuwYpHaIQipACRjDju1OqKw6SFOGFm5Md\n+7Kq6OmVIx2EUFKP0cQrmzNpae9lyaxwxoa68Fbm+zT3tHDzmOtI9I5VOkQhFCMFjCDBKwZ3ezcO\nVx2jz9LLjMQAunr6OHyqRunQhLhiWSwW3vvqFKer25ga58+cCYGsPfkRZ9oqSPGfyJzgGUqHKISi\npIARaNQapgVOptvUw6Hq40xPCECtUrEzrVzORxJCIV8cOM2RU7WEB7mxbP5YPi/ezom6bCLdx3DH\n2FtRqVRKhyiEoqSAEQBcHXAVWpWGPeUHcHexIynCizM17RRXtSodmhBXnON5tWzZW4Knqz2P3RrH\n0dpj7DiTio+jFw/ELUOrHtZN1IWwSlLACABc7JwZ75tITWcdeY2FzEruX1K983iFwpEJcWU5U9PG\n6i9OYq/T8PjCeKp7z/D3vE9w0jrySPx9OOkclQ5RCKsgBYwY8N0p1bsr9jMuxICfhyNHc2to6+xV\nODIhrgwtHb28vDmTXqOZB2+Mxt6lm9VZ61Ch4sG4e/Bx9FI6RCGshhQwYkCI6yhCXYPJrs+lobuR\nWUmB9Jks7MusUjo0IUY8Y5+JVz/JpLG1h4UzRhMZ5sgbGe/S2dfFXVELiTCMVjpEIayKFDDiHDOC\npmDBwp7yg1wd54edTs2u9ArMZpnMK8RQsVgsrNmWR1FFK5OjfZk7KZDVWWup62pgfshsJvtPUDpE\nIayOFDDiHMk+8bjYOXOg6iganYXJ0X7Ut3STXdKgdGhCjFjbjpzhQHY1Yf6u3HvtWD7K+4TC5hKS\nvOO4YfQ8pcMTwipJASPOoVVrmRowma6+Lo5WpzH7u8m8aTKZV4ihcKKwnk27ijC42PP4wjhSK/dw\nuPo4IS6juCf6dtQq+ZgW4qfIb4b4kamBV6FWqdldfoBRPs6MCXQlq6iBuuYupUMTYkQpr2vnra05\n6LRqHl8YR3FnHluLt2Gwd+eh+OXYaeyUDlEIqyUFjPgRd3s3krzjqOyoprC5mNlJQViA1HQZhRHi\ncmnr7OXlTZn09Jq47/px4NjM2pMfYa+x45GEFbjZuygdohBWTQoY8ZO+Ox8ptfwAE6K8cXbQsTez\nCmOfnI8kxKXqM5l5fUs29S3d3HR1KOFhdryZ+T59ZhP3xdxNoLO/0iEKYfWkgBE/abRbCKOcA8is\nz6G9r41pCf60dxk5mlurdGhC2DSLxcL6r/PJK2tm/Fhv5k0O4I2M92jrbWdhxI3Eeo1TOkQhbIIU\nMOInqVQqZgRdjdliZk/FQWYmBqICdslkXiEuyTfHy9mTUUmwrzP3LYhizckPqeyoZnpgCjPPjnwK\nIX6ZFDDiZ433TcRJ58iByiO4u2iJH+NJUWUrpdVtSocmhE3KLmngo28LcHWy49cL4/my9B9kN+Qy\nziOSRRE3yQGNQlwAKWDEz7LT6JjiP4l2YwfHazOYlRwEwK70coUjE8L2VDV08ManOWjUah6/LY7s\n1jR2le/D38mX+2PvRqPWKB2iEDZFChhxXtMCU1ChYnf5fmLCDHi56TmUU0Nnt1Hp0ISwGR3dRl7e\nlElXTx/LrxtLj76ajwu24qJz5pH4FThoHZQOUQibIwWMOC9PBwPx3jGcaaugtK2MWcmB9PaZ2Z9V\nrXRoQtgEk9nMG59mU9PUxXWTgwkNVfG37A9Qq9T8Kv5ePB08lA5RCJskBYz4RTO/O6W6fD9T4/zR\natTsTK/AbJHzkYT4JR99W8jJ000khnsxb7Ivb2a+R7epm2XjljDaLUTp8ISwWVLAiF8U4T4Gfydf\n0muzMGt6mDTOh5rGTk6VNikdmhBWLTW9gm+PlxPo7cTyBRGszl5DQ3cTN4TNY4JvotLhCWHTpIAR\nv6h/SfUUTBYT+yoPMevs+UiypFqIn3eqtIkPduTj7KDj8dvi2FT8CSWtZ5jom8y1odcoHZ4QNk8K\nGDEoE32TcdDq2VdxiGBfR0J8XUgvqKOxtVvp0ISwOrVNnby+JQuAlbfGcqRpH8drMxjtFsrd4xbJ\ncmkhLgMpYMSg6LX2pPhPpLW3jYy6bGYnB2KxwO4TlUqHJoRV6erp46VNmXR097Fs/lha7Er4x+lv\n8NJ78Ku4e9CptUqHKMSIIAWMGLSBJdUVB5gU7YujvZY9GZX0mcxKhyaEVTCbLby1NYeqhk7mThhF\nYEgvH5z6GAetnkcSVuBi56x0iEKMGENawOTn5zNnzhzWr18PgNFo5Mknn2TRokXce++9tLS0ALB1\n61YWLlzI4sWL+fjjj4cyJHEJfBy9iPYcS3FLKTXdVUyN96elo5e0/DqlQxPCKmxKLSKzqIHYMA9m\np7jzdtYazFh4IHYZfk6+SocnxIgyZAVMZ2cnzz33HCkpKQO3bdy4EYPBwKZNm1iwYAHHjh2js7OT\n1157jffff59169axZs0ampubhyoscYm+O6V6d9kBZibJZF4hvrMvs4ptR87g5+HIvdeP5q2sNbQb\nO7g98haiPCKUDk+IEWfIChg7OztWr16Nj4/PwG27du3ipptuAuD222/nmmuuISMjg7i4OFxcXNDr\n9SQnJ5OWljZUYYlLNM4jAh8HL47VnsDJ2UxMqIG8smbK69qVDk0IxRSUN7N2ey5Oei2PLYzhw4IN\n1HTWMnvUNKYGTlY6PCFGpCErYLRaLXq9/pzbKioq2LNnD8uWLeO3v/0tzc3N1NfX4+Hx/U6UHh4e\n1NVJS8JaqVVqpgdNoc/cx4HKIz84H0lGYcSVqb6li1c/ycJshodvjmF33dfkNhUQ5xXNreHXKx2e\nECPWsE6Ht1gshIWF8dhjj/H666/z1ltvER0d/aPH/BKDwRGtdugOPvP2dhmya48EN7jN5POS7eyv\nPsxL1y7go28LOJRTzcMLE3DU64b0uUdCbj7fW0z+mSYWzY4gxN9V6XAui5GQl4vR1dPHf685Rlun\nkYdvi6fdrYh9Jw4T6h7Ev05/EL1O/8sXGWJXam5sgeTm0gxrAePl5cXEiRMBmDp1Kq+88gozZ86k\nvr5+4DG1tbUkJp5/h8qmps4hi9Hb24W6urYhu/5IcZVvMnsqDrI7/yjT4v3ZsreEL3YXDozIDIWR\nkJu9mZW891UuALvTypkS58ctU0fj6ab8F93FGgl5uRhmi4XXPsnidFUrs5IC0RtqePvEZtzsXHkg\n+h7amo20oeyhp1dqbmyB5GZwzlfkDesy6unTp7N3714AcnJyCAsLIyEhgaysLFpbW+no6CAtLY0J\nEyYMZ1jiIsw4ez5Savl+picEoFGr2JleMagRtCtVzulG1m7Lw0mvZfl1UQR6O7E/q5rfv32IDTsL\naO+SE75tyad7i0kvqCcq2J1pk5147+Tf0am1PBy/HIPeXenwhBjxhmwEJjs7m1WrVlFRUYFWq2X7\n9u389a9/5fnnn2fTpk04OjqyatUq9Ho9Tz75JPfffz8qlYqVK1fi4iLDatbOz8mXKEMEuU0FtNPI\n+LHeHDlVS0F5C5Gj5MP7n5XXtfP6lixUKnh8YTyRo9yZGufPwZxqPt1bzPYjZezJqGLB5GDmTBiF\nvW7oWqTi0h3KqeaLA6X4uDuw9PoQXst+E6PJyANxywh2HbpRSCHE91QWG/yTeSiH3WRYb/Ay63J4\nK2sNVwdcxQSna/jTB2lMGufDwzfHDsnz2Wpumtp6eH7dMRpbe/jVTdFMjvY7535jn4ldaRV8cbCU\n9i4jbs523Dw1jGnx/mjU1r/XpK3m5WIVV7bypw/S0GlV/Ovd8WwoXcuZtgpuGbOAuSEzlQ7vHFda\nbmyJ5GZwrKaFJEaWWK9xeOoNHK1OI9BXR6C3E8fz6mhp71E6NKvR3dvHy5syaWztYeGM0T8qXgB0\nWg3zJgXzp4dSuGFKKF09fazdlsfT7xzhWG6ttOWsSFNbD698konJbOZXN8bwdfVWzrRVMMV/InOC\nZygdnhBXFClgxEVTq9RMC0yh12zkUPVxZicFYjJb2JMh5yMBmMxm3vwsh9KaNqYn+LNgcggAWfUn\n+apkB2295+6d46jXctv00fzpoRRmJgVS19TF659m8z9rj5Nb2qTESxA/0GM08fLmTFrae7l9Vjgl\nHCGjPodI9zHcPvZWOaBRiGEmBYy4JFMCJqFT69hTfoBJ0T7Y22lIPVGJyXxln49ksVj4cEfBwLby\nS+eNRaVSkdtYwNtZa/myZAfPHPwTnxdvp9PYdc7Pujvbc8/8sTz/4FVMjPKhpKqVP/89nf/deIIz\nNTLkrASLxcK7X56itLqNqfH+OAVWseNMKj6OXjwYtwytHNAoxLDTPPvss88qHcSF6uzsHbJrOznZ\nD+n1Rxo7jY6GribymgsZYwhGZ3LlVGkTIb4u+Hs6XdbnsqXcbD9SxpcHSwnydua3SxKw12moaK/i\ntRPvYLFYmBMyk6qOGk425LGv8hAms5lRLgHnfBE6O+iYGOVD/BhP6pq7OHm6id0nKqlp6iTY1wWn\nId5zZ7BsKS8X6/MDp9mZVkFEkBtzZjjw/qkPcdQ68Jukh3DXuykd3s+6EnJjqyQ3g+PkZP+z90kB\n80/kTXXhPPTu7Ks8RIexkwWRU9iVXkFHl5Epsf6X9XlsJTfHcmt5f1su7s52/O6uJFyd7GjuaeGl\n9LdoN3Zwb8ydzB41jemBKTho9ZS0lpLTkMv+ysOoVCqCnAPQqL9fhWRwsWdKrB/hQW5U1HWQc7qJ\nXWkVtHcaCfF3UXzFkq3k5WIdy61l3df5eLrquffmIN45+R4mi5lHEu4j2DVQ6fDOa6TnxpZJbgZH\nCpgLIG+qC+dq70JeYyH5zUXMCptIeVUvp840c1W0L84Ol2+UwBZyU1jRwiufZKHTqvnXO5Lw83Si\nq6+bV06spq6rnpvHXMe0wP4DTjVqDWPcQ5kaOBk7tY6i5tNkN5ziYNVRtCotgS4BaFT9XV6VSoWP\nwZHpiQH4eTpSWt1Gdkkju9IrMJsshPi5oNUo0xG2hbxcrNLqNl7enIlWo2bl4rGsL15LS28ry8Yt\nId47RunwftFIzo2tk9wMjhQwF0DeVBfHXmtPem0mqFQk+UVzLK8OjVpF7GjPy/Yc1p6bmqZO/vr3\nE/QazTx2WxyRo9wxmU28nbWW4pZSpgZcxc1jrvvRZE+dWkuEYTTTAq9Co1JT0FJCVv1JDlcdx15j\nR6CzP+ofFDJB6Hwt7AAAIABJREFU3s7MSgrE1cmOwooWMosa2JtRiU6rIdjXGbV6eCeTWnteLlZL\new9/+Sidjq4+fnVzFN80baG8vZL5IbO5Jni60uENykjNzUgguRkcKWAugLypLo6PgxcHq45R2nqG\nJXFzOJBVy+mqNq6ZEHTZRgasOTftXUb+8mE6TW09LLt2LFdF+2KxWPh73mbS67KI8Yzi3ug7UJ9n\nXxedRsdYj3CuDpiExWKhoLmIjPocjlan46h1IMDZb6D4UatVjA5wZWZiIDqtmryyZtIL6jl0shpX\nRzsCvJyGbVWMNeflYhn7TPzfxgwq6zu5bXoYZ+wPkFV/kiTvOJtacTQSczNSSG4GRwqYCyBvqouj\nVqkxmo2cbMzD4OCGr30AOacb8XZ3IMTv8uysbK25MfaZePHjDM7UtrNgcsjAcuntpTv5tmwPo1wC\neST+Puw0g2un2WvsGOcZyWT/CZgsJvKbijhRl0VabSbOOif8nHwGvkB1WjVRwQamxwdgNJk5VdrE\n0dxaThTW4+3ugI/BYche93esNS8Xy2Kx8O5XuWQWNzA5xhdDeDk7y/YS4jqKh+KX29SKo5GWm5FE\ncjM4UsBcAHlTXTxfRx9Sy/ZR11XPotjZfHu8gobWbmYkBlyWv1itMTdmi4V3vjhJVnEjk8b5sHR+\n/3LpI9VpbMz/DIO9O79JeghnuwtfkaXX6onxjGKS33h6TL3kNxeSVptJZn0Obnau+Dh6D/x/tbfT\nED/Gk8kxfrR3Gsk53cTBnGryy5oJ8HLC4PLzHwKXyhrzcim2HT7D9qNlhPm7Mm0abCzYgsHenV8n\nPYSTbugLwstppOVmJJHcDI4UMBdA3lQXz15jR21XPflNRcT6htPVZk/umWbixnji4XLppy1bY242\n7y4m9UQl4UFuPHZbHFqNmvymQt7JXo9ea8+vk36Ft+OlzQNy1DkQ7x3NBN9Euvq6yGss5FjtCXIa\n8jDo3fFy8BwoZJz0OsaP9SEx3IuGlm5Onm5iT0YllfUdBPs4X9ZJ1d+xxrxcrBMF9bz/j1wMLvbc\ncaM3a/LWo1NrL0selTCScjPSSG4GRwqYCyBvqktjsHdjf+URuvq6mBE6kYPZ1ZhMFpIjvS/52taW\nm9QTFWxKLcLX4MC/3pmEg72WyvZqXs145+wy2xWEugVftudz0jmS6B1Lkk88bcYOcpsKOFqTTl5T\nIV4OHng6eAw81t3ZnpRYPyKD3Khq6F96nZpeQUt7L6F+LujtLl8bxNryciEaWrrJLK4nNb2CT3YX\ns+N4GTqtmgcXhvFhyVq6+3p4MO4ewt3DlA71othybkY6yc3gnK+AsZ1mrrAJIa6jCHUNJrs+l9vC\nb8TXw5Ejp2q5fXY4Lo52Sod32WQVN7B+ez7ODjqeWJKAs4OOlp5WXs94l66+bu6NvoNIQ/iQPLe/\nky8PxC6lrK2SL0u2k1V/ihfT3yLKEMENo+cR5hYy8NhxoR48HWLgeF4dm3cXsSu9gv3ZVcybGMx1\nVwXjYH/lfASYzRbKatsprGihoLyZgvIWmtq+P7dLp1UTEejGvBR/Pq/8iLbedhZH3Eys1zgFoxZC\n/Bw5jfqfyAmhl+5IdRprTn7ENaOm49KcwN+/LWDxrDFcd1XIL//weVhLbs7UtPHCB2mYTBZ+d1cS\n4YFudPf18GL6m5S1VXBD2HyuC7tm2OI53XqGL4q/5lRjPgCxnlHcMHo+o1zO3WStz2RmX2YVn+0r\noaWjF2cHHTdMCWVWUv9KpotlLXn5Z109fRRXtVJQ1kxhRQtFla309JoG7ndx1BER5E54oBsRQW6E\n+LmgUll4K2sNOQ25TA+cwu1jb1HwFVw6a82NkNwM1vlOo75y/vwSwybZJ55PCr/gQNVRnh4/i827\n1exKq2D+pGDUNrL89Oc0tnbz0qZMenpNPHpLLOGBbpjMJt7N+YCys6cSXxs6e1hjCnUN5rHEByho\nKubz4u1kN+SS3ZBLoncc14fNJcC5/wRsrUbNzKRAUmL82HGsjH8cLuWjbwvYcbSMW6eHMTnab9j3\nkLmcGlu7KShvobC8hYKKZspq2/nhn2f+no5EBLkRHuhORJAbPgaHcyaXN3Q1sr10JzkNuUR7jGVR\nxI0KvAohxGBJASMuO61ay9SAq/jH6W/Jac5mcowvezKqyC5uJH6M7U2E/E5XTx8vfpxJU1sPS2aF\nMyHKB4vFwsaCz8hpyGWcRyR3jL1NsT1CIgyj+W3yw+Q2FfB58XZO1GWRUZfNBN9EFoTNwcexfx6S\nvZ2GG6aEMjMpkC8OnGZnWjnvfHGKbYfPsGjmGOJGe1r9Pidms4Xyuvb+gqWihcLyZhpav28HaTVq\nwgPdCA9yIyLQnfAgtx9NYG7paSW/qYj8pkLymopo6G4E+lt098Xedc5xDkII6yMFjBgSUwMns710\nF6nl+7k78QH2ZFSxM63cZguYPpOZNz7NpryunVlJgcyfNAqAb87sZl/FIQKd/bk/dqniX3oqlYpx\nHpFEGSLIbjjFF8Vfc7QmneO1GVzlN57rQq8ZmOzr7KDjjmsimDMhiM/2lnAgu5oXP84kcpQ7i2eO\nYUyg9RxS2N3bR3Fl69nRlRaKKlro/kE7yNlBR1KE10DBEuLn8qO2WIexk4KmIvLOFi3VnbUD9zlo\nHUjwiiHCMIZJfsk4aG1rubQQVyIpYMSQcLd3I8k7juO1GRj1dYwJcCWrqIG65i683W3ry8FisbD+\n6zyyS/pHkO6aG4FKpeJYzQk+LfoKd3s3Hk24DwftpS8Vv1xUKhVxXtHEeEZxoi6bL0t2cLDqKEeq\n07g6YBLzQ2fjbt9foHi5OXD/DdHMnxTM5t1FZBQ18Py64yRHerNwxujLfqr4YDS19QxMtC0sb6Gs\nth3zD/pBfh6OZ4uV/lEWPw/HH40adfd1U9hcMjDKUt5ehYX+a9hp7Ij2GEukYQxjDeEEuQQMHNcg\nhLANUsCIITMj6GqO12aQWn6AWclzKKpsJfVEBYtnDs3qnKHy1aFS9mRUEezrzMM3x6BRqylsLmHd\nyQ3oNfY8mnDfQDFgbdQqNck+8SR6x3Ks5gRfluxgT8VBDlYdZVpgCvNCZuFi5wxAkI8zv1mcQH5Z\nMx+nFpKWX0d6QR3T4v25eeroIdsMz2y2UFHfQUF5c/8IS3kLDa3dA/drNf3HJnxXsIwJcsP1J1a0\n9ZqMlLSUDrSEStvKMFvM/ddQaQh3D2OsIZxIQzghrkE2taOuEOLH5DdYDJnRbiGMcg4gsz6Hmycu\nwNlBx96MKm6ZGoZOaxvzCw6frGHz7mI8XO35zaIE9HZaqjtqeSvzfcxYeCBuGYHO/kqH+YvUKjWT\n/JIZ75PA4erjfFXyDTvL9rKv8jAzg65mTvAMnHSOAESOcueppeNJL6hn8+4i9mRUcTCnhjkTglgw\nOQQn/aVthtfTa6K4sr8VVFjeQlFlC10957aDEsPPtoOC3Aj1c/nJ94vJbKK0rYy8xv4RluLWUvrM\nfQOvN8RlFGMNY4g0hBPmFjLooxyEELZBllH/E1nadnkdqDzKB7kfMz9kNj1nwvnH4TM8eEM0KbF+\nF3yt4c5Nflkzf/0oHZ1Wze/vHk+QjzOtvW389dhrNHQ3sjRqMSkBE4ctnsvJaO7jYOURtp3+lpbe\nNvQaPdcET2PWqGnntMJMZjP7s6r5bF8JTW09OOm1LEgJ4ZrkIOx0/UXFL+Wlqa1nYO+VwvIWztSc\n2w7yNTicLVb6Vwf9VDsIwGwxU95WSV5TIflNRRS2lNBr6t8ITIWKIGd/Ig3hRBrGEO4eht6KWnpK\nkc8z6yW5GZzzLaOWAuafyJvq8uo1GXn6wPOoUPGbmN/yn28fY3SgK/+xbMIFX2s4c1PV0MEf1x2n\nu9fEE0sSiAn1oMfUy0tpb1HaVsZ1oXO4YfS8YYllKPWajOytOMjXpbtoN3bgpHVkTsgMZgRdjb3m\n+zZNr9HEt2nlfHmglM6ePgwu9twyNYwpcX74+boN5MVssVBZ13F2dKV/Dkt9y/ftII1aRaifS//+\nK0FuhAe64er00xscWiwWqjpqBuaw5DcX09XXNXC/n6MPkYZwxhrGEG4YjbNu+OfqWDv5PLNekpvB\nkQLmAsib6vL7tPArdpxJZdm4JRzapyOzqIFnlk+84FOqhys3rZ29PL/2GHXN3axYEMW0+ADMFjNv\nZ60lq/4kV/mNZ9m4JVa/1PhCdPf1kFq+n2/O7KarrwsXnTPzQ2czNeAqdD9ovXR0G/nqYCnfHC/H\n2GfG39ORJXMiOVPVSkF5M0UVrXT19A083kmv/X45c5A7oX4uAyM3/8xisVDf1Xh2Dksh+c1FtPW2\nD9zvqfcYaAlFGsbgZu86dP9DRgj5PLNekpvBkQLmAsib6vJr6GrimYN/YpRLANca7uKlTVlMT/Bn\n+XUXtkX7cOSm12jiL39Pp6iylRunhHLr9NFYLBY+LtjK7vL9RBrCWZlw34idANpp7GJn2V52le2l\n29SDu70b14bOJsV/4jmvubG1m637S9ibWXXOZnE+BoeBlUHhQe74ezqed/PCpu7msyMsReQ1FdLU\n0zxwn5udy9lipb9g8frBWU9icOTzzHpJbgZHduIVivJ0MBDvFU1GfQ7O4R14uek5lFPDklnhOF7i\nhNDLyWyxsPqLkxRVtpIS48st0/oP8NtVtpfd5fvxd/LlwdhlI7Z4gf6Tr28YPY+ZQVfzzZndpJbv\n56O8LewoTeW6sLlM8k1Co9bg4apn+XXjmDcxmPzKVlzsNYQHueP2M+2g77T1tlPQXHx2HkshtZ31\nA/c5aR1J9I4bGGXxdfQeUaNcQojLS0Zg/olUxUMjr7GQl0+8zQTfRPzarubj1CLuvCaCuRNHDfoa\nQ52bDTsL2H6kjLGj3Pl/tyei06pJr83ib9nrcbVz5l8mPIaH3jBkz2+NWnra2FG6i70VB+mzmPBx\n9OL6sHkk+8QP7Jtyvrx0GrsobC4mv7l/lKWivWrgPr3GnnD30QMFS4Czn+zFcpnJ55n1ktwMjozA\nCMVFGsbg7+RLem0W/540ny171exMr2DOhCCr+Cv72+PlbD9Shr+nI48tjEOnVVPccpo1J/+OnUbH\nIwn3XXHFC4CbvQuLIm/imuDpbDv9LQeqjvJezodsP72T60fPI8Er5pzH95h6KW4+PbBS6Exb+cDm\ncTq1lihDBJFnC5Zgl0DFdy4WQtguKWDEsFCpVMwImsJHeVtIb0pjYlQgB3OqOVXaRHSosnMbThTW\n8+E3+bg66nhicQJOeh21nXW8mfk+JouZB+Pu+dHJzlcag96dO6MWMjdkJv8o+ZbD1cdZnbWWYJdA\nboqeS3FNOXlNRZxuPYPJ0r+ni1qlZrRbyMBKoVC3EHQjuP0mhBheQ/ppkp+fz6OPPsry5ctZunTp\nwO179+7lgQceIC8vD4CtW7eyZs0a1Go1S5YsYfHixUMZllDIRN9kPiv6B/sqDrEiaSUHc6rZlVah\naAFzurqVNz/LRqdR8+tFCXi7O9De28HrGe/SYezkrrELifGMUiw+a+Pl4Mmy6CXMC5nJlyU7OF6b\nwauH3wf692IZ5RJ4drfbMYx2C0WvHZrde4UQYsgKmM7OTp577jlSUlLOub2np4e3334bb2/vgce9\n9tprbNq0CZ1Ox6JFi5g7dy7u7u5DFZpQiF5rz2T/Cewq20eLtpQQXxfSC+ppbO3Gw3X4Nx2rb+ni\npY8zMRrNrLwtjtEBrvSajLyZ+T51XQ3MC5nF1YFXDXtctsDXyYf7Yu9mfvtsijoLcVN5EOEehuPZ\n3XyFEGKoDdmMOTs7O1avXo2Pj885t7/55pvcdddd2Nn1r1bIyMggLi4OFxcX9Ho9ycnJpKWlDVVY\nQmHTA6egQsWeigPMSg7EbLGwJ6Ny2OPo7Dby0seZtHT0csc1ESRHemO2mFlz8iNKWkuZ4JvIjaPn\nD3tctibQ2Z+FMQtI8I6R4kUIMayGbARGq9Wi1Z57+ZKSEnJzc/nNb37DX/7yFwDq6+vx8Pi+heDh\n4UFdXd15r20wOKIdwrN0zjfrWVwab1xILI0hvSqb5bPscNqlZW9mFctv6p84+4s/fxlyY+wz8+Lq\ng1TUd3DTtNHctSAagLXpmzhRl0W0dwS/nXbfORu4ifOT3xnrJbmxXpKbSzOsM+peeOEFnn766fM+\nZjCrupuaOi9XSD8iS9uGXorPVaRXZfPVqZ1MiU1ix7Eyvj5QzKRxvuf9ucuRG4vFwrtfnSKzsJ6k\nCC9uSgmhrq6N1LL9fFHwLX6OPqyIupvmxm6g+xevJ+R3xppJbqyX5GZwzlfkDdumCzU1NRQXF/Mv\n//IvLFmyhNraWpYuXYqPjw/19d9vZlVbW/ujtpMYWcZ5RODj4MWx2hNMjHMDYFdaxbA89+cHTrM/\nq5owfxd+dWMMarWKjLocNhVsxcXOmUcT7pNWiBBC2IBhK2B8fX355ptv2LhxIxs3bsTHx4f169eT\nkJBAVlYWra2tdHR0kJaWxoQJF37Qn7AdapWa6UFT6DP3UdyVQ3SogbyyZirq2n/5hy/BgewqPt1b\ngpebnl8vSsDeTsPp1jO8l/MhOrWWR+JX4Cnb1QshhE0YsgImOzubZcuWsWXLFtauXcuyZctobm7+\n0eP0ej1PPvkk999/PytWrGDlypW4uEhfcKSb7D8eO40deyoOMiMxAIBd6UM3CnOqtIn3vsrF0V7L\nE4sTcHOyo76rgTcy3qPP3Md9sXcT4jr4XYGFEEIoa8jmwMTGxrJu3bqfvX/nzp0D/37ttddy7bXX\nDlUowgo5aB2Y7DeePRUH0RhqMLjYcyC7moUzxuBgf3nflhX1Hbz6SRYAj90WR4CXEx3GTl7PeJd2\nYwe3R95CnFf0ZX1OIYQQQ0sOHhGKmRE0BYA9FQeYmRhAd6+JQydrLutztLT38OLGDLp6+rhvwTii\nQgwYTUbeynyfms465gTPYPrZOIQQQtgOKWCEYvycfIkyRFDQXExkhBqNWsWutPJBrUQbjJ5eEy9t\nyqShtZtbpoWREuuH2WJm3amNFLWcJtknnpvHXHdZnksIIcTwkgJGKOq70Y+0xmOMH+tNeV0HBeUt\nl3xds9nCW1tzOF3dxtVxftw4JRSArUXbOF6bwWi3UO4Zd7ucfiyEEDZKPr2FouK8xuGpN3CkOo2U\n+P4VQDvTyi/5uh99W8CJwnrGhRi499ooVCoVeysOsuNMKj4OXjwUf69sVCeEEDZMChihKLVKzbTA\nFHrNRurUBQR6OXE8r46W9p6LvuaOo2V8c7ycQC8nVt4ah1ajJrv+FBvyPsVZ58SjCffjrHO6jK9C\nCCHEcJMCRihuSsAkdGodeysOMjPJH5PZwp7Mqou6Vlp+HR99W4Cbkx1PLE7AUa/lTGs5f8v5AK1a\nw8Pxy/F29LzMr0AIIcRwkwJGKM5J58hE3yTquxtxD2jF3k7D7hMVmMzmC7pOcWUrb2/NwU6n4YnF\nCXi66WnoauKNzPcwmowsj7mLMLeQIXoVQgghhpMUMMIqfLek+mDNIabE+NHY2kNmYcOgf76uuYuX\nN2VgNJl56OYYQvxc6DR28nrG32jtbWNhxI0kescOVfhCCCGGmRQwwioEuQQwxi2MU435xMfYA7Bz\nkDvztncZ+b+NGbR2Grl7biSJ4V4YzX28nbWW6s5aZo2ayqxRU4cyfCGEEMNMChhhNWaOuhqAvM4M\nIke5k1PSSHXj+U8eN/aZee2TLKobO7l2UjCzk4OwWCx8cGoTBc3FJHjHclv4DcMRvhBCiGEkBYyw\nGgleMbjbu3G46hjTEr0BSD3PKIzFYuG9f5wir6yZ8WO9WTRrDABflHzN0Zo0Ql2DWR59h+z1IoQQ\nI5B8sguroVFrmBY4mW5TDz0upbg62bEvs4oeo+knH//p3hIO5dQwJsCVB2+IRq1Ssb/yMNtOf4uX\ngycPxy/HTmM3zK9CCCHEcJACRliVqwOuQqvSsK/yINPi/ens6ePIT5yPtDejks8PnMbH3YHHF8Vj\np9NwsiGPj/K24KR15NGE+3Cxc1bgFQghhBgOUsAIq+Ji50yybwI1nXWMGt2FSgU70yrOOR8pp6SR\ntdvzcNJreWJJAq6OdpS1VfJO9jrUKjUPxS/H19FbwVchhBBiqEkBI6zOzKD+ybxpTUdJDPeitKaN\nkqo2AMpr23n90yxUKnh8YTx+Ho40dTfzRsa79Jh6uTf6Dsa4hyoYvRBCiOEgBYywOiGuowh1DSa7\nPpfxcf1toF1p5TS0dPHipgy6ekzcf300kaPc6err4vWMd2npbeXW8OtJ9olXOHohhBDDQQoYYZVm\nBE3BgoUqVQ6+Ho4cPlXLs6sP0djaw8IZo7kq2heT2cQ7Weup7KhmeuAUrhk1XemwhRBCDBMpYIRV\nSvKJx0XnzMGqY0xL9KHPZOZ0VSvTEwJYMDkEi8XCh7mbyW0qIM5rHIsjb0KlUikdthBCiGEiBYyw\nSjq1lqmBV9HV14XetxoXRx0To31ZNj8SlUrFP05/w6HqYwS7BLEi5m7Z60UIIa4w8qkvrNbUwMmo\nVWoOVh/iL4+k8PSKq9Co1RyqOsaXJTvw1Bt4JGEF9rLXixBCXHGkgBFWy93ejSTvOCo7qiltL0Wt\nVpHbWMAHuZtw0DrwaMJ9uNq5KB2mEEIIBUgBI6za9LOnVO8uP8CZ5gpWZ61DjYqH4u7Fz8lX4eiE\nEEIoRat0AEKczxi3UIKcA8ioz6F0Txndpm5WRN9JhGG00qEJIYRQkIzACKumUqmYEXQ1ZouZxq5m\nbh59HRP8kpQOSwghhMJkBEZYvQm+ieyrPESc/1jmBsxUOhwhhBBWQAoYYfXsNDp+N+FxvL1dqKtr\nUzocIYQQVkBaSEIIIYSwOUNawOTn5zNnzhzWr18PQFVVFcuXL2fp0qUsX76curo6ALZu3crChQtZ\nvHgxH3/88VCGJIQQQogRYMgKmM7OTp577jlSUlIGbnvxxRdZsmQJ69evZ+7cubz33nt0dnby2muv\n8f7777Nu3TrWrFlDc3PzUIUlhBBCiBFgyAoYOzs7Vq9ejY+Pz8BtzzzzDPPnzwfAYDDQ3NxMRkYG\ncXFxuLi4oNfrSU5OJi0tbajCEkIIIcQIMGQFjFarRa/Xn3Obo6MjGo0Gk8nEhx9+yI033kh9fT0e\nHh4Dj/Hw8BhoLQkhhBBC/JRhX4VkMpn43e9+x+TJk0lJSeHzzz8/536LxfKL1zAYHNFqNUMVIt7e\nsj29tZLcWCfJi/WS3Fgvyc2lGfYC5ve//z0hISE89thjAPj4+FBfXz9wf21tLYmJiee9RlNT55DF\nJ0t1rZfkxjpJXqyX5MZ6SW4G53xF3rAuo966dSs6nY5f//rXA7clJCSQlZVFa2srHR0dpKWlMWHC\nhOEMSwghhBA2ZshGYLKzs1m1ahUVFRVotVq2b99OQ0MD9vb2LFu2DIAxY8bw7LPP8uSTT3L//fej\nUqlYuXIlLi4yrCaEEEKIn6eyDGbSiZUZymE3GdazXpIb6yR5sV6SG+sluRkcq2khCSGEEEJcDlLA\nCCGEEMLmSAEjhBBCCJtjk3NghBBCCHFlkxEYIYQQQtgcKWCEEEIIYXOkgBFCCCGEzZECRgghhBA2\nRwoYIYQQQtgcKWCEEEIIYXOkgPmBP/7xj9x+++3ccccdZGZmKh2O+IE///nP3H777SxcuJCvv/5a\n6XDED3R3dzNnzhw++eQTpUMRP7B161ZuuukmbrvtNlJTU5UORwAdHR089thjLFu2jDvuuIO9e/cq\nHZJNG7LDHG3NkSNHKC0tZcOGDRQVFfHUU0+xYcMGpcMSwKFDhygoKGDDhg00NTVx6623Mm/ePKXD\nEme98cYbuLm5KR2G+IGmpiZee+01Nm/eTGdnJ6+88gozZ85UOqwr3pYtWwgLC+PJJ5+kpqaGe++9\nl23btikdls2SAuasgwcPMmfOHKD/lOyWlhba29txdnZWODIxceJE4uPjAXB1daWrqwuTyYRGo1E4\nMlFUVERhYaF8OVqZgwcPkpKSgrOzM87Ozjz33HNKhyQAg8FAXl4eAK2trRgMBoUjsm3SQjqrvr7+\nnDeTh4cHdXV1CkYkvqPRaHB0dARg06ZNTJ8+XYoXK7Fq1Sr+/d//XekwxD8pLy+nu7ubhx9+mLvu\nuouDBw8qHZIArr/+eiorK5k7dy5Lly7l3/7t35QOyabJCMzPkBMWrM8333zDpk2bePfdd5UORQCf\nfvopiYmJjBo1SulQxE9obm7m1VdfpbKyknvuuYddu3ahUqmUDuuK9tlnnxEQEMDf/vY3cnNzeeqp\np2Tu2CWQAuYsHx8f6uvrB/67trYWb29vBSMSP7R3717efPNN3nnnHVxcXJQORwCpqamUlZWRmppK\ndXU1dnZ2+Pn5MWXKFKVDu+J5enqSlJSEVqslODgYJycnGhsb8fT0VDq0K1paWhpTp04FICoqitra\nWmmHXwJpIZ119dVXs337dgBycnLw8fGR+S9Woq2tjT//+c+89dZbuLu7Kx2OOOvFF19k8+bNbNy4\nkcWLF/Poo49K8WIlpk6dyqFDhzCbzTQ1NdHZ2SnzLaxASEgIGRkZAFRUVODk5CTFyyWQEZizkpOT\niYmJ4Y477kClUvHMM88oHZI466uvvqKpqYknnnhi4LZVq1YREBCgYFRCWC9fX1/mz5/PkiVLAHj6\n6adRq+XvVaXdfvvtPPXUUyxdupS+vj6effZZpUOyaSqLTPYQQgghhI2RklwIIYQQNkcKGCGEEELY\nHClghBBCCGFzpIARQgghhM2RAkYIIYQQNkcKGCHEkCovLyc2NpZly5YNnML75JNP0traOuhrLFu2\nDJPJNOjH33nnnRw+fPhiwhVC2AgpYIQQQ87Dw4N169axbt06PvroI3x8fHjjjTcG/fPr1q2TDb+E\nEOeQjeyEEMNu4sSJbNiwgdzcXFatWkVfXx9Go5H/+q//Ijo6mmXLlhEVFcWpU6dYs2YN0dHR5OTk\n0Nvby397qbALAAACZUlEQVT+539SXV1NX18fN998M3fddRdd/397d+zSVhSGYfwJNBIE/QsuComb\nikOchODg5iKoEFAyurgJCgGRILg4uIgubqKI4iiiIAgiuDiISBaXjEEHHRSbSMztUCy1Qpc22lue\n33YP5wzfcnm598D79StTU1Pc39/T3t5OtVoF4ObmhunpaQAqlQrZbJbR0dHPHF3SX2KAkfShXl5e\nODo6Ip1OMzMzw+rqKm1tbe/K7Zqbm9nc3HxzdmNjg9bWVpaWlqhUKgwODpLJZDg7OyORSLCzs8Pt\n7S0DAwMAHBwckEwmmZ+fp1qtsru7++HzSmoMA4ykhru7uyOXywFQr9fp7e1lZGSE5eVlZmdnf+x7\nfHykXq8D3+s9fnV5ecnw8DAAiUSCrq4uisUi19fXpNNp4HsxazKZBCCTybC1tUU+n6e/v59sNtvQ\nOSV9HAOMpIZ7vQPzs4eHB+Lx+Lv1V/F4/N1aLBZ78xyGIbFYjDAM33T9vIagVCrF/v4+5+fnHB4e\nsr6+zvb29p+OI+kf4CVeSZ+ipaWFIAg4OTkBoFQqsbKy8tszPT09nJ6eAvD09ESxWKSzs5NUKsXF\nxQUA5XKZUqkEwN7eHldXV/T19VEoFCiXy9RqtQZOJemj+AVG0qdZXFxkYWGBtbU1arUa+Xz+t/tz\nuRxzc3OMj4/z/PzM5OQkQRAwNDTE8fExY2NjBEFAd3c3AB0dHRQKBZqamgjDkImJCb588bUn/Q9s\no5YkSZHjLyRJkhQ5BhhJkhQ5BhhJkhQ5BhhJkhQ5BhhJkhQ5BhhJkhQ5BhhJkhQ5BhhJkhQ53wCg\n13ivQrFdCwAAAABJRU5ErkJggg==\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": "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": 635
+        },
+        "outputId": "beccc16d-ab2f-454c-cc37-ed4b969c61ff"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.70\n",
+            "  period 01 : 143.39\n",
+            "  period 02 : 126.97\n",
+            "  period 03 : 115.83\n",
+            "  period 04 : 108.00\n",
+            "  period 05 : 102.08\n",
+            "  period 06 : 97.50\n",
+            "  period 07 : 93.89\n",
+            "  period 08 : 90.95\n",
+            "  period 09 : 88.49\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Zgxkzjm2P8c2uE+QXlVu7JBEREZujAGNjAlz8GBIyAByLqGxzkpXbtcSAiIjI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\nRDmHBYaIiIhyDgsMERER5Zz/B/tGceKIh2YSAAAAAElFTkSuQmCC\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": "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": 635
+        },
+        "outputId": "7aa7c48c-997b-4877-86ca-2bd10e7e6e0e"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.49\n",
+            "  period 01 : 135.25\n",
+            "  period 02 : 118.26\n",
+            "  period 03 : 106.84\n",
+            "  period 04 : 98.87\n",
+            "  period 05 : 93.10\n",
+            "  period 06 : 88.56\n",
+            "  period 07 : 84.91\n",
+            "  period 08 : 82.04\n",
+            "  period 09 : 79.62\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Qybcd5c4ZmDwyWbxRozAiIiKXogBjA0a1OjMK49bqODFH04hLybNyRSIiIrZN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\n/H8SoLjTuw68mAAAAABJRU5ErkJggg==\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..7cdad9a
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1416 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/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": 1209
+        },
+        "outputId": "04b9a943-2163-46fe-fc1a-cf47c070b95e"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2650.5           541.1   \n",
+              "std         2.1        2.0                12.6       2174.7           424.2   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1467.0           298.0   \n",
+              "50%        34.2     -118.5                29.0       2143.0           435.0   \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       1434.2       503.1            3.9               2.0  \n",
+              "std        1174.0       387.8            1.9               1.1  \n",
+              "min           6.0         2.0            0.5               0.0  \n",
+              "25%         790.0       283.0            2.6               1.5  \n",
+              "50%        1168.0       410.0            3.6               1.9  \n",
+              "75%        1722.2       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>2650.5</td>\n",
+              "      <td>541.1</td>\n",
+              "      <td>1434.2</td>\n",
+              "      <td>503.1</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2174.7</td>\n",
+              "      <td>424.2</td>\n",
+              "      <td>1174.0</td>\n",
+              "      <td>387.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>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>1467.0</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>790.0</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>2143.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1168.0</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>3155.0</td>\n",
+              "      <td>649.0</td>\n",
+              "      <td>1722.2</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     -119.6                28.4       2627.2           535.5   \n",
+              "std         2.1        2.0                12.5       2192.5           414.9   \n",
+              "min        32.6     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1447.0           293.0   \n",
+              "50%        34.2     -118.5                28.0       2102.0           430.5   \n",
+              "75%        37.7     -118.0                37.0       3137.0           647.0   \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       1418.5       496.6            3.8               2.0  \n",
+              "std        1082.5       376.4            1.8               1.4  \n",
+              "min           3.0         1.0            0.5               0.1  \n",
+              "25%         787.0       278.0            2.5               1.5  \n",
+              "50%        1164.0       405.5            3.5               1.9  \n",
+              "75%        1714.2       603.0            4.7               2.3  \n",
+              "max       13251.0      4339.0           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>28.4</td>\n",
+              "      <td>2627.2</td>\n",
+              "      <td>535.5</td>\n",
+              "      <td>1418.5</td>\n",
+              "      <td>496.6</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2192.5</td>\n",
+              "      <td>414.9</td>\n",
+              "      <td>1082.5</td>\n",
+              "      <td>376.4</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1447.0</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>787.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2102.0</td>\n",
+              "      <td>430.5</td>\n",
+              "      <td>1164.0</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>3137.0</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1714.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30401.0</td>\n",
+              "      <td>4957.0</td>\n",
+              "      <td>13251.0</td>\n",
+              "      <td>4339.0</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                207.7\n",
+              "std                 116.3\n",
+              "min                  15.0\n",
+              "25%                 119.6\n",
+              "50%                 180.4\n",
+              "75%                 265.6\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>207.7</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>119.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.6</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                206.2\n",
+              "std                 115.2\n",
+              "min                  22.5\n",
+              "25%                 119.0\n",
+              "50%                 180.2\n",
+              "75%                 263.8\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>22.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>263.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "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": "69147597-8137-4c66-af1e-0864594b1d6c"
+      },
+      "cell_type": "code",
+      "source": [
+        "correlation_dataframe = training_examples.copy()\n",
+        "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n",
+        "\n",
+        "correlation_dataframe.corr()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</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>0.3</td>\n",
+              "      <td>0.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.3</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               0.1        -0.1        -0.0            0.3   \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                   0.1     0.0  \n",
+              "population                      -0.1    -0.0  \n",
+              "households                      -0.0     0.1  \n",
+              "median_income                    0.3     0.7  \n",
+              "rooms_per_person                 1.0     0.2  \n",
+              "target                           0.2     1.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RQpktkNpia2P",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n",
+        "\n",
+        "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n",
+        "\n",
+        "Use this information to try removing features.  You can also try developing additional synthetic features, such as ratios of two raw features.\n",
+        "\n",
+        "For convenience, we've included the training code from the previous exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bjR5jWpFr2xs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jsvKHzRciH9T",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        "    \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "\n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g3kjQV9WH3pb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "varLu7RNH3pf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BAGoXFPZ5ZE3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 635
+        },
+        "outputId": "37115edd-91dc-4c04-b050-4acd40af6c60"
+      },
+      "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": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 166.11\n",
+            "  period 01 : 123.84\n",
+            "  period 02 : 117.53\n",
+            "  period 03 : 116.46\n",
+            "  period 04 : 115.86\n",
+            "  period 05 : 115.42\n",
+            "  period 06 : 114.71\n",
+            "  period 07 : 115.13\n",
+            "  period 08 : 113.64\n",
+            "  period 09 : 113.17\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wCr1v/Us+18EQEREFFQNMN8hubmgnbWpoxyuRiIiIgooBphtoFbFIUiWg1FEM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\nfD3lAAAAAElFTkSuQmCC\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": 365
+        },
+        "outputId": "1d85ad88-5225-4337-8c22-d84a7a00d54f"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7ff5a69d1410>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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h8TgNPf/LD7TDXsHiyKkhjIyHUVdTgZuXz5+2zpJ89ZNrEBFi8E3wcFVxM65z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\nDbKKioqKikoGoBpkFRUVFRWVDOD/B16mDkKvFgElAAAAAElFTkSuQmCC\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": "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": 635
+        },
+        "outputId": "0c95181a-592d-4dfb-eef3-cd31e101f049"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 227.76\n",
+            "  period 01 : 217.50\n",
+            "  period 02 : 207.33\n",
+            "  period 03 : 197.27\n",
+            "  period 04 : 187.34\n",
+            "  period 05 : 177.53\n",
+            "  period 06 : 167.89\n",
+            "  period 07 : 158.42\n",
+            "  period 08 : 149.17\n",
+            "  period 09 : 140.20\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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BG5HvsTF5G1U1VViam/DAxK48Pr0HVuYmfP9rCq+sjiItp6S5yxZCCCFuqSY7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\nrkJggg==\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..3376222
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,1845 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/first_steps_with_tensor_flow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# First Steps with TensorFlow"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Bd2Zkk1LE2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Learn fundamental TensorFlow concepts\n",
+        "  * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n",
+        "  * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n",
+        "  * Improve the accuracy of a model by tuning its hyperparameters"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MxiIKhP4E2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6TjLjL9IU80G",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "In this first cell, we'll load the necessary libraries."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rVFf5asKE2Zt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ipRyUHjhU80Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll load our data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9ivCDWnwE2Zx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vVk_qlG6U80j",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r0eVyguIU80m",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "325763dd-fb9b-4d24-f335-d0ebf1047cb9"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2967</th>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1056.0</td>\n",
+              "      <td>272.0</td>\n",
+              "      <td>964.0</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>128.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4691</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>2690.0</td>\n",
+              "      <td>589.0</td>\n",
+              "      <td>1149.0</td>\n",
+              "      <td>535.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>281.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12472</th>\n",
+              "      <td>-121.6</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>404.0</td>\n",
+              "      <td>74.0</td>\n",
+              "      <td>144.0</td>\n",
+              "      <td>58.0</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>125.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8054</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>2933.0</td>\n",
+              "      <td>565.0</td>\n",
+              "      <td>1077.0</td>\n",
+              "      <td>536.0</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13238</th>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>1096.0</td>\n",
+              "      <td>218.0</td>\n",
+              "      <td>657.0</td>\n",
+              "      <td>199.0</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>65.8</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>540</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>4595.0</td>\n",
+              "      <td>567.0</td>\n",
+              "      <td>1643.0</td>\n",
+              "      <td>544.0</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>362.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>539</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>4775.0</td>\n",
+              "      <td>886.0</td>\n",
+              "      <td>1868.0</td>\n",
+              "      <td>836.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9201</th>\n",
+              "      <td>-119.0</td>\n",
+              "      <td>35.4</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>4635.0</td>\n",
+              "      <td>800.0</td>\n",
+              "      <td>2307.0</td>\n",
+              "      <td>754.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>84.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10855</th>\n",
+              "      <td>-120.8</td>\n",
+              "      <td>38.9</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1438.0</td>\n",
+              "      <td>324.0</td>\n",
+              "      <td>675.0</td>\n",
+              "      <td>268.0</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>119.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6739</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>2701.0</td>\n",
+              "      <td>708.0</td>\n",
+              "      <td>1880.0</td>\n",
+              "      <td>590.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>123.8</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",
+              "2967      -117.8      34.1                33.0       1056.0           272.0   \n",
+              "4691      -118.1      34.1                42.0       2690.0           589.0   \n",
+              "12472     -121.6      37.8                32.0        404.0            74.0   \n",
+              "8054      -118.4      34.1                40.0       2933.0           565.0   \n",
+              "13238     -121.9      39.0                48.0       1096.0           218.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "540       -117.0      33.0                13.0       4595.0           567.0   \n",
+              "539       -117.0      34.0                18.0       4775.0           886.0   \n",
+              "9201      -119.0      35.4                30.0       4635.0           800.0   \n",
+              "10855     -120.8      38.9                17.0       1438.0           324.0   \n",
+              "6739      -118.3      33.9                32.0       2701.0           708.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "2967        964.0       300.0            2.4               128.7  \n",
+              "4691       1149.0       535.0            3.9               281.1  \n",
+              "12472       144.0        58.0            4.2               125.0  \n",
+              "8054       1077.0       536.0            6.2               500.0  \n",
+              "13238       657.0       199.0            2.8                65.8  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "540        1643.0       544.0            7.8               362.3  \n",
+              "539        1868.0       836.0            2.3               118.8  \n",
+              "9201       2307.0       754.0            3.7                84.7  \n",
+              "10855       675.0       268.0            2.9               119.3  \n",
+              "6739       1880.0       590.0            1.7               123.8  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HzzlSs3PtTmt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Examine the Data\n",
+        "\n",
+        "It's a good idea to get to know your data a little bit before you work with it.\n",
+        "\n",
+        "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gzb10yoVrydW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "41715992-25cd-492a-9d6f-1629f8c26fd1"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "outputId": "40dfb8f9-bd07-4229-a9e8-64461a886b2d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an input function for predictions.\n",
+        "# Note: Since we're making just one prediction for each example, we don't \n",
+        "# need to repeat or shuffle the data here.\n",
+        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+        "\n",
+        "# Call predict() on the linear_regressor to make predictions.\n",
+        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "\n",
+        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "\n",
+        "# Print Mean Squared Error and Root Mean Squared Error.\n",
+        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Squared Error (on training data): 56367.025\n",
+            "Root Mean Squared Error (on training data): 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AKWstXXPzOVz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Is this a good model? How would you judge how large this error is?\n",
+        "\n",
+        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+        "\n",
+        "Let's compare the RMSE to the difference of the min and max of our targets:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7UwqGbbxP53O",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "outputId": "8b21ac9f-4deb-40a9-d23b-7bda7b122ac3"
+      },
+      "cell_type": "code",
+      "source": [
+        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
+        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
+        "min_max_difference = max_house_value - min_house_value\n",
+        "\n",
+        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
+        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
+        "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
+        "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Min. Median House Value: 14.999\n",
+            "Max. Median House Value: 500.001\n",
+            "Difference between Min. and Max.: 485.002\n",
+            "Root Mean Squared Error: 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JigJr0C7Pzit",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our error spans nearly half the range of the target values. Can we do better?\n",
+        "\n",
+        "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
+        "\n",
+        "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "941nclxbzqGH",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "85d95485-9031-40b5-a10a-82049bcbeb8d"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = pd.DataFrame()\n",
+        "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "calibration_data.describe()"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.9</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.1    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            0.1    119.4\n",
+              "50%            0.1    180.4\n",
+              "75%            0.2    265.0\n",
+              "max            1.9    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "E2-bf8Hq36y8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
+        "\n",
+        "We can also visualize the data and the line we've learned.  Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
+        "\n",
+        "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SGRIi3mAU81H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "sample = california_housing_dataframe.sample(n=300)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "N-JwuJBKU81J",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7G12E76-339G",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 361
+        },
+        "outputId": "bc081637-5880-478d-b57a-57405adb7244"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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t2rXYtWsXbDYbRo0ahdOnT+Oee+6RfP8VV1yBqqoqAEBRURFcLhf2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\nxaQjF0IIIbyYdORCCCGEF/sv3w4NtR/D6roAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "t0lRt4USU81L",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This initial line looks way off.  See if you can look back at the summary stats and see the same information encoded there.\n",
+        "\n",
+        "Together, these initial sanity checks suggest we may be able to find a much better line."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AZWF67uv0HTG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Tweak the Model Hyperparameters\n",
+        "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
+        "\n",
+        "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
+        "\n",
+        "For each period, we'll compute and graph training loss.  This may help you judge when a model is converged, or if it needs more iterations.\n",
+        "\n",
+        "We'll also plot the feature weight and bias term values learned by the model over time.  This is another way to see how things converge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wgSMeD5UU81N",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
+        "  \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label]\n",
+        "\n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Output a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kg8A4ArBU81Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1:  Achieve an RMSE of 180 or Below\n",
+        "\n",
+        "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
+        "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 969
+        },
+        "outputId": "30460921-88b7-4286-8639-b0e29cdd2bdd"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1\n",
+        ")"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 236.32\n",
+            "  period 01 : 235.11\n",
+            "  period 02 : 233.90\n",
+            "  period 03 : 232.70\n",
+            "  period 04 : 231.50\n",
+            "  period 05 : 230.31\n",
+            "  period 06 : 229.13\n",
+            "  period 07 : 227.96\n",
+            "  period 08 : 226.79\n",
+            "  period 09 : 225.63\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean          13.2    207.3\n",
+              "std           10.9    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            7.3    119.4\n",
+              "50%           10.6    180.4\n",
+              "75%           15.8    265.0\n",
+              "max          189.7    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>13.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>10.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>15.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>189.7</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 225.63\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZBw8ejNWrVyMoKOiJbYa8VigsLMSbb76pLYAIADdu3MDZs2fh5+cHHx8fxMfHIyMjAyUl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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 969
+        },
+        "outputId": "e2554a81-84c1-4cb5-adbf-a97509a6c6df"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 18,
+      "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 : 204.44\n",
+            "  period 03 : 194.97\n",
+            "  period 04 : 187.55\n",
+            "  period 05 : 180.80\n",
+            "  period 06 : 175.44\n",
+            "  period 07 : 172.08\n",
+            "  period 08 : 169.46\n",
+            "  period 09 : 167.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         115.3    207.3\n",
+              "std           95.0    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           63.7    119.4\n",
+              "50%           92.7    180.4\n",
+              "75%          137.4    265.0\n",
+              "max         1654.1    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>115.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>95.0</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>63.7</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>92.7</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>137.4</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1654.1</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): 167.53\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NlFUqDO2t5cZRBnRa75+oU7iej0HLXZP78syKFJZuOET78KG0Dfb1dFpCCCFE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\nd3sYiLcc+dnJx19bsdhgQG8dt12vR6MJ3ICEJCt8ubaExV8U4XQqDL4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1svPHUt7/8hAP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\nCCGEEEIIIYTwiv8PVQAZYIAYhKMAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 969
+        },
+        "outputId": "1984af18-2685-46b1-c8ac-a97991648a2e"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.84\n",
+            "  period 02 : 204.86\n",
+            "  period 03 : 196.26\n",
+            "  period 04 : 189.66\n",
+            "  period 05 : 184.68\n",
+            "  period 06 : 180.84\n",
+            "  period 07 : 178.74\n",
+            "  period 08 : 177.00\n",
+            "  period 09 : 176.18\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         116.9    207.3\n",
+              "std           93.9    116.0\n",
+              "min            0.2     15.0\n",
+              "25%           64.6    119.4\n",
+              "50%           95.5    180.4\n",
+              "75%          140.8    265.0\n",
+              "max         2919.0    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>116.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>93.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>64.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>95.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>140.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2919.0</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): 176.18\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Y7ypoqdfB3dcZ6S4FLcVFEBqkZ2xqEja7k5mLd+Nw+v/fixBCCHGxSVJCCCH+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Su7Jxuztpbg8Mes4GIkclgm4zUZ8BUFk4N4mnH84iwXFxoYcXGwzoD8hs2tbCB5vduFvjVx9L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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb
new file mode 100644
index 0000000..79ff03a
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,1614 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/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": 1209
+        },
+        "outputId": "91c64106-7dc2-4754-a631-37ea0518cec5"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2657.0           541.8   \n",
+              "std         2.1        2.0                12.6       2191.0           424.1   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1467.0           298.0   \n",
+              "50%        34.2     -118.5                29.0       2136.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3160.2           651.0   \n",
+              "max        41.9     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1434.6       503.1            3.9               2.0  \n",
+              "std        1154.1       386.4            1.9               1.1  \n",
+              "min           8.0         1.0            0.5               0.0  \n",
+              "25%         788.0       282.0            2.6               1.5  \n",
+              "50%        1167.0       410.0            3.5               1.9  \n",
+              "75%        1722.0       607.0            4.8               2.3  \n",
+              "max       35682.0      5189.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>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>2657.0</td>\n",
+              "      <td>541.8</td>\n",
+              "      <td>1434.6</td>\n",
+              "      <td>503.1</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2191.0</td>\n",
+              "      <td>424.1</td>\n",
+              "      <td>1154.1</td>\n",
+              "      <td>386.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</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>1467.0</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>788.0</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>2136.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1167.0</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>3160.2</td>\n",
+              "      <td>651.0</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>41.9</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>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": [
+            "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       2611.6           533.7   \n",
+              "std         2.1        2.0                12.6       2153.2           415.2   \n",
+              "min        32.5     -124.3                 2.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           294.8   \n",
+              "50%        34.3     -118.5                29.0       2110.0           430.0   \n",
+              "75%        37.7     -118.0                37.0       3117.0           643.0   \n",
+              "max        42.0     -114.6                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1417.6       496.7            3.9               2.0  \n",
+              "std        1132.7       380.1            1.9               1.3  \n",
+              "min           3.0         2.0            0.5               0.1  \n",
+              "25%         792.0       281.0            2.6               1.5  \n",
+              "50%        1166.0       407.0            3.5               1.9  \n",
+              "75%        1710.2       600.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2611.6</td>\n",
+              "      <td>533.7</td>\n",
+              "      <td>1417.6</td>\n",
+              "      <td>496.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2153.2</td>\n",
+              "      <td>415.2</td>\n",
+              "      <td>1132.7</td>\n",
+              "      <td>380.1</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</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>1453.0</td>\n",
+              "      <td>294.8</td>\n",
+              "      <td>792.0</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>2110.0</td>\n",
+              "      <td>430.0</td>\n",
+              "      <td>1166.0</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>3117.0</td>\n",
+              "      <td>643.0</td>\n",
+              "      <td>1710.2</td>\n",
+              "      <td>600.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.7\n",
+              "std                 116.4\n",
+              "min                  15.0\n",
+              "25%                 119.3\n",
+              "50%                 180.2\n",
+              "75%                 266.3\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.7</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>180.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                206.4\n",
+              "std                 115.0\n",
+              "min                  15.0\n",
+              "25%                 120.1\n",
+              "50%                 180.4\n",
+              "75%                 262.5\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>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>262.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": 671
+        },
+        "outputId": "9a0fcab0-eaef-4148-ad93-2c5dfdb99174"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 153.05\n",
+            "  period 01 : 140.90\n",
+            "  period 02 : 127.35\n",
+            "  period 03 : 116.50\n",
+            "  period 04 : 112.10\n",
+            "  period 05 : 110.79\n",
+            "  period 06 : 111.81\n",
+            "  period 07 : 109.57\n",
+            "  period 08 : 111.62\n",
+            "  period 09 : 110.01\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   110.01\n",
+            "Final RMSE (on validation data): 106.88\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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h5Y3YwgScy7okqYZNF0NMH+WB8qo6fiuJiIjoARhgNOjOjtXTVDtWl9SUSqoT\n2N8J3l274HJCHs7H5Gi4SyIiovaPAUbDLJUWmOw+DhV1lZJ3rJYJAh4f7wN9PRl+PBSPkvIaDXdJ\nRETUvjHAaMEI5wB0M3NFVM5VXM29LqmGrYURwkd6oKyyFusPxmm4QyIiovaNAUYLZIIMc32nQ6Ha\nsbpSUp3RA5zR3dkcF+NycSGWU0lERER/YIDREgdjO4S4BaG4pgTbJe5YLRMEPDHeF3oKGdYfjENJ\nBaeSiIiIAAYYrRrrGggHYzucTD+LhMIkSTXsLI0wbYQ7Sitq8dOheA13SERE1D4xwGiRQqbAXJ/p\nd3asjtssacdqAAge2BUeTmY4H5ODS3G5Gu6SiIio/WGA0bJu5q4Y5TwUORV52HfrV0k1ZLI7U0kK\nuQzrDsahrFJaECIiIuooGGBawUT3EFgqLXAo5SjSSjMk1XCwMsbUEd1QUl6Dnw5zKomIiDo3BphW\noFQYYLb37ztWx25CfUO9pDoh/i7o5mCGs9ezcTmBU0lERNR5McC0kh5W3njEvj9SStNxJE3ajtUy\nmYAnJvhCIRfww4E4lFdxKomIiDonBphWFO4ZBhM9Y+xOOojcinxJNZysjTF5WDcUl9Xgl8MJGu6Q\niIiofWCAaUUm+saY0X0Sahtq8VOctB2rASB0kAtc7U1xKjoLV27mabhLIiIi3ccA08oG2PVFTysf\nxBfexNnMi5JqyGUyLJzgC7nszlRSBaeSiIiok2GAaWWCIOBR72kwkOtjy83dKK6WtmO1s40JJg11\nQ2FpNX6JvKnhLomIiHQbA0wbsFB2wWSP8aisq8Sm+O2S64wb7AoXOxOcvJqJ6CRpa2qIiIjaIwaY\nNjLcaTDczd1wOfcaruRGS6qhkMvwxPg7U0lr9sWisrpOw10SERHpJgaYNiITZJjrEw6FIMeGuO2S\nd6x2sTPFhABXFJZWY+MRTiUREVHnwADThuyN7RDqNhrFNSXYdnOv5DoTh7jB2cYEx37LwPVbBRrs\nkIiISDcxwLSxYNdRcDS2x6mMc0goTJRUQyG/860kmSBg7d4YTiUREVGHxwDTxhQyBeb6/r5jdewW\n1EjcsdrV3hTjA1yRX1KNzUelBSEiIqL2ggFGB7iZuSCw6zDkVOZh363DkuuEDXGDk7UxjlxORwyn\nkoiIqANjgNERE91DYKW0wOGUY0gtTZdUQ08hwxO/TyWt2ReLqhpOJRERUcfEAKMjDOT6mO0d/vuO\n1Zsl71jdzcEMoYNckFdchS1HkzTcJRERkW5ggNEhvlZeGGQ/AKml6YhMPSG5zuRhbnCwMsKvUWmI\nTy3SYIdERES6gQFGx0zrPhHZ8f7aAAAgAElEQVQmesbYk3wQORXSNmrUU8jxxHhfAMD6g/Gob2jQ\nZItERERtjgFGx5joGWOm12TUNtTh51jpO1Z7OJljWG8HpOWW4ejlDA13SURE1La0GmDi4+MxZswY\nrF+/vtHzJ06cgLe3t+rxzp07ER4ejhkzZmDTpk3abKld6G/bB72sfRFflIgzmRck15k+0gOGBgps\nO56EkooaDXZIRETUtrQWYCoqKrB06VIEBAQ0er66uhqrVq2CjY2N6n3Lly/H2rVrsW7dOnz//fco\nKurc6zYEQcAsr6lQyg2w9eZuFFeXSKpjZqyPqcO7oaK6DluP8d4wRETUcWgtwOjr62P16tWwtbVt\n9PzXX3+NOXPmQF9fHwBw5coV9OrVC6amplAqlejfvz+ioqK01Va78b8dq6uwsQU7Vgf2d4KzjTFO\nXMlEUoa0IERERKRrtBZgFAoFlEplo+eSk5MRGxuLcePGqZ7Ly8uDpaWl6rGlpSVyc3O11Va7Msxp\nEDzMu+G33Gj8lnNNUg25TIa5wV4QAfx4KA4NEtfUEBER6RJFa57sww8/xNtvv93ke5qzaNXCwggK\nhVxTbd3DxsZUa7XV9cKQ+Xj1wD+x+eZODOneF8b6RmrXsLExxdmYXBy7nIYryYUYO8hVC522Dl0a\nG/ofjovu4tjoLo5Ny7RagMnOzkZSUhJeffVVAEBOTg7mzZuHF154AXl5//u6cE5ODvr27dtkrcLC\nCq31aWNjitzcUq3VV5cejDHObTR2JR3A6rMbMNd3uqQ6k4a44mx0Jtbsug4vR1MYK/U03Kn26drY\n0B0cF93FsdFdHJvmaSrktdrXqO3s7HD48GFs3LgRGzduhK2tLdavX48+ffrg2rVrKCkpQXl5OaKi\nojBw4MDWaqtdCHYZBScTB5zOPI/4wpuSaliYGmDSUDeUVdZi+/FkDXdIRETUurQWYKKjoxEREYFt\n27bhhx9+QERExH2/XaRUKrFkyRIsXLgQjz/+OBYtWgRTU15Wu5tcJsdcnzs7Vv/Ygh2rg/27ws7S\nCJGX05CSzeRPRETtlyBKvVNaG9LmZTddvqy3JWEXIlNPINhlFKZ4jpdUIzo5H59tuILuzuZ4c25/\nCIKg4S61R5fHpjPjuOgujo3u4tg0j05MIVHL3dmx2hK/ph5HSmmapBo9u1mhv5cNEtKKcfZGtoY7\nJCIiah0MMO2IgVwfc3x+37E6RvqO1Y8GeUJPIcPGIzdRWV2n4S6JiIi0jwGmnfGx7I7BDgORVpaB\nX1OPS6ph3cUQEwa7orisBrtO39Jsg0RERK2AAaYdmuY5Eab6JtibfAg5FdJu+hc6yAXW5kocupCK\nzPxyDXdIRESkXQww7ZCxnhFmek1BbUMdfordggaxQe0a+npyzB7THfUNIn48FC9512siIqK2wADT\nTvWz6YXe1n5IKErCmQxpO1b39bRGT3dL3LhViKh4bt9ARETtBwNMOyUIAmZ5T4FSrsS2xD0oqi6W\nVGPOGC/IZQJ++TUB1bXSFgUTERG1NgaYdqyLgTmmeP6+Y3XcdknTQPaWRgh5xAX5JdXYe+a2Frok\nIiLSPAaYdm6o4yPw7NINV/Ku47fcaEk1Jg5xhYWpAfadS0GOFveZIiIi0hQGmHZOJsgwx2c6FDIF\nNsZvR0Wt+gFEqa/ArCBP1NU34Jdfpe21RERE1JoYYDoAOyMbjHcbg5KaUmy9uUdSDX8fW/i4dMFv\nN/NwNTHv4QcQERG1IQaYDmKMy0g4mzjiTOYFxBYkqH28IAiYE+wFmSDgp8MJqK1T/6vZRERErYUB\npoO4e8fqn2O3oKa+Ru0azjYmGD3AGTmFlTh4IUULXRIREWkGA0wH4mLmjCCX4cirKsDu5IOSakwe\n1g1mRnrYdfoWCkqqNNwhERGRZjDAdDATu42FtaEVIlNO4HZJqtrHGykVmD7KEzW1DdgQyQW9RESk\nmxhgOhh9uT7meIdDhIgfY6XtWD2klz08HM1wITYHMbcKtNAlERFRyzDAdEDelp4IcPBHelkmIlNP\nqH28TBAwd6wXBAA/Hk5AXT0X9BIRkW5hgOmgpnpOgImeMfbeOixpmwE3ezOM7OuIjLxyRF5K00KH\nRERE0jHAdFDGekaY5BGKmvoabE3YLanGtJEeMFYqsP1kMorLqjXcIRERkXQMMB1YgIM/XM264lLO\nFcQXqr8g18RQD9NGeqCqph6bjiZqoUMiIiJpGGA6MJkgwyyvKRAgYEP8DkkLekf2cYSLnQlOR2ch\nIa1IC10SERGpjwGmg3M164qhjo8gqzwbR9JOqn28TCZgXrA3AODHg/FoaFB/x2siIiJNY4DpBMI8\nQmGsMMLe5EOSFvR6OptjSE97pOSU4diVDC10SEREpB4GmE7ARM8YkzxCUV1fg20SN3ucMcoDSn05\nth5LRFllrYY7JCIiUg8DTCcxxPERuJp2xcXs3xBfqP6CXHMTA0wZ1g3lVXXYeowLeomIqG0xwHQS\nMkGGWd53FvRujN8uaUFv0ABnOFob49hvGbiVVaKFLomIiJqHAaYTcTXriiGO/sgsz8axtFNqH6+Q\nyzB3THeI+H1Br8gFvURE1DYYYDqZSe7jYKwwwp7kQyiuVv8qiq+bJfx9bJGYUYLT17K00CEREdHD\nMcB0Mib6xgjzCEFVfTW23dwrqcasIE/o68mw+ehNVFRxQS8REbU+BphOaKjjILiYOuFCdhQSCpPU\nPt7STImwIW4oqajF9pPJWuiQiIioaQwwnZBMkGGm11QAkLygd6y/C2wtDBF5KR1pOWWabpGIiKhJ\nDDCdVDdzFwxx8EdGeRaOp59R+3g9hQxzxnRHgyjix0PxELmgl4iIWhEDTCc2yWMcDBWG2J10EMXV\npWof39vDGn09rRGXWoQLsTla6JCIiOj+GGA6MVN9E0xyD0VVfRW2J0q7Q++joz2hkMuwIfImqmrq\nNNwhERHR/THAdHLDnAahq6kTzmdF4WaR+gtybS2MMG6QCwpLq7H79G0tdEhERHQvBphO7s6C3ikA\npC/oHR/gCiszAxw4n4KsggpNt0hERHQPBhiCu7krBjsMRHpZJk6kn1X7eAM9OR4d3R31DSJ+4oJe\nIiJqBQwwBACY4jEehgpD7Eo6gJIa9Rf09veygZ+bBaKTC/BbQp4WOiQiIvofBhgCcGdBb5h7yJ0F\nvRLu0CsIAuYEe0EuE/DzrwmoqVV/KoqIiKi5GGBIZbjTYDibOOJc1iUkFd9S+3gHK2ME+3dFXnEV\n9p1L0XyDREREv2OAIRWZIMMs7zsLejfEbUeD2KB2jbAhbjA30cfes7eRW1Sp6RaJiIgAMMDQn7ib\nu2GQ/QCklWVIWtBraKDAzEBP1NY14JdfE7TQIREREQMM3ccUz/EwVCixK2k/SmvU3+docA87dHc2\nx+WEPEQn5WuhQyIi6uwYYOgeZvqmmNgtBJV1VdieKG1B79xgLwgC8OPhBNTVqz8VRURE1BQGGLqv\n4U6D4WTigLOZF5FUrP4ddl3sTBHUzxnZBRU4dCFVCx0SEVFnJjnA3Lp1S4NtkK6Ry+T/u0Nv3DZJ\nC3qnjOgGE0M97Dx1C4Wl1ZpukYiIOrEmA8zjjz/e6PGKFStUf3733Xe10xHpDM8u3TDIfgBSyzJw\nUsKCXmOlHqaP8kB1bT02HrmphQ6JiKizajLA1NU13l347Nn//SPG28V3DlM8x0MpV2Jn0gFJC3qH\n9XZANwdTnLuRjbiUQi10SEREnVGTAUYQhEaP7w4tf36NOiYzfVNMdB+LyrpK7Ezcp/bxMkHA3GBv\nAMD6Q/Gob+CCXiIiajm11sAwtHROI5wC4Ghsj9OZF5BcrP4ddt0dzTC8twPSc8sRGZWuhQ6JiKiz\naTLAFBcX48yZM6r/SkpKcPbsWdWfqXOQy+SY5T0VALAxXtqC3vBRHjAyUGD7iSQUl9doukUiIupk\nFE29aGZm1mjhrqmpKZYvX676M3Uenl26wd+uPy5kR+FUxjkMdwpQ63gzI31MHeGOHw/FY8vRRDwx\nwVdLnRIRUWfQZIBZt25da/VB7cBUz/G4lncdOxP3o59Nb5joG6t1/Kh+jjj2WwZOXsvEyH6O8HA0\n11KnRETU0TU5hVRWVoa1a9eqHv/yyy+YPHkyXnzxReTl5Wm7N9Ix5gZmmNAtGBV1ldiZpP6CXrlM\nhnljvQAA6w/Go6GB32QjIiJpmgww7777LvLz7+xlk5ycjM8++wxvvPEGhgwZgn/+85+t0iDplpHO\nQ+FgbIfTGRdwq0T9Bb1eXbtgsJ8dbmeV4sTVDC10SEREnUGTASY1NRVLliwBABw4cAChoaEYMmQI\nHn300WZdgYmPj8eYMWOwfv16AMDly5cxe/ZsREREYOHChSgoKAAA7Ny5E+Hh4ZgxYwY2bdrU0s9E\nWiSXyTHLawpEiNgQt13Sgt4ZozxhoC/HlmNJKKus1UKXRETU0TUZYIyMjFR/Pn/+PAYPHqx6/LCv\nVFdUVGDp0qUICPjfYs81a9bg448/xrp169CvXz9s3LgRFRUVWL58OdauXYt169bh+++/R1FRkdTP\nQ62gu4UHBtr1RUppGk5nnFf7eAtTA0we2g1llbXYdiJJCx0SEVFH12SAqa+vR35+PlJSUnD58mUM\nHToUAFBeXo7KysomC+vr62P16tWwtbVVPffFF1+ga9euEEUR2dnZsLe3x5UrV9CrVy+YmppCqVSi\nf//+iIqK0sBHI22a6jkBBnJ97Ezcj7LacrWPHzPQGfaWRjh6OR23s0q10CEREXVkTQaYp556CuPH\nj0dYWBief/55mJubo6qqCnPmzMGUKVOaLKxQKKBUKu95/vjx4wgNDUVeXh4mTZqEvLw8WFpaql63\ntLREbm6uxI9DraWLgTnGdwtGeV0FdiXuV/t4hVyGOcHdIYrAj4fiuTUFERGppcmvUY8cORInT55E\ndXU1TExMAABKpRKvvfYahg0bJumEI0aMwPDhw/HJJ59g1apVcHJyavR6c/4hs7AwgkIhl3T+5rCx\n4T1ummOG1ThcyInCqYzzmOAXCA9LV7WOD7QxxZkbOThzLRPRKcUIGtj1ocdwbHQTx0V3cWx0F8em\nZZoMMBkZ//uWyN133nV3d0dGRgYcHR3VOtmhQ4cQHBwMQRAQEhKCL7/8Ev369Wu0IDgnJwd9+/Zt\nsk5hYYVa51WHjY0pcnM5pdFc4R6T8Pnlb/D1uR/x6oBFkAlq7U6BqcPccDEmG//dGQ1PexMYGjz4\nV5Jjo5s4LrqLY6O7ODbN01TIazLABAUFoVu3brCxsQFw72aOP/zwg1qNfPnll3B2doavry+uXLmC\nbt26oU+fPnj77bdRUlICuVyOqKgo/PWvf1WrLrUdLwsPDLDtg0s5V3Am8wKGOg5S63hrc0NMCHDF\n9hPJ2HEyGY+O7q6lTomIqCNpMsAsW7YMO3bsQHl5OSZMmICJEyc2Wq/SlOjoaCxbtgzp6elQKBQ4\ncOAA3n//fbz33nuQy+VQKpX4+OOPoVQqsWTJEixcuBCCIGDRokXcpqCdmdZ9IqLzY7AjcR/62vSC\nsZ7Rww+6y7hBLjh1LRO/XkrD8D6OcLJW7w6/RETU+QhiMxadZGZmYtu2bdi1axecnJwwefJkBAcH\n33eRbmvQ5mU3XtaT5tDto9ieuBfDnQLw6O8bP6rjt4Q8fLHlKnxdLfDqo33v+zV9jo1u4rjoLo6N\n7uLYNE9TU0jNWrDg4OCA559/Hvv27UNISAjef/99yYt4qWMK7DoM9ka2OJl+FiklaWof38fTCr09\nrBBzuxCX4vgtNCIialqzAkxJSQnWr1+PadOmYf369XjmmWewd+9ebfdG7YhCpsDMP+7QG6/+HXoF\nQcDs0d2hkAv4JTIB1TX1WuqUiIg6gibXwJw8eRJbtmxBdHQ0xo4di48++gheXl6t1Ru1M96Wnuhv\n2xtROVdxNvMShjj6q3W8naURQh5xwZ4zt7Hn7C1MG+GhpU6JiKi9azLAPPnkk3Bzc0P//v1RUFCA\nNWvWNHr9ww8/1Gpz1P5M85yI6PxY7Ejci742fjBSc0HvxAA3nI7Owv5zKRjaywF2FuodT0REnUOT\nAeaPr0kXFhbCwsKi0Wtpaeqvc6COz0LZBePdxmB74l7sSjqAWWou6DXQl2NWkCe+3nEdPx9OwF9m\n9NFSp0RE1J41uQZGJpNhyZIleOedd/Duu+/Czs4OjzzyCOLj4/Gf//yntXqkdiaw6zDYGdniRPpZ\npJamq328v48tfFy64GpiPn67+fBdz4mIqPNpMsD8+9//xtq1a3H+/Hm89tprePfddxEREYGzZ89i\n06ZNrdUjtTN3FvROvrOgN07agt65wV6QCQJ+PhyP2jou6CUiosYeegXGw+POQsrRo0cjPT0d8+fP\nx1dffQU7O7tWaZDaJx/L7uhn0wvJJbdxLkv93cWdbEwwZqAzcouqsP9cihY6JCKi9qzJAPPnm4k5\nODggODhYqw1RxxHePQz6Mj1sv7kHFbWVah8/aWg3mBnrY8+Z28grVv94IiLquNTaee9+d0clehAL\nZReMcxuDstpy7E4+oPbxRkoFZozyQE1dAzZG3tRCh0RE1F41+S2ky5cvY9SoUarH+fn5GDVqFERR\nhCAIOHr0qJbbo/YuyGU4zmRdwPG0MwhweARdTdXbwTygpz2O/paOi3G5+C0+B04WhlrqlIiI2pMm\n90JKT2/6GyROTk4ab6g5uBdS+xKTH4+vrnwLd3M3vNL/ObWv5N3OKsU/vr8Amy6GeGfBQBgr9bTU\nKUnBvzO6i2Ojuzg2zdPUXkhNXoFpq4BCHYuvlRf62vTEb7nROJ8VhUEOA9Q63tXeFGFD3LDz1C2s\n3RuL56f25HQmEVEnp9YaGCKpwruHQU+mh20SF/SGDXWDn7sVLsXnIjJK/XvLEBFRx8IAQ63CUmmB\nULfRKK0tw57kg2ofL5fJ8Nq8ATAx1MOGyASkZPPSKxFRZ8YAQ61mtMsI2Bpa41jaaaSXZap9vJW5\nIZ6c6Iu6ehErt0ejsrpOC10SEVF7wABDrUZPpsB01R16t6GJ9eMP1NvDGqGPuCC7sBLrDsZJqkFE\nRO0fAwy1Kj8rb/Sx6YnE4lu4kH1ZUo1pI93h7miGs9ezcfKa+ldyiIio/WOAoVYX7nlnQe/Wm7tR\nWaf+gl6FXIZnJ/nB0ECBHw/GIz2vXAtdEhGRLmOAoVZnZWiBENcglNaUYU/yIUk1rLsY4onxPqip\na8DX26NRXcsNH4mIOhMGGGoTY1xGwNrQSvKCXgAY4G2LoP5OSM8rx8+HEzTcIRER6TIGGGoTenI9\nzPSajAaxARvjt0tejDsryBMutiY4fiUDZ29kabhLIiLSVQww1Gb8rHzQ29oPN4uSJS/o1VPI8eyU\nnjDQk+P7/XHILqzQcJdERKSLGGCoTU3vHgY9mQLbbu5BZV2VpBr2lkaYH+qN6pp6fL39OmrrGjTc\nJRER6RoGGGpTVoaWGOsaiJKaUuyVuKAXAAL87DGstwNuZ5di05GbGuyQiIh0EQMMtblgl1GwVlri\naNopZJRJX8cyd4wXHKyMcPhSGqLiczXYIRER6RoGGGpzenI9zNDAgl4DfTmem9ITegoZvtsTg7xi\n9e8xQ0RE7QMDDOmEnta+6GXti4SiJFzK/k1yHWcbE8wN9kJFdR2+2XkddfVcD0NE1BExwJDOmN59\nEhQyBbbe3IMqiQt6AWB4bwc84muLxPQSbDuRpMEOiYhIVzDAkM6wNrTCWJdRKK4pwd5bhyXXEQQB\nC0J9YNvFEPvOpiA6KV+DXRIRkS5ggCGdEuwaCCulJY6knkRmebbkOoYGCjw3pScUcgGrd99AYWm1\nBrskIqK2xgBDOkVfrocZXpPuLOiNk76gFwBc7U0xI9ATpRW1WL3rOhoapNciIiLdwgBDOqeXdQ/0\ntPJBfFEionKutKjWmAHO6NfdGrEpRdh9+pZmGiQiojbHAEM6aXr3yXct6JU+/SMIAh4f7wsrMwPs\nOJWM2NuFGuySiIjaCgMM6SQbIysEu4xCUXUx9t/6tUW1TAz18MyknhAg4Jtd11FSUaOhLomIqK0w\nwJDOGusaCCulBX5NPY6sFizoBQBPZ3NMG+mO4rIa/Hd3DBpasLaGiIjaHgMM6Sx9uR7Cu/++oDd+\nR4sW9AJA6CAX9OxmiWtJ+ThwPkVDXRIRUVtggCGd1tu6B3pYeSOu8CbOpEa1qJZMEPDkxB4wN9HH\n1mNJSEwv1lCXRETU2hhgSKcJgoAZvy/oXX3xR+RUtGyTRjNjfTwd5oeGBhFf77iO8qpaDXVKRESt\niQGGdJ6tkTVme09DeW0lvrn6PSpbsM0AAPi6WiBsqBvyS6qwZm9si6emiIio9THAULsw2GEgJniN\nRlZFDr6/8TMaxJZt0jhpaDd4d+2CqPhcREala6hLIiJqLQww1G7M6zMVPhbdcS0vBnuSDraolkwm\n4OlJfjAx1MOGyATczirVUJdERNQaGGCo3ZDL5Hii51xYG1ph/+1IXMpu2V16LUwN8OTEHqirF7Fy\nRzQqq+s01CkREWkbAwy1K8Z6Rnim1wIYyPWxLmYjUkszWlSvt4cVQge5IKewEusOxHE9DBFRO8EA\nQ+2Oo4k9FvSYjdqGWnxzdS1Ka8paVG/aCHd4OJrh7I1snLyaqaEuiYhImxhgqF3qY+OHid1CUFhd\nhNXX1qGuQfr0j0IuwzOT/GBkoMCPh+KRntuyQERERNrHAEPtVqhbEPrZ9EJicTI2JexsUS3rLoZ4\nfLwPauoa8PWO66iurddQl0REpA0MMNRuCYKAiB6z4GTigJPpZ3Ei/UyL6g3wtsXo/s5IzyvHz4fj\nNdQlERFpAwMMtWsGcn0802sBTPSMsTF+BxIKk1pUb2aQB1zsTHD8SibO3sjSUJdERKRpDDDU7lkZ\nWuLJnvMAAN9Gr0N+ZaHkWnoKOZ6b3BMG+nJ8vz8O2QUVmmqTiIg0iAGGOoTuFh6Y0X0SymrLsera\n96iur5Fcy87SCAtCvFFdU4+VO6JRW9eyu/4SEZHmMcBQhzHcKQBDHQchrSwD62M2tuieLoP97DG8\ntwNSssuw8chNDXZJRESawABDHYYgCJjpNRke5m6IyrmKA7ePtKjenGAvOFob49dLaYiKb9ku2ERE\npFkMMNShKGQKPNkrAhYGXbA76QCu5d2QXMtAT47nJvtBXyHDd3tikFdcqcFOiYioJRhgqMMx0zfF\n073nQyFTYO31n5FZni25lpONCeYEe6Giug7f7LiOunquhyEi0gVaDTDx8fEYM2YM1q9fDwDIzMzE\nY489hnnz5uGxxx5Dbu6dy/I7d+5EeHg4ZsyYgU2bNmmzJeokXEydMc93Bqrqq/HN1bWoqJX+baLh\nvR0wqIcdEjNKsO1Ey76mTUREmqG1AFNRUYGlS5ciICBA9dx//vMfzJw5E+vXr0dwcDDWrFmDiooK\nLF++HGvXrsW6devw/fffo6ioSFttUScy0K4vxroGIrcyH99d/wn1DdLurisIAuaHeMPWwhD7zqbg\nWlK+hjslIiJ1aS3A6OvrY/Xq1bC1tVU997e//Q0hISEAAAsLCxQVFeHKlSvo1asXTE1NoVQq0b9/\nf0RFRWmrLepkwtxD0NPKBzEF8diRuE9yHUMDBZ6b3BMKuYDVu26gsLRag10SEZG6FForrFBAoWhc\n3sjICABQX1+Pn376CYsWLUJeXh4sLS1V77G0tFRNLT2IhYURFAq55pv+nY2NqdZqU8tIGZtXRzyN\nvx5ehl9Tj8PX0R0j3AZJPvcTYT2xavs1rN0fh6XPDoFcJkiq1dHw74zu4tjoLo5Ny2gtwDxIfX09\nXn/9dQwePBgBAQHYtWtXo9ebc++OwkLt3R3VxsYUubmlWqtP0rVkbJ70m49/XfwSX19YD8N6E7iZ\nuUiqM8jbGhe6W+NyQh7W7LiGycO6SarTkfDvjO7i2Ogujk3zNBXyWv1bSG+99RZcXV2xePFiAICt\nrS3y8vJUr+fk5DSadiLSBDsjGzzuNxf1DfVYdfUHFFeXSKojCAKemOALKzMldp5KRuxt6dsWEBGR\ndK0aYHbu3Ak9PT28+OKLquf69OmDa9euoaSkBOXl5YiKisLAgQNbsy3qJPysvDHZYxyKa0qw+toP\nqK2vlVTHWKmHZyb7QYCAb3ZdR0mF9G0LiIhIGq1NIUVHR2PZsmVIT0+HQqHAgQMHkJ+fDwMDA0RE\nRAAAPDw88Pe//x1LlizBwoULIQgCFi1aBFNTzguSdoxxGYn0skxcyL6MX+K2YZ7vDAiC+utYPJ3M\nET7SHZuOJuLb3Tfwlxl9IJNQh4iIpBHElmwY00a0OW/IeUndpamxqamvxb+jViKlNA3Tu09CYNdh\nkuo0iCL+s+kKopMKMGOUB8YNdm1xb+0R/87oLo6N7uLYNI9OrYEhamv6cj083Ws+TPVNsPXmbsQW\nJEiqIxMEPDmxB8xN9LH1eBJuphdruFMiInoQBhjqlCyUXfB0r/mQQcB/o9cjt0LazenMjPTxTJgf\nGkQR3+yIRnmVtHU1RESkHgYY6rTczd0wy3saKuoq8c21taiqq5JUx8fVApOGdkN+STW+2xPTrFsB\nEBFRyzDAUKc2xNEfI52HIrM8G9/f2IAGUdpmjWFD3ODj0gWXE/IQGZWu4S6JiOjPGGCo0wv3nAgv\nC09czbuOvcmHJdWQyQQ8FeYHE0M9bIhMwO0sLs4jItImBhjq9OQyORb2nAsrpSX23TqMyznXJNWx\nMDXAU2E9UFcvYuWOaFRW12m4UyIi+gMDDBEAEz1jPNN7AfTl+vjhxi9IL8uUVKeXuxXGDXJBTmEl\nfjgQx/UwRERawgBD9DsnEwcs8J2FmoZafHN1LcpqyiXVmTrCHR6OZjh3IxsnrkoLQkRE1DQGGKK7\n9LXthfFuY5BfVYhvo9ehvqFe7RoKuQzPTPaDkYECPx2KR3pumRY6JSLq3BhgiP5kXLcx6GPTEwlF\nSdicsOvhB9yHtbkhnsxijSIAACAASURBVJjgi5q6BqzccR3VteoHISIiejAGGKI/kQkyzPedBUdj\nexxPP41T6eck1envZYPRA5yRkVeOnw7Fa7hLIqLOjQGG6D6UCgM803sBjBVG2BC/HTeLkiXVmRno\nCVc7U5y4momz17M03CURUefFAEP0ANaGVljYcx5EiPj22joUVhWpXUNPIcOzk/1goC/H9wfikF1Q\noYVOiYg6HwYYoiZ4W3oi3DMMpbVl+Oba96ipr1G7hp2lERaEeqO6ph4rt0ejto7rYYiIWooBhugh\nRjoPQYCDP1JL0/Fj7GZJ93YZ3MMew3s7ICWnDBsjE7XQJRFR58IAQ/QQgiBglvdUdDNzxcXs33A4\n5ZikOnOCveBobYxfo9JwKS5Xw10SEXUuDDBEzaAnU+CpXvPRxcAcOxL3ITovRu0aBnpyPDfZD/oK\nGVbtuo7NRxNRUcXtBoiIpGCAIWomcwNTPN1rPhQyOdZc/xnZ5Tlq13CyMcFzU3rCxFAPe8/expvf\nnMGhi6mo+//27jy6zfrO9/j70W5tlmRbdrxvSUx2EmBIIJQlASa0MKyhNGk7d7odbu+5M6czndwU\nCr30tpOeM+fMncJtKdBbSNtLWmgpFAiEQmhKEgINhOxOvO+2bMmSbUm2lvuHZMWOnWDHiyT7+zrH\nx4klPfo5X0nPJ7/tCV/aVbCFEGK+kgAjxCSUWIt4oOoeAuEAPz36CwaG/JM+xsrKbH74tau5+zPl\nhCMR/t9bZ/jOUwc5dLJDrp0khBATJAFGiEm6Km81NxVfR+eAi/974tdEopPvPdFp1dy2tpR/+/pa\nNl5RRI83yE//cJzvP/chpxvdM9BqIYSYWyTACHEJ/q5iE0sciznRfZqXa3Zf8nEsRh2f37CQ//W1\nq7nqMid1bT52/Poj/vdvj8g1lIQQ4iIkwAhxCVSKir9f+nmcGdnsadzLB+0fTel4TlsG37hjGQ9/\n6QoWF9k4UtPNd39+iF+8fhK3LzhNrRZCiLlDAowQl8ioNfL1FV/GoDbwq1O/pcHbNOVjli2w8u0H\nLue/37OCBVkm/nykjf/x5AF+9+da/EFZsSSEEMMkwAgxBXkmJ3+/9POEImF+dvQ5eoO+KR9TURRW\nVmbzvf9yJV/+2yqMBg1/3F/PticP8Ke/NsuKJSGEQAKMEFO2LPsybi+/FU+wl6ePPcdQZHp6StQq\nFdetzOeHX1vLndeVMxSK8Ks91Tz09Pt8eKpTViwJIeY1CTBCTIONJdezxrmS2t4GfnP699MaLvQ6\nNZ9bF1uxdNPqQrp7A/yfl47xg51/pbpp8heYFEKIuUACjBDTQFEUtlx2L0XmfPa3fcC7Lfun/Tms\nJh1fuHkR3//K33BFlZOaVi//9qvD/PjFT2jr7p/25xNCiFQmAUaIaaJT6/jaii9h0Zp58cwrVLvP\nzsjz5DqMPPh3y/jO1jUsLMzkozMuHn76EM/tPkVvn6xYEkLMDxJghJhGDoOdryzfioLC08d+icvf\nM2PPVVGQybYvrOa/3b2cXEcGez9uZduTB3lpXy2BQVmxJISY2yTACDHNKm1l3LfoDvqHBnjyk18Q\nCM1cr4iiKFy+MIf/+Q9X8cVbF2PQqXn5vXq2PXmQdz5qkRVLQog5SwKMEDPg2oKrua5gLa397ew8\nueuSLjcwGWqViutXFfDDr1/N311bRnAwzM43TvPdZw5xuLpLViwJIeYcCTBCzJB7Ft5Opa2Mj7uO\nsbv+T7PynAadhtuvLePfvrGWGy4voNPt5/HfHeWHvzrM2ZbeWWmDEELMBgkwQswQtUrNV5ZtxWGw\n82rdHo50HZu158406dh6y2Ie+8pVrF6Uw9nmXn6w86888bujtPcMzFo7hBBipkiAEWIGWXRmvrb8\nS+hUWp498Tytfe2z+vwLskx8867l/I8tq6kosPLX6i4eeup9dr55mt7+wVltixBCTCcJMELMsCJL\nPluXbCYYHuSnn/yCvqHZ37NlYaGN7VvW8F/vXEaOPYN3Drew7ckDvPxeHcHB8Ky3RwghpkoCjBCz\nYLVzBbeW3kR3oIefH/sV4cjshwZFUViz2Mlj/3AVW29ehF6j4qV9dWx78gDvftxCOCIrloQQ6UMC\njBCz5LayjSzPXsJp91l2Vf+ewfBQUtqhUau4YXUhP/z6Wm6/phT/YIhnd8dWLH18xiUrloQQaUH9\n6KOPPprsRkzWwMDMjd2bTPoZPb64dOleG0VRWJpVxVHXCU70VHOw7QPUKg0F5nzUyuz/X0KrUVFV\nYufaFQsIDIY5Xt/D+yc6ONXoYUG2EYfFMKHjpHtd5jKpTeqS2kyMyaS/4G0SYM4jL6rUNRdqo1Vp\nuCJ3FYqicNZTy1HXCQ62fYhOraPAnIcqCUHGoNOwqjKbK6uc9HiDHK/vYd+RNlpc/RTnmjFnaC/6\n+LlQl7lKapO6pDYTc7EAo0TTsL+4q8s3Y8fOybHM6PHFpZtrtfEN9rGnYS9/btnPUCRElsHOraUb\n+Ju81ahV6qS163Sjm9+8U0Ndmxe1SuGGywv47DWlWI26ce8/1+oyl0htUpfUZmJyciwXvE0CzHnk\nRZW65mpteoNe3mx4h7+0vk8oEiI7I4tNpRu4Mu/ypPTIAESjUT483cWLe2vo9Pgx6NRsurqEjVcW\nodeODldztS5zgdQmdUltJkYCzCTIiyp1zfXauAMe3mx4h/daDxGOhsk15rCpdAOrc1cmLciEwhHe\n/biVP/yljj7/EDazjjvXl3PN8gWoVAow9+uSzqQ2qUtqMzESYCZBXlSpa77Uptvv5o2GP3Gg7UMi\n0QgLTLlsKtvIqpxlSQsy/mCI199v4M1DTQyGIhTkmLj3+gqWl2fhdFrnRV3S0Xx5z6Qjqc3ESICZ\nBHlRpa75VhuXv5vX6/7E++1/JUqUAvMCbiu7mRXZS1AUJSltcvuCvLSvlr8cbSMahapiG1+9cwU2\ngzppbRIXNt/eM+lEajMxEmAmQV5UqWu+1qZjoIvX697iw46PiRKl2FLAbWU3szSrKmmhobmrjxf2\n1vBJTTcAGXo1+VkmFmSbyM8ykZ9tIj/biMNqQCXBJmnm63smHUhtJkYCzCTIiyp1zffatPV38Frd\nHg53fgJAmbWY28pvpsq+MGlB5mSDm/0nOqhr6aWjZ4BwZPTHiV6rZkGWkQVZsUATCzYmcjIzEnNo\nxPQLDIZw9QZw5ljQRiPSO5aC5vvn2URJgJkEeVGlLqlNTEtf26irW1dklvHZ8ptZZK9ISnuG6xIK\nR+h0+2l19dPa3R/77hqgvaefUHj0x4xGrWJBVjzQZI0INrYMNGrZIPzTDIXCuHoD5748frp6A3T3\n+unyBOjzn9vlOcuqZ2lZFsvKHCwptWM0XHxfHzHzwpEIuU4rLldfspuS8iTATIKcJFOX1Ga0Rl8z\nr9Xt4ajrJACLbBV8tvwWKmyls9qOT6tLOBLB5QmMCDYDtHb309bdz+DQ6OsvqVUKuQ7jqFCTn2Ui\n12FEq5k/wSYUjtDjC+Ly+OMhxY/LEwsrXb1+evvG3wBNo1bIyswgO9NATqaBUBQ+Ot1JfyAEgEpR\nKC+wsqzMwbKyLErzLNITNguGQmFqWrycbHBzqtFNbasXs1FLkdNMaZ6F0jwrpXkW7Ba99JadRwLM\nJMhJMnVJbcZX723k1do9nOg5DcBljkXcVnYzZZnFs/L8l1qXSDRKT2/gXKgZ0XMTOO8K2YoCTvuI\nYBOfZ5OXZRyzL006iESiePqCdCUCSmBUWOnxBRnvk1mlKDiserIzDWTbhoNKBtk2A9mZGWSadaPm\nHOXkWOjo8FLX7uV4bQ/H6nqoae1NHNucoWVJqZ1lZVksLXNgt1x411MxcaFwhLo2L6ca3JxscHO2\nxUsoHAvrigJFOWYCQ2E63f5Rj7MatZTkWSnJs1CWZ6FEQo0EmMmQk2TqktpcXI2nnlfr3uS0+ywA\ny7KquK3sZoqthTP6vNNdl2g0itsXTASbtsRwVH+iJ2GYAmRlGkb11uRnm1iQZSRDr5m2Nk1WNBrF\n2z9I16jek3hA8QTo9gbGzBeC2O9js8QDSrwnJdsWDymZBuxWPWrVxHuixqtNf2CIk/VujtV1c7S2\nB7cvmLitMMfMsnIHy8ocLCy0zater6kIRyI0tPdxqtHNqQY31c2eUb2LRU4zVcV2Liuxs6goE6NB\nS06OhdqGbhrafdS3++LfvXR7g6OObTXpKM2zUJJroXRBrLfGZtbNm1AjAWYS5CSZuqQ2E3PGXcMr\ntW9S01sHwMrspWwq20ihJX9Gnm+26hKNRvEODCXCTGt3P23xP3sHxl7Z22HVxyYPnzeB2DQNc0Ci\n0Sj9gRBdHj/d8WGd4SGe4aAyFIqM+1irSRcPKPGQMiKgOKyGaQ0Nn1abaDRKa/cAx2u7OVrXQ3WT\nJ9FunVZFVbE9NtxUnkWuPWPenDQ/TSQapbmzL9HDUt3swR8812uYn22iqthGVbGdxcU2LONchuNC\ntfEODNLY7qNuRKjpOS/UZJp0lORZEsNPwz01c5EEmEmQk2TqktpMXDQa5bT7LH+sfYM6byMAl+cs\nZ1PZRvLNedP6XKlQF9/AIG3dA6ODTffAqN6FYZkm3YjemliwWZBtGnOtJ39wZEAZPcTj6g2MGeYa\nZjJoEsFkOKTk2AyJuSmzOeQ12doMDoWpbvJwrK6Ho7XdtHUPJG7LzjSwrDw2GfiyEntSe7hmWzQa\npdXVz6lGD6fi81hG9gY67RlcVmKnqthOVbGNTPOnh4nJ1MbbP0hDh4/6Ni/18R6b81/bmWYdpbmx\nYafSBbE5NbYJtCPVSYCZhFT4MBbjk9pMXjQa5URPNX+sfYNGXzMKCmtyV7KpdAO5Jue0PEcq12Ug\nEDo3BDVirk23NzDmvuYMLXlZRoaGIrh6/WOGq4bpdWpyRg3xZIzqUTEaUufEPtXa9HgDHKvr4Vht\nN8fr3fiDsX8TtUqhoiAz3jvjoDjXMqf2+4lGo3S6/YlJt6caPXj7z02czrIaqCqxJUKLw2qY9HNM\ntTbe/sF4mPEmhqHODzU2sy7RQ1Ma/5pIuEolEmAmIZU/jOc7qc2li0ajHOs+yR9r36S5rxUFhavy\nVvO3pRvIMWZN6djpWJfAYIj2noHEUu/hgNPl9qPRqMjONJB13gTZ7EwDObYMTAZN2gylTGdtwpEI\nda2+xNyZ+jYvwycPi1HL0rLY3JmlZVlkmsa/cnkqc3n8nIzPYTnV6BkVBmxmHVXxsHJZiZ0cW8aU\nn28m3je9/YM0tHupb4vPq+kYG2rsFn1sPk1ebE5NSZ41peslAWYS0vHDeL6Q2kxdJBrhk67jvFq3\nh9b+dlSKiqvz1nBr6U1kZTgu6ZhzqS6hcASVSpkzvQkzWZs+/xAn6ns4VtvD0bruUUu7i53mxHBT\nZWFmSu7t4/YFY3NY4qHF1XuuV85i1MaGg0piQ0J5DuO0h9bZet/09gUTw07Dc2o85y3Dt1v0sYnC\nI5Z0W1Mk1CQtwFRXV/Pggw/y5S9/mS1btgDw3HPPsWPHDg4dOoTJZALg5Zdf5tlnn0WlUnHfffdx\n7733XvS4EmDmJ6nN9IlEI3zU+Qmv1r1Fx0AnakXN2vwrubXkRuwG26SOJXVJXbM5wbqlqz823FTX\nTXWTJ7F5oV6n5rJie2J1k9NunPH2jMfbP5hYJXSy0UNHz7n5PSaDhkVF8SGhEjsF2aYZ72VL5vvG\nEw81De3n5tX09o8ONQ7ryJ6a2DDU+fPEZsPFAsyMDdYODAzw2GOPsXbt2sTPXnrpJbq7u3E6naPu\n98QTT/DCCy+g1Wq555572LhxIzbb5D5EhRATp1JUrMldxeXOFXzY8TGv1e3hLy0HOdj6AdcUXM0t\nJTeQqbcmu5kiTSiKQqHTTKHTzK1/U0xwMMzpJne8d6aHj8+6+PisCwCnLSMeZrKoKrFh0M3MaajP\nP8TpRk8itLS4+hO3GXRqVlRkJYaEipzmebWhn82sZ1WlnlWV2YmfuX3BRA/NcLj56IyLj864EvfJ\nsurH7FMz3gqr2TJjAUan0/HUU0/x1FNPJX62YcMGzGYzr7zySuJnR44cYfny5VgssZS1evVqDh8+\nzI033jhTTRNCxKkUFVflrWaNcyWH2g/zev1bvNv8Hvtb32d9wVpuLrkBi86c7GaKNKPXqVlRkc2K\nitgJ0uXxx3tnejhR38Pbh1t4+3ALapXCwsLMxHBTkdN8yT0f/mCI003nVgk1dfQl5ujotCqWljmo\nKrZxWYmDkjzzpPbTmQ/sFj12i55VC2M1i0ajePoGR00Srm/3cbi6i8PVXYnHZVn1rF+Zz+3XlM16\nm2cswGg0GjSa0Yc3m8d+ELpcLhyOc2PvDoeDrq6uMfcbyW43otHM3FLEi3VZieSS2syc23NvZNOy\n69hbf5AXT7zG2037eK/1fW5deD2fq9qIVX/hICN1SV2pUJucHAuXLXRyL7F5Rqcb3Bw+3cnh052x\npcmNHl7YW4PNomf1YieXL3Zy+aKci66YCQRDnKjv4ZMzXRytcXG2uZdIfHNArUbF8srs2FdFNouK\n7Sm5KV8q1OZinE5YVH6ulyYajdLdG+Bssyf21eShprmXZtdAUn6X1FnvFzeRKTlu98Cn3udSyXh+\n6pLazI6V1pUsuWopB1oPsbv+bf5w6k12n9nLDUXrualoPUbt6DkMUpfUlaq1cVp03HpFIbdeUYh3\nYJAT8d6ZY3U9vP1hE29/2IQClORZEsNNxblm6tp8iYm3da3exG7GapVCeb41MSRUWWBFO+I/uR53\n/wVakjypWpuJqMg1U5Fr5pY153b5nqnfJSlzYCbK6XTicp0bY+vs7GTVqlVJbJEQQqvScF3hOq5e\ncCXvtb7PGw1vs7v+T+xteo8bi9dzY9G1ZGimvpRUCKtRx9VL87h6aV5ih9vhvWfONPdS3+7jj/sb\nRj1GUaA0z5rYi2VhgQ29Lv2uiSWmJukBZuXKlTz00EN4vV7UajWHDx9m+/btyW6WEALQqbXcUHQt\n1+RfxZ9bDrCnYS+v1e1hb9NfuKn4M1xfuA5I7W5wkT5UikJxroXiXAubri4hMBjiVIOHY3XdtHT1\nU5JnoarEzqJCW0ptGCiSY8aWUR87dowdO3bQ0tKCRqMhNzeXdevWsX//fj7++GOWL1/OqlWr+Pa3\nv83u3bt55plnUBSFLVu2cPvtt1/02LKMen6S2iRfIBTk3eb3eKvxXQZCfsxaE5+r2kC22onDYMOm\nt6FTT/1aQ2J6yHsmdUltJkY2spsEeVGlLqlN6vCHArzTtI+3m/bhD43elt+sNWE32HDobdgMNuz6\nTBwGG3aDDbveRqbeikpJvQmVc5G8Z1KX1GZiUnoOjBAi/WRoDGwq28j1hddQG6ihvqsNd8CDO+jB\nHfDQ3t9Bk69l3MeqFBWZOms80GTiMNixGTJx6M+FHJN2+nc+FSIZItEIHQNdNHqbafQ10+hrobWv\njWyTgwprGYvslSy0lWPSJmeDv3QmAUYIccmMWiM35K+jyzL6f5LRaJS+of54oOkdFW7cQQ89AQ91\nvQ3UMn4HsFalxW7IxKGPhRu73hbrxRkOOQYbenVqbHUuxLCRYaXJ10KDr5nmvlYGw+d2uVUpKnIy\nsunq76bZ28a7zftRUCi05LPIVsEiewWVtjIMmslfIHK+kQAjhJh2iqJg0Zmx6MwUWwrHvU84EsY7\n6KNnnHDjCXhwB3vpHHCN+1gAk8Z4wXBj19uw6a2oVem3MiUcCRMMDxIMBy/+PfTp9zFodWTrs8gz\nOsk1ORPfJfxNXSQaoXOgi4Z4WGn0NdN0XlhRUFhgyqXYUkiRtYASSyEF5gXo1Drsjgw+qD1Btfss\n1e4a6nobaPK18KemP6NSVJRYilhsr2ChvYLyzFKZWzYOmQNzHhmXTF1Sm9Q0k3UZDA/iDsZ7cAIe\neoLnws1w8Bl5whhJQcGqs8QmF8fn5AwPWw0HHYv20nd+jUajhKLhWFiYQJgYGTwGL3KfoUhoKv9k\nKCgYNHr0aj3BSBD/UGDMfex6G3kjAk2e0UmeySm7Ll/AcFhpjAeVRu+Fw0qRpYBiayHFlkIK42Fl\nPOe/bwbDg9T2NlDtrqHafZYGXzORaAQAjaKmLLOExfZKFtkrKbEWolHNj/4HmcQ7CXKSTF1Sm9SU\nzLpEo1EGQv7zhqh66Qm4Y0NXQQ+eYG/iRHA+jUqDTZ+ZmItj12eColwklIz+2YWOO1EaRY1erUen\n1qHX6NGrdejVw99H/nn09+GAMt5tGpUmEcqys83UtLTS3t9J+0An7f2ddMS/9w56x7THpDWSazwX\naPJMTnKNsRVm82XidSysuOLzVZpp9LbQ3NdC8EJhxVJIsfXiYWU8n/a+8YcC1HjqEoGmua+NaHzI\nVafSUmEriweaCoosBXO2PhJgJkFOkqlLapOaUr0ukWgE76BvVLjxBHrpGTFs5Rvs+9TjaFXa8UPF\nBYKH4YK3x77r4mFjJl2sNv6Qn/b+LtoHOumIB5yO/k66/N2JE+XI3z3XmDOm1ybHmI02jXsCRoaV\nJl8LDd7mccNKnskZCyqWQoqtBRSY86c8DDfZ903fUD9n3bWcdtdQ7amhvb8jcVuGxkClrTwRaBaY\ncudMoJEAMwmp/mE8n0ltUtNcqMtQeAhP0Is76EFBQa8ZGzjS8YRwKbUZioToGnCNCjaxnpsuhiJD\no+6rUlRkGxyj5tfEem9yUm6n5kg0QteAiwbfuTkrzb5WAuFg4j4jw8pw70qhZephZTxTfd/0Bn2c\ncZ9NBBqXvztxm1lrYpG9Iv5ViTMjO21X9UmAmYS58GE8V0ltUpPUJXVNZ20i0UhsiXw80CSGowY6\n6R8ae326TJ111PyaWMDJIVNnnfGT6XBYScxZuUBYyTU5KR4eBprBsDKe6X7fdPvdVHtqEpOCPcHe\nxG02fWYizCyyVZCVYZ+2551pEmAmQT6MU5fUJjVJXVLXbNXGN9iXmGfTMSLguIOeMffN0BhGzbMZ\nHprKMjguadVYJBqhy9+d2GelyddCk69lbFgx5iQm1xZZCig052PQXPhq1zNtJmsTjUbp8rtivTPx\nQNM3dO6CltkGB4vslfFVTpVk6lP3ciASYCZBPoxTl9QmNUldUleyaxMIBekc6BozHNXpd42ZAK1R\n1DiNOfFem5x4j00uucacxBLi4bDS5G1O9K40+VoJhM+ttBoOK0WWQkqsqRFWxjObtYlEI7T1d8Qn\nBNdwxlMzagftPKMzEWgq7eWYtaZZaddESICZhGS/4cWFSW1Sk9QldaVqbcKRMC5/97nhqBErpILn\nLYtXUHAY7Fh1Ftr6O8aEFacxJzG5NrZ0OfXCyniSWZtINEKTryURaM56ahmMz29SUCg0L2ChvYLF\n9koqbGVkJHFTPQkwk5Cqb3ghtUlVUpfUlW61iUajeIK9o4LNcM9N32B/PKyM3mclXXesTaXahCIh\nGrzNVLvPctp9ljpvI6H4fkSxTfUKY/Nn7BWUZ5ZMarn4VEmAmYRUelGJ0aQ2qUnqkrrmUm3CkXBa\n7qx8Ialcm8HwEHW9DfFAU0ODr2nMpnrDk4JLrUUzuh2AXMxRCCFEWptL4SXV6dRaFjsqWeyo5HNA\nIBSgpree0/EJwWc9dZzx1PJq3R50Ki3rC9Zy18LPzno7JcAIIYQQ4oIMGgNLs6pYmlUFQP/QAGc8\ntbEJwect2Z5NEmCEEEIIMWEmrZFVOctYlbMsqe1Iv60lhRBCCDHvSYARQgghRNqRACOEEEKItCMB\nRgghhBBpRwKMEEIIIdKOBBghhBBCpB0JMEIIIYRIOxJghBBCCJF2JMAIIYQQIu1IgBFCCCFE2pEA\nI4QQQoi0IwFGCCGEEGlHAowQQggh0o4SjUajyW6EEEIIIcRkSA+MEEIIIdKOBBghhBBCpB0JMEII\nIYRIOxJghBBCCJF2JMAIIYQQIu1IgBFCCCFE2pEAM8IPfvADNm/ezP33388nn3yS7OaIEX70ox+x\nefNm7r77bt58881kN0eMEAgE2LBhA7/73e+S3RQxwssvv8ztt9/OXXfdxd69e5PdHAH09/fzzW9+\nk61bt3L//fezb9++ZDcprWmS3YBUcejQIRoaGti1axc1NTVs376dXbt2JbtZAjh48CBnzpxh165d\nuN1u7rzzTm6++eZkN0vE/eQnPyEzMzPZzRAjuN1unnjiCV588UUGBgb48Y9/zPXXX5/sZs17v//9\n7ykrK+Nb3/oWHR0dfOlLX2L37t3JblbakgATd+DAATZs2ABARUUFvb299PX1YTabk9wyceWVV7Ji\nxQoArFYrfr+fcDiMWq1OcstETU0NZ8+elZNjijlw4ABr167FbDZjNpt57LHHkt0kAdjtdk6fPg2A\n1+vFbrcnuUXpTYaQ4lwu16gXk8PhoKurK4ktEsPUajVGoxGAF154geuuu07CS4rYsWMH27ZtS3Yz\nxHmam5sJBAJ84xvf4IEHHuDAgQPJbpIAbrvtNlpbW9m4cSNbtmzhX//1X5PdpLQmPTAXIFdYSD1v\nvfUWL7zwAj//+c+T3RQBvPTSS6xatYqioqJkN0WMw+Px8Pjjj9Pa2soXv/hF3nnnHRRFSXaz5rU/\n/OEP5Ofn88wzz3Dq1Cm2b98uc8emQAJMnNPpxOVyJf7e2dlJTk5OElskRtq3bx8//elPefrpp7FY\nLMlujgD27t1LU1MTe/fupb29HZ1OR15eHuvWrUt20+a9rKwsLr/8cjQaDcXFxZhMJnp6esjKykp2\n0+a1w4cPc+211wJQVVVFZ2enDIdPgQwhxV1zzTW88cYbABw/fhyn0ynzX1KEz+fjRz/6EU8++SQ2\nmy3ZzRFx//Ef/8GLL77Ib37zG+69914efPBBCS8p4tprr+XgwYNEIhHcbjcDAwMy3yIFlJSUcOTI\nEQBaWlowmUwSXqZAemDiVq9ezdKlS7n//vtRFIVHHnkk2U0Sca+99hput5t//Md/TPxsx44d5Ofn\nJ7FVQqSu3Nxcz3eNtwAAA1VJREFUbrnlFu677z4AHnroIVQq+f9qsm3evJnt27ezZcsWQqEQjz76\naLKblNaUqEz2EEIIIUSakUguhBBCiLQjAUYIIYQQaUcCjBBCCCHSjgQYIYQQQqQdCTBCCCGESDsS\nYIQQM6q5uZlly5axdevWxFV4v/Wtb+H1eid8jK1btxIOhyd8/89//vO8//77l9JcIUSakAAjhJhx\nDoeDnTt3snPnTp5//nmcTic/+clPJvz4nTt3yoZfQohRZCM7IcSsu/LKK9m1axenTp1ix44dhEIh\nhoaG+O53v8uSJUvYunUrVVVVnDx5kmeffZYlS5Zw/PhxBgcHefjhh2lvbycUCnHHHXfwwAMP4Pf7\n+ad/+ifcbjclJSUEg0EAOjo6+Od//mcAAoEAmzdv5p577knmry6EmCYSYIQQsyocDrNnzx7WrFnD\nv/zLv/DEE09QXFw85uJ2RqORX/7yl6Meu3PnTqxWK//+7/9OIBBg06ZNrF+/nv3792MwGNi1axed\nnZ3cdNNNALz++uuUl5fzve99j2AwyG9/+9tZ/32FEDNDAowQYsb19PSwdetWACKRCFdccQV33303\n//mf/8l3vvOdxP36+vqIRCJA7PIe5zty5Ah33XUXAAaDgWXLlnH8+HGqq6tZs2YNELswa3l5OQDr\n16/n17/+Ndu2beMzn/kMmzdvntHfUwgxeyTACCFm3PAcmJF8Ph9arXbMz4dptdoxP1MUZdTfo9Eo\niqIQjUZHXetnOARVVFTw6quv8sEHH7B7926effZZnn/++an+OkKIFCCTeIUQSWGxWCgsLOTdd98F\noK6ujscff/yij1m5ciX79u0DYGBggOPHj7N06VIqKir46KOPAGhra6Ourg6AV155haNHj7Ju3Toe\neeQR2traCIVCM/hbCSFmi/TACCGSZseOHXz/+9/nZz/7GaFQiG3btl30/lu3buXhhx/mC1/4AoOD\ngzz44IMUFhZyxx138Pbbb/PAAw9QWFjI8uXLAaisrOSRRx5Bp9MRjUb56le/ikYjH3tCzAVyNWoh\nhBBCpB0ZQhJCCCFE2pEAI4QQQoi0IwFGCCGEEGlHAowQQggh0o4EGCGEEEKkHQkwQgghhEg7EmCE\nEEIIkXYkwAghhBAi7fx/gBXAQ4KfvZUAAAAASUVORK5CYII=\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": "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": 671
+        },
+        "outputId": "9ae7b4da-ecef-4f78-d928-fe9957f2d80a"
+      },
+      "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": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 154.36\n",
+            "  period 01 : 112.45\n",
+            "  period 02 : 100.47\n",
+            "  period 03 : 84.75\n",
+            "  period 04 : 77.26\n",
+            "  period 05 : 74.86\n",
+            "  period 06 : 73.55\n",
+            "  period 07 : 72.69\n",
+            "  period 08 : 72.06\n",
+            "  period 09 : 71.48\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   71.48\n",
+            "Final RMSE (on validation data): 71.75\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lzo4kIiLS6qnAONgQ+yDMJjM1thQAtiRpGUlERORCqcA4mK+7D32DLia3OhNP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\nOAoYERERqTgKGBEREak4/wcjoZEw7OStuAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MrwtdStNJ6ZQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Optimizer\n",
+        "\n",
+        "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+        "\n",
+        "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate.  This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training.  You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`.  Note that you may need to use a larger learning rate with Adagrad.\n",
+        "\n",
+        "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad.  To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "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": 671
+        },
+        "outputId": "5abc3a50-d6c1-45b3-d568-5506eeaa5289"
+      },
+      "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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 85.73\n",
+            "  period 01 : 72.36\n",
+            "  period 02 : 71.03\n",
+            "  period 03 : 71.25\n",
+            "  period 04 : 70.60\n",
+            "  period 05 : 70.07\n",
+            "  period 06 : 70.19\n",
+            "  period 07 : 72.44\n",
+            "  period 08 : 71.34\n",
+            "  period 09 : 69.17\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.17\n",
+            "Final RMSE (on validation data): 69.64\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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qwOeyS0udOnVi69atAGRmZhIcHMzrr7/ORRddxJQpU066zWuvvcby5csB2Lt3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\nAAAAAElFTkSuQmCC\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": 671
+        },
+        "outputId": "49be671d-1c49-4230-e106-dc2465c643fd"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 219.58\n",
+            "  period 01 : 127.02\n",
+            "  period 02 : 116.33\n",
+            "  period 03 : 110.04\n",
+            "  period 04 : 101.01\n",
+            "  period 05 : 87.08\n",
+            "  period 06 : 75.25\n",
+            "  period 07 : 71.83\n",
+            "  period 08 : 71.00\n",
+            "  period 09 : 70.43\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   70.43\n",
+            "Final RMSE (on validation data): 71.20\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Hx2zFCMtnZ4bGwYiIiLQWnYG5AL4WH3qF9cAUUMqx0gIcRRWeLklERKRDUIC5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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "twYgC8FGyxm6"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's print a graph of loss metrics side by side."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8RHIUEfqyzW0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 376
+        },
+        "outputId": "29446a3e-c003-4be8-9dae-0ea0ef8fb78e"
+      },
+      "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": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AVq1axahRoxgzZgyfffaZI0MSQghhZz/88D3du99BZmbmFT///POVvPfeYofGcOzYEaZO\nffCy93/6af11r2P58mXs2/fHVT9/7rnpFBUV3lB8juSwmd22bdvG4cOHWblyJRkZGYwYMYI77riD\nsWPHMmjQIP73v//x/vvvM3XqVN566y1iYmLQ6XSMHj2avn374uvr66jQhBBC2NEPP6wlNDSMjRvX\nM3z4aFeHY1NSUsLKlR/Rs2ef61p+4sR7Kv181qw5dojK/hxWyDt27Ejr1q0B8Pb2pqCggOeeew4P\nDw8ATCYT+/fvZ8+ePbRq1QqjsXz6uXbt2hEbG0uvXr0cFZoQQgg7yc7O4sCB/Uyf/iwfffShrZDv\n3Pkbr7/+b8xmP/z8/AkJCaW0tJSXXnqelJRkCgoKuO++B+ncuSs7dmw/v6w/9es3wNfXl9tua88n\nn6wgPz+fmTP/xYYNm9m48UfKysro1Kkz9933IMnJScyc+Qw6nY7GjZtcFtvrry/g6NEjvPrqXFq0\naMm2bVtJTU1h1qyX+eSTFcTF7ae4uJjhw0cxdOhwXnrpeXr06E1WViZ//LGbzMwMTp06ybhxExky\nZDijRw/lww9X8p//zMffP4BDhw6QlHSOZ599kaZNm/Hf/77C3r1/EBnZkFOnTjJr1ssEB4c4/Hfg\nsELu5uaGwWAAICYmhm7dutleW61WPvroI6ZMmUJqaipms9n2PbPZbBtydwZrbi7J+3dBi7YoiuK0\n7QohhD19uuEIOw4m23WdHZtZGNurcaXLbNiwnujoLtxxRyfmzXuRlJRkAgIsLF78JjNnzuaWW5rw\n1FOPEhISSk5ONrffHsXAgUM4e/YMM2c+Q+fOXXn77TeYOfMFGjW6hSlTHqBjxzsAOHr0CB9//AWh\noX5s2LCZhQuXoNFoGDt2GHfeOY6YmE/o3bsfY8f+lRUrlnHkSHyF2MaNm0hc3D6eeuoZ1qxZTVLS\nORYtWkpxcTFBQSE88sgTFBUVMnbscIYOHV7hu0ePHmHRoqWcOXOa5577J0OGVPy8uLiYBQve5Kuv\nYvj++2/RarX88cdulixZzvHjx7jvvvF2+A1cH4c3TVm/fj0xMTEsXboUKC/iTz/9NFFRUXTq1InV\nq1dXWP562qObTAa7zb2b8Osmjr73Ps1n/hNzh/Z2WaeoXGWT/wv7kTw7R3XJs6fBHTc3+56MeBrc\nr7l/mzat5+GHHyYoyJdBgwayffvP3HvvvSQlnSM6uvzf1M6dO1FUVERkZAiffHKYRx55AI1GQ15e\nDgEBRpKTz9G5c0cAevfuidVqxdfXQIsWzQkN9QPA39+Hxx9/CK1WS1ZWJlqtlYSE04wY8RcCAoz0\n6tWN2NjfKsRbVFQPrVZDQIARo1FPu3ZtsVi8AbBaC3nkkQfQ6XRkZWUSEGBEr9fh4+NJWVkhHTu2\nJyjIF6NRR0FBHgEBRtzcNPj7e6HX6+jWLZqAACONG0dw7Fg86emJtG/fjsBAHwIDbyM0NBSzuZ5T\njg+HFvLNmzezaNEilixZYhs6nz59Og0aNGDq1KkAWCwWUlNTbd9JTk6mbdu2la7Xnl1wSkwWABK3\n7sDa4PKhGWFfAQFGu3evE5eTPDtHdcrz0Kj6DI2qb/f1VrZ/yclJ7NmzhxdffBlFUSgsLMRo9GLI\nkNGAYvtubm4hxcXFfPxxDElJqbz22mKys7O5//6JpKTkoKqqbdn8/GJKS0vJzMxHVcvXUVyczXvv\nLWXp0v9hMBiYOHEs6el5FBWVkJVVSEpKDunpuRQXl1aINz09j9LSMlJScsjJKaSkpHw7u3b9zubN\nv/Daa2+j1Wrp27crKSk5FBaWkJVVQE5OIcXF5d/Lz8/Hai3/2WotIzU1l8LCEnJzi0lJySErq4CC\ngmKyswsoKvpz+2VlKunpeXh42Of4cEn3s5ycHObPn8/ixYttN66tWrUKnU7Ho48+aluuTZs27N27\nl+zsbPLy8oiNjaVDhw6OCusy+kaNUTw8yI/b77RtCiFEbbB+/VpGjBjDBx98zLJlH/Hxx5+TnZ3N\n2bNn8PcP4NSpE6iqyq5dvwOQmZlJcHAIGo2GTZs2UFJSAoDZ7MfJkyewWq3s2LH9su1kZGRgMpkw\nGAwcOnSQc+fOUVJSQv36DTh4MA6A2Nidl31PUTRYrdbL3s/KysRiCUSr1bJlyyas1jJbLDcqNDSM\nQ4cOoqoqJ04c59y5xJtaX1U47Ix8zZo1ZGRk8Pe//932XkJCAt7e3kycOBGARo0a8fzzz/Pkk08y\nefJkFEVhypQptrN3Zzh+NION4WPpePIrStLT0V10vV4IIcTVrV+/lhkzZtleK4rCwIFDWL9+LQ8+\n+DAzZkwjKCgYiyUQgB49evHMM08QF7ePwYP/gsVi4f333+WBBx7mX//6B8HBITRoEIGbW8VLp82b\nN8fT08BDD91Hq1ZtGTZsJP/+9zymT5/JzJnP8PPPP9Go0S2Xxefv709paQkzZkwjOrqL7f0OHe7g\nf//7gKlTH6Rr1+5ER3fh1Vdv7o70Zs1aEB5enwcfnMQttzQlIqIhGo1zpmpR1Ou5KF3N2HMoK37f\nOX785iCNU3dw+4gofDp3tdu6xeWq01BkbSZ5dg7Js3389ts2wsPrExwcwvz5L9G2bXv69Rtg+7wm\n5Lm4uJgff1zHwIFDKCgoYPz40Xz66ddotfY5X65saN3hN7tVd2ERJgDSDSHkx+2XQi6EEE6mqir/\n/OdTGAz1MJnM9OzZ29UhVZm7uzsHD8YRE7MSjUbh/vv/z25F/FrqfCE3eHlgCTaSmhBETtwOgsrK\nUJw0HCKEEALuuKMTd9zRydVh3LTHH3/aJduVigU0bBJAmeJGWqknxWfPuDocIYQQ4rpJIae8kEP5\n8Hqe3L0uhBCiBpFCDjRoaEajUWzXyYUQQoiaQgo5oHPXEhzuQ66HH1lHTlBWUuzqkIQQQojrIoX8\nvAt3r6fpAig8csTF0QghRM1RnduYXq+pUx/k2LEjrFmzmk2bfrrs88GDK7+T/kK71G3btvLllzE3\nHMeNkEJ+Xnhk+UQw6YYQ8vbvc3E0QghRc1zcxrSmGzRoKN2796zSdy60SwWIiopmxAjntnKt84+f\nXeAf6IVeryW9NIS8uN8IcHVAQghRA1TnNqbTpz/FnXeOo23bdhQVFTJ+/Bg++uhz5sx54bIYLnjv\nvfJpxYcNG8WsWTNITk6iefMWts937NjOkiWL0Ol0GI1GXnhh7mXtUo8dO8rUqX/n008/5scf1wHQ\ntWt3Jky4h5deev6KLVBvhhTy8xRFITTCxNGDpWScyiQ8Jwc3J04VK4QQN+OLI9+wK3mvXdd5m6UV\nIxsPqXSZ6tzGtHv3nvzyy2batm3Hjh3b6dgxiry83CvGcKkdO7ZRWlrK4sXvs3//PmJiVgLlfUSe\ne+5FQkJCmT37WbZv//WydqkACQln+e671bz77ocAPPjgJHr27ANc3gJVCrkdhUWaOHowhXTPEPIP\nxGG8/Q5XhySEENXa+vVrmTRpMm5ubvTs2Zsff1zHXXdNIDExkVtuKT9LLj8jLsJo9ObAgf2sWvUF\niqIhOzsLgKSkRJo0KS9mUVHRtkYnjRvfgru7OwB6vZ6pUx/Ezc2NzMxMsrOzOXHiuK043nZbB7Zt\n21ohts6du/HRRx8yZcpjbN68id69+101hksdP36cVq1aA9Cy5a14eHgA4Ovry7x5L2K1WklIOEv7\n9h2v+P3Dhw/RsmUr2+xurVq1sf2h0abNbQAEBAQSZ4cnpaSQXyQ84qLr5HH7pZALIWqMkY2HXPPs\n2d6Sk5OIi9vHm2/+t0Ib07vumlChYciFlh4//PA92dnZvPXWElsb00spyp891XU6HQBnz55l5cr/\nVWhjemG9iqI5/3PZZesyGo34+1s4deoE+/b9wT/+8c/riuF81LZ1X7wPc+bM5pVX/ktERCQLFsyr\nJDsKF7cyKSkpsa3v4qYw9mh3Ije7XcToo8fH5EmGZxC5cXF2SbAQQtRW1b2NKUC3bj344IOltrPj\nq8VwqYvXvXfvHoqLyx9LzsvLJTAwiJycHGJjf7cV6EvbpTZp0pR9+/ZSWlpKaWkpcXH7adKk6Q1k\n+dqkkF8iPNKEVaMjLV9DSVKSq8MRQohqa/36tQwePNT2+kptTKdNe7xCG9OtWzfz2GMP4enpeVkb\n02eeeeKabUx//HGdrY3pmDF/5dtvV/HEE1PJyblyd7Ru3Xrw44/rbI1YrhbDpaKiOlNcXMTUqQ/y\n44/rCAiwADBy5Bgeemgy8+e/xPjxd7NixTIUBVu71AuCg0P4y19G8MgjDzJlygMMHTqMoKDgm0v4\nVdT5NqZQsUXe8fhUvv9iHxHpu+k04FZ8e/Wx67bquprQjrA2kDw7h+TZPmpDG1NHkzamVRBS3xdF\n+fM6uRRyIYRwrNrQxtSVpJBfwkOvJTDEm3NnVLLjtxBitaJcMswjhBDCfmpLG1NXkWvkVxAWYQJF\nIU3xpfD4MVeHI4QQQlyVFPIrCLtoulbphiaEEKI6k0J+BZZgIzp3N+lPLoQQotqTQn4Fbm4aQhv4\nUqDzJuNUEtaCAleHJIQQQlyRFPKrsM3ypg+i4NBBF0cjhBDVlzPbmB45cphTp05e17JpaanMn//S\nVT93RctRR5BCfhVhkeX9ycuvk0tbUyGEuBpntjHdtGkDp0+fuq5l/fz8efrpf131c1e0HHUEefzs\nKnxMnngZPUi3BpMb9xMWVwckhBDVkD3amE6d+iDt2nVgx47taDQaBg4czJo136DRaHjttbdt2zp6\n9Ahff/0FmzZtwGQy8cILM4mK6ozJZCI6uisLFsxDq9Wi0WiYPXsueXl5zJgxjffeW86ddw5n2LCR\n/PLLZoqLi3nttYVs3LiBY8eOMmrUWF566XlCQkI5cuQwTZo05ZlnZnLkyGFeeuk5vLyMNGvWgszM\nDP71r+ddlOmrk0J+FYqiEBZp4uAfRaRllBKenobO7OfqsIQQ4opSPvuEnJ077LpOY4eOBIy5q9Jl\n7NHGFMrPnt9++z0eeug+srOzWbhwCQ8/fD/Hjh0hKKgDAI0aNeaOOzrRo0dvWrS4ldLSUqKioomK\nimbHjm08/vg/aNKkGUuWLGLduu/o3LmbLU6r1Ur9+hGMG3c3zz03nZ2X5OrQoQPMmvUyJpOZESMG\nkZOTw/vvv8M99zxA9+49mTnzGfR6vV3zay8ytF6JcHkMTQghKrV+/Vr69OlfoY0pcFkbU8DWQvSh\nh+7jpZeer9BCtEWLlkB5Qb/llvLmImazmdzc3Eq3f+F7JpMfixcvZOrUB1m/fi1ZWZe3J724fWhe\nXsX1hoaG4+fnj0ajwd8/gLy8XE6ePEHr1m0A6NKl22Xrqy7kjLwSoQ18gT8LuU81/kUKIeq2gDF3\nXfPs2d7s2cb04kYpVWnzqdWWtzp97bVXGT9+ElFR0Xz00XIKCvIvW7ay9V7aqEVV1QptUi9ur1rd\nOPSMfP78+dx5552MGjWKdevWkZiYyMSJExk3bhyPPfaYrS3cqlWrGDVqFGPGjOGzzz5zZEhV4mlw\nJyDIiyy9hewDh1DLLu93K4QQdZW92phWhaIol7UMBcjKyiQ0NIzi4mK2bfuF0tLSm96/0NAwWyvT\nbdu23vT6HMVhZ+Tbtm3j8OHDrFy5koyMDEaMGEGnTp0YN24cAwcOZMGCBcTExDB8+HDeeustYmJi\n0Ol0jB49mr59++Lr6+uo0KokLMJMyrlc0qz1KDpzGn39Bq4OSQghqoX169cyY8Ys2+srtTENCgqu\n0Mb0mWeeIC5uH4MH/+WqLUQr06bNbfz3v69gMBgqvD9q1J1Mn/4UoaGhjBp1J//5z3x69ep7U/t3\n992TmTdvNp9++hGRkQ2vOczvKg5rY2q1WikqKsJgMGC1WomOjqZevXp8//33uLu7s2vXLpYuXcq4\nceP4/PPPefXVVwF49tln6dGjB7169brquh3ZxvRSZ05ksPqTPYRn7ie6eyTmgYPsuu26RtoROofk\n2Tkkz87hqjzv27cXvV5P48a3sHz5+6iqyt133+f0OKDyNqYOG1p3c3Oz/cUUExNDt27dKCgowN3d\nHQA/Pz9SUlJITU3FbDbbvmc2m0lJSXFUWFUWHOaDm1Yh3VNueBNCiLrE3V3H3LmzmTLlAXbtimX4\n8FGuDumKHH6z2/r164mJiWHp0qX069fP9v7VBgKuZ4DAZDKg1dq3tWhlf+1ENPLn6CGVjOOnae3t\njpuHh123XddUlmthP5Jn55AXhIlQAAAgAElEQVQ8O4cr8hwQ0JGvv/7S6dutKocW8s2bN7No0SKW\nLFmC0WjEYDBQWFiIXq8nKSkJi8WCxWIhNTXV9p3k5GTatm1b6XozMi6/G/FmXGvYxhLizdFDKaTr\nAjj9ayz1Wt5q1+3XJTIU6RySZ+eQPDuH5NlFQ+s5OTnMnz+fxYsX225ci46OZu3atQCsW7eOrl27\n0qZNG/bu3Ut2djZ5eXnExsbSoUMHR4V1Q8IrTNcqw+tCCCGqD4edka9Zs4aMjAz+/ve/296bO3cu\nM2bMYOXKlYSEhDB8+HB0Oh1PPvkkkydPRlEUpkyZgtFYvYaqzAH1MBh0pFtDyIv7lQBXBySEEEKc\n57C71h3JmXetX/Dj6gPE70/i9lNf02buLLTe3naNoa6QITLnkDw7h+TZOSTPLhpar23CIi4aXj8Q\n5+JohBCi+nBmG9PrFRu7kxkzngbgmWeeqHJMF7dLfe656RQVFTomUDuQQn6dbIVcHkMTQogKnNnG\n9EbMnbugyt+5uF3qrFlz8PCong1TQOZav271jB6Y/A1kqoHkxH1PoKpW67l3hRDCGZzZxvTw4Xje\neGMBr7++CIClS9/BaPQmIiKSJUsWodPpMBqNvPDC3AoxDh7cm2+//fG6YwoKCq7QLvXZZ6fz4Ycr\nyc3NYc6cFygpKUGj0fDMMzNRFOWKLVCdSQp5FYRHmMlIzSetUEeDc4m4B4e4OiQhhABg64ajHDuY\nbNd1NmxmIbpXo0qXcWYb01tuaUJqago5OTkYjUa2bPmZefMWsHfvHzz33IuEhIQye/azbN/+62VT\nuALXHdPSpSsqtEu9YMmSRQwZMozevfvx00/rWbr0HSZP/tsVW6A686ZtKeRVEBZp4o+dZ0g3hJAX\nt18KuRCizlu/fi2TJk2u0Mb0rrsmXNbGtKioyNbGdNWqL1AUzQ21Me3cuRvbt2/l1lvb4OHhTkCA\nBV9fX+bNexGr1UpCwlnat+94xUJe1ZgudejQAf7v/6YC0K5dB5YtWwL82QIVsLVAlUJeTYWE+6LR\nKKR7BpMftx9T75ubkF8IIewluleja54925sr2ph2796Tzz//lKysTLp3L+/JMWfObF555b9ERESy\nYMG8q8Zb1Zgup9i+V1JSamtxeqUWqM4kN7tVgc7djaAwH3I8/Mg6fAzVDm3yhBCipnJFG9OWLVtx\n4sQxtm79hR49+gCQl5dLYGAQOTk5xMb+ftX1ViWmK7VLbd68BbGxOwHYvft3mjVrXuX4HUHOyKso\nLMJEwqlM0jRmCo8fw/P8MI0QQtQ1rmhjqigKt97ahsOHDxEUFATAyJFjeOihyYSH12f8+LtZuvQd\nHnzw4cu+W5WYrtQu9f77/485c2azevVXaLU6pk+faZe+5zdLJoShapMNJCdm8/kHsYRkxdM5KgD/\nYSPsGkttJxM7OIfk2Tkkz84heZYJYezKP9CIh4db+Q1v+/e5OhwhhBB1nBTyKtJoFEIjzBTqvEg/\nk4o1P8/VIQkhhKjDpJDfAFs3NM9g8g8edHE0Qggh6jIp5DdApmsVQghRXUghvwHevp54++rJMAST\nGycNVIQQQriOFPIbFB5pxqrRkZZdRklqiqvDEUIIUUdJIb9BF7c1zZPhdSGEEC4ihfwGhTbwRVGw\nTdcqhBBCuIIU8hvkoddhCTaSrQ8g62A8almZq0MSQghRB0khvwlhkWZURUO66kPRqZOuDkcIIUQd\nJIX8JoSfv06eJsPrQgghXEQK+U2whHij02nkhjchhBAuI4X8Jri5aQhpYKLA3YeM4wmUFRW5OiQh\nhBB1jBTym2QbXvewUHD4kIujEUIIUddIIb9JYZEXTde6X4bXhRBCOJcU8pvkazZQz8udDEMIuXKd\nXAghhJNJIb9JiqIQ3tBMiZsHaakFlGZlujokIYQQdYgUcju4eLrWfGmiIoQQwokcWsjj4+Pp06cP\nK1asAGDHjh389a9/ZeLEifztb38jKysLgCVLljB69GjGjBnDpk2bHBmSQ0hbUyGEEK6iddSK8/Pz\nmT17Np06dbK9N2fOHF599VUaNmzIokWLWLlyJQMHDmTNmjV88skn5ObmMm7cOLp06YKbm5ujQrM7\nT4M7/hYv0pIsZMftJFBVURTF1WEJIYSoAxx2Ru7u7s67776LxWKxvWcymcjMLL+GnJWVhclkYvv2\n7XTt2hV3d3fMZjOhoaEcOXLEUWE5TFikCVVxI61ET3FCgqvDEUIIUUc4rJBrtVr0en2F9/75z38y\nZcoU+vfvz++//86IESNITU3FbDbbljGbzaSk1Lz+3uG2x9CCyY/b5+JohBBC1BUOG1q/ktmzZ/Pm\nm2/Svn175s2bx0cffXTZMqqqXnM9JpMBrda+Q+8BAcab+r7J18B3MftIN4RQeuQQAeNG2ymy2udm\ncy2uj+TZOSTPziF5vjqnFvJDhw7Rvn17AKKjo1m9ejVRUVEcP37ctkxSUlKF4fgrycjIt2tcAQFG\nUlJybno9weE+nD5eRlLcJvwTM1C0Tk1vjWCvXIvKSZ6dQ/LsHJLnyv+QcerjZ/7+/rbr33v37qVB\ngwZERUWxceNGiouLSUpKIjk5mcaNGzszLLu5cPd6mtaPgqM17zq/EEKImsdhp4z79u1j3rx5nD17\nFq1Wy9q1a5k1axYzZsxAp9Ph4+PDyy+/jLe3N2PHjmXChAkoisLzzz+PRlMzH28PizADx2yPoRma\nNnN1SEIIIWo5Rb2ei9LVjL2HWOw1bKOqKh+88QvW7Gz6KDtp8K9n7RBd7SJDZM4heXYOybNzSJ6r\n0dB6bacoCmERZoq1BtISMrDm5bk6JCGEELWcFHI7s83ypg8m/6BM1yqEEMKxpJDbWVhk+TPx5fOu\ny3StQgghHEsKuZ15GT0w+RnI8AwiN+6Aq8MRQghRy0khd4CwSBNlGi0puQrFKcmuDkcIIUQtJoXc\nAcIjzg+vSzc0IYQQDiaF3AFC6vug0ch1ciGEEI4nhdwBdO5aAkN9yPHwI/PgUdSyMleHJIQQopaS\nQu4g4REmUBTS8KbwxAlXhyOEEKKWkkLuIBUfQ5O2pkIIIRxDCrmDBAQZcfdwI90QSt5+uU4uhBDC\nMaSQO4hGoxAWYaJQ50X6qSTKCgtdHZIQQohaSAq5A4VdeAzNI5D8+EMujkYIIURtJIXcgcIjz8+7\nLo+hCSGEcBAp5A7k7euJt4+edEMwuXHSQEUIIYT9SSF3sLBIE1aNO2npRZRmZrg6HCGEELWMFHIH\nu3CdPM0zhHw5KxdCCGFnUsgdLCzCF0Upv06eJ8+TCyGEsDMp5A7modcREGQkWx9A9sF4VFV1dUhC\nCCFqESnkThAeaUZVNKQWGyg+e8bV4QghhKhFpJA7QViEPIYmhBDCMaSQO0FgqDdareb8dXIp5EII\nIexHCrkTuLlpCG3gS767DxlHT1NWUuLqkIQQQtQSUsidxPYYmtafwqNHXByNEEKI2kIKuZPIdK1C\nCCEcQQq5k/j6Gajn5U66IYRcKeRCCCHsRAq5kyiKQlikmRI3PamJ2Vhzc10dkhBCiFrAoYU8Pj6e\nPn36sGLFCgBKSkp48sknGT16NJMmTSIrKwuAVatWMWrUKMaMGcNnn33myJBcyvYYmmcw+QdlulYh\nhBA3z2GFPD8/n9mzZ9OpUyfbe59++ikmk4mYmBgGDRrEzp07yc/P56233mLZsmUsX76cDz74gMzM\nTEeF5VLyPLkQQgh7c1ghd3d3591338Visdje++mnn/jLX/4CwJ133knv3r3Zs2cPrVq1wmg0otfr\nadeuHbGxsY4Ky6UM9dzxs9QjyzOQ7LgDMl2rEEKIm+awQq7VatHr9RXeO3v2LD///DMTJ07k8ccf\nJzMzk9TUVMxms20Zs9lMSkqKo8JyubAIM2WKG6l5WkqSk10djhBCiBpO68yNqapKZGQkU6dOZeHC\nhSxevJgWLVpctsy1mEwGtFo3u8YWEGC06/qu5ta2oez57TTphhA0p48QcGtjp2y3OnFWrus6ybNz\nSJ6dQ/J8dU4t5P7+/nTs2BGALl268MYbb9CjRw9SU1NtyyQnJ9O2bdtK15ORkW/XuAICjKSk5Nh1\nnVdjMOpwc1NIN4SQvD0WbYfOTtludeHMXNdlkmfnkDw7h+S58j9knPr4Wbdu3di8eTMA+/fvJzIy\nkjZt2rB3716ys7PJy8sjNjaWDh06ODMsp9Lq3AgO9yXXw0xG/FFUq9XVIQkhhKjBHHZGvm/fPubN\nm8fZs2fRarWsXbuWV199lZdeeomYmBgMBgPz5s1Dr9fz5JNPMnnyZBRFYcqUKRiNtXsIJSzCxJkT\nGaQpZgpPHMezUd0bXhdCCGEfiloDb5229xCLs4dtUs7lELPsd4Kzj9C1UwB+Q4c5bduuJkNkziF5\ndg7Js3NInqvR0Loo5x/ohd5TW97WdL88Ty6EEOLGSSF3AUVRCIswU6Q1kHomlbLCAleHJIQQooaS\nQu4itm5oHoHkHzrk4miEEELUVFLIXUSmaxVCCGEPUshdxMtbj6/Zk0zPIHLjpIGKEEKIGyOF3IXC\nI81YNTpSMkopSU93dThCCCFqICnkLlRheP2ADK8LIYSoOinkLhRS3xdFkevkQgghbpwUchdy99AS\nGOpNtoc/WQcOo5aVuTokIYQQNYwUchcLjzSDopBq9aL47BlXhyOEEKKGueFCfuLECTuGUXddfJ08\nT4bXhRBCVFGlhfzee++t8HrhwoW2n5999lnHRFTHWIKNuLtrSJPr5EIIIW5ApYW8tLS0wutt27bZ\nfq6BvVaqJY1GQ2iEmUKdkbRjZykrKXZ1SEIIIWqQSgu5oigVXl9cvC/9TNy4C8PraToLBYcPuzga\nIYQQNUmVrpFL8XYM27zrhmAZXhdCCFEl2so+zMrK4tdff7W9zs7OZtu2baiqSnZ2tsODqyu8fT0x\nenuQYQ0mL24rAa4OSAghRI1RaSH39vaucIOb0Wjkrbfesv0s7ENRFMIbmonbXURKQh5hOdlojd6u\nDksIIUQNUGkhX758ubPiqPPCIkzE7U48P11rHN63R7k6JCGEEDVApdfIc3NzWbZsme31J598wrBh\nw3j00UdJTU11dGx1SmgDaWsqhBCi6iot5M8++yxpaWkAHD9+nAULFjBt2jSio6N56aWXnBJgXaH3\n1BEQZCRLbyE77qA83ieEEOK6VFrIT58+zZNPPgnA2rVrGTBgANHR0dx1111yRu4A4ZEmVEVDaqEH\nJUnnXB2OEEKIGqDSQm4wGGw///bbb0RF/XndVh5Fsz/b8+QyXasQQojrVGkht1qtpKWlcerUKXbt\n2kXnzp0ByMvLo6CgwCkB1iVBoT5otYpcJxdCCHHdKr1r/YEHHmDQoEEUFhYydepUfHx8KCwsZNy4\ncYwdO9ZZMdYZbloNIQ1MnDqqknF4EyGlpSjaSn9FQggh6rhKq0T37t3ZsmULRUVFeHl5AaDX6/nH\nP/5Bly5dnBJgXRMWYeLU0XRSNX4UHj+O5y23uDokIYQQ1VilhTwhIcH288UzuTVs2JCEhARCQkIc\nF1kdFR5hBo6SbggmL26fFHIhhBCVqrSQ9+rVi8jISAICyicNvbRpyocffujY6Oogk78BQz0d6dYQ\n8uJ+x3/YCFeHJIQQohqrtJDPmzePr7/+mry8PAYPHsyQIUMwm83Oiq1OUhSFsEgz8ftKSD2TQVh+\nPm4XPT0ghBBCXKzSu9aHDRvG0qVL+e9//0tubi7jx4/n/vvvZ/Xq1RQWFl5z5fHx8fTp04cVK1ZU\neH/z5s00bdrU9nrVqlWMGjWKMWPG8Nlnn93grtQe4ecfQ0vXB1Fw6KCLoxFCCFGdXVcb0+DgYB5+\n+GG+++47+vfvz4svvnjNm93y8/OZPXs2nTp1qvB+UVER77zzjm24Pj8/n7feeotly5axfPlyPvjg\nAzIzM29wd2qHC8+Tp8vz5EIIIa7hugp5dnY2K1asYOTIkaxYsYK//e1vrFmzptLvuLu78+6772Kx\nWCq8v2jRIsaNG4e7uzsAe/bsoVWrVhiNRvR6Pe3atSM2NvYGd6d2MHh54BdgIFMfSE7cAVeHI4QQ\nohqr9Br5li1b+Pzzz9m3bx/9+vVj7ty5NGnS5PpWrNWiveQZ6OPHj3Pw4EEee+wxXnnlFQBSU1Mr\nXHc3m82kpKRUum6TyYBW63ZdcVyvgIDq1Zb1lhZBbEvJJzVbpR2FeATUni7l1S3XtZXk2Tkkz84h\neb66Sgv5/fffT0REBO3atSM9PZ3333+/wudz5syp0sbmzJnDjBkzKl3mepqFZGTkV2m71xIQYCQl\nJceu67xZfoHlz+2nG0I4vXk7Pl27uzgi+6iOua6NJM/OIXl2Dslz5X/IVFrILzxelpGRgclkqvDZ\nmTNnqhREUlISx44d46mnngIgOTmZCRMm8Mgjj1RowJKcnEzbtm2rtO7aKDjcB41GIe38dK21pZAL\nIYSwr0oLuUaj4fHHH6eoqAiz2czixYtp0KABK1as4J133mHkyJHXvaHAwEDWr19ve92rVy9WrFhB\nYWEhM2bMIDs7Gzc3N2JjY/nnP/9543tUS+h0bgSH+3D2pErGoV8JKitD0VzXLQ1CCCHqkEoL+X/+\n8x+WLVtGo0aN+PHHH3n22WcpKyvDx8fnmo+J7du3j3nz5nH27Fm0Wi1r167ljTfewNfXt8Jyer2e\nJ598ksmTJ6MoClOmTMFolGshUH73+tmTmaSWGSk6fQp9gwhXhySEEKKaUdRKLkpPnDiR5cuX2173\n6dOHadOm0bdvX6cEdzX2vlZSXa+/pJzLIWbZ7wRnH6Zb1xDMAwe7OqSbVl1zXdtInp1D8uwckufK\nr5FXOlZ7ac/x4OBglxfxusQ/0AsPvRvpniHk7ZfnyYUQQlyuShddLy3swrEURSE80kyRrh5pJ89R\nVlzs6pCEEEJUM5VeI9+1axc9evSwvU5LS6NHjx6oqoqiKGzcuNHB4YmwCDNHDqSQ5m6h4HA89Vre\n6uqQhBBCVCOVFvLvv//eWXGIq/hzutZQ8uP2SSEXQghRQaWFPDQ01FlxiKsw+ujxMenJKAsiN24z\ntWd+NyGEEPYgDybXAOGRZqwaHckphZRmZbk6HCGEENWIFPIaICyifC76dEMI+QfjXByNEEKI6kQK\neQ0Q2sAXRTlfyOUxNCGEEBeRQl4DuHtoCQzxJtsjgKyDh66rsYwQQoi6QQp5DREWaQZFIaXIk+LE\nRFeHI4QQopqQQl5DhNseQyvvhiaEEEKAFPIawxJiRKfTkO4ZQn7cPleHI4QQopqQQl5DaDQaQiNM\nFLh7k3bkDGppqatDEkIIUQ1IIa9Bws8/hpam9aPg2FEXRyOEEKI6kEJeg4RFynVyIYQQFUkhr0F8\nTJ54Gd1J9wwmTwq5EEIIpJDXKIqiEN7Qj1I3D1ISsrDm57k6JCGEEC4mhbyGsXVD8wzm9JyXyNy4\ngbKiIhdHJYQQwlWkkNcwFwp5ZmBzipOTSF7xIcf+8TgpKz+mOCXZxdEJIYRwtkrbmIrqR++pIyDI\ni7RkhfAX55O39WeyNv1Exg9ryVi/jnqt2+Dbuy+G5i1QFMXV4QohhHAwKeQ1UFikmZRzuXy/9iQt\nb4umYf/B5O/eSeaG9eTt2U3ent24h4Tg26sP3p06o/HwcHXIQgghHEQKeQ3UukMYKYk5nDmRwbkz\n2fziqaNpqyBa/N9TBGQmkvnjenJ2/kbyig9J/fwzfLp0w6dXb9wDLK4OXQghhJ0pag1spZWSkmPX\n9QUEGO2+TmfIysgnbnciB/84R2FBCVDe8rRF2xDCA7XkbvmZzI0bsGZng6JUi2H3mprrmkby7ByS\nZ+eQPJfn4GqkkFPzDxJraRnH4lOI251IwqlMADwNOpq1DqL5rYEoR/eS+eMPFB47BoB78IVh92g0\ner1TY63pua4pJM/OIXl2DsmzFPJrqk0HSUZaPgd2J3Bw7zmKCsvnYw+PNNGibQiBbtlkb1xPzo7f\nwGpF4+mJd5du+PbsjbvFOcPutSnX1Znk2Tkkz84heZZCfk218SApLbVy7GAK+3cncu5MFgCGeu40\naxNEk4ZelMVuJXPTT1izsv4cdu/VB0OLlg4ddq+Nua6OJM/OIXl2DsmzFPJrqu0HSXpKHnG7Ezi0\nL4niovKz9PoNzTRvHYg54yjZP62/ZNi9d/nd7g4Ydq/tua4uJM/OIXl2DsmzCwt5fHw8Dz/8MPfc\ncw8TJkwgMTGR6dOnU1pailar5ZVXXiEgIIBVq1bxwQcfoNFoGDt2LGPGjKl0vVLIb0xJiZWjB1OI\n251A0tlsAOoZ3WneOphIUwkl235y+LB7Xcm1q0menUPy7BySZxcV8vz8fP72t78RERFB06ZNmTBh\nAtOmTaN79+4MGjSI//3vf5w9e5apU6cyYsQIYmJi0Ol0jB49mhUrVuDr63vVdUshv3lpybns353A\n4f1JFBdZURSo38iPZk28MZ7YTfamDX8Ou7dqXX63ux2G3etirl1B8uwckmfnkDxXXsjdnn/++ecd\nsVFFURgyZAiHDh3C09OT1q1b07lzZ5o2bYpGo+HMmTPEx8fj4+NDWloaQ4cORavVcvDgQTw8PIiM\njLzquvPzi+0aa716HnZfZ3VnqOdOg0Z+tGofhrevJ/m5xSScyuTo4UxOFfng1b03AW2aouRkUnDw\nADnbtpK74zdQFNyDQ1C0NzYFQV3MtStInp1D8uwckufyHFyNwyaE0Wq1aC/5x95gMABgtVr56KOP\nmDJlCqmpqZjNZtsyZrOZlJSUStdtMhnQat3sGm9lf+3UdiGhvnTr04TEM5n8/utJ9u06y45fTrFT\no9Ck9RhaDNXhvvtn0n75heT/LSfty8+x9O5F8OABeAYHV3l7dTnXziR5dg7Js3NInq/O6TO7Wa1W\nnn76aaKioujUqROrV6+u8Pn1jPRnZOTbNSYZtimn9XDjjh4Nua1TfQ7HJZ+/Qe4ch/aB0ac5ze6M\nJijjEEW//Eji6m9I/ObbKg+7S66dQ/LsHJJn55A8V/6HjNML+fTp02nQoAFTp04FwGKxkJqaavs8\nOTmZtm3bOjsscRF3Dy0tbwuhRdtgUs7lsH9XAkcOJLNjeyIajQ8Rne8nwjMb/a4N5P2xh7w/9uAe\nFFx+t3t0ZzR6T1fvghBC1BlObWO6atUqdDodjz76qO29Nm3asHfvXrKzs8nLyyM2NpYOHTo4Myxx\nFYqiYAn2puegZtw9JZqu/W7B18/Asfg0Nuwp4Re/fmSN/Dvud3SlOCWZ5I9WcOwfT5D8yUcUJyW5\nOnwhhKgTHHbX+r59+5g3bx5nz55Fq9USGBhIWloaHh4eeHl5AdCoUSOef/55vv/+e9577z0URWHC\nhAn85S9/qXTdcte666iqSlJCNnG7EjhyMAVraRkajUJkQx/CS8+g27GesqzMine7N2+Boin/m1Fy\n7RySZ+eQPDuH5FkmhLkmOUhuTFFhCYf2JRG3O4GM1PL7FnzNnjQ0leB3ZAtlRw8CoAsKwtSrD97R\nnQkMt0iunUCOaeeQPDuH5FkK+TXJQXJzVFXl3Jks9u9O4NjBFKxWFTc3hQZhnoRkHsJjzyYoLUWj\n1+Pbtg1uEY0xNGuGe0io7Uxd2Jcc084heXYOybMU8muSg8R+CgtKOLT3HPt3J5CVXgCAr0lPhEcG\n5kM/o6Qk2pZ18zLi2bQphqbN8GzaHPeQEJe1V61t5Jh2Dsmzc0iepZBfkxwk9qeqKgmnMonbncix\nQymUlam4aTWEhnjhrebgm30a/fE9qOl/zhngZjTi2bQZhqbNMTRrhi4oWAr7DZJj2jkkz84hea5m\nj5+JukFRFEIbmAhtYCI/r5hDe88Rvz+JU6eyzy8RhsY/HL+mevzdC/DOPoPhxB6sO3eQu3MHAG4+\nPufP1suLuy4wUAq7EEJcQs7Ikb/2nKmewYN9e86SeDqLxNOZpCblUlb25yFo8vXA36MIn9yzGE79\ngS7jz8fY3Hx9MZwv6p5Nm6GzWKSwX4Uc084heXYOybOckYtqxFDPnchb/Im8xR+AkmIrSQnZJJ7O\nJOF0FskJ2WRkAoSCXyjGBjr89cX45CVS7/Q+SrdvI2f7NgC0JjOezZrZirvW318KuxCizpFCLlxK\n5+5GWISJsAgTAFZrGSnncs6fsWeReCaL47kKEAL+IXiGueFvKMUnPxGvM3GU/PorOb9uBUBr9sPQ\n7PxQfLPm6Pz8XbhnQgjhHFLIRbXi5qYhKNSHoFAfbosqv2kuPSXvfFHPJPF0FqfTrZw+X9h1wRr8\nDaX4FiRTLyGO4q2/kr31FwB0/gHni3p5cdeZ/Vy8d0IIYX9SyEW1pigKfhYv/Cxe3No+FFVVyckq\nJOFUJolnzp+1Z5SRSBD4B+FmUTAbrPgWpWA8dxDj1l/J/mUzALoAy59D8c2ao/U1uXjvhBDi5kkh\nFzWKoih4+3ri7etJs9blLVTzc4v+LOqns0hJziWFQPALRPEHX88yTMWpGJPi8f5lO+6bfwZAFxho\nu3HO0LQZWl9fV+6aEELcECnkosYzeHnQqJmFRs0sQPnUsefOZtuG45MTcsgos4CfBfzA6FGGuTQd\nY8phvLfuwPPnjQC4BwXbrq97NmmK1sfHhXslhBDXRwq5qHU89DoaNPKjQaPya+KlJVaSE3NIPH1+\nOP5MFiet/mD2BzMYdGWYrBkYU4/iszWWept+QgHcQ0LwbNocY7v2eDZtJtPJCiGqJSnkotbT6twI\nqe9LSP3yofOysjJSk3Ir3Bl/tsQPzH5gBnc3FbOaiTHtOL5bd2P86Ud0JhPGOzrhHdUJj7BwF++R\nEEL8SQq5qHM0Gg2WYG8swd60uT0cVVXJTMsn8UwWCafL74w/l23inMkEpnZ4akqxZB0mcMM2vL5f\ngz48HGNUNN53RMkNc0IIl5NCLuo8RVEw+dfD5F+PFm1DAMjJKiTxTBZnTmRwPD6Fk8bmnDQ2x6gp\nxJJ6gMAvv8Uz5lMMzeblb6sAACAASURBVFvgHRWNV7v2aPR6F++JEKIukilaken/nKkm5rq01Mqp\no+kcjkvi5JE0rNby/2VMajaWlP1Yck/goVXxuq0d3p2iMTRvieLm5tKYa2KeayLJs3NInmWKViFu\nilbrRsOmATRsGkBRYSnH41OI35/E2ZOQYelEvCUK/5IULPsPEPDb67gbDRhvj8K7UzQe9RvItLFC\nCIeSQi5EFXjotTRrHUyz1sHk5RRx5EAyh+OSSDmnkBJkwU0pIyD/NIFb92Ne/wP64GC8O0VjvKMT\nOj+ZWU4IYX9SyIW4QfWMHrS5PZw2t4eTkZbH4f3lRf2c2oBzng1wV0qxZB0lcM0mfL6IwdCkKd6d\novFq3xE3g8HV4Qshagm5Ro5cf3Gm2p5rVVVJTszh8P4kjhxIpiC/BAADhVjSDxGYcwyjmke9trfh\nHRVNvVtboWjt//d0bc9zdSF5dg7Js1wjF8JpFEUhMMSbwBBvons34syJTA7vT+L44VROmNtwwtwG\nozWbwCOHCNz1LgZPDcaOd+Ad1Ql9w0ZyPV0IUWVSyIVwEI1GQ/2GZuo3NFNSYuXkkTTi9ydx+pjC\nEf+OHPHvgKk4lcDf47Fs2ozB34R3VCeMUdG4WyyuDl8IUUNIIRfCCXQ6Nxo3t9C4uYXCghKOHkz5\n//buPD6q+l74+OdsM5NMZpIQEkIEwirIvqogCFTRulStqFAE7b3Pq6+22ufV9qEqpXW79taLrb19\nrF672b58aVtRtKIPikJdAEUQAwhRVlFWk0ASss1ytuePmWwQCGAyk5l8369OzzqHb76eM9/f+Z2Z\nc9hdWsaRgwpVBfnsKphMXsMhev1rMz1ffRX/wAEEL55CYNKFaFlZyQ5fCNGFSSEXIsF8GQYjxhUx\nYlwRtcfD7Pm0nF2lZVRU9KUisy86Fvk1n1P40ipyn/s7WaNHE7x4Mv7RY1ENI9nhCyG6GCnkQiRR\nINvHuIv7Me7ifhwrr2P3J7Fvvh9hMEeCg/G6EQoO7KHwk+fI1v5KcOIkAhdPIWPwEHmIixACkEIu\nRJeRV5BFXkEWF00fwJGDx9ldWsbeHRUcUEZwIGcEmVYtvbbtoXD94wSzvQTjD3Hx9C5KduhCiCSS\nQi5EF6MoCkV9cyjqm8PUWUM48Fns9rCf71bZp49jX944gpFjFK7bQ8HKVQT79o5dT7/wIvRgMNnh\nCyESrFML+a5du7jjjjv49re/zfz58zly5Ah33303tm2Tn5/Pr371KzweD6+88gpPP/00qqpyyy23\ncPPNN3dmWEKkDE1T6T+kJ/2H9CQasdi3+yi7S8s4+DnUePPYlX8hPRqOULjiA/JfWEb28KEELp5C\n3hXTkx26ECJBOq2QNzQ08NBDDzF58uSmeY899hjz5s3jqquu4je/+Q3Lli3jhhtu4IknnmDZsmUY\nhsFNN93ErFmzyMnJ6azQhEhJHq/O0JGFDB1ZSEN9lL2flrPrkzLKDytUZhahujY9y/dT+LcVHFv+\nEnk33Ehg0kXy23Qh0lynfVvG4/Hwpz/9iYIWv4fdsGEDl112GQAzZ85k/fr1bN26lVGjRhEIBPD5\nfIwfP56SkpLOCkuItJDp9zBqYh9m3zaBed+9kEnT+hPMy6I8MICPiy7j3YypbP3b6+z/5UOEdu9O\ndrhCiE7UaWfkuq6jn3DryVAohMfjASAvL4+KigqOHj1Kjx49mtbp0aMHFRUVp912bm4mut6xj4k8\n3e3vRMeSXHes/PwAg8/vxdevH8mXh45T8sF+Nm/Yz/beM9kfrmDwY08xeGx/im9fQEbvwmSHm3Zk\nf04MyfOpJe3Lbqe6xfuZ3Pq9qqqhQ2OR+/gmjuS6c+lejQunD2DyjEGsfHkbez6FkvO+zhefH2TQ\nj+6j77SJ5F17HZrfn+xQ04Lsz4kheT59QyahP0TNzMwkHA4DUFZWRkFBAQUFBRw9erRpnfLy8lbd\n8UKIs9ejp59Z149g9u3jOa84h2P+Pmw871re21rPJz9/gKpVb+BaVrLDFEJ0gIQW8ilTpvDGG28A\n8OabbzJt2jTGjBnDtm3bqKmpob6+npKSEiZOnJjIsIRIWwW9g3xj7hiunTOavIIsvgwO5r2Cr/Pe\nv/aw6/4HqP1o0xn1ggkhuq5Oe4zp9u3bWbJkCYcOHULXdXr16sWvf/1rFi1aRCQSoaioiIcffhjD\nMFi5ciVPPfUUiqIwf/58rrvuutNuWx5jmrok14nRVp5d12X3J+VsfGcvtbVRNCdK/6ptnJ9nUjjn\nFnwDBiYp2tQl+3NiSJ5P37UuzyNHdpJEklwnxunybFsOpZsPs2ndPiIRG69Vz4DKLZw/LI+C2Tdh\n5PVMcLSpS/bnxJA8d6Fr5EKI5NN0ldGT+nDr9yczfko/LK+fHQWXsPrLQjb+5+OUL3sBOxRKdphC\niDMkhVyIbsrr07no0oHc+r3JDB/Tm5Anh48LZrB6m8Pm+5dQ/fZbuLad7DCFEO2QQi5EN+cPeJl+\n1VDmfGcSAwb34HhGLz7sMYNV/zrI9gf/i7qPt8gX4oTowqSQCyEAyM3z8/WbRvPNBeMo7O3naFY/\n1mVMZvVzG9nz6G+JHNif7BCFEG2Qp58JIVopPC+bG26byBd7j7F+1S6OKOdTFrXo+/hyRp6fRdHs\nG9BzcpMdphAiTgq5EOIkiqLQf3BP+g3MY9f2L9nw1m6+UEdx6MsIAx7+K6MuHkD+1Vejer3JDlWI\nbk8KuRDilFRVYdjo3gy+oIBtHx2kZN0+dueOZ//2OgZ/+ASjrppAziXTUFS5SidEssjRJ4Rol25o\njLu4mPk/uIQxE3pjevyUBsfz6luVfPiLx6gr3Z7sEIXotuSMXAhxxrw+gymzhjL6omI2/Gsnu3a6\nfEQue5/fzsicdZw/5xt4i85LdphCdCtSyIUQZy0r6OOyb45hbEUd61eWcuBQIesihez8n9WMHajT\nf/a16NnZyQ5TiG5ButaFEOcsLz+LaxdcxPXzxpCfo1GRVczqst689qsXOPjy/8OJRpMdohBpTwq5\nEOIrK+qXy+zvTuXK64eRlaFwKGswKz7xsuo//8qxte/hOk6yQxQibUnXuhCiQyiKwsALCuk/tIBP\nNu3nw3f38pk6lANrahm85i9MuHEqgQuGJTtMIdKOFHIhRIdSVZWRF/Zn6Li+bHl3J1s+OsKn7mD2\nLdvN8MBGxs6dhbewd7LDFCJtSNe6EKJTGIbGpMuHM/9/T2P4+VlEdT8l4X48/4f1bPvLMuy6umSH\nKERakEIuhOhUGZkept84kbnfn8yAQo06Ty7rynuy7NEV7PnnShzTTHaIQqQ0KeRCiITIzsng69+e\nxuzbxtI7aFPp7cWqnT6WP/wCh9d8IE9YE+IcSSEXQiRUwXm53HDHZVxzw/nkeKJ86SnilfcaWPHw\nPzi4ZqN8w12IsyRfdhNCJEW/YUX0HdqbnRt2s3HNPg44RRx4v4GCNcsYNa6QwVdMRjWMZIcpRJen\nuCnYn1VRUdth2zpaHaJk7zHGDcojPyejw7Yr2pafH+jQ/36ibamWZ8dx2bNxF5vXfUalFTsOg1Y1\nI8/PYsQ3pqJn+JIcYdtSLc+pSvIcy8GpdPtC/o9Na1hb9TrOsfO4MO8SbpoygqDf02HbF63JAZkY\nqZzng5/s56PVpRyu94Gi4LPrGdZHY+z1U8jICSY7vFZSOc+pRPIshfy0DtYe5n+2PM1xswrXUeBY\nP6YXXcp1F15AhleuPHQ0OSATIx3yfOxAOR+tKGFfpY6j6uhOlIF5FhOvnUT2efnJDg9IjzynAsmz\nFPJ25eZl8urH7/DK7jepd2pwHRW1qpgri2dw1YSh6Jp8J7CjyAGZGOmU5/rKGkpe+YBdhxyimg/F\ndejrb2DClWMoHNo3qbGlU567MsmzFPJ2Ne4ktmOz7uCHvLpnFSFqcR0Vz/H+XDfkcmaMGoSqKB36\n73ZHckAmRjrmOdoQZtur71O6p556Lfahlm/UM376EAZMGIKShOMzHfPcFUmepZC368SdxHZs3tm/\ngRV7VxNR6nAdlcy6gdw0/EouGtIvKR8Y6UIOyMRI5zw7psnONzbw8dZyKrUeAATUEGMmnsfw6SPR\nEtiDls557kokz1LI23WqncRyLFZ/tp6Vn7+FqdbjOirB0BBuHXMVo/oVdWgM3YUckInRHfLsOg77\n137Elvf2cUTpiauoeIky4oJcxlwxBl9G539ptTvkuSuQPEshb1d7O4nlWLy+6z1WH3gbS2vAtVXy\nrKHcNu5qhhT26tBY0p0ckInRnfLsui4VJdvZ/K/tfGHlYaseNNdmSLGP8VeNIzs3s9P+7e6U52SS\nPHehQl5fX88999zD8ePHMU2TO++8k/z8fB544AEAhg4dyoMPPtjudhJdyBtZjsXLpWtYc2QNth4r\n6IXucP5t4jX07ZHXoTGlKzkgE6O75rlm5x62vPYhe+qDRHQ/uC79ClQmXDGawr65Hf7vddc8J5rk\nuQsV8meffZaysjIWLlxIWVkZt99+O/n5+dx1112MHj2ahQsXct111zF9+vTTbidZhbxR1DZ5Yevb\nrD/6Hq4ewrU1+mkj+LeJ19Ar2PEfFulEDsjE6O55Dh08yPbl69hx1EOdN3YdPT/gMv5rFzBgWK8O\n+55Ld89zokieT1/IE/q7qtzcXKqrqwGoqakhJyeHQ4cOMXr0aABmzpzJ+vXrExnSOfFoBreOv4Jf\nf+1nTPDPRHEMDvAx/7HxEX6z9h9U1h9PdohCdGsZffow6c65zLljKlPzy8kLHaKiVuGN5Tv423+/\nzbYNn2OZdrLDFKJDJLSQX3PNNRw+fJhZs2Yxf/587r77boLB5js15eXlUVFRkciQvhKf7uHfL7qK\nR2YsZoRnGtg6e83N3Lv+v/jd+89zPNy9W5BCJJsnv4BR/+sWbvw/13JFcRVF9Z9RF3FZ9/bnPP3b\nd9mwegehhmiywxTiK0lo1/ry5cvZtGkTDz30EDt27ODOO+8kEAjw8ssvA/D+++/z4osv8uijj552\nO5Zlo+taIkI+K1W19fzf1a9QWrcBxRNBcXQm5l/Ed6deT9B36m4RIURiWPX1fLb8DTau2ct+3wAs\nzYumuIwa04spV46gZ0FWskMU4qwl9B6kJSUlTJ06FYBhw4YRiUSwLKtpeVlZGQUFBe1up6qqoUPj\n6sjrL3dOvZby4zP464Y3+MLZwofH3mPTPzcwIe9Cbhl5BX6j875BmwrkWldiSJ5PLXfWLGZdeinH\n1qxl+5ptfO7pz5YtClu2lNOvj59x04fQu0/2GV1HlzwnhuS5C10jLy4uZuvWrQAcOnQIv9/PoEGD\n2LRpEwBvvvkm06ZNS2RInaIgO4t7rpjNogkL6RWaiGOpbKp6n0Xv/oK/bXuFerNjGyJCiLOjer3k\nz7qcGfd/nxtm5jA2tIVguIL9B+tZ/rctLPvTevZ8Wo4jz0YXKSDhPz9bvHgxx44dw7IsfvjDH5Kf\nn899992H4ziMGTOGn/70p+1uJ9nfWj9bOw4e5dlNq6j0laJ4oqiuwbTeU7h2yEwyu9kZurSsE0Py\nfHZcx6F2cwl7X1/L3nAPKvz9QFHIylQZPXkAF4zujaeNhyhJnhND8tyFfn7WUVKtkEPsphWb95bx\n3JbV1AV2oBhRNNdgep+pXDVwBplG93gWuhyQiSF5Pjeu69Lw6SfsX7GK3ZU+jgQG46g6Hh1GTOjL\nqIl98Ae8TetLnhND8iyFvF2J3Ekc1+W90oO8tP0tIrm7UYwoOh6+1ncqs/pPT/uCLgdkYkiev7rQ\nZ3s5suJ1du+PciD7Akw9A1WBwcMLGHtRP/IKsiTPCSJ5lkLermTsJJbtsLrkc1bsfhc7bw+KYWLg\n5fLiaVxWPI0MPT0LuhyQiSF57jiRQwepeO01du84yv7s4TR4cgDoU5zDuIuKUTQIZPvICnpRVXnk\ncWeQ/VkKebuSuZOEIhavbfyM1V+sQ8nfi2KYeBQvs/pfysy+08jQfUmJq7PIAZkYkueOZ1ZUcGzl\n63xWspcvAsOozuzdarmiQCDoJZCTQTAng2COj0C2r2ncl2HIkxPPkezPUsjb1RV2kpr6KC+/v5v3\njqxH67UPxTDxqj5mFV/KjL5T06agd4VcdweS585jHa+matWbHHyvhONkETIChIwswnqAkBEgeore\nNN1QY0U920cgx0cwO17s4+OGp+vdG6OrkP1ZCnm7utJOUl4d4sW1O9lc+SF6789RdBOfmsGs/tOZ\n0WcKvhQv6F0p1+lM8tz5nEgEf6SG8l37MMvLiVaUY1ZUEC4/Rm1dlLCeRciIFfrYeKzQ26rR5vZ8\nmQbBeFEP5PiaxoM5PvwBb0Kfs97VyP4shbxdXXEn2V9WywtrdrKzYXNTQc/QMphVPJ3pKVzQu2Ku\n05HkOTFOlWfHNLGOHSVaXo5ZEX+VlxOtqKDhWDUhJSNW2OMFPtyi4LvKyQVbUSAr2NhVH+uubzme\nkdl+t73rutRbDZi2iVfz4tU8aGpq9ALI/iyFvF1deSfZ8UUVS9/dwSG2oxfuQ9EtMvVMvtZ3KoX+\nXmTovvgro2lcVxN6w76z0pVznU4kz4lxLnl2HQerurpVgTcrYkU+Wl5OKEpTgW/uss8i7M0morbd\ngNcNlaygj4yghu53cXwmUW8DIU8Nx7VKKq0qqsJVRB2z1fs0RcOjefDGXx7Ng0dtPd3mUPXg1b14\n1fh7tJPfo7bRIDlXsj9LIW9XV99JXNelZFcFy9bu5JjnU/TCz1F065TrG6rRVNwzdR8+3UdmU6HP\naKP4t57n1Tyd9qWcrp7rdCF5TozOyLNdX9/iDL681Xi0uqaNLvssGjxBQnoWjuppe5t6FMsbxfHa\nOLqDq9m4moWjWdiaia2a2GoUU4liqhEc1cZRbVzVxtHi44oDZ/GxYKh6q4bBSQ0GzdPUMxBbx8Cr\nedtsGPTtlY9Zq6RMD0JnOF0h77qnbqKJoihMGFrA2CE9WffxQF5+fxd1xiHQoyiaBZqFoptN445m\nEtEtjmvVKLoJytm11RQUfFpzAyDTiA1bNwjaagg0L+uIA85xHSzHxnJMzKahheVY8aGJ5diYLeY3\nLXMtTDu2junG59vNyyzHjC13rZPea8a327h9VVHRVQ1d1dEVPTZU9VbzjJbTTeu1mD7FPEM5YZ3G\n7Zzm31EVVb79nGZsx+Z4tIbKcDWV4SqqqKYyu5oqbzWVPaupGtBA2DbQ7B4E62xy6urIrj1Odp1N\ndp1N3yqXYL2Fg9HUZd/YXd9gBKg3gkRNP9Sf+1myi4ujuLiKi6vGXmgOrubiqg5oNq4WK/yxBkDs\ns8hWTSw1So1iYqm1OKqFozqxhkS8weBo7TcUFBSyPH5yPEGyvdlke4Nke4PkeINkx+fleIP4jcwO\n7Q1IBXJGTuqdvURMm0/2VVIXNglHbcIRi3DUJtRyPD5siJqEoxEidgST6AlFPz7ULRTNjC9rMb9p\n3bN/brOuGHhVLz7dR4bmI9PIJNPwohsqdeFwi8JptijWFrZrx+a7Fo6bmOdFa2ioioaGjqpoqLQc\nV3FxcbFxcHCwcbCx3cZhLOZEUlDaaFi0bkRkZWSg2hoezYtX9+Bt7C7VvfEzHW+Lsx7vCUMPhio/\nlToTZ/rZEbbCzUU6Ut00Xhmq5li4ippoDS5tfxQrtoEbzcAOe3GjGbgRX2wY9eFEMsD0AgqK65Cv\nRinSQhQ4dfSw6whGaslsqMZTV4USjRDRMjE1H7aqYSs6jmpgKxq2qsenY8NTTduqgRWfdhQdR9Vw\nlY45S479/Y2NBCc2jDcU0BwcxcbBwsbCVZxYthS3OW/xcUVRMDQdj2bg0T14NCN2DGie2FD34tO9\nGJqOoigoCicNURRUVQEldrw1LVNPXLd5/RO3k9Mjg569Ou6pl3JGnma8hsa48/PP+n2W7RAx40U+\nYseLv9VU9JsbBBahiE04ahGuswlFojRYEUJWiIgdIeqEsRUzdravWSjxgt84jmbi6BZRzaROOw76\nMZQ2egVcF3A0cFVwVFxHjY97wfXhOhrE57mO2jSOo7VYt+VyrXm8cZmj4rpai/e2XK6Bq3BW/YVt\ncmO9HooDqgOKgxIfosbGNR10zUXTYy9VdVE1F1VzUDUXRXVR4+sqmtt6G/HtxD68bFBiDQrXiTUu\norZNGLOpgWG7Fm7NV2ufKygnXPNsXexPmneGjYWu/P2Nc+W4DjXRWirD1VSFqzjaUEVZ3TGOhmJF\nu9aqwXQjbb7XdcGN+nCj2biRjPh4RvN4JNYjlu33kO33EPR7CPaMDbP9HoKZLcb9BsYpHu/sui5O\nXR3RinLsujpcM4prmrhRE8cycaPxadPEMVtOR3DMuuZljfPDsXUc08Q2TWzLjTUIGot8Y+FvUfRt\nteW0ht2qEWHgqNoJjYbGxsK57zMuEIm/wIy/6s95e2fD8Gj8+4+mxhoEnSz9jipxSrqmomsqfl/b\nP385G6blEI5arXoBwvEGQCja2FCIT0dMGqIRQmYEr9fAjrioSqyLWFVjrVo13opVW7Z8dQW15TwF\n1Bat4Nh7G+e1Xta4rVbrn7BMUWPbV2jeVst11fgZqe24WLYTf7mYloPtOJhWbLp5mYNptV7Xsh1M\n28FuXGY6WOHY8ojtYtoOluVgOx3cMaY4oFqx3hTVBs1GUa1WQ1Q7vtxC1WPjquaAFlseUW0iqgVq\nPajHcRXrK7d5FFR0xUBXDAzFg6EYGGqsByDW+xHj4oLb+hzVxaVlB6ILrdZpuXbzei2Wn/he3Mb/\n0fb/n2p583tNIoTduli3cBtcW4sX5UCrs2mv6ydgxLqCs/2+WEHOb1Gs48U5kOnB0L96N7GiKGiB\nABmBjjtDbMl1XbDt5kaA1VzoT2oExBsRTcuiLacbmhsIkQh2NIoVjeLaLtFwFNu0cGwH17ZxHQfX\ndlBcB8VxUHGI7aAKbuxUGrdxn1Li81Bw48d1bJ3Y/OZxmtZpWh9AUeP9BcoJ26L532t8n6KCppGT\n60PhEr76iUL7pJCLc2LoKobuIXCWD29LtcsYieK67gmFv7nIn9gosE5oQDStbzU3KLw+g9raCJbt\nNDVEbMdtHrfd1vNNByvsxuc7TUMrvl5s3IldUlCs5kaBZsUaCi0aBYpmxxoDqo1yQiMC1cbWLCKq\njaLVNb8vqbk/cY7SsmK3nt/qjYBt4EYDOJEMDMePT8kioGWT7c0mz5dLrt9Pdi9vc4GOn0Hrafab\ncEVRQNfRdB0yOv720mfyueG4LvUNUWpqQtTVhaitDVNbF6K+LkxDfZiGhggN9RFCoTDhUAQraqG5\nDhoWmhZBVaPoWgRNjaIrUTTVRFej6KqFioXq2miOi+rQ5tDjanjRMFw19goUxn43mABSyIXoAhRF\nwdAVDF2lIz4GO7PB5LjuyYXedrDiw9i0i9XYIGhqTMQbCSc0JkzLIuqYOK4b6xUhXhjivSVK0xAU\nRSV+EhS/dqmgnjANoLV4f+Oypt6Zxm0qzdtuXAcF1PiMxlhosV5sujme3r2C2FGLQKaRdsU51aiK\nQsDvJeD3Ajntrm9aNjX1JjUNUWobok3jNfXx6QaTmvpobHm9iaOYKEYExRNB8YTj42GIz1ONMIon\nEru8BujU8d+4KHJGLoToalRFQdUVDKRwSQ9T6jJ0jbxsjbzs9m+u5bguDWHr5CLfcro2yvH6CHWR\nesJuA9kZfpgpZ+RCCCFE0qmKQlaGQVaGAfjbXd+07Ph3caSQCyGEECnnVL8e6CzSNyaEEEKkMCnk\nQgghRAqTQi6EEEKkMCnkQgghRAqTQi6EEEKkMCnkQgghRAqTQi6EEEKkMCnkQgghRAqTQi6EEEKk\nMCnkQgghRAqTQi6EEEKkMMV1T34arxBCCCFSg5yRCyGEEClMCrkQQgiRwqSQCyGEEClMCrkQQgiR\nwqSQCyGEEClMCrkQQgiRwrp9If/lL3/JnDlzmDt3Lh9//HGyw0lbjzzyCHPmzGH27Nm8+eabyQ4n\nrYXDYS6//HJeeumlZIeS1l555RWuu+46brzxRt55551kh5OW6uvr+cEPfsCCBQuYO3cua9euTXZI\nXZKe7ACSaePGjXzxxRcsXbqUvXv3snjxYpYuXZrssNLOBx98wO7du1m6dClVVVV885vf5Iorrkh2\nWGnrySefJDs7O9lhpLWqqiqeeOIJXnzxRRoaGvjd737HjBkzkh1W2vnnP//JgAEDWLhwIWVlZdx+\n++2sXLky2WF1Od26kK9fv57LL78cgEGDBnH8+HHq6urIyspKcmTpZdKkSYwePRqAYDBIKBTCtm00\nTUtyZOln79697NmzR4pKJ1u/fj2TJ08mKyuLrKwsHnrooWSHlJZyc3PZuXMnADU1NeTm5iY5oq6p\nW3etHz16tNWO0aNHDyoqKpIYUXrSNI3MzEwAli1bxqWXXipFvJMsWbKERYsWJTuMtHfw4EHC4TDf\n+973mDdvHuvXr092SGnpmmuu4fDhw8yaNYv58+dzzz33JDukLqlbn5GfSO5W27lWr17NsmXL+Mtf\n/pLsUNLSyy+/zNixY+nbt2+yQ+kWqqurefzxxzl8+DC33XYbb7/9NoqiJDustLJ8+XKKiop46qmn\n2LFjB4sXL5bvfrShWxfygoICjh492jRdXl5Ofn5+EiNKX2vXruX3v/89f/7znwkEAskOJy298847\nHDhwgHfeeYcvv/wSj8dDYWEhU6ZMSXZoaScvL49x48ah6zr9+vXD7/dTWVlJXl5eskNLKyUlJUyd\nOhWAYcOGUV5eLpfl2tCtu9YvueQS3njjDQBKS0spKCiQ6+OdoLa2lkceeYQ//OEP5OTkJDuctPXb\n3/6WF198keeff56bb76ZO+64Q4p4J5k6dSoffPABjuNQVVVFQ0ODXL/tBMXFxWzduhWAQ4cO4ff7\npYi3oVufkY8fP54RI0Ywd+5cFEXh/vvvT3ZIaem1116jqqqKH/3oR03zlixZQlFRURKjEuLc9erV\niyuvvJJbbrkFeUWWWgAAAzhJREFUgJ///Oeoarc+L+oUc+bMYfHixcyfPx/LsnjggQeSHVKXJI8x\nFUIIIVKYNCGFEEKIFCaFXAghhEhhUsiFEEKIFCaFXAghhEhhUsiFEEKIFCaFXIhu4ODBg4wcOZIF\nCxY0PUlq4cKF1NTUnPE2FixYgG3bZ7z+t771LTZs2HAu4QohzoIUciG6iR49evDMM8/wzDPP8Nxz\nz1FQUMCTTz55xu9/5pln5GYcQnRB3fqGMEJ0Z5MmTWLp0qXs2LGDJUuWYFkWpmly3333MXz4cBYs\nWMCwYcP49NNPefrppxk+fDilpaVEo1HuvfdevvzySyzL4vrrr2fevHmEQiF+/OMfU1VVRXFxMZFI\nBICysjJ+8pOfALFnpc+ZM4ebbropmX+6EGlFCrkQ3ZBt26xatYoJEyZw11138cQTT9CvX7+THkyR\nmZnJs88+2+q9zzzzDMFgkEcffZRwOMzVV1/NtGnTeP/99/H5fCxdupTy8nIuu+wyAF5//XUGDhzI\ngw8+SCQS4YUXXkj43ytEOpNCLkQ3UVlZyYIFCwBwHIeJEycye/ZsHnvsMX72s581rVdXV4fjOEDs\nNsYn2rp1KzfeeCMAPp+PkSNHUlpayq5du5gwYQIQeyDRwIEDAZg2bRp///vfWbRoEdOnT2fOnDmd\n+ncK0d1IIReim2i8Rt5SbW0thmGcNL+RYRgnzTvxUZ2u66IoCq7rtrrfeGNjYNCgQaxYsYIPP/yQ\nlStX8vTTT/Pcc8991T9HCBEnX3YTohsLBAL06dOHd999F4B9+/bx+OOPn/Y9Y8aMYe3atQA0NDRQ\nWlrKiBEjGDRoEJs3bwbgyJEj7Nu3D4BXX32Vbdu2MWXKFO6//36OHDmCZVmd+FcJ0b3IGbkQ3dyS\nJUv4xS9+wR//+Ecsy2LRokWnXX/BggXce++93HrrrUSjUe644w769OnD9ddfz1tvvcW8efPo06cP\no0aNAmDw4MHcf//9eDweXNflO9/5DrouHz1CdBR5+pkQQgiRwqRrXQghhEhhUsiFEEKIFCaFXAgh\nhEhhUsiFEEKIFCaFXAghhEhhUsiFEEKIFCaFXAghhEhhUsiFEEKIFPb/AS/G9oVtRQj2AAAAAElF\nTkSuQmCC\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": "693b0884-b944-4e23-a76d-e0929ada8e23"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1296x864 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Xx9jgEMHKxlJ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We might be able to do better by choosing additional ways to transform these features.\n",
+        "\n",
+        "For example, a log scaling might help some features.  Or clipping extreme values may make the remainder of the scale more informative."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "baKZa6MEKxlK",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def log_normalize(series):\n",
+        "  return series.apply(lambda x:math.log(x+1.0))\n",
+        "\n",
+        "def clip(series, clip_to_min, clip_to_max):\n",
+        "  return series.apply(lambda x:(\n",
+        "    min(max(x, clip_to_min), clip_to_max)))\n",
+        "\n",
+        "def z_score_normalize(series):\n",
+        "  mean = series.mean()\n",
+        "  std_dv = series.std()\n",
+        "  return series.apply(lambda x:(x - mean) / std_dv)\n",
+        "\n",
+        "def binary_threshold(series, threshold):\n",
+        "  return series.apply(lambda x:(1 if x > threshold else 0))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-wCCq_ClKxlO"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n",
+        "\n",
+        "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "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": 671
+        },
+        "outputId": "47070cb1-1684-473f-fc6a-85c573427d6b"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 81.87\n",
+            "  period 01 : 76.41\n",
+            "  period 02 : 73.79\n",
+            "  period 03 : 72.50\n",
+            "  period 04 : 72.98\n",
+            "  period 05 : 71.89\n",
+            "  period 06 : 70.80\n",
+            "  period 07 : 70.38\n",
+            "  period 08 : 69.41\n",
+            "  period 09 : 69.17\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.17\n",
+            "Final RMSE (on validation data): 69.51\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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CtUdZvSuTkX1aYjKZnFipiIjI1UVXpm0ATzdPhrUcRIWlkk3ZW89aFujrSZ/O\nEWTnV3A4vchJFYqIiFydFFQaKDF2EB5mD1amr8NqO/vOyYnxMQCs3qX7/4iIiDQmBZUG8vf0Y2B0\nXwqqCtmZm3rWsk6tgokO9WXbwVxKymucVKGIiMjVR0HlIoxoPRQTJpanrTnrOjAmk4mkhFisNoMN\ne7KdWKGIiMjVRUHlIoT5hBIf0ZP0siwOFX531rJBPaLwdDezelcmNl2pVkREpFEoqFykUee5rL6f\ntwd9u0aQV1TFgeOFzihNRETkqqOgcpHaBLbimuD2HCg4TEbp2ZNnkxJiAVi9M9MZpYmIiFx1FFQu\nwf9uVrj2rOfbRwfSOsKfnd+eorC02hmliYiIXFUUVC5B99AuRPtFsj13F4VV/7t2yplJtTbDYF2q\nTlUWERG5XAoql8BkMjGydSI2w8bK9HVnLevfLRIvTzfW7s7CZtOkWhERkcuhoHKJ+kTGE+wVxIas\nFCpqK+3P+3i5M7BbJAUl1aQezXdihSIiIlc+BZVL5G52J6nlYKqtNazP3HzWssR4TaoVERFpDAoq\nl2FIbH+83bxYlbGeWpvF/nybqADaxwSy50g+p4or69mCiIiI1EdB5TL4uPswJHYAJTWlbD2546xl\nSfGxGMDa3ZpUKyIicqkUVC5TcqshuJncWJ62Fpthsz/ft2sEPl7urNudjcVqq2cLIiIicj4KKpcp\n2CuIPpHx5FTksi//oP15Lw83BveIori8hl3fnnJihSIiIlcuBZVGcOYCcMtOnH1Z/cQzV6rdpUm1\nIiIil0JBpRHE+EfRPbQLR4qPcaz4hP352DA/OrUKZv/xQnIKK5xYoYiIyJVJQaWRjDzPzQqT4mMA\nWLNLk2pFREQuloJKI7kmuD2tA1qyO28fuRV59ud7d47A38eD9anZ1Fo0qVZERORiKKg0EpPJxKg2\nSRgYrPjBzQo93M0M6RVNWWUt2w/lOrFCERGRK4+CSiOKD+9BmHcLNp/cTmlNmf35xO8P/+hKtSIi\nIhdHQaURmU1mhrcehsVmYU3GBvvzkSG+dGsbwuGMYjJPlTuxQhERkSuLgkojGxjdBz8PX9ZmbKLa\nWmN/Pun7+/+s0aiKiIhIgymoNDJPN08SYwdRbqlgU9ZW+/Px14QR5OfJhr0nqa61OrFCERGRK4eC\nigMMazkID7M7K9PXYrWdDiXubmaGxsVQWW1hy4EcJ1coIiJyZVBQcYAAT38GRPclv6qQXXl77M8n\nxsVgMsHqnbqmioiISEMoqDjI8FZDMWFiedoaDMMAIDTIm57tQzmWXcKJk6VOrlBERMT1Kag4SIRv\nGPHhPUgrzeRw4RH780nf3/+mgdsKAAAgAElEQVRnje7/IyIickEKKg40ss25l9Xv1T6UFoFebNqf\nQ2W1xVmliYiIXBEUVByobWBrOga3Y3/BITLLsgEwm00kxsVQXWNl835NqhUREamPgoqDjWqdBJw9\nqjKkVwxmk4nVOzPt81dERETkXAoqDtYttDNRfpFsy9lFYVURACEBXiRcE0Z6bhlHs0ucXKGIiIjr\nUlBxMLPJzMhWw7AZNlalr7c/f2ZSre7/IyIicn4KKk2gT1QCQZ4BbMhKoaK2EoCubUOICPZhy4Fc\nyqtqnVyhiIiIa1JQaQIeZneSWw2lylrN+qzNAJhNJhLjY6i12Ni496STKxQREXFNCipNZEhsf7zd\nvFidvp5a2+nTkgf3isbNrEm1IiIi56Og0kR83H0YHNOf4ppStp3cCUCgryd9ukSQnV/B4fQiJ1co\nIiLietwdteF58+bx+eef2x/v3buXl19+mXfeeQcPDw8iIyN56aWX8PT0dFQJLie51RBWZaxnefpa\n+kf3xmwykxQfQ8r+HFbvyqJz6xBnlygiIuJSHDaicssttzB37lzmzp3LQw89xA033MDzzz/Pv/71\nLz744AN8fX1ZtmyZo3bvkkK8g+kbmcDJ8hz25R8EoFOrYKJDfdl+KJeSihonVygiIuJamuTQz+zZ\ns5k2bRrBwcGUlJy+bkhJSQkhIc1vBGFE62HA/y4AZzKZSIqPxWI12LAn25mliYiIuByHHfo5IzU1\nlejoaMLDw5kxYwaTJk0iICCAbt26MWjQoHrXDQnxxd3dzWG1hYcHOGzb9e0zPq0bu07up8h8imtC\n2/GTpI58uuYI61NPcuf47pjNpiavy9U4ozfSMOqNa1JfXJd6c3kcHlTmz5/PpEmTsNlsPP/888yf\nP59WrVrx61//mhUrVjBixIjzrltYWOGwusLDA8jLK3XY9uuTGD2EXSf3M2/3Eu7vOQWAvl0j2LDn\nJGu3pdG9XQun1OUqnNkbqZ9645rUF9el3jRMfWHO4Yd+UlJSSEhIoKCgAIDWrVtjMpkYOHAge/fu\ndfTuXdI1wR1oHRDL7ry95FacAnSlWhERkbo4NKjk5OTg5+eHp6cnISEhFBcX2wPLnj17aNOmjSN3\n77JMJhMjWydiYLAyfR0A7aMDaRXhz85vT1FYWu3kCkVERFyDQ4NKXl4eLVqcPozh5ubGs88+yy9/\n+UvuvPNOrFYrEyZMcOTuXVp8eE9CvUPYnL2V0pqy05NqE2KxGQbrU7OcXZ6IiIhLMBkufElURx7X\nc4XjhqszNjDv8CLGtR3JxPajqay28OjsDfh5u/PnXw5qtpNqXaE3Ujf1xjWpL65LvWkYp85RkfMb\nGN0XP3df1mZupMZag4+XOwO7RVJQUk3q0XxnlyciIuJ0CipO5OXmybCWAymvrWBT9jYAEuNPT6pd\no0m1IiIiCirOlthyMB5md1akrcVqs9ImKoB20YGkHsnnVHGls8sTERFxKgUVJwvw9Kd/dB/yqwrY\nlXf6dO2khBgMYO1uXalWRESaNwUVFzCi1VBMmFietgbDMOjXNRIfL3fW7c7CYrU5uzwRERGnUVBx\nARG+4cSFdyetNINvi47i5eHGoB5RFJfXsPu7U84uT0RExGkUVFzEyNZJACxLWw1AUnwMoCvViohI\n86ag4iLaBbWmQ1A79ucfIqvsJLHh/nRqGcS+44XkOPCeRyIiIq5MQcWFjGqTCMDytDXA/+7/s2aX\nrlQrIiLNk4KKC+ke2oVI3wi25eyisKqI3p0j8PfxYH1qNrUWTaoVEZHmR0HFhZhNZka2TsRqWFmV\nsR4PdzNDekZTVlnL9sO5zi5PRESkySmouJi+UQkEeQawITOFSkslifZJtTr8IyIizY+CiovxMLuT\n1HIIVdZq1memENnCl25tQzicXkTmqXJnlyciItKkFFRc0JDYAXi5ebIqfT0Wm4Uk3f9HRESaKQUV\nF+Tr4cPgmP4U15SwNWcX8deEEeTnyca9J6mutTq7PBERkSajoOKihrcaitlkZkXaGtzMJobGRVNR\nbWHrAU2qFRGR5kNBxUWFeAfTOyKe7PIc9uUfZFhcDCZg9S4d/hERkeZDQcWF/fACcGFBPvTsEMrR\nrBLSckqdXJmIiEjTUFBxYbH+0XRt0Ylvi45yoiTdPql2ta5UKyIizYSCiosb2fr0qMqytDX06hBK\ni0AvNu07SWW1xcmViYiIOJ6CiovrHNKRVv4x7MrdQ35VAcPiYqiusZKyP8fZpYmIiDicgoqLM5lM\njGyThIHByvS1DO0Vg9lkYtXOTAzDcHZ5IiIiDqWgcgVICO9JqHcIm7K34eFlIf6aMNJzyziaXeLs\n0kRERBxKQeUK4GZ2I7nVUGpttazJ3EhSwun7/6zR/X9EROQqp6ByhRgU0w8/d1/WZGygYyt/woO9\n2XIgh/KqWmeXJiIi4jAKKlcILzdPhrYcSHltBVtObicpPpYai42Ne086uzQRERGHUVC5giS2HIS7\n2Z0V6esY2DMSN7OJ1ZpUKyIiVzEFlStIoGcA/aN6c6oyn6Plh+ndOZzs/Aq+zSh2dmkiIiIOoaBy\nhRnRehgmTCw/sYak+NOTalfv1P1/RETk6qSgcoWJ9A2nV3h3TpSmYw4oJDrUl22HcimpqHF2aSIi\nIo1OQeUKdOay+ivS15AUH4vFarB6h0ZVRETk6qOgcgVqH9SG9kFt2Zt/kPYdTAT4evD5huPsPZbv\n7NJEREQalYLKFWrU96MqG09u5KEbe2E2wxuf7SXrVLmTKxMREWk8CipXqB5hXYn0jWBrzk7CwuHu\n8V2prLYyc/5uSjVfRURErhIKKlcos8nMiNZDsRpWVqdvYGD3KCYOakteURWzF+yh1mJzdokiIiKX\nTUHlCtYv8loCPQNYl7mZSksVNwxtR58uERzOKOb9rw/qQnAiInLFU1C5gnm4eZDUcjBV1ipWpq3F\nbDJx74SutIsOYMPekyzefMLZJYqIiFwWd0dteN68eXz++ef2x3v37mXt2rU88sgjFBcXExkZyauv\nvoqnp6ejSmgWhsYOZE3GRhYfX06YTyj9o3vz0E29eO69bXy65ihRLXzp3TnC2WWKiIhcErff//73\nv7+UFY8fP05wcPB5l3fv3p0bb7yRG2+8kZYtW+Lu7s6uXbvo2rUrL774IkePHsXPz4/IyMjzbqPC\ngZNC/fy8HLr9puLh5kHXFp3YnrOLHXmptPSPpk1INF3bhLBpXw7bD+XRo30Lgv29nF1qg10tvbka\nqTeuSX1xXepNw/j5nf9vVL2Hfu6+++6zHs+ZM8f+72effbbBBcyePZtp06axatUqrrvuOgCmT59O\nr169GrwNOb8Y/yimxd2Du9mdt/f9h8OFR2gdGcDPf9KNWouNWfNTKSytdnaZIiIiF63eoGKxWM56\nvHnzZvu/GzpRMzU1lejoaMLDwzl16hQffvght99+O88++yw1NUqZjaVdUBt+0XMqGAZvpb7LiZJ0\nEq4J55bkjhSV1TBz/m6qa6zOLlNEROSi1DtHxWQynfX4h+Hkx8vOZ/78+UyaNAmA6upqBg8ezPTp\n05kxYwbz5s3jjjvuOO+6ISG+uLu7NWg/lyI8PMBh23aG8PBr8fQz8erGf/JG6jv8YcRj3DmhG0UV\ntXyTcoL3vjnEU1P7YTY3rHfOdLX15mqi3rgm9cV1qTeX56Im0zY0nPxQSkoKM2bMACA6OpqEhAQA\nBg8eTEpKSr3rFhZWXPT+Gio8PIC8vFKHbd9Z2nt15PbON/Ofg/P448qZPHrtNG4e1o607GI27z3J\nW5/u5uakDs4us15Xa2+uBuqNa1JfXJd60zD1hbl6D/0UFxezadMm+1dJSQmbN2+2//tCcnJy8PPz\ns5/Z079/f/vho3379tGuXbuLeR3SQINi+jKp4wSKqot5fdc/qbRWMG1STyJDfFi8+QTrU7OdXaKI\niEiD1DuiEhgYeNYE2oCAAGbPnm3/94Xk5eXRokUL++Nf//rX/OY3v2HWrFmEhYUxbdq0S61bLmBk\n60QqaitZemIlr+/6F7++9hc8fEscL7y/jfe+Pkh4sDedW4c4u0wREZF6mQwXvnypI4fLmsNwnGEY\nfHR4IeszN9MhqB3T4+/lSEY5r368C29PN2ZM7UNkiK+zyzxHc+jNlUq9cU3qi+tSbxrmkg/9lJWV\n8e6779off/TRR1x//fX86le/4tSpU41WoDiGyWTi1k430DsijiPFx/jX3g/o1CqQKWM6U15lYea8\nVMqrap1dpoiIyHnVG1SeffZZ8vPzATh27BivvvoqTzzxBIMGDeKFF15okgLl8phNZu7qdivdQjuz\nL/8g7x/4mCG9ohjTrxUnCyp447O9WKy6gaGIiLimeoNKeno6jz32GABLly5l7NixDBo0iNtuu00j\nKlcQd7M79/eYQvugtmzL2cW8w4u4ObED8R3D2H+8kP8uO6wbGIqIiEuqN6j4+v5v/sKWLVsYMGCA\n/fGlnKoszuPp5skDve4m1j+atZmbWHxiGT//STdaRfizelcWy7dlOLtEERGRc9QbVKxWK/n5+aSl\npbFz504GDx4MQHl5OZWVlU1SoDQeXw8fpsffR7hPKF8fX8GGkxt5+OZeBPl58tHKb0k9olEyERFx\nLfUGlfvvv5/x48dz3XXXMW3aNIKCgqiqquL222/nhhtuaKoapREFegbwUPz9BHkGsuC7LzlUtoeH\nbuqFu5uZNxftIyO3zNklioiI2F3w9OTa2lqqq6vx9/e3P7d+/XqGDBni8OJ0erLjZJfn8Lftb1Bh\nqeS+nlOoORXOm4v2ERrozYypfQjy83Rabc29N65MvXFN6ovrUm8a5pJPT87KyiIvL4+SkhKysrLs\nX+3btycrK6vRC5WmE+0XybT4e/B08+Dfe/9DYGQJk4a2I7+kitc/TaXWohsYioiI89V7Zdrhw4fT\nrl07wsPDgXNvSvj+++87tjpxqLaBrflFz58xZ/fbvLXnPX4Vdz/ZBZFs3pfDO4sP8vPrumnStIiI\nOFW9QeVPf/oTixYtory8nAkTJjBx4sSzLokvV77OLTpyT487+OeeubyR+m+mD/s5p4qqSNmfQ3QL\nX34yRPdjEhER56n30M/111/PO++8w9///nfKysq44447uO+++/jiiy+oqqpqqhrFweLCe3BH11so\nt1Tw5p53uH18LGFB3ny2/hgp+3OcXZ6IiDRj9QaVM6Kjo5k2bRpLlixhzJgxPP/8800ymVaazsDo\nPtx0zXUU15Tw78Pvce/17fH2dOPtrw5wJLPY2eWJiEgz1aCgUlJSwgcffMCNN97IBx98wC9+8QsW\nL17s6NqkiQ1vNZRxbUdwqjKfTzM+5J7rOmK12Xjt01ROFeu6OSIi0vTqnaOyfv16Pv30U/bu3cvo\n0aN5+eWX6dSpU1PVJk4wod1oymsrWZu5kTVun3HL8HF8suI4s+an8tSdvfHxqvcjIyIi0qjqvY5K\nly5daNu2LXFxcZjN5w6+vPTSSw4tTtdRcQ6bYeO9/R+xLWcXXVt0IiB3EGt2nCSuQygP3dQLs9mx\nZwKpN65LvXFN6ovrUm8apr7rqNT73+Mzpx8XFhYSEhJy1rKMDN0b5mplNpm5q+utVFmq2Jt/kIRo\nb7q168buI/l8suo7bhtxjbNLFBGRZqLeOSpms5nHHnuMZ555hmeffZbIyEj69evH4cOH+fvf/95U\nNYoTuJnduLfHFDoGt2NnXioRPY4QFerDN1vTWb0r09nliYhIM1HviMrf/vY33n33XTp06MCKFSt4\n9tlnsdlsBAUFMW/evKaqUZzE082DX/b6GTN3vEVKzlYGD/KibHkI//nmMBHBPnRrq2vqiIiIY11w\nRKVDhw4AjBgxgszMTO666y5ef/11IiMjm6RAcS4fdx8ejL+PCN8wNuSsZ0BSGSYTzFm4l+z8cmeX\nJyIiV7l6g8qPL58eHR3NqFGjHFqQuJ4AT38eir+fYK8gNuSvYnBiLRXVFmbOT6WsstbZ5YmIyFWs\nQddROUP3fWm+WniH8FD8/fh7+LG1bAV9B1jILaxk9oI9WKw2Z5cnIiJXqXpPT+7ZsyehoaH2x/n5\n+YSGhmIYBiaTidWrVzu0OJ2e7HrSSjKYufMtam0WYsuSOLTPgyG9orl7XJdGC7LqjetSb1yT+uK6\n1JuGueTTk7/++utGL0aubK0DW/LLXj/j9d1vczJgLTGth7I+NZvoUF/G9W/j7PJEROQqU29QiY2N\nbao65ApyTUgH7utxJ//Y8z6VsRsJqhjA/FVHiArxJaFTuLPLExGRq8hFzVEROaNnWDemdJ1MlbUK\nj07b8PCr5K0v9nHipIY4RUSk8SioyCXrF3Utt1xzPeWWMoJ77aKWCmZ9mkphabWzSxMRkauEgopc\nlqRWg5nQbhSl1mLCr02lsKKU1z5NpbrW6uzSRETkKqCgIpdtXNuRJLccQqlRQFjCHo7nFvL2l/ux\nnf+EMhERkQZRUJHLZjKZuPGaifSLupZycx7BPfew7XAOn6076uzSRETkCqegIo3CbDJzZ5db6BnW\njWqvHAK67OXLjcfYuDfb2aWJiMgVTEFFGo2b2Y17u9/BNcHtsQRk4dPhAO8uOcDh9CJnlyYiIlco\nBRVpVB5uHvyi189oHRALoemYYg/x2oJUcosqnV2aiIhcgRRUpNH5uHszLe5eIn0jcI86RnXwIWbN\nT6WiyuLs0kRE5AqjoCIOcfqOy/cR4hWMR6tvyTEd4M1Fe7HadANDERFpOAUVcZgQ72AeSjh9x2XP\ntvs5ULyPj5Z/5+yyRETkCqKgIg4V6RvO9Pj78Hb3wrNDKquO7GTF9gxnlyUiIlcIBRVxuFYBsTwQ\ndw/uZje8rtnJh5tT2HM039lliYjIFUBBRZpEx+B23N9zCmYzeF6znTeXbiAzr8zZZYmIiItzWFCZ\nN28eU6ZMsX8lJCTYl3300UcMHz7cUbsWF9UjrCtTu92Kyd2C0X4Lf1u0kZKKGmeXJSIiLszdURu+\n5ZZbuOWWWwDYsmULS5YsASA/P59ly5Y5arfi4vpEJVBhqeLjwwspj13PzIU+PHnrIDzc3ZxdmoiI\nuKAmOfQze/Zspk2bBsBf/vIXfvWrXzXFbsVFDWs5kIntxmD2qiIreCX/WrILQzcwFBGROjhsROWM\n1NRUoqOjCQ8PJyUlBS8vL+Li4hq0bkiIL+4O/J92eHiAw7Yt9ZsSdj1Wt1qWfLeS1LIlLNsdxh2j\netiXqzeuS71xTeqL61JvLo/Dg8r8+fOZNGkSNTU1zJo1izlz5jR43cLCCofVFR4eQF5eqcO2Lxc2\nodUYcouL2M4OFhz/kMC1dzGga4x648LUG9ekvrgu9aZh6gtzDj/0k5KSQkJCAgcOHODUqVPcf//9\nTJ48mdzcXB555BFH715cmMlkYmr3W+gU2AW3wALeP/AhR7J0A0MREfkfhwaVnJwc/Pz88PT0JC4u\njqVLl/LJJ5/wySefEBERwd/+9jdH7l6uAG5mN6YlTCHWqw2m4Bz+vmkuOQXlzi5LRERchEODSl5e\nHi1atHDkLuQq4OHmwaP97yXEHIktJJ3HPp3N7qM5zi5LRERcgMlw4dMtHHlcT8cNXU9pTRnPrX+N\ncgqxVXvTytKP+4YMJyLE19mlyff0c+Oa1BfXpd40jFPnqIg0VICnP38c9iiJsUmYPWvI9FvL79a8\nxvtrtlFdY3V2eSIi4gQKKuJSvN29eHDIrTw74DFiPNtiDsxnc+08frvoX6zdc0LXWxERaWYUVMQl\nRfqF8/TgB7i36134mgOwhh7ho8x/MmPBpxzLLnZ2eSIi0kQcfh0VkUtlMpm4NroHPSM68/nhFazK\nWkNRyBb+vOUw3T2Hctew/gT6eTq7TBERcSCNqIjL83Dz4KauY/nj4Mfp4NcFc0AR+z2/4KnF/+Dz\nlENYrDZnlygiIg6ioCJXjBbeITza/x6mx91HoFsLCE3j6+L3eWLeh+w+kufs8kRExAEUVOSK0zW0\nEy8M+y0T247H3Q2qInbz5v43eWnhMnIKHHfbBRERaXoKKnJFcjO7Ma59Es8PfZKewXGY/UrJCFrG\n71b8g7krd1NZbXF2iSIi0ggUVOSKFugZwC+vvYPHrp1GqEckbqFZbLJ+xBOffsCa3RnYdDpzo6i2\n1rD31AFOVRQ4uxQRaWZ0ZVpxOZfaG5thY116Cgu/W0ItVdgq/Qgt683dQ4fSISbIAZVe3QzDIL00\nkw1ZKWzL2UWVtRoPszvDYgcxum0y/h5+zi5RvqffZ65LvWmY+q5Mq6AiLudye1NeW8H8g4vZkrsV\nTAbWggh6+gzljsR4gv29GrHSq1NFbSVbc3ayMWsLGWVZAAR7BREf3oO9BadHVbzdvBnVJonkVkPw\nctMp4s6m32euS71pGAWVOujD47oaqzcZpVm8t2c+WVUZGDYz5HZgbNskxvXrgIe7jnr+kGEYfFd0\njI3ZW9iZm0qtzYLZZKZnWDcGRfelW2hnzCYzQS28WbjrG74+sZLy2goCPQMY324kg6L74WZ2c/bL\naLb0+8x1qTcNo6BSB314XFdj9sYwDLae3MnHB7+gyijHVu2Nb34v7ug3jIRrwjGZTI2ynytVSU0p\nKdnb2Zi9hdyKUwCE+4QyKKYf/aP6EOR19i+PM72ptFSyPG0tK9PWUmOrJcInjIntx5AQ0ROzSSGw\nqen3metSbxpGQaUO+vC4Lkf0pspSxRffLWNN5gYMkw1rcShtrAOYmtSHmLDmNdfCZtg4UPAtG7NS\nSD21H5thw93sTkJ4TwbH9KNjcPvzBrgf96a4upSvjy9nfVYKNsNG64BYru8wni4trmmqlyPo95kr\nU28aRkGlDvrwuC5H9ianPJcP9i3kaNkRDJsJW25bBkcM46YhnfD19nDIPl1FfmUhm7O3sil7G4XV\nRQDE+kczKKYf/SIT8PXwveA2zteb3IpTfHl0KdtzdwPQJeQaru8wjtaBLRv3RUid9PvMdak3DaOg\nUgd9eFyXo3tjGAapefv48MAiSq3FGDVemHO6clOvYSTGxWI2Xz2Hgyw2C3tOHWBj1hYOFBzGwMDL\nzZM+kQkMjulH64CWF3X460K9SSvN4PMjX3Og4DAA10b04rr2Y4jwDb/s1yLnp99nrku9aRgFlTro\nw+O6mqo3NdZalh5fxTcnVmHDirU0mNDS3kxN7E+nVsEO378j5ZTnsjF7KynZ2ymtLQOgXWAbBsX0\n49qIXni7X9zZTxarjZzCSrp1DKe46MJX/z1U8B2LjizhRGk6ZpOZQTH9GN92JEFegZf0eqR++n3m\nutSbhlFQqYM+PK6rqXuTX1nIxwcXsa9wP4YB1txW9PQdyO3JPWgR6N1kdVyuGmsNO3P3sCFrC0eK\njwHg5+FL/6jeDIzuS4x/VIO2YxgG+SVVHM0qsX+dyCml1mIjJMCLMf1akxgfg5dH/Wf5GIbBzrw9\nfHH0a3IrTuFp9iC51VBGtUnEx93nsl+v/I9+n7ku9aZhFFTqoA+P63JWbw4WfMt/9i+goCYfw+KB\nkdWJsR2HMK5/Wzwv8EfZmdJLM9mYtYWtOTuptFQBp+eIDIrpS6/wHniY3etdv7LawvGTpRzNKrYH\nk+LyGvtykwlahfsTFepL6pF8qmqsBPp5MrZfa5ITYvHyrP+9sdqsbMreyuJjyyiuKcXP3ZfRbZNJ\njB2Eh9vVPS+oqej3metSbxpGQaUO+vC4Lmf2xmKzsCp9A18eXYbFqMFWHojPqThuH9if3p1d53Tm\nSksl23J2sSFrC+mlmQAEeQYyMLoPA2P6EuYTWud6NptBVn7594HkdDDJPFXOD38LhAR40T46kPax\ngbSPDqRtVKA9jHj6ePLh1wdYsT2DqhorAb4epwPLtbF4e9YfiGqsNaxO38A3aauotFQR4hXMhHaj\n6B/dW6c0Xyb9PnNd6k3DKKjUQR8e1+UKvSmuLuHTw1+xPW8nAJZTMbSx9eWu4XG0jPB3Sk2GYXC0\n+AQbslLYkZtKra0Ws8lM99AuDI7pR7cWnc+56Fpxec1ZIyXHskuoqrHal3u6m2kbFUD72KDT4SQm\nsN7DXWd6U1ZZy7Kt6Szfnk5ltRV/Hw/G9GvF8Gtb4uNVf2Apr63gmxOrWJOxgVqbhSi/SH7Sfiy9\nwrq5TBC80rjCz4zUTb1pGAWVOujD47pcqTdHi4/zn/0LOFl5EsPqhiWrI4OjB3LT0Gvw92mawxal\nNWWknNzOxqyt5FTkAhDm3eL0RdmiexPsdfo+RrUWKydyys4aLTlVXHXWtqJDfWkfE0j7mCA6xAQS\nG+6Hm7nhoxk/7k151enAsmxbBpXVFvy83RndrzUje184sBRWFbH42DI2ZW/DwKB9UBuu7zCejsHt\nGlyPnOZKPzNyNvWmYRRU6qAPj+tytd7YDBsbsraw8NvFVNtO3+zQLbsHkxL6k5QQc1F/6C9mn4cK\nvmND9hZS8/ZhNay4m9yIj+jJoOh+dAxux6ni6tOhJLOEo9nFpOWUYbX978fZ38fj+1Dy/Vd04GVf\nK+Z8vamosrB8ezrLtqZTXmXB18ud0X1bMbJPywvu82R5Dp8fXcruvL0A9Ajtwk86jCPWP/qyam1O\nXO1nRv5HvWkYBZU66MPjuly1N2W15Xx+ZCkbslIAA2tBJC3KE5iSGE/Xti0aZR+FVUVszt7Gxuyt\nFFQVAhDtF0nf8D6E2jqQdbKWo9mnD+OUVdba13Mzm2gdGWAPJR1iAgkP9mn0QykX6k1ltYUV2zNY\nuiWN8ioLPl7ujOrTklF9W+F3gcByrPgEi44s4duio5gw0TcqgYntRhPq0zjv7dXMVX9mRL1pKAWV\nOujD47pcvTfppZl8eGAhJ8rSMGxmLFnt6enfl58O70JY8MWfdmu1WdmTf/qibPvzD2Fg4GHyINbj\nGrxK2nEy05OT+ZVnrRMW5H3WIZzWkf54uDv+zKSG9qay2sLKHRks3ZJOWWUtPl5ujOjditF9W9V7\nyMwwDPYXHGLRkSVklvWgqDUAACAASURBVGXjbnJjaOxAxrQdToCnc+YGXQlc/WemOVNvGkZBpQ76\n8LiuK6E3hmGwNWcn8w99Sbm1DFu1D7aMrozu1JeJA9te8JRdgNyKPDZmbWVT1jbKLKcvyuZZ04Kq\n7Fiq8yLBdnqOh7enG+2iz4yUBNEuJpAgP0+Hvr7zudjeVNVYWLUzk69T0iitqMXL042RvVv+f3v3\nGRzVdcd9/LtV0mrVu1BBEjaY3kSvphkw8NiJg+OY5E0yk3j8zCTjOPE4iZ02ScjEMyn2OP0ZjxPb\nxHYcAza9mCaa6Zgq1NuqS6tdadt9XkgIZC14EZL2SPv/vEESu6sjfufCj3Pvnsvy/EyiLHf+GXya\nj5M1Z9h6Yyf17Q2EG8JYkrWAhzMX3PNmdaFgKBwzoUqyCYwUFT9k8qhrKGXj9LSzrWg3e8sOodF5\ns8OIukmsnzuZmQ+l9Dr10upsZ++NE5ys/ZQGXyUAmseEty4dT20GtEcxItHaffomNz2atIRIZbb1\n72s2HS4v+89UsO1YKS1tLsJMBh6eNoIVM7KIvkth8fg8HKo4xrbi3djdbUSZrDySs4R56TMxfsH+\nMKFkKB0zoUayCYwUFT9k8qhrKGZT3WZj05X/cbXpOpqmw1OdTTZTWTfnAZpaXVyoKuKa8zxOSwk6\nowcAb0s85paR5EWO5oER8Z17lqRFfeF+JMF0v9l0uL0cOFPJx8dKaLa7MJv0PDwlgxUzs+66StTu\naWdP2UH2lH5Ch9dFYng8j+auYFrKJNmDhaF5zIQKySYwUlT8kMmjrqGajaZpnK27yH+ubKbZ1YTm\nCsNTm4Ehpg69tRkAvTecEYYx5CdPY0pWNnFRYUNq75D+ysbl9nLgbCUfHy2hye7CbNSzaMoIVs7M\nIsZ651M7rS4724v3cLDiKF7NS4Y1nbV5Kxkb/+CQ+nPsb0P1mAkFkk1gpKj4IZNHXUM9G5fXza6S\nfewo2Y9X8wA68qyjWJw9i4lJY3ttyjaU9Hc2bo+Xg+eq+KighMbWDkxGPQsnp7NyZmeJu5M6ZwNb\nb+zkZM1pNDQeiM1lXd4qcmKy+m1sQ8lQP2aGM8kmMFJU/JDJo67hkk29s4FrTTcYHTeKuPChfTfm\nmwYqG7fHx6HzVXxcUEx9SwdGg56Fk9JZOSvrrjvlVtir+LBwGxfrLwMwOWk8a3IfITUyud/HqLLh\ncswMR5JNYKSo+CGTR12SjboGOhuP18fh850rLHXN7RgNOuZPTGfVrGwSYu5cWK413uDDwo8pailF\nh47Zafmszl3WvWvvcCfHjLokm8BIUfFDJo+6JBt1DVY2Hq+PggvVbC0oprapHYNex/yJaayanU1i\njP+9ajRN41zdRTYXbqfaYcOkN7IoYx7LsxdhMVkGfMzBJMeMuiSbwEhR8UMmj7okG3UNdjYer49j\nn9Ww5UgxtkYnBr2OuRNSWT17JEl32FzP6/NyrPoUHxXtpKmjmQhjBMuzF7EoYy5mQ3D2nxlocsyo\nS7IJjBQVP2TyqEuyUVewsvH6bhaWEmoaHBj0OmaPT+XR2dkkx/lfLXF53RyoOMKO4r04PE5izNGs\nzlnGrLTpQ/qCZn/kmFGXZBMYKSp+yORRl2SjrmBn4/NpHL/UucJSVe9Ar9Mxe1wKj84ZSUq8/8Li\ncDvZVbqffWWHcPvcpFiSWJP7CJOTxg+btzQHOxdxZ5JNYKSo+CGTR12SjbpUycbn0zhx2caWI8VU\n1rWh08GssZ2FJS0h0u9zmjqa2Va0myNVJ/BpPrKjMlmXt5LR8aMGefT9T5VcRG+STWCCUlTeffdd\nNm/e3P35hQsXePvtt/n5z3+OXq8nOjqaV155hYiIO9/ETYpKaJJs1KVaNj5N49MrtWw+XERFbWdh\nmflQZ2FJT/RfWGoctWy5sYPTtnMAPBT/IGtzHyErOmMwh96vVMtF3CLZBCboKyrHjx9n27ZtXLt2\njR/84AdMnDiRjRs3kpGRwde+9rU7Pk+KSmiSbNSlajY+TeP01Vo2Hy6mzGZHB+Q/lMyaOSMZkeT/\nrsulLeVsvrGdSw1XAZiaPJFHc1eQYkkaxJH3D1VzEZJNoO5WVAblpiKvvfYav/vd74iIiMBq7fxL\nIz4+nqampsH49kKIYU6v0zFtdDJTHkzi7LU6PjxcxPFLNo5fsjF9dBJr5+aQkdyzsGRFZ/Ds5G9y\npeE6HxZu45TtHGdqLzA7LZ9VOUtDZg8WIVQ34Csq586d46233uI3v/lN99ccDgdf+cpX+MMf/kBe\nXt4dn+vxeDEah9fV+UKIgadpGic+q+HtXVe4Xtb5H6LZE9J4ctlockf0LiCapnG84gxvn/uQytYa\nTAYTKx9YzP8ZsxxrmP9TSEKIwTHgReWll15i9erVzJw5E+gsKd/5zndYt24djz/++F2fK6d+QpNk\no66hlo2maZy/Uc+Hh4opqmoBYMoDiaydm0N2au+l5t57sISzLGsRizLnEabwHixDLZdQItkEJqjX\nqKxYsYItW7ZgNpvxeDx885vfZPXq1TzxxBNf+FwpKqFJslHXUM1G0zQuFjXw4eEiCis6C8ukvATW\nzM0hNz261+PdXjefVBxhZ/E+2jwOos1RrMpZypy0GUruwTJUcwkFkk1ggnaNSk1NDZGRkZjNnf8T\n+dvf/saMGTMCKilCCNFfdDod43MTGJcTz2cljWw+VMTZwnrOFtYzPieetXNzGJVx65SQyWBiadZC\n5qbPYHfpAfaWHuCdKx+wu/QAa3JXMDV5InqdPog/kRChY0CLSm1tLfHx8d2f//vf/yYjI4OCggIA\nZs6cybPPPjuQQxBCiG46nY5xI+MZmx3H5dImthwu4kJRAxeKGngoO461c0cyOiuu+/ERxgjW5K5g\nYcYcthfv4VDFMf7fxbfYVbKftXkrGRv/4LDZNE4IVcmGb0I5ko26hmM2V8ua2HKkmItFDQA8mBnL\nmrkjGZsd16uE1Dnr2XpjFydrTqOh8UBsLmvzVpIbkx2MoXcbjrkMF5JNYIK+j0pfSVEJTZKNuoZz\nNoWVzWw5XMy5wnoA8kZEs2ZODhNy43sVlgp7FZsLt3Gh/jIAExPHsSZ3BenW1EEfNwzvXIY6ySYw\nUlT8kMmjLslGXaGQTXF1C1sOF3P6Wh0AI1OjWDN3JJNHJfYqLNebiviwcBs3movRoWNm6jRW5Swj\nISLO30sPmFDIZaiSbAIjRcUPmTzqkmzUFUrZlNnsbDlSzKeXbWhAZrKVNXNGMnV0EvrbCoumaVyo\nv8Tmwu1UtlVj1BmYnzGbFdkPE2X2vytufwulXIYaySYwUlT8kMmjLslGXaGYTUVdGx8dKebYpRo0\nDUYkRvLonJHkj0lGr79VWHyaj5M1Z9h6Ywf17Y2EGcwsyVrIksz5hBvDB3SMoZjLUCHZBEaKih8y\nedQl2agrlLOpqm/jo4ISjl6swadppMZbeHRONjPHpmDQ33qrssfn4VDlMbYX7aHVbcdqiuSRkUuY\nN2IWJv3AvNEylHNRnWQTGCkqfsjkUZdkoy7JBmyNDj4qKOHIhWq8Po3k2AhWz85m9vhUjIZbhaXd\n08G+skPsLt1Pu7eD+PA4VucsY0bq1H7fg0VyUZdkExgpKn7I5FGXZKMuyeaWumYn246WcvBcJR6v\nRmJMOKtmZTN3Qhom460iYne1saNkLwcqCvD4PKRFprA29xEmJI7ttz1YJBd1STaBkaLih0wedUk2\n6pJsemtoaWf7sVI+OVuJ2+MjLiqMVbOyWTApDdNtN1VtaG/k46LdHK06iYZGTnQ26/JW8kBc7n2P\nQXJRl2QTGCkqfsjkUZdkoy7J5s6a7R1sP17KvtMVuNw+YqxmVs7IYuGUEYSZbhWW6rYattzYwZna\nCwCMTRjN2tyVZEal9/l7Sy7qkmwCI0XFD5k86pJs1CXZfLGWNhc7TpSy91QFHS4v0RYTK2ZmsXjK\nCMLNty6mLWouZXPhNq42FQIwPWUyq3OWk2xJvOfvKbmoS7IJjBQVP2TyqEuyUZdkEzi7083OE2Xs\n+bQMZ4cXa4SJZfmZLJmagSW8s7Bomsblxmt8WLiNstYK9Do9c9NnsnLkEmLCet/V+U4kF3VJNoGR\nouKHTB51STbqkmzunaPdze6T5ew6WUZbuwdLmJGl0zNYlp9JZLgJ6NyD5bTtPFtv7MDmrMOsN7E4\ncz5LsxZiMUV84feQXNQl2QRGioofMnnUJdmoS7LpO2eHh72nytlxvAy700242cCSaRksz88kymIG\nwOvzUlB1go+LdtPsasFijGB59mIWZszFbDDd8bUlF3VJNoGRouKHTB51STbqkmzuX4fLy77TFWw/\nXkpLm4swk4HFU0ewYkYWMZGdhcXldfFJ+RF2lOzD6XESGxbDqpFLmZU2HYPe0Os1JRd1STaBkaLi\nh0wedUk26pJs+k+H28uBM5VsO1ZCk92F2ahn0ZQRPDIzi1hrGAAOt4NdpZ+wr+wQbp+bZEsia3If\nYUrShB57sEgu6pJsAiNFxQ+ZPOqSbNQl2fQ/t8fLwXNVfHy0hIaWDowGPQsnpbNyVhbx0Z33CGrq\naGZb8R6OVB7Hp/nIihrBurxVjIl/AJBcVCbZBEaKih8yedQl2ahLshk4Hq+Pw+er+KighLrmdgx6\nHfMnprFqVjaJsZ0X1NoctWy9sZNPbWcBGB03inV5K5meN1ZyUZQcM4GRouKHTB51STbqkmwGnsfr\n49hnNWw9UkxNoxODXsfs8amsnp1NSpwFgLLWCjYXbuezhisATEkbR6wxjkijhUiTBYup89dI462P\nww1h/bZlvwicHDOBkaLih0wedUk26pJsBo/X5+PEJRtbjhRTVe9Ap4NZY1N5dE42aQmRAFxtLGRz\n4TaKWkq/8PX0Ov1txSWis9B0FZvPf3zr8wjCpODcFzlmAiNFxQ+ZPOqSbNQl2Qw+n0/j5BUbW48U\nU17bhg7IfyiZR+eMJCPJiqZp6CLdlFbbaPM4aHM7cLg7f23zOHC4nbS522hzO2nztHV97kAjsL/6\nDToDFlMEkaZIIo0RPVZr/K3eWLu+ZtabpOAgx0yg7lZUjHf8HSGEEEGn1+uY8VAK08ckc/pqHVuO\nFHH8ko3jl2xMG53EmjkjmTY+HaLNAb+mT/PR7unA0VVsbpYbu+e2kuN2dv++w+2gtaOVmjZbwAXH\nqDf2Kjb+S05nCbIYO3+9234xIjRJURFCiCFAr9MxbXQSUx9M5GxhPVsOF/PplVo+vVLL1DHJ5KZG\nkZVsJSsliujIu5cWvU6PxRSBxRRBYkRCwGPoLDjt2N2OrhLTuVrTvWrjcfZYzWlzO2juaKH6HgqO\nWW9ifOJDLMlawMjorIDHJoYvOfUjlCPZqEuyUYemaVwsamDzkWKulzf3+L0Yq5nslCgyk61kp0SR\nlWIlMTYCfZBOxfg0H47bS4zbgcPjvO3jW1+vdzZgc9YBkBczkiVZC5iQOBa9Th+Usd8vOWYCI6d+\nhBBimNHpdIzPTWBcTjya0cjpz6ops7VSWmOn1NbKucJ6zhXWdz8+3GwgK9lKZldxyUqOYkRSJEbD\nwBcAvU6P1RSJ1RT5hY/VNI0rjdfZU3qAzxquUHi+mOSIRBZnzmdW2jTMhsBPcYnhQVZUhHIkG3VJ\nNmryl0urw0WZzd5ZXGpaKbXZqapv4/a/8Q16HemJkd3FJSvFSmZyVPfdnYOt0l7N3rKDnKg+hUfz\nEmmyMH/EbBZmzCHafOf/gatEjpnAyLt+/JDJoy7JRl2SjZoCzaXD7aWitq27uJTWtFJus+Py+Hq+\nXmz4reKSEkV2ShSxVnPQ3sXT3NHKgYojHCwvoM3jwKgzkJ86lYcz55NuTQ3KmAIlx0xgpKj4IZNH\nXZKNuiQbNd1PLj6fRnWDg9Ku00ZlNa2U1NixO909HhdlMXVfrJuZ0nntS0qcBb1+8MpLh9fFsaqT\n7C07SK2z87TW2ITRLMlcwOi4UUq+HVqOmcBIUfFDJo+6JBt1STZq6u9cNE2jye6ipKaV0ppWymrs\nlNS0Utfc3uNxZpOezKSe171kJEViNvW+w3N/8mk+ztd9xp7SAxQ2FwOQYU1nSdYCpiVP8nuH6WCR\nYyYwUlT8kMmjLslGXZKNmgYrF0e7u9d1L5V1bXh9t/4Z0ekgLaHndS9ZKVFYIwZmf5Si5lL2lB3g\njO08GhqxYTEsypjL3PSZWEwRA/I974UcM4GRouKHTB51STbqkmzUFMxc3B4flXU9r3sptdnpcHl7\nPC4+OqzHBbvZKVYSYsL77XRNnbOB/WWHOFx1HJfXRZjBzJy0GSzOnEdCRHy/fI++kGMmMFJU/JDJ\noy7JRl2SjZpUy8WnadQ2OW+tvHS9ZbrZ7urxOEuYsXvF5eaeL6kJlvt6y7TD7eRw5TH2lx+mqaMZ\nHTqmJE8I2gZyqmWjKikqfsjkUZdkoy7JRk1DJZfmNlfXxbqtlNnslNTYsTU4euxZazTomfpgIsvz\ns8hNj+7z9/L4PHxac5Y9ZQeosFcBwdlAbqhkE2xSVPyQyaMuyUZdko2ahnIu7S4P5ba2rncdtXKt\nvJmqegcAo0bEsDw/kykPJmLQ961YfH4DOWBQN5AbytkMJikqfsjkUZdkoy7JRk3DKRdN07hU0sjO\nE2XdO+smRIezbHoG8yelExHW983ogrGB3HDKZiBJUfFDJo+6JBt1STZqGq65VNW3setkOUfOV+Hy\n+Ag3G5g/MZ2l0zNIiu37O3oGcwO54ZpNf5Oi4odMHnVJNuqSbNQ03HOxO918cqaC3Z+W02x3odPB\n1AeTWJ6fyagRMX1+59BgbCA33LPpL1JU/JDJoy7JRl2SjZpCJReP18eJSzZ2niijpKbz581Ji2JZ\nfibTRyf3+d1CA7mBXKhkc7+CUlTeffddNm/e3P35hQsXePvtt/npT38KwOjRo/nZz35219eQohKa\nJBt1STZqCrVcNE3jalkTO0+UceZaHRoQFxXG0mkZLJicTmR43zeX6+8N5EItm74K+orK8ePH2bZt\nG9evX+f5559n4sSJPPfcc6xdu5aFCxfe8XlSVEKTZKMuyUZNoZxLTaOD3SfLOXSuig63lzCTgbkT\nUlk2PZOUeEufX7e/NpAL5WzuRdCLyje+8Q1+/etf8/TTT7N3714Atm7dyoULF3jhhRfu+DwpKqFJ\nslGXZKMmyaVze/9Pzlay59NyGlo60AGTRiWyPD+T0Vmxfb7e5OYGcvvKDtHsarnnDeQkm8Dcraj0\n/X1eATp37hxpaWkYDAaio29t3pOQkEBtbe1Af3shhBAhwBJuYuXMbJZNz+TU1drO00LX6zhzvY6s\nFCvL8zOZ8VDKPV/HYjFFsCx7EYsz53VvIHfKdo5TtnNB2UAuFA14UXnvvfd47LHHen09kIWcuDgL\nRuPA3QXzbg1OBJdkoy7JRk2Syy2rU2NYvWAUl4sb+N8nhRScr+TvWy/x3wM3WDU3h0dmjSTGGnbP\nr/toyiJWT1jIBdsVtlzexZnqzyg8X0yaNZnVox9m4cjZhBl7byAn2dyfAT/1s2LFCrZs2YJOp2PZ\nsmXs378fgA8++ICrV6/ywx/+8I7PlVM/oUmyUZdkoybJ5e7qmpzs/rScA2craXd5MRn1zB2fyrL8\nTNISIvv8uoFsICfZBCZop35qamqIjIzEbO5smLm5uZw8eZLp06ezc+dONmzYMJDfXgghhCAxNoIn\nlzzAunk5HDxXxe6TZew/U8n+M5VMyE1geX4mY0fG3fN1LOnWVJ5+6AnW5D7SvYHc9uI97C7Z372B\nnKym3L8BLSq1tbXEx9+6OvrFF1/kpZdewufzMWnSJObMmTOQ314IIYToFhFmZHl+JkunZXD6Wi07\nTpRx/kY952/Uk5EUybLpmcwal4LpHi85iAmLYk3uCpZnL+7eQK6g6gQFVSfIuZZJvCmeZEsiSRGJ\nJFkSSIpIxGqK7JcN5UKBbPgmlCPZqEuyUZPk0ndFVS3sPFHGiUs2fJpGtMXE4qkZLJ4ygujIvt2w\n8OYGcvvKDlHcUorb5+n1mHBDOMldpSUpIoGk24pMlMkaciUm6G9P7ispKqFJslGXZKMmyeX+NbS0\ns+fTcj45U4mjw4PRoGfWuBSW52eSkWTt8+smJERyraIcm6OOWmc9tc46ah2dv9Y56+9QYsJ6lpfb\nPo42D88SI0XFDzmw1SXZqEuyUZPk0n/aXR4On69m18kybI1OAMaOjGN5fhbjc+PR32NJuFs2Ps1H\nc0cLtc6624pMPbWOOmqddX5LTJjB3HsVJiKBZEsi0eaoIVtigrqPihBCCDFUhJuNLJmWweKpIzh7\nvY5dJ8r4rLiRz4obSUuwsGx6JrPHpxJmuv+tM/Q6PXHhscSFx/Jg3Kgev3erxPRchal11mNz1FJu\nr+z1ema96XOrMAkkRySSZEkkxhw9ZEuMrKgI5Ug26pJs1CS5DKyS6lZ2nSzj2Gc1eH0a1ggTi6ak\n8/DUDGK/YD+WgchG0zSaXS1dKy/1XeWlrrvIuLyuXs8x6U3dqzDJtxWZpIhEYsKig75hnZz68UMO\nbHVJNuqSbNQkuQyOJnsHe0+Vs/90JXanG4Nex4yHOq9jyU71/w/tYGejaRotrtZe5eXm6aQOvyXG\nSGJE5+pL4s1VmK4Le2PDYgalxEhR8UMObHVJNuqSbNQkuQyuDreXgovV7DpRRlW9A4AxWbEsy89k\n0qjEHtexqJSNpmm0uu23rof5XJFp93b0eo6xq8QkRSQwMjqL5dmLBqS4yDUqQgghRD8JMxlYNHkE\nCyalc+FGA7tOlHKxuJHLpU0kx0WwbHomcyekEm5W659YnU5HtDmKaHMUo2JzevyepmnY3W09L+zt\nKjI2Rz3VbTVcrL/MvPSZWM193823T+OWFRWhGslGXZKNmiSX4Cu32dl5soyjF2vweH1YwowsnJzO\n8jk56LxeoiJMQ/Zi1pslxqdpxIQNzE67curHDzmw1SXZqEuyUZPkoo6WNhf7Tlew71Q5LQ5399eN\nBh2x1jBio8KIs4YRFxVGbNevcVE3v26+511xhws59SOEEEIMguhIM+vm5bBqVhbHL9moaWqnstZO\nY2sHTfYOCiuaudvygDXCdKu83F5kbvs4Mtw4ZFdn+kKKihBCCNHPTEYDcyek9Vrt8vp8tLS5aWzt\n6C4vn//Y1uSkzGa/y2vribWaiYsK7ywvN1dquj82E2sNw2gI7luO+4sUFSGEEGKQGPT67pWRu3F2\neDoLjL2DptZbZeb2r10ra+Ju125EW0w9TzVF3VqVufm1iDD1V2ekqAghhBCKiQgzEhFmJD3xzu+w\n8Xh9tLS5ehWY28tNdYOD0po7r86YTfqeReZz19HERYURYzVj0AdvdUaKihBCCDEEGQ164qPDiY8O\nv+NjNE3D2eGhofW2lRl7749ruu5r5I9O13ntTWaylf/7+IRBv+BXiooQQggxTOl0OizhJizhprve\nBdrj9dFk76Cp1UVj17UyN1dnOlds2mlo6cDj1TANcnOQoiKEEEKEOKNBT2JMBIkxEcEeSi/D45Jg\nIYQQQgxLUlSEEEIIoSwpKkIIIYRQlhQVIYQQQihLiooQQgghlCVFRQghhBDKkqIihBBCCGVJURFC\nCCGEsqSoCCGEEEJZUlSEEEIIoSwpKkIIIYRQlhQVIYQQQihLiooQQgghlKXTNE0L9iCEEEIIIfyR\nFRUhhBBCKEuKihBCCCGUJUVFCCGEEMqSoiKEEEIIZUlREUIIIYSypKgIIYQQQlkhWVR+9atfsX79\nep588knOnTsX7OGI2/z2t79l/fr1fOlLX2Lnzp3BHo64TXt7O0uXLuW///1vsIcibrN582bWrl3L\n448/zv79+4M9HNGlra2NZ599lg0bNvDkk09y8ODBYA9pyDIGewCD7fjx45SUlLBp0yYKCwt58cUX\n2bRpU7CHJYCjR49y7do1Nm3aRGNjI4899hjLly8P9rBEl9dff52YmJhgD0PcprGxkddee433338f\nh8PBn/70JxYtWhTsYQnggw8+ICcnh+eee46amhq+8Y1vsH379mAPa0gKuaJSUFDA0qVLAcjLy6O5\nuRm73Y7Vag3yyER+fj4TJ04EIDo6GqfTidfrxWAwBHlkorCwkOvXr8s/goopKChg9uzZWK1WrFYr\nv/jFL4I9JNElLi6OK1euANDS0kJcXFyQRzR0hdypn7q6uh4TJj4+ntra2iCOSNxkMBiwWCwAvPfe\neyxYsEBKiiI2btzICy+8EOxhiM8pLy+nvb2db3/72zz11FMUFBQEe0iiy+rVq6msrGTZsmU8/fTT\n/PCHPwz2kIaskFtR+Ty5g4B6du/ezXvvvcc///nPYA9FAP/73/+YPHkymZmZwR6K8KOpqYlXX32V\nyspKvv71r7Nv3z50Ol2whxXyPvzwQ9LT0/nHP/7B5cuXefHFF+X6rj4KuaKSnJxMXV1d9+c2m42k\npKQgjkjc7uDBg/z5z3/m73//O1FRUcEejgD2799PWVkZ+/fvp7q6GrPZTGpqKnPmzAn20EJeQkIC\nU6ZMwWg0kpWVRWRkJA0NDSQkJAR7aCHv1KlTzJs3D4AxY8Zgs9nkVHYfhdypn7lz57Jjxw4ALl68\nSHJyslyfoojW1lZ++9vf8pe//IXY2NhgD0d0+f3vf8/777/Pf/7zH5544gmeeeYZKSmKmDdvHkeP\nHsXn89HY2IjD4ZBrIRSRnZ3N2bNnAaioqCAyMlJKSh+F3IrK1KlTGTduHE8++SQ6nY6XX3452EMS\nXT7++GMaGxv57ne/2/21jRs3kp6eHsRRCaGulJQUVqxYwVe+8hUAfvzjH6PXh9z/P5W0fv16Xnzx\nRZ5++mk8Hg8//elPgz2kIUunyUUaQgghhFCUVG8hhBBCKEuKihBCCCGUJUVFCCGEEMqSoiKEEEII\nZUlREUIIIYSypKgIIfpFeXk548ePZ8OGDd13jH3uuedoaWkJ+DU2bNiA1+sN+PFf/epXOXbsWF+G\nK4QYIqSoCCH6nyH8xAAAAr5JREFUTXx8PG+++SZvvvkm77zzDsnJybz++usBP//NN9+UTbGEED2E\n3IZvQojBk5+fz6ZNm7h8+TIbN27E4/Hgdrt56aWXGDt2LBs2bGDMmDFcunSJN954g7Fjx3Lx4kVc\nLhc/+clPqK6uxuPxsG7dOp566imcTiff+973aGxsJDs7m46ODgBqamr4/ve/D0B7ezvr16/ny1/+\ncjB/dCFEP5GiIoQYEF6vl127djFt2jSef/55XnvtNbKysnrdoM1isfCvf/2rx3PffPNNoqOjeeWV\nV2hvb2fVqlXMnz+fI0eOEB4ezqZNm7DZbCxZsgSAbdu2kZuby89+9jM6Ojp49913B/3nFUIMDCkq\nQoh+09DQwIYNGwDw+XxMnz6dL33pS/zxj3/kRz/6Uffj7HY7Pp8P6LytxeedPXuWxx9/HIDw8HDG\njx/PxYsXuXr1KtOmTQM6bzCam5sLwPz583nrrbd44YUXWLhwIevXrx/Qn1MIMXikqAgh+s3Na1Ru\n19raislk6vX1m0wmU6+v6XS6Hp9rmoZOp0PTtB73srlZdvLy8vjoo484ceIE27dv54033uCdd965\n3x9HCKEAuZhWCDGgoqKiyMjI4JNPPgGgqKiIV1999a7PmTRpEgcPHgTA4XBw8eJFxo0bR15eHqdP\nnwagqqqKoqIiALZs2cL58+eZM2cOL7/8MlVVVXg8ngH8qYQQg0VWVIQQA27jxo388pe/5K9//Sse\nj4cXXnjhro/fsGEDP/nJT/ja176Gy+XimWeeISMjg3Xr1rF3716eeuopMjIymDBhAgCjRo3i5Zdf\nxmw2o2ka3/rWtzAa5a83IYYDuXuyEEIIIZQlp36EEEIIoSwpKkIIIYRQlhQVIYQQQihLiooQQggh\nlCVFRQghhBDKkqIihBBCCGVJURFCCCGEsqSoCCGEEEJZ/x8TzV+KkvAlAAAAAABJRU5ErkJggg==\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": "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": 671
+        },
+        "outputId": "9a9e10c8-9be9-4518-df00-cad497801f05"
+      },
+      "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": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 108.90\n",
+            "  period 01 : 106.11\n",
+            "  period 02 : 104.79\n",
+            "  period 03 : 103.24\n",
+            "  period 04 : 101.96\n",
+            "  period 05 : 101.75\n",
+            "  period 06 : 100.73\n",
+            "  period 07 : 100.76\n",
+            "  period 08 : 100.05\n",
+            "  period 09 : 99.91\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   99.91\n",
+            "Final RMSE (on validation data): 99.54\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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U1sju9bG3mFm05QTpmYW/RFtERKS8U4ApIpPJxOhGQ3C0c2DJ8ZUkZVwvdFs+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\nYII=\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..0783e84
--- /dev/null
+++ b/intro_to_neural_nets.ipynb
@@ -0,0 +1,1161 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/intro_to_neural_nets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "eV16J6oUY-HN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_wIcUFLSKNdx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n",
+        "  * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_ZZ7f7prKNdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n",
+        "\n",
+        "One important set of nonlinearities was around latitude and longitude, but there may be others.\n",
+        "\n",
+        "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "J2kqX6VZTHUy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's load and prepare the data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AGOM1TUiKNdz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2I8E2qhyKNd4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pQzcj2B1T5dA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1209
+        },
+        "outputId": "a92924d9-7a80-42c3-c7de-3c2a9fb36749"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.7       2634.9           538.2   \n",
+              "std         2.1        2.0                12.6       2206.3           425.8   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1444.0           294.0   \n",
+              "50%        34.2     -118.5                29.0       2109.5           431.0   \n",
+              "75%        37.7     -118.0                37.0       3135.2           644.0   \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       1421.5       499.8            3.9               2.0  \n",
+              "std        1164.3       389.1            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         782.0       279.0            2.6               1.5  \n",
+              "50%        1161.0       406.0            3.5               1.9  \n",
+              "75%        1714.0       601.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "        text-align: right;\n",
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+              "</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>2634.9</td>\n",
+              "      <td>538.2</td>\n",
+              "      <td>1421.5</td>\n",
+              "      <td>499.8</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2206.3</td>\n",
+              "      <td>425.8</td>\n",
+              "      <td>1164.3</td>\n",
+              "      <td>389.1</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>1444.0</td>\n",
+              "      <td>294.0</td>\n",
+              "      <td>782.0</td>\n",
+              "      <td>279.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2109.5</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>1161.0</td>\n",
+              "      <td>406.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3135.2</td>\n",
+              "      <td>644.0</td>\n",
+              "      <td>1714.0</td>\n",
+              "      <td>601.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.3       2664.8           542.3   \n",
+              "std         2.1        2.0                12.5       2115.5           411.0   \n",
+              "min        32.6     -124.3                 1.0         18.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1501.8           305.8   \n",
+              "50%        34.2     -118.5                28.0       2176.0           442.0   \n",
+              "75%        37.7     -118.0                37.0       3179.2           658.0   \n",
+              "max        41.9     -114.5                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       1449.0       504.7            3.9               2.0  \n",
+              "std        1107.3       373.2            1.9               1.2  \n",
+              "min           8.0         4.0            0.5               0.1  \n",
+              "25%         807.0       288.0            2.6               1.5  \n",
+              "50%        1180.5       418.0            3.6               1.9  \n",
+              "75%        1754.0       614.0            4.8               2.3  \n",
+              "max       15507.0      5050.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
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+              "</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>2664.8</td>\n",
+              "      <td>542.3</td>\n",
+              "      <td>1449.0</td>\n",
+              "      <td>504.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2115.5</td>\n",
+              "      <td>411.0</td>\n",
+              "      <td>1107.3</td>\n",
+              "      <td>373.2</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</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>1501.8</td>\n",
+              "      <td>305.8</td>\n",
+              "      <td>807.0</td>\n",
+              "      <td>288.0</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>2176.0</td>\n",
+              "      <td>442.0</td>\n",
+              "      <td>1180.5</td>\n",
+              "      <td>418.0</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>3179.2</td>\n",
+              "      <td>658.0</td>\n",
+              "      <td>1754.0</td>\n",
+              "      <td>614.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>15507.0</td>\n",
+              "      <td>5050.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                208.3\n",
+              "std                 116.5\n",
+              "min                  15.0\n",
+              "25%                 120.3\n",
+              "50%                 180.9\n",
+              "75%                 266.0\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>208.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                204.8\n",
+              "std                 114.8\n",
+              "min                  15.0\n",
+              "25%                 118.3\n",
+              "50%                 179.1\n",
+              "75%                 262.1\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>204.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>114.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>262.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RWq0xecNKNeG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Building a Neural Network\n",
+        "\n",
+        "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+        "\n",
+        "Use **`hidden_units`** to define the structure of the NN.  The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it.  For example, consider the following assignment:\n",
+        "\n",
+        "`hidden_units=[3,10]`\n",
+        "\n",
+        "The preceding assignment specifies a neural net with two hidden layers:\n",
+        "\n",
+        "* The first hidden layer contains 3 nodes.\n",
+        "* The second hidden layer contains 10 nodes.\n",
+        "\n",
+        "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+        "\n",
+        "By default, all hidden layers will use ReLu activation and will be fully connected."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ni0S6zHcTb04",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zvCqgNdzpaFg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural net regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                             \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U52Ychv9KNeH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `DNNRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2QhdcCy-Y8QR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train a NN Model\n",
+        "\n",
+        "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+        "\n",
+        "Run the following block to train a NN model.  \n",
+        "\n",
+        "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good.  We'll aim to beat that.\n",
+        "\n",
+        "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+        "\n",
+        "Overfitting is a real potential hazard for NNs.  You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+        "\n",
+        "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+        "\n",
+        "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rXmtSW1yKNeK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 671
+        },
+        "outputId": "6c68afe2-a6e5-468d-cfa8-6507ac5ed7ad"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=10,\n",
+        "    hidden_units=[10, 2],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 182.41\n",
+            "  period 01 : 216.21\n",
+            "  period 02 : 178.61\n",
+            "  period 03 : 174.19\n",
+            "  period 04 : 180.45\n",
+            "  period 05 : 171.34\n",
+            "  period 06 : 170.94\n",
+            "  period 07 : 177.86\n",
+            "  period 08 : 181.41\n",
+            "  period 09 : 212.58\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   212.58\n",
+            "Final RMSE (on validation data): 206.98\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+CmECk4JazFZo1UrUlUdmtyNTo3B4nViTXw9FlAw5FnKDHTVqJWrLDOgbnYR7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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "O2q5RRCKqYaU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "j2Yd5VfrqcC3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IjkpSqmxqnSM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 671
+        },
+        "outputId": "e0c811d2-b005-4a68-f71d-1c93836581e2"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.46\n",
+            "  period 01 : 157.37\n",
+            "  period 02 : 149.41\n",
+            "  period 03 : 140.14\n",
+            "  period 04 : 138.21\n",
+            "  period 05 : 122.56\n",
+            "  period 06 : 116.67\n",
+            "  period 07 : 112.46\n",
+            "  period 08 : 111.31\n",
+            "  period 09 : 109.44\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   109.44\n",
+            "Final RMSE (on validation data): 108.68\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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jnEcguyAHS8JXITE7SaM47k2tMbaXEzKy8vHjlnBkZGm2YB4REVFtorMCRqFQ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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": "vhb0CtdvrWZx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "f130d54d-044c-4bde-8dba-a191506d62aa"
+      },
+      "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": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 108.09\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..ff9f6a5
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1650 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_pandas.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "YHIWvc9Ms-Ll",
+        "TJffr5_Jwqvd"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/divyarachel/Assignment-5/blob/divyarachel/intro_to_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "rHLcriKWLRe4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to pandas"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "QvJBqX8_Bctk"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n",
+        "  * Access and manipulate data within a `DataFrame` and `Series`\n",
+        "  * Import CSV data into a *pandas* `DataFrame`\n",
+        "  * Reindex a `DataFrame` to shuffle data"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TIFJ83ZTBctl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n",
+        "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "s_JOISVgmn9v"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Basic Concepts\n",
+        "\n",
+        "The following line imports the *pandas* API and prints the API version:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "aSRYu62xUi3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "e3c6c112-a007-4163-c9db-1a0d75f98c22"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import pandas as pd\n",
+        "pd.__version__"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "u'0.22.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "daQreKXIUslr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The primary data structures in *pandas* are implemented as two classes:\n",
+        "\n",
+        "  * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n",
+        "  * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
+        "\n",
+        "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fjnAk1xcU0yc"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to create a `Series` is to construct a `Series` object. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "DFZ42Uq7UFDj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "outputId": "16aa4414-fff4-44cd-b11e-b514a231861e"
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    San Francisco\n",
+              "1         San Jose\n",
+              "2       Sacramento\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "U5ouUp1cU6pC"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "avgr6GfiUh8t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "dc0c6eae-faee-42d9-9393-1ef6e9e49f6a"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
+        "population = pd.Series([852469, 1015785, 485199])\n",
+        "\n",
+        "pd.DataFrame({ 'City name': city_names, 'Population': population })"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population\n",
+              "0  San Francisco      852469\n",
+              "1       San Jose     1015785\n",
+              "2     Sacramento      485199"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "oa5wfZT7VHJl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "av6RYOraVG1V",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "ac8980ad-a463-4332-8483-5a6cb6269975"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.562108</td>\n",
+              "      <td>35.625225</td>\n",
+              "      <td>28.589353</td>\n",
+              "      <td>2643.664412</td>\n",
+              "      <td>539.410824</td>\n",
+              "      <td>1429.573941</td>\n",
+              "      <td>501.221941</td>\n",
+              "      <td>3.883578</td>\n",
+              "      <td>207300.912353</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.005166</td>\n",
+              "      <td>2.137340</td>\n",
+              "      <td>12.586937</td>\n",
+              "      <td>2179.947071</td>\n",
+              "      <td>421.499452</td>\n",
+              "      <td>1147.852959</td>\n",
+              "      <td>384.520841</td>\n",
+              "      <td>1.908157</td>\n",
+              "      <td>115983.764387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.350000</td>\n",
+              "      <td>32.540000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.499900</td>\n",
+              "      <td>14999.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.790000</td>\n",
+              "      <td>33.930000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>1462.000000</td>\n",
+              "      <td>297.000000</td>\n",
+              "      <td>790.000000</td>\n",
+              "      <td>282.000000</td>\n",
+              "      <td>2.566375</td>\n",
+              "      <td>119400.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.490000</td>\n",
+              "      <td>34.250000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>2127.000000</td>\n",
+              "      <td>434.000000</td>\n",
+              "      <td>1167.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>3.544600</td>\n",
+              "      <td>180400.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.000000</td>\n",
+              "      <td>37.720000</td>\n",
+              "      <td>37.000000</td>\n",
+              "      <td>3151.250000</td>\n",
+              "      <td>648.250000</td>\n",
+              "      <td>1721.000000</td>\n",
+              "      <td>605.250000</td>\n",
+              "      <td>4.767000</td>\n",
+              "      <td>265000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.310000</td>\n",
+              "      <td>41.950000</td>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>37937.000000</td>\n",
+              "      <td>6445.000000</td>\n",
+              "      <td>35682.000000</td>\n",
+              "      <td>6082.000000</td>\n",
+              "      <td>15.000100</td>\n",
+              "      <td>500001.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+              "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
+              "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
+              "std        2.005166      2.137340           12.586937   2179.947071   \n",
+              "min     -124.350000     32.540000            1.000000      2.000000   \n",
+              "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
+              "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
+              "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
+              "max     -114.310000     41.950000           52.000000  37937.000000   \n",
+              "\n",
+              "       total_bedrooms    population    households  median_income  \\\n",
+              "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
+              "mean       539.410824   1429.573941    501.221941       3.883578   \n",
+              "std        421.499452   1147.852959    384.520841       1.908157   \n",
+              "min          1.000000      3.000000      1.000000       0.499900   \n",
+              "25%        297.000000    790.000000    282.000000       2.566375   \n",
+              "50%        434.000000   1167.000000    409.000000       3.544600   \n",
+              "75%        648.250000   1721.000000    605.250000       4.767000   \n",
+              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+              "\n",
+              "       median_house_value  \n",
+              "count        17000.000000  \n",
+              "mean        207300.912353  \n",
+              "std         115983.764387  \n",
+              "min          14999.000000  \n",
+              "25%         119400.000000  \n",
+              "50%         180400.000000  \n",
+              "75%         265000.000000  \n",
+              "max         500001.000000  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "WrkBjfz5kEQu"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "s3ND3bgOkB5k",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "42249c8c-b567-406b-d2ec-f5784d9a4b05"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-114.31</td>\n",
+              "      <td>34.19</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5612.0</td>\n",
+              "      <td>1283.0</td>\n",
+              "      <td>1015.0</td>\n",
+              "      <td>472.0</td>\n",
+              "      <td>1.4936</td>\n",
+              "      <td>66900.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-114.47</td>\n",
+              "      <td>34.40</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>7650.0</td>\n",
+              "      <td>1901.0</td>\n",
+              "      <td>1129.0</td>\n",
+              "      <td>463.0</td>\n",
+              "      <td>1.8200</td>\n",
+              "      <td>80100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>-114.56</td>\n",
+              "      <td>33.69</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>720.0</td>\n",
+              "      <td>174.0</td>\n",
+              "      <td>333.0</td>\n",
+              "      <td>117.0</td>\n",
+              "      <td>1.6509</td>\n",
+              "      <td>85700.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-114.57</td>\n",
+              "      <td>33.64</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>1501.0</td>\n",
+              "      <td>337.0</td>\n",
+              "      <td>515.0</td>\n",
+              "      <td>226.0</td>\n",
+              "      <td>3.1917</td>\n",
+              "      <td>73400.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-114.57</td>\n",
+              "      <td>33.57</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1454.0</td>\n",
+              "      <td>326.0</td>\n",
+              "      <td>624.0</td>\n",
+              "      <td>262.0</td>\n",
+              "      <td>1.9250</td>\n",
+              "      <td>65500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
+              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
+              "2    -114.56     33.69                17.0        720.0           174.0   \n",
+              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
+              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
+              "\n",
+              "   population  households  median_income  median_house_value  \n",
+              "0      1015.0       472.0         1.4936             66900.0  \n",
+              "1      1129.0       463.0         1.8200             80100.0  \n",
+              "2       333.0       117.0         1.6509             85700.0  \n",
+              "3       515.0       226.0         3.1917             73400.0  \n",
+              "4       624.0       262.0         1.9250             65500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "w9-Es5Y6laGd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "nqndFVXVlbPN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 398
+        },
+        "outputId": "f2700a3a-5ba6-42e8-d7f9-68dd657679f8"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.hist('housing_median_age')"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fadf35b2d10>]],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wQNnZ2ZKkvLw89evXryGOCQCAn4Uaz6g//PBDFRcXa8aMGb5to0aN0owZM9SmTRuFhIRo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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "XtYZ7114n3b-"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Accessing Data\n",
+        "\n",
+        "You can access `DataFrame` data using familiar Python dict/list operations:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "_TFm7-looBFF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 107
+        },
+        "outputId": "c5d37137-77f1-4f0e-e0e0-66d6bc8a20a4"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n",
+        "print(type(cities['City name']))\n",
+        "cities['City name']"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.series.Series'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    San Francisco\n",
+              "1         San Jose\n",
+              "2       Sacramento\n",
+              "Name: City name, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "V5L6xacLoxyv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "outputId": "22a344a6-d1eb-4b7a-faaf-1aa101f35f6e"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities['City name'][1]))\n",
+        "cities['City name'][1]"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<type 'str'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'San Jose'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "gcYX1tBPugZl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 129
+        },
+        "outputId": "df1dc85a-92b6-49c2-b335-219a3427996e"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities[0:2]))\n",
+        "cities[0:2]"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population\n",
+              "0  San Francisco      852469\n",
+              "1       San Jose     1015785"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "65g1ZdGVjXsQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "RM1iaD-ka3Y1"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Manipulating Data\n",
+        "\n",
+        "You may apply Python's basic arithmetic operations to `Series`. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XWmyCFJ5bOv-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "outputId": "0432b615-ee6a-4011-896a-4604934b08ac"
+      },
+      "cell_type": "code",
+      "source": [
+        "population / 1000."
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0     852.469\n",
+              "1    1015.785\n",
+              "2     485.199\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TQzIVnbnmWGM"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "ko6pLK6JmkYP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "outputId": "ce554b07-0e13-4afa-c276-3a1f3bc8964b"
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.log(population)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    13.655892\n",
+              "1    13.831172\n",
+              "2    13.092314\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "xmxFuQmurr6d"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n",
+        "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n",
+        "\n",
+        "The example below creates a new `Series` that indicates whether `population` is over one million:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Fc1DvPAbstjI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "outputId": "fceaccba-f2f2-4c86-b98f-524ead610f77"
+      },
+      "cell_type": "code",
+      "source": [
+        "population.apply(lambda val: val > 1000000)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "ZeYYLoV9b9fB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "0gCEX99Hb8LR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "23ef4380-9542-44e2-d9ff-ec22de60b30f"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n",
+        "cities['Population density'] = cities['Population'] / cities['Area square miles']\n",
+        "cities"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density\n",
+              "0  San Francisco      852469              46.87        18187.945381\n",
+              "1       San Jose     1015785             176.53         5754.177760\n",
+              "2     Sacramento      485199              97.92         4955.055147"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "6qh63m-ayb-c"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #1\n",
+        "\n",
+        "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
+        "\n",
+        "  * The city is named after a saint.\n",
+        "  * The city has an area greater than 50 square miles.\n",
+        "\n",
+        "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
+        "\n",
+        "**Hint:** \"San\" in Spanish means \"saint.\""
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5OlrqtdtCIb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "b84db6c4-ced8-44be-ecd4-f0a1e53ab996"
+      },
+      "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": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "0                       False  \n",
+              "1                        True  \n",
+              "2                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "f-xAOJeMiXFB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Indexes\n",
+        "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n",
+        "\n",
+        "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "2684gsWNinq9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "2edd5acd-4b98-478b-bd26-f5e4354a0354"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names.index"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "F_qPe2TBjfWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "f8e83b92-e5dd-4546-8ae7-0451c796373b"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.index"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "hp2oWY9Slo_h"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "sN0zUzSAj-U1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "04503d51-50d0-42ab-83fb-3b15775c0898"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([2, 0, 1])"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "2                       False  \n",
+              "0                       False  \n",
+              "1                        True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-GQFz8NZuS06"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
+        "Try running the following cell multiple times!"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "mF8GC0k8uYhz",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "be16fe78-26c3-4ffb-b99b-50d07dcd15db"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex(np.random.permutation(cities.index))"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "2                       False  \n",
+              "0                       False  \n",
+              "1                        True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fSso35fQmGKb"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8UngIdVhz8C0"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #2\n",
+        "\n",
+        "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "PN55GrDX0jzO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "outputId": "8da9c9f4-1de6-4374-f875-f9da58bdb944"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([0, 3, 7, 2])"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469.0</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199.0</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco    852469.0              46.87        18187.945381   \n",
+              "3            NaN         NaN                NaN                 NaN   \n",
+              "7            NaN         NaN                NaN                 NaN   \n",
+              "2     Sacramento    485199.0              97.92         4955.055147   \n",
+              "\n",
+              "  Is wide and has saint name  \n",
+              "0                      False  \n",
+              "3                        NaN  \n",
+              "7                        NaN  \n",
+              "2                      False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "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": "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..5b8055e
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1502 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Logistic Regression"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LEAHZv4rIYHX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n",
+        "  * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CnkCZqdIIYHY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9pltCyy2K3dd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Frame the Problem as Binary Classification\n",
+        "\n",
+        "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n",
+        "\n",
+        "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "67IJwZX1Vvjt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and prepare the input features and targets."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fOlbcJ4EIYHd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "lTB73MNeIYHf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kPSqspaqIYHg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FwOYWmXqWA6D",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1209
+        },
+        "outputId": "a75241e8-5627-425f-9f90-70de984651d4"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2646.6           539.1   \n",
+              "std         2.1        2.0                12.6       2169.9           415.6   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1462.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2129.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3149.0           651.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1430.6       500.7            3.9               2.0  \n",
+              "std        1140.8       379.1            1.9               1.2  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         792.0       282.0            2.6               1.5  \n",
+              "50%        1168.0       409.0            3.6               1.9  \n",
+              "75%        1726.0       606.0            4.8               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2646.6</td>\n",
+              "      <td>539.1</td>\n",
+              "      <td>1430.6</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>2169.9</td>\n",
+              "      <td>415.6</td>\n",
+              "      <td>1140.8</td>\n",
+              "      <td>379.1</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>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>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>792.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2129.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3149.0</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1726.0</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>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2636.6           540.2   \n",
+              "std         2.2        2.0                12.5       2204.2           435.5   \n",
+              "min        32.6     -124.3                 2.0         12.0             4.0   \n",
+              "25%        33.9     -121.8                18.0       1461.8           296.0   \n",
+              "50%        34.2     -118.5                29.0       2121.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3155.2           644.0   \n",
+              "max        42.0     -114.6                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1427.2       502.5            3.9               2.0  \n",
+              "std        1164.6       397.3            1.9               1.1  \n",
+              "min          17.0         4.0            0.5               0.1  \n",
+              "25%         782.0       281.0            2.5               1.5  \n",
+              "50%        1164.0       409.0            3.5               1.9  \n",
+              "75%        1699.2       603.0            4.7               2.3  \n",
+              "max       28566.0      6082.0           15.0              34.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2636.6</td>\n",
+              "      <td>540.2</td>\n",
+              "      <td>1427.2</td>\n",
+              "      <td>502.5</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2204.2</td>\n",
+              "      <td>435.5</td>\n",
+              "      <td>1164.6</td>\n",
+              "      <td>397.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>4.0</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>1461.8</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>782.0</td>\n",
+              "      <td>281.0</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>2121.0</td>\n",
+              "      <td>434.0</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>3155.2</td>\n",
+              "      <td>644.0</td>\n",
+              "      <td>1699.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>34.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_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",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 635
+        },
+        "outputId": "824da08d-cad6-4380-af60-c3bd6cb8518e"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_linear_regressor_model(\n",
+        "    learning_rate=0.000001,\n",
+        "    steps=200,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.45\n",
+            "  period 01 : 0.45\n",
+            "  period 02 : 0.45\n",
+            "  period 03 : 0.45\n",
+            "  period 04 : 0.45\n",
+            "  period 05 : 0.44\n",
+            "  period 06 : 0.46\n",
+            "  period 07 : 0.45\n",
+            "  period 08 : 0.44\n",
+            "  period 09 : 0.44\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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OcnKy83FQUBBlZWXodC1/SaSlpTF27FgiI4+3Di8oKKChoYElS5ZQVVXFsmXL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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JjBZ_q7aD9gh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Can We Calculate LogLoss for These Predictions?\n",
+        "\n",
+        "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n",
+        "\n",
+        "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability.  For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n",
+        "\n",
+        "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily.  Remember, `LogLoss` is defined as:\n",
+        "\n",
+        "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n",
+        "\n",
+        "\n",
+        "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n",
+        "\n",
+        "Given the predictions and the targets, can we calculate `LogLoss`?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kXFQ5uig2RoP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "7a6c6df8-e715-45de-c256-0db528c21139"
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                  validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "\n",
+        "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "\n",
+        "_ = plt.hist(validation_predictions)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AgNQhqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAY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\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": "5YxXd2hn6MuF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)  \n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UPM_T1FXsTaL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 635
+        },
+        "outputId": "b16df9d5-8d92-4a83-d52a-50aaf1cf8a8f"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.59\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.56\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.54\n",
+            "  period 05 : 0.55\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.53\n",
+            "  period 08 : 0.54\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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kJBRnzpzC999/g7fffq/V6+Ljd6J3b2/Mm7cQe/f+gj174gEAtbW1eP/9TyCXyzFnzp+R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\n2DVpZoQQQghh16SZEUIIIYRd+/8ldst57Ei8HwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-Xo83_aR6s_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n",
+        "\n",
+        "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n",
+        "\n",
+        "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DKSQ87VVIYIA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "outputId": "8606e9b2-d549-405f-f9d5-3c893c360e62"
+      },
+      "cell_type": "code",
+      "source": [
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AUC on the validation set: 0.72\n",
+            "Accuracy on the validation set: 0.76\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "47xGS2uNIYIE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n",
+        "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n",
+        "obtain the true positive and false positive rates needed to plot a ROC curve."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xaU7ttj8IYIF",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "e8fd29de-cac3-48b3-d6a2-f849587353cf"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "# Get just the probabilities for the positive class.\n",
+        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
+        "\n",
+        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
+        "    validation_targets, validation_probabilities)\n",
+        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
+        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
+        "_ = plt.legend(loc=2)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1m2Ljs8LdbM7fjsVm4dbQW1gQdzeeMvt1KlLCQqik1WShpKqZF/791UX3PTxn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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PIdhwfgzIYII",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+        "\n",
+        "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+        "\n",
+        "**Verify if all metrics improve at the same time.**"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XKIqjsqcCaxO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 671
+        },
+        "outputId": "836883d8-fd4c-4d2c-d71d-85d0e65c4537"
+      },
+      "cell_type": "code",
+      "source": [
+        "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=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)\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": 14,
+      "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 : 0.56\n",
+            "  period 03 : 0.55\n",
+            "  period 04 : 0.54\n",
+            "  period 05 : 0.55\n",
+            "  period 06 : 0.55\n",
+            "  period 07 : 0.54\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.71\n",
+            "Accuracy on the validation set: 0.75\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Hxkb4kFNYxeGzV9taXQghhOgRpJhRsWCXQCJ7h5NenklKcWqLvjkTgtFqFL7c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\niJIMmFVuAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wCugvl0JdWYL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VHosS1g2aetf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One possible solution that works is to just train for longer, as long as we don't overfit. \n",
+        "\n",
+        "We can do this by increasing the number the steps, the batch size, or both.\n",
+        "\n",
+        "All metrics improve at the same time, so our loss metric is a good proxy\n",
+        "for both AUC and accuracy.\n",
+        "\n",
+        "Notice how it takes many, many more iterations just to squeeze a few more \n",
+        "units of AUC. This commonly happens. But often even this small gain is worth \n",
+        "the costs."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dWgTEYMddaA-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 671
+        },
+        "outputId": "05017d04-5020-458e-9571-2883efae3604"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000003,\n",
+        "    steps=20000,\n",
+        "    batch_size=500,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.50\n",
+            "  period 01 : 0.49\n",
+            "  period 02 : 0.48\n",
+            "  period 03 : 0.48\n",
+            "  period 04 : 0.47\n",
+            "  period 05 : 0.47\n",
+            "  period 06 : 0.47\n",
+            "  period 07 : 0.47\n",
+            "  period 08 : 0.47\n",
+            "  period 09 : 0.47\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.81\n",
+            "Accuracy on the validation set: 0.79\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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mzZuHZ555BomJiVi1ahUAYPHixfj+++/x6quvoqysDB4eHpYq0aaoHT0wyDsC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\ntv0AAAAASUVORK5CYII=\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..2e720d5
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2702 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/multi_class_classification_of_handwritten_digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mPa95uXvcpcn",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Classifying Handwritten Digits with Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Fdpn8b90u8Tp",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c7HLCm66Cs2p",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n",
+        "  * Compare the performance of the linear and neural network classification models\n",
+        "  * Visualize the weights of a neural-network hidden layer"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HSEh-gNdu8T0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2NMdE1b-7UIH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4LJ4SD8BWHeh",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 233
+        },
+        "outputId": "d3c2b93b-5797-414d-a576-b269c44040fc"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import glob\n",
+        "import math\n",
+        "import os\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "mnist_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "# Use just the first 10,000 records for training/validation.\n",
+        "mnist_dataframe = mnist_dataframe.head(10000)\n",
+        "\n",
+        "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
+        "mnist_dataframe.head()"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "      <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>6747</th>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>440</th>\n",
+              "      <td>7</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>8711</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5825</th>\n",
+              "      <td>2</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",
+              "1336    1    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "6747    8    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "440     7    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "8711    3    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "5825    2    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "1336    0    0    0    0    0    0    0  \n",
+              "6747    0    0    0    0    0    0    0  \n",
+              "440     0    0    0    0    0    0    0  \n",
+              "8711    0    0    0    0    0    0    0  \n",
+              "5825    0    0    0    0    0    0    0  \n",
+              "\n",
+              "[5 rows x 785 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kg0-25p2mOi0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains '6', then a human rater interpreted the handwritten character as the digit '6'.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PQ7vuOwRCsZ1",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dghlqJPIu8UM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren't all 0.  For example, adjust the following text block to print out the values in column 72."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2ZkrL5MCqiJI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "4f4ccb8f-1163-4056-cc17-afdce59f9c65"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_dataframe.loc[:, 72:72]"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>72</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1336</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6747</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>440</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8711</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5825</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>528</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3532</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1167</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5730</th>\n",
+              "      <td>58</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4012</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "1336   0\n",
+              "6747   0\n",
+              "440    0\n",
+              "8711   0\n",
+              "5825   0\n",
+              "...   ..\n",
+              "528    0\n",
+              "3532   0\n",
+              "1167   0\n",
+              "5730  58\n",
+              "4012   0\n",
+              "\n",
+              "[10000 rows x 1 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vLNg2VxqhUZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JfFWWvMWDFrR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def parse_labels_and_features(dataset):\n",
+        "  \"\"\"Extracts labels and features.\n",
+        "  \n",
+        "  This is a good place to scale or transform the features if needed.\n",
+        "  \n",
+        "  Args:\n",
+        "    dataset: A Pandas `Dataframe`, containing the label on the first column and\n",
+        "      monochrome pixel values on the remaining columns, in row major order.\n",
+        "  Returns:\n",
+        "    A `tuple` `(labels, features)`:\n",
+        "      labels: A Pandas `Series`.\n",
+        "      features: A Pandas `DataFrame`.\n",
+        "  \"\"\"\n",
+        "  labels = dataset[0]\n",
+        "\n",
+        "  # DataFrame.loc index ranges are inclusive at both ends.\n",
+        "  features = dataset.loc[:,1:784]\n",
+        "  # Scale the data to [0, 1] by dividing out the max value, 255.\n",
+        "  features = features / 255\n",
+        "\n",
+        "  return labels, features"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mFY_-7vZu8UU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "e6c0965a-fe1e-44e8-9caf-0f99a6b51d28"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.8    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 7500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4-Vgg-1zu8Ud",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "c4b36752-6f4f-4f76-f741-26ddd081c0e2"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets, validation_examples = parse_labels_and_features(mnist_dataframe[7500:10000])\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    0.6    0.0    0.2    1.0    0.2    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 2500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wrnAI1v6u8Uh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Show a random example and its corresponding label."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s-euVJVtu8Ui",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        },
+        "outputId": "8a5c75d8-7197-47f7-bb45-6f8d71e23a97"
+      },
+      "cell_type": "code",
+      "source": [
+        "rand_example = np.random.choice(training_examples.index)\n",
+        "_, ax = plt.subplots()\n",
+        "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n",
+        "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n",
+        "ax.grid(False)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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WAGBALAHAgFgCgAGxBAADYgkABsQSAAyIJQAYcNwRvViPRk6ePNl8zU8++STUdfqd1+s1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\ngAGxBAADYgkABv8Crimdf4LZLwkAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ScmYX7xdZMXE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Build a Linear Model for MNIST\n",
+        "\n",
+        "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n",
+        "\n",
+        "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix).  The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n",
+        "\n",
+        "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cpoVC4TSdw5Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  \n",
+        "  # There are 784 pixels in each image.\n",
+        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMmL89yGeTfz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OeS47Bmn5Ms2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
+        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The training features.\n",
+        "    labels: The training labels.\n",
+        "    batch_size: Batch size to use during training.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns batches of training features and labels during\n",
+        "    training.\n",
+        "  \"\"\"\n",
+        "  def _input_fn(num_epochs=None, shuffle=True):\n",
+        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
+        "    # gets a good sampling of data, even when number of steps is small, we \n",
+        "    # shuffle all the data before creating the Dataset object\n",
+        "    idx = np.random.permutation(features.index)\n",
+        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
+        "    raw_targets = np.array(labels[idx])\n",
+        "   \n",
+        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8zoGWAoohrwS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_predict_input_fn(features, labels, batch_size):\n",
+        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The features to base predictions on.\n",
+        "    labels: The labels of the prediction examples.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns features and labels for predictions.\n",
+        "  \"\"\"\n",
+        "  def _input_fn():\n",
+        "    raw_features = {\"pixels\": features.values}\n",
+        "    raw_targets = np.array(labels)\n",
+        "    \n",
+        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size)\n",
+        "    \n",
+        "        \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "G6DjSLZMu8Um",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, and a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `LinearClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create a LinearClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(),\n",
+        "      n_classes=10,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ItHIUyv2u8Ur",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n",
+        "\n",
+        "Stop if you get anything above about 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yaiIhIQqu8Uv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 987
+        },
+        "outputId": "9edc3223-e59e-42bd-c3f4-7e7d35dbf49f"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_linear_classification_model(\n",
+        "             learning_rate=0.02,\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",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 15.38\n",
+            "  period 01 : 12.05\n",
+            "  period 02 : 11.01\n",
+            "  period 03 : 7.05\n",
+            "  period 04 : 8.10\n",
+            "  period 05 : 7.56\n",
+            "  period 06 : 6.96\n",
+            "  period 07 : 6.22\n",
+            "  period 08 : 5.75\n",
+            "  period 09 : 6.27\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.82\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1mzZtoqqqChsbG5YuXQrA4MGDefbZZ41VQp80dWwAXm72vLPGltYKPzxGnmF/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\nZcoUUx5eWIgBzrZE3eJNSVU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9X6HxO8dPQ29nkl+k0Y51Kq+Gv3+b3v0417ypvnyb8z3H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\nRJyQ0hZCCCHixP8Dxnxpm+b6CrMAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RkcgytGVnZ4fRo0c3eszb2xve3t5yhCMiMklaGdrieSRERKrFREJERBKwIiEiIkkEC/kS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\n/wCo5dtlqdrPugAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "266KQvZoMxMv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lRWcn24DM3qa",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here is a set of parameters that should attain roughly 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TGlBMrUoM1K_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 987
+        },
+        "outputId": "520af557-176b-4c64-bfba-41b585278f4f"
+      },
+      "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 : 4.09\n",
+            "  period 01 : 3.92\n",
+            "  period 02 : 3.61\n",
+            "  period 03 : 3.63\n",
+            "  period 04 : 3.61\n",
+            "  period 05 : 3.33\n",
+            "  period 06 : 3.26\n",
+            "  period 07 : 3.23\n",
+            "  period 08 : 3.23\n",
+            "  period 09 : 3.19\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.91\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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P9VyhEEIIUzNaeI8cOZJZs2YBkJSUhLe3d8VjVlZWWFlZUVBQQFlZGYWFhTg7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\nxMEovEVERByMwltERMTB/H/qoOOWugULQgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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kEFmGtuzs7DBu3Lhmj/n4+MDHx0eOckREJkkrQ1u8joSISLUYJEREJAF7JEREJIlgwSAh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\nSUVORK5CYII=\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": "TOfmiSvqu8U9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "evlB5ubzu8VJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "4554e423-8dc0-40e5-e8c2-de335c25fbde"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          1       2       3       4       5       6       7       8       9    \\\n",
+              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          10    ...       775     776     777     778     779     780     781  \\\n",
+              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          782     783     784  \n",
+              "count 10000.0 10000.0 10000.0  \n",
+              "mean      0.0     0.0     0.0  \n",
+              "std       0.0     0.0     0.0  \n",
+              "min       0.0     0.0     0.0  \n",
+              "25%       0.0     0.0     0.0  \n",
+              "50%       0.0     0.0     0.0  \n",
+              "75%       0.0     0.0     0.0  \n",
+              "max       0.0     0.0     0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kdNTx8jkPQUx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, as well as a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `DNNClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  # Caution: input pipelines are reset with each call to train. \n",
+        "  # If the number of steps is small, your model may never see most of the data.  \n",
+        "  # So with multiple `.train` calls like this you may want to control the length \n",
+        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  # Create a DNNClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.DNNClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      n_classes=10,\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZfzsTYGPPU8I",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 987
+        },
+        "outputId": "33498fbe-7ad0-4951-bc90-5a5a40b8d44b"
+      },
+      "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": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.64\n",
+            "  period 01 : 4.46\n",
+            "  period 02 : 2.92\n",
+            "  period 03 : 2.16\n",
+            "  period 04 : 1.89\n",
+            "  period 05 : 2.54\n",
+            "  period 06 : 1.89\n",
+            "  period 07 : 1.78\n",
+            "  period 08 : 1.84\n",
+            "  period 09 : 1.71\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.95\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PN3WxnypO5VRxKgDedp4sGnwfgU7+HVy1EEJ0H/PmLbjqbS5NOzpp0o1Mn36b6Ytqg1nC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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NFYqIyCwIglWLHqYmS8SMjAyEhYVh0qRJ+OWXX/DMM88gIiICwcHB+P777+UISURkdgRB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\nkjCREBGRJP8P8r9yVOOPHO0AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qXvrOgtUR-zD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we verify the accuracy on the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scQNpDePSFjt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "554804e9-fcd2-4f1a-ae77-44555ba44efe"
+      },
+      "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": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EVaWpWKvSHmu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "e2210003-5c42-4f96-c7d5-db1a8680c468"
+      },
+      "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": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.95\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WX2mQBAEcisO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Visualize the weights of the first hidden layer.\n",
+        "\n",
+        "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
+        "\n",
+        "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
+        "\n",
+        "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eUC0Z8nbafgG",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1174
+        },
+        "outputId": "5ca4c226-cbf9-4aad-ff64-7b40df45162a"
+      },
+      "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": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n",
+            "weights0 shape: (784, 100)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7Ycec6FijXfp30JuW/96jXrvzlKagrv4KpEydhyHHyCAL4a4zmgo677PwG2vf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\nTU7+0H59r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WOM2XcRazjH8rQEeQxpVCcqJAQXZlKmtI2PoDHtr6Dnoh55HxNnp5uboZkz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\niMgQzZ/eCj0e6jWoblTmMgiO3bXN2leuDzHY4drq4bH6yte9djDZS/pis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ylmnfg+lx1IrkUtS81rduqH7x5H/IbjET/dr3IGvVp3tzsc+eiEhcesKn9osUesmT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\nd5d+Sa+fPaewvnMNKH1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3vFnr8X3Nb0IeGtut96iuVYALY84YDAaDwWAwGAwGg8FgMMwh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\nV/1KKLJ3rBMa3taDWh+84Isr8R3kY8KRmyI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77ZgE+A/ExepykTIfmtnkCvhm1OyFv0bJ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\nY/vUdrKpTbYs4Ccj2CPwZxpqkLW5xqluENcTscqOiPokMWShznUUkq01t/V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U58IeOxef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Le5LhPXwBkJNYt+h19/32lz3Q1Pml/0CzXhfh//IWx5k60aa1zHa4DGerBW3u/xEfq815qo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7+oVbZ4l2w6RVb9p10Ikjs2QdhZhUuS75G9bcjg7KuluD1Vgj4hdCgx+VJy3l2bI9cSHW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WAwzCHsxxmDwWAwGAwGg8FgMBgMhjnEXTVnCp5c4LXbD2r+MvPkr74Om9XPP7FF9WNL3K4T4Bq+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\nLhO6+ir8+0+PQlp14/nns3aucoylyhJYfZvSvPItSCG12dAXJt9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6lOWlrZNxDRvegrXo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Lcoz+HA2n7k27AJDYxFD5EAX/5s2WfmiQ/Rm+bxu56lvBfuf9SKr/nplVb8xv98l/KqRL1d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\niu+LtJSXcAgLeWOMGexFnUycjf493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mNejZGuOyJK1xzhehajtD/oPq8tvDk+Xw8GKc7V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HifNxHs0f6L1ddK7ej7lY5oxhGIZhGIZhGIZhGMYMYj/OGIZhGIZhGIZhGIZhzCD244xh\nGIZhGIZhGIZhGMYMcs2aM2FR0KT3nNVWkyUPQUO459k9Xrs0W1vYRt8AjR3rzVg/LyLyxlHo9D65\nFZrqCzsvqH7lW/G9l96CVnh4DLrSd0+eVO9Z9QjqifAxzLtPW4YW1KK2TM0x1PFIjNG1O5q7/w97\n7xke13Wl6W6kQgWEAgo5FohAgARzzpkSkxIl2ZIcZLctx2tPz3RwT3dPB9/H7unpdruT3e0sW7ai\nlXOgSJEUc84ESOQMFEIVcpof89zzfWtb4n2e6+LFn/X+2mTtqjp1zt5r73OwvvVB87b1azjW60+d\nE/3SqUZH4Z2oG3HxedmvYrNteRldWJfnzZU1HEJnoEceI23khKVNzlkNPf1YGPrt7G2yNk06ae0T\n06AdTkyR1pHJlajHULRqA753ELZmXK/IGGNGI9DpZlRWOu2614/IY6Dzzpr02ifkea/aCY0nW8NX\n7L5H9OuoOeS0uf5MoqXLtvXh0SSpiOoelKWL17jORzPpjW+2S818fjre9/6HmCPLSqX1aWoQ16qX\nxoevRNZbSK2APjOe7EnrnkLdhOGg1NazLW0G6cIT0qxzSfV71qyEVn+sR2qKzz6Hugq5fhzf0Kis\nPzBK+nG2PHelSbu8WDfVnFlqok7pHmjKuXaTMcY0vgINfeYK2PGde+OC6Lfqs6gRwbblXFfBGGMa\nuzGmb3wf9TUWLZfxJns2ahg9/qeoLfO5733Kae9cLGv91HViPSiMDzrtqWE5ByJUg+zEaxhzJVtk\nTZg+sp8t34rj6zoiNf0NL+JcpFK9kme/+5Lox7Wv1prokjqPaldJp1LjI6vl3nOYf5ev1ot+q+5f\n5rRraL5Uf1nWWAtdwPxji93xXjm+B2m8X7+Etat0FuZYihX741xY/vNpfeL6UcYYM9yDa9j4HNbc\nDy9JC9JVlTSuqC7WuFVXII7qPEXq8JtiY+WegMf27WDOFszF1HJZA+/Mv8O6c+Wf7nLag13S/rTi\n/jud9tUnYXGft61M9Iu8dspp99eQBXWpjOWZy6Ctb9+P+hDuPKyF09NybW54/wN83ix8Xkp+UPRL\nCuB6Dw9gXnVel/a9w2Tdyvr8OJ+sm+Qny15+T9ZKWedi2vZFjSKt70Dfn1Qm9xhmEt/ryYUte+i8\ntD5lS+qOYzgvJffJWg+jdC5yluC1oV65zo70IA4nleKYuDaeXR/BX4GYcuNxrGneoKxLwJ8XmIex\ncuOJU6IfW71ynZapVGlrPjWG/TDvlSJW7cjMVVZtrSiTthhjKd4aZ+4M7L97TqMGi69QxjNDdZQS\n/FjXi8nO3BhjOtn6nN5TsFyuFH0dqPPkTUNtKK4z018r6x2mlWD+seV95Ka0Jt/iQk2rf3j2Badd\nmiNrbSS58Tu4lqKvSI4LrokzNYJrPHBNWktnb759VtrBB7BPy7DqTnHNyZZuHNOlJllrMC8N43vj\np9Y47fRqeV4aXsA6NB55xWknFcjzMtaPtaznFMYOf0/xnHzxHt7Xtl865rQ79sm6S8UP4v4hNh57\nOa5vYowx/ZewZmRQfO+orRf9hptxDcv+APut6UkZPxd9VdY4iTaJNHdSy+S66KOaa93Hce3cVk3R\nQ7/FeVu6FucpfZmst9p3GrGTf2fngXrRj+MZ1z/lOrYjVh2veKqDlkgxsOEdWful7D6M26lxuj9Z\nKscFj6WOfTi+njR5rHz9G6hWXOmji0S/yRG5jtto5oyiKIqiKIqiKIqiKMoMog9nFEVRFEVRFEVR\nFEVRZpBbypq6P0T6Xv4uaefaQPaDcZSOnL0lKPrlLkCa9sgI0gnZlsoYY3ZQ2jzb2dopsSwd4ZS/\noiVIXbQlDZyOxOmT4VqZahhDj6r4e985f1702zAHKa3DZE/J0ipjjLnwOKRaWSVIWw0kJ4t+LkvS\nEW045WywRaYbZq2GJR9bCfrS7NRkvDY8QvbUZdKCtOc8UgeTssjOcEDK4qp2P+S0O+r3OW2WMuXP\n3yje098LWUSkByl1afOkHen0FNIDQyQtmPfNDaKf243f3nYeaXi2HV9m6RIcQxfS1JpekWn9WRuC\n5nYxSXKRWMuCr5esMkdGMQbDw9IyrjAD12p5GdLuuwakJW52Ds6LSKW1rN+a30R6YPYajJdQO8ZH\nYEWBeA+nWLPldtZKaQ3ZexnXrXYfJDBZWTJ1fYqudVsfUrFLF8pU5lhKI4/zoG3b/BburjC3Ey+l\n13ccahCvVX0ZUpdEL9I4w9dlnBpqxfW69B5knxNT0qYwPg6ptnyN3dlSLpiYiPmzbi5i2yt//bLT\njozIOXHvn0DqEUsS1bl7Pif6nXvyP532pq/D7np8XI65EYqdKSQxyVgix0/tL2BDzJactjRvzv8l\n7YGjCZ+/iWGZmtp3AeM2ewPGYGBYyn0nBvE+TyLm2M1fSell2hKkAYfbcM4OX5M2srwWTkwiNddX\njDmbu0FKbVrfx/zN24T1/ebjUhbMdqftNMfePSePldfdFeX4vIzyTNEvsBTnop5kUgU75B6j55iU\ntEWblDK2Opep6Iu/jpT6micgcWJZiTHG9F3Z77QzliENOildygd2kzSq4UlI82yZsZnCvmNyFNfR\nnUkWwu/I/YiL5LUhsn92pUrJJtuyF8950GlfOP9D0a9i+wM4nClc03BYfu/UFI6P7Wxrnj4g+mWt\npVgsw/zvTT7Z0LO0wBhjpiZwfE0kGWXLZWOM8eThGgTo/Hd9KCUXWWuxxjW9hzgUWCzndmI62dzH\nQDbD0l+W5xgj7Xbdudiv2VLpqQnE+N6rWLt8s+S4ZCtelgcW3F0p+vHxeTNwDdlq3Rhjei/T/m2+\niTopJMdre1/KRxKSMDdZnsaWx8YY46/GOnbsCeznKubIvWwuSbR4P9J84rDo1/AW9h2zH4HdNY8z\nW0bjy8dYCpNk021J9Nsu4tr5qWzCnQsXin4XSPbz6kuQ129ft0T0q6/FeCqbj/nG1s3GSElItBmk\nfUn3YTl30pdhjuRnIe7mpslxy1b2nQdgZdxqSYoCJHNJDaLdfU7uqRIoBpbesx7vqcS6wxbbxhiT\nmIIxNkr3jqOD8v6ONc1hkgFGGqQk0EtrcKwLY8fll/G55RTWu97v41pnVMj1c7gZ++aCv7rPRBve\n09j28nEu7PUK7sReeagjLPpt/8ZWp936KvYZo13Smrs3jN8SoDIqPV3yPpXX3ZE2vMdL8y3BOp9v\nPo51KNWDNXLpDjnHhttx7LGJtGe2xnBCKuJQYxPW2Vi3XHf4OEoeRrBs23dD9EtfJCVeNpo5oyiK\noiiKoiiKoiiKMoPowxlFURRFURRFURRFUZQZ5JayJpZPTFkVozOWI4U3uQcpia1vydSdVEppTk1F\nKl4kICvcL/2jbU675xLS2bLqZXoTVyX3JCN9aIgcWDY8JuUrL/7T6047hxxdYmNk6mb1NlSVHiBJ\nSHGmTCvLqEL65I19SNkqqJLpre++B1nTtjlIYSrYI6UTPceRgmrWmajDkphES0LFjjGuFJzPsTEp\nQ+q9huvFqZwul5Q1FaxASuVgGG4TdspxXBw5OfmRmpYdRDrcwIBMrw9k4uSEepD2lxSQEpYrP37T\naacvxzWJjZVpb6EmpGlzle7mkwdFv/7LqLZe8dAWpz3rk/LzhjrkWI0mMTQXYyxZUx9V5M9agnnZ\nHJJymNp2yJ/Wb0LlcHeXTOkfasTvSCI3EZYbGmNM3hZy6qLwkFONsd7xzk3D+JdAVsjV2XleG2OM\ny4/xUbIK3zNwUbql8Hx2U3p/ymw5Lm+8jDTWnEU4R8H7pSNHyytIZS5faaJO/ZMXnXb2Vil9aHge\nx1h0L/7/4AnLrakf8WP2SqT1152sF/2KFyB9u+tduHmkzpbxrL0OTk7V39iJF/4FcTN1gZQOtryM\n87Tgm59w2u//5bdFv+DdcMTpvwpni8FGOVdCEaSqpuXCRe/03/9K9PMvQgozj5Hrr10W/Ub7KH02\n20QVTg1nFwBjjBkYIEcWljGQzMAYY641IIU5TJKxFI+Mz94OfN73XnzRaVcUSLkXS2r/7GsPO+2h\nJqSaD7bLc56+APO07UCt0+4OyxTlTD/ivS8RsaIyX7oZLCVpWQvFHv+ATK0Pk+tUNslqJ4Zk2rgt\nIYo2l36JOVGyTUqqQicgOxiJkCx6oXQNGaAxHaDz6fNJCdlYALE3dydJcVxyXRym9PB+cng5fAAS\nspIsORfnfwa2csOtmEedR620bJJu1U085bQzl0mtUcuVt512pB4p+tPj0umH5UDeXFy7nI0yrvWS\n45ipNlFlgM5R72npwsQOHwW7aM9lmUf1XcWaMtqJuMHyV2OM6TyIfWmA9r+RJjmvUkoh22CZYxZJ\nf+Pc8rNjyTlt8Abe47McqFj+00Wyv4CVIh8hyX7GKlzfriONoh/LpMw0pCO8vzfGmOEWGROiTdNL\nkIgX0prxf8AFYwlCR63co6ZWYV1bvAfShcQMGaNZBhNpxfwdbpFS2xyW1NKYaXkVa1/F56XDU9dZ\nxFEujVD/Xq3oN3svXGc3UbxlabYxxhy/ju/auwpS3eY6ef9UUo7rNU3SvOYPpBwo03I9iiYd7+G7\n8q17nM6DkBv5yIEsUtMr+rHz6BDNv8B8OR57TyI+X7gA+UrlF6XFZmIS5mJ8PORL/jLcF0Ra5Z5y\niOTDLW/iunkzpSNRHUmQs7djj8rj0Bh5b3Hzl3hPU7d0+qreAMlh2jxcp7on5f5vNsnfbwfddD+a\nkCyd02LofpFLUHB5D2OMSV+I85uYg/PG8dUYY0q3YJyMdMHlbtFeuS9nh70EP61jr0DWP2q5Qi4M\nBp121qqPLt9hjDENhzBuszuwtk5PyHuSCZIFr/oy5n1CirwP7KQY60rGfi7TKt1gf76NZs4oiqIo\niqIoiqIoiqLMIPpwRlEURVEURVEURVEUZQbRhzOKoiiKoiiKoiiKoigzyC1rzsSTdVS7VUtmagw6\nOq4XEFwRFP1cLujv+vpQgyWtSFr69TXB6tBfCd3XeFjaYrOdb7wPerg00lLa2vUNmxebj4Itpo0x\nJnwdtTvmzIeGsO2G1Hd68mC1Nn8xNI4Rq47CPV/a7rTZWq7lkrRRZM347YDtxmxrxnA9NJ8JdD6n\nLH15HGnjfWlBp137wvuiX/oiXIf+K9ByFm2X1n9DQ/VOu+86zsdwOvTungw/v8W01eC1rFmwOq0/\n8pbox7bvXaS7n56Q9rM8fkbI1tmuI8H6645zqFOTUS3rCsT7pLVjNBlq+3jNd97WWR/5/8vukuP+\nJlnndtfg2qRmyZoQKVWo1+LNwVifsjSSbIXNxJNWmLW4xhgTqYOmeoLm9viAnOe2bvf/wa7RkLkS\nOn62AAzfkPV2uFZGUhDjKiZWPp/2WNaT0ebYFWjIV1kWfJlk1dpxGBrtz37vEdHv1b+CxfVgfb3T\nXrddXm//XMTRjudw3mufkLWcFvwh6jx5PEGnnbEGmntbt58+D7r9hg+g+c5dFxT9kopwrtvexhqS\ntV7Widr+N5902q3nYF1c+XVpiR1pQbw69lP0m7NhtujnSb999Up4nRhNlxrqyQjGINdoSsyS529R\nMTTVrecR/772D/8g+v3d177mtDuaUWNiz4oVop/HhVgWm4AxnU621b3npG28l9ZSP62fvW9Ly+Tg\nElyroYuovzK3SFrUBvJxzvMXY+z458gaKYPN0PTzOjNYI+dsQprUckebZLIfn56QhUiSK1DbI6cI\n52m4PWL1Q02D/lrUEIj37hf9OM6M9eIc9l+RdQf6azG2LtH15uubTlavxhgTOgc9/gdk8eyKk/Fl\n3d3LnXZCEj6v67hVmyYFsZLrDqb8Ti0FrAeNZIlua/oHwpgjCx4wUSVyA/HArokWrsG5ZPvszgPS\nbpfnJtuijoflPjJtAWLeUDP2etNWDRveG+dsQP2dQXpPx2lZg4qtml23GPcTVD8wn+oktX8ga7tx\nbaRBsvllG16bwCrsc3pOy1iRszH4se+LBrweTI5Nfmy/G29jDxfcUCpeO0iW9wtX4f4idFzut5Me\no7hXhP1JXKK8Heq/jrnJNVNy76TaWvsvivdkr8bvuPTvR512gVXXqf8S6uVMTmEeBZLl3P7Wf/uU\n0+46i2tSdKes6dJ3EZ/notp73nZZT5Bt1aONl2rJtL0j7xdzt2OvPB7BXs/ezzW9h/flrQ067f7z\n8h5sYJBiypdRHHCwTdYNGvViT+5KxvVsfAm1SuKTZF2V4N2Yi7wvLVgj9yJTU/jsSDdi6EiP3BNc\nfR41Y+Z9GvdBnT87Kvr1X8Lx5axD3Jj1iPSuP/3PqLe56+/vMtGGa2N1n5BxaopiYiyNJdsiu+8S\nrhfXWnGny30Qj8fu05in7lS5Dw8sINvyOuwT4q7hnNk1fJYswp6wnfbTvgx535+egz0q15XxBuX9\nJ9fYGR/CPo9rCxpjTGAx9lxthxCXfda9hX0vbqOZM4qiKIqiKIqiKIqiKDOIPpxRFEVRFEVRFEVR\nFEWZQW4paxrrQdpWnFem7sQlUcr2KFK/UsoDot/Jv3/BaWctQ6qzyy/T1FLL8L6RLqReZq+QqYuR\nFkr9WoJ0r6F+pF9x2pMxxhTuQnpT85uQdqSQTbAxMl3dW4D0wkLLToxTn0a6P956seP9eqcdWIZU\nJ0+PTG+69ARSkUsWPGSiTYSkS8mz5G8WqVWU8ei2bOMmKI1roB32cgU7ZHol29UlpiK9cqClWfRr\nfuFdp53D0heyNx8LyxTcziNN1IYVaNEeKZGr+SnOZ/EDkA+4LMuzvmuQ9hRvR2rk1JSU2LSfRAok\nW7b3HJMpf5yOXPCYiSpTlDttS86Y2tdwrInxcjwO0TwtqcJctNPB2RquldJTA8uknaGHZIE9Z5By\nG0+xItay/bYtPz/q/cYYk+jHMcSRLXa8FYc4hbzvNGKKp0jOMW8x/s3p+HZO+lCdtLKMNiurEYuS\nZ8tYGe9FnGEp4m//+29Fv3u/DZ9tln3e+I2Uo7B94Nf/8VGnXfMrKWv69iN/h/az3/vIz7atLH/y\n9Z877b1f3+G0Bxvk+RvuxBye/SVIcVieZIwx09OIL5MjaPdclCnp3QcRA5Z9Bp83cE2mtLYeRPp7\n5n1bTTRJLkcMHe2W6bwJ6Ri3Hftg0RhYKa2vG16BdWxGIT5v1+bNoh9bqz7zr9912gc/lNd6w8ZF\nTtuVhrg7RPawI5aNJaeUj3TCxjLWkvrxmj7UiM9bu3656Hfy5TNOO6cdKcG2rIklNSM0Pni/Yczt\nTcG3SSqWKcwcjzxZWNO8uTKujPXhmLuPY42bKJNzu5nsdwt2IwbEuuS5jpDN+NZHYNfJsquXH39P\nvGfXnHVOexHZhyaXyN/UdRLrVWAh4rAtWZyktb6V0sEHrvWIfomZeN9gL8bP0JiUA1U//NGy8miQ\nuxl7h5GQHN+8VrS+hn2f1zovkyQVGuvC9czeIi3BXST3Gg2RXMKSIbEsn6VMCVQmYNY90iqW5QNJ\nJZAHeizpfdt7SJMfIAlHzgopm2nfX4/PoL2cKyCPNXcb9tcsCx6sl3F8tH/E3E56L0CWk27Z1Te9\ngFg571HYCI90D4p+izfCp91D9r2/s8aTHXfdAUhOOhrlGrL4S5CxDFHJgtBZyAiLdy4S7xnpJwtz\n2utkWTa6LAm/8Cz2wvetlHLVhGSMmYKtuFaTY1ISwdbuPWSxXvJpKYkJXZD3XdGE74umx6UE3kNS\nkn6yrvcWpIp+c+gekfdDA1fltSlaE3TaN36B/czNDvn7xiexP1z/qdV4gezG7XvWqSmMdX8V5vL1\nZ98W/Vj+1E2xNX2B3ONmkGyml86/3yvjbv5uyBSHaWyPWjKpOY/IMRdtWN43ZUkMs9cH0Y/2D/WX\npN138EFYxael4d6q/aYsg5FZjPHuWol531NzRfQbIdkU70uL78K9n/tdKeHL34nzyev5aKeMG10N\nVM7kYZxbtvY2Rp6L5CLEaPt+bJL+7aL7mIHrcv38f0MzZxRFURRFURRFURRFUWYQfTijKIqiKIqi\nKIqiKIoyg9xS1iTS/8mFwhhj3LlIUyvagNTSoVbpKpNBKV5ucsEZG5Bpkq3vQj5Rvhdp6HFxHtFv\nxIP0wslJpB311yDtLc4tpQ/jg0i3m33/ThzDWJfo51+ANNGYWKRUuymt2RiZ7nT5ZaRzJVjuCCUb\nUaG8myQw2ZuCop/XcpaJNrlrq5x2uEnKRzgllytp8+83xpjOw41OO2kWUrr6rnSKflz5vGAF0gjb\nGo+LfpVfhdvSxChS1m7+Gun6/oXZ4j1Zq1FFPCmb0rITZRps6WcorcyDlMWxYXmex3qp2noXfl/7\n/jrRL20BPn+E3DrsKu+xifL6RxMXjelsy+kmNgHfW3oHUubt4yuitLyrL8FloHCRTLnlKuxljyCN\nuOeSdPXwpuO8JKzHOOo6gX79Vjoqp5rfSp7VQe4IsZRm2XtZjrcUkiMkZmP8DjfLqv1JZRizY5T+\nzhIaY6RD3e2goxOyhaKqueK1A9+DXKFyCWLq3X8pK/KHzrebj+IsOTcZY8z81C1O+9f/FTKk7Y9u\nEP2q2hATmo/C8eLIq5AHrk+R52XTaqT08ljqG5SpoEUkhUgmGemU5ehy5UmkJi/7o01Ou+e8lA56\nS5AGzemtJyzXjPUPS2eFaBImVyFbmsauBezqd/m5c6JfVj7eN00uaLOyZcwrSKdzRvN30x3S4W+0\nG7EsdKj+I4+7/JMyxT09iJTglg9POO1gpnTlYVlswR7IWFvfqhX9Vn0a53ycZBAD12UMGG4lZzxy\nyim8X0o9Og9JV51ow3JaV6qUe8R7EXPYtcV2WBhqQpzJXIWU/MYXZFp2JjlgvPi3Lznt1ZbDWupc\nnPtWWoe8KdgH7X5Izt8PXsTa+r9++Uun/e9/9EeiX8EyHEPDs5dwbOuk61baHIzBPnJJ8VlyIHY1\nHO/D9S7fJOVAIXKZMdK08femg/Yl7kwpE5iimOUhx63JQSm7Yheq2Hisfbbct5ccdgo2I21/bNCS\nwpJkousgjm+gB3vjqs/KE8EOKeygyXIJY6S0OH0KUvlRS+KTTs5SMSQtDizME/26ZYAoQgAAIABJ\nREFUz0A2yjKFOJ/cO9jyhmiTwKUDLDUjj7s2uk+IiZEdeV/N7mhTY1Jiw3PdPw+ylfw7y2W/ZPQr\neRDzNDUV7XBYyjl6L2JtZrevI/+4X/QLLsb1buzCfciVZnm9l8+BpDt8imQRk1KqNTGB65NJcmaW\nUxpjzMQg7XfuMVElMYAYNWjtv6784JjTHh3HMZTPmif6XfvpKaedTHM2Z1NQ9Bun35G2CGM97aAs\nheBOwBxm+WKYSmeUlGeI99x4Ams1l7eIc8vb5Zqfw23JNwtjdKhV/vabNzHHNj8IB98ckggZI9cZ\ndgX0V8r1mOPD7aCL5Lk+S+7rzcI+uu0ApOMZa+U9BEsJXS7sdYoq75ff1fWm04600j5j/nbRr6Pu\nA6c9QK6Iwu3QumftOo65FEfy4dytslRKSiudXzpu23WQ48ZwN66PfY/D98455Dg2YsmpPNnyuYKN\nZs4oiqIoiqIoiqIoiqLMIPpwRlEURVEURVEURVEUZQbRhzOKoiiKoiiKoiiKoigzyC1rzhTeB006\n6yeNkfo7tpOMT5Qf6UqHDvHc06hhkJMmtWy5O1Gf5fz3Yb8dWC0tXJNL2AoaGtm8ZdDgR3puGia7\nCDVshoelpTPjK8AxxSbgudXADWmBxValRdU4vlCt7Nd8EJrxlExoFxvfrhH90mfJugXRpvs8zkdg\nntSX91yAJrrvIjTV+duk/pYtKxtfhJ5+LCRrB5U8Ag3phR8+77RtfTDrMtkucMk3v4Tjbjkk3pNT\nDB1iKITaGENDskZMIAvWou03UccjxrJ1dvlxHVlnWXzPAtFPHDvpCX25yaLfiGV5F03Y1nO0V1rO\nTpP+eKgFuvaEJKmZD51D/YBC0jyPWXbAPNc7T+Lcsu2tMca4M6Dp7KfaQ1xbxEhptMleid/ReQI1\nJcKWFXIG6aaHyXbSrquyYBr1d/z0vYGVMm54c3CtRuj32nWCsjfIej7RZu59GFsn/12O783fwviu\nexJadtsyNL0aGuuBOtQ/Wb9poejHlpAb96B2ENtRG2PM4hWI86/+fJ/Tvv+PdzvtOCuuJ1IMTOkg\nq3OrzlGkDjritHk4bvvzOL5MjkFP3n1IHmvZF1GrId6FY1gzLPXBA1cpFm8xUSWwArVFpidlPYOx\nXpxzriUz/9OyRkznB2RR3I5YuPdbe0S/oTbMZ67V4s6UFrvi+JahrsQY1X4J18j1qf/KQXw2xY3c\nrbNEP/4d/HmzHpFxkuMf13Ka6pfniOvMcKzgGkLGGDPcJuNNtAlTrTePVVeOa8u4SRs+ZVnEegth\nrc2/v7td1iE5/R9Y87nOzJRV8yqTLHcHazF3+JyNhWT897gw557/xT867V4rpg7W4Zja2zAWgnnV\not9NqrkQIYtsHvfGGHP2adSHWPp52KWy3bMxxmRY74sm/jmoFxDnkTGl4/16p12wB7XY7Dnb8Oxl\np51cib0Y130wxphpqiXTdhjvSZ8vrXNHezEOcu9AfYPqEtR86qo5K97D8ZBrEdjW7dNUW6Sfrm/G\nErneudMwr0YHMI56L0mr4RGaY4V0juqfkTW8Ei278GjD67BtYZu7EXuGaaoJ0WHVpGp5FXOsN4SY\nOjYh14aKCR4L+DzLcdvExmP/1PYBardMrkQM7L0iz+fkKK5PxxXUnwlF5FjqPoCaT1+9806nnWPV\nE2z/4KPrbqVS3UdjjEmhuiSDTajLWXRPlegXunj7rLTjPDhfXPfGGGPcGVTPkvbhg40yTqaQjfzE\nAK0HVj0RXl+4JmiKZU+dTbWchui8BMgi+/3vvCXes/SR5U67/V3EsrYuWbOS7wtyaP8xMCj308sf\nxufdeBzzvuRhWW8ntQL7KLY85z2AMcbUvYtxPkeWZokK3nzslWPi5D1T05sYtwGyindnyP2Ix4tx\nPDSEMTw2Jtek2Fi8L7MU+46Wi++IfhyLs1cEnXbH0Xqn7fLLuoiRGsS9OLoXsmvJ9J6hGo4UBHxB\n+YyCx+3EEMZf3iZZw6b5TdTiGerAtctcLtfBnlOyvpSNZs4oiqIoiqIoiqIoiqLMIPpwRlEURVEU\nRVEURVEUZQa5payp8wDSkVrqZDpcQQVSmvzVSBFrffuG6Je/A/KY0mVIT7St+epJKsP2uJEb0jYs\nZRbSNYdDkFJMTeL42BLQGGPGx5E6NzqKfo1vSHtTtjZrfh1pjIEl0n6w/R2kuuXtwu+zbccGG/C9\nfZRCnZIjU1VTKqWVW7ThtK3UCmnLNkoSD7bDHOuXqdMxZK+cXI7UX5eV7sopeNkbg0572soZdaVC\nkuAiWVN7DWQV/sIK8Z6BAVyvgXpcR7aSNsYYl6veacdTqmVikjzPIbKRHOlAKm3HUTmGpyitn8+R\nN/vjLdajDduwjVjp1h5KQxygcZZaIeVyLEXpPI/fnpQqUxInKGU0RBbwqZa1eS+lyEau4XtZUjI6\nLOWQiSRzHCUZwMSA7NdDNngsMVi7RtoBs+QinsaRLYlgCdsUpYaHTrSKfunL5FyPNsM0P1Z/a6t4\nrfV9jLvAcqSp25KLjiOQIvoKEEvcOVJmN1CHFFKWl1Z8WUpsmE/vgsTpuT//rdO+47HNoh+nTtd8\nCEvlys2Vol/+dsTHD77zttO2ZY7r/myb037tL2E1fPd3HxH9Hv/mj532js9udNr9F6TFeoGVzh1N\n+snOPdzYL14rImkAS2BsyU7aQtjQp8ViLU1Ikqm5mYtoDi/CuB2ol+nBiZQ23kv2x3lbkHLbebRR\nvIfnDktW6l+7KvpVPAz5Eqd5d34gP88/H/sAlx/rgscal9NT+F62nbTtrNMW5ZjbSSztE/qudskX\naXxOUgqzJ1vGSk51rn0aUsTZd0upUC5JUNrOIbZlz84S/bqOQV5b8ghiHVsen3hDSmKyU2E5y2no\ntgR09ZK5TjvFg3gwHpGxl39vHlmB9lmSiAX3L3LacbQGs224MVJSGm1YasSSEmPkHJsg+2xez40x\nJnUu9gW8TZm2+nGsTSX7XVt2yhKlvisYV54szB07td4fxDkLXYeU2F8m16OBRlyDeC/2Nu37pbR7\nrBf76YzVkJj4iuQeNY4+Y4jkw7Y81ZaERxu2b+84VC9eYwln3yWcz6EGGXvzd2O/6KWx6kqXUhdh\n+Ux79K4PpYQogeIRx4q635zH9wRTxXtYMlewHNe0NCDXxdBp2MsHluIaJ1nXp+cw4kHmeppXlswn\njuXZNIg7jzV9fL8oU7sP90xBkp4YY0wC3bf1nMRvGrgupUIVX4BsmecI338YY8zhH0ESXlaBtctr\nyX3r9kECVEjXo/M04umCuyx5Lu3j+wfQrtoir2ESldiofRL3JsEtZaIfj98ckgyzdMkYY8LXsG/m\n/VGcV96mVz0k5evRJt6Huc/x1Ri5rvO9Ht9bGGNM5xWsUbnVkLx2nD0v+vH9Y1Ie7q3ifXIfFJ+E\n+F33DN0HtiAGuBNlzPIvRvxPpvHXe6Fd9GtuwHVIiMP8GLwp7w3WfmW90w6dwfy114mcDXjOEefC\ntRtslfHKFZBxyUYzZxRFURRFURRFURRFUWYQfTijKIqiKIqiKIqiKIoyg9xS1pS1ruhjX0uZjXTr\nc8+dcdqlS4KiXy+n/1BldJa8GGNEGjE7JdlpVTceR0qTm1KMuyjNe+6nl4j3dNUfc9rhm0ijC12R\nqfApJAOJtKDae5aVphufjPSpIernzZPpu4NNeM1fhs8eH5Ap7uyAcTuY9SmkwY0PyO/itNZkuqYj\nIVlxnFN1+TOyl0tnj9AVpCx2HUFK5eSQTOPNpLE1MYjzwVWwO8MXxHsCs/FdLA+xJSzs/hSphywu\nIVX2S6UK9ymz0R5qGxD9OH04bx3LJWQ6m7/q9j3r5DHDMiYbP0nkhhrl78hci/TmtDGkEQ93yLTs\nvtNI+4txfXS6rDHGnHgXKYpzS3E9OZ3wzEsyBT/xXaRfJ+Zi/g6NyDmRPI5z7smDrCd0RcoPSj+B\nivdhcgYabpa/PURprFmrcazenVJi2G/FhGiTQOnittND7kaM7+P/sN9p94Rltf5d397rtCPNOB/h\nm1ICyinbqVUY38OW/M6XjZTP5/7i1077/u/ge2zHuvYzkGZkpJC0ykorfvkvXnTaO761w2nbrje/\n/EN875YdcDcYjcjfNEmSmHMvYy1Y+dha0a/1DaRYF0dZ4dRHMcXrk1Kc3nOYO/65kKwMWTGKz5Mr\nFfFq1Iq7LB8bo7ibu8pKk7+KlPwpknfwe9yWDNOVgu/lNPvgLvnZLHsJrEQKed3LV2S/o4j9PN5s\nYuOx1rPEZNJy3ApflWMu2tQcgYzQluPxGsJOESmlUirKsiyW7l59Ua5dC7+A1O4JOp/pi6VsRTgC\n7SOZ4zKc9zmVQfGeWJKceHJxje/55g7R74Ofwp1r5QNwb0v0yzFc/BAkWexoZUv4stfCkaP9QL3T\nLtw1W/Rrehkyubyv3W2iCctVw1aM4uvmIWdFl/V7+ZyzU+HIkHTS4jHd8g6knN58uYawJMhDe8KJ\nYXyeLfViKVPoFNYqWy7gIVcU3nvyPs4YYwpoz8rykHjL0SqVxnOI0v1jrdIALKu+HQx34VplrZGO\nRcPtWP9YWjZu7ZvFHomuty17H2rF5/FcTLDOtZ/2h95MrJFjJG23z1NgIcXHJ7A/Sr1bSurZ7TaJ\nyzg0SPeiIDn6RJrwWoJXSjg69tc7bZYwT1iSRW+RlGFFk2XfgEuqLVuuewrxkOdH5VeWi37jEVzD\nUbq+w5ZjUTAba2vaAuw3fYXy98Xtg5Qwlu5hyh6CZNTeN7Eb3pxHIN1sfVW67A6SrK54D60flutX\n9wnslXi82PeLPA76LmEfas/teOvf0YbLTESaZcznEgOeHKw1MfFyHvjyME/bL+P+m99vjDHDNBdj\nSZLbQeuJMcYklZMT6zJI/lPIrc92MRSus+TslmyVeygqhaw8fydk+PZc5JIRRXS9e617Bo6jPB5t\n162s5dLRzEYzZxRFURRFURRFURRFUWYQfTijKIqiKIqiKIqiKIoyg+jDGUVRFEVRFEVRFEVRlBnk\nljVn2C4qc62su8LWhLPXw8Iu0bKH+rh6Kqz7NMaYqTFo0YabobvnehPGSNvDmg+hyc5Jg16v+SVp\nBcr2krEutAu2lop+7W/i81xu6PpCZ9tEP9aWD1xFrZvmg9LOsHAjPn+wEfo1tng0xpgbr+N45+0x\nUWesD1q8JuvcxPvwO1nXx+fMGGN6z0NHx/USWvfLz/MWQGNXsAvjYrhD1lxIJHvDBLJui5A1WlKh\ntBWseQL2eWwPyRbK9ndxvQS2iDPGmEmyc297Axry4gfnin6tb+M1rkUQa9kSdpMNau6X7jLRxEta\n66EmqQNlHesU6cszVhWIfi1kc+8jTbZtBTdBtR94/iVYVreb/gDWclxrg61Yy+dK/TiP/ba3cDy5\nK6T+coquDY+pJLLEM0bWFAqdYnvKXNGP6wpMkd1lnKVH59pDtwN/FbTSl390XLz2/uOoCbH1S5uc\ntjfbqk9wGeMsoxp1arx5Um/NFq+sU+44JC1DfTvwm7d+foPTTkjE/MuYmybec+kF6Ol7I5hvhZbF\n597/+ajTnp7GuOo8LfXbcbFUZ4xqk3lTZU2OR//5s077zPf2O223te6wFXm0KdwGq8y+87JukH8e\najkNUd2jkbaPr3fF64ttD8tWnnMehv59oFmuSWP90OqPdWH97DyIa+0pkLUxkmhepS3CfBlutWpu\nUcybIs141kJ5bbieRTPZ0maUynoLSdWYA6Lmg2Xfa1uIRpuiMvxmrqlmjDGhY6gT0BfBNWg7L+01\nZ+9FTYjSe7Fu9Fo2qQ1PouZCYhbGqq1r55oa7VfxGRzj3Zadt59099efxrxMTpd7p1WfQH2HlvdQ\ni6HnaIvoxzUruG5LzloZy1vfQfzmuD7SI+uTZFrrUDThMTNq1Rzg2oW87+u36pYZWhs8VD8msULW\nIOm7iNoCPqrdYdeAiKNr1UN7ArMQ32PbxvP6addEYDqOwL4+Ywnmn13PgK9Nwc6P34cN1KCOQirV\nq2NLaGOMifPc3joXYv8wKGv9JFBNrpEOxLbCu2VtIy6Jx7W7+LoZI+u9sP16zgK5L295i/Z9qRRv\n6YvcWXIu3vwV6qDxcdvW6d5CjLPOo6jNmGHVoGJCxxB7fGVyPebYznt6uw5HQorcK0eTxucvO+1E\n67z0dmNNKa6E1fDkmDwvN6lOT3IF9nqDdTJOjo/jffwZXLPGGLm+cGyN1JOF+gkZ/2KpXk7BnZg7\nxZ+w7gtojoVO4tpMjUyKflkbETd9+bRHs2o4nvpn3N8s+OIKp23b2Nf87LTTLvq/HzDRJiEFsWli\nsFu85p+DtZtr1nUekXvKAO0NuOaTz9qDhGje8zOBzNXyfmCkG/OZn0twHSGPtS5yjb1ksqhvOyDv\n010BWu+oHqNdT4rLKPWcwzHYsZzvM13JeM27RO7jW97F+Ml+xPwOmjmjKIqiKIqiKIqiKIoyg+jD\nGUVRFEVRFEVRFEVRlBkkZnrayq1SFEVRFEVRFEVRFEVR/n9DM2cURVEURVEURVEURVFmEH04oyiK\noiiKoiiKoiiKMoPowxlFURRFURRFURRFUZQZRB/OKIqiKIqiKIqiKIqizCD6cEZRFEVRFEVRFEVR\nFGUG0YcziqIoiqIoiqIoiqIoM4g+nFEURVEURVEURVEURZlB9OGMoiiKoiiKoiiKoijKDKIPZxRF\nURRFURRFURRFUWYQfTijKIqiKIqiKIqiKIoyg+jDGUVRFEVRFEVRFEVRlBlEH84oiqIoiqIoiqIo\niqLMIPpwRlEURVEURVEURVEUZQbRhzOKoiiKoiiKoiiKoigziD6cURRFURRFURRFURRFmUH04Yyi\nKIqiKIqiKIqiKMoMog9nFEVRFEVRFEVRFEVRZhB9OKMoiqIoiqIoiqIoijKDxN/qxdO//menXXvs\nhnitd3DQaa/ctdhpT49Pin6e3GSnHedJcNqXnz4r+i34gxVOu/39m/i8iSnRz52T5LSTiv1Ou+1t\nHF/68jzxntSygNMebBlw2i6/W/SbHMWxxyXi1Iz2DIl+R5465rQXb6122pHaXtGvaO8cpz1Cn9H6\nRq3oNz097bQ3fvvbJtq0Nr7otEf7hsVr4Zs45t7T7U47e1NQ9Itz43zkzV/rtCcmwvK7Tp5w2unV\nOU676fXrol9gSa7T5mvc9hau49CAPNbMRbiuiRlep331lYuiX9Xd85y2Nwfjb3JMjs304iqn/f7f\nPI733ztf9Os60OC0q7++x2n3NcnfVPObc05723e/a6LJlfd+4rQv279311ynnVSIOWH/3pjYGKfd\ncaDeaQ+2yWtYsLPcaY92YZ4Pt0dEv0gT5pKb5lJHS4/Tjo2Vz3/n3L/AaV985ozTLlpYKPpN07RP\nX4RxNNY3IvqN9WKM+IooHrwl41V3d5/TLpiLcZSxQn4vn5cVX/tTE21qj/3KaTe9ck28Nj6J6xWo\nzHTauRtniX5917qcdkwcrqknO1n06znditcobvYcaRH9sreWOO0kOof9Nd1OeyIyJt6TVp3ttIc7\nMS76r3SLfinl6U7blYoxEjrbJvplLCtw2i0UK/J2VIh+4RsYW+HraI/2ynERG4Pzsv5v/9ZEkyPf\n/47TTl8i15rWtxDbR8bHnfYUxXhjjMldkO+0k4pTnXZCilyT2t/FOM67E/NyYnhc9OO5Hb4RctrN\nxxuddnBjqXhPAl2P6UlMOG+uHEfNr+F6hDsQK2KMpOwhzO2uI/jeeG+C6Oejdbv3LNaciQE5xjLW\nYm5Wbvy8iTY3T//aabe8UiNey9uNc91Oe4vAygLR79qrl5x2fjWuac+1TtGvYBPm8BDFTTMlx4Un\nD+d+4BrG9zTFcm+JX7yn+STOdXZ5ltNOmZ0h+nXuq3favlJ8RkJKoujnzUtx2jdfvIz/98mxGevB\nniC5LM1pd51oFf2Gx3Bdd/3935tosv8v/9Jpc4wzxpjWWoytOfdhbI5ZeyC+Hl11iF9Jbvl702nP\n0kd7pfQV+aLfeHjUaSeX4LxceOq00y7dUC7e48nGscfEY80c75dxrfbNq0571hbExliXXGfb3693\n2nl3lDntwcZ+0W/gKn5vKII4XrV7ruxHY3HFV6O/LjZcedZpNz4t9zeG1rj8XThvvRc6RDdvAcZt\nE8XN+Lg40c9Hc8yd5XParjR5vcM1iKOZq4voFczZriNN4j2ptG53vFfntNOW5Yp+8XQvNDWO2Osr\nTBX9+i7hN8bE4RqnVAREvw663mM9GN8894wxpupzS5x20ewHTDRpqnnOaY90y3smvsfpo+tWuKtS\n9JuaQJzru4wYGpMgxzfPl9h4XN8p6/6T4TVyuANjfdpam725GEft+3AvWrB7tujXcQj3BbF0fO5M\nn+jH9728v+o+Jfdhw61YWwto3xNp7BP9+q9g/7f8y39ios3Zp//Facf7XOK1wELsd3jPYGLlbqDv\nKq5dyizsAVvekve+Ebof53la8dgS0W9iZAKfTeMilmJl3zkZD3K2Y7/Tex6v+YrlHOs9g1g+3o/Y\nPTklnz1kr0MMmBzG8SSmeUQ/bz7Gj6Gx1X1Krov8/KJ81WeMjWbOKIqiKIqiKIqiKIqizCC3zJw5\n/h4yAVZsWyBeaz2Dp378BCjBetLGf3VLSMVfaOY8ID+v7W08UfPk48l22wn5ZHq4Dk/NiobxlzXO\nUnnzH98S71m6Hn8F6KO/tuZvk39JPP3sKae97g83O+2xfvkMy5eI35FIT0mvHZR/fUtrQFbKxCD+\n0pmxSv71zX5iH21qf4K/2PQNySfa2SV40j/3G5uc9pV/2y/6tYXwWyINeJJbtG2x6DfYiCeh/Ffg\nUSvrgp/0B+gvT7O/uBHf0y7/up5I2RndZ/AUctNffVr0C9Xhr0ueAP5ydfPpk6LfxXr8Oy0JT+Jz\n5svflDkXT/dHRvC9g80Dop+dJRJNGt/B/LD/Ct9/CU/Sa+gva9UPy9/B2WChRvxVqOwe+Veyafpr\nLv+1hv+qa4wx05Pol0dzqeMHh532gkeXifd0n0DcyPDjCTZnvRhjTO3rV5x26yWc88R4GbLy1wad\nNv9FId36S1X6NP59/U18duqcLNEvJt7OB4guHfvrnbb9ZH7e11Y57Ru/RGbh+JDMkuCMkbzt9FfR\nFjkek4I4p+3v4q94udtlJg5n37S8gXg90oGsqelxeawZizFnO+izSz4ls84u/geyDNPL8Jf8zBUy\nBkaaEFM4C6bpt5dFv+JPIlOR/+I/bWUgJBenmdsFj/UhK+usmNah2AT8JWiEMtCMMWbgOmUl0fX1\n5cu/6hTvxdycGEG/XivzyE1/SeRzkZ6Dz0spl39t7TqKtXUshHPO89oY+dcp/utW6SNyDR+hDCqe\nV/WvXRX9stYUO23OyOQsTmOMCdciRpmNJuq078O4TaqQ46XrELJRMlZjn1H3uvwtcz+5yGkP1uP4\n3S65D/q4TIv+izLDpvcU+qXMw9ps6K+UIvPGGJOeiWvM2SP81z37ewfrMd/s7OTwdcoYmI+sRc5W\nNcaYhnc/es+Wu7lE9Ova32BuF5yx03hW7hUrKfuDM//66BwbY4yPsvsmKHvRzjroPN6M76V5ameG\njbQjJnDmUSAZ5yhsZRh2n8Qax3+h5QwLY4wpuxN7kUY6/7kri0Q/3itxdmnbOfnX+rI9iFeh57Hf\nF3PPGJM2P9vcTrqP49olz5EZXxy3eK2y9wycCZ+/Lui07b/+c6YE74narIz2wErMl3iOUzTPI9Zc\nHOtBHOWM1JFOGf87TyB+J6Tg+Dy5MvuLs2pGmjGues7KMZxIv5EzNkt2y8yUAb6uMhHk94aP1ZMl\nf8cAZbwW7MQXN/z2kuhXdA+y2dMXYM/We1lmRXR+gJjCmWFxiTJLKlyHa8WZ35x5FO+V4yPRj0yI\nwDKMAf4NxhhTcAeyuDgLJskal62UxTXcKscLk0nrTN912svOlXNvsElmv0Wb9IU475yFa4wxdb85\n77R5HmWuk/FnilQo43Tv682X9xDjvO+gdai/Vp7r1HLEhCG67/JXY5/BzwCMMWaQznU+7ZOv/vC4\nPFb6Ha4EzPPszdY+mbKDWCXDMckYeV/oK0QWjX2f33uO5vAq8zto5oyiKIqiKIqiKIqiKMoMog9n\nFEVRFEVRFEVRFEVRZhB9OKMoiqIoiqIoiqIoijKD3LLmzNK10OxyBXBjZO0H1rRyhXJjjMm9A/p8\n1s+OhD6+mnftIWg/K3dIHVkbuam4SdfIDh9bvrRRvCfWBR1i9xVovGtfuyL6rfj8aqfNrhu2Sw1r\n1FhLOv++haIfV9ofC+EcXT91U/SrWkuOJCtN1Fn0xw857a6a0+K1sQFUp/7w715z2mv+7B7RL+si\n6ulkVJNryISlf6Rzc/UZaJjnflpW3+b6BD3HoNdkvWfY0ngmkAY1tQIaxGu/eUf0yycNamws6lLM\nffR+0S+vFXU9WDM6PCg18pd/gLoZ5Z/BNQ6u3yb6XXrjn8ztIm816jSUkyuBMbKOSyAALTxXKDfG\nmDFyDOM6M92W40C4C9eGHTqyNhbLfuTgMErje+4DOEdt70jXpJyt0HGG06BnTQzIiufzv7Dcace7\nETe6rAr37N7E+tNLT8hxnlkEjTHP34khWVcgc6V0b4o2rIuNS5D66PaDiJ3jdFyjvVadqI3Qso9H\nMH/b3pNxJZfOdXI5uakcbBT9MtZCL+yn2gI8F+3PZq144X3QiXONImOMKXsAzmn9VGW//5qsucB1\nUgp2Ix5yjR5jjBmjmNpCLjrB++U60UdxPl+WwPi9uf4E4kZyjpyL7LTCdkZ2zYaxLsyXaXLoGGyR\n8fTGK1ij8lbgOvVamuxCckRgtxguT8Wx3hhjMpaj7k/Ti6ilMtQq6+hkrce8Z134+Z8eE/3yyYFq\njOoGle6tFv3YuZC/K2S5r5R9Sq6n0SbSQ3UgZNkVM041SgZorI5OyDouTAKwAW63AAAgAElEQVTV\nvIhzybnd0wMdemZi8Ue+xxjDRjCifkyE6vHEuuW2bXoCmn4XzdlbaeG55lhwm3QOar/KDjH4jO7z\nss5FHNVYS0hC3Qa7hk2/Vecumoy0Yq2KiZG/d7gd17fnIo7d7ZXuVKmV2Et4LqAWiNct+7VS3b2p\nZlwob6GMAYaOo49cTefeiTX3jScOiLfsfmyr0+b96kiH3HuyE4iL9uA1+6VzJNc86mrDcds12xJ8\nWFvnkkvllHUNe47TurvWRJ0RulbhkPzNXDNmmuq09Z+T9ZryyMmp/yJqdiRXyVpb/RfwWt6duD9J\nXShjtCsdc6mL9ljxdM7clrueK4B/83VkJ1hjjBmnOJI+G/vaOJe8Pv45qDsVQ/WfRq37p/o34PwY\nKMV4TkiWY3isX64B0YTrvdj1WdipMXwTsSc+SdZU6iWXH65bw66rxhgz0onfP0p7QLu2jzsTdbJS\nSjEOmul+0Y5XXnLzYSeeKesaspuli/o1vypdOOMpNvqC2IdZ4UrUecpYirV0tFfee9t1iaIN7814\nHhkjXQO5ppJdY2iK+sXSGhK5IevKcV2nnqOYY74CWXtvkurt+edinz9MNf9sh7C+s1jH2Mmp2KpN\n030MtcS4huPNX0hH6axNQRwP19HJk/E/vgzXO1KHa5pSJuMQ19H5KDRzRlEURVEURVEURVEUZQbR\nhzOKoiiKoiiKoiiKoigzyC1lTd3XkGLGqXLGGJPoQjoapx2mzM0U/WqegvVW+UNIm9z3Y5nWeccf\nbnfa7guwpx610tSyV5HdGNnJTZBtpJ1i1fQSUrbZepetWI0xZqyP0sfYJsyyvr7xAqXIkpX2UItM\nB5/g9HJK27etkG3JWLTpa4MEwZYJzH4UluGhU0jpHWiRUpeJQaR5d1+G5Cs5KC1IEymNMO4anv21\nvCxT/TxFSAWrenQXvrcL8qnAojzxnhqyBPcV4/05G6VuIYbSrSMd+E3HH39V9CvajJTW2XdDxnX9\n1ZdFP05dZbvi2ASZ9rbsC6vN7YLlOxHLcnaiD+OMU+9sG7z4ZMzTRkq9zFwqz3MCpWjmkKSh53Sr\n6MefNzGMND+29vYvzBHv6T6KFMLcLSR5tOxIb/4KkrjgQ5DGsCWqMTL1v+E52JbOvkdKKTil39WE\nsMcW98YY07Afn1EinYKjAp9Djh3GSCkI48uVaZMsxeJQUv45aZ3eS6n8qVWIy4M3+kS/ulchncma\nhxTrIbomSSXSHjImAXOMj4dtl42RUtZkSuuctmJg02tIM+bzkrdDSi7Ygrr43ir6f5lG7bcs0qPJ\n3K+scNq27XS4AXMzhaRGIUsSwinBbOfKcdYYY2Ip95mlI6UPyPHNttCc5s1Sj5aXZAzOXA+Z1Nkr\nkIhtWbFe9Ot4v95pJ2bj2rgsiUTTWcztrHz89otPSolh+TbYuw41QsbFFrDGGNPyGo63uMpEnSKy\nQo3UyTmRVIHjn6TYFkiTc7GPrLATKB7y+40xxj2ItPyzT59y2qleaU+dlIbz2/0h1uDUeRjPnjyZ\n4s+22Cz7a/1QynP9RVirs2men31RrmPzd2GfdumNi0573l3zRT/e0/D65LLsbFnuFm38i7G+pCfI\n74mQTCC9EvGv/ZKcixMvIP6J+dIj18+q9ZBbsuzHVyhT8NnqdTa9Z2IEe9Sdn9sk3jM5itemxiEJ\nsMKkiK/hEZzznFyZMs/W5qOU7u8LyjgeacDYaTkKuStLKI0xJrlCfn60GezDMeZa383SFP799t6T\n5TI+sli39xbFD0DW0HcVe5VxSz7iJhlR/UsYI36Sx8TES21KLEmwOZb3XZHykIJtkE+w/LXljRrR\nj+dVzhZaMyxpY1oR4k2EYmrWWilF72ep+y4TVVrexH2BLalkS2qOmdOTUlLEUnkv7XtYNm+MMXm0\nd+TzPNol7xfFZ9PaXPoJyOYnJuR7WObUdQIx2JMr4647HeOSZdS8fzbGmAz67WMDuJ62TGqEymeE\nzuG+xVcs5+xo9+2TiRpjTBJ9X/dxWUYgYxnuFcZJJt1/VcrUWerZR3L2SJeULGZnYEzznO08LNcu\nvldPITkQ30/k7SgT7+G9RT7tI4e75fUOkzV57wWsDbGWNJnlaYk5fN8v7dG9tD6zNHKgTq4nvny5\nbtho5oyiKIqiKIqiKIqiKMoMog9nFEVRFEVRFEVRFEVRZpBbypqyFyEd69IHV8VrKz+7ymlH6pEu\n5s6WkqKcFZAhXf8NpApL188V/dr3wQ2ksRYpXROTMvVLpJ2GkCa0ogJpS1d+dEL+DpJCcRrdzdfk\nb7rRjpSmokykwSaVyhTlhWuRYz1CaXRvvnRY9KsuQnpmyTo4pxT3yJT7lKpbV23+fWn/oN5pp1bL\n7+5rQDp8MqWVccqyMcZ4ySEoEER6c0/dedEvQI5Kw+TEkW2lV/ZRRfDBPlx7Tv9Pr5bV8+d+A6nA\no2Gk4yYmy7S/xrdwTFX3Pei0+1fK1NLzr6DfUANS2zprZb8Vf7Lbade/chLH3SSdVSr33m1uF74i\npMDZzi8RSmtnt7SST8k0dHbSYXeqGEuKkkEyp7onLjhtlmIYY0zDMaQeJpIUhR1IBq7IdMecTfiM\nmp9D7pBSLudY8YOIDzzn+TwYY4S7ybz/AseL0NV60a2b0kQ95GQxYjmxBR+UcSnacKolV+Q3Rson\nCvdA+tG+Xzol5WzAOeRU4vTFuaLfwFWkCHMKakq1lJ7G0djPXAEJJ6dY244GOVuRVnzm53DtGbfi\ndfUuyG+uv4nU8FnrZApq/jZ83pWXaMwVyNjoJhmbO4C0YluuGVgkz0U0Ydcj20mBHRdCxyFhC37S\nkiFRTE5fgPT5yTF5/kr2fPRac+jHh+T3DuM4AslIq527Fe+3XQff+9VBp71gFjmAWa5OQ/S94ySh\nTAvKOZtHqfphSuGNt+TDkTrsFzhWpPRKiU/RvdJVIdqwm2LPNcv5hdzxpmnopy+XEtCG9zD/2AGv\n35IxZK/D57FUu2DPbNFvqB1rZgLJxSdJEmM7WbCjCLtXNHTL2OtLQr/rFxG7LdMQc/41rItFhVhr\nGt6Rkgue6+mZOCZfoVzrx31SqhdNXCQ57josXej6u3Au+NrEeaTMJXwZ54nX1oxxGfNOvwuJlzcR\nY3peQI7b8RDGVQbFU3aSmbY+e5QczC4fwXmuXFoq+oXIdWSQZE0JIZmCf+wCJIFLZ2Hv+f7po6Lf\nYpr3gVmQ64y0yVjxO4MkymQtRLz2V8k96uQwxs84yZDrX7sg+iUmkISW9hNeS47CUq72DzFmKix3\nOJZWZNN9DGvNXKkytvGaG0uSJ/9sKQtjWe/FFzHfZm+S8YD34bx/6/pAjvXhfsR/lqK3vinnLDta\nRZvcLRhn/ddk/OOSERkrsO8Zt2TqLCXpJ6lQ5nLposnrS3oFvjdhiUv0G+pADGDHp7Q0lCDo6nhb\nvMeThOMr3Ih431snZcEZhbDWHQntw/8vkfu6MN0fdx2GTGpqRK71Bffg2neQK3GK5eqTueL2Oory\nniZnY1C8xpKnG7/E/fzklIxnIy3kokcSt9mPSun9tV/gHoBlzbbcd9ajmJsslc/agLGeVbpCvIcl\nTywDTLVKtJiddNy014n1yMcj7JzHe/cey0G27TzGLd+DdZI83JjflWXaaOaMoiiKoiiKoiiKoijK\nDKIPZxRFURRFURRFURRFUWYQfTijKIqiKIqiKIqiKIoyg9yy5kykBrq+6o3Sy5ItwYRFqlWr5Pzz\nsGlM86EuxamDl0W/lbsWOe1FZD0WOtMm+rHtNFt59g9Cszs8JnWMtS+gBo2fjmH9H28R/UrIejhQ\njXox5/5pn+jnSYbO2Z2HGghV+VJrePQ67GHL7oCeMKlMas2OPH0cn7HlCybapMyGxi61VGpfm9+C\nJjW4B3rApndlLRl3Fs5bxxCsQNPKgqLfjac+dNp5d6AGQbxHakEzl0GLnZZBtnZzUEsh0ixrurAl\nImvz7TF36TC0of657zvtc+9cEv14nKRR3YeUSqlJrHkCx9R0EzVx5myXNRHe/PN/cdp7v/99E03Y\nRjfBK89lajmuKduhsx2fMcb4q/EbWVNu2xqPk31j1iZoOq+/eFH0S0vG2Gcb2bpDsOW1tai+YtQm\nSKXzHLosaz40n4eOk/WnbC1sjDFDo6iBEUsWrj2WBeCcL2GMjZCVXscBadk3cpttCplIo7TvZa39\nuX/DPMqslDWGml7B+J4cgk531LIITMxAjQm237XtyAvvQmxy+6EpLnsYmuoL339PvGeELBFL16Au\nwmCDnLOsqy2ogn7bRbVGjJE64rxyjNOwFQMGOsnKnjT9+Zbl9tWfIUYVfvd+E03Y9tZj2aGHTmDc\nsd1izU+knfQwjVuug9Z4tkn0K1oIfXm4Ftr1xbukz/tbT6J+TMUKXA+uMxNjzZ3SHJzn/gjGTp5l\n1eyieBMK4fznZEtd+MUfofZQajZqlNnWojUXUUNp0d3Qknvz5fd2HkFdhQJZeiMq9J1B/Y7grkrx\nWtdBfHfKHMSpm+9cF/0yClHbwkW1dRIz5bnpOorr6idb7IanZEytaUPM5uvDtS281vXhWgoJyei3\n6q4loh/Xyrh0FHuiijxZR2eQxmbBCMXoiKxDsuhe7Nma30PMb39b1siKT5Y1XqLJmSdRA27B3kXi\ntQyqdcB2u4OWbTpbuPZfRK2M2Fg5XxZtRt2aKwcxDk69eEb0W3o/zvtQK+ZL3QGco4JFsm5EyznE\njWQP5kvbZbmG//df/MJpf++LX3TafUNy3Vq3HvHhG9/5d6d91wpZlyFzI9b30EnUyLLHL1vC3g7C\nV7H3HmqS1rSpczFfvPmIKzlWHRKuh3H5SVyT7DWyBtIwrV1L/2Sv0+65fkX089P38rw69gPsBys3\nyxoxXDMsqQTjyt6PxMTjGNxUKyc2QdYOYlv1TqqrFlgh7zXGKF71HEatjbSlsvbaINW8M7Ik4e/N\nGNXwGrZqFnE9PN6vs1W4McYkz8I5S6RaTknps0S/kR7UG5qcxHf1WHveeKrbNT2J742kYv4memSN\nI85Z8Hhw3DGzZDyYmsLeK70cx9d+Ut7bJvoxd3LvwEIWY8WXgeuoV8T3TuNhWQOOx8TtoPsQ1qpi\nq1Yex7OCPRVOu/VVWdvIU4g1iWPJmR/JmldVezEIX//hu05755fkvfmVH2O9Kn1wHo6H6n6eO/yk\neE/xXtyfdVBtKZ7LxhiTtwrHEMnA3Kk/JNexbKq/0/Q8YkVglZyLbLl++t9QhzY9kCL6te9HfdAi\nGUaMMZo5oyiKoiiKoiiKoiiKMqPowxlFURRFURRFURRFUZQZ5JayprERsraqlParNU/CRislB+k6\naZad67y7kDLEKWYpp6X98YnXIX9asBoSqsMfSru8XY8h3Sm5HXKOl57ej2O1bLj2/g9YHHefRHph\ndv4dol9WHtnWxeC5VcneDtGvoBrv6+054rQDi2V6cMIvcXo51TD48DzRL+14vbmdpMxC6vWJ7x0Q\nr8377FKnffi7rzjtRY+tFP3YXu3Kc5A8ZebJNHxOx0vKIGnY914R/QrvQRr5wf/1XafNUrDH/uQB\n8Z6n/+w5p12UgXTrqj0y9a6zH6lubW8hlTg+Vj6L3PzFDfjex3Edt/6PnaJfcMUepz2rD6n73adl\nqmphpbz+0YTP/wTZSRpjzARJW1hGVHekTvRzn8QY9HkgK8neJlNGmd5zkHFNWfInfrTLFtHFy5BG\nnJAi5SuX34K0rGwZ2fdaFsyJJFnsGkAqpS1Z7Kd0bv9JxJTkCinfC13A72D7TFvqkeW9fSn4xhiT\nS5bRQy0yfXuwDf9O8iIVNn2BlDX1XkA8inMjDdq2LL56EKmmc0n6kFQiLZDbybZxuBHHUPQA0kIL\ndlXwW0wipbnPWnuP0x4b6xH9+PzmfX4HfkPvcdHP54MsKc5NNtGWPW4eWXi3vY+0U9u6s3D77bMM\nHbgO613/XHlt2GK8+zilly+S/dIppdmdDXlgcZyMUVOUJs9yy3qSSBhjTBlJYE4fwByrKkMMHgjJ\nVPN5j0Hi0F+L33T2iZOin49sg5PcmM8eS4bkboHUtPRTkMi63UWiX8EduDbX/gPpyrM+I6Vaoz3S\npjzaTFF6ff1rV8Vr5Z/EvoVjYNHqoOjnK0LMGSKrzRhrrak9gVi85r9uctocu40xxtOD+ePJhizO\nT7Lb8UEZAxufu0z9MM7s9O3+y5gjhbR+cnw1xpglZL08PIKU+vSkJNGPrbUTKF4ba53o65CfH02q\nd2MvNdIjpT2xCbgGkRuQBEZ6pfwzdzvmbBxJY9kW+f98BuRQbDGesVrKa9IrIdnuOI55OvchyK5C\np1rFe5b90Uan3Urn1WVJAp9Y+ddOu5MsXOfvlTbQvNf+6Y//3GnHJcotP4+Jonux7259V8aX8DU6\nF3tM1Mnehr2Ay9oz9F/FMba+hv1hqEeOK9dx/LbyXVi7EpLkPBgj2+nmQ4g/WZZMqv8GfvMoSZA9\nLpzbREvWynOWZSo2lw5CmryEZHAuaw3n2JO5DnGULaeNMcZFcz33TuzB7bl47Te4z6re/bGH9/8J\nH0nOEq1xy+Op+D7IA73Zcg1h22VfDmLr+LiUN2fNxjlrPPgBPq/Ako7sQ9xNIslUcgHLF+WeL9yK\n93R8AJvtwHIpX0kNIgYkJWHuJPhqRT8uzZFUimOw9/E8Z3nMjg1IWVO4DrHMyCUzKuTtxvrc9KIl\n9ZuP39z2DvZf2ZuCol/fWYzbFNqLz9pYJvpdeAbyw/XbsWfopfcbY8yNDux5m/8DEsgld5IkqVvu\nb976zhtOu3I27kkGh3tFv+zl+L2DJJOafbe8r+S55F+I81CwfI3oNjSEsV5Ia1L2mqDoZ98/2mjm\njKIoiqIoiqIoiqIoygyiD2cURVEURVEURVEURVFmkFvKmhLikDo3aqWMJgWQvucLwoFl3ErBivPg\nK979EZxzusNh0S8jmSocH778kf9vjKwoH6Gq69vXICUqf6dMwTeUWs+pY3FxMvXuyns/cdppc5C2\n5MuVqXKTk0iLnSDpV/+1btHv5A2kN921FxKa7lMynaly11xzW6HMxuxyWZm8kyQe8x7BOYx3y1S/\nSBhpnZv/+jGn3XL6sOh3/udIEy3fhfM0+8vLRL+BWqR8rvzKOqfd/l18z8WXpGPUzq9sddqTwzjv\nf/Ot/xT9/uKv/sBpP/mj1532o9/aK/px1XOWMsXHy+s9Po40uIyMzTiGOW+JftnLrHEXRcLknMap\nkcYYk0yuADxPs1Jliuz+51ApnSVKd04GRb/eM0gpZHc0W1JUQin07HziK0U6qttyfajahvRPTuv0\n56aKfkNdGDtXWzBfdu9dJ/rxfOYxceh5KZtZdz8kHMV7IKlreEXKGcI3cZ6NNLaICu3vI2XWkytl\nAk0HkCY694twl6r5hXQDKX0EuawTg4i3dc9LlwA/yTvZ4SUpV8aAyXk4b+yi9Mx3XnLaC4NB8Z41\nf4EYkJCA693Tfkj0yyuG5Gl4mGR1Ppne2nAU6cM8L7PWSqeNw/8KWWbFSnxG+gIpp+29KKWo0YTl\nq8OdUiLRua/eaRfci3HG6fjGGBNL62L3CaxpGZYLx+nXIB9u7Mb6cs+nN4t+z/7iHaddSe47DY2Y\ny2u+vlG8Z7SXUm6X4FhZBmuMMa//HdKDl22CjMRXIOcsMzWFazg6Kh002O1qimSYfVekY5sty4k2\nvhKM294LzeK17mP4N0tLJkel/JLHavdBSHx9s+S5qViDscpuJcMtch8UHoF7yb/+GvPvG/H3Ou30\nRXKsz/oMJC0s7al7RjpBeTIQD3oHMW4vNkjHuh7am22qRmr3rN3SsbPpDYzpfJLzJVjSjA5ymYk2\ncW7MI3YmM0a6swg3xirpxshOiIVbsE+JzJOSbXbiGSOZccmKu0S/zibsc/0kRfT4EXfZnc4YY/x+\nfG/CTozLseGQ6BefiDWDXUFsx0p25Ou/RHK2PdKVzJ2Jz2PJky2nqrsk50fUoeMP18nfzHuaVJJV\nxNdJ10oXj2+SAwWqpVwphqSjScXYO41bLoYRcvW6cRxrc24m4uP0uHSj5Ovgr8b1Hg1JiWZFPI5h\n4Cries4mKTHneCM+u0yu4SwPYifdtjelxCZ3aYG5XYQuYc21pWR8DxI6j/XAdqfKmIc4OdCMMTfY\nVC/6TdF5jyUp1EiXXI85FqWQq3DPJXxe0fJt8lBzcc7HF2NMpBTLuDHcg/ExOoD9ZmJASt0Kd2PO\nsVPYZNKE6DcewV6Ona9sd83E2+ycZp9Dhl17S8jJqfF5KX8apzX+zDNwqqxcL++Riudjbn74LiR3\nLGMyxpjlZRgXpeSkfOYtlD1hybUxxrT24r7NQ7GiME/Ondb9kIH3XUCsLPu8dP/jkijz937NaYfD\nct/Na3DOOsg1J8fk9eZ7JiOXEGOMZs4oiqIoiqIoiqIoiqLMKPpwRlEURVEURVEURVEUZQbRhzOK\noiiKoiiKoiiKoigzyC1rzsT5oIu1dW5nLkLLuGkBrKRsDSbXr5hXGnTabJlmjDFpS6CjHiFLystH\nakS/IdJzXWuFVn9uHLRrLr9t44ZjDe5EIYmao78U/eJIB5yWvsppT0xIy77zP/6N057zOfgKdk9K\nXe4I1eiI9+JU+wqlHr35ZWi3q7aaqMOWXcX3yPo2bjfOW6gB2rlL/3FM9Euvgk7vylMvO+0xy+6U\nLQwDpA1MSJDWxi21+M2s0bv/b6Gt77BsdFmnvW3No0775d/+m+j33E9RC6aNdIesLTfGmKRc6NAj\n7dD/pRVK69fRUYzhkRHoZcMN0pItJhYa1Iy1JqqwxeeEVdeJz98kWbMmWDVn2GabbVF7jsoaSKnz\ncK25PkT3AVkfYWoCn5e1OYjvpbhhW8WyBaSL2vz7jDHmydeg268uRt2RM+9fEv3WfQ4n+iLZ8i1d\nK8f5tXdQW2bWSuhALXNwM94/am4nxfdifoyEZB2vnvMYg9d/Dp1uhOpQGGPMEFlux5Alc9lD80U/\ntnJm+8XmF2SdnVSyBbxO9ttsqbvov0nfza46aKyTclCzgi2ZjTFmbOAppz3ah98x3i9/E9cm49oH\nDc/K6z1BluuDVIepsU2Ozcy1cg5Hk/ANfC/r2I0xJntL0GknUD2k9OV5oh8PvKFWHPvgzT7RLdmD\ntYzrRHUc+/gaEHM3oTZI5jLUGOg5J2u/ZK/EsQ73ot6LN13afnOdmZx1eM9QhzznKWXQ5A/QOHBb\ndrMnfog6ZUsfW+207Rozo9b8iDbxFKfyKnLEa8llqCtx8y2sVbExct/SdQBa++q1VGPorLw+2bNx\nTlvfxn7Etgv/zs9+9pHH+pV7UBPNXyrrRqSmwla2a3Kf085cJscc1zbi3zHfqifVG8H+KzWAWhah\nE3KdyKWxMEx7tr7zsl5A5hpZ8yOa8HrSflGO78xZGI+JAdQjCd+QFsc9p3FepqoRXyLN0r43leZ6\nSj6uQW/vh6If16FKLc102mNk4erNlHXjEhNpzZ3EuA9dvyn6xXmorg4NxUCVrM3V8j7qDaUtZBt2\nub71kZV26mycr5rDslbJ3O1zzO0kdBrXLiZe/s24rxHnLbkc18C2Im6vwzqUW475Froqx23GHNRH\nqn0K1y5rnTyHbIudmIBYMUZ1Ju06Eu4c7Ksmh/Ea3wcZY0yA5ibHvaQsGYcyV+O7hqg+Vd91Ocd6\nqBYn/w7eoxljzHhY1i+JJt4cxAq7xk7eHagZwvUsB6y52H4U9yAxCRgHaXNknRDez0TqsWYmWWtI\n1hrsA3gv66/E501PW9fQjbpv+QuwV+yoPSL68frE9YAy5slxNNDQ/pH9pifltUkOIiZwrSH+fcYY\nk2rVzIo2vjzU3BzpkPVnInTeuWZKYJVck9LnUW2oH51y2nbNtrbLmPdusqj/xI4Nol9HI/YTHbXY\nqyzYjH1+YkDe96+q2OS0T3wfduv2sweuV+iidSIlIGusJW5ELbDWhhepn6zj1XkIcSh4L9bmSEur\n6Md73o9CM2cURVEURVEURVEURVFmEH04oyiKoiiKoiiKoiiKMoPcMq+maC9SGVtel1agVQVIY3r/\n10hTLs+VNo/ZbDvahpTMBL+0veo8CAlLJqVIrf2qTG8681PYAfvIOitvG1IVr/zbUfGeRPquz2/5\nqtPeNG+e6Hfvn0Oi1HwR0pi+S9Li000WuPX7kC41a4e0N33YxVbkSPM7/45M1V+0e4G5naRVI8WM\nbW+NMab2xXfxj5iPl0iwNV4vWeaNh6Q84crLsDZbPx+2aeGuG6Kfm1Ig/VVI/a35KeQcnf0yrfj6\ni7Dpfenpf3HaA+fl9dm6GOcz5w6Mi5CVbu3JwnVMK5QpbExcHNJbexsxD7y50ub90k9gI14RZVnT\ncDPmzvC0lBPw2Lp+vt5pZ96UluBssZuShdeCD1aLfpd/CEnbObJZ3fbAGtHvnWcx71dWz3baA31I\nhcysyBTvcWfjnE8MIsX2+DvSNp3TiAMkwSpbKy2YX/rXN532iiqMt+5rckxU7kQsc1PqojtL2lmz\nHfftoOG3mPvBB2X8GZ+gtNt8zFN/qpTOsAwtQHIZYQNujCEVjPjNESudkqVcmSkYFxVfQEpm/RvS\nmtyQRK7pJqQdb56Wtt8F6ZCHlOYgZbv8IRnz4ljuloJ4nVws0/9Tj8HeliVAWRuC8vMSb50y+vvQ\nexppyt58Occ4nXuMzquvUPZreBbp29mbgk778EFpyzinEq91DUDOluiSVrz3fwZ6WGEhnLXUaefd\nJVOPj/3gfzrtSAs+u2C7nGMs/4n3YByN9so5FktyhMIlsCdtOb9P9Fv33+9w2u2HIduI3JAy0Z52\nXN+SBQ+ZaMO28VOWHLvvHNaKjGLMvzivPO/pQxjfiWn4vMwSmXp+5iikhCxT7B6QkumvfuITTjuN\n4p47B2uQ2y2vY0ICxlZ/LWQCLOUxxph3L2BtzvEjviwsCYp+kWEcX2Ks7BcAACAASURBVFcHrsnQ\nqJTEzM7E518nq+HypSWiX9vrkMiUrzRRJXQKafHBjaXiNbZ9jZAEedqSemQuh+xqYgQxeLRLyuoi\nFFMmx/AZ0xMyVZ9lOXFxuIaxbnx2TIyMT5wmP0xWtiwVMcaY0X7El6Em7I/6L58S/Qp3Yj0e7sbe\ngeUvxhjjpbjEkot598j4fOHFc067Wipco0Ia2cO7LDl2xhhkJjXPYgzbkuScUkhVcjdDkmvLJbsu\nQLqbNAvry8B1Kcllq/L2PsSidQ/RILYOgmV2LKOp+Oxq0W+oG9/FdtLT03IsJeUhvqQHcU0joQbR\nz7UVkg4P2aNnbQyKfixLiTZD7diX+q19H58LlsNmzpP77vh4HHvbGeynR3ulTGqaNjd8zm25dEct\nSZBnf7QcKCVF5ij4fOX43lHI/tKLpVR+PA+xNikJv2Ng4Jzol125HK/1YH2P98hx2VeD9dRNtvC8\n1zLGmDj37dvbGGPMKJ3DwVo5Xsq/hP3EUBPWrqK160S/piO4VwuswXrlsfbbXRcQvzd/FTIkW3rk\nq0MpkPT5iBX1z0C+ae/dk2jvuOy/rHfaU+NyjrH1fNm6B+kVObnbztGzh3wcT+c1GXtn7UVJFI8H\na0vd6ZOin69Eljex0cwZRVEURVEURVEURVGUGUQfziiKoiiKoiiKoiiKoswgt8yPijQilY+rkBtj\nTP4OpH71/QBpmMV3Voh+J55GatqNDqQKP/xHd4t+6QuRqnT5CUhbPC7psFO8NOi08yjV7fSzSC3q\ntFKFS0ch6+kg955N964Q/dhFomITfoedBpu3EemF7R/KqvZM7lp8Rn89fntVgnwmxil/t4OzP0SV\n8YVfWSVey9+O6xiux7kJnZHOB4U7IH0pICeU3778n6Lfjj+Hq0S4DfKLlDyZiu1aijTM+Hik1hbd\ni8rXkSfPivcsJfeYI29gjExYaX/sRFH1lS1OOzFVSumSkiB1aTwGd6BYyy3gmX95zWl/82d/Q6/I\ntLfgDjnuokliDtIc7XT1thOQeiy4E1KZX/zwZdFvx6JFTpvn71ifTBkdJ0ecLXdhjrz9zCHRj52w\nWELDUiZPgZRznHsZKZ/xcUhHLc6QKaeTdAy17ZCR5DXKdFmWUTa1IwV17hbpLjHSjtTueC9iiidb\nxjVbGhRtXFRRvvN4k3itZDdSY3vIjSdtvnRwSKOM85aXILMLrMoX/cJU5Z9NZjIsJyOWPMXGo+ME\nuVKwtMUYYzpIXtQ7iPgfHpJSgGyas5WfXey0s4IyDZYd8aanMZ8bL3wg+rEDxnAY47b/spTYjHbg\nOEqkQvP3JmtT0Gn3XpBSyaQgyUbppDe/dE30K9iNtYFls9XzZol+4/+7vfcMj/M6z4Qfog4wGPRe\nhyAA9t67RIpqlET1YtmSZSvrKHbWTl8nV65syW7Kxut8djaOS2RZxZatXimJKhRJsVPsBEiQRO9t\nUAcd3y+/9/0cS8x1rQYX/jz3r0PNmcG87znnOecd3aUHUpJPqiCNiY7SNWoutfN2Ym13NUOOlpqj\nx4YT0mJpLbpSLX8h6LcTY0QBvu5u1W9iAuPRXoO9NHOulk0OD2NfyFyBOTvgJFXFxUwvfZvTGYed\ntK+0FagrLCMcdaQulY14bVkMxvTsyc8/F6yZh/H55Fyleu26LajRqYsg08hYAKlQdLROpRgexhzk\ns0rdHv0dllLqHaf1/eCtt1W/b958s9dOIfnrlSs69ebiEUiVF25H7eq/pGto/u36TBhJBMoh++ir\n1LKUYZIHJVdgfylctl3162qGjJcp78FbV6t+g104E7F0cDikpRScnNRViXuUNhdzfWxYnxU41ZAl\nmW6aTQLJnBIoVSXZqc8DDVhLR58GHX/5PStUv5YPa/B3qabEJutz97wbdCJJpDFYh+/bfkXXgdRl\nWAdJAexV7n7XdQjzs3kP7nugIl31SynHXODxbttXq/ol0DNPDN2btr2QFEVH6zpc00bSFJJml9bo\na8rZ9NkpQrWvaenDLJI8xSRAjpUyX6cX9ZIki9PH4tL0mTcqdvr+f3wS7ROdx3VaXfb6YnoN4+SE\n36l1MElywQvPayuIrGJKTptHZ0fn8wKlGHv+bF6/nQ1asj0xcpDa+A5ZFVrqNzaCNRyOxpyYnPz8\nRCyWPI72O1ItTm+i87SbuBXnyPQijcQc1JjogK4D4yQdytuOs0rHZS1n5zMDX1fls7pf8VZ8xiv/\nhOesOTk6MTIrh+SHVaiJxXdj3xkb1Pe9/ROMSclO7KtRfsdS5SDqf4wPNgluGm/plp1eOxyGDUtS\nuk6jDA/i77J8NdZ5/hys07YdLow5YzAYDAaDwWAwGAwGg8Ewg7AfZwwGg8FgMBgMBoPBYDAYZhD2\n44zBYDAYDAaDwWAwGAwGwwzimqLunpPwepAJ7a/BMZSF86DP3vPkXtVv1QpoVTf8HvKF3di6WIpP\nXfAw9GEcqyoikpqN147/w5PoR/p0N57yZ29DU720HHrvQ29+qvp19UN3nnQIf7fcibyNi4PGMTEf\neu/4eB0jfvUd+Ji0noAG80KT1m7v/D2tgY40+N5U/UxrWkvvgx/AmV/jfiy+f7nqV/8mIgxL70Sk\ncvfAgOp37t+g38tZjpjf6ue0f8zmv/kjrz02Bg3h2ecQKTnn+nL1nt/8O+LNFxdDw5qbruN2G9rh\nPdLbAG3gUKOeF4FsaAoDQXyG6zlz99cQC8t60qP/8BvVL5Vjf6+TiKKnBjr+uRtL1GsNR6Bx5O9+\n59o1qh/H4LXur/Xawy16DDtpHVx4HePJMcsiIusXY233dOM9b3/wsde+ebmeRzxfSrLgH5O1WK+d\nhi7MiewU6FddD5tcilH0z8b9H2rUes4YioIfpAhSd6yTgnouRRr528s+97Wu0/A06KQY4XQnHrL1\nA/gEJBZAH1z9bpXql0UxnFE+6N/DzXod9F1ELa76FJG4QdL+d/frOVLTDm39wkL4SV2/SPuLLHoA\n4x/IwZplza6ISHgA1z4+hDXm+kgU74LGOD4dNdrV76Ys1Zr8SGKAvHx4HxQRmUUeBK3vwvcgNl33\nu/IKNPTlD8IUJ+ysxd3P7PXa2xdjH+oPa716QhHmAY9n/BqMzcSEjkJmDXRgAbTlg87aOf0yane6\nH/rqeV/X0ZUD5Fk2OQqtfnR8jerHXgI95LfjL9Zre9iJT400ug5hT47L1D4us6JgXsBR5wN12jsi\nqgn+DuEmjF0gQX8ee2hFJ2I/9jmeemMhjFFyKXwV4uJQK0NdOrozJR1rbLAeY+dL1N4EhZnw0Gio\nw7nla9u2qX6zyAiCfWYWX6djb9lLoJ98ANy57sbbRhJn6VxSvlZHafdVYR2MUax91nztE5WcBcem\nWdkYm6GhatVvpBvzkb1KQqdbVb9Rmre953AWYW+RnOU6lnc8jPvcRX5/ORu1P1jVT3F+Gx3H56Xl\np6p+7L2xdCfqy+4nP1LdUmieFpHvW2pQ18/OI3RmvV0iDvZ3CVXpmt93Af9OX4MzZedB7WvC65T9\npOIcr4eUTL73qNcp9+n5feh/PeO1T9fhjMXnoIw8fV6Ynw8PDfYX2fPOEdVvI3nQJJXjM5ouaK/H\njAycfQrvwDwd7hxU/dLJl67mafj6pezUZ+iOK9rnLpJo/Qh1PntLUL0Wpu9beCPOjVFRukZFR2N/\nafgYcz09S8cO592Atc5eI/HOfjzWj3XP576Rdnyf+mp9zzMD2EujyGvI96iu6X3kT5hGFoezHCOd\nnuYzXruNzt3Zzjk+dA57YeIOfIfURdp/pf0w5n2hLnmRAX39knu0dyP7kvZeQG3jOHMRkez1iJCu\n/Q3OOucb9PwL78HYrV+K9ef67CTPw16YNJt8xigqXXn2iEj+dtycyUnMg+5L+uyZQp5FsfSckDwn\nQ/WrP/YePo/PN060eRT5RIXOwxPS9W0svkvXGxfGnDEYDAaDwWAwGAwGg8FgmEHYjzMGg8FgMBgM\nBoPBYDAYDDOIa8qaMteDEh1u1XTrug8Q08jRu3lpmubnD4KONkr0VqYdioic333Oa+ekgqI5Mqap\n04XbQHH0l4BeeG4/qEq3bNYRiEwHj6G/68vTMbrH9oEim70M9MmEbN1vagp00vxFW7123bE3VD+m\nGjLliu+XyPRHafM93PLXj+i/PYFo0M1/ibE7/X0dYZtLUXi9DaAvsrxIRGTdd7/ktYcGar120Q1a\n3jI1RTF5TyPyefNf3ee1K3/8nnpPPs2t9f95i9d+7q9eUP0e+8FXvfaln4BOmneLlpSMjICOzDRl\nlzJa9R7iTlMXgO4bGtT95qzWko5Iwp9EEcwUESciUnojokpZBpJYqqnODW9A9pK/A/ei5cMrqh/H\nWs9fjX5DThzkobP4vIp8rJd71q3z2oMjWkrRP4wakFOGeznmSBg4Dn2SotLd6Os+oo0nFqEe+Jx+\nI52Y5xkr8V2Znigy/Wux+ieg6uZsn61e49jLhQ9hvXQd0zLI6BjQJtuv4PpL1gZVP46bXxEPSrRL\nGW2sAc2fa8WrRxAx+eI776j3fPPBB712CknBSlfpeNOBGtBge+Ixz9zvkFSEudpCMaiZawtVv9E+\nzJ+xPsytyTHn8wo1DTqSiKV4yd7zHeq1gltBIx8Zxb1ML8tX/TLX4bqOP4mo28V36NxvfzzRbEmC\nUFaiP6/+HOZI8RJQiidofjd+clC9JyYRe+FIF9bH6z/eo/q19GAMb1qKONGLT2p5zdzHENOblIl9\ngWWrIiLNe3F2yN0c9NpdpzS9vPSB6aunIiKz4vD/pkaciOym85ArLXwQa5Gj3EVElm8G7fv0AewT\nSzdpynLmaow3nwVuv17XAD6fxMSjhjHdP5CqP7urEes0PgNzZHxAR4u+/N4Br72CIu4DKToK9Hgl\n5DxNJC8NvaX3O5abzlkGiv7v1NTh6aup+SRpbjqpKfPZQexjSRS53fKpjnNNyML1pxahTo72672r\n6kXIE0p3YM+dGNLX13sG8oSsrSRdmIQ1QH+LptYnZoNCn7sJx/KeC+2qX1wsXiu8EXtzx379eedr\n8O+xE/h+2Y40meV3RXQmqH33kuo3++a5Mp3gOhp0pBTdp3BOY9le4S79nYZaIK0epXhzd/4lZ+Gs\n0d+JWsQR5iIiFV9a5rV76KwXE439l+WBIiInrmDvWjUHsoqKPC3b/rQGZ+j2U5CN3rpmpeqXtRl1\ndITOSHHJWg7UV411WvooanTvZV17x3s/P+b5i4Jl5SoLWnSMNaN+t7Y7YAlPuBnjmbNN18mJUYyp\nLwPx6uNOnPIonRFayFqikqwllpZoeVEiSexqL2EfmPWL06ofS+x4jvVe1GeCjBU4E7GUabhL7zlJ\nsz9bUt9XpT8vdXHOZ/aLFFgWN8uRK/lycK9nRdMZ3VljI7T+AmW4rt6z+pqzUrGHHDgJ+dNNd29Q\n/XI3oN7GxEDylV6C+tVyUlt2DJJ8v7cS97DoFr1/XnkW52SWIk4O633Ml4d9YopqOZ+jREQCZajl\nvmy8p8+Raw7m4TrkM+RpxpwxGAwGg8FgMBgMBoPBYJhB2I8zBoPBYDAYDAaDwWAwGAwziGvKmphC\n6GLOHaAeslMzU8xENNXw+C9Bv2VKrIhIQR4oqMEHIUM6/i8HVL/O/aCuxhGFN5gNicTHR8+o96RR\nwkRTN9ydU/2aznvXn+702kyLTM3QqTfj46BLDQ3Veu3+K92qH7vEj7SDzpVTrp3wL+6+4LUX3CjT\nislJnZxQ9x7GpGAbqGOLvrVe93sRlLMBcshe/Wc3634f7ke/q5DBLP5P96h+vb2gxM/7Mj6jvRKU\nuuXf/qp6T+re3V7bl4L0itu/rtOu/H7QXQvuwHcYdaQzMTGg+DYcvOi1C2/SDvdrvomUsV5KGWN6\nq4hIdoWmpEYSXT2Yc8sc2u9QM9ZpVyMkCHMoTUlEJCoWv8V2HgfFk+emiEjyQqxFpsbHJOlkkc2b\nQJ9lSub7v8Ka5SQfEZHZtE7Zcf/8T4+qfhu/jPnHCTgXXzmr+hWvC3rtzJUkw2zvV/1YAsMSzYsk\nWRPRNMs50zCcRfeAUtm2t1a9ljQH9zCBKI/hZi0pTaRUm5RM3M/6T3QqzsZdkHdyKtXAlR7Vr6AI\na6nmBNZ5JlHg//Chh9R77vyjW7126Axo5yPdei4Fd2z22gkJkNt0tu1X/Sr/BdKe4H1I0+hzamq4\nAetgsB33Jd6vad5NuyHNKNHBKF8YsUQpd6UPncdBg86hxIKYgP5+IaLxs1wp2qHIluaAwpy3DhR3\nrsEiIrl5oNJOhEEh5/FImZel3sN78xit8y3rtLTqyAmskZzloGhzopGISC+lRDGNnRMQRERyNuA6\nWKYWFaf/XxGnfwR1YGJE0NeKuZQWTFevRXXg2lgSGZfqjONJyBrSk0CHT8wPqH5jdJ2J+bTvvObU\nnw2YM6M+1PXJSci+h7v1PjYxgnvNNTrWSal5+Ku3eO39b4ECPtag6dspiTjDbdiIGx+frc9LoVO4\ndp63XLtERIbbdf2KJEIDkJtMOa/FZeI6uP658zZ7Dij0vd2QPEXH6v29NYSzRCGdD4+evaj6cdrL\n4R/htZsewDlCnO+QkAVZAFPr/UWOPJOWiJ+SC9scKVkMpcyMk0S4fIGWoR87hvk3m2wH8tfqfm76\nYaSRsQZ1xZXxsgSWx7HuhQuqH0sCs6+DfKTrqP48TmGZRffJlZnwuaOAZGf8vJMU1mtsYiLotf/s\nhz/02judRLR1FThrt/fi3kYn6frPkgl+vuh1akD2JlzvpZ9jDmeu1HKqgl0VMl1InkPpkLG6lvOY\ndh7EM1xCga4VQ02oycV34KwU7tQ1hPcUTkdy9+NhSmW62oZ6dYFSg1bP1XYHJz+FpG/hbKwDng8i\n+hzF7WgnlSd7NeZITAKtc7cGkM1G7fOo98kL9b6dkKUl+5HG7K9g/59yimrbflgqxKZgr4mO17WS\nn7UyVkD+9cS2x1S/+ETMmWAXZMyu7P38D5AA6yf7gu6rkO3lrilS7+E0Rj+drRv3aMlm6lKcsToP\n8Hu0LURiAf5uywdINc3ZomVxXKMGKdkz57qg6tfyHj5j7hb5HRhzxmAwGAwGg8FgMBgMBoNhBmE/\nzhgMBoPBYDAYDAaDwWAwzCCuKWs69REo7mV5ueq1dHKMZnp03auappuQAsr2HKJUxiZriUQSUeJa\n94HOPPd2zUm/9CaojJlE4S1cB2rR8Ss6fWbtDsgv7vzSd7z2D//kT1S/lnfg3B6VgFuTvlA7ije8\ng5SaNLoPs29fq/r1t4Hi3tsC6mLeWk2/WrpGp0tFGpn5oHT1NWvpA1OwxilZp/bXWj4ym6RmgVSM\nyeiodqAep+SCvB2QrbRVaSft5jdALVv8R6DtsWP+4KBDF14J6mvLQczN0u23qH7nn/2N1y65ExKg\nwQZNzb38PNJLWL7zyT9+oPqt+RakGdkrKL3I+byre/C+pXdrSdEXRZIP1MiJYe18r+RzlLaT4NDQ\nuV+AEnZiHOf/8UF8xsQI6NJ9XVoqNGcL5kHoDCijNz12ndceuKrlF742fId+eq3sXq1bGKwDHTDc\nhL+bmqIpnUwb7CD3+LRF2tE+myQmUxPgajKFX0RkYEjThSONwUbQdlnGJKLlMr50fC9frh7HGKLN\n8r1xwZToKeKnVnxFcyhbj6OmEvFecihJxk2sY0puRhmo0o37dYJPdDSu4+yvnvLao136PvO8rX8J\ne0j+rZpyTAxmKdqFNdbnpFI0f6TrXCRRsxt1KTld35fkCtDfq19DjVr5nc2qH8vsSkpw/3rP6XSW\nzAWYx82HQCnOnKulsTwnxvpRywZofVx8/bx6T+8QaPyz8/F3TlVfVf1WLYTMs+YI7mtWupZcsMx4\niOZ56iJHxvsqKNsFy7Euu8/r5JMsJ/kr0ijYFPTa/Y5MzBeL+9lE55HwqE4D4fNJ12vYM4c7tUSi\n9wJkbE1XcZ2cxiUi0vcCpdFsx/7JaVqubIg/u/EiEq+K5utEr8R8nLG2fxkrfbBO72Nd1fi83jrQ\n9X2tOq0peT7mevspnHVKbnakE7M0fT+SyMgG9dw/W8/HKB/OEuEWyCJYNiQi0njyI689RtKezsNa\nDrNsA2QWVftxfuG9WUSkYknQaxc3f7akq9uR7nz0c6RjLp6Luss1TkQkPhVrrOEN1KGufr0PxNH8\nzSCZVWuNTn65QlKP7I/weSsf1WfZ6ZY1xafhurLW6/MxSxwG6Fww0qn3kFxKP+ylhJukUr3P9pyD\nDDdQSlIcZ15E0/zhNMC8m7AuQ05a36KlqHX/feIbXrsvrL8rJ4+OjuPMPOXIOfpIRj/Wiz3DTaA6\n89Qxr730a2TD4OhS6n5N5+ZlElHwec6VpXAKU+cRSEf4TC8iMkLrr+0g9jveV0VExki+FOvHc6Cb\nKnzpAOTNp2trvXY7SRRfP6Ql9Q8+cIPX9lFyE8s4RfQ5+ZMzOEPt+ob2pqh9BTIzTrJLLNb1KpNS\nnfgsmDpf75/j4c9OvooUouNRO7rPtuoXKTmV04hb3tfP3OkrIKfrr6U9JFPvn4OzUFfYRmXQkd43\nUGpgbz2S6Dht7tNf6zPfOKUir+nFOXLhf9I2JQlkkREVhzXf7tgOqIQmek7lZx8RkXZKzsunWtHy\njr5HE44U1YUxZwwGg8FgMBgMBoPBYDAYZhD244zBYDAYDAaDwWAwGAwGwwzCfpwxGAwGg8FgMBgM\nBoPBYJhBXNNzpiwfurHglxap1z783vteu7wcGtHBER1lVrgWWq+Pn/nEa29+cJ3q174X+sJJ0lO2\nnteat4qd8BDhyFDWB9/z2A71nn/+37/y2v/lq1/12gOODnTJ7fB+OfnviHbNqtG+KinzoVFr34fv\nPVASUv3SFkArWNUMTfbs7PmqH0cATgdYL9vq6OjmPAD9XTgEzWj+TdrrIdwJvfmVp57z2lE+HaG2\n+Bv3eu2YGOg1T/7wKdUvoRA66IEOxNqxF0Pdm9r3ZhZ5iuRuDnptvz+o+sVQzN5QGzSobpR2LkWb\nsUZ2Ycmtql/NbkRDj3ZBwxrt175J9QeheVx6t0QUpfdj/bV9XKtei0uD5r14EXSrDa9UqX5zv4HI\n0I5T+Iw0R9Pacw4ayozV+DyO+hMR6bsEHWjJnfCMqXsV4xa8Swubp6awtkOX4a8Rbu5T/Xw5+FsF\n19G1H9U+RONUKzg6NtymtcesRWads8+J+S3aOn1RkyIivaRPLXYi0ftqsP66z8A7osOpPyUF0NZ3\nk0dJQpyej+w5U0U+H/Hp2mfHlwVPG55LHIc85kZUUj2IXYC1XLRFexX0dUHjPlSPMc5Yp7Xm1dXQ\nofN1jDhRw3PugcdH1U/hY5VP/jgiIlmrp8+vpHBj0Gtf/fiyei25CzH0yYnQQ5/+wSeqX9l9WC9d\nx7F3DTTodTDnYfilsR9B3vWlql83+Sj4yB+o9zzmB2vuRUQe+rNdXjtMccflA7pORlFMJvvMNLRq\nv4V5pVg7HJ/Z9Wmz6rf8ifVee4RqcnKZjrNueY802hGupyIiceRzMdSh/VQSUxOpH9ZEnlMDJ8Ko\nPwN09nFjUvkeHrus5wzj/odwduklPwsf/d048h0REcnZirl/+SzOI+yjJqLrNUfdxmfoz1t2K/xo\neipRryZHtEa+hzwYAuS91EGaexGRqTHtPxFJJBQFPve1vguom6mLsce5Wv/G3djTOYKaI45FRDr3\n4ZyyaiPVoU+1R9OVc7j+ND9qa83uM157yVZ9BlxXgH2SPYV4LxbR0docP+36OjV8gLXDfjTZKdrn\n4q412n/ht5ia0GMWn+n/zH6RQvUz8HUsfUA/a/ScQW1LoTN1SkWm6tdxGONTdwbt/KC+N+wlE0Xe\nEbEBvX+yv03m+kK8QDYuzed1bVMx9I/ivHXhxdOq3zmKco6PwXfoadf1v7sNczA2GjUk4HidLXxo\nudfuvYi6MenMdfZhijTUWSzJ8RQtSnW7i8jvesWxP1UqPWfFp+kzy+Wff+q1o8kfdLxP1zyOM7//\nFnhTrimHj1qOsyayN8Abtfss1l98lnNuIk/HvAac19jPUUT7/fGzLe8/IiJd5NvFvlg8niIiw3SW\nLdFHyIig4xjmZteJFvVa9kbcm6u/RD0rvlN7Y/kycG9yl+C5Oi5Or9kzT/3CaxfunOu1+x1ftbM/\nr/Xai4rxHdgDrjWkn7/X0hhnL8NvGYmp2ottZAhnpNJ193jtQIn2Hq38MXydim7BZ7tephMDODez\nH1zhrrmq33/kxWbMGYPBYDAYDAaDwWAwGAyGGYT9OGMwGAwGg8FgMBgMBoPBMIO4JsetphWUrsKw\npi1t/db1Xrv9E1Bp+4Z0hGSIaGGL5gW9dkKOpuVVPA6ZU+snoJlm+LTk55NfHvLa8RQXuPYreP+/\n/Nfn1HtuWLLEa6cn4e+erdf02+Z3QTfOygANLy5VRyW2UkxrzvVBrz0aGlb9QpWgS+34CqJUmVIs\nIjLYRLSoyCYwi4hI9hbEjOcu0rKD+gMfe+3Gj0HPXfyElp0NNIAy1taJmLMNf7pN9av7eK/Xjg1A\nMuLGMG98AvKnU9+D7GzpH9/ntUd69qn3cCxgbjEoig3VL6puFbsgS4qJAT2u68QvVb+a5yH1YOrh\nsb5Dqt/Wv7rJa0dFga5Z/bSO4Fv+xAaZLrTvxxrrb9HU1yBJOBreQsRnQqamYcbFIfrOX4jxHKjX\ndECObY0nyUvuxnLVb3QA9MqoKIx1XDromo3vX1DviSG6K8v5YgJOnDfJaDrPYV6O9uo1xjK4jKWg\nLnad1nRMllm07q/12r4CTZ8MN+l7G2mwfK7TkXtkr4U8dKgV62XJ45p6zjTKIZJSxMXocs6x3ckU\nOdh1VMe4xmfitdTFiLxMKkQN5FhLET3Gzfsxxk5yp2QsxZw7c6XWa5c60plFG0H55ProykMuvQDJ\nXMl2SC99TtT3xMj0Rb8mleC+ZOVoCnM8rbnSr0KqMDmm6eWnd4XZWQAAIABJREFUf3LEaxeuwrhX\n7NBy0sY3IePj6M1wh5btDV7FGn75PcgwfSQRWx4MqvcMUMQlx9JmODHdHJ85HsJ8m7OwWHcbBp2X\nY1WTgprSzjLbCZLKJObqMczZrqVqkcYwSZn8znmE53fTKUjugo6siSf8/FWIzQw36/3uzY8w3p9U\nQqpXlJWl+r39Ksbu+hWQvnFsKdcGEV1HN38H57KaZ86ofizhuHwS18RSDBFNqR+swbxKmqNlZ0P9\nmDOTNEeyKP5dRCTgvC+SYKmkzxmbwFzE7zZSHHruqkLVjyNX3z+j7xljZAzzu+YVnO3cmOS6drx2\nx2pQ+osyQel/86X96j1zcnDPZhHdfcntS1S/2GScRYfoHJC9XFPmx0jSlkcl9OibJ1W/XIp0Lr0J\nssSTzx1X/fJzcS8rNkrEkUcS89rfnFevccx2z2lInMb7tNR2lGpT8QKciZqq9FmAZbNTE1i/acty\nVb8h2j/ztkNG2vQOnhMWfXmFek//VdRU3mfTA3pu9tOc4XnVM6Dr+vx1JM2giPGmt6vl8zDSRbV8\npZZwxDrnrEgiew3WVeiSlmIP0DNOUjH2g9E+fZ4brEO/CdpPon36XBZHUsyTx7FHsvRLROS7jz3m\ntWtpXXLNu0r/XURkLkniWRoabtJ111+C/XjN/VjnfL4UERmivTmb9jR3LPheFNyMcR/t1fVlpF1L\ncCMNljLNe0KfPdvoWX/uN3DN7rmc47752aDyledVP95rzv8rrERqOrSUi5/1uV4HAhjHEmcvzSQJ\npIowT9SScL8f9zoUwj49MaLj6uP9uI5o2nMrD1xS/dY9jgLJ8r6WD7T8VdlEaCWniBhzxmAwGAwG\ng8FgMBgMBoNhRmE/zhgMBoPBYDAYDAaDwWAwzCCuKWva/MQWr73/Rx+r19Y/isQFTlZZtFXrcg68\nfcJr3/GXt3ltTp4QERkfhpSC6Yl1pzV9L4Zoa0zj770Aatq3/uvD6j3/7Y9/5LVvXAaq+cSk/g5v\nfgxK04JCUPTS6zQ1kKUZnNY0FtI0y/zbQJeKIwobUylFNBVyOpA5D5TXc//+knpttBt0tOV/DIlS\nX61OCWAqemZystce7tYyNk5+af0AVGKX+nv4f8Glu+R2zJlwGPczuSxDvYdp6OEwHMVd6UPDEczV\n7GW49tgkLZFb/J0bvfbEBKiMw92aWlrza1CdWX5T8ehm1W9qSjvFRxK520GZb/3Z4c/tl0apDROU\ntiMicvwfQSlc+92veu0r599V/XI2Bb32+BCuKRBYqPolZIM6XHsWn83SDn9+snpPN6VPxGRiHfkL\ntWN++yFIDgs2gD7ZNHRM9WPH/E9/dNBrp6VoGnEmyZpYphifoSn9bXuIenifRBwDJOcpXKBpmJ3H\nITVIWwyKdf+VbtWv+xhop5x8U/trTQdPmQcafc76Yup3TvVLo6QjTga48jI+z5UCBEjac+FnoMCn\nO2t2gCRY5XmQnSXn6XnByQUTNOdcR/vZt2A9xyVjLQ7U6Ro61jd9a5HprtFOTUmkvXCwEdfec0bX\n03n3I4UpfQ7WdsMHWk7Ate3qPtDpM9P1eklZjLmUGI/70ksyYzfNKyEXkr7C7ZBPdF/SEjZO6oqK\nw/7bX63vuS8Xa/HM25TkkKdlUulrsBY52aLzuJbbhVuoDk+DYjSKKNWxjnSZkzny5mMtth7UUuhA\nLuZxRwOSRzr7NQV+GUnKdt0C2nPthUbVjynb/jm4NywdZ3mbiMjYJM4TnLAWm66vabAF32n2XNTu\n1947qPo9tAEy6Bg/5nfjPk3LLtyMujFYj7necFpfUwLJuys2SUSRRPeov0onv4wm4nzIc9+VnLGk\nuSAdEqx3T2oJ0AT1W0rjebVVJ4quKoM0MUTrL0hJUD97SZ/D/vrxx732mltxRk1zJGK8FuMonbBp\nn5Zj8Rk6YwXq7tCorosHL+r0w99i5WNa/n7512c/s1+kwHva2ISWgKZxsifVnFmOPYCSO5RjHyor\n0HsNnxnGSEoy6Mi75z10h9eOjsa97sxEnRqs0++JJulgfA7+ztVLek3UkmyjsQvzdnGxloqyxLf+\nZcgh53xFp2A2vIlkzuE2nJOb3tSSi8I7nMSYCKKLJGeciCki0nkC92yYZDlqrxeRaEq1mxWNa6/e\no5NHJ1lOSs9qxffq+KI9P0CqcGku6jjPlevu1HO95T3UueT5mEehXv1cUERS6uFOrPOcjSWqXw/t\nLbFUT6ec58/sFZDbhLsxz12peDYl1U4HUkgO2kpyUBEtD+q9hDkck6jPFkO01yRmYc9kaa2ISDTL\nCitwhgk458N4WrN87mM5VUVQ74tTtC+mU1pT/Yndql9yKWp+Gz13xKfrNC2W/7//rx967R3f2q76\ncQIlz+E45/P8JZ+dYPZbGHPGYDAYDAaDwWAwGAwGg2EGYT/OGAwGg8FgMBgMBoPBYDDMIOzHGYPB\nYDAYDAaDwWAwGAyGGcQ1PWc4Umvbn96gXuu9DJ1k7QFo9CYcvWhFPvTlrMXKmqf9K87+8+t4zxPw\nmMjZpDWYlU/CwyadNGqH9p722snHtLarZxAax/ZeaKMzkrQvBUeNZlJ05TtPa7+du/8C3jmseWPd\noYjIWC90v6M96DdQq3Wqdaehc1v+oEQc3VfhVeAjHayIyPyvIpI6PAgfF9Y2i4jEJkHnx5F0wdjF\nql9SHnwuwu3QaKYt1zGFOavhx1P9NDTvHR9DWx+Yn6nek04+HOPj5BHTpe/7LNJ2d5yBZ1Hzca37\nzd0CjedAAzSe3Z/q6MUh0siW3QQ9eU91rerHvkdZX9fr5YuCY29d7wjWYI6Q3vj8Oe0RsHAxrrfu\nk4+8dnK59glhnwvWTA4P6/sXHQ1fBo7OHbiC7+rG0HM0K7eHy7SeNyYR2tyxMfTLX79c9Ws9CS38\nCvJfYc2riEgPed1whGHY6dfao9dmpBFDc3OsX+utOVa48XV4AUxNaG1y8kKsi6hYaLRztgVVv6R8\n1tJiLqSv1h5a7CMyPgjPitRi+DmkL9Xrt5/mI7vC5F6n44/rX4VWPER1uLtaj3fhAP4ua3svn9H+\nJ8VF8GDoJ2+oOXfq/aT3tI7HjCTYV8CNXz33k6NeOzUfmuKc64OqX28Vokaz50JfHbxxq+p3/B9/\n7bVLVkPLPjGs91mOIL1hFfwIEovg15CyQHu/pAaxt/bWYQ/iaGYRkX0/p2huirTscmJf1xcgD7Ig\nAzUlKkEfMzhKlWt30wldXwpWap+jSINr21iPjgJNJT+ocDPtNWPaxystgFqcHcS6TO3RZ4uuHkTB\nnjoOH4hFFUHVr7Md9ecXT77ltdmzZqnj/eUnD5rR7jC1nWsiL4FoH8ZkNXkeiYi88NQer33jatTb\n1AKtke85CY+JNPI1KXM8cYY79P4cSfSdg+9B7s36OoYpEndiGD5Rrr8Q/5v9TrYvXar6rVuH+/7B\nXvJSpLhsEZE8iqf++Dx8u27YgX7ffFAf9LppLdV+Ap8HX7aeR6Gz2MdSaI4OOP5PaaswHh2Hca5j\nn0YRkdxUjCl/B65PIiJl931G1msEkbkea53jdUVEJkcxdo3HcS1Lf097hdQ8C9+dEaor7D0hIvLh\nj+Fbt/6OlV574JK+h5018BxKL5nvtdlTpKdS7zMte2u9dnULzpGut88jW1Hnj5KvWnGmPvOOtOM6\nkudh/da9rP3lisi3MUS+ce7ac/0ZI4lwE2rcmHOmTKDnjvYD2GuKd81X/TpPNnvt/kt4xpy7U3vJ\ncMR1xzG85/RTR1U/rq9nL2JdlQSxbyfmBfgtav+Lpr0rkKCfK1s/xOflUBR891ntQZU8B+ewyTHU\nF1+6fhbrrcF84ectf4GupxwxPh3wkw9c6x4n/rkA9ShzSRD/PUH77ITLar12XBzmgs/xeByNxR5V\n9T7Oitkp+po5dpp9P/PWfX5dCtXj7BhD+132nHWqX+3H8NwcIV/TSSdKm9fi6Di95pgChU5h/DPW\nwHspe22R6sf+m6ItpETEmDMGg8FgMBgMBoPBYDAYDDMK+3HGYDAYDAaDwWAwGAwGg2EGcU1ZU8ZK\nokYe0zGX5/ZVud1FRGTdVzRlqPsEKGd+iseqe/+Q6jc2DrpX9U9BGd17Wse+3vHw9V77hafe89qF\nRKNefc9K9Z71X8Z3OvE8okrPNTSofhxvN7+PKHrjmt7E1MC290Fty9yiJVhC/UIUM8e0LBGRsi3l\nMp2ofwURfGWPaP7UR//taa+dHgC9r/zrK1Q/pmDxffKla5pafwP6+SnCsOXdK6pf71nQQfNvxfWf\n+wXGp7Bcx/71XMBnJ2wARZ+lZSIiQw0YO6b+unKgkRDFzGYRbS5Zj88SitzuuVzrtTnKUkSk9K4I\n54QSOOYxY76WJ7TtB32PKcHzSjWN7swZjMFWR47BaH4fMjiWQgRyNCU2Lg70x0SKwh4lOd+4E+dd\nfBdorCzXGXBiLJnWOdwDydTUpI5LHSEpYTTJc2KdNRZP83SU4jMnx7VkKDczTaYTpfeChtlGFGgR\nkeI7QU1muZI7v8/QGmFJEseFi4hc+skRr51GtXw0pGPthyjyed97n3rtNcvwfTIL16v3jPXv9dos\n9eA4TREdH5gdwhxpI3mpiMjxSsgPb3l8m9cuc2jYcSkY19ztkFC5UdUFd1TIdKH3Iij/E2E9v5ng\neqmKYhmzdZ1ceM9XvHZMDOrk0JCOrsxeC7p/EtGNXdlpz0lQomdF455lUYR6ep6u6fHxqI19Mdjf\n3/yejpoMU/zu/33+ea/9/Pf+VvVLpHrP0ZU9Z/XYVP0Ccyx/U9BrRzux6a6kI9JgaU+gQtPwa2nP\n5NjVrGLd79OD6De/BGM1MKDXWJIPe8UInSd6OvtUv7978UWvXV4ASvTC1ZDTsnRJRGTgMuQYLA8p\n2OmcK2gtDVLEfW5Jluq2NRFz9Ww15uOIcw5aWQYZUVQc9p0Yv95np1NK4S+HZKD2tUr12iyaT6kk\nLYhL0/KEpCCkPWVFqFG+PVrex/PFT+OZEdCyiH+gmOxwGGN151rIcOYV6KjhwWHU+OW/j1rbfUbX\n064a7H8sQc3eqmUFfF5gaeiGO1epfk/9K+wESnMgGR242K36sYykVJeRiCAuDfezt7JDvcbze959\nS7z2xac+Vf1YssVx0hODukYX0bMCS4sz1usxmb0E0rOODkQys/RGnJjjlh5813yKZf/mQ7erfk1X\nUBPnfE7Es4iWy4TovqQtz1P9BmgPb/wIUpQFj+vxbnoXZ7ugdiT4wpgIoz6w7FlEZIzOMAn5WC9t\nB+tVP5YR5W4jibSzN0TF4NwTpPjs1vccGQ7Jeiv6cM/is2g/dsoTS+LC9CwR55wpc7fDJoBjsVPn\n6XrqS8d3mBzHXOy9os+yKeWQtLUdqMU15Ov60n8Za7MgKBHHcCuk/vEZulYW3IQ9ZWqK9wO9EFJS\nMO/6+/EMn+jE2qfNx5pLW4D6M9Si98VAEGspJh5jNzWF+z5rluaaJGTi/BCfiDFpPrtP9Ru4irl6\n6Ajkghec3wfWVeBMufEGPEfz846ISOYGPHfx7wNRO6JVv+iEa/78YswZg8FgMBgMBoPBYDAYDIaZ\nhP04YzAYDAaDwWAwGAwGg8Ewg7gmr4Zpk4kOtYqpzgvLQamM8WsqaO42UL/CHXCD9zuO/vlbQU1r\nO4I0g2W9OhGC6f67dm3x2l0XIZOJSdS02r4q0AHbSa50z73XqX5MhWxpBHV92w2aGnj0J0gXKpwN\nKtaJF0+ofgu3QBZw4RzoTatu0SkAU460ItJg2u7khKafjRMdLzYZ9+3qc2dUvyiSy9z1le1eu/aF\ns6pffTXo9aUrMC/ybilT/SbH8Hd/+GdPee0//N9f9dr9NZoaya7qXRch0emr1MkCq/7g21678u1f\neO3kuZqS3vIBKJBT9H3KHt6o+sXEgB4XKIG0gOeziMje/4Fklbu/r+V9XxTjlGbTfFFTnctvwjyr\n/QC01REnWYTX7Ds/Q1pTfpqW8iTGg76ZVAo6YU/dZdVveJCkeuRw7ydquDu3Q5RoNT6I7+MmRvE8\nHSKaZcM71arf+2cwT8vzQFutKNASn5wdTEHFZ7vyp4AzRyKNBJJq9HfopKhRkqqwXOvSbk3XTw/g\nM3LXY121H9OU3oLbQcM88OP9XjuYo2VxkyQv46SROV8GdbPx1AfqPVzneb6MULKZiE6Hy6AEkUBI\nz7n8Gvx7kiSu0U5yUN95rHWeI93VugaoRJYlElGMUuqgSznOXwVZSTHJI9MXaRnhxMQItUFxHx/X\ndN7Z23ZQP/zdK29+qPrFEf04eB/46nwv+/t1wkdfCOPevBvras+pU6rf4zvwHR6/5x6v/fy7mh68\n7GLQa6++HSk/mWu0XKD9EmrAaEhL9hjVb13w2vOu/9xu/89g6YdLt04pxXzspmTKNCe1bClJdoYp\nzSF3ma4/LBOJi8YZJsqh6//9I494bV47nBQRFa/p0VmbQKPmM5tLSR+ow/Vy4ljJPTr9iSWmKVW0\n3zXr/Y4liw0fYD+efYdOYBmomb4EvHA95BzxsbpW5NwAWcQEJW80fqjrZN4GSP9YmjE4ounq8fVY\nm2uWQXL9/sGTql9FIWrA5gV0rg3hPiy7U8vLWY4cppSpISfZc5KSQQIV2JvdOSGURDZ7Ds1FJ7no\na9+5y2tX7cZ6q2vTKUTlWXouRRpNryLNaHhEpxjmUTpS4xt4NkhI0d+puwNzIXsW1g5LW0REkhdC\n4tBDyZycCCMi0t192Gv7fBjT/A2o65U/0nV45b2wVDhOzwNpnVqKmFcECYufas1QvZb7MqJpjPuv\naNnZhcOo37mUdDPqyF/zSIoTaXAi65T7nEHydl8WxsZN0spZC6lkTAydI6f0ObK3geQi9Ew451G9\nrjghmM+YmeU4FAz2aSkxJwqNL8H35nUpomsK7yXRvs+XQ46RPJDPESIiQ62oLyyt4veL6HkwHcha\ng/2k7pUL6jX32n6L6jffUf/me52Yg3WVnqfTVuPjcRaNobNxb6Z+rhwMYX/pOgcpXDI9n4Q79dkz\nnuSrg2HItn2ZOiXLT8/H3//DZ732tx9+WPVj+WoPpdlVHdbPRfM34dzNEnM3he4/kvsac8ZgMBgM\nBoPBYDAYDAaDYQZhP84YDAaDwWAwGAwGg8FgMMwg7McZg8FgMBgMBoPBYDAYDIYZxDU9Z4aaoIEL\nN2t/hJgo/K6TNAeayarntF49ax48WcrvhldJR5X2NBnph2YvazX0nVNTWpM4SLrp5mp4Xiy4ExrC\nhrcvqfd8eA5RXg2d0Ipt3KrNCPYexXcqo3i7yWEdIXm0GvrOwhJo5oKFWo+euQpa++1L8Fr9C1rH\n19wF/ejS+yTiyCZNtatXXPnoGq8dIu+W/Ou1NnXP3yJetfoQtLRf++8PqH55N0Az+v7/2eO1r1ui\n7w3PLfYKeff7iEdfc4Men8o98N7Y8J3rvHY4X8/Nj//mf3ptP+kdyx/WUdevffcZr33n33+NXtFx\nhof/Dl4ySQHoUVOX62sqv276ItETC6F3nO94OFS9Cn3m0scwnr1VOpKy+Q2K4KOYx+REHfPLuPA6\nPjsrXftEZW+BFpx9S1TkaL72cuglb5DENP15jD6KK+ZY3oDzefeWI3b50N7TXttXqHXmceT/wVrP\nyVG9tsf6ptf/aZh0seUP6vnN/jzs4ZA/V88z1sjWvYGaxTGHIiLHvw9PEK7X6at1DCfroGOuYOyG\n2rBGE5xY43rSIk+QbxVHe4uIdH+KGs0xo1nJehy7B6DnTu5FTc1cpb07Os7CIyCVfG8ynPhK9o6I\nNDie+srzeh/jezHvEWTO+gO6nrK3zPAw9NBTU9pvofUk5nTOMniDjHRofTXvwYf/ET4IK/9gg9d2\nY3l7T8NX4rl98CRaVqq/a/582rs6UFOWBYOqH/thsJ/Uuae1F1tGHr5r9jro291rylpfJNOJ9gvw\n+slzvI1GQ/BqyJiP+dh/VfugsY58ksa+y4kPL7wR3lBRRzDeMQHtj8deTm++/onXjiWfmgf+Ypd6\nz8BV1PUU+q7DjgY/JhGfzT4NjW9dVP14Ln34Psbu+u0rVb+0xTjbxWdC3991uFH1C7VpH6VIoqsL\nnz131yL1Wizd21hB/fen6v0uPh3/nhiGx0Rwo14H9QfhTZFJ/nzBbO3hlU2eH3y26adY7ZOvaJ+a\nssXYS/vIv4J94kREAgm4z+zvVbdP++hk5GGPGO/DZ/hLUlU/jshmj7ryhTqam+fEdCAuG2OQkp+j\nXus6iujq1MW41xPOuTyNfE6Uz4cTdcv+UqlL8bdmOf5PDR8c89q5W+Bf5PfDUyJlqR579rlYfT+8\nKmOc+hKXAk+0tgOIOs/eVKz69VWT3xWtt5rntCdHmh8+Gv4MtFs/0H4q/tkY/+K5ElH0XsDekLVB\nXwd7v7BXy/iQHsOhTsz9sT7sVyPd2p+FPVDjknEvAwHt5xkXhzocW0hePKPY+zJztqj3hMOIUGY/\nmszZ2i+lv7vKaycV0nob0muWx35yFM8WY316XsaQn0sx+Xb1OZHbA1f0HhRp9JzHPRty9uSecxgT\nH50JB6q1B1LaIszVUYpR74/WvnftHUfwGu2t4Sb9TFe8C/cjeyl8vAY7sdcMO36HvP+xt6RbDw68\nfNRr33I9zO1cL04+vy5dgv18/mJdr3rOYW7FUuR77wXt45V3/Ry5Fow5YzAYDAaDwWAwGAwGg8Ew\ng7AfZwwGg8FgMBgMBoPBYDAYZhDXlDWlkxSl7pKmLS1cCkpO7SFQv4qWaypy8lxExnVdgbwoqUjT\nKwebESHX+nGt154I6zjgtCWgEFWfAx2QI6uYXiwict/DN3jtXzz5ltce69f0s9se2uq1mY7/3Gs6\nRvautWu9du4NoL66tKrREKh4g42g3xbdo6Mmhx0pWKTRTPHDMUmaRp2/A/SslHkYqziflgkUkgyG\n6V27/8+7qt+df4toRpbOuLHYqUS/nk/Rk5dbIFuoPnRFvadiI75rQgrmZu5mfU373gAd9eE/ecJr\nh+o09Xf7tyGzm5hAdF24S9MI1/6Xu732+DjGuLemSfWrfRWyq8Waef6FkUByHqbOiogsegh0y3ai\nyPY36FjGzbev9tptJyhaLknHSTc0g54aLIOsJC7dp/qx9IijPM8dgqywtkNLq+JiUHJufwSSpM5D\nul/aCtDBOw4iOs+No/Nlg8JbQRTy2GR9TU1vYw1kbwVlu/3jetUv7+ZrUw2/KGpeBK0zOkpfS8oC\nrL/5j0FC0LqvVvULnQU9Mnsz6MMu/bX8NtA/w62QDfE9ExHpOoy5kEaSp54zoLeOdmla8QRFuzNd\nf/YCXTcuncCa88VhnZ64qtdiSRbel0BSxMbXtOQiJR/U5MQ8UJszl2v5U+W/HZPpwhTtL+UPaxp1\n9XOQIXUcwtxqCukI+JK7MTap2Vi/oXYtd8hYhPGNisL6y3CkjfGpqAnZRHvuOo19jO+riMhAEvpt\nmjfPay/etkD120/1dB1JgRNy9ef1E7W5i+aOKxdob8I87frRQa8d3KxlJJPj0ysx9FMEfFJQn0cG\nLuPecC1pOazrRaIPrw0Mg74dGhpS/QZex9ln5e9DatbjUJ05CnvzfJwTfFTza17S1PCiWyCzmCJ5\naUp5puoX7kANGO3Bdx3r0XHmHAl+453rvfbgVR3rPE5ns8QirEufI4EUPaUjivR0kkc6sbzdJyCH\nyViN9TLpSB77SP7bRbJbf5LeZ4tWYy1W7sceV1am12I6/a2oWNT47uP4PvkkcxMRGRsA1b72Aqj6\n4xNaYl12C9YpxxOnNOqaHkcx6oNE72/7UMtchkkuwpKpyRFHFjq9S1F66lA73DNqxlrcz8BsSA36\naKxEROpOYm321ECasnqnlqOwrKb3HMa+rV0/46z5JmTwiYk4F3RchdRvrE8/Q7AMKWcDzhnjYd2v\n8S3MH5bfDTZpCSCfd4Y7sS6T5uh6NVyJz+cYa7df8px0mS4U3oo6NNyl6x/Lytmqwj2nzaIjUf5i\nyI26mo+rfslZkHCHh3Dm7e3WxSY9C7U2FIKEJikJtXXWLP0YPDaGOhdIxTWNj+so7YRknDlq3znk\ntbPW6mfgwSacw2eRzCV1kZbEsRRvJISa7O6D/Kw7HUiZi7PYQK2u+Xx2jPVj3vpydP05/OMDXnvr\nX+zw2q7ka3IM19Z6pEE+Dx3HURNHu3GWSl+O82pCnt53eC/lvaH3rN5ze2mv/toDN+PvdOgz7/It\nJFMnaaQ7PuF6rOHcG3GmqX1R25mkshyqUH4HxpwxGAwGg8FgMBgMBoPBYJhB2I8zBoPBYDAYDAaD\nwWAwGAwziGvKmibHQKksuE0ngQyTi/M4uU4zrVZEZP/PkAKx489v9Non/nm/6rfsCdBnWcpU/uBW\n1a/9NKhBax9Z57UrX0Rqxvx7dQrK1Vfxnq5+uECfPaep9devBY0ujZiQgTPa3Z9TKUaI7s+0QxER\noVSYEXotar6m/ncPajlUpMGpOgP1WupS9zLuTdlXIHu5+PSHqt+838drBfVEKXRomJU/gvN13jrQ\ngDOW6oSYd/8O6U8bHsI4BhpBW00q1RTMoUZ896EeyJ9+8zcvq373/BU0RUf+4RWvvebPb1P9Btrw\nGdHRRE+v11Q+RkYR7sNIpu63+NsbP/d9XxS8JmZlagph7SuQU42Ng7KbtUC7iHMySHIuaHmuVGjt\nTlBBL/8K6yrekbb802uvee3TZ9BvwUKkyvzlffeq94T6QQ1tPlDrtRsdKdkyokn2UUJbspM20UvO\n6OMkN4lL1RKstNsRTTDQgM9LW67vUe0bcOCv2CARB49P7jYtoQqUYO4zLdiVXHQfAz2ek5bSF+pU\np0sk7cmn+j3ozO/i+zFeTFU98P2PvPb8bfPUe+oaMF5vffqp1+5xatmqGxZ77aE61IrBEe2Yv/Jh\nWlc0zwp26n3n4rOQgHIVbXxHy4ZyNuq0iEgilmSAJ/81hmbbAAAVN0lEQVTtkHotJwgpSc6WoNce\ncCSGnJDTMxtzeMKh/eZfB/r1yAjGve+iXi9RMdhrxilpaCCMfsOtmpadvhK0bK4Np94/p/qtWIwx\nYJlaSbGeb/k3Q6rReQxSuXk36Hne/D7kqlmU1tT0hpaw5e+cPgq+iEjyIswgV5IcT+kxnKqR48jn\nhhowpyu2Iy2o3UlJmSJade2vcX/HHPlI+SPL8Nk1WKeNdZCJ+X26tvHcz6QxTSkKqn5CKqcRqi+p\ny/Q4slwpOv7zj4icBBNLUpTYFC1ViHdklJHEKK2XoWZ9Fkkg2eMgjVNMqv5+yZT05ifZzGCdXrNM\nwU8giSYn4IhoSdBgPcmgSd4w8WqVek/aSowBy+M6+vQ1LR3Fd2BJTcoSfaYcJblS3tag145J1JKh\nvkrIesZrcN5PKtdrj+VZ04HSOyClbNqta3nqQvztrk+b5fMw/3asv2MvQAYz3q/3mqv7LuM9d2J/\nSgnpe1j7S6zT5AU4K/K8cveZrHw8rwwMUI2v1DU1OuGz15UrPe0jqehoLOaFezZm+4h+krVGOylR\nUXHXfOT7QuDPZnmXiEgOpTdxLYyO19+Pnx9bLyKtLqtMJ8X1tuN+smVCyYYbVT9XsvRbNJ3G82fZ\nuofVaz4faqjfj72rq2uv6hcTAyln6a2Q6DcfP6L6pVRgXrXuxf6Ze52W8YbbsT/HUbpQuEUnF6Uu\n0WfWSKOHUgwTC3Tqae1vIKllixFX2lNcgteuPoMzW+oKvddERWNtp9L5l88mIo48lGS4ja+gjvaF\n9fNJCcmkj76BM2phRobqt+M2PH9ePorxmb9dn3lr3sLfSs7CfXGT7NLX4LvzHhkf0PuOaBXu78CY\nMwaDwWAwGAwGg8FgMBgMMwj7ccZgMBgMBoPBYDAYDAaDYQZhP84YDAaDwWAwGAwGg8FgMMwgrilA\nPPUk/ENW/oE2YGjZA23W+SuIsBu6oPWdt/3nm7z2pZ8j5mz+gzqCdHIcetei2+APMTmpYx7TF8K7\nJDqatJ+l0ISGHW19Wy+0vl/fjvjkxEKtpxskXwCONdswd67qV0Aa+g9+sc9r56dp7Vn+HOjuBkg3\nmLOpRPVj/fJ0oO8ydKvDzVq/OPsB6HRb9iHer/QhPT6zZiECrvMAIs9K7luo+sWTH0ogiPsx2qfH\ncdlWvK+JPAgylkKTyO8XEen8BH+3+iw0hHd8+2bV78RPEM+aEcAYH/r711W/Dd+9x2tffQXRbxwL\nKiLS8gHm+thazO8rTqRp5iJ89xwtY/3C8DvR8+rvkmeALxfXGx0Xrfqx/ri/CprggKMvZ++kgm3Q\nbU6O6FjPrVcwd/50F3x+6jsRcTkyoj00WkLwUSjNRpQg+w6JiNS9i7mYtRhrftSJaIzPpAjhNHgx\nxKVoX4aOw5g7HAE8PqC/H8c8Tgc4PjvcrOtUShm0sBwTHRWrxzGWtKt8ndHR2tuh7OuI/htqhXfB\nvJ0PqX59fYh/Humn8VkEP5D9rx5V70ki34s/+e6XvTb7VYiIJJDfRP8l1KGA45vR/lGt185Yh1zB\nqmd0NGbOCsSqxqfDF8Rfotdsz6eIkJZbJKI4/1Pci5RE7Ufmy8e4jZDvQ6wTD8sxjxf3wDOqYLbW\nk/c3wY8mlvwicjbrPWQ0RB4T12PNhi5hLbqR1nHkDXLlLPbwkXHtgzKLvNOyU3CfC27VfkBjg1hL\nIYp1d+tGP3lvcE3KWK/zJNnzbjoQR/Xi6nuX1Gspfoxr9vVBrz3k6P87WuB3EFeNWjTlxDrnbJ/t\ntflsUfOqjtc89iP4LExMfnZ+cfeArhsrdsGnJon2iauvHlT9wk347lG0N7jzorUFY5dbgJrEEb0i\nItHkF5C+EjW675L2m2g+Df+hFV+WiGKS7pHP8bbp3I+az55bUXH6/0nyvchaAJ+B+HXa96Du0Lte\ne9U34S+XnKXPh5/8z2e8dvAmRPEW5cGTyd2bY5MxF1fdivGcHNVrgGtK4Q6KLu7RcyJEXjKD5F3k\n7jlp5AERoIjxqBj9/dizZ7ox6ezB7PEyQl6Xk8585PFe+6W1eI9zZshMRw079zL2vlS/nj/sb5e7\nBes3Nhbn0qgoHbc+Ooo9rr8V36f3fIfqV3Az5iPXlGif9mDxFyZ/Zr9B8lkRERklH47srdgbXB/M\nprdR50rmS0Qx1Iy6njJP+/fMonPP+DD2ie6zbapf5krs70PkZ9mXpuvkaC+uNzEf96j10gHVL5rO\nTonZqGW8rsbHnfhyetZprn0V36dV135/AXlgtmFdhZyo5tBpXGMUeXg17dZ7TtZ6iuCmPZf3XxGR\n3gs0l7ZIxMFzifcqEZEsOpsl0lmnzXmuzLke66WePDHZ01BEpPMwIrLryVctVKfnd3wsPbuQt0zZ\nrZjE8ef1fWf/osWruP7ra2LPzaxkzKWWwzrau2BT0GvH0HX4af6JiHSegC/WRD/m+uiYPle170cE\nfOly+R0Yc8ZgMBgMBoPBYDAYDAaDYQZhP84YDAaDwWAwGAwGg8FgMMwgrilrWvLoKq896sTR9bWD\nCrZwDmLSZsW5dEhQvzopFrAiRdMBp4ieypTg0FVNLTrxLOJhiyiuK3Mt6HCVr5xV79n8BOLtmOLI\ncXsiIosptpSlBOm5mjIf4wfFiiNhV/6+ln6N9IB+Nf4xKExuVHNLj6ZwRRrMWg7ev1i9dv5fD3vt\noVFQsFxKeecZyASKbwGdNiFFR6M1nkd8YOZionIn6KhSpuplrAB9ODkXlMzLL+1T7wn34n7WtIPC\nFjinZTksNQg+APlUIE/HHg52glIX3LXGa0dFaerdpXd+g8+owN8qofsgMr3U33qKsMu7RUfT+otB\nv+05TXIOhw7Jcp6MNVgvoyEtORvpxlgx7bT4Nk3f3rQRkfXjRN+L6sLfZRmPiEgiSfiYnh/9gY61\nLyQa8QRHk17WdajodtDQT/xfSAJCNd2qX/nDoIqHzuOa4tJ0HUr0a7lNpMFx3xNDWgLUexlyAJYg\nJJVqeV/qPMjBYmNRm3qu1Oq/RZ+fkI2xb2/c+7nfLyk96LUD5ahLS9tnq37hQcyZzkNYRxlrtBQg\nPgNU8SmKos1I1pJSjnKOTYbcJjlb92OqKlP3xaHCZ22avijtLIosT3NiLcdoHYSJ6szSAhGR7C2o\ncz0vo25kOtKeyl8ihpJj2OdsLVP9mPp/+Ht7vXYdSQx3/dmt6j1Djfi75SswvunL81S/joPYgxNp\nPMed+duxD9KoxFTU4OhETdWPT8BYTw7jM5Ln6IjL3otaChBpcKxwaoqOsI1NRx0IUQ1MLNIU5sxM\nrL9YmsM5O3RMqo/kvrW/wvkk2ZGrjtehbnGk8iiNfXhUSzGHKPJ5gGJ0Yx1ppy8H36GpCjLw2Zv0\nfrJoA+Ygn5cmhjUtO4Yi5VkalLowW/Vz41gjidl3I4K5jc5YIiIJRfi7XcdBNWcau4hIXyXWCMsd\nCp3Y9CSKek3Jxt8dH9eU/s1//Ti+UxViddNKsWbT07WMt7nhNfydjaDgx8To2h9qQAx0Rsb1Xrt1\naLfqN1iLetPdjHZwu64biSSDTirGXB5s1GcZV04caXQcQO3wJet5G0P1Y5TGLn2hls60nYR8jqPs\n01bpeubLwzrI82HeshRDRCRQhLPeaD/Onk2H8dyQ5Mhp+bDNdaNwpz4rslSezwFZTjR3/TuQviQE\ncFZJLNZ1iKPPWcrE8mgREZmcPtn2aC/OZiFHrsTPTBy7nLJA14qOow2f+Z5wh35+iKJ5EMjFWXZq\nSktBO84iln2gDusgmSTkTZXvqvckZGEv4D3CrX9Xn4YkLn8n1izXWRGRlPmYp217a/F3nLrYSTUq\n7zrMRZbUifyudDzSYFl0f61+NmUZ/XAnxiTbOW8Nt+O13OuCXjvK+X2g+C7U0bi9+GxXQpuzBZ9x\n9kn8BsCyq+B9i/gtMjaA+XjoB2Q/kp+p+vHaiYnF5xXdoZ93eum8yXYIruwsh9ZwCz27lN2kZeDV\nP9a/P7gw5ozBYDAYDAaDwWAwGAwGwwzCfpwxGAwGg8FgMBgMBoPBYJhBXFPW1HUMNMEEhx5XQWlL\nHYdAReMEBBGRcZJDlS4F3afq3zWlZ97XV+LzjuDzjrx3WvVbtQUyFaZfjxHNyKX9XngWyT6pGbiO\n1fevUv18GTp547doPaDpskxX33YDvnf9S5WqH1P8Oc3ATV9ZvW2JTCfCLfi+V54+pV5LCGC8UjJA\noS28QcufOCGmn9KfMhZoSUz+jaDNNu2FFMfvJCBdfAnU7pRU0AgH+0547WV/tEm9Jy4OFMiKTtBg\nA1maQt7XhvSnMNHrYpO1m3fbJ/iM0l0YH05uEhHZ8T/+0Gv3dmI+XvrZCdVv1V88ItOFaD+W6tlf\nfapey0wBxbXwbsh8eJ6KiCTk4D5Pjn1+ygX/mym3vVWdqh+v9QlKeliwBXTAmsM16j3rHl3vtdnx\nfKhZ06jZgf/q85grc6lOiGiJYHwM7lHxrZqS2LYX3yNjLWj7be9rOZVLF440kpJATfblaSkFU5CT\nCrFeWNYlIjJFtOD2c5ARJjnpZv4C0IJT0iDrqn7nDdUvdS5onvV7IXNk6iZTwUVEwpex7tOWQ9rT\ndkjLUCdIJlB0L2SjLe9cUf3G+vC3OMkkoVCPxzAl8SWXg5rMNFoRTdePNNIpHe38M3otFqzgNAN8\n9/omTQ9OHcY9G5/APRqs0/Kn8jux34UpvaLVSRLIppSLHErYyc4FZTcuoPfmix9jLwjejPXiJp1x\nmlvXEZwJ+p3EkMQg5mwaSVvCztgISfuaKyHD9DljNuYk/EUas6JBR3Zp1CxXq6eEE1cqGk3Ue5Y0\ndDgSG38Z1iYnDCU683uQUi9SsvEay6mi4/X5oes4JErB+zFfLj6n9/o+SrlYeBso4F2Hm1S/y/sg\nBVh8P2Ikxhz5a2wq5gnP20CZlqcNORKHiILuedpyLbHmmpBK0lA3SavvIva1mATsIXUHtdyBZcup\n9+I+x8VpmvzYGNYFyxxD9dhr3GQ9lsA3HcX9L9qp97F4kuEe+f4/ee38m7RciaWh4w2oLz0nWlW/\n9iGcgcaoDuWs0JKunvbpTWuKpeQ4N6mFJWmZtHe7coIJkram0p7kJiXV1eMeRJHsuuOX+u/Ovxvn\n8p5TeE8mJdY0v6ETdwrugHyJ03c4MVVEJJNk5f0kQWPJj4jIbJJ9DFFtCJRqKf8lkr+yFDgqVv//\nd5a7RRoDV3GNyfP1muCzxEgHZFf9zn0Z78PzIq9ntscQEcnfzFFTuMaJCS0xjE/HeukjuVf9S0h/\nSnLSSvm8xelZU056Xnwu1jCnEGWu1tJkPtvGZ1IK4Noi1a+f5v0IpS+O9WkpvzoP6kefiKDjMGpC\ncoUex0E6b7P1BUtDRUQS8nG2nRWNexPnSBZHyaoiaz1+H6h/QadzdZ1EDZh3P9bl2V/i/JVTrL9r\nGp3TShaQjUNHWPWbouvIvQk3lJOkRERSFkCe1vQhannpXP13G99CTeC6duXnOnnUV6DP/y6MOWMw\nGAwGg8FgMBgMBoPBMIOwH2cMBoPBYDAYDAaDwWAwGGYQ9uOMwWAwGAwGg8FgMBgMBsMM4pqeM6wd\nDp3Xfh3N+2u99iRpPXsqtb48Pw1aX9Znx8fpeM1T/wavA47Y3XCn9oVJo+g11om3kndEXpr2XojP\ngO4wjtpHnj+q+i3ZCh1j+lJ4kORv01FmHFfZUAktXHCFjhOr2wPtMF9T6VytSTx1Cv1WPCwRB0eM\nJSXrOK/KX8J/gvXRl578RPUbCkGvuerP7/Ha0dFaQ3j5HYxjRz2igZcuWqv6JSd+tvdGxeOItB4P\na417TCJ096m50Hx31mhtvS8dus6eVmiFR7t1HGQK6WLP/+A9fO9era8e/v+e8doczR2bpCO3p6Z0\n/HgkkbIY8549U0RErr4Jr6MaitzOWKY1+Bxl3Ep+Oz6KRBURydmO+T5YSzGtw3o8cuZSpDPFyPac\nRa2Yd7uOt1M+VuS3EJ2gS1HPWYxbLsUOsxZXRKR2NzxXMiqgCZ2l5ejKZ4DjmEcHtT+VNGufnkhj\njPTCqa5W9U1oVYvvRi3qu6J12RzLm0Ka4MbdWv8eKIOWerwE8YNurHMgiDhf9jTgdkq5/q6jvzrj\ntTlikmOSRUQyqI72UIR5T6deY0t+D+u++hdYz+zHIiIy79EVXjtUiXkWmKN14w1vVHntwu9IRFH/\nItbbnJuduEXyN/BlYZwyix2PgLegqV70IHw9WOMuItJxCOt0cgT3IpCrvUr6q1BrfaT35kj20CXt\nvbDw8dVeu+skfEt6L+h+qeS/kksR0a7Pzyh5koTIxyMuVe8R+TdiD8pYBS14zxnth5HsrI9Ig7X8\nrv6bfWaKb4WPhHtvkhfj3gyRJ5DPiUlV0a+zsd5inJjxwltwbzgmtJO8nKJ8ulbG0mfUkVY/tVif\ng8ZqMH+6DqIO5+/UfiXZ5DPG0dI9p7R3DF+TkH/PaJeu0X1N2i8ikjjzLLwL/T49z3oGMT9L2lCH\nYh1fxE6qI+EGrJehQb3fZS3Bftp4BL50fRe7VD+O0mXPD452HR/UXlXVr2HfTg7g/BJu1/sRz7HO\nVtTxMcejIe8mxKOzN5AvU/sq1ryFOll6O/YcN663bOd8mU7kbMWZY/hX+pr9JbReaO673kF8pkya\njXrL51oRkU274Mt3+Vl4CGaRD4yISLgFc4H9NYbJy693UJ8pZ5GXWv7NGAPXc7GJfClSKBKc43pF\ntK8O+06N9uq5WfEleMp1kz+HG6XNe0ikQY+BymNGRCSTPNF4To8P6nnWT2cd9thJyNX1uZn8LKMp\n7tpfpPdF9ivKvwHjkUW+MB3HtLeI0HWw75nr15NEdTxMsdJJBXqsU8qwj/H4cmy4iEgseb2x/2nI\n2XPYR2c6MMFzxDlIu+PwW/Rf0mfU0EnsFYEFuP7f8Xaj5/lGio0vvm+B6tf6ETwj2X9oySP4fSA+\nVd+XIVq/xXegfvH5X0Sk/nXUwKFGvMf1axqnZ4UU8nyqeUnX3oyl2Cf4fuVu1b8juLXdhTFnDAaD\nwWAwGAwGg8FgMBhmEPbjjMFgMBgMBoPBYDAYDAbDDGLW1NTU1H/czWAwGAwGg8FgMBgMBoPBMB0w\n5ozBYDAYDAaDwWAwGAwGwwzCfpwxGAwGg8FgMBgMBoPBYJhB2I8zBoPBYDAYDAaDwWAwGAwzCPtx\nxmAwGAwGg8FgMBgMBoNhBmE/zhgMBoPBYDAYDAaDwWAwzCDsxxmDwWAwGAwGg8FgMBgMhhnE/w8L\ny1ipTo08DwAAAABJRU5ErkJggg==\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": "qQAnmNSlZpFq",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1136
+        },
+        "outputId": "22004025-2ae2-495b-a0c5-76b60246f89f"
+      },
+      "cell_type": "code",
+      "source": [
+        "weights1 = classifier.get_variable_value(\"dnn/hiddenlayer_1/kernel\")\n",
+        "\n",
+        "print(\"weights1 shape:\", weights1.shape)\n",
+        "\n",
+        "num_nodes = weights1.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(weights1.T, axes.ravel()):\n",
+        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
+        "    ax.matshow(coef.reshape(10, 10), cmap=plt.cm.pink)\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "weights1 shape: (100, 100)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1440x1440 with 100 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FkS_bLu6Zv8J",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "outputId": "fdfccc1b-f87c-4624-b311-a22a79fc4256"
+      },
+      "cell_type": "code",
+      "source": [
+        "weights2 = classifier.get_variable_value(\"dnn/logits/kernel\")\n",
+        "\n",
+        "print(\"weights2 shape:\", weights2.shape)\n",
+        "\n",
+        "num_nodes = weights2.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(weights2.T, axes.ravel()):\n",
+        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
+        "    ax.matshow(coef.reshape(10, 10), cmap=plt.cm.pink)\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "weights2 shape: (100, 10)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1440x144 with 10 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ 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..1a6299c
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1175 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "sparsity_and_l1_regularization.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "yjUCX5LAkxAX"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/divyarachel/Assignment-5/blob/divyarachel/sparsity_and_l1_regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Sparsity and L1 Regularization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "g8ue2FyFIjnQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Calculate the size of a model\n",
+        "  * Apply L1 regularization to reduce the size of a model by increasing sparsity"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ME_WXE7cIjnS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n",
+        "\n",
+        "L1 regularization is a good way to increase sparsity.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fHRzeWkRLrHF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and create feature definitions."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pb7rSrLKIjnS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3V7q8jk0IjnW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pAG3tmgwIjnY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1209
+        },
+        "outputId": "2ae0d23a-d966-49d7-f81a-e9e1eb8bed16"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2650.7           540.1   \n",
+              "std         2.1        2.0                12.6       2183.5           419.7   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1452.0           295.0   \n",
+              "50%        34.2     -118.5                29.0       2136.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3165.2           651.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1428.9       501.9            3.9               2.0  \n",
+              "std        1097.7       381.6            1.9               1.2  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         788.0       281.0            2.6               1.5  \n",
+              "50%        1171.0       410.0            3.6               1.9  \n",
+              "75%        1720.0       608.0            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2650.7</td>\n",
+              "      <td>540.1</td>\n",
+              "      <td>1428.9</td>\n",
+              "      <td>501.9</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>2183.5</td>\n",
+              "      <td>419.7</td>\n",
+              "      <td>1097.7</td>\n",
+              "      <td>381.6</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>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>1452.0</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>788.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2136.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1171.0</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>3165.2</td>\n",
+              "      <td>651.0</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>5471.0</td>\n",
+              "      <td>16122.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.5       2626.8           537.6   \n",
+              "std         2.1        2.0                12.5       2171.6           425.8   \n",
+              "min        32.6     -124.3                 1.0         15.0             4.0   \n",
+              "25%        33.9     -121.8                18.0       1475.8           300.0   \n",
+              "50%        34.3     -118.5                29.0       2107.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3114.0           644.2   \n",
+              "max        41.9     -114.6                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1431.1       499.6            3.9               2.0  \n",
+              "std        1260.1       391.4            1.9               1.1  \n",
+              "min          13.0         5.0            0.5               0.1  \n",
+              "25%         791.0       284.0            2.6               1.5  \n",
+              "50%        1156.0       407.0            3.5               2.0  \n",
+              "75%        1724.0       597.0            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              34.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2626.8</td>\n",
+              "      <td>537.6</td>\n",
+              "      <td>1431.1</td>\n",
+              "      <td>499.6</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>2171.6</td>\n",
+              "      <td>425.8</td>\n",
+              "      <td>1260.1</td>\n",
+              "      <td>391.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1475.8</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>791.0</td>\n",
+              "      <td>284.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2107.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1156.0</td>\n",
+              "      <td>407.0</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>3114.0</td>\n",
+              "      <td>644.2</td>\n",
+              "      <td>1724.0</td>\n",
+              "      <td>597.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</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>34.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.2\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gHkniRI1Ijna",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bLzK72jkNJPf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_buckets(feature_values, num_buckets):\n",
+        "  quantiles = feature_values.quantile(\n",
+        "    [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n",
+        "  return [quantiles[q] for q in quantiles.keys()]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "al2YQpKyIjnd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"households\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"longitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"latitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"housing_median_age\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"housing_median_age\"], 10))\n",
+        "  bucketized_total_rooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_rooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n",
+        "  bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_bedrooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n",
+        "  bucketized_population = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"population\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"median_income\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"rooms_per_person\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"rooms_per_person\"], 10))\n",
+        "\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "    set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n",
+        "\n",
+        "  feature_columns = set([\n",
+        "    long_x_lat,\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_total_rooms,\n",
+        "    bucketized_total_bedrooms,\n",
+        "    bucketized_population,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hSBwMrsrE21n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Calculate the Model Size\n",
+        "\n",
+        "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "e6GfTI0CFhB8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def model_size(estimator):\n",
+        "  variables = estimator.get_variable_names()\n",
+        "  size = 0\n",
+        "  for variable in variables:\n",
+        "    if not any(x in variable \n",
+        "               for x in ['global_step',\n",
+        "                         'centered_bias_weight',\n",
+        "                         'bias_weight',\n",
+        "                         'Ftrl']\n",
+        "              ):\n",
+        "      size += np.count_nonzero(estimator.get_variable_value(variable))\n",
+        "  return size"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XabdAaj67GfF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Reduce the Model Size\n",
+        "\n",
+        "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n",
+        "\n",
+        "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n",
+        "\n",
+        "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G79hGRe7qqej",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1: Find a good regularization coefficient.\n",
+        "\n",
+        "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n",
+        "\n",
+        "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n",
+        "\n",
+        "Again, the model will train on the entire data set, so expect it to run slower than normal."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1Fcdm0hpIjnl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    regularization_strength,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    regularization_strength: A `float` that indicates the strength of the L1\n",
+        "       regularization. A value of `0.0` means no regularization.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 7\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on validation data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9H1CKHSzIjno",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 599
+        },
+        "outputId": "11719e1e-ac65-4869-8191-2938ea48272a"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    # TWEAK THE REGULARIZATION VALUE BELOW\n",
+        "    regularization_strength=0.0,\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": 9,
+      "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: 795\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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IY1Ax46D6dKpqRHnwRA7f769fI8qL7ugwijZeYXx/+kd+St9vkzFFREQam4oZ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\nAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yjUCX5LAkxAX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hgGhy-okmkWL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n",
+        "stronger regularization gives us smaller models, but can affect the classification loss."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_rV8YQWZIjns",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 599
+        },
+        "outputId": "975ac6e6-382e-4918-c9c5-a1519fef6982"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    regularization_strength=0.1,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\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: 754\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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woGNfJvqNoaCmiO9kukkIIUQPJM2MCQrv68yIMHcycyvYe/xyp8eb3S8ad2tX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\ni+XcK1AQdwAAAABJRU5ErkJggg==\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..2412a6e
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,970 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/synthetic_features_and_outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Synthetic Features and Outliers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jnKgkN5fHbGy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Create a synthetic feature that is the ratio of two other features\n",
+        "  * Use this new feature as an input to a linear regression model\n",
+        "  * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VOpLo5dcHbG0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n",
+        "\n",
+        "First, we'll import the California housing data into a *pandas* `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S8gm6BpqRRuh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9D8GgUovHbG0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "dab7a3c0-c045-4597-dfa6-f6c72d9e6015"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import sklearn.metrics as metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>622</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>32.7</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>2061.0</td>\n",
+              "      <td>391.0</td>\n",
+              "      <td>1169.0</td>\n",
+              "      <td>400.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>142.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11258</th>\n",
+              "      <td>-121.1</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>1106.0</td>\n",
+              "      <td>195.0</td>\n",
+              "      <td>505.0</td>\n",
+              "      <td>187.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>192.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10367</th>\n",
+              "      <td>-120.2</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>866.0</td>\n",
+              "      <td>252.0</td>\n",
+              "      <td>369.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>70.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7880</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>3979.0</td>\n",
+              "      <td>510.0</td>\n",
+              "      <td>1351.0</td>\n",
+              "      <td>520.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14379</th>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>3598.0</td>\n",
+              "      <td>500.0</td>\n",
+              "      <td>1296.0</td>\n",
+              "      <td>533.0</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>500.0</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>9068</th>\n",
+              "      <td>-119.0</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>2474.0</td>\n",
+              "      <td>625.0</td>\n",
+              "      <td>338.0</td>\n",
+              "      <td>141.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>195.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5947</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>846.0</td>\n",
+              "      <td>271.0</td>\n",
+              "      <td>1153.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>155.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4609</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>1161.0</td>\n",
+              "      <td>254.0</td>\n",
+              "      <td>882.0</td>\n",
+              "      <td>236.0</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>158.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14241</th>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>830.0</td>\n",
+              "      <td>228.0</td>\n",
+              "      <td>368.0</td>\n",
+              "      <td>174.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>342.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8109</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1488.0</td>\n",
+              "      <td>313.0</td>\n",
+              "      <td>1221.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>171.4</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",
+              "622       -117.0      32.7                34.0       2061.0           391.0   \n",
+              "11258     -121.1      39.0                16.0       1106.0           195.0   \n",
+              "10367     -120.2      37.7                13.0        866.0           252.0   \n",
+              "7880      -118.4      34.1                27.0       3979.0           510.0   \n",
+              "14379     -122.1      37.4                36.0       3598.0           500.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "9068      -119.0      37.6                20.0       2474.0           625.0   \n",
+              "5947      -118.2      34.0                52.0        846.0           271.0   \n",
+              "4609      -118.1      33.9                37.0       1161.0           254.0   \n",
+              "14241     -122.1      37.4                25.0        830.0           228.0   \n",
+              "8109      -118.4      34.2                36.0       1488.0           313.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "622        1169.0       400.0            3.5               142.0  \n",
+              "11258       505.0       187.0            5.0               192.3  \n",
+              "10367       369.0       165.0            2.9                70.2  \n",
+              "7880       1351.0       520.0           15.0               500.0  \n",
+              "14379      1296.0       533.0            7.8               500.0  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "9068        338.0       141.0            5.0               195.5  \n",
+              "5947       1153.0       281.0            2.2               155.0  \n",
+              "4609        882.0       236.0            4.4               158.0  \n",
+              "14241       368.0       174.0            3.4               342.9  \n",
+              "8109       1221.0       296.0            4.0               171.4  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6kNgrwCO_ms",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll set up our input function, and define the function for model training:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5RpTJER9XDub",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VgQPftrpHbG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
+        "    after training the model.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
+        "\n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "    \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "      metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "    \n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Create a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
+        "  \n",
+        "  return calibration_data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FJ6xUNVRm-do",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Try a Synthetic Feature\n",
+        "\n",
+        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
+        "\n",
+        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
+        "\n",
+        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
+        "\n",
+        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
+        "the final RMSE should be.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "isONN2XK32Wo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "H9EYFHuUsBMa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 969
+        },
+        "outputId": "008f81a1-54f8-4858-dfa0-1373cefdeeea"
+      },
+      "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": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.75\n",
+            "  period 01 : 189.68\n",
+            "  period 02 : 169.74\n",
+            "  period 03 : 152.84\n",
+            "  period 04 : 141.16\n",
+            "  period 05 : 133.59\n",
+            "  period 06 : 131.09\n",
+            "  period 07 : 130.78\n",
+            "  period 08 : 130.92\n",
+            "  period 09 : 132.51\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         200.6    207.3\n",
+              "std           92.1    116.0\n",
+              "min           46.0     15.0\n",
+              "25%          164.5    119.4\n",
+              "50%          197.5    180.4\n",
+              "75%          225.6    265.0\n",
+              "max         4395.4    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>200.6</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>92.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>46.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>164.5</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>197.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>225.6</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4395.4</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): 132.51\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Elyd/3RZwOAiKH139iapZNwxmlJBWoDoqsyTOq8/ILtajoiHC10F9FJeq7Dlk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\nIiIiIiLiE/8f+17P0ROtU8YAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZjQrZ8mcHFiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Identify Outliers\n",
+        "\n",
+        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
+        "\n",
+        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
+        "\n",
+        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P0BDOec4HbG_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "outputId": "5636afec-4a57-49fa-f658-c8e93455ea90"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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W46SU3kPlHdNSOj4REYnn0KFDUKlU+MY3vhE6VlxcjPPnz8PpdKK3txdWqxWl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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 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": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1299
+        },
+        "outputId": "cf1375e7-6573-45d2-adfc-cf39ad43f9d4"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")\n",
+        "\n",
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.82\n",
+            "  period 01 : 189.07\n",
+            "  period 02 : 166.76\n",
+            "  period 03 : 146.51\n",
+            "  period 04 : 130.49\n",
+            "  period 05 : 120.52\n",
+            "  period 06 : 114.83\n",
+            "  period 07 : 111.26\n",
+            "  period 08 : 109.86\n",
+            "  period 09 : 108.71\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         191.0    207.3\n",
+              "std           49.6    116.0\n",
+              "min           45.7     15.0\n",
+              "25%          159.4    119.4\n",
+              "50%          191.1    180.4\n",
+              "75%          218.0    265.0\n",
+              "max          422.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>191.0</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>49.6</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>45.7</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>159.4</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>191.1</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>218.0</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>422.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 108.71\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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62NpcgNdXKs1fKkrNscWipJJSuamO5avUlnrquP0Gq69MWm6GEZxeq3gdHLwH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dBvsP31HjZv7+LQ0UEAPG6ZObPzMvYdZEtwykRQAzIb2JhoQTRXaOscpLs/VneLIIpTpbIiP6V9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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "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:"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/validation.ipynb b/validation.ipynb
new file mode 100644
index 0000000..4045a03
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1515 @@
+{
+  "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/divyarachel/Assignment-5/blob/divyarachel/validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zbIgBK-oXHO7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Validation"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WNX0VyBpHpCX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Use multiple features, instead of a single feature, to further improve the effectiveness of a model\n",
+        "  * Debug issues in model input data\n",
+        "  * Use a test data set to check if a model is overfitting the validation data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "za0m1T8CHpCY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), to try and predict `median_house_value` at the city block level from 1990 census data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r2zgMfWDWF12",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8jErhkLzWI1B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First off, let's load up and prepare our data. This time, we're going to work with multiple features, so we'll modularize the logic for preprocessing the features a bit:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PwS5Bhm6HpCZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "# california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "#     np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "J2ZyTzX0HpCc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "sZSIaDiaHpCf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **training set**, we'll choose the first 12000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P9wejvw7HpCf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "25a38f28-c5d1-43c8-9fe3-4b3e930c3b4b"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>34.6</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>2655.7</td>\n",
+              "      <td>547.1</td>\n",
+              "      <td>1476.0</td>\n",
+              "      <td>505.4</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>12.1</td>\n",
+              "      <td>2258.1</td>\n",
+              "      <td>434.3</td>\n",
+              "      <td>1174.3</td>\n",
+              "      <td>391.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.8</td>\n",
+              "      <td>-118.9</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1451.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>815.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.0</td>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2113.5</td>\n",
+              "      <td>438.0</td>\n",
+              "      <td>1207.0</td>\n",
+              "      <td>411.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>34.4</td>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>3146.0</td>\n",
+              "      <td>653.0</td>\n",
+              "      <td>1777.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.8</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       34.6     -118.5                27.5       2655.7           547.1   \n",
+              "std         1.6        1.2                12.1       2258.1           434.3   \n",
+              "min        32.5     -121.4                 1.0          2.0             2.0   \n",
+              "25%        33.8     -118.9                17.0       1451.8           299.0   \n",
+              "50%        34.0     -118.2                28.0       2113.5           438.0   \n",
+              "75%        34.4     -117.8                36.0       3146.0           653.0   \n",
+              "max        41.8     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1476.0       505.4            3.8               1.9  \n",
+              "std        1174.3       391.7            1.9               1.3  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         815.0       283.0            2.5               1.4  \n",
+              "50%        1207.0       411.0            3.5               1.9  \n",
+              "75%        1777.0       606.0            4.6               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "83f7ac7b-a7a4-4949-c638-46e8fffd23ba"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.describe()"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>198.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>111.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>117.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>170.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>244.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                198.0\n",
+              "std                 111.9\n",
+              "min                  15.0\n",
+              "25%                 117.1\n",
+              "50%                 170.5\n",
+              "75%                 244.4\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5l1aA2xOHpCj",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fLYXLWAiHpCk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "1ed2a68c-8080-4c89-ecf9-19584530f24d"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>38.1</td>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>31.3</td>\n",
+              "      <td>2614.8</td>\n",
+              "      <td>521.1</td>\n",
+              "      <td>1318.1</td>\n",
+              "      <td>491.2</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>13.4</td>\n",
+              "      <td>1979.6</td>\n",
+              "      <td>388.5</td>\n",
+              "      <td>1073.7</td>\n",
+              "      <td>366.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>36.1</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>37.5</td>\n",
+              "      <td>-122.4</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1481.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>731.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>37.8</td>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>2164.0</td>\n",
+              "      <td>424.0</td>\n",
+              "      <td>1074.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>3161.2</td>\n",
+              "      <td>635.0</td>\n",
+              "      <td>1590.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>2.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>18.3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       38.1     -122.2                31.3       2614.8           521.1   \n",
+              "std         0.9        0.5                13.4       1979.6           388.5   \n",
+              "min        36.1     -124.3                 1.0          8.0             1.0   \n",
+              "25%        37.5     -122.4                20.0       1481.0           292.0   \n",
+              "50%        37.8     -122.1                31.0       2164.0           424.0   \n",
+              "75%        38.4     -121.9                42.0       3161.2           635.0   \n",
+              "max        42.0     -121.4                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1318.1       491.2            4.1               2.1  \n",
+              "std        1073.7       366.5            2.0               0.6  \n",
+              "min           8.0         1.0            0.5               0.1  \n",
+              "25%         731.0       278.0            2.7               1.7  \n",
+              "50%        1074.0       403.0            3.7               2.1  \n",
+              "75%        1590.2       603.0            5.1               2.4  \n",
+              "max       28566.0      6082.0           15.0              18.3  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "e19d1b04-a1aa-44a5-87a1-bbbd8b4fe4b8"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>229.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>122.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>130.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>213.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>303.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                229.5\n",
+              "std                 122.5\n",
+              "min                  15.0\n",
+              "25%                 130.4\n",
+              "50%                 213.0\n",
+              "75%                 303.2\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z3TZV1pgfZ1n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Examine the Data\n",
+        "Okay, let's look at the data above. We have `9` input features that we can use.\n",
+        "\n",
+        "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense  will get you far.\n",
+        "\n",
+        "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4Xp9NhOCYSuz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gqeRmK57YWpy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's check our data against some baseline expectations:\n",
+        "\n",
+        "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n",
+        "\n",
+        "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n",
+        "\n",
+        "If you look closely, you may see some oddities:\n",
+        "\n",
+        "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n",
+        "\n",
+        "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n",
+        "\n",
+        "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n",
+        "\n",
+        "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fXliy7FYZZRm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Plot Latitude/Longitude vs. Median House Value"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aJIWKBdfsDjg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n",
+        "\n",
+        "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5_LD23bJ06TW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "outputId": "8581f48f-9d2d-4a45-eb4a-c7fc0564fd9c"
+      },
+      "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": 26,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M\njtd3Ha01mbTDrAkWoUE2+eZj2SapdIb9TQavbgFbpxlbazJmeN+v99brqqiptNmwLU4i4VFbE2DZ\nolImjZM0oUIIIc6+gG0wf86pDar5vmb3wfx723YfSLO/3mXi6KHPKgghJAjI8fKGTE4AAKCBF9+K\nsfqVGA1HsxXQo891sGRhCWUVYZrafcqLFZfMCQ44B+B4isMGf/shi/tWpemKZ+/jZlzG1yo+urhv\njeO5kwze2Orlnw3ovp3WGsM08TyfN7b5NNansC2YMsbiK5/IXkspxdJFZSxdNPjeASGEEOJUbduX\n4dVNKZrbPIojBrOn2Fw5N3xGRuh9nc1ql4/nQywh+9uEOFkSBPTT0JJ/A20yDfGuvj+3tLs8vKqd\nojIHO5RdU//mOw63Lg8zbsTQv9LRwy3++VMm67dnaGn3qa0Oc95UG6NfhTlmmMmCmT6vv+PhHVPH\n9VSsTsaltCJCIp7piQtwXNi23+V/Huvg1qWy7l8IIcTZ8/buNA88FSPee6SOz55DLp1RzQ2LT3/B\nvmUqRg+z2BYbOBtQW2MxbYK0c0KcLAkC+gkFFTAwF7/WGv+YHrjW2VSdPUFAY5vPE6+k+NLHB5/q\ndD3N06+n2HXIJZWBkdUGV1wQYN7M4KDvAfjQxTaTRxls2OWxdb+P64EyugOAtEMwaBEI2fieTzKe\nW0Fu2ZMitsiiWA5cEUIIcZa8vCHVLwDI0sBbW9NcvSBEccTM+76TsWRBhMNNDl2xvnY6YMHShWUn\nNRMvhMiSIKCfqWMtjjQPHGXwHA83zzyk7+cGBgcaPBrbPGor81d2v1mVZNOuvk1NTW0+B454fOZ6\nxdja4/8qpo41mTrWJJbwuft3LumUB1pTXB7CtrPvNUwDx82dzYgnNR1RX4IAIYQQZ4XWmsaW/Mtx\nuuKarfsc5s86/SBg5qQg/99N5byyPklLh0txxOCimWFWXFFBc3P0tK8vRKGRIKCfaxeFaOvy2bbf\n7V2DX1YEdS3xvK83rdxKzfUgnc5/qu++epete50Bj3fENC9vyHD7NUP7VYSDCu37ZFIOruuRSjqE\nIjYl5dm1/246NwgYXmUybJCgRAghhDhdSinCAciT8A7TgKrSMzcINWlMgEljZOmPEGeCBAH9WJbi\n0x8uYv8Rlz11LuUlBrMnW/yf+1Js35O7Y9gwDYKRUM5jI6sNRg/P3+HeU+fiDHJm19G2wQ/zOtY7\nezJ0tMbxut/i4eNkXHzPp6g0jNdv2ZJScMl5EZkmFUIIcVZNmxCgoTU14PHxIy0mjZGsPUL8JZIg\nII8JIy0mjOz7au768jj+8zckBvNsAAAgAElEQVR17D6QwvOgssKmI2WT9vo6/OEAXHZBANPI3+Eu\njgzeEQ8Hc59zXM26XZqWLogEYO5UKOs+dv2VjaneAKC/VCLDonNN9tsm7V0+ZcWKOefYfHRJKS0t\nsZP5+EIIIcRJuf6KCB1Rn637MmS6J73HjTD5+NIiyd8vxF8oCQKO4Wt456DJ4VYTT0N1ic+SC03+\n5mM1Oa+ra3T58+YMbV0+JRHF/FkBpo4bfLRj3owAL6/P0NiWu25SAbMm9v0aOuM+D78EDW19r9m8\nD1Zc5DN9nMHR1vzrLj1PU1Ou+PBluRuTh1r5ZhzNpr2ajAMzxkNliewhEEIIMTS2pfibG0o4cMRl\nT12G6nKT2ecEMJTiaKvL27sdQkHF/Fknl05bCHH2SBBwjNVbbHY39H0t9W0mR6M+y2ZnR+V7+Non\nFndo6/BpaIaumEcm43PulPyZfixLcdOSEI++mKK+OduRj4Rg7nSbyy7oe8/qTbkBAEA0CS+9DVPH\naIpCio48+59MA2oqTq3j/s4+nz9t1HR0Txj8eQucP9ln6VwlIzhCCCGGbPxIi/HdM+laa373fJy1\nW9Mk09nnV69Ncf3lYc6bevyseEKIs0+CgH4Otyr2Ng5c09/YBpv2Wyyc6nLgSIY/PBfNObrcsAw6\nopq6RpdPXaeYMTH/pqUpY2z+8RMWm3Y6RBOa2VMsKkv77qe1pq4pf9maOmB3vWb6RJv65oHrgSaO\nsph0CqclxpI+z63XRPtteUhl4M3tmmEVcP5kCQKEEEKcvFc3pnl1Qzon8XZzu88jq5NMHR8YsBRW\nCPHukjUf3VwPNh0KUlxsUlpiEAmDZfVVXS1Rg4yj+dXjXTkBAIDv+nieRzwFr25KH/c+pqG4cHqA\nKy4M5gQAvdfKn1yot4wfvizCpDE2dsDEsi0sy6Sq3OJjSyOnNGq/fhc5AUAPrWFn3XEKI4QQQhzH\n1r2ZPCfvQFuXz2ubBm4iFkK8uyQI6PbG3hBH232aGhO0NiexLEUkbOB178K1DHh1fYK2uElpZTFl\nVSUUlUUwjOxXqL1sVdfSPvRMP8dSSjGyKv9zlSUwdYxiw06ne7mQgVIKZRh0JRQvbRjkPPUTSDuD\nd/QTUkcLIYQ4RamBx+70Sg6STlsI8e6RIAA42qF4fWOc3Ts7aWxIUH84zo6t7XR1OYRDinTaZXSV\nx6b9iqLSCIFQADtoEy4KUVpVjGEYaJ2t0IrCp/eVXnZutsPfX9CGi2dkj01/c4uDM/C4AbbsdWjr\nPPkAZOyw/KckA3RETz2gEUIIUdhqq/K3h5YJU8bJamQh3mvyrxB4aaNLW2vukEUm43OkLsbEyWUE\nTJ+KsENH3Byw5MayLcIlIZLRFAqYPeX08iHXVhrcsdTnje3QHoOwDedNhrHDspVpa2f+7ECJFOyu\n85hflrvEKJH0ee71GIk0TB1nM31i7masc0aDZWhcP/dz+Z5PR5dPc4dPTbnEikIIIU7O4nlhdh9y\naWrPbbdmTbaZNu74B36tfSfOaxvjdHR5VJaZXHphMefPiJzN4gpRcCQIAI62ZpfSBEIWppkd1Xcy\n2Ww/ra0pTFOx+7CPp/Ovubcsk5Jik4Xnhbhybijva05GScRg6YX5nysOKzqiA0fubROGVyre2uaw\n57Df/ZjHph2ttLRlP99zr8Psc4L87Y1lmGb2s/haoX0Px9EY3WccaF/jej5oONqmqSk/7Y8khBCi\nwAyvNPnbG4p54a0UR5o8bFtxzjiLFQvDx33f6je7eHhVO+nuWe8D9bBtT4pbr6vgsrklx32vEGLo\nJAgA8DWRkiCW3TeKbgct0kmHRNzBDtrsaghQWp4dIU+lXFynb2Sjoszgyx8tpziS/7Tgodh12GfD\nHuiIQVEIZo2H8ycPHIGfNcnicNPAhZYTR5n8+R2fzXtyl/A4aQvIBgGeDxt3pPnjqzGWX1LMG29n\niCY0lvJxHT1glqMoDGOHS/YGIYQQp2ZEjcXtHyo+8Qu7+b7mxTWx3gCgRyqjefHNGIsuKO4dsBJC\nnB4JAgAzaGP5uR14pRSBkEU6mSGdSVNRXoxp+ti2jW2bxKIZHCfb4b7kXPu0AoAtB3yeehNS/Sq9\nA43Z9J2XnpsbCCxdECSW1Gze5RBNZM8HGD/S5NzJFo+/NnANvxWwsB0LJ9O3cXjTjgzrdsZJZMCy\nDdAKJ+1iBczejc4AM8cblBbJUiAhhBDvjqOtDnWNeTa+AXVHM7R3eVSVS9dFiDNB/iUBgYAJyYGP\nG4ZBIGTjpF0M5eK6EAgYGKZBKGyB9pg5XlFdDvevStPalT3Ma9ZEk4XnDtw/kI/WmrU7cwMAyKYK\n3bgHFkzX2FbfdQylWDw3SFOnItPgo5WiPWnwxvb811dKYdq5QUBDq4vZL2YxLYPisjCm7xIIGhSF\nYNo4k2Xz5a+HEEKId09R2CQcUiRTA5e9hoMGoaAMTAlxpkgvD7CP8y1oHzSKRMIjGfcwTYVpmkTC\nBrdcbhFL+Pz+T07vaYig2d/g0xXXrLi4b5Ow72vW7tTsO+KjyW70XTBD4fnQ3JH/3u0xqGvWTByh\n8DW8vV9xqEWxvwE6YgEMKxs5JNPZ/5SRLW+eT3HMH3ODE8/1iXYkqK4K8/VbA9i2wpCTgoUQQpyi\nLfs9tu7XpB3N8ArFotkGRaETd+BLi02mTQixcfvAkbmpE4KnnYFPCNFHggDAdx0810RrjWn1jeB7\nrkcimsQO2qTSHo7jk0n7hCMmJWEYN9zg/z7RPwDI0ho27HK5/HyTSMjA15rfv+Sz5UBfZ3z7QZ+9\nR+DmxQZBG5J58ilbJpREstdbtc5kZ31P5WdSXJKdwejq7EvmbygDj9woQGuNm8k9Q0DlWU/pe5pk\nymPjXk17DErCmoumKoK2BANCCCGG7rm1Lq++7eN1N0c7Dml2Hfb55DKLsiEsMb3tukpiiWZ2H8w2\njErBOeOC3HZt5dksthAFp+CDgCPNLjt2tNPRla2tDMPACpgEIwFM08RxPAzLJBQO0dGexOuu1cbW\naLTWA1Kf9eiKw57DPrMnG2w7oHMCgB576mHDLhhfC5v2DrzG2GFQU2aw+4hiZ/3AznggaBEKW6SS\n3dmNbEXKzwYNkK04J4ww8VImqYwiEjLYss/NWfffn+P6PLuu7z6b9mo+slAzukZGXoQQQpxYe9Tn\nre19AUCPhlZ4aaPP9YtO3J5UV1j809/Vsm5rgoYmh9HDbS6YGRnSElshxNAVdBDguJr/90hHbwAA\n4Ps+mZSPYZj4tgY0ShlEwhaGoTANmDjc5/JZPkpBKKCIJvIfthXtXtO4t37wkxFf2OgTCBhYZjZ7\nT08HfnQ1rLgo+/OBJgXkr/xs2+wNAsYMUyycFWD7QQ90dl3/5ReV8dq6DrYecPE9CB5O4AxyuPCx\nFWxrF7ywEe5YOmjxhRBCiF5v7/NJpPM/d7g5/6BZPoahmHdu0RkqlRAin4IOAv68IUFdY/4eseu6\nmHYQw1QYJvjaoLI6zKUzPOZP68nCo5gy2qC5I//Jumu2+sybphlk4L37PgrVc+Kwymb7KQ6kGV1h\nUBqx2VvvsXV3hvZOME1FqChAIND3a+sJGoI2zJtuMHOCxcwJVvdzmv9+tJM/b0j2jcoYNobh4fu5\nlbFhQKR44BkHh5uhpdOnukxmA4QQQhyfeZzBelNSewrxF6Wgg4CW9vyddwDf9bO5iLXOHiCGYtwI\nm/nTckf1r1lo8/Y+j1jimAsY0NwJ63Z4zBxvsGGXxs0zCNKT71ip7L18DV2pAM+vTbJ5j0s8BalM\n9jUOkE67lJSFCYVt0Brb9Jg4QjF/hsnsSblpStdtd3h5XerYW2JaBrg+PXFAOKiIlEYwrYFpTj2f\nQWcOhBBCiP4uOMfgz+/4dB3bJgLjayUIEOIvSUEHAdUV2U5vMBxAGYpMysHvGTJXABqUIhDM5s+v\nLh0YNNiWoqrcJJbqe04p1bu0Jp6CiSMN5s/QvLVd4/S7hGEojH7DJkoptNYYpoFlGzS1Z5cc9T8Y\nRfuQiGcIBE3SSQfDVASKQpjWwAhj+4HBeu+KBbPDjKw2sC2YNzPIQy9qDhwd+MraChheIRW3EEKI\nE4uEDBZfaPDcW7nLgiaPUiy+8NTP0xFCnHkFHQRUVAQpq1a9JwWHi33SyQyJrmS2I28YhIsswiET\n388ur8mnpkxR1zRwuYxpwIQR2Q708nkm08f6bD2g2dcArTGFYagB6/AV2U6/ZZu4jk++W7oZj+bG\nKKlEBu37HK1XdMSqKIn4jK3pe50/+EQHaQeunNu3/OeSmZrWLoj2y8oWCsCCGcjpjEIIIYZs3jSL\niSN81u3wybgwpkYxZ7IhbYkQf2EKNghIO5rn13m9AQBkMwOFIkG0r3EdD9PKdsZNO5vvPzPIwPol\n55rsa/DpiOU+Pm2sYtKovuuPqzUYVwvbDsIjr2nybvZV2Udd5zg9eCBcHMAOWsQ6kmjf5+D+dlbZ\nVXz8UpeK7hPaJ4wy2bQ7f6HbYtl9CD1ByORRBrcu9lm7K5vZqDgM50+CscNlL4AQQoiTU11msHz+\nwPZDa9heb3CgySTtQHmRZvY4j6qS/INs/bPdCSHOrIINAtZu92iLDnxcKYUdzHb6i4qDVFQGjz1q\na4CR1Qa3LrF59W2PxlafgK2YNEpx9dz8X++kEdkzANw8/XyFwnE8XMfvLk82r78yFH73pgLTyh5Y\nZpomqlIRbU/gZnxiKc0Tb1l88koXpeCSOQGee8slnsi9kRUwiaYtXt7sc+FURUn34Su1lQbXLTjB\nhxVCCCFO0Zu7TTbtt9Ddg2ANHXC41WDZeQ7DyvpaW9eDI10msYzC14qw5VNd7FMWOlGLLIQYqoIN\nAvIdztXDMBTFpWGCQYtg0CKdyVY6w8sGr3zGDDO4dcnxR80dV2Mo2HJQ4Xr5hzW0r4l2JLEtmDjK\npClq4ZM9wMzzfFzHw7D67mPbJqGwTTrpYJnZEf43d5pcPM3DMhXDhoVoOJrBdX1QYFsmgZCFUorn\n13m8vgWmj1V8eJElmRuEEEKcNfEU7KjvCwB6RFMGmw6YLJ2TnbnWGg60W8QyfW1dNGOS6DCYUOFS\nHJRAQIgzoWCDgMmjFK9szj8abwcsikuDeJ7GcTwScY/qMsXYKpfB8vUfz646j5c3Ohxp8bFMKC0y\n8Twb0xwYNISDmvPPz6b5XP22iVZ9dzRNA9M0sG1FOtMzU6AIhQN4nk9JiUU6ozjQAheT/WAjqy3a\n88x4aK3RGmJJWLtTE7A9PnRxwf51EEIIcZbtPWqSzORvQ1uife1hV0oRy/M6z1e0xA2Kg8dfLiuE\nGJqCXfA9qsakpMTGDpqY/TL0GIaitDxIIGBhWQb1dVEyGY8jzS4P/AkOHD25EYj6Zo/fPJVg844E\nLS0pWjs9Dh31SMZTA3L1A0wba3DtJUHiGZOGtvzXtG2DSROKKCvLdto9z6esIoJpKiJhRTQFv/6T\nwR/XKmZPsSk6Jv2/1rr35OMeO+t8PF9GV4QQQpwdIXvwNsYy+p5LOIMfkJkZZBZdCHHyCnLod18j\nPL0WHN8kGDTRAY3vabTvEykOYFlGNoe+Uihl4Lk+lm3SmdC88jaMv3po99Fa8z+PdXH0aF+etHQy\nQzASJBQJEu2IU1pR3Ls5d3gFXDorOxXaGjeZON7CMCAW8zja7PZukHJdjR0wGV4TAlI0JR2S8QxN\nzQbDasJoFA3t2f/2HvGorjQpSmlSKZ/2qI/WmmPjj1gSMg6Eg6f//QohhBAAR1p9Gls1E0YoJtXC\nhv0ebbGBqUJHVvY1SrY5tGBBCHF6Ci4I8H340yboiOfm5zcthWVZ2LbZmwrUdbMdZjfTl0XocCtE\nExrTyJ6yGwoMPiqxdkuKuiMDz09PJ9LYAQutNUVGknGjI1SXwbyp2U74jqMBHNtmWHX22tWVNuVl\nLjv3pNAaAoHsBI5hGlSWB4l2OXS1J+lsS1FWFsLrN1OaciDZkX19JGgQcTJE4wPLWlECwcDJfZdC\nCCFEPrGEzx9edtnboHHdbNs2fZzBvOnwxk5FZ7JnIYIGz2P3wQy2r5g3XVEZgZa4T8o9drGCpiKc\n59RNIcQpKbggYMdhaOrI33HPLs8xu3/WpJLZw8O0YfSOwmuteeA5l6NtPqYBY4crls+3GV45cGXV\nxu0DT+vtkYylCEYC+K7LRy/te7wjYVDfaXPsVGh5mcWI4TaNTU7vMiDoy+FvmApPa3zPJx7Pv14y\nkVZEIhbRuJPzuKHgvMkGhuRgE0IIcQY88orLzrq+UftkGjbs8gkHHD62EF7eavDOfognPDLde9wO\nNGg64rDsIsWYcpcjnRbx7qVBtuFTVeRTEZGZACHOlIILAhKD98t7O/q+n11n72Q8lKFw0hn8iJ3N\n0OP6HGzsG4nYflDTHnX4/A0BAnZuJ/pI82An9oLnefiuj3PMSEdTfGDmhB5lpSZamRQV2X1lRpOM\nOyjDoKjEJhZzsGyrt1I9VsoxmD/dYH+jTzwJ5cXZAOCScwvur4IQQoizoKXTZ++R/J31nXU+1yzQ\nNDQ5tHfkPqeBTXs1i2b5FIUNJle7xDMK14eSYHYGXghx5hRcz2/6WPjzNk08NbCjrVR2BgAgk3bR\nvo9lG8SSDqaVpqw8QDQxcJS9sU3z5laPy87Lfp1aa576c4KOuMIKWKCzswx+92ZcrTWqu6M/subY\nX8HgoxzhsIV3bGq1qJM9R8DXaE/T0Z6kqqZ40GsoBZfOsbjukuwUrW0x4NRiIYQQ4lS1delBD9dM\npMHxoKkj//PxFPzyiQwfWWQzcZQp6UCFOIsKLq4uCsGcCWCogRWLaWaX/biOh+t42EGLjuYYvq/x\nXJ+wctBe/gqpLdo38v70a0mefi2F6yuUyh70ZVomRr9hDKWyJyouvqhvJ25ju2LPYUVdg8/hoz5t\nHX7v/gQA55hK1XV96g9HsYMmyjCIRzPEo2kS3ct9smlAc8s7sgoqisFQioCtJAAQQghxRo2uUZQU\n5X+uqkQRsCBo539ea019s8tDz6c40OCRcSQIEOJsKbiZAIAr50BlCew8rElmwPcVnTGfWMLD9XxS\niQzptEs6kT1RTHWvu/e1ZrC0ZaVF2Q6+72s27hi4GRjAMAw8x8M04IJZET58WYTa6uyvoLFT8cKW\nIIl+h6NkMuB4muFVoNCknb7nXNenqTGBFbDx/ey8gutmDxOL2B4zJyr2Nii6En3lLYtoFs3Qcvy6\nEEKIsyYSMpg9weC1LbnLUi0TLpxqoJTCMjyUMrEsI5uAw8129n3Px0k6tOsA//m4Q02VzdTRsGK+\nIQdaCnGGFWQQADBnYva/LM2WvQ73PtyV97U9h3pNGGEQT+sB+wqqy2DBDIP12zPsOuTSkv8yKEMx\neVyAGxYXc8743FycWw5ZOQFAj2RSM6o4wzm1Lg+8bJLxLTxPE406ZByfQCB7mnA07eA6HkqBh8Gy\nCyCZ1uxoCHCkOU3A1GQyHpt3Q0MLXDTNxDKlQhVCCHHmLZlrsn13nLrGDK4LxcUml1wYYf6MIL6G\n9rhJUXEAw1DdZ9doEvEMmZSLYZq4jotpGXTG4a2d4GufDy8cmFpUCHHqCjYIONb0CTaVFTZt7cdk\nzjEN7KBNRQksX2AzcZTPK5s9jrTo3uxASy60+M3TSbbv79kvYGLZBr7n5xwIFrA0E4d5VJcP7Hx3\nJvKvzPK1Ip0B0wDfyXDkqIPvZ0fzTcvAsrJLeiw7O5oSigR7R0vCQVh6UYBX1iV44jWPzn6pQTfu\n1tx2tUlZUcGtCBNCCHGGeR5sPZhd8z95pObBP3aw+0Cm9/mODp/X1kWZM8Wkri2IHeobCFNKYVmK\nUNgiEU13z2x7vXv0AHbWZQe2wkEZvBLiTJEgAHBczdE2n6vmh3j2DUUi4aE1GJaBZZlUlBh8+NIA\nrgfVZYrPXm/T1qmxLEVNucFTr6X6BQBZSikM08gJAjpa4zz4SCtPvXCUZVdUc8tHRvY+FxhkfSRA\nOKBpj/ocadK4/dZHeq6Hb2uCIQvDMLCDFqGwTXGob9mS72teWJ8bAAAcbtY895bHX10pQYAQQohT\nt78Rnlmnae3KtjsvbARlVDJldvawzXhXiqb6Dlo7ff70ZgK7LJT3OrZtEgiZxDIOSqmcPW3RJLRF\nNaMkCBDijCnoIEBrzfNrXTbv8WjtglAAxo0MEgloosns9OSUsSbXXl7O/Y938IcXHVIZqCqF886x\nuPqi7Ne3rz5/Xn6lFIZh4DoOyViCzqY2ADq7XB5d1ci40WEWzq0AYGyVR0O7wbF7DiqKfKbUejzx\nuiaVOfYO4Do+ppVNZWpZ2fJksxBlO/fb9jk0tOb//AePZjcOy+ZgIYQQp8L14PHXfY62ZJekAli2\nSThi42QUkUiAUCiAHbQ4vLeFzTszTJ7ukS8vSU+b6bs+hqF6z8EBKAlDLOayanuGsmKD+eeGsSxp\nu4Q4HQUdBLz6tseLG73e8wFSmWzHeOoYxRdv6hupuP/JKFv2943ot3bB6nUu2/elqSrRtHd6aJ0/\n005tucv6t+rRfm6GA8eFN9Z19AYBs8e6RJOKvUct0q4CNFXFmkvOSWMY0NA2eIYEz9XZDES2wvPA\n9frKmnaP8z4/m5BUqlEhhBCnYuMen/qGVG8AAH0Z9krKgoSCAVJpTUlpmHBRgGTK5Uh9nPJhZQPa\nTM/ziXUmegOAcHEQ3d2c+W6Gnz3QQbp7xe7zr8e55ZpSpk7I3V8nhBi6gg4C3tnXFwD0t++I5s0t\nDnvrPRrbfI626ezyIKNv5EIDh45qduzJ7hJWpsIO2DmVmmlCSTA9IADokUj1VZpKwaJpDnPGuuxv\nMSgKwIThHj0DIdZxVu2YZncGI626X9tXhtmTA1SVZgOXY42uUXJKsBBCiFO2cWcmJwDo4ToeqYRD\nUVG2jUmloagkhOsmicU9ipIpApFwznsSsTS+p1EGREpCmKZBIOBTZDq8vSX3YIH6Jpf/faaLb/9d\n9dn7cEJ8wBX0gvBoIn/n3PHgydcybNzl0dCi8f3sacL91/dDX+pQAO1pPDe3IvQ8iHklg95/9IiB\n6yJLIprZYz0m1fYFAAATavN31pUBwZCNaSpCYZNwxCKe8vG6Aw/bUlxyrjEgJ3NlKVxxnmRaEEII\nceoGO50espt7K4o1kUi2q1E9rAjLzo49jq92mTzCp7JYY+CTiKdJJjKEi4OUV5cQKsqO8F822yDW\nEct7/cONLmu3JM/wJxKicBT0TEBFkaIjmi8Q0KTzrL/Xmpw19P4xI/za09Cvs60MRXOXYsq0Gg7W\nRUGDk0yjtWbMyBDXLxs25LJecb7BvkaPuqZ+11cQClnZA8mUoqUxSihikwla3P9Mhk9fk61EF8yw\nGFbus2G3TyKlqSxRLJxlUFla0DGgEEKI01Rdpth7OP9ztqmoKFV0JsC0oKwkwCGVbUvPGWsyd3o2\ngHj8Dc3mfQbFZZGc95uG5q0tGTrSAQJhyCSdAfeIxgcPQoQQx1fQQcD5U03qml2OGcBHMfg6+p4g\nQGuN7x5T+ajc2YEeSR0mXJx9baQkzOgaxec/UUtleWDIZbVMxV9dbvJ/n1GkMxoUBAJm7xkG2c3A\nikQsQ1FRgJ0HMrRFbWpqsu+fONJg4kjp9AshhDhz5kyxWLvNJd+q12lTQr1LWYsiFpYNtp19YFil\nSTLtc6ARpo2GhjZFU/8VP1oTj7l0ZHxQFuFiE8MwScX7DuqJhBSzp+bPNCSEOLGCDgLmTbdwXVi3\n06OlQxMJZTvL2/Z5OAMHHHIoUxEMB0inMvTEDIaZv5Pten21o8agNabw9ckvxakoUUwZbbC3Mc+h\nYolsgQ3DoPVoFDMY4MnXPKZOHPBSIYQQ4oyYPt7msvN9/rw50zugphRMnhBi+jkRsk2kwrYNPM8j\nnXZBKVa+6oMy6Upkz8EZXa05fzIk04rDTR5NrQ5ev8QWSikCIZt0sm+f3dxZIYZXFXQ3RojTUvD/\nehaea7Fglkk8mU0RaluK36R9Nu8ZuNEpWwlZvT/7vk9VbRntTV2MHmbSGjPxjhkN0b4mk8xdW5RI\nal5dH+XmaypPurxLL/B56i040JTt8HuuTyqZobMt0fsa19cYWrP3iE9Diyu/ZCGEEGfN9ZcFGTU6\nyKZdLmgYPzZEeZmF1tCVMNA6O3CVSrp43TPoh4+kKK0oArKZ6g42ZZcJ3bEM7vnf3ACgh2Ea1FQH\nKY/4zJ4S5OqFRe/ehxTiA0j6h4ChFCXdSxG1hgtn/f/s3XmMZMd94PlvRLwr77qrurv6Yl88xEsU\nKZEUrVuibMuybHg8NmyPbQwMrBfCLLAL24vFYoHB/GN4F2vYwIwxM4Zn1iNjNLZnLMuSrNOSKIki\nJd5Uk+y7u/qouyrvfEdE7B8vK6uqq4rdzUMS1fEBCDYzqzJfZTUjXkT8jiI9aVlaNSwvdgcdev1A\nbar+k9c0Ftxz9wi/9RHBP3434ZtPJ2wslJClKTrLtrxnku4ccvRqSiH8s0cM/+ufNAlCj7ibofXW\nmEiTabQR/Ncvd/i1D+5cASjTliePpyyuGsaGJA/c7uMpVzHIcRzHuX73HYSJkZDFliLRkm4MjbZk\nuZUvAKy1XJ5ZL1OXJlvnxZkFOHXJEnh5mezt/MIHy9x37FW6azqOc93cImCDJIOvvxwxW1eEZcHu\nMkxPF5m50KLV1ltqGq91BU6Mh7WGkSGfQlkg4nwVEAQKiLBG025srmBweP/rq23sk9Fpbb35tybP\nVUiNJU0yzs8pGm1FdZsNk9klzae+2OXi/Ppg+90XUn79oxHjw65ykOM4jnN9hIB9Q5rpqubbJ0PO\nLSnWChBaa1mab7O0sH5ibU3e9V5563ONBRabcHhaMjO/dX6bHBHcc9jdtjjOG8Vlim7w9PmA2brH\nxvZZBsWuPeUdu+oqJUk1xKnl6RMWIRWFQkChEKBUnrhbGSpv+p67jka8667Xd4z5f/5OFXtVyVJr\nLVma5ddqwWhLt2d44dFyE6cAACAASURBVNz2r/H3j8WbFgAAM/OGz3wzfl3X5jiO49xc0szy2HMp\nn388QSUt3nWox/6RlKX5FqdeXuTUK1e1rheQxJvDbn0PDkzAhx/wufMWxcY0u7Ga4GMP5+WwHcd5\nY1zXkrrX6/GzP/uz/O7v/i4PPvggv/d7v4fWmvHxcf7oj/6IILj+Kjc/zubq2+9+SyUplT3arc3H\nl2shQpWCpd62LG7TkAugUPQ5vD9ESTh6IOJj76ttaof+WpQixXve7vGV7/YGCck60+sNzQQYrWk3\nu9TbEtj8szXbhjOXt+Y9AJy5rGl1DeWCWyM6jnNjbpb5wll3aUHz6a+mzG3obL9nTPMrHw747vfa\nLC9uzosLQo9qrUDvqkXAkT2wazSfd37joyGnL2pOXdKUCoIHbvMIfLcAcJw30nXd5f27f/fvqNVq\nAPzJn/wJv/qrv8pf/dVfsX//fv7mb/7mTb3AH6ZtQuv7BMXC5ptoISEs5DX6J4cspQj8HSJoykXJ\n7/3LSf7335niFz889IYNZKWCHOz65/kJ679OIQRxLyVLMuYXtpY6ijN2rICUpDs/5ziO82pulvnC\nWff5xzcvAAAuLVo+/52U4dEyU9MjVIeKVGoRIxP5fxcrBUZqknIEo1V44Bh84uHNc+OhacVH3hnw\n7rt8twBwnDfBNRcBp0+f5tSpU7z3ve8F4IknnuADH/gAAO973/t4/PHH39QL/GEaKW2/CihHMD5k\nKZZ8glARRh6lcojvewgst+2FobJk/9T2r3tgkn6i0xtrdDigPFxiaKLK8ESV2liFsJjvsq31D7DW\nslTf+nONVAV7xrf/9QsB7Z5rwOI4zo25meYLJ7fSMJy7sn0S7/lZgybvZl+pRQyNlpiYqlAseSgl\neOjOkP/lFwX/888JPvqAdEUpHOeH7JqLgD/8wz/kD/7gDwb/3e12B8e5o6OjLCwsvHlX90N2x3RC\nKdx8PCmF5a4D8La9lsCXhJFPEHqDHIHpMcst/Zv/n3mnYN/Exu+FW3bBR9/5+ga2Rsswu5ihN3Rj\nsdby1ClJVAhQKj8R8AOPUrWIH3mI/mAqhKSwTQ6yFIJj+7ePBjMIvv3C9qFCjuM4O7mZ5gsnF6d2\nS8PNNWkGcwsZzWZKkhi6nYzF+Q6ddkIQSpablnrrxirlGWP5/Le7/N9/2eBf/4c6f/a3TZ55qXPt\nb3QcZ4tXzQn4u7/7O+655x727t277fPWXv//vOPjlRu7sh+B8XGYGLM8fRpW2xD6cGyP4NZpAVQQ\nfsL3XtIs1C2eglrB8p67PSYmosH3/95By3MnU+ZWDHsnFLcd8HZMKr6W+eWUv/gfSxw/1aPTs+zb\n5fPBByt89JEaz59KuLK8deCTUhAVQlqNDp6flzS9744S4+N5cvLJixkX5g27RgTVqkCqFGss1uYn\nAELmYUUrLfFj8Tv7cbiG18pd+4/GW/na38putvliJzfbtY+OWvbvWuH8la0lP6USmP7cAgzmwlYz\nxfMVz522PH/asn+X5KcfjLjtwLVLf/77v17in7633jV4cdVwYXaJ3/3lUe69rXjD1//j4mb7e/Pj\n4q187W+EV10EfP3rX2dmZoavf/3rzM7OEgQBxWKRXq9HFEXMzc0xMTHxai8xsLDQfEMu+Ifh3umr\nH6mwsNDkzmkYjTSf/lrK5VnNZWM5dQaO7VP82qMRfj/kZ89w/g+kLC6+tmsw1vL//mWd0xfXB9YL\nV1L+y2eXwaR0Mh9rwRiTT679vAAh87KlVlvwLe9+e4n7jxkuXGzyme/CuTnQRiCEpRxaRsbLeFKQ\nxBmNZjwozayk+ZH/zsbHKz/ya3it3LX/aLzVr/2t7GadLzZ6q//9e63X/s7bBPPL0N1QWE4p0Ei8\nbTbBpMwbh1lrMFZw5pLm//tCm996VDBc3jlAYWFV88Tz7S2PtzqWf/jGKtNjb80T7Jv1782P2lv9\n2t8Ir7oI+OM//uPBn//0T/+UPXv28Mwzz/DFL36Rj3/843zpS1/ikUceeUMu5MedsXBh2eOfnoGV\nVobph+akGbx4RvPZb8X8wnujN+z9nnsl4czF7ZqMwZMv9Hj/gz5GazZWCc0XAxajTb+tumD3ZIgU\nhi89A6eviA1fK2j2BFKCHypKlYBSNWTuchNrLW876PoEOI5z/dx8cfO671aPobLgWy9knLlkSY1A\neXKQm7adLNOkmR3kATTa8ORL8JH74fQlzYU5y2hVcMctEtWvpvfSmZTODhWs55ddHpvj3Kgb7rrx\nyU9+kt///d/n05/+NLt37+bnf/7n34zr+rEyuwpfOV6k3lWUhuG2aonVlZjTJ1dZO+E+MaOx1m4J\n/Vltar7xVI9mxzBclbz3vohS4do32HNL2Q79EqHeMuwdt9jtxjzLoC275ymWm5bvnIkwoeToIWi2\nNLPzyeC6jQFrNPW6xvMEI2NFSLrcfdiVB3Uc5/W5GeeLm9WhacX3T4Lw4VpFYPNTbEsQSMbGiySJ\nodmIqbct/+nzMScv2kG1Pt8T7Jn0efsRyeiQ7IetynzDa0OeXDFyScWOc6OuexHwyU9+cvDnv/iL\nv3hTLubHkbXw2HGod9dv3JWSjI4ViOOMmfMtAHqxxZj8CHTNy+cSPvWFNssbqvM8dTzhN3+uzL6p\nV4993D3hIQRsF0Y7XFU8c9KiTb7jb6xFkCcBS5WHBAkp8HyPF85klBckE2MBpZJHoaDwfcGFi+vb\nKbVagNfW9GKDkIJO5vPF78Mn3v3aPjPHcW5uN+t8cTNLtWXmOvK+rc1z0Ky2jI0X8X2F7ysCX3Ly\nUpteotAmQ0jA5k3Izl9OWGpFHNmtGBopkJn13jhxJ0Frw+23XDufwHGczdx27zVcXlXM17d/rlZb\nL7szOSI3dTK01vK5xzqbFgAAc8uGz36ze833vfNwwOG9Wwe1MIB33RXSaBt0ZvKdEMsgP0BnBgSE\nxQAvUCSpYHk55dSZNssrecOWasUjCvNr9TxBoeARhpK4m6H72y9nZyHTN1a1wXEcx7lJ2e03rdae\nNMb0u9xbglAwMVUkitb3If1AUaoWKFdCqsMFfF+BEIQFD2Msaao5cVGgbV7wQoh8o6tYDvmp+0o8\n+uAbF47rODcLtwi4hm6680ck+zf9hRAeunPzDfvckubsDh15j59J+d7xZNvn1ggh+O2fL/P22wIq\nJYHvwf4pj1/6YIl7joUsN7aPf7TWgsnDkpQnSdOMpJeSZbCwmNJoaoyxVKv54Fup5NWLfF+CsGht\n8HyVNwzbmpLgOI7jOFv4nmD36PbP9e/92T0Kdx8LmdpVIYq2bnJ5Xj7fKiUplEIgP+UOQo9eJ8n/\n66qoH6EU+/cWkdKFAznOjbrhnICbze6hjJdmobvNPbvJNHccVDz4Np/bDm7+KM2r7orAf/tqj04M\n77l3c/TkCy81+daTy3R7hgN7C/z6T09ggDixVMsS2c85WKzv/OLa5KcBaZwhhKC50qYyVMTzBL4v\n6CUQ9zSVimKoFqxfr7EkGZRKgjSG77xoeN/b19/TcRzHcXbyU3cJFuqW1db6Y3ZD7P7MAiw1E0Yn\nw21v2j1PDMJgpRSEkU+aaoJA0euYfE7dZuprtFxSsOO8Fm4RcA3FwHJkNzx/Lq+2sybyDO+5zzBZ\nLWz7fbvGFJ5i5yYqqeW7L2Y8fJc/qI7wt5+b5a8/e4U4yUe5x55Y4clnVvk//tUhhir5rom1lpfO\nZSyuaozO6/pv7UMg8gpBNt/ZV75Hp51QrkYkab5jkySWJEuxI3lIU6ed4YceNtZIKcg0PPYipMby\n6P1uEeA4juO8ur0Tkt/4sOHJlyw/OGdZbdktm2GdnqXY7lGqbJ07PU9SrQbU6/mumxAiD1ewebhQ\nXhHPIOXmE/qJEQm4hYDj3CgXDnQd3n0r3D0dM17OGCpopodTHrylx2RVs1CHLz0Nf/e44KvPQr1f\nwlgIwUht549XSMHCquXCbD5wLa8mfPbL84MFwJpXTnf49N/PAnmC1H/8+y5//vc92p18dyXPC9g8\n+AmRv7/nKaQQKCUxmSYIBCAwBipVj147JY01q/WEdkejpMBai9xQ1u34OUucutwAx3EcZ2fWWk7O\nZJyeyXjkTsFodefT8ImyJvA2PylEvvsfBJIoyuegLNMEkUeWZiglWFls0270aDW6ZFker7p7FB65\nx+UDOM5r4U4CroMQcGwq5dhUuunxVy7BF58SdOK1nXLBiUuWjz1gmR6HB++K+MzXt+vqK5FS4iko\n9zdDvvH4CvXG9kH4J87kK4vPfyfm+NmtRwtGW4RYL09qLYMKQaafHyCVZGwsHyiNhTgxGG0xVlOv\n5+8rFHi+wuj1RUWjA/Mrlr0T7jTAcRzH2WpmLuNvv9bj/BWNsTBUFlQrCmv9bU6q4fAey3JsuLic\nl9Nb27hao5QgSTK6rYTUE8Rxitkw9Vlj6XUS7n+b4tF3eoNGnY7j3Bh3EvAaWQuPv7RxAZCrdwTf\nfjl/7EPvjPjAAwUCXwyqGUgl8cM8GffALsnESH8QfJX3WhsbT13cuRui7cdc2n6zMN+XYAVGW6y1\nlCt+Xm2h/zXNZkaS5XkGAGEoMamhWPTotNcXO4UAht/ajUwdx3GcN1i7B197Dv76MfjLr1guLuYb\nTACrLcvMbIYntm5sTY/BfUclhcAipUBuE9IadzMay/kGWpZZsFtnSGtguKgZrbrbGMd5rdz/Pa/R\nfB1mV7Z/7soS9NJ8Z+MX3l/kD36zwvSUTxD6hFGAlJLpccHHH1lPCn7PQ8MMVbc/mDl2SwmA5FWq\n9Vjym3+d6rzmcuizMYOqWI5YXOyRZYZuN2N4yGdsrEBmBIWiwlpLWAiwFvSG0qCHdkO54P6aOI7j\nOLnlJvyXr+UbYScuCawKqI2WKZTW5zRroRIZjk4LShHUSnDnLYJf+aBCScHhyQxfbY0XiuOMuSut\nzaFEOxSn2KkCn+M418eFA71Gov/PdiGP4qoqZpOjPr//G3njriuLhpGK5O23eoNW6ADDtYCf+8gk\nf/3ZK3R76+E4tx0p8csf3wXAnjG5Y2t0IQRSSoLIw/e9/CKMQQhBuRZhrKTZzOj1NOWKolzyWFqM\nSdO8z4Dn5X8VklhjLURBvgD42IPumNVxHMdZ99gPYLGxeW6QUlAsh3kpz7WJ0Vh+4yMeaWaRkk1z\n3p4Rw/23JBy/5LHaUYCl006Zu9La1An41eQJwY7jvFZuEfAajdfyhKRLS1uf2z0K4VUlkIUQ3HXI\n565DO7/mJz46ye1HS3zjO8t0Y8Mt+4p85H1jBH4+0L3vvoDzs5rlxvoAKYBCyadSDel2EtrtLM83\n8CRCSWojxU3vkaYWKQSLCzFKClotQ6eTUSoFeQxmN6MQwG8+ClPDCsdxHMfZaHabeQ9AeYqwENDr\n5NV9OokkzeyOMfu37s44MpUxV5esNjSf+kKHON3mC7fJMJYSPv6ISwh2nNfDLQJeIyHg3bdbvvAU\nNDrrA9xo2fJTd1gyDT84nzfcun0fFK9zrDp2qMyxQ+VNj2ltefZURqcHv/yhkKdfzlhYMWgr6dqQ\n0fESQgisLdJqxFw4t0K5WiAIt//1riynJImmWvVYXs1o1XssXKqTpilSSuxohW5XwvBr/ngcx3Gc\nn1Di1TbgN9ywdzKPf3zS8LGHdt5QUhJ2Dxt2DwvefbfHN5/NNjWqnJ5QXJwHsyEeVsj8hPvF85K3\nH3k9P4nj3NzcIuAG9VJBkkE5shycgl9/v+WpU5Z2D2pFuO8wnJ2D//EdWG7li4PvvGR5+2F49x03\n/n4vnc/43HdS5pbzgbVUgPtv9fjEeyP+89cjSmp9cBVCUKlFTEyVWZxvEUbVbV8zzQy+L+j0bH/x\nYPthTYIs09RXm/zlV2v8wrstbzvowoEcx3GcddNjsFDf+niWanrdFCkFXuChlOD5M4Z33g4TQ9c+\nWf7ogyFH93k8dzIj03DLbsmlFY+WFhhjiHspQgqiKM89uLCAWwQ4zuvgFgHX6QcXJN8/49FLQCrJ\n1CgcmdRIYZkaExwcSwn9vCPiPz4lSLVAKTDG0uoJvnPcMl6DY9PX/55xYvnMN1OWNoT/tLvwzWcz\neloh1PaDarEcoi+3yJIMY/KkYT/wUEpirSVLNWkKQSCw1tDrpIPKRdZaslhjgM89mZdyiwK3EHAc\nx3Fy73kbzK9aLi2tzw3GGNIkIyoGg2o/Whs6PcmffSbj0QcsD9x27VuOQ3sUh/asz21zT+T/llJS\nKIabvnabpsOO49wAtwi4Dv/0TMZXnvNQKt9tz2LDqbZlblkwMhQSp3D8ss/0UMITL1kM+QIAQEpL\nlhkyI3hpxm67CEgzy+yyoVoU1Mrr56xP/GDzAmCNsXD2UkZtYvvrzQdGSxxn6CxvtR53U/zQw/O9\nwWltEmfE3WTwfWtlTK2xtBtdvOEyT520PPwaTjAcx3Gcn0ylAvza++HZ05aZRcvpi4aVVgZCDMJQ\nszQvMmGMoRsLvva05q5D6oY3lY5Ow3Nn1suPrhHA4T1v0A/kODcptwi4hl4CT54wjI/6BKFAAElq\naLYMq6sp2JTJCZ9mW3JqIcSQsLFmkOh37I17Kb1kayDll7+X8NQrmqW6JQzgyB7JJ94TUC1JOvHO\n16WEJcs0nrf1NKDbSfsNv/KGYVbnrduTXoa1FqXW+wUkccpaLSOxoaxRa7VLbbhML9ny8o7jOM5N\nzlNQ8A2vnNO0ewIp+3ORAOUJlK9I+xtRQkK9Dd9/OePhO70NjS37gag7lAAFOLwb7jsCT5+CtT6W\nSlqmRy0LyzATwfj4m/qjOs5PLLcIuIaXLwqKJS9vvtUXBgqvJkliTbOZsWtSEfqSOBUUC4p6urmg\nv5R5QdHlhmFja4ZvP5/yle9lgx2OOIEXzxriNOF3Ph5xYJdCimzLDgjA9ITg0moPWSn2Xz/X7aYs\nzrUZ3NhfVchUZ3ZwSiGEQEjIEo3y8l4B1lisNQgpsVimx17Pp+c4juP8JHrqpR6f/nJMN85LUfuB\nR6EUIsibVHq+xAsUOlsva/21pw2PH0+ZGMrnnqWGxJLnGLz/XsFYbetGmRDwkXfArfvg5RlodQwX\n5g2nrwjOXBF84znDU6da/Ow7LZ5y8UGOcyPcIuAamqm/aQGwRilBpayYm89oti1RCKQ772hIBZdm\nY1rdiHIh/5rnTultb/DPXDacuaQ5tk9ybL/kpXObewOMVOCRuzySVPMX/1inVA2RUhL3MhbnmnS7\nCaVyAcjzATa5qtRaVIxoxm2M1ijPQypJGqcUqwHVYn4U6ziO4zhrnjsR86kvdEj65TwtlribYIyh\nXC1iTd7FXimJ8gSmP4V1k7yR5kozn4ekMkgpWW3lOQa//VFDMdw632YGVuIAWfIoRXCoalluQJJA\nu53x4rmEgm/4yP2urLXj3AjXaeMaSoWddxbWInGkEoN76yzb2sxLSlBKEceGVy4LLi5L2rGg2dm+\nIYo2cHkp31359Y+E/NQ9HtPjgolhwT1HFL/+aMjUqGLflMf/9sseftxg5swSly+sksSaYqkw2NnP\n4zI3vI8QGGMHjylPIZUkLAYEYd5oTABRIRycYDiO4zjOmm8/Fw8WABulcUaWrXfxtcYONsbWQmM3\n2jg1za/Cd49vfU1t4J9eKXJh2aPeBm0FYSiZGJGM1Cy1Wt4n5+QVN1c5zo1yJwHXUA6379ALsHeo\nyeqywpOSNDOEytKLN3+9EBCGalDF4JWFIqdXJFjDxG7FUqOxpQ+K78H+Sdn/s+BnH/J5+bzgwqyh\nWhJMbeiSWCpI/tWvVOjGhn/732PmVvJBV2uNTjRZpgkLfv4eIl+wQD74WmuRUhBGPr1OQqlSQCmJ\n9BRRKaTRETxxAt517Pq6NzqO4zg/+RZWdp4XsyTD9/t5Z6zPM1Lm85YQ67kAV09+S82tc83XTxS4\nuGiJ+zlyUlpKBcFoDapFQatjKBQk3bYP6C3f7zjOztwi4BpuGU+ZWVKs9ja3APaVZvdoxrDf4lS7\nytFdPdptxWLdI03NoO15EEiklKSpJoxUP5HX4nmSQjli7z7LhfPNTa99ZFqydzIfROPU8p8/3+Pk\nhfXQoW89l/FLHwg5sGv96FNJQaeV0Gub/iC7/npGGyanh2is9gbHshtZ29+xkfngHBYCfF8hJTx9\nWnHHvoxK4fV/lo7jOM5bX7kgWFjZ/rmo4A+aifkePHDvMHFsWFhMmLm0udKEvKrGZ5oJ5lYFEzWL\nEGAMXJjP8+XWGAPNtkVJwUhVUAwtxkrCUOIWAY5zY1w40DV4Eu6cWqFWSFDSoIShEqUcGO3ghwHV\nqkfgGQqBZayqEQKCQBFFHlHkDXY/0lhz4MBa8648RtL3YGQkYO+EIvRhqAzvuFXxqx9ar4X82W/F\nvHJ+c+7A7LLhM9/sbQrzWa5rFlbzO/yrTxbSRKOUpDa89U7eWEuvEwMWa8APPKyxmH48ZzcRHJ9x\nf00cx3Gc3J1Hgm0fj4o+I+MlwijfXywVPYJAUql4HDxQ4MD+cFCYQik4erRMpZw/ICUsdEL++xMB\nn3nSZ7EhuLAkNy0ANur0LAhIsrwfgedJ6h0XEuQ4N8KdBFwHzxccnuygDVgr8NSGajvSp1bM6yNP\nDRnGK4aF5tXJSZZKRVEbWr+5T1JLIRIgJYcPRuzeZakW4YFjEG4YX09f3H5nY2bOcnJGc3Rf/ius\nliWV4vZ5BsqTSCVQngRhMdoOFidxJ84TuDyJlIKJXWUunUuJIoXvy/zkwEUDOY7jOH0femfEworh\nyePpoPpPoegzvmcIqSRB6GF0wq5Jj9n5jGbHsH+3x+6pCOlJrlzuMTFRoFIJKESKl0+0KBR8fF9h\ngcuriq+9KDi6e+edfW3AE4ZM56cDvhJ87QeK99+hqRXdpOU418MtAq5DEIWQGvKcps2DS2p8xqsZ\nSliKAYwPW1a6oHW+I68UhIHA9yOszeP1rbV4niVNQWeG52fWE3BPX7H8zAOWfeP9Ov7bJF+tXUVj\nww1/MZIcO+Dx/eNbv0Gq9XhMKQRpprESjNF0W3mgpRCCciXA9z1GxysUiwHGWAJPc9veneM/Hcdx\nnJuLEIK776gwG0s6rQQ/kBRK65tcYajYNVGiUlGUQs3zJyyvnEnZP+1RKvrcflvAWsxQGCr27yvR\nbG+eWxebgluMRQqLsVt3+H0FK3WD7yvCfg+f5Qa8eFHx8NFsy9c7jrOVi/O4DuNDRbbdDrcW6SmE\nyP/84jmYrwtKRUmlLKhWBOWS3FRi1Nq8cZfJ8pdcXU1YWWqxNN+ivtphpWn5zvH1agq7xrb/FQ2V\n4Y6Dm9dwB/eWCCN/UNBHCAgLPuVqkYUrTdJUE8f54GiModvuISR4vspv+PtHuFEpP4qQUnBwKj+h\ncBzHcZw1Y1VLGEqqw4VNCwDIQ3s8X7HWi3LPVF6cYuZyRrdrEGJth79fpW7b+v4CrGD/+NbTAIFl\n73Cbdk8grKUYSTwPKhWPiwtu08pxrpc7CbgO5aLHcEFS71o2Di/aSqQQXF60fO8HkswKpMwoRJax\nMX9Lz4AkNVhj8X1JmkEh0Jw/U19/vmtY6rVQskA3VhRCeM+9PpfmNY3O+usoCQ/c7lMI89e/vCw4\ndUXwwgXL0FiZNNVkicYL8kRkay1pnNFY7WxayyhPYXV/EPYl3W4K1qIGXYgtd+93A6rjOI6z2dSQ\nZc+o4cLC1tr8hUgigDSD5TpEAdQqkkZLo7Uh8iSd1KI1qMCSZls32aSwTNYMd+/P+CebMFcPSLSk\nFGoOT3bYPxZTKYKKO7RllaaR9PDp7FB623Gcrdwi4DrVCpJSYGjF+X10KQBPSf7mW3B2bn233hho\ndzRqRTAysl5RyNg88TZOLe1ORqmYnwqUygHt1obMJwuL812EKANwZK/Hv/iZAt9+PmWpbihGgrsO\nezxwe76z8rUXFMcvSDKTLwiigiFLNWmq8xwA+p2BlaDTSjctTMLQZ3zUp9WzCKHwPMnlmVVGJ6tI\nmfc+OD9r2TP6Jn6wjuM4zlvSuw5lLDUkni+JCgKtBUlqqfaTfdPM0orBIBiqSnqJpRtbpMjDdzo9\nS6Ascc9wdU+a6VHD9Kill6TceyDG2jwPQMn8lBtguJSwmATsLy1xolOlHitK0dbXchxne24RcAM8\nJRnaEBqzUIeZxe0Hm05XM2y9QQ6AAMJQEgSCbtdQb2iGd8HIeIlOO9lc0tNAo5MRBfmv58Autakc\nqLWWV85rvnvccG5BUygFg7rMUkrK1ZB2M6ax0qFcLRBEHjo1eL7E9xVxL+s3DIOVhua++4Y4N5PS\n7WRYC43VDkHgkaWGS0tv+MfoOI7jvIUZC199TvHKJUmqAQxRTzA57lEtK9J+SL7AkmYCrfOeNkNV\nw0rd0ktFXgJUw1LdkhmBFHn8fymCPSOGh49lm3oKCLHeoHONJy0XmkNMlHoIKYm7mkcf6DfFcRzn\nmlxOwOuw3IJMbz/YGGPR2pJlliSBJGWQGByGEqUEcQqep9izv8bddw+xf39psMPxZ/8geP7U1tpo\nxlr+61dS/tPnE46fzei0EpbmW7QavcHXSCkpVUKshW47Jkmyfgk1RaEUUhmKBouGJDbMXIzZMxWg\nPIUfSOJuRrcdY4xFuLHUcRzH2eCJE5IXLyjSDfNfL7bMLWQIYQc367K/a6+UQEpBsaAYHZEYBErm\nz6/tfxkr0EZw74GM996R4fe3KAPfQ+8QlWpQaCO40B7GmpSj+yUjFXdb4zjXy/3f8jrsHYNStH38\noZSCJBUkaX6EqTWsdVNXShAEguV6vl2SppZ6C4ZGIu65ZwTflyhP8amvZJyf25wU9d0XM549ublv\nABZazZg03ZpAlWWGTrOXhyOx9v6KQmm9DqnOQHoSa8AYQZqkdDt5laEDk6/ts3Ecx3F+Mm0Mgd2o\nF1va/YaVkM91oQ+hD80OgGSkosAKekk+J+oN05ZFcPaqHANPKXo63Nr/Rks6WUQpMqx2QxptH99z\nu1aOcyPcIuB150EThQAAIABJREFUKIZw67Rlu8pBhcLWZKmNg50U0OnknYXjWCOkotGC1abh8JEK\n1oLve/z7z6T8x88b2t38m0/O7LAlYqHbyU8OrLX0uhtKhYq8RKg1ZnC06vkKP1QolS8+jAGtNcOj\nEdZYslRTKxruvsUlWTmO4zjr4h1KVwObknylFJSKgsDP56U0M3jKYkW++YXNT8uv9dpdU2A1LtFN\nfeLMo5WGLMZVNB4SS7udsJyWWGm8UT+h49wc3CLgdfrA3ZZH7jBMDVuqRctQGYpFhbXQbmd0Ohlx\nrDd199XaEoagfJ84ziiEkolRhZQCIRVJBmEgEVKQZoaFhuDf/oPk1MV0EGu5rf5btFsx7ebm8CCj\nDZ1mQqveHTwuEIyMFigUPHS/SlAp0GAs1hiWG5bPfddVB3Icx3HWDZe23xwyxuB7FmPzMCAhLIWw\nHxZkYaSsCXyDIM8r6PY0jUayaX4c2qbRV+hBR0csJ1UW4xr1pIyxCmOg3jIsn7xIqXUJJdymlePc\nCLcIeJ2EgIdug3/xAcP/9NOGOw8CCIzJm4UZk+94xLFByjxXIArzAdEYQa+XMTYWUCkrhmsSIQRJ\nAsMjAaYfCFmqhLQ6GX/+uYyLS+yY82SMYXG2wdzFVYw2GJN/f5bqQVfHbiclSzXWGCanIqb3VQDL\n4lJeOejSTBNjDUHkY7GcvASd2A2sjuM4Tm5XLUVnW8NP282EizNtIL/xVxIKgUFYjfIs1aKmqOLB\nAqFeT9Da0u3mu1vlyHDn/q2vO140ZHrrPKSkYWrEknkFHn7ujyjb5hv8kzrOTza3CHiDXVnZ/g49\nyyyBbygVBIGfZ0t1uxlT4wGT43l8fqmQNzwxJj9GxVoqlRBjYNdkgajgE6d5B+DNCwFLmmQszTWp\nr6w3FDDarH+dGHwpcTdlaChgfKKEtbBaT2m1MoxOQYUMjVWoDBWxxtJoa5bqbhHgOI7j5BaXExbn\nWnTaMWmSEfdSVpfbLC+0mJ3t0utleNJQjAxSwFAxphgKCoGFNGbEq2+awnRmODSp+fBdKePVrfNN\nJbJk6Vrobf6PIH/t0aqlVpaMts6TfOOzfOkZwzbrE8dxtuFKhL4BVruSC8s+nVQiA0G5aGht07DE\nmrxCQpIYLs/GaG1ptPK8gDwUCAJfYqwh7mn8QFGqRkgJhVJebjRNU3zfp1wS7BnJd1pePtsjibcf\n9ayxiKu6Mfa6KWdPr7JYCQgKAVIKqlWfThvGdkX0OglCQJJofGkZG/K3fW3HcRzn5uOpfB7ZlHvW\npw1cudjm4fsL/QhVSzHohwkZ6IoCe+R5TvSODL7HWJDCMF7becMp9POSoFfzPdgfzgIwlV3kK3MB\n57+S8i8/LFx1O8e5BrcIeA20gefOwMVF8iZdQUCxlN8oFwp5PwB/VbNS3xxPb4xhZdUwv5AQ90Ns\nerFheTVjbMRHZ3ljr2LBZ3UlASRC9OMp+0GV3WaMP5I3Cvu1jwQIAf/Xn3WuvsQt1voVAGhtEEaw\nutJjzJfURksAlMqSdjsliDykFAShx3AhpRC4kdRxHMfJvesOn8eeSWh1tz7nKclyXdNsaSplhSdN\n3i9AQ72rCD2DSA3d7vrGlRBw/KKHJ+F9d27e0FpswDOn4HJdUyoIDk2LTVWAjLEcefpT+demVaaj\nZa5kIxy/mHDHXhfs4Divxi0CblCm4b89Bmdn1wchIWImxw17doVAHspTq0hWG2ZQ1ixNDWfPb5/V\nm2UWbewg9l4IQZxohMybrZw/ucitd04gpUD5CmstFkU7huGyYN+Ux4unty/XIOT6dXq+JEvNpq7B\nzUbCcH8RIKXo5y3kJxZBoPjET732z8pxHMf5yVOrKD74QMhnvhlvKt1ZKPmUqxHdTka9ZaiU8yIZ\npv9F9a7iQKXFxWSSMPKpImi1Mnw/v1k/tyB5+ULKlWUoFyAKBF95VtCJ1+NZryxY7r9dUC7l32Oe\neZrlv/wiwaNHODH1fubmygilefxlyR17f5ifiuO89bhFwA16/KXNCwDIE4DnF1NGhrxBaVDflxQL\neXfgLLP0ejuX9YlCSaNpB5V/tLEEYT54+oFHUAg4e6pOZbiQ36RrgxdGPHHS49F7Mx59KGJ2UbN4\n1cmDUmpww++HinIlZMRvcn5O5icY5CE/zWZMpRJibd7gzOaV2xiqCPZPub8ijuM4zmYP3R1wplFG\nSUGWWRotDWK9NPalOcueyTx6P1QZ5Ugzv+pTiWd5oX0rAGGYV8XTJs+Fa3QFf/1NBtXqPGWxUuY5\ncn2NNrx03vKOQzHm+edJ/82/xrZSTlwepzF6FN1KGK+AChTgkgMc59W4O7wbdHFx+8eNgeWVjD2D\n/gCWu/Zn7K5qPv89S8tuH1ITRYIkWz+yTBJDr2dQSvYrKECxElFfbCOVxGhLqRJSKgfMrlrmVgVn\nVorccXeZZicj68ZcuNjBeiFRwcf0a/57gQdCsHdS8Iv3L/HtE0W+/XIRKQXt/k6MlJJ+QSGMsdxz\ni2C7HgiO4zjOzaubCB4/W2RyYv2mf1xbFpczGk1DEHq0e/mpszGGUGkkljS1PL56jE6/grVY62Fj\nLQaL0XawAIB+g01jEL7YdIK9fH6F7v/ze/DM04PH4o5AKYHvS7SBoYLELQIc59W5gLkb9Gq3xEKu\nNw6rFTT3HcjYPWq5Y19eDu1qYSiYnIgAS5pqOt2MRjPFmHw3XkpBluUhRVExQKcGFShK5bx7Yqbh\nyy8EnJr1WekoMkIoVJjcO0alVsAPPMLIp1gOUf3k4E4qGa8ZHr27xa17enh+Pognccbtu1YR/esv\nR5YHjroFgOM4jrPZiYWAbra5IaZSgpEh1c9jE/05yrJYlyhhWFiVjJZT4mTjd62FwK6/xpZkXpsX\nuNik08VuWAAApOXh/qJCkFrFcGVrw07HcTZzi4AbtGd0+8elhIlRReBZioHm1ol4MJi96zZ4751Q\nK4NSEAaCiTGP248WGB2SDNckSknabY0xFjMY8PI+AsZAoRzSbPaoDRUol73+2CnoJFcPdAI/UJsG\nUiEESuW/6qEojzmKAnjHoWSwU2MtvONQh4dvbQF5EzTfnRM5juM4V1ntbH+D7fuSSllijMX3BGma\n0Yx90kzgy4yRSsbt+zLGh9Z26AVef56REjxPUiyHWxYCV29HVS+8uKnEqClVaL3/43lX4tQiBewf\ndacAjnMt7jbvBj10G1xcsJyb35AYDExNeJRL+cA4VU0ZLq7H5wsBuyd9Vm2A1gKl2HC0mXdUHK4J\n6g1Lr7fhKDQzJEnebbhZ75JlhuHhAkEgiWODNptCMAfWknrjeGP1BUEx0LzjQH3wWCnKr9EaS+Dn\n13Noqkc7LXKrq6rgOI7j3KBqWbC4ZDmwV9HtJ/TG+BwY75KKkG6s2D+l0ZllueXhKUEqLErmIT+e\nlycYW2PpdrJBWOyaIh32v/xVrBAIa8n23kLnF38L/4F3kl1O8TyBsIJbxpIdrtBxnDVuEXCDfA/+\n+XvgK88Lzi1IpITRYY+RofW78VasgM3VeuabCiHkYNdjI2Pz04FaNaDb7Q1OA9aqLujM0OuklKsR\n1kq0tnieQEmL2fpyAJvasPcf4f6DLYaL6wuDejcYfO2BCY2vLH7Bsn9ME6eSKLjBD8dxHMf5iTdU\n1LS2nEKDwFApSXZPSNJUoqrgdVOkNUShYKXuoa1ACpiesCy3oBBCnOS9BzINpZJPo5GiraE2HHBo\nLEV5gl5iGalAR46Q/ps/pf78kxB3Sd/+MPgBgbUUIoExgtGqRrl9LMe5JrcIeA2Uglv3S8JquO3z\nQvcgbkFYZqEheGXW5/KKxAhDMQLf2zg65cm3UliklHheXlFooyTOBmXWjDHEscZamB41XFje+v5p\nqun18pv9fBdFUClJwvFxZmKPveEcqx3F989XUErgKcs7dl0CIjqx4IvPeHSfgINTgo89YCkX3pjP\nzXEcx3nrOzqRcHlVUW8ZFhdTokgyOuJRjRI8TzE6HNCutygon7GxNrXFE1yo3kMzDiiFeUiq7+en\n0UJKShF0YqiU823/Tie/mS8WPBIki6sSJcAPDSIA6UnSe9615bqkkihlieOM589JRsqWPaPWNQ1z\nnB24RcBrNFXNuNjwSfXW7YYhu4S3NMMVuZ8vn9lNvKH6TxxDrWIIg7XHLAJIdX7KcOygx/mZlJVm\nXjEh6aXE/a6MOjOkqcFow1hZU/QtvY6hl4BSkihSFAJYWo3pdrN+gpakXJYcPhCCkMxloySZ4YVz\nBdomQIiEyWrGkRf+hnhiHy/XPkg3ya/t1CXDpx+T/PaH3CDqOI7j5FZbmu8900QphedJWu2My1fy\nU+z3vF2SZiVmVzwyBe890CBsNZlrFJDCEvkWYTVYxS27Us4vFPA8i+4YsAJPCSoVj+XllDQzLHU8\nMm3JgAuLikJomRi3m8qGAsSJJdN5/sDFRctSWxGFil3Dhve/LaVa/JF8VI7zY80dmL1GoQf7ainq\nqqo/w2KFQ94FhNUUerMk2eaBylhob2jwm+/UWwqyRxAIlFJ8+AHD3UcM77tf8qGHAw4fyLsRe57E\naM3PTP+ATrPNd16SdHp5edI0NXTaKednOnQ6Wb+iQr5waDY1s7NxP0RIciWZxKsNMTTsUygqxqZH\nOf22f4a/MIM5/tzg2pQSzC5bTl5xKwDHcRwn9+eftxRLAYWih/IlQehRqoQEvuKbz2iW6yY/Mhce\niVbI2hAHRtrUihm+Z+mmCiU1BV8TBNCNAQRpZjHWIoQgCASFwtZ9ym4MrdbmvjtpZmm08hN0o/N/\np6nFIri8ovj6cf/N/kgc5y3JnQS8DnuHM2pRyvxcA2MFVdlkj5pD9hcGQ16HsbDJQlzd9H1pZsEY\npLKUgpTxUpta0OOpi/lAFQVw7IBE27zKT/U+SZIKGj2PyWKL20ZXSVLB3509tql2sjZ5V+BEb66K\nYLTl5NmEVivjvnsqFAJDLxGMjfi02xrlKbrV3cwfeh+3nniWb/vvp9lM8QNFlmouLXkc3e3KhTqO\n4zjgBz5RKJmc8AZlPZeXNSvG0KtnxEke5iOF5dTKKCbqIAtQjWJ6WUCqJaUgJQMkeUPNKMzLe1pD\nP0E4r2rX7W6t8jMcZTTaAisEWkOrYzEGtDaDUNiNrqxIFhuCsaqbxxxnI7cIeJ2qoWEkPIcwWzsC\nGyAzWw9bPGW4d888ntwcZrOr2iHrJZxdGmJ3rU0nk0S+IQoltx4JefJ5zSP7rgCwf6iFFAbL5uSs\nq49I1wghOD/T4+D+iMlRSa2o6aU+1bIikBm+snSH9jAuHmdkNMTzJcuLHSyWyHf1lh3HcZxctSqZ\nGA+oNzTdnkFJGKopSkXJuTTve+N5Ag/NcjeAeJjpGvhSE5OH7GRastLxKYSaNBMUi/1FQL+oRam4\nucLdRuM1y7FyzOMnPOrdfI7NMku3kw4Kaqz1xgHQRlDvuEWA41zNhQO9XlJhg9K2T62kFVbSrc+N\nlBJ8tTXOvua3uad6FoFltlEAaymqLmApRoJySVKaHAMgVJqH9sxufVMLgQ+jQ/m/12ht0BouX+mh\nJISeRQhLqSxRyiAEWOlxoVXFxE2CQJCkGY3lDvNL6db3cRzHcW5KI8Me7eef5baVb/Cg9yQP6G8R\nvPQtOu2EKJIEvmS4JqiVNFJBQ5cQ/fw3AIEg0RIEDJcyAs8gBXhe3mAs8gwPHY1R27TnHC4Z7tqn\nOThh+OcPJYxEMfXVhGYjIcvyr1dKEEXrm1el0LBnZKdaeo5z83InAW8AXZ2GLEFm3cFjRgWY2i4q\nC4bE5GE9SQqVMOO23U2W2z7L7ZBCoNldy2/0pwp1VAqBTGllEWGWUvQSJJZl6fPxdyfM9SapiCYj\nYo537Znn6YVdg0RegF3jlnfdBsWCoNW1zMzCd1+ATiu/kfdVnodgAayl3bIUI58k6xHV53k8vZeX\nXu6ye9owMlriwkqX4xfh4z/cj9RxHMf5MSVOPsexQxGz6h00qBEQM1mbY+TKE1zZ9RBKpIS2w1TF\nEIaSWmQQCLSRCGEQWBo9n+FCShTCaM3SigXaauJYcGxfytEpgzApz5xTLNTzcty7hw3vOpoNGllK\nCb/0sOWlywE/OBvT7AiaPYkXqE29eA5P6S0lr63FFbxwbnpuEfBG8CP0+K3Y9gLoHkgfUxon6UQM\n1ySd/k26wLKrajkxV2OhGWCRgOXsYpnRQouSF3B0WNM1ERZBnCkkGYGCo2NNAmkIPcvp1kEqpZhS\nb5n33N7hhYUxstSSZZrdUwHFQhuAckFw20GICooTl0a4MlPnwP4Ia6GXCpSAY3sT6j3J6krG8VcC\nXrHTeL6m1cgo1zyCSDFUcuFAjuM4Tm7PlM9JdesgHDUhZIZ9TOzyya5cYGxvhUuXYor7JSdnQ3bd\nIsBmdHURTxqkUHgSwsCiDdSbGuFBt2NRSqBNfnd+ZLfh8C7Dalvgq63lqtux4OSsB57PbQcNx6ZS\nLixYXrpkqXcMhcBycNJw74H1sKLnz8JzZ2ClDcUQjuyGR+7IFxSOc7Nxi4A3ipSYyuTgPzMDL14O\n6WxoqGIRXG4EdHr5wLfWDL3RC1htD7G8HGPvkPhSkxlFagRxJgg8jS8MAQkl1SH0QkSvSz0ag0wx\nUUlY6UaAotmDbiIpBOtHn5PDhnMLisNHhykVLUlmafdgeiwhkJbVrsGsLPH9pWkAglCRpIYsSXnk\nnQWEjoHoh/AhOo7jOD/uGtHUlnw0gEXG2RtcoNkNmM+G+PQXljh8sEVZZGgUXRkSKIs1gmohz6Nb\nXDG8/HKbI7cGWJs3r6xE6/OXEDBc3hoWdHlV8eSZYMMcG3J+0ePhIz0+tmv7ENbnzsCXnoZU54uM\nVhfmV6EbWx59x+v8UBznLcitfd8kMyv+pgXAOoGSkCQGnRmstRhtEVJQrfg8fbaMMBoFWCuYa1VA\na3QrryvqkRKpjMrKeWJZxvMse4dbg1c3Gs7MF0g35FMVAkMxSEmNpBsLupmkWsxbtFsE1ULK/loT\nJfKBd2xEEoSSobLh+8926RpXXs1xHMfJ9XbYFDJ4pFGNi91REi25MCuYGoopez1qfpvxYBUh8hv/\n2UXN0qrluRMQFAIW57t5U0xjefmi4sz8zrcn1sILF4Mtc2yjp3h+ZudW98+fXV8AbPTyRWh3t/kG\nx/kJ5xYBb5I02znY0FrodA3NtqHTNRhjsMYSRpJ2T9BKJEpqLAJjoat9xttnuNSs4SdNRnqXUDqh\nWj9HQcQU/fXKRBZoJz4XltbPTZUwvH3fKtbkJwBnL8FqR+Y9DATEqaDoawqBwffgjsOKQwcilNU0\nW3D2fPJmflSO4zjOW4int79jVqRcknvR1mOoqpic8KlMTbCc5mWyI9mj27OcvSx5+ZzgsWcs7S74\ngSJN802oLIP5huRLzwacX9h+Hl3pCJZa29++LLUU2TZFhYyBldbWxwE6seD8wjV+aMf5CeTCgd4k\n4+WMkwsBxm4dxJJ0/WgzTfOKCWGQ11oOAoGne0wUM5a6ZQIf2knAebOLlo4gy9i//CzCGgqNWbLx\nu/DE1qoHrZ6HsXnWQdE2qZYs+8faZLJCuST4/9m78yDLrrvA899zzt3e/nKvrKqsVSWVpNJily0b\n4R1ovAHNAIPDTcMMA8xAxDDRQQdBhAk6Jmamo2GADv5o2vQMHdF09BgDxqZNA8abbNnGi3aVttqz\nqnJf3363c878cbOyKpVZJSHLqirpfCIkpe577777Xry4Z/ud329uSRN6kkaQk+ZlrBmw5+AwocoZ\nGzIY6fPQ85JaM9wxzanjOI7zxjQQZZTN0GLrKnFVt7mUVkkSy9QuxbGDdYRQtPIaNdVDCs3z04JM\nS6wtMuRZW1S2DwKJ2WjKrC3CZ//mMZ9DIwlvvR2aVXj0pObsjCHVgraWNJvRllo5UEyE7ZQIVAgo\nhdCNtz/mKctI7dX5bhznVuIGAd8jQxXDZD1jprV1aTLNDJ1OjqfYmK0Q5Lkl8C1ZVtwMx8o9fC/g\nWPg8s2YPs70yoT5EPWnhyZSlaB/j3bMok6CtR41V3pR8gyeCt3HEO8uCnSA2VSIxILQpQ3aZLPcZ\nq1aZHQjKYZGlYX45x2Y+99bO88LyPpRS5Eja/YQ0l6SZpTlcYveYAnbO1+w4juO8sciwjNSayLSJ\nKeHZDCUNvWCESlUzNqqZGjM0y4LUgEHR0RVI+swsX+l2XM7pb42lWo8QolgJuEwbwbdPwjPTUA0y\nzs5cPeHVo9vJ2TtV3TIQGK5qdiptIwTcNglLre2P7RuDiaHv8ktxnFuQGwR8D90/lVANLUtdRZwJ\nltYtk80+99yboZSl3VecmfOZWQkwxhYVEqVlf7PNsh6hK2tYIxAyYLrtMdWw5CXBYvMoNS9GdlaZ\nLK9ipcft9Xn2tz/BSAkW7BjfUO8m8Cy58VnJmozINUbrKVp2WOjVKQWSlb5gj7/I+eUyF/WejasW\nrHU9tLF4qigytnfMFVhxHMdxCgaLJqAvikmuVPhFus2NhBe7Gjm1UvF3IFNSU2z6PbtY3lwdv1wU\nDIoMdkpJggB6PcNVDyFEMXvf6srLb7BpbTWh3ghoNEIAqqHm2J5rh6+++55iE/ALMzBIBZ6y7BuD\nD771u/9OHOdW5AYB30NSwO0TKbdPQJJZnpvTjNavzKiPNTT1ssYY6MWSZj0kHeT4UlPOuvT9Cutr\nIRaQSoEXEdkErUqsVA5SFQGRyskszIy9lSPrn8LGZcZLS9xVPg/UURI6XpN+LAlMQkl5QB0pLdbC\noysHtl13P5OceGqFWqNIJ3rn3gEQvjZfmuM4jnNT0xshojvl2ZdSMFROECLEIinJAYHMODcreHS6\nvvk85UkiJZBCUKl4KAmVEvT7xXm11mgNWWZQSiKVLJJZ2K2TUjKLuW0yxLMJd0zmVMJrT1pJCR98\nAN7Rg+kly2gdJodfne/EcW5FLtj7NWKsYaS2PaQm9OGefX3ee3gOIST1MAEBkR2A9cBTJBkIDBk+\nMs+o6lWmsz3k1REGlAFLX1SId9+BXVlEAkNee/M9pLC05RhPn/W4sOgDFq3BkzvfLGcv9ZFKMTZe\n5vZ9llrkKqo4juM4BbvDXrfLpBRkNkBrgUCQaJ+SSnlhvgJsdOJtsRfO8xRRSSGlQCnwPYnZ2BiQ\nZ5osM5j8yux/ECl40VuP1TQfeovgzQey6w4ArlavwD0H3ADAcdwg4DWSanvN6oTNimZXtc9wdpHJ\n+gCBQClDXa8yEa3hK4MvYyazc2grqOoWfRMy2x9CJQNyEZLj0ylNENuAPDP0ZJPqYB6sJTcSg8dC\nOsT8uocUBkXGocntG4o7nZTZ2T5KScLQo1oPKQWuWJjjOI5TUOr6ne3UBKS6KIbZz3yUhLv25Rht\nsabI1FMUuDQYU5yr2CBcvP7yhuFeJ0V6xUEpBZVqwMhohVK5CEOSAu484Nonx3ml3CDgNeLJ68yc\nCANKcXdwikOjXayFiu2hpCYiYXejjxeEjMklAtMnI2RYrHE+30O9NY22korXY1WM8IU9v4jOc/q5\nT22wQrV1ifWehwGUlEgVUQo0hyZzmlHGykrMYJAzGOSsrSXMzRdrsf1+jjGWOPdJcxc15jiO4xTu\nm0qu+ZiUkOSCOFWkuShSUVNkwPO8rSFEJrebM/9SFh1/zxMEAfR7GVoXoUAASsmNFQhBuRIQhIq3\nHJUc3ffddWPSDE5ckDw/I9Au/4XzBuN6d6+RciDppoJMv3gGxRISI7KE0WyOmewI6/2A26MOuR8R\nZx5lP6FZ8jBeFWnWWFST1JM5Vvxh+sEw5e4iojrGAI9WXicfm2Ly5OfR+/dQCducPddn/wFBrEMC\nX1KPimqKZ2cta+sZa+tXqitKqfADTZZopLB4SmwUV3Gbgx3HcRw4Oql5fjann3qbLUPRubesrSZg\nfUabgl4SUgszslywGlcolQxpVhTIzDZSZV/OBhQGAm2KaJ9uJyeODVIJSmUPrS0CQakkMFqQZZZ9\nu0N+/F16W4rQf4zHzkienFZ042Ig8chpw9uO5BzZ7do7543BrQS8RoQQDJU9wrwHdmPmg5wyXSIS\ngrVZtAy40BlmKWkwECWkEOwqdRhPzlHxM1IT8LS4l6rfZzy7SLOckHgVAt3DCkVJxORGEPtNQtuj\nk4UgBHeNtjkx7VMpSbTJ8JQlkIbnzu18o/N8D2s1oQ/1yFAvbQ8bchzHcd64fuRNMWmaAQYhLNYa\nFub7LC6mzMzEtHsQZx6ZFsx1KvSTooiXEALlSTy/6Lz3uylpnOL7MIgt3a6m2ylGBsqTxb6B0NvY\nLAy1ahH+k2vxXQ0AppcE3zrtbQ4AANZ6kq8+59Ppv/LvxXFuJW4Q8BoKlGSsWWXX3HcY6p5jJLlE\nrTdHafYFopnn6YzfTq2UE+uAjhrFJ8NXhqpeZ6+apd9LmTeT1OhgylU8BaHMwCsWdHKjMNoyLyag\n2sAawylzO6iAldWc3Ei6rYRmZGgGhiSHoabH3j0heyZDwqC4oQohyJKcCzMxdT/mOpFMjuM4zhtQ\nri1LSzFZalFK4PuKXZNldu8p0+9rFhZTcm3opgFznSr9eGvqTykFxhjy3LKymtIfaLrdHGNASIHn\nSXxfIsTG//uSMFQMEksUCpSEU/OKb54O+OoJQ3fwj2uoTs4qcr39Nf1EcOKi22fgvDG4cKDXmheQ\njhygdu7bhN1FpDWk5RFW9z1AUptA9U2xEdjL8MlQWUygB+xRl8i8JhWvR47Pev0AJoVK3mLgN8it\nYikpE6mEga3gpT3WvXFWGOZCNyBJYiSG4ZJhKLQ8dR4mxgJ2TYR4GxuvRkd8ZuYSZi710LkhSSzn\nZiz377+xX5njOI5zczEaxsbKlCuKsWpM6GnW+wFKFckput2MVttQGpMYrVlcyjfbGigGAVoXq8zG\nwKWZmCyz+J5CSPAChefJzdl+peTGvgCDFwg8JfjGqY3U1XPwlB/xloMphydeXmB/kr2yxxzn9cQN\nAm6E5i5ceG+RAAAgAElEQVTm73w/3qCNMDlZeWhzt1ScewyX+vgSchNQG1xCAtJotCwxUopJVQWE\noMEqXp7SKo+SEWCkz6HGGpmtcKlyJ6fFnURY4kyChTxLWe36/McvBWgDeW544fSAiTGf0ZEAz5NM\nTgScObmCFxQ/jbm1G/g9OY7jODelVizZO26IAoMVPknuIYRmotohSSL6/ZQ4MfT6UApBa4tSdrNT\nr/WVzEAAaWIQUpDnuqghEEmCwOPyU4QoVqmlBKsNIgq2XE+cSZ6Y9tk3unPF4BdrVq4d9z9Sd3sC\nnDcGFw50A5QCH09J8lKdrDK8OQAYZArQ7G+sA2CFQgsfKyUWQa+xhyptlO4h0ewenGYl3E1KGRBo\nLVhPykwv+jxZfR8EHp3YY7WlGR6WrKxpZBCCkJspQEslj7mFlEFczJ4EgWJyssTwWA0Az/1CHMdx\nnBdZ6PmUSxLlKTwliEJJo+aTmhIT9Zh7DhQZhJbXYbCRTOhy59xaS5pqpNoejmOBKPIIQ7/o9G88\nRcmixoC1IOzO+9S6ieLs4ssL5bn/gGaosv08k03DXXvdPjjnjcF18W4AIQSNUkTkeygpwBqMzqmr\nNofqywjyy08k90pY6dHz6gQKov4K4VNfR0rBbO1OWuEkUKRWa/V9lnsel1Z8Aq+Iv5xdsrTXE24/\nENEYKm3bSCWExPMkq6tX1j9Hx6qMjVfwPMHU6Gv2tTiO4zi3gExDrOUO7QlUygIjQs6uNADNUF3Q\n7Rcz66VSURQsCASBL1FSEkaKSsWjUvU3z5Om+WZlYCGK+P8ihail18uYX8pot9Mdr02bl7c3oFqC\nDxzPuGO3plkxDFcNd0/lfOgtGcr1jJw3CBcOdIMoJamXI5J+i8z0EVdNXkirSa0EIRC9FvF6G//A\nLg7qM6jBGnqoQmotwcnHyA49AEFIe+Cx1lMsrIcYayj5hlbs024nvOOBCr1EEgaWXn/7DEet6qE3\ncjVrbdHWQ0oYGVK85143I+I4juNc0Ukkxu7cU7ZAGEjGhnKefW7ArvGQVgeUguEhj1IMcQJhoOh0\nMzxPEkWKNDX0+8UEWJ5bOp2UatXH8yXGCIyFNMlJk2LVetDPaDTCLe8d+YaDY/nL/hwjVfgn97/8\n5zvO640b795AxmjybPDiKugIAQEZOk7JPvHHJP/t0+Tf+RqeTlFJBz0ySaedM7R6kspf/AFrHcFM\nq0KuJcYKet2MCwsSY+DwwSrdxMMi6XYTOu3B5gzLZZ4SlKJiqbU/MGzs1WKiKSmFOI7jOM6myLdY\na4lTuLhgOH0hZ25Zk2VFGk8pYbhWbO71VVEdeKihCPytG4MrFY8wVAghCAKJ54nN2H9rodfLEGJj\nFcAUtQLCyCeM1Ea60CutpxKWo5MZpWCnK37l1jqGFy4Y2jtMoDnOrc6tBNxA/ThnMRkiMT4SS0UN\naPqd4iaIRQY+5X/6U+hnniQ98QTmrvuQ/Q7aK9FdTsj23EH4tT9GPfY1grs/ROhfLrcuWVnXTI5o\n1uMyQkCWGfoDi5QwP9Ni7/46Wkt8H8qR4fAew+lZTbd/5aZajdxNz3Ecx9mq5FkWVi3CxEwNZ3gC\nVnseM4uKaiUgzzS1qqRaLUJTm3UYGfLItSW5KopHIBAbQf+X56akvDJQ0NqSZxrPV3h+Eb4qpcXz\nPOJBTpoadg0bhiqKyVrC1MirV/I3zSyfflhzaqYY7JQjOLrP8KMPFnsgHOf14CUHAYPBgN/4jd9g\nZWWFJEn4lV/5FarVKr//+7+P53mUy2V+53d+h0aj8Vpc7+vGIIXpdpnMXIkDGpiI1HhMRGuAxcNg\nmuOU3nwc0+tiTz+LkJbADpgYLHKmcjvh3R+isnyKNDPUyoZGTZImUK1CvWR4/PkuB/ZHGCPQRlCu\nFKE+y0sD9u2NqFUVU6M55Uiwf0LzzLniJxH5hrv2uGVSx3FeHtdWvHEYA8OlHocnYgJV9N73DkNr\n4PPktCHWEa1uysSIAAyjQ4rAN7S7ckutgKuXwdNUY+0Oefv7OY2mQglQPsSGzRoC/RgOj1s++FbJ\n0tKrNwAA+MzXNE9fVVCzH8NjJy2+0vzIg27+1Hl9eMlf8pe//GWOHTvGL/7iLzIzM8PP//zPU6lU\n+N3f/V0OHTrExz/+cT75yU/yS7/0S6/F9b5uLPbUlgFAQdDRFZq6QyRihDX4uo/wfYJDt5GXa2Se\nj590CbIWC/MWdeiHOHj6s8SxQVUER3etUS3VqJcht7BrVPD8Cz3uvrNKFAmsgWYzZHGhx8JyzuSE\nYqXjUyllVCONrxSjNcM9UxkTTZcmzXGcl8e1FW8c3zltOTx+ZQAAoCQMlTOO7JI8dSFkdiHn6P6i\nk9HNPDxl6cdXzmEtmxuLjbEbm4oVvf6VzrwxFikEUSAYbcDFBUu5BIMYopJCCMvc6qvfTvViy+mZ\nnc978pIl19atBjivCy85CPjgBz+4+ffc3BwTExP4vs/6epHGstVqcejQoe/dFb5OxdnONxCLop9H\nRP4AlSV4duOGGEQkXYt3dIrg3FM0hcf+z/9bnvup3+HcxPeB0Fyal4weChkq51QqitwISoHgwF6P\nlfWc0SbMzBuqVcXQcESuBa2WYajp0R0YyqHhp98+oBy9hl+E4zivC66teOPwVEbgbe8kCwGV0JBn\nKdZaZpcs1VqxqVcbwUhVI4CRalEj4MySR24kSgq8yCMKFcrLabczrLUYbZDKZ6hmKJckeW6ZGres\n9z3WWzlaW3z50oOAXix4bs6jl0pKvuXIRMbQdeoErHct/eQa5xpAnBbZhRznVvey17Q+8pGPMD8/\nz8c//nF83+dnfuZnqNfrNBoNfu3Xfu17eY2vS+I6Ny5JDllOczC7eSxd63L+j77AsT/835j7ziWG\npypUJocZ+tJf8MyxH2Xu6Q7DY1WSXDJcicFKWrqKFxiOjGsePSnZNw4myxF+iO95CCmQ0uJ7lrWu\nolrSbgDgOM53xbUVr3/hdTbfWixSWEolj9Onltk1WcEAkS944LaEREOqBa2+YKRuWGoJLscFCSEo\nlxSt9Xhjtl1iNLTahnIoEBLWWhY/Ap1bTG5p9UCba7eni23B109FdJMrK+/Tyx4PHE7Yf409BKMN\nQaMCrd72x4ZquIQZzuuGsC9OFXMdzz33HL/+67/O8PAwv/qrv8rx48f57d/+bSYnJ/nZn/3Z7+V1\nvu6cmc85u7D9BhSQMOnNoP0KAkuQ9Wh0LxLMnOHE//1Zpv7Xn2T95ALizNN4734ns0+2mXnfT/PF\npwL2TA1x/1HJRLTOSPscj8u3Mjz/NONHR3hyboSJSo9qmDDdGaPdVyAsQ1VLGCoWVwXfd6TH/bfV\ntuV+dhzH+cdwbcXr24mzXUze2TGf/sXViMdPeaz3NKdOLHP49iYHDg9TCiH0LHuGk8v1MTEW5lcV\nM8v+lnMsLw/I8yubhKMQxkZgtS3wPUsY+iwupUgByvd45zHJD79l5znNv/wHy/nF7ccnmvDRd3HN\n9u4vvtjn89+JtxwTAn7snSU+8KBbBnBeH15yJeDEiROMjIwwOTnJnXfeidaab33rWxw/fhyABx98\nkM9+9rMv+UZLS53v/mpvkLGx2qt+/TUJzUjRiotqwAC+NNRUlzwoNs5ZIA6baCR7/LPsevA20laf\nfHGV3omLjL3PQw01OLanxxcf98nyjN3xGZpPfAt16QLD39dk+Et/Rvnun2O03mBXPM1SeJhd+hJt\nswc/VIzUDOsDhbWWSLS4OJNTCv3rXPlr53vxvb9W3LXfGLf6td/KXq22Am7d9uJW//293GsfrcCz\nMwET9a0FuzoDj7lWBCJndXEAQJwIPGkxVjLIBL1EbmaekwJG65r5VYU2RYffWotSCnPV7H6ew/SM\nIQxhfMSj3bH4viSJNcqHkzOGu3d3MFZQCq68Ls1hbrXMTtnQF9YtL5wfMFLdOQveO++xZJnkufOG\nTgyNCtx7SHL8toylpVc3acYb5Xdzs7nVr/3V8JKDgEceeYSZmRk+9rGPsby8TL/f58iRI5w+fZrb\nbruNp59+mv37978qF/NGIgQcGNZ0EkM3KSoihrJPmm1/bhbWaQ0dJhyeJt8/Rfbnn0PYFG/uHOZU\nn5Hqgxw7kLKQwKA2xOH1Z2mvD7jvkX/LcmWI0CRMMEPZjxlincx2GaorhIR0o+pjrWToDDx8T980\ngwDHcW4drq1441ASTi+UaA98hioZShjasc+ltRKVMszNp8SxQQgo13wCX+B5kGbQjeWW9NOBD82q\nYaVddNSzzJJlWwMU0txitMVaQRhI0tSgpMUPihCflTZ8+tESBslIVXP37oy9w3oj3fbOBCDEdcJy\nheAHjyve92ZJrsFX1141cJxb1UsOAj7ykY/wsY99jI9+9KPEccxv/dZv0Ww2+c3f/E1836fRaPCv\n//W/fi2u9XWpFlpqYXEjavWunZc/D8qkS20W/uATHP4nd7LwcIzVKek3H0dIQTmwHKutshqPQGOE\nIdWhdX6GkfsPsXhxiYNmjksvtDl8fJHHuRffF1RLmjT3KQVQ9/rMdyqUohSXwM9xnH8s11a8sRyd\nyJnpRaytlDGmqAg80rRcnMlYmO0wOdWg0VAoJQk2atj4Hiyue1gr2NXMNuraWLK8aAMDZVjpbJ0J\ns7YoTKZNMRDo9AABzaGAXs8SJwYEm9n2FtsenYHkfeGA4YpltKa5tLZ9JWCkqhkqX3sQkG3sXSj5\nELiMoM7r1Ev+tKMo4vd+7/e2Hf/TP/3T78kFvZHJ68wy2FaLS597gvq+YaJGRHl3FUYmyRaWGKSG\nlbzOveOznOjtYf6O97B/9QmC2VnM7AXOd25nVJ2h9f98kbWzR+Hv/g21j/wC2Y//AlIWFR5vb66w\nNKhxYb1CoiWTjczd+BzHedlcW/HGcsdewd99qsP+fSXKZUWWWZ47mfL0U6scu2+MJBd4KmT3hGKQ\nFJtppbD0+hqBIs1g31hGJGLeMdVi3UxweNzwB582gA9cnsa35JkhTzVRPUBKQbkkyTKL70OSQhBs\nTbc9yCSn5n3edjjlvn0pnVjSGlx5TjnQ3LcvY6cmN8vhqZmQC8semYHhquXAaMZtYzss0zvOLW6H\nbT3OjRIFwY5Ll6K9xsz/+YfEiy1MpunOrVIda5CvtCEzXPr9P+IjB05wPptgfinhfOVedFAhHK4i\n8wT19a+z8sXvUPnpf0r35EX0cpfws3+CuHAKay2jpXU8BYHKEVmf/voSf38i4NSiCwtyHMdxdmJ5\nx1sijk7lTNb6lOlS8lN+/Mf3sn9fhJSKsVFFp2cIPMMgKVYDQl/S6WmM8Hj0BckL5yw11eNgOE06\nGFAqB0Qlj6ikiCJFFHkEftFVGR728TyB70vaHcMgtlTKkijc3pXppUVrOlyxvP+eAfdOJexpJhwc\nHvDDxwbsbu6cGeihFyKevuCx1oVuHy4uCR49F3Buxc2KOa8/bhBwE/E9RbUUYZZWsLnGphnxY08x\n/y//d9onzhdPUlB7+3Hs0fvw0x7VeyfoP32JPKiwklRYnE/pdeDry4fI9xwgqISohUWqk03EHXfR\nONTEZBYx6FJ9+FPsqawwWWmDEPRiyR3ZM9xRvsTxsQt86QmYWXU/EcdxHOcKay0LbctoLacUWOpV\nyaF9Pm+/NyDybZEJKIChqmJ1LacWGbQ2KGEIAtCm2B8wPOTzyCmPmVYJqxNsZ5Z6tDVBvxCCMPJo\nNjzqdR8hBKutogOf55Ydp/OB8lUbhM/PpHzpK0t85rNzfPIzC3z8z9Z5+tT2QgALLcHMisJcFZlr\nbVEb4IU5NynmvP64Ht5NJgp8hkaarPz6v2L+n/0SS7/8a6RPPgOA36yw72c/hFetwj0PEP7gu5l4\n614a3/8mOj3Bvt0eCIuf9jgbHeNT4T8jN5JdBwLG3nY70eEpKgd3oaJiWbR+/js0RFHIJ8kEpxcr\n9AdQWZ9hLGjz1oNt/vZxN/vhOI7jXNHPLPEOCXJ8ZamFKUIIPAWeZ/A8ycWFIrtOoAyH8hdQQrC+\nnmKRNJsB35puoq0gUjn3TaxsO6+UguGRiCQ1zC7k5Fe99+rygOnzHRbm+2RZMTgIPcORiSJ8p9XT\n/Oe/7nLqQk6uiwHI2Us5/9/fdllY2fohnpnxuVbJgfXuy9sUbC0stWClU/ztODczNwi4CQVDTaZ+\n5ecpN0KE74ES1O7Yy22//CNU9o8DYKXCjE4xdOwQh9/cwLTWqZQku8Z8jpVeYK5f5tn5GtkP/Bj7\n3rEb2xxheDBNfvAudr33TqKJBrWaYPDw11nuBjx+cZjlbon5bBhlMsLeKlPNHp5vEfGtmULLcRzH\nefUl18mQ6UtDu5OhtcZTAp1DN4aF5YwwMIR5B5n38T2JNpahhsdy26OdFykPh0rpjudNUsPMfP6i\nWXpLPNDkmaHbyViY6zNUznnb4YSRatED/8ojMSut7Uk3Wl3LVx7dWgfAu04//+UkBjo5I/gvX1H8\nyZc9/uRLHp98WHFx+aVf5zg3ipvmvUlVj9/HXf/mf0HPzGDynNKu4c30ZEZIJBojYP6JOUY/+hbW\n7UFIEnxfElfG6XZyohC6OqD3lg8zkS5SXp1jfWgX3s/9c3bPneGJ+3+Ju77173jh8XkulHYBEIni\nBixNjpAwOaRRnQXy6NbOYe44juO8OtR1HhMYTp3s4vkSbXyEhHpVst6Gfj9noXo/t81/lYXag/T7\nIVEkqEaGgQ1JrYdGEQSCTjtDeQLPU/jKEidg7NaeeDzI6HYSolIRJpQkhiGvz/6RK/Obrc7Osf8A\n7Rdl5Jsc0pxe9NgpsWijfO3zACyswxeeUgySjXbawsyq4HOPCT767pyyqzLs3ITcSsBNTAdl/F1j\nlCZHtgwA8lIdz2SofID98E8Sj+0nr4wzHp+jESZ41hIPcqb2+szZ3bT9STydwOIlvLEJ8sYuqrft\nYayW8/xtP0mjUfwMGrLDveWzAOTSp0eV0XoO0v1MHMdxnEKtJPB2bBYsVa9Pzesx6Of0BjA6EuKp\n4vlDtWIg0I4mwKQoafCV4YGpVVayOsv9iIu9JkJYVpb7LC30WFvpUQoMoQdcTheqNb1uwupiD50b\n8vxKZ36ptTUGZ6h+7SFLs7r1Qxye0EzUt3f2A8/QrOU8fCZAXyNe6KnzcnMAcLVWX/D4WdeGOjcn\ntxJwM1OKuDaO1RqpU6yQpGGNwCbIPCPDp7aniUUyIpbwkhb37ioTrl7A8/ayb1zR8kbJEk08yAhy\nje33qI8M00+nkMLA6C7kzCr70uc5Pr5AIHNyFTBfvg1Q5FqQN/be6G/CcRzHuUlIIRgqGZZ7GosC\nBAJNIDNKKuPNR+GhExKlFI2qxVqoVzRCGNa7krXoDoY86HYzvJqkk3h8+xQcnhhjzQzjqaIjbi0M\nBprpmZSx8QpBAEmcszDTQV/V8de5wfeLzn45vNIRtxaO3lFjJaswiC3z830WF4pKxkM1yXveGr3o\nc8EPHUt49Lzh/HJxvkY559DEgHJoiDOfr56OeM+RZFvhsP72fcabevG1H3OcG8kNAm5imReS55ZS\nZwEv7WOkh6lZBrVxAtumH41SyS6RWMOd5jnWyzVKep5PnDzKgw+UqZUNvSRkPlbktVH04ipLn3uM\n0f/+B8jCKtJoqqEmnHmOd5/5HOU3v5ne4buZrR4lVk0kOQoJXnCjvwrHcRznJuLJjLo3IDMeFoEn\ncpQsZsmHGzC5u0KWWxpVi9aWfcMpxnhUqx7zizFhENAMBuwaq7HaGqfdzbgQ1ajVwLxoR208yMgy\nje8rwsij0YxYXe5vPn65P16J4PgRS5pbpIAnZ0osdj3GJ4snTO2rcfFCm6TV5gMPlhltbu8CBT4c\nnMjYNdzDV1uvoxxkjNcF5+c1WpXRwGQ9ox5ZatG2U21qlF/BF+w4rwE3CLiJaSNozD2Dl1+ZRgj7\nq/TSAd2hKQyWXEWUeksMnnmC6Mi9fPPsLg7ct5d6TWGwICH0Ybp0F6PqIVY/8XdMfeAYbV1CDE0A\nmvGn/it4ltapaVaO/xxCChQ57TTg6O4b9/kdx3Gcm5MnPYQo6su8mLES5Xk883yPo4d8RpqCcgSX\nVhS5hqXFAVobxiYz1tqC0aZCKc3ifI8wrFEtK8bGQpaWiul1a9kcBACE4ZWuixDQHC0T+YK9Qxl/\n9U3BaqeoYFwq50zt8TYjWqUSHDhY54H9HsOV7ZuFL+ulltDbOewn8jSPnQkIK0WQ/7nlgL3NjPsO\nJpyek3TirSsEIzXDmw5d+70c50ZygWo3sfLaxS0DACi2K5Xac7CyhLSWKO/TbsHC47P0Rcibu1+i\nWffwlcYaKKscJSyJLNP+gY8ydNsoph8T5W18ZSgvn9u82XnLM/iXTpIYj9iUODgsqEYux5njOI6z\nled5eGrnecSzCwHPvdCl3y8y+jxzKuPJU4L1vkevZ/B8RTywrPV9Ls3laAvDQ5I8t7TWYqJIMjFx\nZWpdyq1Vge3GSoGQUB8qEUU+KI/ptYBLy0VoTqcPi8sZ56YHW67NIphvX3/+s+Rfu9MusEhxpV3M\njeD8qk9fe3zgeM6+MU3oWyLfcmjC8KG3aAJXYsC5SbmVgJuYF3d3PK50RrRwmrg9oFTpczEbx56a\nIfjUX1I7spugM4NpTOCJDGNgLOpS9QZUKzmld+5n4T99gvr/+D/RHwimPvV/bZ5XAOM1gRqVxX4B\nx3Ecx7mGclTm7FyfRiXHV9BPBOcWAh49U8YPLEms6ceWQQKrLRgb16ytpSgJvifwymXKac5qS1Gr\neAwPW5KkaHsqFZ9SSTEYaEplH8+7PAiwVEJDrRlRqYWbqwMASimCwJCmVzb3rrfz4hyl6+U02mqk\nLFjoFnsEXizOJJVaQLpl/7BgoePxln05U2OGQWqQoliFd5ybmRsE3MSsCoDejo9FSRvVBeGXqT3y\nOebOzGG1of5jH8D++R8jVzLEv/x1FnoN3jQ6jTUZIs2xD76X3fFn6D3zOFPP/tGWc4qJKbxDd72s\nfMiO4zjOG1ucKT77nTr1ck6zopld8emnRWdbKUG9JjY65IJcQ7utyTKDlIbmUICHplyVICWVSk49\n9VldKUKA8txgLSgli5l+wJOWI5Oa3sAjFTvn3AxChVKCJNEYYzEG2t18cxAgsEzUr1PoAAh9iRIC\nY+2W9jDVgktLklRu3yenr5o3K7ltdM4twoUD3cRMY2LH41p6hNUS5fYC6WqLYOks8aU22coy0enH\nuX2fJv/AB1n6959gJOpQsV3WzQhKGGbrd9Nf7BCuXtx60koN9a4PI1w6UMdxHOdluNxBXm77nJ6L\nNgcAl+2bCqnWfIaGik68FFCqeExMNvF9hY67ZLkljXNmFyxCetTrxXN7vZwsK8JuTJrwpgMpP/bW\nhPfcnVEKrx2mao3F8yXlir8ZQtQfmI0QIsveoYyR6+wHuGx3w6cWSgappJ9I1roKtCKVO9fMqUdu\n9dy59biVgJuYKTWwSoHWm6VLrBBI3wMpUL0uXsey/O1prLWMHG4iOh2CQ3fQ6Qc0vv9u9tZbWHza\ng4iyP4ySIdNfucQdf/XbqG9/Abu2hChXUcffg5xwqUAdx3Gcl6ccwnBdsNqVmyMCnRu0tpTLknLZ\n48A+j4WllKEhRRgGVKze2NhruHPS8PSsz8JCQqUcYLQhjlO6XY/p6SvZf9o9y1g1Y3yjps2xfYan\nL1heXNTLGovRkKU5UdnH8yVCWDItmZ3p88EHYLLx8jrrQgiaZZ/mVZl9rIWVQc5CZ2ucTz3SHB7d\nudKx49zM3CDgJiZMBmEJdI7VurjJen7xX2OIF9aJ9gyz/PQalaky1oPlp6YJO5a9b76P8ac/z7OL\n/4LbxixpZln3R2nma9R/819gVpfwf+AnbvRHdBzHcW5Rz89IepmH8q50xqUUKGMYHwsQQqAUjAz5\nXJo1NBsCnUkyYzHaMNcfZr1niGNQEqxNmbnQZ/ZSjFISdXW8/1X9/fGGJZCaOFOIjcVra9isHaC1\nxZgiTaj0FULAwqplbsmyu/nKP68QcHwq5vSSYaWnMBYaJc3hsYxgozdlLSy2Jet9xXgjZ6jskms4\nNy83CLiJmVITay1CefCiLAw6SemtxFQ6CeWJCN3XLD22SLI8YPxtLcJ2laWvfQ17ocKlX/gVyrbF\n6Ys1HhxdQdxxlLn/998z9Zv/CpSPNQaLQQi1rQCK4ziO47yYtfDUtCTXW9sMIQRBoKiUroSWBoFA\nSpib6XPoQIlWD5Tv0Yo9ur0eQSBYXRmgc1104K1GCIHQBqkku0dg366t77Nv1PDsBbEZknS5tIA2\nBm2K2gRSFmsFRhcPPvS0ZLENb7vdMLxzVM9LkhJun0g333N61efJmYhMC3xpaPcFy21Z1E6QAbub\nOQ8eSVAu0ta5Cbmf5U1M1yfR5dFtx22es/TQCcbecRfl4TIqioiXE0xsyfuapJ3Q/vTn8ZRkX+tx\nDmbPMtSU7B2O+fyFQ/RkDfmjP4V96iHi3ir9ziKDzhKD7jJpsnNGIsdxHMe5LNOw1t25C6E1xMnW\nsJs8M6ysxHiexVhBHGssYDJNueSRppow8otOvS0688ZY6mV475sU8kUTVO+82yBFsXn48gDAWEu+\nsRogRLE6IKQgywxKCowVnJzz+KtvK7pbM4e+IqeWAp5fDFkfePRSxXrsk6Pw/eJacyO4sOrz14+H\nGAOnZgWPnZGsdr7793acV4NbCbiZCUF84G34Z7+NXJpGKEW23mHlO6epHNpFbTQEY9BpURRsMF9k\nEhosG+JLa4zcPQw6JWBAY+F52uXb2TMesJ5U2TtUo3fhDO3/9kUaP/wOAKzJyeIOQij8oHQjP7nj\nOI5zE1MSAs8SZzuvHntXxe/EsWFxvoc2lulLKZVamSS1BIEhKoWkqUFIgTEGz1N4viKJM8JIsnsy\n5NEz8PBzgqGK5d6Dhsg3/MOzliwxxGlRBEwpiTEWC1fCkwTo3IItViKUEvi+ZLVjeehZwYePXz9L\n0GWvdgUAACAASURBVPXkGmZbHi/elyClIAosaXbl2Hpf8h8+5zHIBCD4hxcsRyYNP3i/3jENqeO8\nVtwg4GbnBWRHvp9Sfw3VXkCVYOrdRzYfjldblIYF/ZkrL+lPL4MSrL+wCFgqJ89StW3C2/czVJL0\n8waJhLN7f4LRf/dz1N77NuRV1UzyrO8GAY7jOM41KQlTo4ZnLm5fDShFgiAojqep4eyZDpaiIz47\nl7DX8xkMNDrXIKDbLZLuD/oZQhSz934oiOOc+TXwPEG3k7PWUUXV4czS61+JtTfaYozG9yVKCYSU\nWL31moQQIARSFtcxvy45vexx2+grGwisDSRxvnPtAfmiw54n6KcWsbGBIc0Fz1xU1EqW7zvqsgo5\nN44LB7oVCEG69x50prcsieZxSuu5c5R3RfgVhRqq0Xz/28FAUIuIl/v0znc5/3/8R+h12LPwdQ40\n1vFNzMCWSXKPo//ze1j+T5/Z8nbWuJuS4ziOc33vuktzcFyj5EYFXyySnEE/Zn4+5sKFHo8+ssJ6\nK6cxVEJtFPzqdovY/3Y7xxpI0yIDntwInPcDhacUvi9ZW+4jAM+XdFoDWq2Y3Gzvukjgv3vQcN8h\ngRQ7d20ERehQo6GQEha6HskrXAwo+VsrB1/NvuiwFAJpss1Kx5edW3RdMOfGcisBtwg9eoDFR09R\nHSnjVSL0IKF16gLJ4irKV5T31qn96IcpHT1A66FHSDsxbGyGSpfWyeZmKR8+SlfH1GnTt3UyI+js\nuZ38r/9my3u5WgGO4zjOSwk8+NG35syuCubWinCdb59WnJuB1ZU+xoAf+lTrIZ6viEoe62sxvW6K\n8hTWQpzkGGPwfUWa5Phh0fnXmUB5ivZ6n+Vlychoibjv0evEyB3aKGNhcV0wNQYnpne+XqmKDQfW\nCHRuybRguSvY07T0E8OTp4tm875DUCtfvx2shpbhsma5t70blb9oYBEEUK4oevHW2J+rQ4Yc50Zw\ng4BbSJZLlv7hyW3HLTD1P7yf8g+9m+5si9JIjf7M2ubjo8d2YdcSBpUxSt1lamnKeng7Ruf0R6YY\n/rF3bTmf55dxHMdxnJdj97Bl93Ax6fSt01CuhpSr2yv6SikplTz6vXQzNCaJc3xPYYzBWEs5CvE8\nRRgq6s2QXjsm7qUk1SJk1Q8Ua0sdGiPb0/ucmQNtLY2KodXb2om/vB8ABFmmsSh6PYMdsXzrOcNX\nn7Z0NkoTfP0EvP1Ow7vvu/5A4K5dMU/PRqwNFCCwttgL0Ltq07GUlnKQM5NuDx0arrn0oc6N5aZ8\nbyHhvcd3PB5NjjP69mOUukuoQXvLAADAClg7tULymf+KCSLGuqcYlUsMB11Ea5l88gAAUnr4Uc3t\nB3Acx3FekfHG9Tu2QgrsRlir0aZI4WkMeW6JSj5RySNLc2p1j3JJkWWaNNX0ujFQhNqkaU6abJ9G\nn1kRfONZ6PQMYmMjsFTgB5KotJECWwAIPE8QJ9DqGr74+JUBAEAvhq8+ZTk1c/3Q2HJgeWD/gLdM\nDTg6ETMaxSSpoZias4S+ZbRhabf1tlSq5dBy30EXeuvcWG4l4BZS/uBPoVtrKJkiGw1skqAvTDP0\n5ruLnMpY/Hh7is/WySVsvszuikcwc5rurjuIpMVLUtLyEMlSj0f6+7ljMue2mrspOY7jOK/MA7dp\nzsxLBunWOUZBkTknSw2eJxESdGrIsqLN8XxJrR4hBOyaDPF9xdLCAGshz3KMiZASsqTYRzDoJfiB\nt1nbRiqx+fcgAaU0pbKH1jAYZGRZsQrheZIwkqQphDLj778jSbIX7SKmSIF64pzlyJ7rf14hYLSq\nGUVzYBj2jxqeuijJjARrqXqGqSnNRA3m1z2SDIaqlvsPGvaNuZUA58Zyg4BbiLCa+vt/CJFftdb4\n9rch4zZkxTH/wD7Ul79B9rm/Q/3h71F98x10HnoMgLlvniMY+wLhL9+N/IeHKY/fjjq6i13Tf89X\nho+z1otQMuHg2PYbouM4juO8lFoJ3nlnzheeDjaPeRsd9MEgK3rNFP8flhRKSbxAUi77KCUJQ4nA\nsrQw4OJ0GyhSYAupiPsp/Y0VgSzV3HvQcGrOI9NiW6FLrS3WQhAopBT0+zlaZzQaPkIopM2Ynrf0\nE4HyJHm2fQLslcTsj9cNP3i3Icvhr78FXz8JcQqlAI7syfnJB8HbOamQ47zmXDjQraS7uHUAACAV\nWVjDbuQqzkWAV68S/MRPEn7yU0z+8k9vPtXzPVaemEV11+j/6V8y0j6JEIJGPMdPhH9Frg0n59y4\n0HEcx3llrIVTCwFhoDb/UUoipcDzFGC50l+XjO+qMDZWplLxiaIiZKc/0FycbqO1Js801XoJow3t\ntaIWzuUsQkrJYlXhJSrdK1WE/2htSVNDr5ezsp6zvlG0S10jWf/EsGBpHf7uEfj01+HLTxShQi/H\n33wHnpkuBgAAgxSeOgd//+jLe73jvBZcj+9Wkvd3Pq48cj/CywasehPFIQnp8G6ysQRZCtn/z99D\ndOwQs3/+MJx8Ft3pE+4eZz6vsCfXNEWbnxh6iL8fvGvn93Acx3Gcl9DuCxZbO88vFhtz2czus5G6\nfxspBTrXqEBRa5SISgHWWurDFUAw6MdYa5lesIzUDDMrO6QM3dgTULyPQClBnlu63Qxj7OZxIQRS\nCfZOVcAaVldT+n3N7hFoVgX/+YvQT66c9/mL8OPvgF1D1/4OBimcndv5sTNzkOXgu96XcxNwP8Nb\nyXXCB3M85r2DzAaHN4/5StOiSe1T/4UR7zxGZ+x92wT4EcpPScb3M/TQJ8AaVL9NdbfPPcmzwG3f\n+8/iOI7jvC69OE/+1a6etY9CQa0qN8KFINeWOLYkCCb3j2x7XRD6pElOqRzR7wyYXzEoaRmuh6y2\nryoeZixJnIOAWi3cOFY8pvWV51lrEcKiVJHdR0iPvXs8KrLP++4x/NlXxZYBAMBKBx5+Gn7qOvNl\n3f61Vwx6cTFIcIMA52bgwoFuJf7OWXtSLflG/n08Ze9jkF0JNqyJFikhuj7MTO0u/HqVxpCiXLaE\nx47S/uJDZF/8W6y1yDzFzxMOpSeu3C0dx3EcZydGE1x6itKzn6d84m8Jz34T0W9RL1vGGzu3IcZa\nPE9ircUYw/paUkxCqaKSb+BLKmVJnu1cwevqYlthudhzMLNUrAbkWU6eabJMk8QZWlu67ZQ0ydH6\nygbky6sRVxPAoF+8pzbQHI5Y7UoW1nf+6DMroK+zda5ZhWbl2o9Vomu/1nFeS24QcCupTmC9rXeP\nzCrOZ1PEslgmjXOJNlBNlznQeYrAtxihSP0yamUWUSoRxm3Kgcb72z8n68ak3Rg8D5UNKJsW8szj\nN+bzOY7jODc/a4nOfINw7hm83jJqsE6wco7S6YeRSYe79mTblgOMseS5xfOLGH4pJWHkcfp0Z0vn\nXilBtbp9mlxrQ5Ze6XlfvaLw3HmNkMXgwhqz5a17vYw41ggBnifxfbUlBMna4lxZZjGm2K/QGly/\nayQ2/7Uz34M79+382N37i3Bdx7kZuJ/irUT5MHQQWxlnRQ9zKR3nycFRZvJdVz1J4MfrHG19jXK6\nRsn0CUoevspRl84iazVEGhO8/8Pw9vchlETVqzAygchikmgIdfKbN+wjOo7jODc31Z7HW5/Zfjzp\nEMw/j68scVLMvue5KWbnU7PZ4YZiL4AfeESRx9ra1tgZX1oEGp0brLVobUgGW1P1XD1w0Nps7C8o\nBheeJ656zBarDIHa3ERcKvnb9iJYC0ZbohA8Ydk3BhPNnT//npGX7si/73545zEYb0IpLM713vvh\nHXdf/3WO81pyUWm3GqmgOs659YjODhUIAcbj80RmQCpLVPJVemqKRrIE43v4/9m78yBNj/rA89/M\n53zvt+7q6vs+dEvoaCQECCOEAYFtbDAz9jjs2ViHY8yGZ8fgWMfaDkf4j1mHY9fY4XBsrD3r8Y7H\nXmAxgwcEBoQsARJIQrfU91HdXff5Xs+VmfvHU0dXV1WrJbWk7lZ+IhR0v8dTWW8X9eQv85e/n5ga\nQScZ4aYNODtvxz/3EtJ1EY5D6gSQzmNa8zinn0NtufEt/uYsy7KsK53TGEesc0hNdubo7jf4riFe\nI6tncfK+OGkXjqTVyjh19ByVesC2HV20OoqZ8SZpZhYO9Upcf+V0JY0z3IXEemMEWpuFHP88EBBC\nLZUIdS6YsTuuICx4dNop8rynggB6alBxFY4D99wA3/wxNM+LUXqrcO8l3BqFgPfeCPfeAJnKy4K+\nShEjy3rL2SDgKlULFY14dRAQ6Bab0+MApG6BQtogcwTFqZO0S104qcHLOhR0A8d3Kf0P/47sO/8A\nx48S3/I+HCU5sfWD7Bo5YoMAy7IsaxUjvfWfc1yqRdjcqzk6uvoepbI8N18ulOWUUhL6mvGxFuNj\nLSbH2hSrBTIt8vQeY9BagcjLXBtjSJOUrv4KhYKg09akiUZlGqUUvp+PLSy4GJ03IVsMDgAQLOwW\n5Kk/vu8iEBQKgk2DEteBGzbldT33b4aBGjx5FDoR1Ctwx558Zf9SdWJDlORnAV6tlKllvdVsEHCV\n2tqV0oglc9HyP6GrY3YlLxIQg1YErUkmi9uJE0EjlQx3HWSf8wyer3AbU8hiQtI1SP2ue4gOvYJP\nSlqugd+LGD30Nn53lmVZ1pUq7duJN3EEJ1lZttoAqp632H3fdRlTDUkzyvcMjGEhNWjxIHA+OQ9C\nwexssnSNudmIKNZ4gYfrOWQL3XyzTGEw+L5HvbcC5JPqjZsKjIzERO2U2ckm/UN57U4hBUHg0mrl\n1/Z8h3rdo1R0aLTyFKFC0cPzHAJPs2e7h+MI+kspheU+Z3RX4f5bX/tnNNfSfO0HihMjhjiBgW64\nfZ/krgN22mVdOexP41XKc+CmoZiR+YzO2WFc1WFzcpS6ngGjIUtxEEybGgXZpt23m24aZEEPMp5A\nYNCdNvNhlb6eAWT/LFJlOEJSV+Nk4TqlDSzLsqx3Ni8g3nwrwfAzyKyDlhKRZWgnRBkJxvDDwx7N\nWILIz9AKkVfmKRTcpTKdnicohvDi8ZVleJTS+AtpPZAHAUIIat3lFa+LIkO56OC5ktSV+L5DuxVR\nKod5/r+E3p6A6emYJFZo5VCrhriuYnpW4Xn5TkW1LBHCUA8Vu3oS3ihjDP/wXcXJ0eWUqZEp+MYT\nmlKouGGHbRlsXRlsEHAVkwI2lmNKzUeR2eqixAJDt5kiDrtxdABjp6CrAFkGSlOKJjnObrSj2dQ1\nh4dGGqh1JmgPbkfGLdzABgOWZVnWSqrcS7RxHzKay2f4nSbO2DDhj79KtOEAp2cfXPUeIQSeJ5DC\n4PsSoVN+/IMxsmx1SdGlRmICWDhQnKUKKcVSx+DFwKJadYlihRf4NOc61BeaiiFAGcHNN5R5/uUW\n8/MpeoMhDCSOVCidBxyvHOowPgIH90u8oTdeL+Xl05pTo6vPTKQZ/OSwtkGAdcWw1YGudtJBFypr\nPpW6IX6tgOdokFA48jTlaAapEzCQJXlDsePRAPPV7YwWd6CQzHTvRVV6GJuJWeN3s2VZlvVOZgxi\n9iRTkcchuY9DYi8z4UbU5t3oco3w3AvsiF9Y863FQPAbH874xK0tnnlyZM0AwHGdPF1IG4xe3DVw\nMBpUZpbOFZSKDlIK4kSB0RiTnws4/5pCCKIUbryujBSglcJxBJ6fnzmIOinGwNQcfPNJzYsn3/hN\nb2zarNvbc659kU5qlvUWs0HA1U4I0oE9GLlyU8cAUX0DWroExEycmIGN2/GSJkIplB9wVm6kQEKc\nuZzztpB6RcbD7Zz29hFpD2MMX3/OdjWxLMuyztOa5LDazmSwBeMXMX6R0WAHh7wb0b1DCGAnx9Z8\naynQuA6AoK/bw3EdXM9FOnn5Ttd3CcL8cK8R4Dj52QEvWD6MrJXB9wWDgyFzczGjZxp0WilRJ8V1\nHLI4BvIKRaiEmZkUITSbN4UEQR5ctOZTGrMxndZyCaM0g2eOvvEgYKBbrNtGoFa0h4OtK4cNAq4B\nWf8uOjvuIi7WSb0CcaHO/MBeGv27cXWKpyNuOvn/UR/qQpe7UGmKOnWK9PQwVTNNlgpwPTyd0nZq\nxNpjTlVpJAFBKDg5bn9MLMuyrNxwu4jwfKRcTtuRErQXcLa0B4BKsFZLXcPOQY3Whv/7aw2m5par\n9kgpcVyHIPTyXQAW6v4vpP50WitTXvt7fZIo5dCL08SdlDRJUZnC9T2kgO6aw/6hJvfvHObs2Qbt\npkIpSFOFlIY4zkiS1WMcmcqDgRWjNoZzU5pTYwqlX30lf/8WydbB1ZN9z4Vb9tj7qXXlsGcCrhFp\n9yZiZ6E6g3AxCALVQWJAZdDdj3Ac0IYsE6QvvUBp8xBpuoUgEJS9CIxG6hQQNLICp2Z8XE/y0pjP\ntv7VZw4sy7Ksd54OBZw1FrSlgJniFrYCXVs3sD/KOD0paceCWtGwc1Bx2w7FD5+LGB5dK0iALM0n\n8ouEFAvnAc5foTecPjHH9NTCfUmAyQzFso/ne3iepKvucuS04URxI0MbfUYnEzqRJpgx7NhZZ+PG\nAkeONFd9/UYH/ubb8Av3QrUIJ0cVDz2hGB43aJNX+bnnBod37V1/+iSE4NP3OUvVgaKF6kB37pf2\nPIB1RbFBwDVCOD5ID6FTXLNyGUPELczemxDGIAA1PgpA4pbxpU8lTNDCIxM+JT0H9BCnLu3Mpys0\nBJ7NYbQsy7JyFy13LyTDOx6gZ8d+3i8zkgw6iaAcmqUuu1Oz66fcGHPh3xebgBmSJMX3PbJUMTe/\nvDDleg6VWpFWI8L3XaQrKZUcZpuS9nTC3r0FTp2Yo6unxPCZBtu3VymVHCpVj8b8yk7E0pGMTsP3\nnoX7bzN86ZGMqbnl58em4es/VHRXBDuG1p/QV0uSf/VBSTta7hOw2BvBsq4Udl/qGiGEQIbV1U9o\njeN7CCnAKIgispPHaQc9jG55N4dmuhkqN2hnDpkWOFoBhpmmS+CB52qq4dorNpZlWdY7jxTrLwy5\nZEz3XocWDi+cgidegXOThvPnv4O960+eL2yopbVBa00QejSmW6hMEbVXlvEMCj4Iges7xFFKoRQy\nPWfItERlhlMn50jilPmZNlKAFIJ2x9DXF573dcFx5VJ34bOT8PhLakUAsKiTwFOHL+3sQDEUdFeF\nDQCsK5LdCbiGyEIdhETHTdApMu0gswhXpQg0ZBnZ0Vdo+T2cPPBzTJs6zXlJlrVwBDTSkIAmUQKt\n1KVaMDhCcePQGr3fLcuyrHekvjBlPPJX7QhoA5vTY8zGJf72u9sYmYbFGp9PHoWP32WoFuG2/QHf\nezLixNmV9xbpCMLSwqFgY1BKk0YpjuegjcBxHbIsAQxSSqQj8AIPx3GAxUpCBiEFrbYmCH2arTZT\nk4p2s4M2ee+BTEEn0oShQ7nikST5hP78ACRKDXON9YOdZsfukFtXPxsEXGNkWF3eEVAZZT3N2ZEG\naQat0Rkm/fcwc+8tBIFk0MCRkzDb9sBxQDpkuo/MCLrKCldkbO1K8exPiWVZlrWgWg5J54aZ8YeW\nc4OMoVePUOuM04q7GZk+P0IQnJ2C7zxr+JmDeVrMgQN1Jpst2q14YaXfp6uvRBh6jJ6dI8syQFIo\n+wgESpmFvgEOpWqIWcgbWpy4K2XIUkVYzHe+00STpBqBIO7EaGUQKqFSr9GJ8wpDZ0/PUCqX0Dpb\namC2qNE2HDonkQ5otXrVv16yK/vW1c9O765ljkurtJ2nTho6iYSagBqgIekYSoGiqw7d5ZRXxitk\nBrqKAb3FjK6Cor9i8OwZJsuyLOsCG5JzDKbDtP06Qgp81caNW3iNSUbjoTXfMzwhiFND4MF022No\nWzdaabQxOAslQjGGsOARdfJmX1oB5OcCVJbvDKhMs1iIX8o8DchxHKQj8sm+EERRRhzlOw1ZmmGU\nJs3ynP84UiSJYmy0zdBmn3LFo9lYDgTycwj5WQbPkyRmuV8BQLUEB697YzfHM+OK8RnDzo2Svr43\ndCnLet1sEHCtMoZs5DBadbin6GIqLrOxz8Nn91AsBziOJEolhVBRdFO0yWsnHz+XcfMuYQMAy7Is\na12qewuFE4/jyUm0H+DEbaRWxG6Zbx7bseZ70iz/L/AgXThqJh258nCiEPkZNlia6BtjUKlamJwL\nVKoRMi8rqrUhiTKCUCwEB4IsM7QaCSrTGG2QSJTOuw0XCy7tdkqWKTzPRWWKWjWk2YhJkgzHWTkt\nEkLgunKpOpHnwifukQz2XPqRSmPgySOC42OCVgSNpmJqOiOONcUQbj8wzwN32IPD1lvPBgHXqOiH\nX6Pz0NeR7QbagNy4merPfZr7tx/m60d2UKoUwHfoCSMCVzNYadFMQ0YnJKzb69CyLMuyQNU2EA3d\ngD95BK/TwABZoYt0ww3URjwm1jhQ21eD0sJZ3N6KodFZ/ZpyqNl3wPCNH2R5zVGTp+MopZHu8sRb\nZRotDW7eeYwsUyRxRrHk0mpE5KcD8gDC8SVID+k4OI5AZYbJsSZpqqhVXYwGP/A5eXiCeneBcr2C\nXqcfQJrB5Cyw9dI/q+88J3nmuIClFmKSUtVBzUa0I8UjT0dIXB64y7vYZSzrsrPVga5B6cs/Jv7K\n/0vJV1QGa1QHqwTz48R/+X8QSsO7+04yNZORKUNVT+FnLYq+puDl7dSLnt0FsCzLsi4u699Fe9/9\ntLe/m/au99LZex+m1s+tOw2eu3ISHbiG23abpSMEt+5QlIKVr5HCcGCT5kMHQ/7XX6twYIsgSzO0\nNnlnYXd53VIIQZZkKJVvKWSpwvVcjDF02nkakOs6aKUpFAuUSgX8wOPM8CxzM23mZ2PSOCONM2Zn\nIowxBKHP7HSHcvmC9dEL6pZOzV/6ZzTXgleGzw8Aco4rKZSWJ/2vnLJV+Ky3nt0JuAZ1vvifKPWW\nlzotCiHwyyHCSUi/+WUKD/wb9iRNzrbqFAoRgWoTa4lwMrYMOgxW7S8jy7Is6xJIB1VfeQbglp1Q\nDA0vnoJmJ2+6deM2w44Ny6/Z1GP46LtSnj3hMNuG0IOKF/HSc7M8/kNFT93l/jsrnBqD1kV6VWql\ncRwnDxQciTGGNM4wWiOlxPM9lFYUiiFaazrtlPHxFhgw2jA83GJoSxdz023kQge0iXNzVLrLRJHK\ne+tccGi4XLz0j+fYqCBK107zcc/b2WhFi/0QbEqQ9daxQcA1yMkiZDFc9bhX8Ilffgn/gZTthTEm\nsiqDpRYOmi45z7H5LrbVOmSpA/7bMHDLsizrmrB3I+zdePHU0sG6YfCWfNX+saca/N1Xp2mdV3rz\nmZfbDA6VaUWrt6YXqwMtHthdrO9vzMIZgsygpQIBjuPgeJK4mSy8B8o1n+ZcQhznO+BzM52la8Vx\nRt2VGJORKb1iI6C7AgcPXHoSRSmAPDFp9eT+/Ov21KQNAKy3nE0HugZd7HCRSRWl00/idOYoZPME\n8Uz+uFYUXSCZ5W8f8fnLb3qMzrxFA7Ysy7LesZQyPPQv8ysCAICpWUXSjrnwnJrWeikNiDXud4v5\n/EobsjRDCIExeTAggKDoEXc0jitxPWfh6gYh8z+F5WBh9V/gOJIgdCiVPTYPCH72XpdS4dKnTrs3\nGvprawdDSZwHQIEHd+y3ObjWW88GAdegLFn7F47RhvnxhM5D36U2eYT7n/tD+P63oTVPe6LBnuo5\nBisRB6/LqFfgiz8MiNM1L2VZlmVZlyRKNVMtxVRL0U700ir+omPDMcOja99sRicStg3kZT5VpsjS\njCzJMCpPn/GDixymNSAWUoQWdwp6Bqt5VaBUUSoHdPWVSWKF60kwAs93kELSbi2OR+C6Dl3dBSr1\nEql5bQkUUsAHbtT0VZd7DQgMQqV4JmHHkOSXP1rh1r02McN669mfumtQu+96/ObLeIWVvxzbEy2i\niSZ01yg5RapkdLSgMHqS67ITRN5BYu1RKsDeLYqZBnzzGZcHb7cdgy3LsqzXbrqtmY+WJ/2N2FD2\nDT2l5fQXzwUpQa/uyYUj85KaazXsko5ECoG+IKjIV/4XmomRlxyVUlAo+fiBS7sV4/oSA/i+S6sZ\nE4QBadKmd6DG3FSTsFrEW+iUuXj9+Y7kB4c9Brtiaq/hXMDGXvjX79e8MmxoxrC1zzDYJTAmRAhB\nX1+BiYnGpV/Qsi4TGwRcg/p+87Mcvf9D9F3XT6G3hFGa+dPzTDw3QXmrz8zDh9l+2xOkUQK1Almp\nC9VuY4DIBACEPmzuN4zPOIANAizLsqyVWhF85XGXqYZAG3AduHVHxt3780lzJ10ZACxqJhB4hkqQ\nBwHbNgbs2ORz9HSy6rXddY+RqTWiA6Do5yk/qxnMQg6+MQYpBUKIpUDCZJru/irN+QilFGHBZWaq\nhco0nVbM/GwbN/SXggDXWU6a6CSCF4Yd7t772gpoOBKu27o6WLGst5MNAq5B0nUpHryFke8+QTKf\ngga34lLeWkBXu4lnTpDMNMnaHeSufZhKnaTYx7yuM6crS9dxXSgGFz/YZVmWZb3zaA1/+z0370a/\nIFPwoyMeUZLygZsM7XVSUwGi1FDJ15wQQvDJB7r4qy9NMTG9vOi0Zcjjur0lxn68OjgAqJUF77vd\n55++n5BkLJ29FTJPAcrSDNdz8TwHrTXzsx2kKylWQwQCKWBseJqN23pJ45R6T5Gx0zM4C6VFjTFo\nrRk/16BWH1z6ukfHPGolyfWbrsx82WMn23zju+OMTSRUKi733tnNXbfV3+5hWVcgGwRco9zP/THO\n/i9T+6e/QWYJxnVJ7vskycGPUTj5q6RZinPbu5FbdkJ7nnZ5E7EooFmusDDfgpu223KhlmVZ1kpP\nHxMrAoDzvTjsct+N6UX7Tl6QwcP+nQX+4N8N8u0fNphraAZ7Xd5/V4WxKc1jzyQka8y3B3oc7r7J\n58vfaZFpges5IMTC2QGFyhRSShxXkiX5n+MopVItkKUaR2iidsr8bAfPd6j3VJg8N0etr0inTKtO\nDAAAIABJREFUmdDTXyJJBI7rMDPVoqunBIDSkueGHQLXsHvwytopf/7lef70/zrF1MzyB/bUc3N8\nZmKIjz8w8DaOzLoS2SDgGtVREnX/p+jc/6kVj0sg/M3/EeeuTciwkC/nRC3E1AnktptxhEEZmGkI\nqqFg34Yr6xecZVmW9fY7ObF+XRGlBaMzUC0LGuvsBgTu6lSYcsnlEz/VteKxzYOSm/b4/PjFlbsB\n5YKgf7DIobOCLDMorZFS4LgSpTQqyxewlMo7CaexyvsIKI3K8nr8szN5A4K4E7FvfxdRJrnxjo0U\nix5Pfv8sWZri+yGlaoGxkVm6eko4Dvi+wCA4NeVecUHAPz40viIAAEgSw0MPT/DAfX0Evq0HYy2z\nQcA1yqxRk3hR+d47kXIajEF0GjhPPkZNO7Q2XU+c+iQpbO0ybNp1ZW51WpZlWW+vSuHiz3/vZY9f\nuCuh6BvaF2TzhC5Uw0vPh//lj1Xorbd5+XhKO9YY6RPWSpyYDjkxbdi8q5ckVnkX4cyQxBlZqmjO\ntZGOzCfuVZ/WfLKQ4qNwXYdKNWBqIqMxF7G9x2Eq7ebo8Zi4FDO0qczkaJNte0pobfBcF98XBL5Y\nyuXvrNME7O2itOHEcHvN50YnEp55YZ47b7VpQdYyGwRcowquIcpW/4Jyspja7CsIRyPGziBffArI\ne4PVRl5ky423A2sfwrIsy7IsgPfsV7w0LFmrCZYjNeWS4OvP+nz0loSGa4hSgyHfAaiF4jUdim1H\nhnp3gTtqBaY7LsPT51e+E/iBh+u5aGXwjcHz8hKfpUpIqeozPxuB0YSFAM8TeL4hDFxEV5H5uRjH\nc3jk8Q4f/0jE+Jjk2LFZbrixl9HRFlrnpUir9ZBqxUVrw2KLgqJ/ZZ2ZkwJ8b+2VfimhXLK9CKyV\nbBBwjeotGTrZhYGAoev0ExR+8pU131O+SLlly7Isy1pUDOG2nRlPHXNZGQgY9myG/m7DS6ccjIFq\nKKmubmJ/SR5/SfPo84ZWnrmDECl+YCiWV7a1X4wphBD4gUuWaYzx8DyPsKCYnmjg+S5+4DIx2mT/\n9VWSJCUoBhhtmJmLKYWa+bbA8xzOjURobRg9M0dQ8BjYkNcElTIvPyox7Oy7snbLhRAc2F1mbGJ6\n1XO7thU5sKf8NozKupLZIOAa5TmwpaaJZcDM2DRuZ5auxglqzZfICgV0p7Pi9abag7P75rdptJZl\nWdbV5t7rDDiGw2cMSQKVIuzYCLWFInNdFcPjhyDNoL8G+zYvT9YvxfiM5uFnDfF56UTGQBxlOK4k\nCJenMPnOwvLKvOtKEiFQShMWPMqVkKidIIC4lTI70yaO8hQijQYER4YljiPo7S+hlEClCsdzSBKF\n4yyvorsO3LwpZlvflVc441c+tZHxyZiXDreWPo2NGwL+zS9ssiVJrVVsEHANcx3om/gJAy8+ijDL\nv6ycoUGSsTFUM88dNH6Iufl94NqtAMuyLOvS1Spw1/XrP/+95wRSSsDw9DH4xLsNpeDSrv3MMVYE\nAOeL2glpklEsB/nq/Br9AoRkRZUgMKhMk6aK2ekOSWIQUqBihV/wmJh1yLIMISRCLlxPCDrNDtXq\nchAQuIYd/VfWgeBF1YrHH35uD4/9aIaTZzp0VV3uf28fQWAPBFur2SDgWqYysuPPrggAAKSUuBu3\nk6UOJizB3tthcOvbNEjLsizratVbUsxEq3PNkxRGpzTbtxU4eaqDEJLhSfjuM4aP3fnq100VtOOL\n9BnoZLSbMVI2qfUUqdRW5htlmcZoQ6YNaZJhBPiBn9f/1wY/9MhUilb5PbG7v8rYZEoWa0pVj+Z8\nvNR1WC80HFvUU1Y4rzKnPjKsOHw6w/cEdx5wqZbfukm4lIJ77+rm3rfsK1pXKxsEXMPk3Bg0ZtZ+\nThrMB34R3JV5lUrDmRkXY2BTd4ZrFw8sy7KsdQxUDPORRrGyadi5SRjsD5FSsGdniZcPN3Fdh+EJ\ngdJm3Ul0ksF3npWcmhC0I4egoImjDKUMznkT8SzLUJnCOJKZiRZSCooLWwy+LxBIzp2axfNcStWQ\nNFXUuko05zoUywGu5wJ5Tn+hFCKEYGayjXQEg5tKeI5hakyitUY6ghefneCm2wbRStNbXGd7AtDa\n8Hf/HPP8UcVCg2K+/3zKh+/yufM6u9tuXVlsEHANM34BHBfU6m1L43hEjz+CGj2LqNYo3PsAp5oV\nXh7xacT5qs5LI4p9gwk7+q7MbU/Lsizr7eU6sHdAM9U0vDzikGTQ6DgIx8NfmLMXQkF33WO+qclU\n3p5mvSDg609Kjo4sP+l5Do4jmZ/tkGQG33dJkozWXAdjyFN9XMncVIuu7gK+LwhDB61ciiWX5nxC\nz0CZYjmf6Lu+pL+7myzTaLW809CcbRO1E7TWTI636O0tUKqXyOKMeleRsTOzNFu9tNuap2KHXYNq\nRVCy6JGfpDxzeOXue7MNDz2ecGC7Q6V4aStrxhhmGxrfE5QKdjXOenPYIOAaZsrdiL6NmNFTq57r\nnBuj9chXl//+2Hc4fte/p9F/09Jjzdjh2TMBtYKmp2zLhlqWZVmrSQHTTYeTEwHnVwryPaiW8sZc\n5bLDfFPTVwdvnZnH6AycHF89sZZSUCh6zE63yZKM1ny0ouOwzjRJnFGrLa+0O66g3l2kOZ8wM9mi\nUAooVQqUqwW0NkSLzQuModOMSKJ04a+GViOlUvHRmWJgsAxCIh1Jq5XfBycbgj/9Yszd10nuvH7l\nbvqRM2sfFm604YkXM37qdn/N58/34xc6fPuHTYZHUzxXsHurz6ceqNHXbads1uVlw8trnHfrB8mq\n/UtVAgyCqKOZ/sGTK184OcLWJ/56VS/3VElOTNktTMuyLGtt7Vjw7LDPhT0DkhQ6cf7nNNUUA8Pt\ne9bP8z87JcjU2hVspJP3JIijZClX/3xaaZRauVil0vzvaZzRaSUImQckSZQSRylgSOJ0KQBgocCQ\nygzDx6eYHZ/l1NFxtDE4riRL811xIQRT8/CVhyOeP7qyTGh2kY3zNHv1vgKvnIj4L1+b5dhwSpJC\nq2N45pWY//OLMyh1ZfUlsK5+Ngi4xslaD53bf5boup8i3nEH7Rs/xPgTL6DT1b+pqhOHqI2/tOrx\ndI2mY5ZlWZYFcHTcJUrXnk4kaR4A1PyYn323YdcGmG9pHn9J88wxTXbexLa/ZpBi7Ynu4oFeL1h/\nUWp8PKLTye9tnVbC2EgDACFFHiQsBAVZpug0OgwMVdmwuY6zcPhNa43jSYzRNGbzxgRRO2FqdJae\n/jIjZ/MzdmmiaDcTohSeeHFlELChd+3PwXPhwPZXX8l/9Mk2zc7qz+DE2ZQfPttZ4x2W9frZvaV3\nAiHJNuwBwKgMs85ShUTjJK1Vj1dCmwpkWZZlrU1d5BZhtGFHd5vrb5IYY/jWk4ZnjhraCzsE33/B\n8MHbBHs2STb3wVC34czUyoUnYwxRJ59sCyEQC7n4xpil1gBh0UdrmJ9P0SrjxJFptDY4jszLgwqQ\njkBliuZMG6Nh5Ow0W7b3M7CpzpkTkxhtCCsF4s7K3YY4VgyUC8xMt5gcmwOcpU3zuebKb/6+2zxO\nnNOcm1z5+M27HbYOvnrH3pnG+h/m2NSV1ZzMuvrZnYB3GOG4uJu3rflcp76JmaGVDcOqoWJ3//qV\nECzLsqx3ts1dGa5cewV/Z3/K9Zvz554+YvjhS8sBAMDELHzjR4Y4zV/zsTs0USdFL0QWaapozkd0\nWinSyVN5HEcu/SelwHEl9d68G26WGUaHZygHKdfvDSmV8rXOIPRQmWZ6bJ5OOwEMrbl8IOVagUIx\nz9VXSUq9u7AwujzY8H0HIyAIQ8bOzZOmy3n/4QX192tlya89GHDvTS67Nkn2b5N84l6Pn//ApTVH\nqF2klGhv3a7bWpfXq/5EdTodfud3foepqSniOOY3fuM3uOeee/id3/kdTp06RalU4gtf+AK1Wu2t\nGK91GRQ++DNk505jZqaWHwxCyvd9iK39MNVUGKC7pLluKOYiu6+WZVmAvVe8k/VWDdv7Uo6MeZx/\nLqC7pLhx8/Ii0qFhc+GxMwBmGvDUIcO7rxeUQrhxa8L3n9cIIFtI4ZFOvgPQaXby3H+TnxPwA4+w\n6ON6y6vs77mjxM6NRQBGxjO+9I0W7WbE3FTzvK8q0NnCtaWgf6jOiVdG6bQSugYqy2cAhCBNNGmc\nNx0TxiDIdxi01mRuie++6PPe/fFSxaNaSfLgvZfYEe0Cd99S5IWjMZ1o5Qe1ZYPL3bcUX9c1Xy9j\nDN94ZJbHn20yN6/oqbvc/a4KHzho/z98rXD+4A/+4A8u9oJ//ud/plAo8Ed/9Efcfffd/PZv/zau\n6xJFEX/+539OkiTMzs6yY8eOi36hdvvqXU0ulYKrdvxrjd3p6cfbewNogyiVcbftpvixX6R0171s\n7FLsHkjZPZCyqSsjeBsXHq61z/1qYcf+9ihdahvVK9TlulfA1Xu/uNp//t7I2DfWFQU/z+kv+Yat\nvSl37ogpnFcM58eHDHOrM04BGOoV7NiQBxB7t7hMzSpGZwXSkTieA8YwMzFPlqqlFCCjDSrLMNpQ\n68l3ArTKuG5rSnGhrGalJNFKcezkedsPAoQjqHYVqHaVAHA9h/mZNirTlKsFjNHEnRQ/8EEIEIIs\nzVCZxgtdiuWA7t4CXb0lZtuSTMOm7teXOnv+Z9/f41IrS6ZnFfMtTeDD/u0Bv/RgjVrlrb0hf/mh\nab700DTTs4p2pJmazXj+UJswkOzeFq4a+9Xmah/75fCqP1E//dM/vfTnkZERBgYGePjhh/nsZz8L\nwKc+9anLMhDrreVu2kb5X/362z0My7KuEfZe8c5jDIw0JPORJNOCwNPctDWhu7jYaRdePiM4N+0g\npaFU0CzN4M8jBWzpX/nYp38q5JNKc+yMplgQ/ON3WkycXaPnjYE0yei0Y4LAY3q8xWNPpHziw8ur\n1QN93lLlHykFSEEap2zctmnpNfk8Pw9CiiWfqXGNXwiQQiIExFFKlmm80KNUKVCrh1QqyxHOuWkH\ndl6enP27bylx8KYiY1MZhUBSr776WYLLLUk0P3i6gb7gnytT8OiP5/nQe2oruihbV6dLDis//elP\nMzo6yl/+5V/yW7/1W/zLv/wLf/zHf0xvby+///u/T71efzPHaVmWZV0F7L3inWN41mG6szxBzRKH\nTiKBjFpo+MbTLifG89KeAFI41Osps7MrJ/O7NsKujasnlK4j2bs1X9F31jlzAGA0TI/PIxB0mjEk\n+SHkxUl9b13gCIORDjrTSAy7b9i49DzkVYAWewfMTreX0pAAhJALKUgGAfiBRxCsnJgn65Q2fb2k\nFGzoe/tycc9NJIxNrV1EZHQyZb6pqFftGYWrnTBrFdxdx8svv8znPvc5kiThs5/9LB/5yEf4i7/4\nCxqNBp///OffzHFalmVZVwl7r7j2tRPNE4dTsjV6Y3WVBVFH8q2nVqfHuA70lVMmZxS+C7s3u3zs\nngKee/FJ9D98fYL/56uTaz4npSRYSI9I2gmVsuQ3f7VnaZK/uS+kvx4wMZ0yPid46OmVaUlpmjFy\naob56fzBvExo/pwjJa6/MNkVgoFNNbQylMsutXph6RrbB+AX3nPt1FqZmUv5t59/mUZr9T9wf4/H\nX/1vBwj8a+f7fad61TDuhRdeoKenhw0bNrB//36UUkgpuf322wG45557+LM/+7NX/UITE403Ptq3\nSV9f5aodvx3728OO/e1xtY/9ana57hVw9d4vrvafv9cy9qmWIFNrr1Q324rjwwpYncaSKdjQLfjk\n3YuTfsXsTHPV6y501w0+f//fBdkaDbekI3EcJ5/0h7BhYDm1p+SDzBKmplIkMFiFB2+D//rdjOmG\nIEsV0xMNola+C2CMIUsVjpuPffF/EQIpoNVISKKMxmy+Q1CtBYSuZmdfzMTE6zsTcKX+3OzfGfKj\n51Yf4jiwK2R+IYq6Usd+Ka72sV8OrxrGPfnkk/z1X/81AJOTk7TbbT7+8Y/z6KOPAvDiiy+yffv2\nyzIYy7Is6+pk7xXvLHnRiLUTCVy53jO519P3thhKbj5QXmrsBXnKTFAMCIrB0qTf9SUfPFikGsBA\nWdBbkivSfgC6K/DT79IMHxvn3MmppQAAwPEcjDZ5nwABSIFcCDCUMiRRniKjNTTn2mztSXnv/pgt\nPddeP51f/fk+bjlQxF+I9cJAcOdNJX75Z/re3oFZl82r7gR8+tOf5nd/93f5zGc+QxRF/N7v/R4H\nDx7k85//PF/60pcoFov8x//4H9+KsVqWZVlXKHuveGcpB4ayb2gmq9N4qqFmQx1OTazeCXClYefA\na58wR4lhuuVS7a6SxinaaIIgQEiBUnqpr0DoS/ZsDimGF08vevGEIiiGZEmG1hqBwPVcpCvzqkNK\nUfALOE7+PRhtlncFFrSaKUePt2jPS2o3C8rFays9plJy+Q//dojjwxEnh2P27AjZNHh1VzGzVnrV\nICAMQ/7kT/5k1eNf+MIX3pQBWZZlWVcfe69459lczxiedRcCAYEjDfVQM1jR9JXg7LRieGp54iww\nHNis2ND92vcCDp3KmG3maT5+6K94TkqBXkhdH+pzKFzCPPXcRJ6uduG1IE8BUrFCK72U1iakWNGL\nACDTguEJGJ7QnJmAX/2wIPCvvYo5OzaH7Ngcvt3DsN4E9mi3ZVmWZVmvWeDCrt6MViyIs3x3YPEM\nrevAx96V8fxpzeiMxHFgW79i1+ByADAypXnpdJ51c9NO6Kmuv5JeKealOtcqZbL4WCGA99zir0r/\nWXPsF5msG2MICy5Ga7Ioo6fHJ1KrIwvHWb7G2Un4/oua+25xSFJIFRSDvPSoZV2pbBBgWZZlWdbr\nVgoMa/Uuchy4ebuG7SvTf4wxfPNJw9NHIV2oQvmjQ3DwgOaGnS5nZj0SJSh6mu29KQXPsH1IsnVQ\ncHJkdRRQKQi2DrocvMFn//ZLK6t5w06Pp19JOb/DMYDWmjRNkY7P3e/byEvPTWKyiB1DIafHzFI1\nJMeVq9KDzkwYvvx9GJ7Iv69KEQqeoRBAXw3u2geFYO2oYGrecHIMBup5B+afHEoIfcGNe3wcW4/f\nepPYIMCyLMuyrLfMS6cMPzq0clU/TuGxF2Ay9giLyyk6402XWzZ1qBXgwfcEfPE7MSNT+RulgF2b\nJP/+l3poNtqvaQy37vP5m681MDiIhUm2VpokThBCYIyh2UzYd30vjz18mk9+UPDgewK+/ZTi8BmD\nlKt3LY6eNSBSlMp7FLQ6Dq4r0drwyjAcOQu/+D5Dpbg8qVfK8J8favPcsfwzEBhUmjI52kJrw1Cf\nw4PvLXLjHpuLb11+NgiwLMuyLOtNFacJaZqiMaSpxHd94nTlRDpTMDal2FpcfqyVOBydDLhtc8SW\nAYf/6VMFnnw5Y66l2dzvsH+bQyF0aL6OSo9JojBG5XUSDWRZhkDgOA5aG2bnMgY3VBjcUMaRgk39\nDh+6UzA8qYiT1dcTQiAdiecJEBC1U4zJAwFjYHQGHn0B7r/N8OyRjCgxTLUcnjm+3JTLIJCeT7Wn\nzOxEg3MTin/4VottG12qpbe+c7B1bbNBgGVZlmVZb5p21CFK4qW/bxmAT7w745+eKNCKVk5s1+pf\nOtfOJ9FCgOsI7rr+4ik/xhhmGuB7UC6sn0ojBGhtYDHFR543FgN9ffnqe1+vx3W7fBodKAaSD9xi\n+JfnNM3O8sulzAMAyK/pBw6Fkk/UTnGc5WDn6DnN84ciRqfyFCnHAS/wKFyQT+WHHq4ryTLNzLzm\n0acjPvKe0kW/b8t6rWwQYFmWZVnWm0IptSIAWNRf19xzfcqhUZ+5ec3MfD75LxfdpQn/kteQEv/M\nUcUPXlCMTIHrwrYBwYfvcuivr07fuW6Hy3NH0rUvJKBeD4mjjN0bDH//iODsZJ7CtKHb4SMH4b/9\nwBAnICSr0oOMNriuREpQmUIrgxe4zM4bZqaXz0goBaqdIh1JEC4HN1IKitUQlRlajQ7PHlV85D2X\n/jlY1qWwQYBlWZZlWZeV0pAo0Nk6k2xgy4DGq/pkyjA1ozh8UtPbpdEXBAFdRXVJVXaOndX8tx8o\nooWYQyVwaNhw+HTELdszPnFfBfe8ij437fV57ki6dtUhA4dfmeH9txd56pWQmeby+4YnYablUClq\nsvVaHgiBWPhPpZo4zlDKYMzab0jjbEUQoDJFay6iq7+CENBMJV99LObj99izAdblc211trAsy7Is\n621jDIzMKmaHh2mePc3ZGYeZuLRmac9FriMY6HW55fp8gutItfRcNczYN7BGAv4anjyklwKAFWMS\nLo88nfGfvjK79NijP2nzxe8qwlJIUAwJigFSnNeNWEjajYSona4IABY1O4JwnUo/QuQr+cYY0jTD\nL7gIAVmaEbXXDorOT4MyxtBpxmSpImonFMsh0nF4+qhcM13Ksl4vuxNgWZZlWdZl0RodZmPjBJ7U\nSJUyFB9j1N/GXHkT9aC14rWRWtmoq+AZZo1L3YuolxwqAWyspRyf8GhEksDT7B7IKPprT4Qb7fUn\nyNKVPHuow+mRBCnhSw+nSEcQBA5xrNAa/KJP0l4OOFLtMDG//vfaVRF0lfLKP3rhSwsBnucghCCJ\nM+JOhuM6BKFL1MkQEtbcDDCQpXmDsqid0JrLDxzoTOP6Dp12TKFe4PvPp9xz4+oGZ2+X6bmM06MZ\nG3odPNeWMr3a2CDAsizLsqw3THcaFNUkcfcgkeODyvCSFkPzR+m4FbTvIYXBGOgon9lk5UFXIfMG\nXL5IKQfQVzY8/EqB2c7ygd1Tkx7v2h4zVFcXfvmF0ptrBwI608QJvHI84eFnFEMbq5RrIb7vkCaK\nxnzEyJl5nCDvFiykoFBwqRXW/36rRfjQbZKXTin+63c0XuDgeQ4YiDoJzbkIyCf33kK34b66w/j0\nyrEXAujECp0ppCPwfAfXk2SpxnElAtDKEEcZT76iuOfGfLfg0Sdb/OTlNp1YM9Tv8aF7qgz0XFqf\nhDdqrqn50ncjjp1t0Imhry54136P+++06UpXExsEWJZlWZb1xjVHyYr15YR+xyUt1ADo6oySOLvw\npGaiJQiclE3FSZSRtLKQubSE0gK04fh4iBGa0VlvRQAA0E4lz5/x2VDrrDon8K59ksNnNJ0LUoLS\nJCNq5w/Wqw613hLdfcsBiOc7dPfmKUsjZ+bRjqarr8rWQcHd1wuOjhimL0gJKhcM79qd//nAVoe0\n06HdBMeRaGMwejkY0UqTCUG9DL/+MwHf+XHK8bOKTBk29Tt0dRd47iRLnY7DIoRFn5nxBpXuIkpp\njMkbmXkLLZn//hsz/PNjjaUdiFeOx7x0NOKzv9THUP+bu1NgjOG/fDPiyPByMDMxa/jmEwmlguDu\nK2inwro4eybAsizLsqw3xBiDkpK1TvCmfglfxFSLIa4jqfkRBVfhOZrQzegOmtT9JkmWp/xMzgoC\nzzDZXHuKMtOWTDRWP7dzSPLgux0qocYYg1aauJMwP5U3EdiyweWmfSGV6tqr1ZVamJf6lIJi6HDw\ngKQYwMfugm0DBs8xuNKwudfw0Tugp5q/TxtDbx7r5BN2vcZuhFZs6IYkMXzyvpDP/VKJ/+VXyvzc\nfQVOToilAGCR57v0DFaRUtJs5DsKvb0BN++STMykPPZUiwu/zOhkxtcfuUj+0mVyZFhx7OzqnRit\n4SeH1j8Ibl157E6AZVmWZVlvkMGIddYVHReCACkMcRqvihOEgKITkWZ1hCOplzO29WjOTK2XYy5W\nTYAX3bTL4YYdgv/8tSbPvtKh2dYIAds3enzmI1XiTOL5azfd8jyJEAaVaYrVkB8dM8RZQrkiOHij\nwACehA0V8FxoRZqvP644MaJpJh6Op9CZXnUIWmcZrU7G07Pw8rGIO28I+Ln7iggheHkY1mt2LKRk\nbrqNzgz1uk+1EnDXgYhvPdak1V67ytCpkUs7RP1GnJvMz1Cs5WLnMqwrjw0CLMuyLMt6gwRIF8zq\nFWKUQoVl3NmT+FoSOyWMXDkRD11F4Gak2mGw26GnnNJVVnRmVwcWtVDRX12vNmdes/9XPl5l+r1F\nnj0c0VV1uHFPvsqfKUPR13TS1YFAEiuSWOG4DjOTDYSo8LUfZHQiQ73m8MF3u3iuYLRpGKoa/u7b\nGcfPLU96HcdBCkmaZktHEwSaTnu5I3AnhkeeitnU73LXDQHhRTJntNIIYejtC9myrcqu/gRHCsJg\n/SQO/zUezu1Eim8/Ok27o7npujL7dr56Q7Jtgw6eA+ka/9T1ik0wuZrYfy3LsizLst4QIQR4hTVL\ngWqRpwmJLKKg25SzmVUlclIlSZXEdwzCyS9y3VBCOVg50/Rdzb4NKfIS5rrddZf331Hm5n0F5MIb\nXAd2DyrSVJEkaqnkpjGG2ZnlJflWIwYEN+4PuG6H4eTJOb74UEwrljRiyStn9YoAYOlzkIJKyWFT\nn2RjL0SdbNVrjIHnj+Yr9vs3w2D32lOxes3nhpv62LajRikw7N2QX+vuW0sM9Ky9hrtvR/jqH8yC\nx5+e43/+wyP8zZdG+eJ/H+cP//cT/OlfnUatt82yYNuQy+4tq4Mo34M79r81B5Oty8MGAZZlWZZl\nvWFuoRvteGjyhXADKCRaekizPBl2TUbWTomy5SlIIwmQUuI7GrnQvKunbHj/3oi9Awkb6yk7+hLe\nu6fD9r7VE+tLdXIMDp/RtNsZnY6i0UhpNhLGRxqMnlnOpx/aWML34PhZuH6Px9YtJaYnmvzjt5rM\ndQTz66TwAAz2OvzWLxbZOrB+pBKn+UTbkfDg3T710vLEWwD1imBog0foGwaqGXfuiKkV8tf4nuTn\nH6jTU1+eiEsJN+8r8Imfql/S59CJFH/75RHGp5Zz+JPU8OiP5vjqQxOv+v5feiDk9v0u3VVJ4MHm\nAckn7g24zQYBVxWbDmRZlmVZ1hsmpcQv95O0Z9FqMTfd4OoUT6/MVa87czzd3MRgOIMWuV9FAAAg\nAElEQVQRLmOdKkU/QwpDwY0QC+cLSqHhlq2XJ8+9HcO3fuIw31menBsDSaaZme4sPSYkbNtRJ4o1\nc/Pw1GHNrQd8Tp9u02om/OTFhH3bXGDtYKQU5tffudnje0/Fa+6ODPUuT7/2b3X5tQ/B00cNUQJD\n3bBnkyHTHbSBYI2Z2ruuL7FvR8j3nmjQjjW7t4bcvK+w6oDxeh7+wQxjk2sf4n32lQY/+9P9F31/\nGEg+86ECtXqZM+fmKRUE8hK/tnXlsEGAZVmWZVmXheN4hOVetErQrSmcpIFco3a/IzQbghlGoy6+\n9pWTVLsibru1RrEcsK06B3Rd9rE9c1ysCACWxywpV0PiToqUgltv788bXxmJrPtMTCh2bEiod5eY\nnmpx+HB+XqCnBlNzK6/lOXDTzjyAuWm3x3U7XV44ujJYGOqTfOCOlRWKAg8O7l99rYspFx0++v5L\nW/m/UDtaI6F/QRxf+uFe3xNUijap5GplgwDLsizLsi4bIQSOG+CUujHJ3Krn8ymmoOjESCH4vXue\nZ24q5m8e3scDH6jS3ffmNJzqXGRDoVjy6NlXZ8vWKo6TBwqOI3AMVKo+rU6KH7ioJCNqZhw/Kbjj\nQJlSqDgzbtAGuitwx36HG3fms3chBL/28Qrf/GGHo6czUmXYPOBy/8GQWvlVZvhvsluvr/KPD00Q\nrTHh37rp0s8VWFc3GwRYlmVZlnX5uSFaBkgds7j+np8VEBghSfFxpeZMz63s9Z/iP5Sf568f282e\nzUNvynC6K+s/19MdMHBBk63F7BbXEcTKQZsEJBhtyGJDOxH8+oMep8Y07Qh2bZIrqvNkytCK4EMH\nC3z0PVdWqsyOLQXe/a463/3+zIrHhwZ8Hvxg79s0KuutZoMAy7Isy7LeFKLUT9Y4h8N51YCEJKJA\nhyKuyJhx+ki9IslAPwfHR1CNEK9y8Zz01+OGrYYXT2tGZ1amr3gu1Ourp0OLufxKGZTwiTsdPM8j\nIUVIQZRKhMhLZp5PG8O3fpTxwgnNXBMqJTiwVfLhu1ycSylr9Bb59X+9kc0bAp55sUknVmwZCvnY\nB3vZOGh3At4pbBBgWZZlWdabQoZlmnE/TjqPT4JGElNgTnaTKUmvGmUmK1FKZhBSsHV7geMvNbmu\nL8J4l3cy6jrw4B2aR1+Cs1MCrWGgbghLHtkFjc6MMSgt0NrgOxqjIUsVnu9RrBYIQp/ZpqbRgkpp\n5Xu/9eOMR55dDnpmGvD9FzTaZDx496tXzzFa5SVUpXvJB31fDykFH/tgHx/7YN+b9jWsK5sNAizL\nsizLetNUqjWmRv9/9u47yu7jOvD8t+oXX36dAxqNRESCQWDOSSSVLVmWzKFlW9JKa61sr9bjMJqR\nZ3zOeNZx7fXujHd0pGN7pdVYtmVLNk1JVCIpUswZRCJy6Ebn8PIvVu0fD0Cj2Q3mBLE+50gk33v9\ne/V+jYNXt+rWvU1m3F4QEqUFKoVyNM6qYCeptRGATDBHMz9MPCIQc8fQvetf0vWjWPPIzpg40bxj\ng00uc+aDqsUsvPfidldfDUgBzTDi2THJRNVCCEEcaxotRaulyGagq2xz9HgAop0i5Pku9fkmGsF3\nHnO54nyHwVKMbbVTgHYeWr6R2a7Dilsv0Xju8hP7iXnFvuOaIBJkbMU5ndP0dHpIv/iS7oNhvFwm\nCDAMwzAM43UjhKDQHGegsZeWVQQ0ubSKpRMiXPrTEQQg0EgpUKvWkOgprLCOdrPtmp1n8ORzMT98\nbIqJ2Xa1mx89HnH1+Q43XbL84eL5eso//bDJ5LxASMFQr8VNlzis6wx49oALQhDHpxr+0gqg2kiZ\nmk7xMx6V2QZCClKlaTVCdh+SFHtLHJx2WNMdUfZi5hvLj7XSgLmapr9raRBweEry6EGfMF2Ylo02\nilwajTA80EB6L97J1zBeLhMEGIZhGIbxurI7B2G6QjGZWfR4XRTorh8CKVEIml4ntvbJTO1FVo+g\n3BxpYYCkc/WSa85WU+64P6R2WuOuagN+8FjMQLdky5rFqTf//MM57rynShS1V+pt12Z0MsfIpMcF\nW/JEydLJeZzA6FiM1oo4StFodApoiIKI6oniR63EYte4j1AWQ0MOk5Mhzebi0qCFbLsJWHucigd2\naqbmoZCrUw0Ewrc4PfsnTB12z/awonPcBAHG68IEAYZhGIZhvK7sfBl7tEXLLSCFJsXCSVt0tQ6e\nqhzU8juoWWXWTt2LwxxKlLGEQM4eQFs2aWlo0TUfejZZFACcFCfw9N50URDwwwcqfPN784tel0QJ\nteka48LCPxzhFs5UmlQQRynNeoglLZI4JYmT9uHgZrTodc3YIZt3WJnxmJhoMDcTnMrr3zQs8V3B\nXE3x9bs1YzMKrTWWpdu7JQXo7c8ueue5IEMrguVOEjRaKfc8FjBfU3QUJNdf4pPLvLmlR42ziwkC\nDMMwDMN4XVnVcey4ST5eOmvXQN3vYaxjC5nKOKoVMdmzhu5oHLwMApCVUeJsF9LJnPq5MDpzU6vg\nec89+FR92depVFGv1olCD/cMJUSDIKI628JxLXr6C8zPNGg1WwgEWi1+n5MBjWUJenqyNGoxrpVy\n7mqL91/VnnL96MmUw6MxadLekRASHKf9nJ8JKJYWDkRLqbGspelQB0civnJHjcnZhfMHj+4I+MQH\ni6wefPHDx4YBYNq8GYZhGIbxulJeHs3yB2JDO89o9ztw7RRR7qTRsZIn3GupOgtdg2USkNQniOsT\naN2e+K7oPfOqd2/H4unNyHhyhldC1AzoLGqy7tIDvWEQMzXWwHEtyl05LLv9Tz/TnmjnC4t7CySn\nNeK1bUG5M0PR13zoWgf7RBOyJ/dEpwIAaBcCisKEKEyYnY3QeiGw6M40yWSXpgLdcW9zUQAAMDmr\nuOOeMxxIMIxlmCDAMAzDMIzXlcp3k+aXb0KVZgt0yVlyIsC3Qxq969l7RLHDvWjh52V7pVzHLZJm\nu8HVxZtt1g4uncb0dQquecfi1fD0zJsG2K7L5Vsk150b019OsYTGsTSWSKnXIjq6s/StKJ2a+Fu2\nRaGcQwAXvKPr1HXiRBM8ryuxZQmm6+0xHptI+fr3WwTB8tWD4jglSRStVjuSKHkB21YGS84DzNdS\nDo3Gy17jwGhMpb789Q3j+Uw6kGEYhmEYr7tg5Tb8o09gNaYRgBIWYbZMs3Mh199Ck7VCtCoSygL3\n1S/k6tzTRF7+1Gt00mq/Vgo++X6fe5+CXQcDlNKsPFHtp/S82v25rMN8FC4Zk+M6CFswMZ1w0zrN\n6p6YegCWhO8+Iak3l+9V4NiCbZf2USxniBNNkkIzWPyaJNEkiQYEf/2vTXYdStEIhBAopUCDPC3V\nR6t2P4LeQkI502TkWJ07x2D9cMBlWz3kiUZjSrX/txydglIvEPEYxmlMEGAYhmEYxutO+wVa669D\n1iaJ5kZRmTzKzSx5XSuAtYMpiRZscI5wf3wZF/jTp12ofaBWCEHGk/zS+wtMTb1wYsOFW3I88qxF\n2ApQiUJIge06ZPIZKtPz3PdEk+svzmJZgsKJIfV3wOHJpdeypebGKwocnfOo1DmVvnN6Y6+TK/pR\nmCJ0cqJ3wMLzUkqUUiilkLI9diEllgWt2Qo/errFybn8w9sjtu+L+NSHClhS0FGUDA/YHBxZmuK0\natCmXDBJHsZLY/6kGIZhGIbxxhACVexD9axZNgDQGg5Pe6zMzbHKHqVXzjAedi6+hOW+7E66m1cJ\nhIBiV4lST5lSd5lcKUfUCoiDkLHplMPHF6fYXLpeMdS9eMldoNm6SlHKgVLtiX6zqWg0FM1mO50n\nTRXVakK9HtNsRIsPCpymHQgsrNrbtqSnBA9vbyEsC8d1cFwHy7F4Zm/EfU8GJ26h4F1XZSnlF9+D\nUr79+PPvTa2Zcsd9Df76X2r8/ffrjE6e+XyE8fZidgIMwzAMw3hD2V6BZquJbS2eZI9WfIa7YtAp\nnWoGKaHLqzNSzTNUrAMS6b38Drqb1mVIGxPUWh6O5wKaqBUSNAIsx8FxBLns4smzY8PPXpHy1AHN\n+Hw7RWhtn2bTkGa+qXl0n0162vxeKQgCTZomzM60cC3Nb3xY8udff4GBaZBSYLk2jmvj0QLLxpIL\na7QW7U7GO/bH3HBxO3A6b73H537B4sdPtJivKcoFyTXv8Nl3XPDlO2OiWNPXKVnXr7jjvgaTMwv3\n+YndIR95Z55Lzj1TSVTj7cIEAYZhGIZhvKGkZXPX9k4Gy00GOiJSJTg65XL/cwWuWFelpfM8WlvD\nxzY10U3NXbv7uf3yaYo5F+lkX/wNnqej5HDx+XnufbhKUG+delwIges5rBty6O9aWlrTseDSDUsT\n8KerkjRt5/s/37o+za/cbOHYAq01mYxFjCCJ04VWxLTTiCxb4uc8hBB0l6BSX0gPWnS/pGSutvix\ngR6b2961UNf0H+6JeXr/wlhHpxVP7gUhchQ7FM1aQJKkNFrwvYeabNvkYlkvb0fF+OliggDDMAzD\nMN5Qx6cVu0dcth9xlzz3zLE80s8RtBKebKxl/2yRJE3YPVHk8g2vfNL6P/18P1IKfvJ4jShSSFvi\neC5r1+T46M1naBJwBrN1wXIBAECYSBxbcHRS84MnIbV9cgVBmiqiICYKErTWpGlKrtBO33EsuHSj\nYPtzElj+1K/rnPmzHxhJeWZf2j4wLNrBzcm0oDRN8X0Xy5bMz9TRSjM2rXh2f8SFG1/f3YD9RwJ+\n8GCNsamYrC+5YFOGW68unjrk/EolqaYZaHIZgfUqr/V2ZoIAwzAMwzDeUJPzEC+fKk+1aeGpmCRO\n2TPVgXQlaSo4VslyiWphv8KmuLYl+PRt/Xz853p56JkWs5WUzrLNlRdkTtXwf6ly3pkr8PiuJk40\ndz4C01U4GSxYlsTPumiVQpIyMODgZSy6yzabhlK2rpHMztvsOhQte93hgeU/+HNHUr7+g4jkRKq/\n1rq9y+BYSCnRCipzDUodOTI5j2atfbbgXx5IOTiZ8L7LrRcMMF6pvYcD/vvXp5mrLvyi9xwKmZpN\n+KUPdr3AT55ZqjT/+pOQXYdSag1NR0Fw4Qabmy99+edEDBMEGIZhGIbxBlvdD77Lkrr6AEorWo0U\ny5ZMtfJsKNZoZLKgFTKsQLb8qt7bsSXXXrS0AdfLsXkoZeeIYra+OHXHlpoNAyn37LDQjk1fnyRJ\nNK1WTLOZIoSgUM5R8OFT71ZkPUlPT56pqXauzzUXujy+K2a2ujjIyPhw0WaP5yZcEgWdmZT+UorS\nmu88FNM67T6e3AWIghgv47YPHwtNtdLAddopT9KSRMrmyb2aZpCyflBxfEpRyMLVF3j43qufUH/v\nJ7VFAcBJD29v8K5ri/R2vvzOxt+6N+ShHQsHmyfmNN9/JAYBt1xqzji8XCYIMAzDMAzjDVXOS7oK\nMaMziyfRWmtUolGpIpN3sW2L0XmfgWLAqq4muUOP0NpyC7wOq77zTcFIxSVMIONoVpZjRqcFO0cs\nqk1BR15z6wUxGa99SPiGcyMe3OswMS9RWlDKKs4dSohSyUjFJZNpj9FxwPMspAyp1xO0hkQ4/NV3\nA379g4vHUMhKPvpOn+88GHJsXKGBgS7JhVt8jjaKBJX2/TqIpnc+wYvrjM8uvyshpSAMIlDt+4kQ\nKFshBPhZ79TK+Z6jiid2hugTlYoefjbmozf7bBh++ZP00x2fXH5Ho9nSPLWrxa1Xv7zrNwPNzkNL\nKxtp4AePBNy4zcZ+pdtEb1MmCDAMwzAM4w13+WbJ39+TIKVsN9DS6lQAICR0drYr4bRii55CwJzq\nwArryNoUqtj7mo5lrGqxe8IjTheCkmNzNscnU8Ko/djYvOav75a8+8KItQOa3pLmZy6OmK4KghgG\nOzVSwB1P+jz/vICUglzOoV5PkFIgpaDRsqk2Unp6Fo9l02qHjatsjoylxAkMD1rcvz9HKz49YBJM\n1hwy+MDy3YNBEIcxWmmk1X7PJFbky7nnTZYFliVJVHvVfrqi+df7Q37jdhv5KoKtjHfmKvTl4suf\nrI/PplQbyz+XKsl/+K8z/MlvvLZ/Ln7amT4BhmEYhmG84c5bK+kva+IwIQpikjBtr1gDhaJ/aqVa\na6i2HLSSkMbIoPqi1w4jzQNPB9z/VEArfOEOulrD4Rl3UQAAgJB0lOxTk3bLkli25K7tCyvYQkBP\nSbOyW2NJaEWC+ebyUyvHsXBdgedZJy4vODK5/CFgIQSrB23WD9uMVVxa8fKTZsdzWFRy6Hkf7OT9\nlJbEsix0mi5ZLdenve6kYxOKfUfPcGjjJTp3/dI+EADDAw6XbH35FZ56ypLM0nPkQPt+BanL/iNL\nu0IbZ2aCAMMwDMMw3nBSCj5wtcNA1+LV5mzeoaOrPYFUSqM1RMrBV/X2hHXfM4j9T4FefgL94DMB\nf/Q3Vf7+By2+8cMWf/g3Fe59IjjjOGqhoBouPx1yHTi9+Ey7qo1k18gZJvq2xrXOFHRo+np9Bgc8\nclmJSjRDPS8+DUuW/5gAKC1OdVBe9LjSpEqdCqTyBR/LFkTh0l2DNFGLmpad1Apf4I1fgg/eVOLy\nC7J4p2X9DPU5fOwDna+oOlAhK8kvH1cAYNmS7z9YfwUjffsy6UCGYRiGYbwpVg9Y/PrPSZ7el7J3\nVBMol0zORoiUnKfIeorD4xJle1zX/BbJfAV59BB696NEj97D5HwnXLUNff55CCE4PpVwx49bNE9b\nEJ6vab59f4uVfRbrhpbmoVuinbxz5qn7YkLAVHX5Up6OBQMdKQcnl07uPVdQzDu0QigUBJW5gO2H\nLc5Z/cI7FYOlhP1TaulOBVDyE1xb0wjS0/oL6IUeBkJTKOdAwtxkleFBh3wRZqrguSC1Yqq6NHe/\nuyzYsubVnQmwLMFnbuvh0EjIrv0BpYLk8gvzL7sS0+muucDim/edeYfi9ahy9NPMBAGGYRiGYbxp\nwkhx9EiF6cmIAI+BNX24noOyNB2liGI2oae2g2phNbMD11DMPErHcz/Ga06gH3iIx/74ixQu38a6\nv/wvPPSstygAOPUeMTy2M1o2CMi6mnImZa61dEoURu10oec7b2jpAdWTNgwkzDZswkQQp5Ak7R2F\nYh5sqx0oxAg6ulyePqzY87cR/Z0ea3oSpuY1x6YFcSLoLikuPkexslsz3BFzcNpFn3bWIO+mbOiP\nKeQs6q2U9HkpPbYj6eguE4UJk8fmUEnKLZcX2bbFZmJOU8zCzLzgq98VzJ1Wjchx4OoL3NdsQr1m\nyGPN0GtTueeKC3z+8d4qUi6kNJ3qhxCnvO/6l9fv4e3OBAGGYRiGYbwpDo8E/LevjDE6sbAandlV\n5YLLV9HZU6AR+JzTH9DZl2HWHkZJh/lN1+JWxsiN76W4qszIPYeo3v8oR77wJwQf/I9nfK8znQ0Q\nAtb3ROwYEzRPy72PIr1ocgzt/Pmcq+gsLv8eO8dc9k+4YAk8C1ytEULjOeJUCozjaBIlcBybIGhh\nWZLJimSi4hIEijhuT+arLYvJecnPXJawZSCi6CuOVyxSJSj4CloN/uGuFtMziiRSiBN5/9A+A1Ao\n+lRm6sydKD/q+xaHjqdcspVTaUjFnORXPpjlvqcipiuKXEZw0SaHrete3S7A60UKwYev9/ine6NT\nnxUgiRO2rtb0d781x/1WZYIAwzAMwzDeFP9w5/SiAACg1QjZ9+wo179rI3EqODrtcc6QQ09ynAln\nGGyXxtBWcuN7ke7CRLD60BMM3tbkTFOb/q4zV6TpyCouX93i6JxDmAgyjkIoxb2zLiePT2qtyXkp\nt1+1fOnL+ZbgwKRLqhdW0Nur1ALfjtFCEqcWllBkPUEQpFiWpJTTaCkIQnAcQXxa2n49EDxxQGKp\nmH2jCa0QekrQkU34yRN1mqcfdUgVTt4mk3XJ5FwsS7YPBNsW0pJoIbnvyQjXafCzN+UX7ku3xUdv\nfoFk+7eYqy/02bzG5kvfrDNX1XiO5t+8J8OWdWfPZ3irMEGAYRiGYRhvuHozZe/h1rLPzU43mJoO\n6ej0iWJopC5dchpfNQisPFGpDw3k169g8ycdnvv/HiWp1Lh4VcSTox5Hxhbnja/olVx/0QunpDgW\nrOuO0VqTJCE6jfk3V1gcnc0TJoI1vSm5F7jEsVmHRC2fQqO1ZqijwXTdoxa0p17lvMXMbEKtpSmd\n2FmQUmDbnOr+C3BwDCanFnYkak0Ai1g7LCoPqiGNEnJ9hXbJ1VQRNGMcd/Hq+PZ9ET9zvcZ6Fbn5\nb7auks2//8SraxpnmCDAMAzDMIw3gVIadYYCNFq1n4/i9gHWJ0c6WDV8HEeHBORpFQYZ3/Quund/\nn/q6DWz81QJHfnCY/Kp+/uc++PYDAYePtxtzrRqwefeVPhn/xSvxKJUSNudR6cJq/4p8Ey9bxrKW\nTpm0hmogidPlzw6c5IiIklND5wVhYtFoaTQC29KkqSRN2gd5tQYpJbatSZL2BVvLFjYSuL5N2AyR\n1sLnisKENFE4jqRWaZEuU1qoWle0Qk0+e/YGAcZrwwQBhmEYhmG84Yp5m7XDPjv3Npc8V+rMki/6\nACglGJnzUMMQiQypglg7zK+5krlv3cO68+bZt/E6evvOQ1gWhRzcdkvuFY0pCmqLAgAArWKiVoVM\nvmvR4/Mtwd5Jj7mWBQg8K8WxNHG6dHLd4QcUZJ3Icsm5DvWmRZxqPFeSKpirxEhp4Zw4jLuwI6AI\nwuWr4Tiug+1ZtGotvKwPGmxb0FMSDPbAQ+PL77J0FC0yvgkADNMnwDAMwzCMN8kHb+6kq2PxeqTr\n2azd1L/QLAywLUlL5KmTJ1IOINGuR7xmM7Wh85k/MELvB659VWPRWpMmyzebUmlEmi6k3qQKdo75\nJyoKtccZphauq7Hk4i2BDr/BmtIsUkBWtohTcG2NJRQD5YhqNaGr0yaKEhxHcvK8q5SCnrIgjs5U\nElPj+R6ZQoYkjnE8m94ej54+j4NTLgNre8gWFnfXEsBFm12sV1Cn/41UbaR860dVvvyP8/zdd6uM\nTp6pK7LxapidAMMwDMMw3hRbN+b4wq8O8f/+S5Xx2RTPdxk+p5ti+bSOshqKecEIq1H69GmLgGIH\n9b2HyHZ1oqT9Klc29Qvm9OgTzcmU1ozNp/TlqvTloRnbTNazpLq9I9BbaCGTEB1HdMoKq0pzpKLE\nyXSf4zM2q3tDomaMbSksyyFNNKWijSXBdSStE+U+g0RiW4IkXVql6GSqj+u5NGst/JxHoWxTKgiO\nHE/BslmztoOxY7MEsSDraS7bZPPuq19+t9430rGJiC//Y4Xx6YXg57EdLW57d5FLtprDv68lEwQY\nhmEYhvGmGej1+Pynevjbh3OkzztYa0lIUs35g02UfN6URWvCH92L97HraMxlkdHyVXteOoGVJNjT\nR0gzBZJSz8Iz0sKyXLTWzNVDLKlPHRLOuQk5J+bgXBmlJQrBls4JOid2kQ0q6FAQZspUejdSaVqs\n6IxxbFjXPcUz0wM4riRKBMW8JAgFJ3t+CQGpkrgZm6S+eCVcKUUSpydeJ7BtSTbnMjkVIhGoNEVI\nQSJsnFyetJUQA0fnBNWmppR76+4E3HlvY1EAAFBrar7zkwYXbfFfUbdhY3kmCDAMwzAM400lBFy5\ntslPDmSRp9KANFprunIRA50hQaII1UJ5nvTgQdz6NPnz16N/eBR3/gjsfZBkZo7qgUmOfGcXsquT\nzvfeRM/PfwDpOiTzVRACu7S4qZSqVRB7H6VwfBeOCkFaxKUequsvRWVL2E4WIQSNMCFKl+4WZN2U\nrmyLqUYOV6YoN8t8z0b85+5G5nL4rXnS6QNsbtZxypfhegIZR4zN2+RyEinFksntyf/WSlMoOEhL\nEsUprUaCYPFrhZBYtkRpQaUl8XybZivF8+xF1z06ofn2Qwm3v3NxmtBbRao0h0aXT/0ZnUjYczhi\ny9rXpvGYYYIAwzAMwzDeAtb0aboK8zxwIEMtcPBsxdbBefJ+O+3F0jHgoeOYdPt29J3fYsV//iwT\nh+a4Nv8M8Wg38eBavI4O+ntz9Fy6mh1/+E9U//5rjP7R/4W2fVQzQLo2+W3nMfibv0I9aDL/6G7k\nmlV4526ldOgwKj9M58gT5FRKcd+jNC/7MI7XPmgcp2coZwRk7AStNUIoGolLxivQKA9RqI1CNo/f\nmkdbNuvrjzPlbObRuTXYjiROBCu6QqbrWSxLo5Smp0tSb0AQpvT0+Agp0bqdBhTkU2amm2Qtl2Y9\nQqWKcncWrcF2LaIYMr5gfi7BtiVBsLi78eFxTZRoXLsdHMxWEn70aIvpuYR8VnLFBRnOWfkmBgkv\nsNBv9gBeWyYIMAzDMAzjLUFKzbbhyrLP+S64c4cJd+wl050l+3ufwDm6j57Dj6NrVayJMcThfcQr\n1jF/4fV0ju9g+Pd/jYn/+6us/sx72f37/0Aw1SSNbSo/eoDqI0+jLQsr46EaLewN64h/99/RNf8s\n0xtvQB99lJycxp2fJEglYn6CfBIQ9W4i9QtLxpcoQRgLZhs+5WxIqn0svw+3No2DAAFxqQfmZ7h7\nZA2eIxCpIutCVy5hfB5sS9DTKbFtST6rqdYBYbXPAKSaMBJkMjbdPVkyGYvJ8QZBK6RvRZm52QDP\ncwFNPts+uxCFMep5Oxdx0u5D4NpwdDzmy/80z+TsQnDzxO6AD99U4Jptb/zZAUsK1q5weLK69ID2\nyj6bjavfmjsYZytTHcgwDMMwjLcE3zlzV1/HtuldtYqhd99E10WXkS2vwDu2D6oVxIkDvTIKcA7t\nJPvco0z1bkV6Hv2/+lEq+ycZ/v1P4F+2GeKEzd/6U2TWoXDrtaz87ldY+a9/Renn3kX9T/+C+fNv\nxQ+mCOeqJP2rqB3YR2N+jj3eeTycv5nqyAyFsR2LxpYqmG54aC1oRjZxPSRMHUYz65lPchztvgRl\nu2DZkM1zbuE478rczzWFZ7h8fYVG2J6O+R5YJ+r+27agkGvfj5N5/+6JObDrWrruYk0AACAASURB\nVEgp6BvIMzjciWVJHMcCAZ4DSoFKNWm8dOeiv1OQOZFR8+37G4sCAGj3JfjBw03i5AUaH7yOPnBD\ngYGexWvUxbzkPdfmlj0PMF9XfPuhhK9+L+Yb9yQ8d+xM1ZSM5zNBgGEYhmEYbwm2ZZFxliYpSCnI\nee3HhZQI24bxw4jJY0teKwB77AjSdlC5AqOFc0lHx+jIQend14KGw3/4Fc696/8hCVNaTpmoeyXe\nB95Lxy+8H/XIIzgqJu4cQNk++8qX0xAlLqr+kF51nMnudzBZdZFRuw5/nAjGqlkqLZ8MDRAS79B2\nXBnh+5KRnouZ3j9DtTDcHp9lsc3aTlHWWe1PolLNyGyWjAeus3iSa9uQzcDJpr8nS3sK0W6m1j5L\nwIk0JFCpotFMmJhOiOMU210cVHkOXHWe1e4orDVHjy+ffz8xk7Jj//LlUl9vgz02v/OJDt5/fY7L\nz/d55+VZfucTnVy0ZWlloIlZxV9/J+GBHYo9RzVP7Vf87Q9T7t+eLHNl4/lMEGAYhmEYxltGIeNS\n8F1cW+JYEt+16ch62NbiCa2oziA4w2p1FCCEZu8xSVOUyA6UseOAjnN6AdDHj6OqdVb/8jVoLBQW\nsXaxrrqatN5EF8o4OQ+KRRK/wBF7PbFwWd3ciZRwpHQJ6uA+js7leHa8g9FKHoAuZuiUM6x0pyFN\nkVox468kuOMugnoEcbRozFLFHJ30yWUlGb993FecFgcI0U6DymehkGv/txDtSqbpiQVvKQRKabRu\nBwHjkwmNRkoap9i2hbQWLljOw7mr21M/AbxQoR3bevMy8HMZi/dfV+CTHyrz0VuL9HYun71+z1OK\n6edlj8UJPLRTEUZvzk7G2cQEAYZhGIZhvGUIIch6Dh25DJ35DKXM0gAAQA2eg3KWrxSjCmWUFqRa\nkLWaWF1diDikEbe7EPdfu5nZr92BM7SC5NChk+9Mavs4l19CqSDxLE2cKdGZi4hwOO6sppjM4KgQ\n15esivdwdf1fuV7fzXqxD4AsTc619jDZfxHZ6nEiZSOFJp2cxKnPYh/ciTytI3FVFaiL4qkGYWKZ\neffJib/rQMZv7wCoExk8Wp/4Gd0OAvIFr33YOEood7YPC2u1MBmersB8feE+rz3DAeChPptz1731\n8+9Hp5c/qD1fh+0Hz3yI22gzQYBhGIZhGGefcjfJirVLHlaOR7z2PILUQRe6yTzzEN65GxHZLEmt\nBq5F8Zx+wkPHkSpm7rO/SfN/fOPET0t0oQxKU4sKNJ0yjqOxZcqE7EMj0bSX4ueGLsIRirJV4QL5\nFFvFdlZ6E5RFlbrXQ4E6Ngk2EVYuT2FyL7I2h1WbA0AjSIqDbFsr2dwfUPDSUz0COPGK57OthR2A\nk4QQKAVSQiZrgRb4GQfbsZASVq/K4fvtC0sBu45Kdo8IUgUfvDHHcP/iVfZyQfL+M+Tfv9XIF5jF\nuqb0zYsyQYBhGIZhGGcdISTx5e8h3HwxabmHNFcg6RsmvPQmosFzONQaQMYN5r7yLQpZhcqV6avv\nZ+V7L6J5fA4rn0VMTaJHjtP48t+QTs+gtcZq1Yn27qLy6E4mJlISJcl7ilDmmN8/QSJc0iimVRpk\ne/ctpArqbgfr2U2hZGPLFCUsFJqBcC+zTZ/oIx9j3/C7EWjSKCb1y0S9m8gODnP+KljXnXDFmibn\ndIeU/ISTAcDzdwaU0sSxao/ztM0RrTWOY5HxJWGzTv1EdZ1SwWJgIMPmzSWyWQtpWzx20OF7Tzl8\n/X6bVuzw2x/v5OduznPNtgzvvirL5z/ZwYWb/Dfot/jqrOpbPlDpKcO5a8wU98WYO2QYhmEYxlnJ\nyXUQbL2a1i230Xrvxwmu+xla/Rs42FqBH9WR/+532PJvP4BIFU7SonV4ioF3nsfoXc/Qdeul7P3b\nR9upNNMzBP98JyCoN1OqA5vouOF86l/7Jo3II1WabH2M/X/+Tbj3+3RmAnjyEXzR4rHiu/DjGmMd\n55G0WoSVFr0TT6KffQpxZA8ZGTE3cC71jmEmOrYQrr2SYPhSkvLKU5/j+HjAnd8fZ9+zo2zubeLa\natnUIKVOHAJG4XkncvsF5PMWA302riu56MISa/pj0iSho9yOFDK+xarhLFs2+NhWilKK6arknh3t\n3YKbL8/xsfcW+eCNBTqKZ88S+i2XWEsCgUIWbr7IelPPNJwtzp7ftGEYhmEYxmmk5TK0dh3P7T5G\nJGxi7TIVlvCjCgOPfZPVf/4JpC0gDknmash8gee+fDe9H7qGsQf30/j6HQsXixPCVDKpB+nL96H8\nhM73rWbnjKCYUZS/8TfMBwL/kXtYc/Mgx6ciCqRkGuMcLG5DpRJ/NiQzNEz8f/53nK4s6Xm3YYuU\nuarNOfIgB9a8hxWHfsze2WHKBYeNfSF/8aX9fPdH4zSa7TyfO743wTtvHaYwONBOPTohTdu7H73d\nDtOzMaWcRmlBnIAlLY6Ph5SKFrFy2LQxz48fi5meTentFliWpKPDpasQs6pf8fReqLdSZmoWf/L1\nmMGOlFsucxnsXnr2Yr6asOtAyIGRiDQV9Hfb3HBpFs998XVkpTSP72iyc3+IlPCOzRnO2+Ajlotw\nXoF8RvKp9wme2JMyPgcZFy7bIinmzBr3S2GCAMMwDMMwzlq2bdPb303QDKhUQzbFz9LX2IXcmgeV\nQKxI/RIjoxGNoy3SBI78139efJFCHnnLzVRDD8uBluhA5z1kPs/UzoQ16yp4nTl6vvxnVD//e2QI\nCC++gb7nHsDJF5ksbiO1XKZ7L2RlvJ/srTeS3v9jUr+MrS1SJcjOHYJSB0c6LmPdX36axm/+Ad9/\nsotv3jmC0guT4vGpiLu+e4TPfKbIWDOPkO0dgDgBEHieIJ+zCEIo5CXFTEwjshFCMD2bUCpaoH3W\nDQQcr8TMWNDb7aK1oNK06S1GDK+QPLk9IlewCGLBM/sSxqZTPnyDx5O7EybnU1xbUKmEHDraPFVp\nRwiBtCTfvr/BxefluP19Pj+6v8LOfQFhrFg54PKea0t0lW2U0nzpH2Z4ZHvr1Ge7//EG11+W42Pv\n73zNfv+WFFy6ZWE6q5TmyT0xU/OKwV7J1jX2axZ0/LQxQYBhGIZhGGc9P+vjZ33QRaL5PFZ9EjTE\nvevQfonezRC/713s/cX/bfEP2jbyZz5EpW8zaIGb1vDSkJrbw1wzS2F+lENzK7js5qtIBjqoXHM9\nOgzI+RH+4e3ITe8gSj1SoXHCmPnSEM01fRSrszSf2U20aQ2em7IncxXbgj2M660UPvMJRv/3P2fL\nf/hf+T+u3oHb6fPv71pLLWyvxE/PxDz86BQDG/N0pJP4qsmkM0Qq2g0DXEfguO2JbSu2KbgBcQxR\npJicSenvtujqcqgph0olpbtTA4IoFkSppKcYU8hbxKkiaLarFU3Oaf76zoBUWySRalcW0hbYLkTt\nMwZaa5RSBIHg4Wda7Dg0Rb0a0qoHAOw9HPHcwZDf+HgvO/cFiwIAaDdVu/fRBu/YlOHc9Uvr/r9a\nE3MpX/9ewJHxdmUgIWDdCotffq9PPmN2B57P3BHDMAzDMH56CEHaMUS0chvR8Da0Xzr1lNPdyYa/\n+0v8X7wdfc0NqFveg/q9PyL5tc8DAq1h3ZFvI3M+ldgl46R0HnqcPQcjqqVVeDLG/eWPcfjBI6wQ\nx4k3XYBVmwEUemyagWP34EZVam4PzQ1XUrfyBAoGijVimUPWKzhxFZEtwH0/5LnqEPX8APrZ3Xz1\nk8f5o9/u4V1Xt8ueds48y4ftf+KS8n6umP0mPxt8lfPDh9ofUYLvLlT8aYQCIWX74HCkiSJNUxXw\nXEkUp6SqfdRY0/4/SyjiRNFsRMSndRXWwqajO0dnX55cyUdIgeu7i1bStdJorUmTlCSFQkcez3dO\nPT8yEfPd+yrsPBAs++tJU3hyd2vZ516tf743PBUAQLuE6v6RlG/d++Y0PnurMzsBhmEYhmG8bTil\nAlv/4HNs310lKA6eelxrTefkdjITe/BLNjOWoNazBmv1mvYLPI9QuuTcBPvydxBHNZr5QfQDT7Gy\n90kOfW8HpaHDNFavIpXD1N0unK19DMt5VLNGS+eY7d3I5t3fIchsBQTNyKJvQ4meXB/7fnSYlR/o\nof/8q/kvlx2m+pO9hM4KOmd2UVl/Ce7hpxjomUPwDM84mzk6qujtlniOIIwkYdBCCoEgbf9TtA/9\nCqDZSsllLRxb4zspQmmmpwJq8y0cb2ECr1JFvRpgWZJM1gGtadZDMsUMcSsmjtodhnOlDK1aiFaa\nJEkpdReYHJk9dZ2jYxHlwsJ1n0+9DiX8p+dTDoymyz53YDQlSjSubdKCTmeCAMMwDMMw3laEFGy5\n94+Z2ngTzfIqhE4pzh+guO8BrMoMTimL3YgIuzYws2Iz59gWOTchET4FOYdtB4j9e0jcPuSxowzW\n97P/q19h+t1rKZ03yVBmJ7HI0DjSonvAwhrZiRgQpIHL1NobSWaOk1k/SF91D+GKPqZya8jIp5jQ\nfazqajJWG+L8m85nzO8m5/TQMX+Q2uYbiXc/Q7HDZWX3RgQWxycSujsklgXzcxHZvItLk8nZPCsH\n26vuSaKoVBIsS9KZTyh4EcdnBLOTVZIowXYXcuaV1sRRSkxKHCV4GQchJQKwXRut2/sJuWK7ERmA\nSjW2v7ixmOdINq7xeGzH0hV/KeCCja99KlAj0CfOTSwVRpo4Nr0Dns/cDsMwDMMw3naE1vQ+9o0T\nLXmB0zrrWq0KXTiM4VIsBJzTWUG26qhCDo0kDGP8VFAa24W7ukw4NUe2O0drLqB7bB/eoUcobNnC\n2B/fQc8vXUMydogVF8QcHnNx3/kuwtIq4l/4X/D+2x8z+vFfZXDmOIUVWfbIPrqtiB31MkOdw7hH\nn6Pa2ctU8RL6amMU84q9mfWEKXSVFMcnYa6Sks1Y2LakXmkxkjr4bsBgv4dOUuJYnUoTmptXdGXg\nqSfniFrtswBRK8LLtlOQVKrQqUZYApDEUYrtWMSBADSu72LZAiklftYjChLQoJLFK/Dnrve57tI8\nO/YFPL1ncVrQFRdmueB16EOwoseit0MyObd0m6G/S5I9O1ofvKFMEGAYhmEYxtuKEBIxuAb93Fw7\ncfy05rzS97EFCKfdiXdNT8SAPU+y5wnSjVfgBzOEs7OIqTEaTz+H7PJp7jqOU84Rph7R+AzVx3cT\n7Zkh3bmX+Se6SY8coyMISZsdJDmfxjtuobr5Rla9d4rkwON412xk/C++ytz5HdQqCteRTAVZhjM+\ntVwvk3OCfXMul2SrjEUdZHwLKRRCauYrMUJCNudQnW+RJprAkzRbMWLfblZMTrBh00b2N4aYrjlU\n6rB9Z/O0u6FP9STQul1dB6XBhjQReL5D1ApBtCf/+VJ7FV+c1q63Vmlfz3Hg0vNy3HJVESkFv/YL\n3fz48Tp7D4UIKTh/vc/lF2Zfl2o9tiW48jyHbz8YLtoRyHhwzYWuqRC0DBMEGIZhGIbxtiOv/QDp\noR0QRQsPWhKvo0DcDFFXXsWANUKfVQdATI+Rad1NdUZT9muo53Ywv7/GxH1TrPuFq6juOkp+RZZ4\nzCWaajL948cBmHt4J/FohczGFdjFDqxV50CS0ohtjvRdxqqLJLNuluCXf50wtmikDsVMhKMj0nIP\nc4HPSNWhFWV4xLmESGeQETQamjBUSClpNFLSVCOEIE01MoW9+xr0DG8k/9RDbFyxjtzIPp5qrCfG\noW+ozNjRORAsOvgrhECh0ArSVCFtgW0L8kWfRj1qB08nU4fS9oq7lPDOSxyUKnLBxgznrFpYcrcs\nwY2XFbjxssIb8Svlum0uhZzgiT0xtYamoyi5bKvNltVnPp/wdvaiQUCr1eLzn/88MzMzhGHIZz/7\nWW644QYA7r//fj71qU/x3HPPve4DNQzDMN66zHeFcbaRPYNw+68jvvVlUCnStnBLBdJEkXauoGJ3\nMFSo4QiFnhyH555FyQKjX3mMSReEbeH1FEnrMcHoDDpOSaOE1tgcjbE6xCkUBIXBMrNHK8SNCJEc\nJ/TzKJUQhD5xdiU9ZU2S2hwrDWNJsC1FKRsxUD9IPbQ5FAwxX0lwPIvZsIALTM4oYiXadfslRGGK\n7YDtSHI5Bz9jESeanh6byQ2X8I9P9vHJtQ+yK1xDlDrkCu30Hy/j4Z6o7KO1Rp0IJDTt3RGtBdJq\nV02ybAt5YvU/TRRx2F5uX79S8qF3Ft+U3+Fytm102LbRTPpfihcNAu655x62bt3Kpz/9aUZHR/nk\nJz/JDTfcQBiGfOlLX6Knp+eNGKdhGIbxFma+K4yzkRzYgP3zv4Le/gBqaoJQ2jjbLoRsmZWZOlpp\n1PHj6Pu/D0pReXIfaT3gZAZ8a7JBfmWZw//yNAA6VUw+PU0w0z4Q233+Orx1g5TjEPmLv8z+uTXI\nVoGu+DjNtJ8kLpJlnLGwAy0sBJo4FWglKR17iqPlG8m5MVMxdHQIWi2FrxSF2jGmvWFsWxBFiihU\ndHRKiiWHckeWoX5NZaxG1o1pDKxDKBvh2GzunmfPbBd+1qN3ZRcgFqXJSKFJ0xTHEYSBwnEltmWR\nKvC9dlWfKIyJTgQAfZ0W777SeyN/ZcZr6EWDgPe85z2n/n1sbIy+vj4AvvjFL3L77bfzp3/6p6/f\n6AzDMIyzgvmuMM5KQpB0nYO4rBPZmIQ0JkESP/0YenwUGnWYmQQgqkdM7ZxecomoFhBX23XoWxMh\nlp+g0/Yhg+zG1WTWFMgNnsdEYQhHFjlaKfP0pEtHZ4rjWkyGHVQaNvl4im6nzoF0JfWWhT+8iqzM\nMKTnGbHKCK0Y6IbCwWeY8AYZUoc54q+h1WqX6azXBX0DBRwbekshxekxeksWz7pZHAmPJ9vwHUlH\nLmXv3oBMzqNVD2nWA7TW2K6N6zkIKejvzzIyUiebc0mihKu22rz/Kp+5Wsr9T8XUGjalguRn39lB\nFLw+Nf+N199LPhNw2223MT4+zhe/+EUOHTrEnj17+NznPmf+YjcMwzBOMd8VxllHCHSuizTXdeoh\nqxKRjhxDTU+2a+VPNZl4YpKkvrQGZTS/uPpNGi5UymkcGsXvXoVYsw5fR2SkZmY2QeHiOBLHFtRD\niRYWxZLNxh3/QveaS9jNZYhykVVOg/rIDKv7i9RCh6yn8MePEmzYSDYXIwJBT7dDqxETRQm2nSXr\npQhhY3d2kcYpji2JI810M0OjBf1dUK+2KHcVsByLNFXEYUzYighdm1wxw+RUwNDKPBsGUi7bLOku\ntTsZdxQsPnCtderzlQo2U8v3BHtdRLHiO/fMsvdQCyFgyzlZbr2uE9syh35fiZccBPzd3/0du3fv\n5rd/+7cZGBjgd3/3d1/WG/X0vDGHQl4vZ/P4zdjfHGbsb46zeew/DV7tdwWc3b9DM/Y3x2s+9p7r\n0Fddw77/+Acc+6tvEMy8jJnuyUpDEqjVqB6YZ3B1ixnLYk51kKYxadI+tOp6cFH5IA/NbGEqLDL/\nxH4KgaD3yvPRQpKrjzPx/cfY8PGNPL7fw1Yh8py1DA1YzCSrSJuKJIFSh0d1roVrCxqBxXQFtqzO\nMTJpIy2BpTTzdc3kVEw5b5Em6kTNf3A8hyRO2o2/ooSgEZIt+FQqIRsuL7D5nBeurflC9z4IFVNz\nKV1li6wvz/i6lyJOFF/447088Wz11GNPPFvn4LGI3/u367Hkyw8EzuY/86+FFw0CduzYQVdXFwMD\nA2zevJlGo8H+/fv5rd/6LQAmJyf52Mc+xte+9rUXvM7UVO21GfGboKencNaO34z9zWHG/uY428d+\nNnutvivg7P2+ONv//JmxL1X6zGeYfGw/wT0PLTwoJdJ3Uc0XDgwGrhggqMPEXY+z+tYN+FEThYXn\nJURRSivS5DKKOJUIAUpZVH/204w7eTqdGXS1gtOcJfKKuEHEqn4FoaC1+lw8mVKPBXEMSaJJEs2a\nQYFlgUxgti6RwkJYDo1GQi5nE4SCaiXg0DGX9ERlnyRu71rYrk0ctLsBp6lCpZpGLeLr329Sqba4\neGN7ujg5p9hzRJHLwoXrLPr7i8vee6U0d/wkYseBhPk6FHOwebXFh67zXvGq/XfvnVkUAJz0wOPz\n3HHXKFdfUnpZ1zvb/8y/Fl40CHj88ccZHR3lC1/4AtPT0yiluPvuu0+dEL/xxhtf0l/qhmEYxk8v\n811h/DSSvseGr/4F09+8i/rjz2BlfLo+8j6yW9ZTufchxr70P6jd98iiPgMAnVs6sTMuSUWho4SZ\nJw7inHMtHU5MX7ek2ZTMzMbkfIeRzBBxpEFC3NFPrZWjKx1H5rOkzXn6btrMwcinrxwyUc9QbUBH\nJj11gDdNIQxTtm6z2DMGqWp39U1SQcaO6ezw6O8WTM1BEivGJ1M83yGJUsJWjJCC1Sscsp5g++4I\nx5FksjbVakKiBN9+MKWQEew4lPLsAUVwoqLq/U+nfOw9Ed35pfftzgcjfvLMQupUtQGP7EyBkI/c\n+Mq6du09dOazBzv3NV52EGC8hCDgtttu4wtf+AK33347QRDwn/7Tfzr1l7phGIZhgPmuMH56Ccui\n5yPvpecj7130ePmGKynfcCXVh59g5HO/jU5S3LxLYbiABqrjMbUdxwE4+q1n2LLtflZdMkTG6eDY\ncQuVgps0UMImSSL6cy0CXAbrT1NWTXZmz2dDr0f2uV2ct0owIc6n4Csm5y2O1SxK+YhsJkOvnKLh\neziORRBqNBrv6D6qa1dRzLa4YGOOINTM1QApEGiU0tQq7Um1EHDxeQ7nDPts3RDz8F1HcLu2EgYJ\nWiuCBP7xxzHVul5USWh8VvO336vz2Q/ai1b3k1Sz8+DiDsIn7T6c0go1Ge/MuwFjMyk/eSZhel6R\n8QTnr7fYtsF5wR0EcybglXnRIMD3ff7sz/7sjM/ffffdr+mADMMwjLOP+a4w3q6Kl1+EGlyDU53A\nyjhUJ1KaY/NEMw0ApG8RVWrtXQRRJchkKeTz2LbEyjiAZkPnLOfIw+wQ5zObHWYOcFpNGn4eZ+UG\nMoRE2sG1U/pKcCS0mKulKKlZn5uia1WRWdWJSjQgyB7fj2cNEqeSJG037dJa43oOpbxNJiuZGm+S\nLzis7Id1K9sB+9phh+FLa/xEaxoFh1ZLo5SiUtfEYYrtWFjWQnB/fCr9/9u78zi7qjLR+7+1pzOf\nU3MlqSSVkHkgJMHIjKIypfHVi4Bc9crVt+37SkOrfdUXh9t6u/3c7n7x029Ptz+ILbytEulGaecJ\nQVAQImEKIQlJyFzzXGc+e++13j9OUkmlqpIUGSrVeb5/wd777POcTW3WevZe61m8tEOxbtmR7mS+\naBjOH/Nq5JDhPPQPa1oa7XH37+sM+dbPSwweNUpn296QvkHD2pUpntk0jD7m1K4Dl6yZ3sMpp4o8\nphFCCCGEOAVOLMbQtk56XzzA4Ja2kQQAQFdCki0paD9AYqidILCYOwPmOG3UJkJqnDz95TiBXV25\nN0zX06NmUJsMKf7maeK5DoJEHQC2ZYh4mqSVJ51QzPXfoKbBA9elEDqEBixbUQotLNumPx9hMKfI\n5qtzAFJxhzmtKVLpGHVNKebNz7BsSXLUE/74gtk0bPoZgYYF81zQYKofJ/DDkQnFh+WPGaWTiClq\nkuM/mc8koD4zcdfz1y/4oxIAgFDDxtd8Llqe5J1X1OAetQ5YxFPc+PY6Vi4eZ0ySOCFJAoQQQggh\nTkFh63FWw9YQrYvxxr/+HqdtFxhNY6JMPtVK0i4QdYoke3ZRdlOUQxtba1AWKuaRa72Q6N7N+G4c\nR1XH6GPg0vZ/p6nG8K6GV0nHQ4phhI4+Dz8AS0Hf8qvJlmz6CjGKZYuDXZpcXhNqTRAYsrkA17Ox\nHAc/HP1UXrkOjckSkajLUNFm1QVl4l5AqVBdTyAM9MixERcWzRnd4XdsxYULxn/Sv/ICh6g38dCd\n9l497vbBHLz6Rsj/+f6ZfOHOVv7gmlpuekcdX/pEKx94T/PE114c10mXCBVCCCGEEGMF/WOr1hyt\nZ1M30RkRcjvbKM2EzqEYZV/T3qO5IL+bFr+LwL2M0qAiEgd0yEA+TmpWA0O/bafyrjSqbChUXMJy\nAdo7qF07CJlaPFVhR0+aviGFparj/XM+tPda7G8vMHNmklBb9PUV8Ms+Pd1FolEb3zeUKyGeM7rj\n7Qz1Erv2GtRLFqVCyNrFedLNF9C2v59XthRQiSiOW+3kr10aoaVxbKf+hss8ADa/ETKYNWQSiuXz\nbW660jvudXLHzx0ASESr37N0YZylC+PHPY84OZIECCGEEEKckhNPTC11ldn/b8+RurIPx0kTlBTx\nTIroz37Ag4k7+PiyASJeIzG7QtSNYnKDkHHpeGoryt2A/8E/IjQWq3Y9wt5l17DM2wfKIbQ9LB0Q\nagdlaUplQ6WsGRoO6eos0dQYw3MVjm0RYOFGXGK2ZmAwxLEVhcCjUKkQ90IilSHUnl1Yq5eSTFiU\n8gHNlTbqi7t5aeG17NxZwA9DmmsdLlzg8P7rU/T15cb8Vksp1l8e4bpLDLmiIRFVuM6Jr9EFLRZd\nA2MnFc9qsFg+f3SGMJzXPLcNhvKGZAzWLVE0HGeokRhLrpYQQgghxCnwZjSe+CAD+T1dNHjDpP12\nHNsiWeqh/PJWwmKOUMVIhQNEjY+vLdqDegbKKXSygQNPbUMXi9hoBrxmktk38FRAxYpSctMQBnie\nQmtFLhtgKShVFH4lpL2zBJbC9SyMMtgOlMsa11U0NTgE2qFzwCPdtZ3Y3q0ULrmWIAxJpl2aTBfZ\njiy77UXUlw6w9uI6IhGbXFHz9jUu1gkW6HJsRU3SOqkEAGD95R6L5lijUqqGjOKmK0Z/14FuzTd+\nbnh6i+HVPfDsVnjg54bt+8cfTiTGJ0mAEEIIIcQpqFt/zUkdV7O8kVi+j/5CnKgdYP+P/87AKx28\nb+hR8l0DNDsDzAj24BAQjzqE2pD6zKcoJBqp3fRT0naWaDhI8L2HyakkI58MdAAAIABJREFUQ9Fm\nir7NwYEoyoRobbAdhRd1GcxqlIJSSWMphW1bGAOeA7GozdyWCI5T7QbmgigFFSe3cC3l0KY+VsKq\nFHhX/8N0N1yISsTJFPYTT0VxXIfBrGbj66e/LGcsYvGx90T50A0eb1/rcNMVLp+6PcbiuaMHrjz5\niqFnIKBcrFAuVKiUKgznNU9tNmMmLouJSRIghBBCCHEKZt/zx9jp45epTC6fQ9M1F2MP95PzGpnR\n+wKVrbtw0i4N5Q4yA7tIOGV8X1EbyzEn2seK/LPUNMdYduNcnLoUdf5e1PKLKO9vY2BHO6GyaRuI\nExqbQtHguTB/drUkaCqh8GIOtm0deopuiEYsZtTCzCaLec0+rn1o6I2yiGa7aTnwGxIqS9TVXJd+\nHtPdTy45k1luH/3DinS+jXgyQiRi89hzecLw9He4LaW4aJHLTVdEePtaj8gxE4lLFcPre32MNli2\nhXIUxkCl5HOgS9PZL0nAyZIkQAghhBDiFFjRCBc++32wx+9WKc9hxVfuILOwmf5YC62fuJbof/so\nQc5n/j3/lfLBDmpy+4lkuzDDgySCQcJf/hitLaywxFP1NxNtqce1DL4dQ7seA0WPVw9m2NaZxlLg\nB4a6Gpv6FHieTSbjYFkW9dEChAHDQ2WWL01QKGtm1ARkYgGzMiVcOyQeDjMz3E+iPEBjf7XSkVcY\nZOPl/zeeFdBc3svG/GLmD7+ArSs4nsNAX4nfvlI+m5cZreHhJw1ezCOejBKJOigMgR+iFPiVACXr\nhp00SQKEEEIIIU6RV5th1if/cMwcYTvmsfwrH8ZNeJhCluCRHxDrqK4kvPDP78BVPm7aJUjVo4OQ\ndPYg5uXnGf7KfRQHyxw0LQSWhR1xsJVF0D9EYe6FPB97B/v7k4BCWQrfN3ieRa7iYNuKmfUWmZjm\nD2c+xlXWb1mzKkUs5jKUVXh2UI3ZMdTFylwQbMOlui1SHKASwGP2DZSSM2iM5tjcVUdWJ1nkb6Up\nlsOyLILQsPNAcDYvMT9/Adr6bWzHxrIUrueQSMXwPBu/EqC1prlWsoCTJdWBhBBCCCFOg9n//Y+I\nzKhj8JHvERQqxFrqmfWfriC9ZBZ6904CL03/k0/RcOMlNN/6NuzCIKVnniBwklDXTClbQed8ig9u\nAMB//Amia6/mkhU2beUZzI4OoIOQzovfB9bhajmGeFRRKiiUMviBTU1Gk45V+Pzyx4gEJZY67bRH\nS3SVkmgU4VEFeBIqz0r/xZF/Nyh2tMfpzXksmlVE93byy/7V2PiUtEvWZAj9EDfinNJT91AbntpU\nZNd+HwMsmO1yzboYtj3+SYsV2NE2drtSikjcI58rgWLUwmfi+CQJEEIIIYQ4TRo/eAuNV67A3b0R\n5ToQBgSvvIDONDK8bSsLbpyN21hD+OwvKA3nyHYME712PWE0Qax9J8W9+9Fv7AcFevfruL37UPHF\n9OVcFlu9hAf3M7TwtpHvi0QU8ZjCaXSIZzsIapoBi0poY8XjmMEcEatCtNSDHyQxBtoHXBbFqk/x\nC4FHaBS2qo6l35mfwSuDKRwrJN/Vw6P9i8Bo/rP6LjvNAorao1jIEom5mFATBOCcZPWfw7Q2fP27\nw7yyozKy7eXtFV7f6/N/3ZbGHqfqUM8Q5Evjf49tWyil0GdgjsJ/ZJIECCGEEEKcTq3L8FsWol5/\nHsoFzLJ3QtMckut66bn3f6Je2IopVygTIX7F28n8HzcQf/Vpcq9vQfUUUJEIJu/T/1ov9ff+FcN3\n/r+kIhXCwMDvnqL2muspRNN4LjiOhSmXSe55jcW9P6N31bvpSSxgsBAll24g7Q4R+IZuXUelolGW\nReHQUH5joHM4wsO5G3hbdBMBNr8YXgdAuQK7Cmlm0ca11hMMqxp+rG+gXPSplH2a6jM89UKOrbsU\nMxssghBqUxZXXuQyo/44q34BG18tjUoADtuyq8KzL5e4cm1szL66JDj4dHbkqJQDLFuRSMdJZeKE\nWmO0wXPlLcBkSBIghBBCCHG6OS5mxeWjNnl1DTT/2Vco79xMkCtQP28ulgXevq2Eb+xi+I02Op7u\nQOeLAPjZCio7wMUHv8vghdcy9KvnKWzdz/w5P2TP1XePnDf63BM0fPsvabyhmWLfcnL1Cykf7KS3\nfhZpdjMYxOnXKQwKpUAbRbGs6M8qDvTa+P4svlVej8ECZaG1IQgAyyUolNlgv5c8CfyyT3aggNaG\nMDTE4i49gz49g+GhCkSarXsDPnBdlIWzJ+5i7tznT7xvvz9uEpDL+7Tv7SObOzIPIT9UpFKqEI1G\nQEFjzfGTDzGaJAFCCCGEEKfKaCgOgQkgkgInOu5hTiyDtWgNhZ98BzvXA3tfJ9/WxdC+Prqf66KS\nL8PhYS0aBnYNUvzmkyT+/FLyfoLU7Bhm6AAArgqo84aYmdpH+8FuCoN1lAoGO9dP4vWXKK9Yhyrl\n2daeplyr8SIOrguua9GZi9E7qNHaoDWAgzEGE2jC0GAMYDvsKs4gN1wCBkf9jsAPsawj9WWMMSil\nGMrBr1/wj5sEHG+RMXuCfvxPnsqSK4Q4roPWGh1WFwYb6stRSQRYlsWy+dKtnQy5WkIIIYQQp6Kc\nhVwnKqiOszG5HohlIDWL8WbPWrEkzrr1vHrThzHFHLocYsIxh4ENyihy29uYXeygp1JAJ9PUpDWX\nNuwkZlfw7BCz/lLKm95B3vMpffMh7BVbwUpjB6uwdEhv1mJ3dw9LV82svnlwFY6tqM9YDAyNrvAT\nhAaOGlo/3kRby1J4UYfhgeJI5/9oB7tD/MBMuFLwRUs8nttcIjxmgV+l4MJF3pjjCyXNlt2aaDyK\nUgpjDDrUlEtljDYoo3nLihjrL4+M+31ifFIiVAghhBDizTIash0jCQCAQkNxAPK9E34s2trChc98\nn/TFa8ZPAABCaFo3h2idS9rxSTQlcW7+EN6KC8l4RbxDi30pyyJ9zTr6563D3rODaPtO1CWXk8ge\npL/o8st9sxjsL4xEF+pq59x1FenkMR31oxIA19YE5bFrAcSSEQJfUykFGF3tkB+dCDj2uLnPiJUL\nPa66OIpz1FN/x4ar1kZZvWRsR/67vyrga2vkO5RS2I6NF60mDO9YF+HD6+MTVhYS45M3AUIIIYQQ\nb1ZxEBWOneSqgHC4m7B3EGfuApQ19rmrm05x+RMP8ern/5r99z4wZn+0MU7dikYGd3XhJzJ4qxsx\nWjPQspJ6XcS2jvTY/XlLeG3WJVzwZy3MbN9I34VrKLb/hO9uXUR/KUYsUT1WKYge9bC92rGu7jP6\nyPkUMH+WxVXLEjy5qUhnrybQCi/i4rgWg335kWN1qKlojRdxAaiUA17dUWL10ui4bxKUUrz/+hRr\nlkZ4ZXsZA1y0JMKSeWPfAlR8w44J5hDYtk1djc36q5Lj7hfHJ0mAEEIIIcSbpSdeMEsN9aAe/wmF\nbAn7XbcRXXfVuMfN+OTHSRa2s/uRV6gMFrE8h/oVzVzwnhV0Prcd5SbY+6+/Y8b6t1DpHebx7HWk\nvDILa/pYWt8HQG9mITUFzYEF13HJ5XG6/QF+0L+O37cPAJDKVCfbxiLgHOr9+YFhKFsdk1MT19Qm\nAnqGbYyyqMtYZGptyrbLf3lPlNxQmb97OI/va4LQYNsWYVD9rFKKwA8ILAsdhnTu6eOrO+GiZSk+\n84fNE9buX9zqsbh1bMf/aBXfUKyMX/pTKcXb1yWIeDKw5c2QJEAIIYQQ4s3ykph8D4pxOqpD/dhB\niXgMgk0/xW9pxZ01d+xxSlG64HIW3xYQb65BORZ+tkjPS7vo29yLu3Qp1sH9BC86RGY3AJCtRNjc\n00zc8Yl7IXuzDWgs6pIFOsImoo7PmtW1qEqezTsNi5bWkopDMl79SmPAVZoVLQGZuOHCVs3OPo90\nzh0VWiW02DvgYuWKROMe1qGa/MYYwlBTLlbfghiqw4L62ntH3ii8tGWYXz0b59rL02/68iZiihn1\nNvs6xo6Zqs9YXH9F6k2f+3wnqZMQQgghxJvlxSE6tpNrcll4/dWRf3fCEv7PN0x4mvrb38/+rbBj\nw0be+N7v2fuTzRx84gDD+wuU93Sw6LpWen6xicritSOfCY3Nq30zeL53PvpQl05ZkFdJSnYCjcVV\nl9Xy7msSYHk4libU4AdQqUDBt5nTbLF2gcaxYbg0frewULF5o8fFduxR4/IdxyYScdFagwYUpOsz\nR10Ew+PPDE3mao6hlOLK1REio3MTHBuuWB2ZcPKxODF5EyCEEEIIcSrSszGWB5Uc9LZjBnth+2YY\nOGpicBDgHWfki1KKhV//G/Z/+n+Q3/QylaEibjLKBe9bztxrlzHUlkVrxbO5VaM+N1R0cANNLKpQ\nSpH0B2iM9WKF0FOeQ0MyYG1DOwdej9HrpKmvHd1p7s7ZLGyqjrk3TNyh7h4cf0iOZVuEYTjyG2zX\nqU4oOHR4pTLRrOeTd9mqKFFP8dyrZfqHNZmExdrlHpevGr8Mqzg5kgQIIYQQQpwKpSDVDDQTPvoA\nVn54/OMixy9hacWizPvf99L3//wvMokCidm1hJWQ7t/vZc+vdtHzma/SlRs9Cda2IZMwtHdXmFUP\nSwovkIm5dLszSUeKNEcH0U6c1X2/5KXozcDoQvyBPvxkH6J2SDkY+zagUAzpG9Bjth/+oEJhMARh\nABpiyQSlXAFjDI5j8Vdf76JSMcye4bL+6hQzGo8/D2A8a5ZGWLNUSoCeTjIcSAghhBDidJnROv72\nRBK17C0ndYq6T99Dt1nAq/+2i1ce3ExnX5rOT/89B5veOuo4YwzGGEolTVO9IlvQ6GgSFYS4nsWM\nxDCupXFMQH1xP66t4Zi5C+nIkc790KBPoTj6yb3vG9q7AuLx8VfxCoNwZA6ACQ1BEFTLd8Yi2I5F\nf97m9T0V9rT5/PaFAn/37T66eydeMVicPfImQAghhBDiNLFu/Aj821ehp7O6hgBALAEXLMNeccVJ\nnUNZFrPu+gjc9ZGRbS0V+O7GCqpYBGXhde4lteslKqsvZahxHpWsplzWbI6t5GJ3B7NoY0A1otBY\nYQXf8mjK+DSlArqyCQDiXsgFjUfKm+YLhi37KjgOWBZEPIUfGIolqEtAPgv+UTmC1ppSoXRkwTBd\nTQQAHNchVZcimogS+AHFXIlyoUxnT8CGnwzyyTsaT/FKi1MlSYAQQgghxGmiHBfe/xnM5ifgwA5w\nXNTydTBnVXXW7ps0lIcL/vZu7Od/h3E97GK1Tr9unY/9Z/8bNWsmvYMW7eUkpdwqrolsJWoXUSaE\njv10N1zE0jklLKUYKESwlCIZCfHD6gD+Yhn291RrHPmHqp6WjyrNuXi2oiVjeOJFH8tS1RV7ixX8\nyqGDTfWtwGG24xBNVMfsO65DIhMn9EMCP+DVnSVe3l5g9dL4m74e4tRJEiCEEEIIcTrZDmrNdbDm\nutN2ylf/9WlqX3qOeR+7jszqC7A8h9zOdvY/9AT+D79F7s7P45crRCIOFe1xMGimRhUpapvh1GJa\n39aE4xgCbQi0RaAtCr5Nb85hxcwy+9oDhgrjTwxOxQyXLIH7v1cgNzh2YTSoDk0adQnc0cOHLMsi\nmoiQGwwIQ8Pjv8tKEjDFJAkQQgghhDjHRbe/xPI//yD1ly0b2ZZcOIv08rm89P89jz/UQdzJkEk6\ngKIrbCQfZumvxHEdmyWmkyIZ/NAi1Ec6+4G2eKPHY3B44io+rU2GqGfo6J54YTStj8wtsOxqh/9Y\njufguA5+pUJbt39kGJGYEpIECCGEEEKc42Ysb6B23dhx9PHWJuZeu4LCrtdoveStBLbBtTWDeZe8\nXUMhcAgKhnWpfko6wVAhNaYUaK5i43gWx04aPiziVjvr0YiC7Nj9SsFFiz36hhXFwMaORsFAxQ/H\nnDIS9wh8n2hESQIwxSQJEEIIIYQ4x82/diWW7h13X2pxC4lwHp6do2RClJtguFLBTdhkIj5d5Tgu\nIZSKHByaMc4ZDHVpC9sKRr0lANDa8NKOkHJJsWSeR0dvcWxsLQ5OMoPxFUdX7rdsi1KxWglIh5rQ\nD7Esi0g8yvIF41cbEmePlAgVQgghhDjHRTOpCfeFpTJNLQ7JJ39AS/tGkk6OefEeXF0gZvlE7CIG\nUGiccXp+jmUILJflCyMkIkce3etQ45dDsnnYuM2QSMdZvcTDPeoRcutMhyULknQOjH2qb9sWCkO5\nUMYv+0dtV9x2Y+2bug7i9JE3AUIIIYQQ57ggPQt7YB8Woxft0qGmXDOD1gNP0F4eoKFuNiYcJJHf\nQVfsStCKhelujAHleUTcECtQVMLqk3jLMtQkQiKuQRub1Us9nnulSKEEgT/6u3YchLtvruFgh8/O\ngxXmzU6yeLbh35+ZYCExqonAsdIJhefKc+ipJv8FhBBCCCHOdY6HCRX45ZFNQVh9CzB34CU8XaKh\nJcJOfw7lBx8g8drTePEEWR2ndfAFdDaLcj1q40UsG+bW5klFfWZkfOIRg21BIqIZKtkUCnpMAgDV\nMqXDecOCuR43XJ7kqotTWJYi6k4ctj6qapAXd9BaU98YZ9MuCzP+FARxlkgSIIQQQggxDVh+Bbvr\nAFZ/F9ZAD17nbmL9Bw/tdIg01aJrm+n97TbUUB9esZ+mRJ6+xALCYjV5MEYBiqgb0piuoBQcLuxj\nW9XyoTXJ8SfsZhLVp/jHWr1w/ERAa025dNQwIGURT0WpWHF+s8Xi2e3SDZ1KcvWFEEIIIaaBIN0I\nBqz8MFZuEEtXy3oawHgRep7fycyuFwn378PP5okP7KIhViBv12HyeQqBxUAhhq00obFwLQ1UFwgz\nBkINqUjI4jnjf//SuYqIeyQJ6Bv0+def9PHjX/UQ0wPE3SMlRMMwpJAro4MjbxQiMZdUKkLnwUEM\nim0H1agViMXZJXMChBBCCCGmgaBpEWH7VuxSDnVoLI0BTDxFWAno39JB7aoSicYEvTSQmjOD9j6D\nbSfI6TjFShTP0TiWRqnD3X8AhTaGQsliXr3P/CUWGM22fYahPKQSsHSO4sZLjjw7fm1ngQce2Udn\n75En/c0NOaKZDBXtUin7o8qDWpYiU5ugVKhweBzQYN6iPxvSXHOmr5wYjyQBQgghhBDTQSROdl83\nyeULsMoFMAYTTWKMZviZpxnc1Yu7q5PYombyd3yRgUqMSuCDl6THbcG2IeH59AzZ1CVCQnOkU+8H\nCpShohTaKG68xOZdFxuyBUjGwXOOvAEwxvDoL/pHJQAAXb0BK+sKdJcToxIApRSNszIA2I41si/i\nGlKxM3e5xPHJcCAhhBBCiGlizw9eI7/xJcKyT6gVYV8PuSd/y55HNqEHsnT+3QasS68gEoHOoTjx\nSJnAh7bkCvzAQinFvg5o63UoB0cG8isFiZhiuOSwb6i63XUUdWk1KgEA6O7z2bWvNG58uw+UWb4o\nSTwVwXZtIlGXRCaG1od6/sqgrOr5WhsM8bELC4uzRN4ECCGEEEJME07LPF78n/9O08UtxOoTVIZK\ndL5wAF3SEPMgCBhcdyORIIoXsWiI+QRbX6ar+a0MFBRDebh2cQev9TfTqAyhqVYZypcViWg1GciW\nqpV7JlrQNwwNeoLKPlob2noDHNfFcY8kGaViQCFXQqFobqmtTkg+A9dHnDx5EyCEEEIIMU1k3nUl\nJjB0bTzI3p++Tvsz+6oJABCvjYKvKadmkivZhIGhYjzqX/0puSL05xwiDng2NKQCCCtoHZIrW2hj\nc3g9L21GjeYZY2aTxwVzo+PuiyUiGMslnnDJ1ERwvWpXUykFykKbENe1sSzY023xL4/BUGFy1yAM\nDbl8eOTtgnhT5E2AEEIIIcQ00f2NhyfcVzIO7qwUab8TP1+mx15JXOXoufy9aBNS8hVGQ1uljkTM\n4DqKWJglcFPkKxG0VoAh5hqsCd4CQLVD/9531vDA93rpHzxSEcj1bFIN9biuTeBrSoUK9Q0xQg39\nfSVsxyY/eKhUqQbHtdjT7vOzTR63X33iDr3Whg0/6GLT5hyD2YCGOpcrLk7z3usaqkmGmBR5EyCE\nEEIIMU3kXtg84T6dD2lc3ESsazuD0dnV8f/FenRtM3FXMzSsCY1NMfQItY1rhbi6xNz0EBHbB1Vd\nK6AhHpAvc9whPxe0xvhfn1nAdVelWbkkQX1zhrmLZ5NIV2f6WraF47m07R8kFndIJl2iUYvQGIwx\nI4uIaW3Y0x7Q1nvi3/7gI5388Ff9tHdXKJYM+9vKfOeHPXzvZz2Tvo5C3gQIIYQQQkwbuhJMvDOf\noxjOxGuaD14UFcKBLptV6V5i0XqiBUOprHAyBh0GJMmiTBHLcWiI56mYOIODmh9tsyhUbDJxw9IW\nzVsW6pH5Ab98JsszL+Xp7g/IpByWzY+w4sJGwr1jn8RblsJ2HHq7cmTq4qSTLrnhMpWKj6NtIp5N\nPltGWTF6hqGl4Tg/rRDy7EvDRBJRbNtBKUUYhvjlCj96fID3XteA48iz7cmQqyWEEEIIMU3YNemJ\n9zXW4C2eC/EEaqCbSqFMabjAQJgiCBUrG3qwbZgRz1JjZ7EISTl5ADLRgO6ekJd3WwwXLYJQ0Ze1\n+N12mxd3V7uLT2zM8cgvhzjQGVCuQHdfwFOb8vz+pf4JY3I9h0o5IJFwSCVdvIiDCUOMMaTTNs0z\nE8RjinlNx//d+9pKVEwE1/OwbAtlKRzXIRKPUgngt5tyk7+Y5zlJAoQQQgghpomZn/lvE+6bcfVS\nYpkoQxWHmleeoC7lM+RH2F+ciQ4BY8jEAtJuntmxXookGIrOBKB/2OKNjrHdQoPi9bZqtaBnXy4Q\njrPCb1d3Cb/ij91BdYKx49gYrQk1OK5NuRwQakO5okkkXFbOs6lJHv939wxqbMces92yLJyIx4Gu\n47whEeOSJEAIIYQQYppoevfVzLt5Lco+qgunoOGt81n47hVU+vLUdmwhHjOU8hVyRYuhvEXPoMWw\nU8+c+DBRJ6BAioqKExiXsg+vHYwSYuE4CuuY3uFwQdE3FNI/NH5HOwwNxfzYdQO01hitydTH6O4q\n09FZJhZTFAsVlFL091eoy8ANbznx7x4Y1hNO/rVsi3hMurSTJXMChBBCCCGmCTsSZf4tb2Xeuy+i\n/anX8bNl6le1kFnYTKgsSjv2olLPk4nm2Z2OUCpqUOD7ASVfMc/bQ0gDvTQBhmIFdhxM0pv1que3\nIRKx8H1DpVItPTqUC/irB4fB9lBWGXPMjGHPhVjMJQzDkY66MQajq8OB8rkQYyCbDcgPFwh8KBV9\nojGP5pTBPon+e1PdxF1Wz7V527oTvEoQY0gSIIQQQggxTahYirKbJGZC5ly7YtS+YuDiugo1sB+V\niTJcADA4KmTV7C4KqoaMW6DH12ijyBVh47YU5qiBIdVFwhSuC2GoCAJNfrhMdT6yTTQeoZgb/dR/\n+YIoixa7/H4b6GNWGFCWwoxsMuSzPpZjUykFRGMeEfvkav1felGcx5/Lsadt7LCjq9fFaayVLu1k\nybsTIYQQQohpJFj+Nsq4o7aVQ4ed//Aj/FKF0ms78KNpCgcOYtmG2pRmUX2ONbUHwXLwQ5tSYLF5\ntz0qAYAjqwQrpbAsw2B/gcG+/Mh+27bJZKpvDZJxi3UrY3z05lpuWGdx02WKuU3VcygLLFthHTW2\nqFwKUbaFZSmwFBHXsGzOyf1my1J87NY6Vi6K4B3q72eSFrfdkOa/vLtukldQgLwJEEIIIYSYXmpn\nMjTjUsKffBs7Gad0sJeBre2EpRJ+YYDaixbxb7EPcNm2h9jceg8aGx+HmK0JQodB6nBsRVOtYm/n\n6FPbR829LeZ9BnrGVt1ZtzLO5RfWsGhBBr9cHNl+8WLFxYthwxOw+5jzGmMol30sy0IpRSRicdF8\nqJu42NEYMxpc/vSORvoGfXIFQ0uzi2PLImFvlrwJEEIIIYSYZmLLlvPLtV/kjZ+/wsFfvUy+rQ3L\n86ldtZAXmq8nVzOX5H+6nUTCo6M3ZPdwI7EwxxC1+CoKQF3KoA4N31EKXJdRT+7zufK4311fazNv\ntkdNevxnye+5DBbO0tWJwcYQ+CGFXBm/XC0tZDuKW672eNeaN/fb62tcWmd5kgCcInkTIIQQQggx\nDc2cFefBdz6AXRzmioFf0B+fxebUFQAsqo1izZ5HrMulUNDsG4hT483Gj9SOfL61IWTFzJDf7XRp\nGxjdJaxP+OwtFMZ8Z1OtxdVroseNKxGD299usa8z4J9/WCJXOJJo1KYVn7sjhqzrNfUkCRBCCCGE\nmIZmNzm0tKRob4MnvVtHtjc1xZg1K4YVrWBZCmXZaOXS4TfREK2W+bQw1MQNCQ+uX1Vha1tIx6CN\nMdCU0aycHbCoPs6vNpbZ3xVgK5jf4nDTVTFikZN7At86w+Ev/kiq9pyrJAkQQgghhJiGWmoDFi2I\nU1cfoaeniNFQVxclU+MR9TQeZZTy0GG1jKZjVYfjuJahIaFJVOf3Ylmwck7IyjmjVwJbucBjxQUu\ng9lqGc90Uh7f/0ciSYAQQgghxDRkKbi4tcRGHSWZzBzaaoi6hrpYiWJJUy4HDPQVWTzPZVlzBWVB\nTdSMWRBsIkopatMy9v4/IkkChBBCCCGmqVk1mnctGebFAy6V0MVzNLMSwxQqFpt7a8hmfRbPc1kz\np0R98uRq8ovzgyQBQgghhBDTWCrucOVCzZb9eYaK8NLBDEM5i862Ia5a5fC2i2yUkgRAjCZJgBBC\nCCHENGfbiovmVxcQK1cqBCEkYpEpjkqcyyQJEEIIIYT4DyTiKaT7L05EpnkLIYQQQghxnpEkQAgh\nhBBCiPOMJAFCCCGEEEKcZyQJEEIIIYQQ4jwjSYAQQgghhBDnGUkChBBCCCGEOM9IEiCEEEIIIcR5\nRpIAIYQQQgghzjOSBAghhBBCCHGekSRACCGEEEKI84wkAUIIIYQQQpxnJAkQQgghhBDiPOOc6IBi\nscg999xDX18f5XKZO++8k6VLl/K5z32OIAhwHId7772XxsbGsxHNN+0mAAAH/UlEQVSvEEKIc5C0\nFUIIMb2cMAn49a9/zcqVK/nYxz5GW1sbH/3oR1m9ejW33XYb69ev56GHHuLBBx/ks5/97NmIVwgh\nxDlI2gohhJheTpgErF+/fuSfOzo6aG5u5ktf+hKRSASA2tpaXnvttTMXoRBCiHOetBVCCDG9nDAJ\nOOz222+ns7OT++67j3g8DkAYhmzYsIE//uM/PmMBCiGEmD6krRBCiOlBGWPMyR68bds2PvvZz/LD\nH/4QrTWf/exnmT9/PnfdddeZjFEIIcQ0Im2FEEKc+05YHWjLli10dHQAsGzZMsIwpL+/n8997nO0\ntrbK/9SFEEJIWyGEENPMCZOATZs28cADDwDQ29tLoVDgmWeewXVd/uRP/uSMByiEEOLcJ22FEEJM\nLyccDlQqlfjCF75AR0cHpVKJu+66i/vvv59yuUwymQRgwYIFfPnLXz4b8QohhDgHSVshhBDTy6Tm\nBAghhBBCCCGmP1kxWAghhBBCiPOMJAFCCCGEEEKcZ85IEvD73/+eyy67jF//+tcj27Zv384HPvAB\nPvShD3HnnXdSLBYBePbZZ3nPe97DzTffzCOPPHImwpmUycQOYIzh9ttv5x/+4R+mItxRJhP7v/zL\nv3DLLbfwvve9j4ceemiqQh4xmdj/+Z//mVtuuYVbb72Vp556aqpCHjFe7FprvvrVr3LppZeObAvD\nkC984Qt88IMf5LbbbuP73//+VIQ7ysnGDtPjXp0odjj379WJYj/X7tXTSdqKqTGd2wqQ9mKqSHsx\nNc5ke3Hak4D9+/fz4IMPsnbt2lHbv/KVr3DPPffw7W9/m9bWVh599FGCIOBLX/oSX/va13jooYd4\n5plnTnc4kzKZ2A975JFH8H3/bIc6xmRiP3DgAI8++igPP/ww3/nOd/jGN75BNpudosgnH/tPf/pT\nNmzYwNe+9jX+8i//kjAMpyjyiWO///77mTlzJkdPufnNb35DsVjkoYce4pvf/CZf/epX0Vqf7ZBH\nTCb26XKvjhf7Yef6vTpe7OfavXo6SVsxNaZzWwHSXkwVaS+mxpluL057EtDY2Mg//uM/kkqlRm2/\n7777WLVqFQB1dXUMDg7y2muv0drayowZM4jFYvzt3/7t6Q5nUiYTO0B/fz8/+tGPuP322896rMea\nTOwtLS1s2LABx3HwPI9oNEoul5uKsIHJxb5x40auuuoqPM+jrq6OlpYWdu3aNRVhAxPH/qEPfYgP\nfvCDo7bV1tYyPDyM1ppCoUAikcCypm5E3mRiny736nixw/S4V8eL/Vy7V08naSumxnRuK0Dai6ki\n7cXUONPtxWn/i4rFYti2PWb74RJxhUKBH/zgB9xwww20tbXhui6f+MQnuP322/nxj398usOZlMnE\nDnDvvffyqU99atzPnG2Tid2yLBKJBABPP/00tbW1zJw586zGe7TJxN7b20tdXd3IMXV1dfT09Jy1\nWI91otiPtnr1ambNmsU73/lOrr/+ej796U+fjRAnNJnYp9u9eqzpdK8e7Vy7V08naSumxnRuK0Da\ni6ki7cXUONPthXMqwT3yyCNjxnrdfffdXHXVVeMeXygU+PjHP85HP/pRFixYwPbt2+no6GDDhg2U\nSiVuvvlmrrjiCmpra08lrLMS+/PPP49t26xdu5a9e/ee8XiPdqqxH/byyy/z13/919x///1nNN6j\nnWrsjz322Kj9Z7PC7WRjP9amTZvo6Ojgscceo6+vjw9/+MO87W1vw/O8MxHuKKcauzFm2tyrx5pO\n9+pEpuJePZ2krZgef3/nUlsB0l5IezF50l5M7n49pSTg1ltv5dZbbz2pY4Mg4M477+Smm27i5ptv\nBqC+vp4LL7yQWCxGLBZj0aJFHDhw4Kz8oZxq7I8//jhbtmzhtttuo7+/n0qlwpw5c3jve997JsMG\nTj12qE6i+uIXv8h99913Vp/snGrsTU1N7NmzZ+SYrq4umpqazkisx5pM7ON58cUXueyyy3Ach+bm\nZmpqaujq6mLOnDmnMcrxnWrs0+VeHc90uVcnMlX36ukkbcW5//d3rrUVIO2FtBeTJ+3F5O7XU0oC\nJuPrX/86b33rW0f9wDVr1vA3f/M3lMtllFLs27eP2bNnn62QTtp4sd9zzz0j//zoo4/S1tZ2Vv5I\nJmu82MMw5POf/zx///d/f05e78PGi/3SSy/lwQcf5O6772ZgYIDu7m4WLlw4hVGevNbWVn72s58B\nkMvl6OrqorGxcYqjOjnT5V4dz3S5V8czXe7V00naiqkxndsKkPbiXDJd7tfxTJf7dTxv5n497SsG\nP/nkk3zjG99g9+7d1NXV0djYyAMPPMCVV17J7NmzcV0XgEsuuYS77rqLxx9/nH/6p39CKcWtt97K\n+9///tMZzhmN/bDDfyh33333VIU+qdhXr17Nn/7pn7JkyZKRz3/mM58ZmVR1Lsd+11138a1vfYsf\n/ehHKKX45Cc/yWWXXTYlcR8v9r/4i79gx44dvPjii6xdu5Z3vOMd3HHHHXz5y19m586daK358Ic/\nzB/8wR9Mi9g/8pGPTIt7daLYDzuX79XxYl+0aNE5da+eTtJWTI3p3FaAtBfTIXZpL6Ym9jfTXpz2\nJEAIIYQQQghxbpMVg4UQQgghhDjPSBIghBBCCCHEeUaSACGEEEIIIc4zkgQIIYQQQghxnpEkQAgh\nhBBCiPOMJAFCCCGEEEKcZyQJEEIIIYQQ4jwjSYAQQgghhBDnmf8fmcOYFvVGzu4AAAAASUVORK5C\nYII=\n",
+            "text/plain": [
+              "<Figure size 936x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "32_DbjnfXJlC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n",
+        "\n",
+        "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n",
+        "\n",
+        "**Go back up and look at the data from Task 1 again.**\n",
+        "\n",
+        "Do you see any other differences in the distributions of features or targets between the training and validation data?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pECTKgw5ZvFK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "49NC4_KIZxk_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75<sup>th</sup> percentile value for total_rooms per city block?\n",
+        "\n",
+        "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n",
+        "\n",
+        "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "025Ky0Dq9ig0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3:  Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n",
+        "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JFsd2eWHAMdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n",
+        "\n",
+        "By the way, there's an important lesson here.\n",
+        "\n",
+        "**Debugging in ML is often *data debugging* rather than code debugging.**\n",
+        "\n",
+        "If the data is wrong, even the most advanced ML code can't save things."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dER2_43pWj1T",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BnEVbYJvW2wu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n",
+        "\n",
+        "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xCdqLpQyAos2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 4: Train and Evaluate a Model\n",
+        "\n",
+        "**Spend 5 minutes or so trying different hyperparameter settings.  Try to get the best validation performance you can.**\n",
+        "\n",
+        "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n",
+        "\n",
+        "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rzcIPGxxgG0t",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "CvrKoBmNgRCO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wEW5_XYtgZ-H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D0o2wnnzf8BD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n",
+        "\n",
+        "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n",
+        "\n",
+        "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n",
+        "\n",
+        "See how much better you can do now that we can use multiple features.\n",
+        "\n",
+        "Check the data using some of the methods we've looked at before.  These might include:\n",
+        "\n",
+        "   * Comparing distributions of predictions and actual target values\n",
+        "\n",
+        "   * Creating a scatter plot of predictions vs. target values\n",
+        "\n",
+        "   * Creating two scatter plots of validation data using `latitude` and `longitude`:\n",
+        "      * One plot mapping color to actual target `median_house_value`\n",
+        "      * A second plot mapping color to predicted `median_house_value` for side-by-side comparison."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UXt0_4ZTEf4V",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # 1. Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(\n",
+        "      validation_examples, validation_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # 2. Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zFFRmvUGh8wd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 635
+        },
+        "outputId": "03cbba44-1485-4c44-bdab-55ee83ef7eb6"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 226.04\n",
+            "  period 01 : 224.64\n",
+            "  period 02 : 223.27\n",
+            "  period 03 : 221.90\n",
+            "  period 04 : 220.58\n",
+            "  period 05 : 219.30\n",
+            "  period 06 : 217.97\n",
+            "  period 07 : 216.64\n",
+            "  period 08 : 215.33\n",
+            "  period 09 : 214.02\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+uNDSX1Ze0+BHcUSgCX7ddg0bIBhswl1QLlmw4arDRNR7MMAQxQGj2tBmqjcA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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Xyz6n1YHbGef",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(\n",
+        "      validation_examples, validation_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "i1imhjFzbWwt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 635
+        },
+        "outputId": "cb798376-3e18-47fc-e5af-9fe1abd97046"
+      },
+      "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": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 207.37\n",
+            "  period 01 : 189.89\n",
+            "  period 02 : 177.18\n",
+            "  period 03 : 168.61\n",
+            "  period 04 : 163.79\n",
+            "  period 05 : 161.91\n",
+            "  period 06 : 161.26\n",
+            "  period 07 : 160.93\n",
+            "  period 08 : 161.42\n",
+            "  period 09 : 161.90\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SilTqpIERQgghypEbN+KJjNxZrGWnTp1OlSpVH/r6hx/OKa1Ypc4ibyUghBBCiAebM2c2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\nGCIiIgo5DDBEREQUcv4PBtXYz8rXw/AAAAAASUVORK5CYII=\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": "_xSYTarykO8U",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "9bbeeaf1-5b2b-41a7-9199-4efebcd08dcb"
+      },
+      "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": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 160.65\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file