diff --git a/class_classification_of_handwritten_digits.ipynb b/class_classification_of_handwritten_digits.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "class_classification_of_handwritten_digits.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/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": "NtztmisP-6EM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "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": "469caa90-75c2-4ad5-f854-6a3a39adc840"
+      },
+      "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": [
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+              "      <th>8162</th>\n",
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+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 785 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      0    1    2    3    4    5    6    7    8    9   ...   775  776  777  \\\n",
+              "4609    5    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
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+              "8162    4    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "3531    3    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "4609    0    0    0    0    0    0    0  \n",
+              "5894    0    0    0    0    0    0    0  \n",
+              "5715    0    0    0    0    0    0    0  \n",
+              "8162    0    0    0    0    0    0    0  \n",
+              "3531    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": "c59ebd87-adda-403c-a9ea-1eabefa7e6f0"
+      },
+      "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",
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+              "\n",
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+              "        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>72</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4609</th>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>5894</th>\n",
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+              "      <th>5715</th>\n",
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+              "    <tr>\n",
+              "      <th>8162</th>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>3531</th>\n",
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+              "    <tr>\n",
+              "      <th>...</th>\n",
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+              "    <tr>\n",
+              "      <th>466</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7367</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>722</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5127</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7181</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "4609   0\n",
+              "5894   0\n",
+              "5715   0\n",
+              "8162   0\n",
+              "3531   0\n",
+              "...   ..\n",
+              "466    0\n",
+              "7367   0\n",
+              "722    0\n",
+              "5127   0\n",
+              "7181   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": "2440b744-d0ae-454d-e3d2-401489028c39"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
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+              "      <th></th>\n",
+              "      <th>1</th>\n",
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+              "      <th>6</th>\n",
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+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.8    0.2    1.0    0.2    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 7500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4-Vgg-1zu8Ud",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "ff50ac05-e604-4f73-efc2-339ac80b91e1"
+      },
+      "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>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    0.6    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 2500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wrnAI1v6u8Uh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Show a random example and its corresponding label."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s-euVJVtu8Ui",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        },
+        "outputId": "b0b9dffe-4ec8-4506-f8ba-85ffe708b64a"
+      },
+      "cell_type": "code",
+      "source": [
+        "rand_example = np.random.choice(training_examples.index)\n",
+        "_, ax = plt.subplots()\n",
+        "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n",
+        "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n",
+        "ax.grid(False)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFXCAYAAAAro2x+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAE2RJREFUeJzt3W9MlfX/x/EXcUQ5ipIoLLfMb4lG\nZrPmPyj/AK7SzfwzbygTc3kDK5yk5siptWyhiJZkDqRsJcudRbq5asG0Wg4Rw1kNt0JrEVkSAiUK\nmiDfG+139vMrxpsTh+scfD5u6fHTdd5nV3vuOpzzuQhpb29vFwDgH93m9AAAEAyIJQAYEEsAMCCW\nAGBALAHAgFgCgAGxRI8ZPXq0zp0716X/JikpSRUVFV36bzIzM7Vr1y7z+i+++EKjR4/WL7/80qXn\nwa2FWOKW1tLSom3btikyMtLpURDgiCUc19LSooyMDD322GNKSkrSli1brvv3Y8eOae7cuZo2bZpe\ne+017+OHDh3S7NmzlZycrKeeekoNDQ03HHvbtm3at2/fTZ/7jTfe0BNPPKH+/ft33wtCr0Qs4bh9\n+/bp0qVL+vTTT3XgwAHt37//urfep06d0ocffqj9+/dr3759+u6771RTU6O1a9dq27ZtOnz4sCZN\nmqSXXnrphmOvXr1aixYt6vB5v//+ex09elRLly710ytDb+JyegDgqaeeUmpqqkJCQjRo0CDFxsbq\nl19+0fjx4yVJs2fPVmhoqKKiojRhwgSdPHlS165d08SJEzVq1ChJ0sKFC/Xwww+rra3N9Jzt7e16\n8cUXtX79evXp08dvrw29B7GE43766Sdt3rxZP/74o2677TadO3dO8+fP9/774MGDvX+OiIjQhQsX\n1N7eroqKCj3++OPefxswYID++OMP03N6PB6NHDnSG2SgM8QSjnv55Zc1ZswYvfnmmwoNDdXChQuv\n+/c///zzuj8PGjRIYWFhSkhIUG5urk/PefjwYVVWVurzzz+XJDU0NGjBggV6/fXXNXnyZN9fDHot\nfmYJx9XX1ysuLk6hoaEqLS1VdXW1mpubvf/+8ccf69q1a6qvr9eJEyc0fvx4PfLII6qoqFBNTY0k\n6dtvv9Urr7xifs6CggKVlZWptLRUpaWluuOOO1RUVEQocVNcWaJHpaamKjQ01Pv3V155RU8//bSy\nsrK0a9cuJScnKz09Xbm5uYqLi5MkjR07VgsWLFBDQ4OefPJJjRw5UpK0adMmPfvss7p69ar69++v\ndevW3fB827Zt07Bhw276IQ9gFcL9LAGgc7wNBwADYgkABsQSAAwc+YDn1Vdf1TfffKOQkBCtW7dO\nDzzwgBNjdKvy8nKtXLlSsbGxkqRRo0Zpw4YNDk/lu6qqKj3zzDNaunSpFi9erN9++01r165VW1ub\nhg4dqq1btyosLMzpMbvkf19TZmamTp065d0XvmzZMk2fPt3ZIbsoOztbJ06cUGtrq9LS0jR27Nig\nP0/Sja/rs88+c/xc9Xgsjx8/rurqank8Hv3www9at26dPB5PT4/hFxMnTvT5e3+BpLm5WZs2bVJ8\nfLz3sdzcXKWkpGjmzJnavn27ioqKlJKS4uCUXdPRa5KkVatWKTEx0aGp/p1jx47p9OnT8ng8amxs\n1Lx58xQfHx/U50nq+HVNnjzZ8XPV42/Dy8rKNGPGDEnSPffcoz///FMXL17s6THwD8LCwlRQUKDo\n6GjvY+Xl5UpOTpYkJSYmqqyszKnxfNLRawp2EyZM0I4dOyRJAwcOVEtLS9CfJ6nj12XdxupPPR7L\n8+fP6/bbb/f+ffDgwaqrq+vpMfzizJkzWr58uRYtWqTS0lKnx/GZy+VSv379rnuspaXF+3YuKioq\n6M5ZR69JkgoLC7VkyRI999xzHd61KJCFhobK7XZLkoqKijR16tSgP09Sx68rNDTU8XPl+JfSe8vX\nPEeMGKH09HTNnDlTNTU1WrJkiUpKSoLy50Wd6S3nbM6cOYqMjFRcXJx2796tnTt3auPGjU6P1WWH\nDh1SUVGR9uzZo0cffdT7eLCfp///uiorKx0/Vz1+ZRkdHa3z5897//77779r6NChPT1Gt4uJidGs\nWbMUEhKi4cOHa8iQIaqtrXV6rG7jdrt1+fJlSVJtbW2veDsbHx/v3SWUlJSkqqoqhyfquiNHjigv\nL08FBQWKiIjoNefpf19XIJyrHo/lww8/rOLiYkl/36cwOjpaAwYM6Okxut3Bgwf19ttvS5Lq6upU\nX1+vmJgYh6fqPgkJCd7zVlJSoilTpjg80b+3YsUK797y8vJy7zcZgkVTU5Oys7OVn5/v/ZS4N5yn\njl5XIJwrR7Y75uTkqKKiQiEhIXrxxRd177339vQI3e7ixYtas2aNLly4oKtXryo9PV3Tpk1zeiyf\nVFZWasuWLTp79qxcLpdiYmKUk5OjzMxMXblyRcOGDVNWVlZQ3Qeyo9e0ePFi7d69W+Hh4XK73crK\nylJUVJTTo5p5PB698cYb+s9//uN9bPPmzVq/fn3Qniep49c1f/58FRYWOnqu2BsOAAbs4AEAA2IJ\nAAbEEgAMiCUAGBBLADAglgBgQCwBwIBYAoABsQQAA5/vOtQb73YOADfjUyx7893OAaAjPr0N527n\nAG41PsWyN9/tHAA60i0f8HDjIgC9nU+x7K13OweAm/Eplr31bucAcDM+fRr+0EMPacyYMVq4cKH3\nbucA0Jtxp3QAMGAHDwAYEEsAMCCWAGBALAHAgFgCgAGxBAADYgkABsQSAAyIJQAYEEsAMCCWAGBA\nLAHAgFgCgAGxBAADYgkABsQSAAyIJQAYEEsAMCCWAGBALAHAgFgCgAGxBAADYgkABsQSAAyIJQAY\nEEsAMCCWAGBALAHAgFgCgAGxBAADYgkABsQSAAyIJQAYEEsAMCCWAGBALAHAgFgCgAGxBAADYgkA\nBsQSAAyIJQAYEEsAMCCWAGDgcnoAwN8aGxvNa1evXm1a98EHH5iP2dTUZF6LwMWVJQAY+HRlWV5e\nrpUrVyo2NlaSNGrUKG3YsKFbBwOAQOLz2/CJEycqNze3O2cBgIDF23AAMPA5lmfOnNHy5cu1aNEi\nlZaWdudMABBwfHobPmLECKWnp2vmzJmqqanRkiVLVFJSorCwsO6eDwACgk9XljExMZo1a5ZCQkI0\nfPhwDRkyRLW1td09GwAEDJ9iefDgQb399tuSpLq6OtXX1ysmJqZbBwOAQOLT2/CkpCStWbNGhw8f\n1tWrV/XSSy/xFhxAr+ZTLAcMGKC8vLzungUAAhbbHYPUr7/+al5bU1NjXnvfffeZ1kVERJiP6bS3\n3nrLvPbAgQOmdd9//72v4yBI8T1LADAglgBgQCwBwIBYAoABsQQAA2IJAAbEEgAMiCUAGBBLADAg\nlgBgwHbHIPV/d32y6Mrt83bu3OnLOAGtubnZvLZ///6mdVFRUb6OgyDFlSUAGBBLADAglgBgQCwB\nwIBYAoABsQQAA2IJAAbEEgAMiCUAGLCDJ0iNHTvWvJbdJnbWXwR35coV8zH79u3r6zgIIFxZAoAB\nsQQAA2IJAAbEEgAMiCUAGBBLADAglgBgQCwBwIBYAoABsQQAA7Y7BqnRo0eb11q38AG4Oa4sAcCA\nWAKAAbEEAANiCQAGxBIADIglABgQSwAwIJYAYEAsAcCAWAKAAdsdg9TFixfNa12uW/s0T5kypduP\n2dDQYF47cODAbn9+9DzTlWVVVZVmzJihwsJCSdJvv/2m1NRUpaSkaOXKlfrrr7/8OiQAOK3TWDY3\nN2vTpk2Kj4/3Ppabm6uUlBS9//77uuuuu1RUVOTXIQHAaZ3GMiwsTAUFBYqOjvY+Vl5eruTkZElS\nYmKiysrK/DchAASATn+Y5XK5bviZV0tLi8LCwiRJUVFRqqur8890ABAg/vWn4e3t7d0xBwAENJ9i\n6Xa7dfnyZUlSbW3tdW/RAaA38imWCQkJKi4uliSVlJT45asZABBIOv2ZZWVlpbZs2aKzZ8/K5XKp\nuLhYOTk5yszMlMfj0bBhwzR37tyemBUAHNNpLO+//37t3bv3hsffeecdvwwEAIHo1t7aEcTCw8PN\na/v16+fHSQLf9OnTzWutP39vbW31cRoEK/aGA4ABsQQAA2IJAAbEEgAMiCUAGBBLADAglgBgQCwB\nwIBYAoABsQQAA7Y7BqmmpibzWn5Hkp11a2hJSYn5mCNHjvR1HAQQriwBwIBYAoABsQQAA2IJAAbE\nEgAMiCUAGBBLADAglgBgQCwBwIBYAoAB2x1vAY2NjU6P4KjbbrNfE0yaNMm07t133zUf85lnnjGv\nReDiyhIADIglABgQSwAwIJYAYEAsAcCAWAKAAbEEAANiCQAGxBIADNjBE6SuXbtmXtuVX24Gm5Mn\nT5rXfv311+a148aN82Uc9ACuLAHAgFgCgAGxBAADYgkABsQSAAyIJQAYEEsAMCCWAGBALAHAgFgC\ngAHbHYPUgw8+aF772muvmde2traa1rlczv6v097ebl773Xffmdd+9dVXvozzj4YPH97tx0TP48oS\nAAxMsayqqtKMGTNUWFgoScrMzNTs2bOVmpqq1NRUffHFF/6cEQAc1+l7qebmZm3atEnx8fHXPb5q\n1SolJib6bTAACCSdXlmGhYWpoKBA0dHRPTEPAASkTmPpcrnUr1+/Gx4vLCzUkiVL9Nxzz6mhocEv\nwwFAoPDpA545c+ZozZo1eu+99xQXF6edO3d291wAEFB8imV8fLzi4uIkSUlJSaqqqurWoQAg0PgU\nyxUrVqimpkaSVF5ertjY2G4dCgACTaefhldWVmrLli06e/asXC6XiouLtXjxYmVkZCg8PFxut1tZ\nWVk9MSsAOKbTWN5///3au3fvDY8/9thjfhkIAAIR2x2DlNvtNq/t06ePee3Ro0dN66ZOnWo+Zltb\nm3ntDz/8YFr3/PPPm4/55Zdfmte+8MILpnWvv/66+ZgDBgwwr0XgYrsjABgQSwAwIJYAYEAsAcCA\nWAKAAbEEAANiCQAGxBIADIglABgQSwAwYLvjLaArWxM/+ugj07px48aZj/nmm2+a127YsMG0bvv2\n7eZj7tu3z7zWuo00Ly/PfMzq6mrzWu7gFbi4sgQAA2IJAAbEEgAMiCUAGBBLADAglgBgQCwBwIBY\nAoABsQQAA3bw3ALq6urMa3Nyckzrtm7daj7m+PHjzWurqqpM6+6++27zMf1hwYIF5rWNjY1+nAQ9\nhStLADAglgBgQCwBwIBYAoABsQQAA2IJAAbEEgAMiCUAGBBLADAglgBgENLe3t7u9BDwr6amJvPa\nH3/80bTuzjvvNB9z4MCB5rUuV3DswD1//rx57a5du8xrN27c6Ms46AFcWQKAAbEEAANiCQAGxBIA\nDIglABgQSwAwIJYAYEAsAcCAWAKAAbEEAAO2OwI++Ouvv8xrp02bZl77+eefm9f269fPvBb/nmkj\nbnZ2tk6cOKHW1lalpaVp7NixWrt2rdra2jR06FBt3bpVYWFh/p4VABzTaSyPHTum06dPy+PxqLGx\nUfPmzVN8fLxSUlI0c+ZMbd++XUVFRUpJSemJeQHAEZ3+zHLChAnasWOHpL/vHtPS0qLy8nIlJydL\nkhITE1VWVubfKQHAYZ3GMjQ0VG63W5JUVFSkqVOnqqWlxfu2OyoqSnV1df6dEgAcZv40/NChQyoq\nKrrhfnt8PgTgVmCK5ZEjR5SXl6eCggJFRETI7Xbr8uXLkqTa2lpFR0f7dUgAcFqnsWxqalJ2drby\n8/MVGRkpSUpISFBxcbEkqaSkRFOmTPHvlADgsE4/Df/kk0/U2NiojIwM72ObN2/W+vXr5fF4NGzY\nMM2dO9evQwKA0/hSOuADvpR+6yGWgA+6Esu+ffua1+bk5JjXrl692rwW/x57wwHAgFgCgAGxBAAD\nYgkABsQSAAyIJQAYEEsAMCCWAGBALAHAgFgCgIHpd/AAuF6fPn3Ma9PT081rf/75Z1/GQQ/gyhIA\nDIglABgQSwAwIJYAYEAsAcCAWAKAAbEEAANiCQAGxBIADIglABjw2x0BP6uurjavffTRR81rjx8/\nblo3aNAg8zFxc1xZAoABsQQAA2IJAAbEEgAMiCUAGBBLADAglgBgQCwBwIBYAoABO3gAP7t06ZJ5\nbVpamnltfX29aZ3H4zEfc+DAgea1txquLAHAgFgCgAGxBAADYgkABsQSAAyIJQAYEEsAMCCWAGBA\nLAHAgFgCgAHbHQHAwGVZlJ2drRMnTqi1tVVpaWn67LPPdOrUKUVGRkqSli1bpunTp/tzTgBwVKex\nPHbsmE6fPi2Px6PGxkbNmzdPkydP1qpVq5SYmNgTMwKA4zqN5YQJE/TAAw9I+vuOJC0tLWpra/P7\nYAAQSLr0M0uPx6OKigqFhoaqrq5OV69eVVRUlDZs2KDBgwf7c04AcJQ5locOHVJ+fr727NmjyspK\nRUZGKi4uTrt379a5c+e0ceNGf88KAI4xfXXoyJEjysvLU0FBgSIiIhQfH6+4uDhJUlJSkqqqqvw6\nJAA4rdNYNjU1KTs7W/n5+d5Pv1esWKGamhpJUnl5uWJjY/07JQA4rNMPeD755BM1NjYqIyPD+9j8\n+fOVkZGh8PBwud1uZWVl+XVIAHAaX0oHAAO2OwKAAbEEAANiCQAGxBIADIglABgQSwAwIJYAYEAs\nAcCAWAKAAbEEAANiCQAGxBIADIglABgQSwAwIJYAYEAsAcCAWAKAAbEEAANiCQAGxBIADIglABgQ\nSwAwIJYAYEAsAcCAWAKAAbEEAANiCQAGxBIADIglABj8F1o2RbG5kPSwAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ScmYX7xdZMXE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Build a Linear Model for MNIST\n",
+        "\n",
+        "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n",
+        "\n",
+        "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix).  The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n",
+        "\n",
+        "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cpoVC4TSdw5Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  \n",
+        "  # There are 784 pixels in each image.\n",
+        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMmL89yGeTfz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OeS47Bmn5Ms2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
+        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The training features.\n",
+        "    labels: The training labels.\n",
+        "    batch_size: Batch size to use during training.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns batches of training features and labels during\n",
+        "    training.\n",
+        "  \"\"\"\n",
+        "  def _input_fn(num_epochs=None, shuffle=True):\n",
+        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
+        "    # gets a good sampling of data, even when number of steps is small, we \n",
+        "    # shuffle all the data before creating the Dataset object\n",
+        "    idx = np.random.permutation(features.index)\n",
+        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
+        "    raw_targets = np.array(labels[idx])\n",
+        "   \n",
+        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8zoGWAoohrwS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_predict_input_fn(features, labels, batch_size):\n",
+        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The features to base predictions on.\n",
+        "    labels: The labels of the prediction examples.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns features and labels for predictions.\n",
+        "  \"\"\"\n",
+        "  def _input_fn():\n",
+        "    raw_features = {\"pixels\": features.values}\n",
+        "    raw_targets = np.array(labels)\n",
+        "    \n",
+        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size)\n",
+        "    \n",
+        "        \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "G6DjSLZMu8Um",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, and a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `LinearClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create a LinearClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(),\n",
+        "      n_classes=10,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ItHIUyv2u8Ur",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n",
+        "\n",
+        "Stop if you get anything above about 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yaiIhIQqu8Uv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1092
+        },
+        "outputId": "c6462a15-ef05-4a4a-b199-b72ec8fe4169"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_linear_classification_model(\n",
+        "             learning_rate=0.03,\n",
+        "             steps=1000,\n",
+        "             batch_size=50,\n",
+        "             training_examples=training_examples,\n",
+        "             training_targets=training_targets,\n",
+        "             validation_examples=validation_examples,\n",
+        "             validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.68\n",
+            "  period 01 : 4.30\n",
+            "  period 02 : 4.02\n",
+            "  period 03 : 3.85\n",
+            "  period 04 : 3.90\n",
+            "  period 05 : 3.91\n",
+            "  period 06 : 3.92\n",
+            "  period 07 : 3.84\n",
+            "  period 08 : 3.81\n",
+            "  period 09 : 3.95\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.89\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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y8/MBtF9OtKPlRq8Ue95XINYnBvcNuQsfpH+GN4+9jyeGP4wQdZDYZREROaTbom68bC+5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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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EQUBUVBR8fX31z12+fBmvvfYa6uvr8eCDD2L58uWttsUTEomIVEvo4K11hw8fRl5eHlJT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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "266KQvZoMxMv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lRWcn24DM3qa",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here is a set of parameters that should attain roughly 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TGlBMrUoM1K_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_linear_classification_model(\n",
+        "    learning_rate=0.03,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mk095OfpPdOx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Replace the Linear Classifier with a Neural Network\n",
+        "\n",
+        "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n",
+        "\n",
+        "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rm8P_Ttwu8U4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "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": "XqNEjZphBR4o",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        },
+        "outputId": "c3ac747d-ad5f-4f9d-ad7c-f3908e950318"
+      },
+      "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": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 5.25\n",
+            "  period 01 : 4.01\n",
+            "  period 02 : 3.51\n",
+            "  period 03 : 3.25\n",
+            "  period 04 : 2.93\n",
+            "  period 05 : 2.74\n",
+            "  period 06 : 2.65\n",
+            "  period 07 : 2.65\n",
+            "  period 08 : 2.38\n",
+            "  period 09 : 2.40\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.93\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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d/DzuHtx1bmzM3s5LR18npzbP2qUJIcSAI+HcA4qicOf1MagUhY+2p9Out9/R\nzSN9Y3l64q+YFjyRwoZiXk76K+syN9Km73+rpQkhRH8l4dxDof6uzB4XQklVE9uO2vfZpJPGkR8M\nW8LDY+7D29GLbbm7+MPRv3C+JtvapQkhxIAg4XwNbpkeiauTlvUHsqmqa7F2OWY3zHsIv014lFmh\nUyltLOe15LdYk76eFn2rtUsTQgi7JuF8DVwctdw6czAtrXrW7LLPwWGXc9Q4cFvMIh4Z9wB+zj7s\nzN/Hi4dfI71qYHz9QghhDRLO12hGXDDhAW4cTC0hI7/a2uVYTLRnJE9OeJS5YbOoaK5i1bHVfHRu\nLY1tTdYuTQgh7I6E8zVSqRR+ODcGgA+3ZWAw9O91t6+FTq3lluiFPD7+IYJcAthXcIhHNz7HusyN\n5NUV9vs1yIUQwlaoV65cudLaRQA0Npr2PqaLi4PJP/Mb3u6OlFY1cfpCJZ5uDkQEupvlOLbK08GD\nycEJKChkVl/gXFUm+woPkVx6kvq2Bjwc3HHVuli7TLtizu9ncSnptWVInzt60BXFaCOnO2Vlpt2a\n0c/PzeSf+V3V9S08ufoQWrWKP/xsEi6OWrMdy5Z5eDuy++xRkkqOc7oijbaLC5cMcgsh3n804wPG\n4OXoaeUq+z9zfz+Lb0mvLUP63NGDrsiZcy856jSoVQrHM8ppbTUQF+VjtmPZMndXJ9wVT8YFjGZW\n6FSCXAJoN7RzviaHtMp0duTt5VxlBq2GNnwcvXBQ66xdcr8kZxmWI722DOlz92fOGgvWYXfmjh/E\nnhNF7DiWz8wxwYT6u1q7JKty1DiSEDiOhMBx1Lc1cLz0FEklx8msvkBWTTZrMtYz1CuaeP/RjPYb\nibPWydolCyGETZLL2n106nwFf/70BEMHefKbO8eiKIrZj2lLetLn6pYaUkpPklRyvHM5UI2iZoTP\nMOIDRjPKdzg6OaPullwCtBzptWVIn7u/rC1nzn00arAPY6J9OZ5ZztGzpSTEBli7JJvj6eDBdYOm\nc92g6ZQ3VZBUcoLkkuOcKE/lRHkqOrWOON/hjA8YQ6x3DBqVfFsKIQY2+VfQBJbNieb0hQo+2ZHJ\n6ChfHHRqa5dks3ydfFgQcR0LIq6jsL6Y5JLjJH3nl7PGiTF+o4gPGE2MVxQqRWb7CSEGHhkQZopj\nOWlpbTdwMqsCRVGIDfeyyHFtQV/67KZzZah3NLNCpzLCdxiOagdKGsvIrDnPkeIU9hcepqq5GieN\nE54OHgPulsF3yeAZy5FeW4b0WQaEWcQNk8M5cLqYzYdzmRYXhL+nDHbqKUVRiHAPI8I9jMXRN5BZ\nfYHkkuMcKzvFrvz97Mrfj7cb3TiQAAAgAElEQVSjV+fUrBDXoAEd1EII+ycDwkzo8JkS/rE+lbFD\nfHl4SZxFj20t5uyz3qAnrTKd5NITnCg73bnhRqCzP/EBHUHt7+xnlmPbGhk8YznSa8uQPsuAMItJ\niPVn57ECjmWUc/p8BSMHD8y5z6aiVqkZ6RvLSN9YWvVtnK5II7nkBKcr0thwYRsbLmwjzC2E+IAx\nxPuPlsVOhBB2Q86cTSy3pI7nPjhKgJczz/8kAY3avgc0WaPPTe3NnCxLJan0OGcrMzAYDQBEeUQy\nPmAMY/1H4aazrznncpZhOdJry5A+y5mzRYUFuDFrbAg7UwrYnpTPgolh1i7J7jhpHJkYFM/EoHjq\nWxs4VnaS5JITFxc7ucBnGV90LHYSMIYxfiNw0sj9fyFE/yLhbAaLpw/myJkS1u+/wKQRAXi6dj0i\nT/SNq86F6SGTmR4ymarmalJKO4I6rTKdtMp0Pj6n6VjsxH80o3xjZbETIUS/0OOpVPX19eh0OsrL\nyzlz5gyBgYEmHTHbn6dSXU6nVePkoCElvYz6xjbGxdjvoCVbmg7hpHFksEc4U0MmMiFgLG5aV6pb\na8mqvnBx5Pc+ihpK0Kg0+Dr59KsR37bUZ3snvbYM6XP3U6l6FM6/+93vqK6uJiQkhNtvv52ioiIO\nHTrE7NmzTVakPYUzQHiAG8czyjl9oZIRkd54uztarRZzsnafu+KidWaI12BmhExmjP8onDROVDRV\nkVlzgaSS4xwqSqLN0EaAs1+/2IzDVvtsj6TXliF97j6cezRa6cyZM9x2221s2rSJxYsXs2rVKnJy\nckxWoD1SqRTunBsDwH+3pWOwjXF3A46iKIS4BrEoKpHnJv8fv45/iGnBE2loa2D9+c08vf/3/PPM\nx2TX5lq7VCGE6NSje87fDOjetWsXjzzyCACtrQP7J56eiBnkyaQRARxKLWHfySJmjA62dkkDmqIo\nRHqEEekRxi3RCzlUlMyeggMcKU7hSHEKYW6hzAidQrz/aHTqgbk/txDCNvQonCMjI1m4cCHe3t7E\nxsaybt06PDw8zF2bXbhtVjTH0stZsyuL8UP9cHaUf/RtgZPGidmDpjEzdArnqjLZk3+QU+Vn+E/a\np3ye8RVTghOYHjIJHydva5cqhBiAejTPWa/Xk56eTlRUFDqdjtTUVAYNGoS7u7vJCrGXec5XsvFQ\nDmt2ZXF9fGjnpW57YUt97quKpir2FR7iQOER6tsaUFAY4TOMmaFTGOY9xKqbcNhTn22d9NoypM/d\nz3Pu0b82aWlpFBcXo9Pp+POf/8yf/vQn0tPTTVagvZs7fhD+Xk7sSCkgv6ze2uWILvg4ebEoKpEX\npvyWu2LvIMw9lNMVafztxLs8f+hlduTtpbGtydplCiEGgB6F8wsvvEBkZCRJSUmcOnWKZ555htdf\nf93ctdkNrUbFD+YMwWA08uG2dGxkUTbRBa1ay8SgeH4z/mF+M/5hJgbGU9VSw/8yvuSp/S/w4dk1\n5NcVWrtMIYQd69E9ZwcHByIiIvjkk0+4/fbbiY6ORqWy72UpTW10tC9xUT6czKog+VwZ44f5W7sk\n0QPh7oO4a/gd3Bp9IweLjrKn4CD7C4+wv/AIUR4RzAidwhi/kWhUsp6PEMJ0evQvSlNTE5s2bWL7\n9u08+OCDVFdXU1tba+7a7M4P5gzhTHYln+zIYFSUDw5atbVLEj3kqnNhbvgs5oTNILXiLLvzD5BW\nmU5WTTbuOjemBk9kWshEPB1koKQQou96tAjJoEGD+Oyzz7j77rsZMWIEb7/9NrNmzWLo0KEmK8Te\nFiG5ElcnLS1tBk5mVaBWKQwL97J2SX1mi302J0VRCHD2IyFwHOMDxqBSVOTW5ZNWmc6u/P0UNhTj\npnXB29HLpCuQDbQ+W5P02jKkz90vQtLjXakaGxu5cOFCx1zRyEicnEy7mYA9j9b+rubWdn67+hD1\nTe38/r6J+Hn2700ZbLXPltSib+VocQp7Cg5SUF8EQLBLIDNCJzMhYByOmr6vrS59thzptWVIn7sf\nrd2jM+ft27fzk5/8hKSkJL7++mtWr17N4MGDiYiIMFmRA+HMGUCjVuHhouPo2VIqa1tIiA2wdkl9\nYqt9tiSNSk2YeyjTgicx1HsIbfo2MmsucKo8jT35B6ltrcXX0RtXnUuvjyF9thzptWVIn7s/c+7R\nPed33nmH9evX4+3dsSBDSUkJK1asYObMmV2+p6mpiSeeeIKKigpaWlr4xS9+YdK1uPuzicMD2Hms\ngJT0MlIvrr0t+j9FUYj2jCTaM5Kallr2Fx5mX8EhduXvZ1f+foZ5DWFG6BRG+cZadc60EML29Sic\ntVptZzADBAQEoNV2v9LVzp07GTlyJPfddx8FBQXce++9Es4XKYrCD+fG8NwHR/lwezrP3ZuARi3/\nWNsTDwd3FkbOZX74dZwoT2VP/gHOVmVwtioDLwdPpodMYkpwAm46V2uXKoSwQT0KZxcXF9577z2m\nTJkCwL59+3Bx6f4S3cKFCzt/X1RUREBA/758a2phAW7MHBPCrmMF7EjOZ15CmLVLEmagVqkZ5x/H\nOP84CuqL2FNwkCPFKaw/v5mNF7YxLmA0M0OnEOEu//+FEN/q0YCwiooKVq1axcmTJ1EUhTFjxvDw\nww9fcjbdlWXLllFcXMzf//53hg0b1uXr2tv1aDQDa2pRbUMrP/vDdgxGI39/Yg5ebva5raS4VGNr\nE7uyD7I1cw+FdSUARHmFM3/ITKYMikensf0tLIUQ5tXj0dqXy8rKIioqqkevTUtL4ze/+Q3r16/v\ncnrJQBmtfbkdKfn8Z2s600YFce8NsdYu55r1lz7bIqPRyLmqTHbnH+BU+RmMGHHROjMl6Pubbkif\nLUd6bRnSZxOsrX0lzz33XLfPnz59mqKijmklsbGx6PV6Kisre3s4uzVzTDChfq7sO1VEVmGNtcsR\nFqQoCsO8h/CzuB/z3OQnmBc+GwWFbbm7+H8HX+LvJ9/nTMU5DEaDtUsVQlhYr8P5aifcSUlJvPfe\newCUl5fT2NiIl1f/X3TD1NQqFT+cOwSA/2xNp6VNb+WKhDVcvulGuPsgTpV3bLrxu0Ov8NW57ZQ2\nlsu67EIMEL1eEPhqqx8tW7aMp556ijvvvJPm5maeffZZWY+7C0PDvJg8IoCDqSX86cMUfrkkDg/X\nvi9cIfqfbzbdmBgUT05tHnvyD5JUepx/Hf8fAJ4OHsR4RRHjFU2MZxQ+TvIDrxD2qNt7zmvWrOny\nje+++y6bNm0yWSED9Z7zN9raDfxr81n2ny7Gx92BFUtHE+pv+9Ns+luf+6P6tgYyGs+RnJtKRvV5\n6tsaOp/zcfRmqFcUQ7yiiPGKkrW9TUC+py1D+tz9Peduz5yTk5O7fG7MmDG9r0h8j1aj4t4bYgnw\ndmbtnvO8+J9kHlg0krgoH2uXJqzMVevCvOiZjPUYh8FooKihhPSqLNKrssiozuJA0VEOFB0FIMDZ\nryOoPTvCWuZRC9E/9Xq0tqkN9DPn7zqSVsK7G9Jo1xu48/oY5sSHWrukLvXnPvcnXfXZYDSQX1dI\nenUW56oyyaq+QIv+2yURg10CGeIV1XF27TkYZ62zJcvul+R72jKkz92fOfconO+8887v3WNWq9VE\nRkbyi1/8wiQLjEg4XyqrsIY31pyktrGNOfGhLJsTjdoG79n39z73Fz3ts96gJ7cun3NVWWRUZZFV\nk02boQ0ABYVQ16CO+9VeUUR5RuKkkbn1l5PvacuQPpsgnP/6179y4cIF5s+fj0qlYvv27QQFBeHh\n4cGePXs6R2X3hYTz95VXN7FqzUkKyhuIi/LhZzePwMmh12P4zMIe+twf9LbPbYZ2smtySa/uCOsL\nNTm0GztmBKgUFWFuoRcHmEUR5RGBTi0LoMj3tGVIn00Qzvfccw/vv//+JY/df//9rF69muXLl/Pv\nf/+7z0VKOF9ZY3M7f//iNKcvVBLq58qKpXH4eNjO2Y699NnWmarPrfo2ztdkk1GVRXp1Ftm1eZ3z\nqNWKmgj3QZ1n1pHuYWjV3a+hb4/ke9oypM99GBD2jYqKCiorKzuX66yrq6OwsJDa2lrq6gZ2c83N\n2VHDitvi+HB7BjtTCnjhX0n8cmkckUHu1i5N9EM6tZZh3kMY5t0xt765vYWsi2F9riqT8zU5ZNVk\nsyl7O1qVhkiPiM7BZeHuoWhUtnXlRgh71aMz5zVr1vDyyy8TEhKCoijk5+fzs5/9DB8fHxobG/nB\nD37Q50LkzLl7RqOR7cn5fPx1Blq1ivtuGk78UH9rl2V3fbZVlupzY1sTWTUXOFeVSXpVFgX1RZ3P\n6dQ6ojwiiPGKYqhXNKGuwahV9rcevnxPW4b02QSXtQHq6+vJzs7GYDAQFhaGp6enyQoECeeeOp5Z\nzj++SKWlTc/SWVEkTgy76oIw5mSvfbY11upzfWsDGdXnO6ZuVWdR3FDS+Zyj2pFoz8jORVFCXAPt\nYp9q+Z62DOmzCcK5oaGBDz74gFOnTnXuSvXjH/8YR0fT3fuUcO653JI6Vq05SVVdC9Pigrhr/lCr\n7Qdtz322JbbS55qWOjKqsy7Os86krKmi8zkXjTPRXoM7wtoziiCXAKv+4NhbttJreyd9NkE4P/bY\nYwQEBDBx4kSMRiMHDhygqqqKV155xWRFSjhfm6q6Fl7/30lyiusYFubJg7eOwsXR8oN37L3PtsJW\n+1zVXN25IMq5qkyqWqo7n3PVuuDv7IungweeDh54OXjg6ejZ+Xt3nZtNXha31V7bG+mzCQaElZeX\n89prr3X+efbs2SxfvrzvlYle83Jz4Ik7x7H6y1SOZZTzwr+SeeS2OAK8ZJEJYTlejp6da4EbjUYq\nmis7w/p8TfbF0eA5V3yvgoK7zg1Px4vBfYUQ93BwRyuD0MQA1KPv+qamJpqamnBycgKgsbGRlpYW\nsxYmrs5Bp+bBW0fxv11ZbDqcywv/TOLhJXHEDDLteAAhekJRFHydfPB18mFKcALQsYJZXWs91S01\nVLXUUN1cc/H31VRf/HNBXSE5tXldfq6b1hVPx+8E9zch7vjt72V+trA3PQrnO+64g8TEREaOHAlA\namoqK1asMGthomdUisJts6MJ8Hbm31vO8fJHx7hn4TCmjAyydmlCoFJUeDi44+HgTjiDrvgao9FI\nfVtDR1i31FB1McA7A72lmuKGUvLqCro8jovGuTPALwnx75yVO8pqaKIf6VE4L126lKlTp5Kamoqi\nKDzzzDMmWXhEmM6M0cH4ejjyt89P885XaRRXNnHL9EhU/XBAjhhYFEXBTeeKm86VQW4hV3yN0Wik\nqb3pYlh3nHF3/v5iiFc0VV4y9etyjmrHLi6hf/t7J42Tub5MIa5Jj2/mBAUFERT07dnYyZMnzVKQ\n6L3hEd48fVc8f/nsBF8dyKa0qpF7F8ai09reoBshroWiKDhrnXHWOhPi2vVVoab25svCu/p7l9S/\nOx3sclqVFp1ag8EmtgPqGQUIdx9EQuA4RvuNxEEu8duFXo+0sJHNrMRlgnxcePqu8byx9hRH0kqp\nqGnmoSVxeLjIX1hh/5w0jjhpHAly6XoznhZ9a2eAf3vpvCPIa1pqUVTQrjdYsOq+aTO0kVaZTlpl\nOg5qHWP8RjExMJ4hXoPtYt75QNXrcO6P8xcHCjdnHY8vG8v7m9I4lFrC7/+VxIqlcYT4yd6+Qjio\ndQQ4+xHg7HfF5/vjFJ+SxjKOFKdwtDiFw8XJHC5OxtPBgwkBY0kIHEewa6C1SxTXqNt5zjNnzrxi\nCBuNRqqqqkx6aVvmOZue0Wjky/3ZrNt3AScHNT+/ZSQjI31Megzps2VIny2nP/faYDRwviaHI8XJ\npJSepKm9GYBBbiEkBI5jfMAY3HVdz621pP7cZ1Pp9SIkBQVdj44ECAm58uCN3pBwNp9DZ4p5b8NZ\nDAYjP5wXw+yxpvv/Jn22DOmz5dhLr9v0bZyqSONwUTJnKs9hMBpQKSpivWNICBxHnO8IdFbcdcxe\n+twXvV6ExJThK6xn0vBAfN2deGPtSf695RwllY3cPjsalUpuTQhhr7RqLeP84xjnH0ddaz3JJSc4\nXJxMasVZUivO4qh2YKx/HAmB44j2jJT70zamxxtfmJucOZtfaXUTqz47QVFFI2Oifbn/5uE46vq2\n+pL02TKkz5Zj770ubijlSHEKR4pTOpdb9XLwJCFwHAmB4wh0scxud/be554wya5U5ibhbBmNzW28\nue40Z7KrCPN35ZdL4/B27/3iDNJny5A+W85A6bXBaCCz+gJHilM4VnqSZn3Hqo/hbh3TsuIDRuOm\nM98g0oHS5+5IOItLtOsN/HdbOruPF+LpquOXS+OICHTv1WdJny1D+mw5A7HXrfpWTpaf4UhxCmmV\n6Z33p4d7D2ViUDyjfGLRmvj+9EDs8+UknMX3GI1Gth7N49MdmWi1Ku6/aQTjYq48taQ70mfLkD5b\nzkDvdW1rHUklxzlSnNK5ZKqTxpGxfnFMDIpnsEe4Se5PD/Q+g4Sz6Max9DL+8WUqbW0GbpsdzfyE\nQdc0h136bBnSZ8uRXn+rsL64Y/50yTGqW2oA8HH0YsLF+9NdzRXvCemzhLO4ipziOlatOUF1fSsz\nRgfzo3kxaNQ9+8lY+mwZ0mfLkV5/n8FoIL0qiyPFKRwvO0WLvhWACPewzvvTrlqXa/pM6bOEs+iB\nqroWVq05QW5JPbHhXjy4eCTOjle/xyR9tgzps+VIr7vXom/lRNlpjhSncLYyAyNG1IqaET7DSAgc\nx0jf2B7twS19lnAWPdTc2s7q9Wc4nllOkI8zK24bjb9n97v0SJ8tQ/psOdLrnqtpqeVoyTGOFKd0\n7gjmpHEi3j+OhMCO+9Nd3SaTPks4i2tgMBj5dGcmW4/m4eqk5eEloxgS6tnl66XPliF9thzpde8U\n1BdxuDiZpOJj1LR29M/XyYeEgLEkBMbj53zp0sH9oc96g542Qxuthjba9O14OrijVplulz8JZ3HN\ndh0r4D9b01Gp4N6FsUwaceWF86XPliF9thzpdd8YjAbOVWVyuCiFE2WnaDW0ATDYI5yEwHGM8x+N\ni9a5V302Go20G9o7wtLQRqv+8v+20mZov/T3+jZaDa2dAdtqaKVN33bZ69poNbTTpr/0dQbjpbuT\njfUbxU9HLTdZryScRa+kXqjkzXWnaGrRs2haJDdPjfjeJSrps2VIny1Hem06ze0tnfenz1VlYsSI\nRlEzwjeWSN8QauobLoZn28XA/E7QXnzsu0HcbmjHiGkjS0FBq9KgVWvRqXRo1ZqOfb1VuouPadCq\ndehUHcuhjvSNNdmxJZxFrxWUN7DqsxOU1zQzaUQA9yQOQ6v59rKO9NkypM+WI702j+qWGo4Wd9yf\nLmwo7va1akWNTq1Fq7r4S61Fd/H3um9+r/72z1rVpb/Xffc9Xbzum8/TqDRW2wJZwln0SW1DK2/8\n7yRZhbVEh3rw0K2jcHfWAdJnS5E+W4702ryMRiPFjaXoXBQa6lo7zlC/G6xq7YDZhKO7cB4YHRB9\n4u6i4/EfjCUh1p/M/Bp+/68kiioarF2WEKIfUhSFIJcAhvlFEeYWSqCLPz5OXrjpXHHUOAyYYL4a\n6YLoEZ1Wzf03j+CmKRGUVTfzwr+SOZNdae2yhBDCLvVtv0AxoKgUhcUzBhPg7cQHm87y509P0NRu\nZFyUt9Xu2QghhD2ScBbXbMrIIHw9nPjr2lP8bc0JokLcWTIjimHhXtYuTQgh7IJc1ha9EjPIk6d/\nPJ7Jo4LIKqjlTx8d45WPj3G+sNbapQkhRL8nZ86i1/w9nfjt3QkcPlHA53uySM2u4kx2EmOH+LJ4\nxmBC/cy3UbsQQtgzCWfRZ4OD3fnVsrGk5VSxdk8WxzLKOZ5RzsQRAdwyLRJ/L2drlyiEEP2KhLMw\nmdhwL377o3hOZlWwds95DqWWcDStlOlxQdw0NRIvNwdrlyiEEP2ChLMwKUVRGB3ty6goH5LOlvL5\nnvPsOl7IvlPFzIkPYeGkcNwuLmAihBDiyiSchVmoFIWE2ADih/px4FQxX+y/wJYjeew6Xsj8CYOY\nNyEMZ0f59hNCiCuRfx2FWalVKqaPDmbSiEB2HS9gw4Fs1u/P5uvkfBZODue6caE4aE23BZsQQtgD\nmUolLEKrUTF3/CD++MBklswcjNEIn+3M4ol/HGRHSj7tesPVP0QIIQYICWdhUY46DTdMjuCln0/m\nhsnhNLW085+t6fx29SH2nyrCYLCJfViEEMKqJJyFVbg4alkyM4qXHpjC9eNDqa5v4d0NaTzz7mGS\nzpZiI5ulCSGEVcg9Z2FVHi467rw+hvkTwli//wL7TxXz5rrThAe6sWTGYEZEyrrdQoiBR8JZ2AQf\nD0fuWRhL4qRw1u09z5G0Ul779AQxgzy5dcZgYgZ5WrtEIYSwGAlnYVMCvZ15YNFIFk6q4/M95zmR\nVcEf/5tCXJQPi6cPJjyw683JhRDCXpg1nP/0pz+RnJxMe3s7P/vZz5g3b545DyfsSFiAGytuG01m\nQQ1rd2dxMquCk1kVjB/mz+LpkQT5uFi7RCGEMBuzhfOhQ4fIyMjgk08+oaqqisWLF0s4i2sWHeLB\n4z8Yy5mcKtbuziLpbCnJ50qZOjKIm6dF4OvhZO0ShRDC5MwWzhMmTCAuLg4Ad3d3mpqa0Ov1qNWy\n4IS4NoqiMCLCm+HhXhzLKOfzPefZd6qIg6nFzBoTwo1TwvFwlXW7hRD2w2zhrFarcXbu2I1ozZo1\nzJgxQ4JZ9ImiKIyL8WNMtC+H00pYt/c8X6fks/dkIdePH8SCiWG4OmmtXaYQQvSZYjTzhNLt27fz\nj3/8g/feew83t64H87S369FoJLxFz7XrDWw7ksvHW89RWduMi6OGxbOjuXl6FE4OMtZRCNF/mTWc\n9+7dy6pVq3jnnXfw9Ox+KkxZWZ1Jj+3n52byzxTfZwt9bm3TsyOlgI2HcqhvasPNWcsNkyOYPTYY\nrZ38wGcLfR4opNeWIX3u6EFXzHZ6UVdXx5/+9Cc++OCDqwazEH2h06pZMDGMmWOC2XY0j81Hcvn4\n6wy2HMll0bRIpowMRKOWxfCEEP2H2cJ548aNVFVV8cgjj3Q+9tJLLxEcHGyuQ4oBzslBw83TIrku\nPpSNh3L4OjmfDzadZeOhHG6ZHklCbAAqWW1MCNEPmP2ec0/JZe3+yZb7XFXXwlcHs9lzvBC9wUio\nnyu3zhjM6GiffrckqC332d5Iry1D+myly9pCWJuXmwPL5w1lfkIY6/dd4GBqMa//7ySDg925cUoE\ncVE+ciYthLBJEs7C7vl7OvHTG4d3rtudfK6M19ecJMTPhYUTw5kQ6y/3pIUQNkXCWQwYIb4uPLh4\nFPll9Ww6lMvhMyW8/dUZ1u7JYn5CGNNHB+OgtY/R3UKI/k3uOYs+6c99Lq9uYsvRPPaeKKS13YCr\nk5brx4dy3bhQm1vMpD/3ub+RXluG9Ln7e84SzqJP7KHPtY2tfJ2Uz46UfBqa23HQqpk5Jph5Ewbh\n7e5o7fIA++hzfyG9tgzpswwIE6Jb7s46Fs8YTOKkMPYcL2TL0Ty2Hs3j6+R8Jo8IJHFSmOyCJYSw\nKAlnIS5y1GmYlxDGdfGhHEwtZvPhXPadKmL/qSLGxviROCmMqGAPa5cphBgAJJyFuIxGrWJ6XDBT\nRwVxPKOcDQdzSEkvIyW9jGFhniycFM6ISO9+N1daCNF/SDgL0QXVxV2wxg7x5VxuNRsP5XD6QiVn\nc6sJ83clcVI444f5oVbJNCwhhGlJOAtxFYqiMCzci2HhXuQU17HpcA5Hz5byj/WprN3jyIKJ4Uwb\nFWg3m2wIIaxPRmuLPhmofS6tamTzkTz2nSyiXW/A3UXH3PGhzB4birOj6X/mHah9tgbptWVIn2Uq\nlTCjgd7nmvoWtid3TMNqatHjqFMze2wIcycMwtPVwWTHGeh9tiTptWVIn2UqlRBm4+HqwJKZUSRO\nDGf38QK2Hs1j0+FctiXlMXVUEAsSwgjwdrZ2mUKIfkbCWQgTcHbUkDgpnOvHh3LgdDGbDuey+3gh\ne44XEj/Mn4WTwogIdLd2mUKIfkLCWQgT0mrUzBwTwvS4YJLTy9h4MIeks6UknS1lRIQXiZPCiQ33\nkmlYQohuSTgLYQYqlcKEYf6MH+rHmZwqNh7MITW7itTsKiIC3Vg4KZxxMX6oVBLSQojvk3AWwowU\nRWFEhDcjIry5UFTLxkM5pJwr4811pwnwdiZxYhiTRwSi1chcaSHEtySchbCQyCB3Hlw8iqKKBrYc\nyWX/qWI+2HSWdXvPM29CGDPHBOPkIH8lhRAylUr0kfS596rqWth2NI+dxwtoadXj7KDhuvgQro8f\nhLuL7pLXSp8tR3ptGdJnmUolhE3ycnPg9uuiuWFKODtSCtielMdXB3LYciSPaXEd07D8PJ2sXaYQ\nwgoknIWwMhdHLTdNiWD+hEHsO1XE5sO57EwpYPexQhJi/UmcFN7tT9hCCPsj4SyEjdBp1Vw3LpSZ\nY4I5eraUjQdzOXSmhENnShg3zJ9REV7ERnjjL2fTQtg9CWchbIxapWLS8EAmxgZw6nxlxwjvs6Wk\nnC0FwNfDkdhwL2IjvIgN98bjsvvTQoj+T8JZCBulKApxUT7ERfnQYoR9Kfmk5VRxNqeKvSeL2Huy\nCIAQPxdiw70YHu7N0DBPGfEthB2Qv8VC9AOh/m7MiQ9lTnwoBoORnJI60nKqSMuuJCO/hoKyBrYn\n5aNSFCKD3BgW7sXwcC+iQz1kK0sh+iEJZyH6GZVKITLIncggdxZOCqet3cD5whrOZFeRllPF+cJa\nsgpr2XAwB61GRXSIB8MvXgKPCHSTVcmE6AcknIXo57QaFUPDvBga5sVioKmlnfS8atJyqjoDOy2n\nCjiPk4OGYWGenWfWwb4uss63EDZIwlkIO+PkoGF0tC+jo30BqG1o5WzuxZDOruJYRjnHMsoB8HDR\ndQwuuzjAzNdDRoILYSdx/8sAABFgSURBVAsknIWwc+4uOhJiA0iIDQCgvKaJtItn1GdyqjqnawH4\nezpdHAXuxbBwL9ydZSS4ENYg4SzEAOPr4cT00U5MHx2M0WiksLyBMxfPqs/lVbH7eCG7jxcCEOrn\nevF+tRcxg2QkuBCWIn/ThBjAFEUhxM+VED9X5o4fhN5gIKe4nrScSs5kV5FZUEN+WT1bj+ahvjgQ\nLTbci+ERXgwO9pDdtIQwEwlnIUQntUrF4GB3Bge7c8PkCNra9WTm13ScWedUkVVYQ2ZBDV8eyEan\nUTEk1IPYCG9iw70ID5CR4EKYioSzEKJLWo26I3wjvAFobO4YCX4mp5K0nCpSszt+Abg4ahga5tV5\nZh3o7SwjwYXoJQlnIUSPOTtqGDPElzFDOkaC1zS0kpZT2TnALCW9jJT0MgA8XXUMCfUkKsSD6BAP\nwgJc0ajlMrgQPSHhLIToNQ8XHZOGBzJpeCAApdVNnM2p4kx2JWdzqzl6tpSjF9cE12pURAS6ER3i\nQdTFX7IuuBBXJuEshDAZf08n/D2dmHFxJHhZTTNZ+TVkFtZ0/Leghoz8ms7X+3k6En3xzDoqxIMQ\nPxfUKjm7FkLCWQhhFoqidIb15JEdZ9ZNLe1kF9WSWVBDVmEtWQU1HEwt4WBqxzxrB52awUHuFy+F\nuzM42ANXJ601vwwhrELCWQhhMU4OmksGmBmMRkoqG/9/e3caG3X173H8Pe20dJmt+97SBeUWUBC5\nCoJ6I+o/mkgEtYhUfWJiiA80aCQoosGYlMTECAT3hNQYquAaFdSrGLwWlSsgNEK3+beddlpau8y0\npUDbuQ9+7VD0oii08+vweSV9wGSmc+bXhk/P+Z3z/VI7Mquua/GNKTdqyEiKC963LsxykpEUR4Q2\nmkmYUziLSMhEWCxkJMWTkRTPoiszAegbOE19i49aTw91LUZge3/x8t1Ii8y4KVYKshzBsC7IcKg4\nioQd/UaLiKnEx0QxqyCJWQVJAAwPB2ju6KO2uScY2EfqOzlS3wmAxQJZyTaKsp0UZjooynaS6orV\nMS6Z1BTOImJqEREWclJt5KTa+K85WYDRzGO0IEqdpwd3qx9Pey97DjQDYI+LojDTGQzsqRkOpkSp\nr7VMHgpnEZl0HPHRzJmWwpxpKQAMDg3TdLzXCOuRr4O1HRysNbpvRY4EfFHWaGA7SXRM0exaTEvh\nLCKTnjUygvwMB/kZDm6+OgeATt9AcEd4bXMPDa1+/t3q56v/9QCQYJ9iLIOP3LvOTbOrVriYhsJZ\nRMJSoiOGREcM86anAnB6cIh/t/qpazaOctU297D/WDv7jxkVzayRRpGUmUXJpLtiKMhwkOSM0exa\nQkLhLCKXhChrJNOyXUzLdgEQCATo6BkILoXXNvcYu8SbzxRJccRFGTPykWYg+RkO4mN07lrGn8JZ\nRC5JFouFFFcsKa5Y5s8wiqScPDVE98AgB35to76lB7fXx6G63zhU91vwdWkJscGgLsh0kpNq03K4\nXHQKZxGREVOiI5mZ5SLNMSX4WE/vSeq9PtxeH/UtPtxe/1lVzayRFnJS7RRkOMjPtFOQ6SQ1IVaF\nUuSCKJxFRP6E0zblrJ3ho1XNjKA2AruxzY/b64OfjdfETbGSH5xdOyjIcOBQkw/5GxTOIiJ/w9iq\nZtfNygCMzWaNbb3GDLvFR73XR5W7kyp3Z/B1SY6YMcvhDvLS7Tp7LeekcBYRuUBR1shgG8xRvSdO\n4x4T1vUtvrNaaEZYLGSnxBubzUY2nWUmxRMRoeVwUTiLiIwLW+zZZUhHW2i6xyyHN7T5aTzey7cH\nWwDjnnd+uj04u87PcJDoiAnlx5AQGddwrq6uZtWqVTz44IOsXLlyPN9KRMTUxrbQvKY4DTAqmzW3\n943MrHtwe/0ca+zmaGN38HUuW/RZ966nqtHHJWHcfsL9/f1s2LCB+fPnj9dbiIhMatbICPLS7eSl\n24N1w0d7Xo8uhdd7fRyo6eBAjVGK1AJkJMeTn2HsDC/IcJCVEo81Use5wsm4hXN0dDSvv/46r7/+\n+ni9hYhI2Pl9z2swSpG6RwLb3eLD3eqnpaOP/zncChjHubJSbOSl2clLs5GbbicnxUa0NpxNWuMW\nzlarFav1/L99QkIcVuvF/UVKSbFf1O8n/z9d54mh6zxxzHatU1LsXF6YEvz30HAAT5uf6sYujjV2\nUdfcQ4PXR0OrP/ic0W5ehdkuCkZ7X2c5iTNRhTOzXWczMc2Ni66u/ov6/VJS7LS3+//6iXJBdJ0n\nhq7zxJks1zrOamF2QSKzC4wZ9uDQMN7f+mlo9Rsbzdr8NLb10tDq5+v9TcHXpSXEkptmLKXnpdnJ\nTbNhj5v4M9iT5TqPpz/748Q04SwiIv+cNTIi2Pd6Icb56+HhAG1d/UZYt/YGQ3vskS6ARMeUkSVx\nO7kjoe2yRavpRwgpnEVEwlRExJmCKdcWG48FAgF+6xmgoc1PQ1svjW1GK82xm87AaPoxGtR5IzPt\nZHXpmjDjFs5HjhyhrKyM5uZmrFYru3fvZtOmTbhcrvF6SxER+QsWi4VkVyzJrljmXp4afLy79ySN\nbf6RZXFjOfxIfSdH6s9UOYubYiU3zTZmSdxOemKcCqeMA0sgEAiEehDARb/3oPsZE0PXeWLoOk8c\nXeszek+cNgJ7TGi3dZ69Pyg6KoLc1NElcWPHeGbyXx/t0nXWPWcREfkHbLFRFE9NpHjMsa4TJwdp\nOj5y/3pk89nv+2DraNeFUziLiMh5i51i5bIcF5flnLlFeer0EM0dfcGd4g2tfjztfWcf7bJYyEiO\nM2bZ6XbmFqfjjIlU8ZRzUDiLiMgFiY6KJD/DqAU+6lxHu5rb+6isamX7f9cQZY2gIMPBtBwXl+U4\nKcx0qjTpCF0FERG56P70aFern+bOExyqbqe6qZtjTUYtcYsFclPtTMtxclm2i2k5LpyXaB9shbOI\niEyIsUe7RjeE9Q+cpra5h+qmHmo83bi9Rreur/Z7AKNoyrRslxHYOS5SXbGXxHEuhbOIiIRMXEwU\nVxQmc0VhMgCnB4dwe/3UeLqpbuqhtrmb7w57+e6wFwBnfDTTsp3GUni2i5xUW1ge5VI4i4iIaURZ\nI4Mbzm6fbyyFe9p7qfH0UN3UTbWnm/3H2tl/rB2AmOhIirKcTMs2Ztb5GY6w2BWucBYREdOKiLCQ\nO1Lw5Ka52QQCAdp7Bqhp6qa6qZsaTw9H3J0ccRvFUiIjLEzNsAfvWRdlObHFmqfZx/lSOIuIyKRh\nsVhIdcWS6orlulnGRjNf3ylqPN3B2bW7xU9ds4/Pf2gEICslfiSsjY1miY6YUH6E86JwFhGRSc0R\nH83cy1OD5UgHTg1S1+ILzq7rW3w0t/fxzYFmAJIcMVyW4xzZaOYiMynOdJvMFM4iIhJWYqKtzJia\nyIypZ9ppNrT5qRnZEV7j6aGyqo3KqjbAqIRWlGXcs56W4yQvzR7y4igKZxERCWvWyAgKM40iJ/+6\nJpfhQADvb/1GUDcZu8IP1nZwsNboyhVtjaAg02GEdbaLwiwHMdETG5cKZxERuaREWCxkJceTlRzP\njbOzAOj0DVDt6aamqYdqTzdHG42v0efnptn41zW5/Od/pE3IGBXOIiJyyUt0xHBtcTrXFqcD0Ddw\nmhpPDzUjO8LdXh8HazsUziIiIqESHxPF7KJkZhcZxVEGh4aJnMBiJwpnERGRvzDRG8TUq0tERMRk\nFM4iIiImo3AWERExGYWziIiIySicRURETEbhLCIiYjIKZxEREZNROIuIiJiMwllERMRkFM4iIiIm\no3AWERExGUsgEAiEehAiIiJyhmbOIiIiJqNwFhERMRmFs4iIiMkonEVERExG4SwiImIyCmcRERGT\nCctwfuGFFygpKWH58uX88ssvoR5O2Nq4cSMlJSUsW7aML774ItTDCWsDAwMsXryY999/P9RDCVsf\nf/wxd9xxB0uXLmXPnj2hHk5Y6uvr45FHHqG0tJTly5ezd+/eUA/JtKyhHsDF9uOPP9LQ0EBFRQV1\ndXWsXbuWioqKUA8r7Ozbt4+amhoqKiro6urizjvv5JZbbgn1sMLW1q1bcTqdoR5G2Orq6mLLli3s\n3LmT/v5+Nm3axI033hjqYYWdDz74gPz8fFavXk1bWxsPPPAAu3btCvWwTCnswrmyspLFixcDUFhY\nSE9PD729vdhsthCPLLzMmzePK664AgCHw8GJEycYGhoiMjIyxCMLP3V1ddTW1iosxlFlZSXz58/H\nZrNhs9nYsGFDqIcUlhISEjh27BgAPp+PhISEEI/IvMJuWbujo+OsH3hiYiLt7e0hHFF4ioyMJC4u\nDoAdO3Zw/fXXK5jHSVlZGWvWrAn1MMKax+NhYGCAhx9+mBUrVlBZWRnqIYWl22+/nZaWFm6++WZW\nrlzJk08+GeohmVbYzZx/T9VJx9dXX33Fjh07eOutt0I9lLD04YcfMnv2bHJyckI9lLDX3d3N5s2b\naWlp4f777+ebb77BYrGEelhh5aOPPiIzM5M333yTo0ePsnbtWu2jOIewC+fU1FQ6OjqC/z5+/Dgp\nKSkhHFH42rt3L6+88gpvvPEGdrs91MMJS3v27KGpqYk9e/bQ2tpKdHQ06enpLFiwINRDCytJSUnM\nmTMHq9VKbm4u8fHxdHZ2kpSUFOqhhZWff/6ZhQsXAjB9+nSOHz+u22HnEHbL2tdddx27d+8GoKqq\nitTUVN1vHgd+v5+NGzfy6quv4nK5Qj2csPXSSy+xc+dO3n33Xe6++25WrVqlYB4HCxcuZN++fQwP\nD9PV1UV/f7/uh46DvLw8Dh06BEBzczPx8fEK5nMIu5nzVVddxYwZM1i+fDkWi4X169eHekhh6bPP\nPqOrq4tHH300+FhZWRmZmZkhHJXIP5OWlsatt97KPffcA8DTTz9NRETYzV1CrqSkhLVr17Jy5UoG\nBwd59tlnQz0k01LLSBEREZPRn4YiIiImo3AWERExGYWziIiIySicRURETEbhLCIiYjIKZ5FJyuPx\nMHPmTEpLS4NdflavXo3P5zvv71FaWsrQ0NB5P//ee+/lhx9++CfDFZG/QeEsMoklJiZSXl5OeXk5\n27dvJzU1la1bt57368vLy1UEQsSEwq4IicilbN68eVRUVHD06FHKysoYHBzk9OnTPPPMMxQXF1Na\nWsr06dP59ddf2bZtG8XFxVRVVXHq1CnWrVtHa2srg4ODLFmyhBUrVnDixAkee+wxurq6yMvL4+TJ\nkwC0tbXx+OOPA0av6ZKSEu66665QfnSRsKJwFgkTQ0NDfPnll8ydO5cnnniCLVu2kJub+4cGA3Fx\ncbz99ttnvba8vByHw8GLL77IwMAAt912G4sWLeL7778nJiaGiooKjh8/zk033QTA559/TkFBAc89\n9xwnT57kvffem/DPKxLOFM4ik1hnZyelpaUADA8Pc/XVV7Ns2TJefvllnnrqqeDzent7GR4eBowS\nt7936NAhli5dCkBMTAwzZ86kqqqK6upq5s6dCxhNZQoKCgBYtGgR77zzDmvWrOGGG26gpKRkXD+n\nyKVG4SwyiY3ecx7L7/cTFRX1h8dHRUVF/eGx37dGDAQCWCwWAoHAWTWmRwO+sLCQTz/9lJ9++old\nu3axbds2tm/ffqEfR0RGaEOYSJix2+1kZ2fz7bffAuB2u9m8efOfvubKK69k7969APT391NVVcWM\nGTMoLCzkwIEDAHi9XtxuNwCffPIJhw8fZsGCBaxfvx6v18vg4OA4fiqRS4tmziJhqKysjOeff57X\nXnuNwcFB1qxZ86fPLy0tZd26ddx3332cOnWKVatWkZ2dzZIlS/j6669ZsWIF2dnZzJo1C4CioiLW\nr19PdHQ0gUCAhx56CKtV/52IXCzqSiUiImIyWtYWERExGYWziIiIySicRURETEbhLCIiYjIKZxER\nEZNROIuIiJiMwllERMRkFM4iIiIm83/auJWqg6ASogAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PjxzliIgsklaGtngeCRGRaskXJImJiTh27BgEQUB0dDR8fX2Nz/322294+eWXUVdXh3vu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\nEUny/7J+UG6z9DfrAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TOfmiSvqu8U9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "evlB5ubzu8VJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "8b9deb62-7dbd-44c4-c370-2cabf55f24e1"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          1       2       3       4       5       6       7       8       9    \\\n",
+              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          10    ...       775     776     777     778     779     780     781  \\\n",
+              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          782     783     784  \n",
+              "count 10000.0 10000.0 10000.0  \n",
+              "mean      0.0     0.0     0.0  \n",
+              "std       0.0     0.0     0.0  \n",
+              "min       0.0     0.0     0.0  \n",
+              "25%       0.0     0.0     0.0  \n",
+              "50%       0.0     0.0     0.0  \n",
+              "75%       0.0     0.0     0.0  \n",
+              "max       0.0     0.0     0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PDuLd2Hcu8VL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "29065944-6d0d-42d0-ea92-37a7483c8b75"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.95\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6sfw3LH0Oycm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XatDGFKEO374",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code below is almost identical to the original `LinearClassifer` training code, with the exception of the NN-specific configuration, such as the hyperparameter for hidden units."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kdNTx8jkPQUx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, as well as a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `DNNClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  # Caution: input pipelines are reset with each call to train. \n",
+        "  # If the number of steps is small, your model may never see most of the data.  \n",
+        "  # So with multiple `.train` calls like this you may want to control the length \n",
+        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  # Create a DNNClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.DNNClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      n_classes=10,\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZfzsTYGPPU8I",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_nn_classification_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    hidden_units=[100, 100],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qXvrOgtUR-zD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we verify the accuracy on the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scQNpDePSFjt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "EVaWpWKvSHmu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_test_input_fn = create_predict_input_fn(\n",
+        "    test_examples, test_targets, batch_size=100)\n",
+        "\n",
+        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+        "  \n",
+        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+        "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "WX2mQBAEcisO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Visualize the weights of the first hidden layer.\n",
+        "\n",
+        "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
+        "\n",
+        "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
+        "\n",
+        "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eUC0Z8nbafgG",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1172
+        },
+        "outputId": "d3279422-3a86-4986-9136-39575a3abc76"
+      },
+      "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": 16,
+      "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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3vXoL5b162+tWvPIW2Bn3dKLthyfz/mbaEzdY8WcPvmTFM26ZSnlNYi7f9R7q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bfU55pxjSmx+i8C6fQXgY5DiITqasCYz71Ov/Vv+9t+rzz/Ewu98u6nd6t6i+9e7JX5jXjzpipV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\nNbnv/RuiX9FtMpUznmTWQQL0wb8eEX9jBySWprW8Iyv1Fy1F6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ZwEXHmYB+Eeq6I/pcwf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Cjm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\nXwvq0co70H7kyhmyFcR/3nOrE3tzMP9ef+pDkTdpC+r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KTshTPw9ZU08DKF39LZIGNeku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QDRH2cURVEURVEURVEURVEGkPPKmricMDhbuq54+H67o5JTouOXhHK2sx+jRCx+UYbIaylA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O6rk5NCB7Wg1nT73il9BXJmXVKPFZVBLewcKoZ8rFt6z+jtS/lHuCZuXKGtVwGPMicy3u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\nrfoQuvv4SGj0BnqkVoxteV2J0Kgt6pW9RVjnxzatEda1u6j/CseDA1K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Gp/U85Ix1ixHz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WS/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\nVfN3UNMkf5OutTF2E+OgaCM0+DOO603rz6Afn/8b0IJH1upaIB2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th+pFP55bHN3JkZnG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pQDifQKom8v+KqUYKWfxtzkFL3YhfL71VGKVdY/BnYtDjZgDo77Zeogr01OXgq2ZE3xhTij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\nSneedTajJEHZPjz3LJql52LOzbg3O/8RsXvTl7VL1PFH8ByU8WPtY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ymdduPoV03/SFWhZy5qk3vXbWUqReNmzW8qeGLUh7TCWpFcsRGrfodDHfEFI0s5ciNZKr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\nVqbQWjg7i1gezFqp+vV3HvbayQE9Zn7LcI+W26RlQ9aaMQ9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7XJZjpEWPt5WfwGexVJKl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\n",
+            "text/plain": [
+              "<Figure size 1440x1440 with 100 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kL8MEhNgrx9N",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits.  You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n",
+        "\n",
+        "It can be interesting to stop training at different numbers of iterations and see the effect.\n",
+        "\n",
+        "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n",
+        "\n",
+        "What differences do you see visually for the different levels of convergence?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
new file mode 100644
index 0000000..ca9e17d
--- /dev/null
+++ b/feature_crosses.ipynb
@@ -0,0 +1,1657 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "feature_crosses.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/feature_crosses.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "--AKWgDykiUU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Feature Crosses"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F7dke6skIK-k",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Improve a linear regression model with the addition of additional synthetic features (this is a continuation of the previous exercise)\n",
+        "  * Use an input function to convert pandas `DataFrame` objects to `Tensors` and invoke the input function in `fit()` and `predict()` operations\n",
+        "  * Use the FTRL optimization algorithm for model training\n",
+        "  * Create new synthetic features through one-hot encoding, binning, and feature crosses"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NS_fcQRd8B97",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4IdzD8IdIK-l",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, as we've done in previous exercises, let's define the input and create the data-loading code."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CsfdiLiDIK-n",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "10rhoflKIK-s",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ufplEkjN8KUp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "41b2ae6e-5a0d-4841-d213-8bca4847d64f"
+      },
+      "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/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2638.8</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1428.7</td>\n",
+              "      <td>500.8</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>12.5</td>\n",
+              "      <td>2172.0</td>\n",
+              "      <td>423.2</td>\n",
+              "      <td>1128.3</td>\n",
+              "      <td>385.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1452.0</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>788.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2120.0</td>\n",
+              "      <td>432.0</td>\n",
+              "      <td>1164.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3141.0</td>\n",
+              "      <td>649.0</td>\n",
+              "      <td>1723.2</td>\n",
+              "      <td>605.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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       2638.8           539.4   \n",
+              "std         2.1        2.0                12.5       2172.0           423.2   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1452.0           295.0   \n",
+              "50%        34.3     -118.5                29.0       2120.0           432.0   \n",
+              "75%        37.7     -118.0                37.0       3141.0           649.0   \n",
+              "max        42.0     -114.5                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1428.7       500.8            3.9               2.0  \n",
+              "std        1128.3       385.0            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         788.0       280.0            2.6               1.5  \n",
+              "50%        1164.0       408.0            3.5               1.9  \n",
+              "75%        1723.2       605.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              52.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.4</td>\n",
+              "      <td>2655.4</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1431.7</td>\n",
+              "      <td>502.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.7</td>\n",
+              "      <td>2199.1</td>\n",
+              "      <td>417.4</td>\n",
+              "      <td>1193.6</td>\n",
+              "      <td>383.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.7</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1484.0</td>\n",
+              "      <td>301.0</td>\n",
+              "      <td>794.0</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2151.0</td>\n",
+              "      <td>439.0</td>\n",
+              "      <td>1172.0</td>\n",
+              "      <td>412.0</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>3173.0</td>\n",
+              "      <td>648.0</td>\n",
+              "      <td>1718.0</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>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5050.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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                28.4       2655.4           539.4   \n",
+              "std         2.1        2.0                12.7       2199.1           417.4   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.7                18.0       1484.0           301.0   \n",
+              "50%        34.2     -118.5                29.0       2151.0           439.0   \n",
+              "75%        37.7     -118.0                37.0       3173.0           648.0   \n",
+              "max        41.9     -114.3                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       1431.7       502.2            3.9               2.0  \n",
+              "std        1193.6       383.4            1.9               1.3  \n",
+              "min           6.0         2.0            0.5               0.1  \n",
+              "25%         794.0       284.0            2.6               1.5  \n",
+              "50%        1172.0       412.0            3.5               2.0  \n",
+              "75%        1718.0       607.0            4.8               2.3  \n",
+              "max       35682.0      5050.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>206.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.2</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>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                206.7\n",
+              "std                 116.2\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 179.2\n",
+              "75%                 264.6\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>208.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>122.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>182.1</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>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                208.9\n",
+              "std                 115.5\n",
+              "min                  15.0\n",
+              "25%                 122.5\n",
+              "50%                 182.1\n",
+              "75%                 266.0\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oJlrB4rJ_2Ma",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NBxoAfp2AcB6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hweDyy31LBsV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## FTRL Optimization Algorithm\n",
+        "\n",
+        "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S0SBf1X1IK_O",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "1Cdr02tLIK_Q",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "7e04e30b-0cd8-450d-9c35-2d05a7db89f5"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(training_examples),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 208.36\n",
+            "  period 01 : 133.16\n",
+            "  period 02 : 111.59\n",
+            "  period 03 : 287.88\n",
+            "  period 04 : 277.27\n",
+            "  period 05 : 303.94\n",
+            "  period 06 : 290.74\n",
+            "  period 07 : 250.07\n",
+            "  period 08 : 248.70\n",
+            "  period 09 : 252.15\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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rnnvuWbKyMs/7/P3331NfIdU7SWCEEEKIJiQ7O4u1a7+p1Wvvumse8fEtz/v8U08trK+w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\nRETE7/x/loNB7BzF3tEAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i4lGvqajDWlw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## One-Hot Encoding for Discrete Features\n",
+        "\n",
+        "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n",
+        "\n",
+        "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n",
+        "\n",
+        "| # | feature_value |\n",
+        "|---|---------------|\n",
+        "| 0 |             2 |\n",
+        "| 1 |             0 |\n",
+        "| 2 |             1 |\n",
+        "\n",
+        "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n",
+        "\n",
+        "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n",
+        "|---|-----------------|-----------------|-----------------|\n",
+        "| 0 |             0.0 |             0.0 |             1.0 |\n",
+        "| 1 |             1.0 |             0.0 |             0.0 |\n",
+        "| 2 |             0.0 |             1.0 |             0.0 |"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KnssXowblKm7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Bucketized (Binned) Features\n",
+        "\n",
+        "Bucketization is also known as binning.\n",
+        "\n",
+        "We can bucketize `population` into the following 3 buckets (for instance):\n",
+        "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n",
+        "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n",
+        "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n",
+        "\n",
+        "Given the preceding bucket definitions, the following `population` vector:\n",
+        "\n",
+        "    [[10001], [42004], [2500], [18000]]\n",
+        "\n",
+        "becomes the following bucketized feature vector:\n",
+        "\n",
+        "    [[1], [2], [0], [1]]\n",
+        "\n",
+        "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n",
+        "\n",
+        "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cc9qZrtRy-ED",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_boundaries(feature_values, num_buckets):\n",
+        "  boundaries = np.arange(1.0, num_buckets) / num_buckets\n",
+        "  quantiles = feature_values.quantile(boundaries)\n",
+        "  return [quantiles[q] for q in quantiles.keys()]\n",
+        "\n",
+        "# Divide households into 7 buckets.\n",
+        "households = tf.feature_column.numeric_column(\"households\")\n",
+        "bucketized_households = tf.feature_column.bucketized_column(\n",
+        "  households, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"households\"], 7))\n",
+        "\n",
+        "# Divide longitude into 10 buckets.\n",
+        "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "  longitude, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"longitude\"], 10))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U-pQDAa0MeN3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train the Model on Bucketized Feature Columns\n",
+        "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n",
+        "\n",
+        "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YFXV9lyMLedy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0FfUytOTNJhL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "808d561d-423a-40fd-f8ca-0d4df11b8d87"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.28\n",
+            "  period 01 : 143.04\n",
+            "  period 02 : 126.62\n",
+            "  period 03 : 115.45\n",
+            "  period 04 : 107.61\n",
+            "  period 05 : 101.72\n",
+            "  period 06 : 97.24\n",
+            "  period 07 : 93.63\n",
+            "  period 08 : 90.68\n",
+            "  period 09 : 88.29\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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owIiIiDQz69evqdPzXnvtHxw9mnnJx5988tH6KqneKcCIiIg0I8eOHWX16lV1eu5DDz1G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\n/wO0+h6ssJPvegAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZTDHHM61NPTw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JQHnUhL_NRwA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ro5civQ3Ngh_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RNgfYk6OO8Sy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "7842f7d9-c64a-4341-8646-59a030316b5a"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.23\n",
+            "  period 01 : 143.04\n",
+            "  period 02 : 126.63\n",
+            "  period 03 : 115.47\n",
+            "  period 04 : 107.48\n",
+            "  period 05 : 101.55\n",
+            "  period 06 : 97.05\n",
+            "  period 07 : 93.45\n",
+            "  period 08 : 90.55\n",
+            "  period 09 : 88.13\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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kzfGNZscSERFpEFRgTDSiR0u83Z04lOyPu92db46t5VxZkdmxREREHJ4KjInc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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AFJ1qoZPlQcs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Feature Crosses\n",
+        "\n",
+        "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n",
+        "\n",
+        "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n",
+        "\n",
+        "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-Rk0c1oTYaVH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train the Model Using Feature Crosses\n",
+        "\n",
+        "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n",
+        "\n",
+        "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-eYiVEGeYhUi",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xZuZMp3EShkM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "7fef1e57-e1a0-40a6-fadb-72ae094fd983"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 162.83\n",
+            "  period 01 : 134.65\n",
+            "  period 02 : 117.59\n",
+            "  period 03 : 106.32\n",
+            "  period 04 : 98.34\n",
+            "  period 05 : 92.33\n",
+            "  period 06 : 87.72\n",
+            "  period 07 : 84.22\n",
+            "  period 08 : 81.30\n",
+            "  period 09 : 78.99\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TB4Xi6uxI3uEOACzXLIyIiMhlUYExQStXR2IGtac42xNfSzsSc49wKOeI2bFE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\n7yjAiIiISN5RgBEREZG8839J0w/hPR05fAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0i7vGo9PTaZl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3tAWu8qSTe2v",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-_vvNYIyTtPC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "42a1a94f-2e97-4389-b661-da2b52dc6d30"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 162.93\n",
+            "  period 01 : 134.84\n",
+            "  period 02 : 117.78\n",
+            "  period 03 : 106.43\n",
+            "  period 04 : 98.34\n",
+            "  period 05 : 92.42\n",
+            "  period 06 : 87.74\n",
+            "  period 07 : 84.17\n",
+            "  period 08 : 81.28\n",
+            "  period 09 : 78.91\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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EpM5RgREREaln1q5dVa37vfnmaxw/nnzR2x9//OFrFemaU4ERERGpR1JSjrNy5fJq3XfG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\nJOMowIiIiEjGUYARERGRjPP/TP/jl4sWG88AAAAASUVORK5CYII=\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..bfce63e
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1590 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "feature_sets.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/feature_sets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zbIgBK-oXHO7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Feature Sets"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bL04rAQwH3pH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F8Hci6tAH3pH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F5ZjVwK_qOyR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As before, let's load and prepare the California housing data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SrOYRILAH3pJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dGnXo7flH3pM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jLXC8y4AqsIy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "0adfc8d0-749e-434c-9424-c3eeccda95b7"
+      },
+      "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/html": [
+              "<div>\n",
+              "<style scoped>\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>2654.6</td>\n",
+              "      <td>541.2</td>\n",
+              "      <td>1437.0</td>\n",
+              "      <td>503.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2187.5</td>\n",
+              "      <td>421.1</td>\n",
+              "      <td>1149.9</td>\n",
+              "      <td>385.2</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>298.0</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>2137.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1169.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3167.0</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1734.2</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>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       35.6     -119.6                28.6       2654.6           541.2   \n",
+              "std         2.1        2.0                12.6       2187.5           421.1   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1465.0           298.0   \n",
+              "50%        34.2     -118.5                29.0       2137.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3167.0           652.0   \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       1437.0       503.4            3.9               2.0  \n",
+              "std        1149.9       385.2            1.9               1.2  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         792.0       282.0            2.6               1.5  \n",
+              "50%        1169.0       409.0            3.5               1.9  \n",
+              "75%        1734.2       607.0            4.7               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2617.4</td>\n",
+              "      <td>535.1</td>\n",
+              "      <td>1411.7</td>\n",
+              "      <td>496.1</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>2161.7</td>\n",
+              "      <td>422.5</td>\n",
+              "      <td>1142.7</td>\n",
+              "      <td>382.9</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1452.5</td>\n",
+              "      <td>294.0</td>\n",
+              "      <td>780.8</td>\n",
+              "      <td>279.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2109.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1164.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3111.2</td>\n",
+              "      <td>640.2</td>\n",
+              "      <td>1697.2</td>\n",
+              "      <td>597.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>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>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.5       2617.4           535.1   \n",
+              "std         2.1        2.0                12.6       2161.7           422.5   \n",
+              "min        32.6     -124.3                 2.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1452.5           294.0   \n",
+              "50%        34.2     -118.5                28.0       2109.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3111.2           640.2   \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       1411.7       496.1            3.9               2.0  \n",
+              "std        1142.7       382.9            1.9               1.2  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         780.8       279.0            2.6               1.5  \n",
+              "50%        1164.0       408.0            3.6               1.9  \n",
+              "75%        1697.2       597.2            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              34.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>206.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>178.8</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>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                206.5\n",
+              "std                 116.0\n",
+              "min                  15.0\n",
+              "25%                 119.0\n",
+              "50%                 178.8\n",
+              "75%                 263.8\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>209.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>17.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>183.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>267.3</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                209.2\n",
+              "std                 116.0\n",
+              "min                  17.5\n",
+              "25%                 120.8\n",
+              "50%                 183.7\n",
+              "75%                 267.3\n",
+              "max                 500.0"
+            ]
+          },
+          "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": "b9bb9ecb-a4a1-4310-bdc1-17a882ff171e"
+      },
+      "cell_type": "code",
+      "source": [
+        "correlation_dataframe = training_examples.copy()\n",
+        "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n",
+        "\n",
+        "correlation_dataframe.corr()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>-0.2</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                  -0.2       -0.0                 0.1          0.1   \n",
+              "\n",
+              "                    total_bedrooms  population  households  median_income  \\\n",
+              "latitude                      -0.1        -0.1        -0.1           -0.1   \n",
+              "longitude                      0.1         0.1         0.1           -0.0   \n",
+              "housing_median_age            -0.3        -0.3        -0.3           -0.1   \n",
+              "total_rooms                    0.9         0.9         0.9            0.2   \n",
+              "total_bedrooms                 1.0         0.9         1.0           -0.0   \n",
+              "population                     0.9         1.0         0.9            0.0   \n",
+              "households                     1.0         0.9         1.0            0.0   \n",
+              "median_income                 -0.0         0.0         0.0            1.0   \n",
+              "rooms_per_person               0.0        -0.1        -0.0            0.2   \n",
+              "target                         0.0        -0.0         0.1            0.7   \n",
+              "\n",
+              "                    rooms_per_person  target  \n",
+              "latitude                         0.1    -0.2  \n",
+              "longitude                       -0.1    -0.0  \n",
+              "housing_median_age              -0.1     0.1  \n",
+              "total_rooms                      0.1     0.1  \n",
+              "total_bedrooms                   0.0     0.0  \n",
+              "population                      -0.1    -0.0  \n",
+              "households                      -0.0     0.1  \n",
+              "median_income                    0.2     0.7  \n",
+              "rooms_per_person                 1.0     0.2  \n",
+              "target                           0.2     1.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RQpktkNpia2P",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n",
+        "\n",
+        "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n",
+        "\n",
+        "Use this information to try removing features.  You can also try developing additional synthetic features, such as ratios of two raw features.\n",
+        "\n",
+        "For convenience, we've included the training code from the previous exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bjR5jWpFr2xs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jsvKHzRciH9T",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        "    \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "\n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g3kjQV9WH3pb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "varLu7RNH3pf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DSgUxRIlH3pg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 639
+        },
+        "outputId": "10d2b092-5a8f-4568-d93b-4a86e249f950"
+      },
+      "cell_type": "code",
+      "source": [
+        "minimal_features = [\"median_income\",\"longitude\"]\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 116.17\n",
+            "  period 01 : 115.47\n",
+            "  period 02 : 118.32\n",
+            "  period 03 : 116.56\n",
+            "  period 04 : 114.72\n",
+            "  period 05 : 114.82\n",
+            "  period 06 : 115.58\n",
+            "  period 07 : 114.24\n",
+            "  period 08 : 115.80\n",
+            "  period 09 : 115.24\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tensorflow_estimator.python.estimator.canned.linear.LinearRegressor at 0x7f92b483a048>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hhBCOJQmM6HONhkYKa4sZ6DsAV42LxWvO5lSiKDAmLtDi+Wnx4fh5u7L3ZDEN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\nIYQQwuJIAiOEEEIIiyMJjBBCCCEszv8HpvlGw5FGbekAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IGINhMIJ5Wyt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BAGoXFPZ5ZE3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "004f2491-f1e1-4f28-f132-7d76dbed1b6d"
+      },
+      "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": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 165.04\n",
+            "  period 01 : 121.28\n",
+            "  period 02 : 118.95\n",
+            "  period 03 : 116.46\n",
+            "  period 04 : 115.76\n",
+            "  period 05 : 115.18\n",
+            "  period 06 : 114.78\n",
+            "  period 07 : 115.10\n",
+            "  period 08 : 113.83\n",
+            "  period 09 : 112.99\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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iItIfFGCOktViZmS+i72VNkyYtKSAiIhIP1CA6QOFBW6MsJUMexZlvnLCkXC8\nSxIRERnUFGD6QMc4mJRINm2RIHsC1XGuSEREZHBTgOkDHQEm6Gv/t8SrhR1FRERiSQGmD2S5HbhT\n7NTtSQagxFsa54pEREQGNwWYPmAymSgscNNYm4TVZKXEpx4YERGRWFKA6SNjhrnBMJNhzaGiqYpg\nOBjvkkRERAYtBZg+UljQPv7FHswkYkQob6qMc0UiIiKDlwJMHxlV4MYEtDS0r0yt+WBERERiRwGm\njyQnWfFkp1BT6QB0J5KIiEgsKcD0odEeN61NDpLMSZT4dCeSiIhIrCjA9KH2+WBMpJlzqQ7UEgg2\nx7skERGRQUkBpg91DOQ1t7SvTF2q26lFRERiQgGmDw3LSSHJZqGpNgXQytQiIiKxEtMAs337ds4+\n+2xWr14NwA9/+EMuvPBCFi1axKJFi3jrrbcAePHFF5k/fz6XXnopa9asiWVJMWUxmxmV76K2qmMg\nrwKMiIhILFhjdeBAIMCyZcuYMWNGl+033XQTs2bN6vK8Rx55hGeeeQabzcaCBQs455xzSE9Pj1Vp\nMVXocbOtrB6nJVUz8oqIiMRIzHpg7HY7K1euJDc3t9vnbdq0icmTJ+NyuXA4HJx44okUFxfHqqyY\n6xjIm2pk09DaSENrY7xLEhERGXRiFmCsVisOh+OA7atXr+bqq6/mxhtvpK6ujtraWjIzM6P7MzMz\nqampiVVZMVfoSQMg4m//V5eRRERE+l7MLiEdzEUXXUR6ejrHHXccjz/+OA8//DDTpk3r8hzDMA57\nnIwMJ1arJVZlkpPjOqrXZqU5aKxxwgioCVUf1fGkK/0sE5PaJXGpbRKX2ubo9GuA6TweZvbs2fz0\npz9l7ty51NbWRrdXV1czderUbo9TXx+IWY05OS5qanxHdYxR+S4++NRL8gjYsmcHNQVHdzxp1xdt\nI31P7ZK41DaJS23TM92FvH69jfq6666jrKz9ksp7773Hsccey5QpU/jwww/xer34/X6Ki4s5+eST\n+7OsPlfocUPYjsuSTomvnIgRiXdJIiIig0rMemA2b97M8uXLqaiowGq1snbtWq666iq+973vkZyc\njNPp5O6778bhcLB06VIWL16MyWTi2muvxeUa2N1qHRPaOUJZ1Jh2UNO8jzxnTpyrEhERGTxiFmCO\nP/54Vq1adcD2uXPnHrCtqKiIoqKiWJXS70bluzGbTLR53ZDWPpBXAUZERKTvaCbeGEiyWxiWk8K+\nPZrQTkREJBYUYGKk0OMm6E3FhIkSrya0ExER6UsKMDFS6HGDYSHNkkV5UwXhSDjeJYmIiAwaCjAx\n0jGhnaUlk2AkRKV/T5wrEhGKONhyAAAgAElEQVQRGTwUYGKkIMtJcpKFQH37ytS7NQ5GRESkzyjA\nxIjZZGJUvpv6vU4AShVgRERE+owCTAwVetwYzSlYTVb1wIiIiPQhBZgYal+Z2kwq2VT599Iabot3\nSSIiIoOCAkwMdQzkJZCOgUGZryK+BYmIiAwSCjAxlJZiJ7tjZWo0oZ2IiEhfUYCJsUKPm+b6VEAB\nRkREpK8owMRYYYEbozUZu8mhgbwiIiJ9RAEmxtrHwZhwRrLZ11JHU5s/3iWJiIgMeAowMTYiLxWL\n2UTI5wagxKdeGBERkaOlABNjdpuF4bmpNFRrZWoREZG+ogDTD8Z43AR97bdUK8CIiIgcPQWYflDo\ncUMwiWRTKiXecgzDiHdJIiIiA5oCTD/omNDOHszCF2yirqUhzhWJiIgMbAow/SAvI5kUh/Wz+WA0\nkFdEROSoKMD0A5PJxOgCN75azcgrIiLSFxRg+kmhx03Er4G8IiIifUEBpp8UetIgYiWFDEp95USM\nSLxLEhERGbAUYPpJoad9IjtTSzqt4Tb2BmriXJGIiMjApQDTT1KTbeRmJNNUmwKgdZFERESOggJM\nPyr0uGlpdAFQqgAjIiJyxBRg+lFhgRsj4MKMWT0wIiIiR0EBph8VetLAMJNsZFLRVEUwEop3SSIi\nIgOSAkw/GpGXitViJtKURtgIU9FUGe+SREREBiQFmH5ktZgZmZcandBOl5FERESOjAJMPxvtcRPa\nvzJ1qbc8ztWIiIgMTAow/azQ48ZoScGKTT0wIiIiR0gBpp+1r0xtIimUSXWghuZQc7xLEhERGXAU\nYPpZTpqD1GQbrY1uDAxKvRXxLklERGTAUYDpZyaTiTEeN/669hl5S3y6jCQiItJbCjBxUOhxY2hl\nahERkSOmABMHhZ40jDYHNpIp0Z1IIiIivXbEAWb37t19WMbQMrrABZiwtmZQ39pAY6sv3iWJiIgM\nKN0GmK9+9atdHq9YsSL6/Y9//OPYVDQEOB02CrKcBOpSASjVOBgREZFe6TbAhEJd1+r55z//Gf3e\nMIzYVDREFBa4afO2r0yt+WBERER6p9sAYzKZujzuHFo+v096p9DjJqKBvCIiIkekV2NgFFr6TqEn\nDUJ2kgwXpd5y9WiJiIj0grW7nY2Njfzf//1f9LHX6+Wf//wnhmHg9XpjXtxgNjw3BbvVjOFPx59a\nRm1zHTnOrHiXJSIiMiB0G2DcbneXgbsul4tHHnkk+r0cOYvZzMh8F7vqnNhSocRbqgAjIiLSQ90G\nmFWrVvVXHUNSocfNjo/TASjxlXNy/rQ4VyQiIjIwdDsGpqmpiSeffDL6+I9//CMXXXQR119/PbW1\ntbGubdAr9KQRCbTPCaM7kURERHqu2wDz4x//mH379gGwa9cu7r//fm655RZOPfVU7rzzzsMefPv2\n7Zx99tmsXr26y/Z33nmH8ePHRx+/+OKLzJ8/n0svvZQ1a9YcyfsYkAoL3BCxkhROp8xXQTgSjndJ\nIiIiA0K3AaasrIylS5cCsHbtWoqKijj11FO57LLLDtsDEwgEWLZsGTNmzOiyvbW1lccff5ycnJzo\n8x555BGefPJJVq1axVNPPUVDQ8PRvKcBI9OdRFqqnZDPRTASpMq/N94liYiIDAjdBhin0xn9/v33\n3+eUU06JPj7cLdV2u52VK1eSm5vbZfuvf/1rrrjiCux2OwCbNm1i8uTJuFwuHA4HJ554IsXFxb1+\nIwORyWSisMBNc337gGjNByMiItIz3QaYcDjMvn37KC0tZePGjcycORMAv99Pc3Nztwe2Wq04HI4u\n23bt2sXWrVs577zzottqa2vJzMyMPs7MzKSmpqbXb2Sg6jKhnZYUEBER6ZFu70L6xje+wbx582hp\naWHJkiWkpaXR0tLCFVdcwcKFC3t9srvvvpvbbrut2+f0ZEK3jAwnVqul1+fvqZyc/rtF/MSJ+fz5\n7VTMWCgPVPbruQci/XwSk9olcaltEpfa5uh0G2DOPPNM1q9fT2trK6mp7QsPOhwOfvCDH3Daaaf1\n6kR79+5l586dfP/73wegurqaq666iuuuu67LeJrq6mqmTp3a7bHq6wO9Ondv5OS4qKnpv9Wh0x1W\nTIYZa1s6ZY2VVOzZh91i77fzDyT93TbSM2qXxKW2SVxqm57pLuR1G2AqKyuj33eeebewsJDKyko8\nHk+Pi8jLy+ONN96IPp49ezarV6+mpaWF2267Da/Xi8Viobi4mB/96Ec9Pu5Al5xkxZOTQm2DC3Pu\nPsp8lYxJHxXvskRERBJatwFm9uzZjB49OnrH0OcXc/zd7353yNdu3ryZ5cuXU1FRgdVqZe3atTz0\n0EOkp6d3eZ7D4WDp0qUsXrwYk8nEtddeO+Rm+R3jcbOn3I09t30cjAKMiIhI97oNMMuXL+eFF17A\n7/dz/vnnc8EFF3QZcNud448/vtuZfNetWxf9vqioiKKioh6WPPgUetJ4Z6tWphYREempbgPMRRdd\nxEUXXURVVRXPPfccV155JcOGDeOiiy7inHPOOeAuIzkyhQVujFYnFsOuACMiItID3d5G3aGgoIDv\nfve7vPrqq8ydO5c77rij14N45dA82Skk2a2YmtOpad6HPxi7QcoiIiKDQbc9MB28Xi8vvvgizz77\nLOFwmG9961tccMEFsa5tyDCbTYzOd7GjPhWrs5pSbznHZY2Ld1kiIiIJq9sAs379ev785z+zefNm\nzj33XO655x7GjdMf1lgY7XGzfWsaVmC3t0wBRkREpBvdBpivf/3rjBo1ihNPPJG6ujqeeOKJLvvv\nvvvumBY3lBQWpBH5QDPyioiI9ES3AabjNun6+noyMjK67CsvL49dVUNQoccNQQfWiJMSbxmGYRx2\nvSkREZGhqtsAYzabufHGG2ltbSUzM5PHHnuMkSNHsnr1ah5//HEuueSS/qpz0MtwJZHpTqLF58Zr\n3kNDayMZjvTDv1BERGQI6jbA/OpXv+LJJ59kzJgx/O1vf+PHP/4xkUiEtLQ01qxZ0181DhmFBW7+\n7XVhS9tDibdMAUZEROQQur2N2mw2M2bMGADmzJlDRUUFV199NQ8//DB5eXn9UuBQUuhJI+JvDy27\nNR+MiIjIIXUbYD4/BqOgoIBzzjknpgUNZYUeNxG/G4ASn8YYiYiIHEqPJrLroEGlsTUy34U5Ysca\ndFHqLSdiROJdkoiISELqdgzMxo0bOeuss6KP9+3bx1lnnRW9Q+att96KcXlDS5LNwvDcFPZ6XYRs\nlVQHaslPyY13WSIiIgmn2wDz2muv9Vcdsl+hJ42KyjTsWZWUeMsUYERERA6i2wAzbNiw/qpD9iss\ncPP37Z9NaPeFgpPiXJGIiEji6dUYGIm9Qo8bI+ACw6Q7kURERA5BASbB5Gc5SbYnYW5No8JXSSgS\nindJIiIiCUcBJsGYTSZGF7hoa3QRMsJUNFXFuyQREZGEowCTgNontNs/Dsar+WBEREQ+TwEmAbVP\naNcRYDQORkRE5PMUYBJQoceN0ZyKybBS4lOAERER+TwFmATkdtrJTkvG8LvZ46+mJdQS75JEREQS\nigJMgir0uAn63BgYlPkq4l2OiIhIQlGASVCFnjSM/eNgNB+MiIhIVwowCWqMx02kqWNGXt2JJCIi\n0pkCTIIakZeKOeTEFE7SnUgiIiKfowCToGxWCyPyXIR9bupa6vG1NcW7JBERkYShAJPACgvSCPvd\ngOaDERER6UwBJoEVetxEmtIBDeQVERHpTAEmgXWZkVcT2omIiEQpwCSw3IxkUqxOTEEnJd4yDMOI\nd0kiIiIJQQEmgZlMJgo9aQS9bvzBAPta6uNdkoiISEJQgElwhR53dEK7Em9pnKsRERFJDAowCa7r\nytSa0E5ERAQUYBLe6AI3Eb8bDJPuRBIREdlPASbBpSbbyEtzQUsqZb5ywpFwvEsSERGJOwWYAaDQ\n4ybU5KYtEmRPoDre5YiIiMSdAswAUOhJ0zgYERGRThRgBoDCzitT604kERERBZiB4JjcVCxtaWCY\nKfGpB0ZEREQBZgCwWsyMzHMT8buoaKoiGA7GuyQREZG4UoAZIAoL0og0pRExIpQ3Vca7HBERkbhS\ngBkgOk9op/lgRERkqFOAGSDGaEZeERGRKAWYASIrzUGqOR3CVkp8uhNJRESGNgWYAcJkMjHGk064\nKY3qQC2BYHO8SxIREYkbBZgBZLRn/7pIQKlupxYRkSEspgFm+/btnH322axevRqAjRs3cvnll7No\n0SIWL15MXV0dAC+++CLz58/n0ksvZc2aNbEsaUBrH8ibDmggr4iIDG0xCzCBQIBly5YxY8aM6LYn\nnniCe++9l1WrVjFt2jSefvppAoEAjzzyCE8++SSrVq3iqaeeoqGhIVZlDWij890Y+2fkLVWAERGR\nISxmAcZut7Ny5Upyc3Oj2x588EGOOeYYDMNg79695Ofns2nTJiZPnozL5cLhcHDiiSdSXFwcq7IG\nNKfDSn5aJkYwST0wIiIypFljdmCrFav1wMO//fbb3HnnnRQWFvLFL36Rl19+mczMzOj+zMxMampq\nuj12RoYTq9XS5zV3yMlxxezYR2vS6Gzebkij0VaNJSVMpjM93iX1q0Rum6FM7ZK41DaJS21zdGIW\nYA7ljDPO4PTTT+cXv/gFjz/+OMOGDeuy3zCMwx6jvj4Qq/LIyXFRU+OL2fGPliczmUh5GpaMaop3\nb2FKzvHxLqnfJHrbDFVql8Sltklcapue6S7k9etdSK+//jrQfkvw3Llz+eCDD8jNzaW2tjb6nOrq\n6i6XnaSrQk1oJyIi0r8B5qGHHmLLli0AbNq0idGjRzNlyhQ+/PBDvF4vfr+f4uJiTj755P4sa0AZ\nlpOCta39slGJxsGIiMgQFbNLSJs3b2b58uVUVFRgtVpZu3Ytd9xxBz/72c+wWCw4HA7uvfdeHA4H\nS5cuZfHixZhMJq699lpcLl0XPBSL2cyo7CxKWpyUeMuIGBHMJk3nIyIiQ0vMAszxxx/PqlWrDtj+\nxz/+8YBtRUVFFBUVxaqUQafQk8au2jSaHVXUNO8jz5kT75JERET6lf7XfQDqOg5Gl5FERGToUYAZ\ngAo9bgwFGBERGcIUYAagTLcDlykbDJMCjIiIDEkKMAPUmIJMIoFUynyVhCPheJcjIiLSrxRgBqiO\ncTAhI0Slf0+8yxEREelXCjADVGHBZwN5tS6SiIgMNQowA9SoAheGXxPaiYjI0KQAM0A57FYKUvIw\nImYFGBERGXIUYAawMZ50Iv40qvx7aQ23xbscERGRfqMAM4C1zwfjxsCgzFcR73JERET6jQLMAFbo\ncRNpah8Hs9tbGudqRERE+o8CzADmyUrB2pYBQKm3PM7ViIiI9B8FmAHMbDYxOisfI2RjV6N6YERE\nZOhQgBngxnjSiDSlUddaT1ObP97liIiI9AsFmAGu84R2JT7dTi0iIkODAswA17GkAGhCOxERGToU\nYAa4tNQk0k05gJYUEBGRoUMBZhAozMsj0upgd2MZhmHEuxwREZGYU4AZBMZ43Bj+NPwhP3UtDfEu\nR0REJOYUYAaB9gntNJBXRESGDgWYQWBknguatTK1iIgMHQowg4DdZqEg2YNhwO5GBRgRERn8FGAG\nibEFWRgtKZT4yokYkXiXIyIiElMKMINExziYYKSNPf7qeJcjIiISUwowg0SXCe18WthRREQGNwWY\nQSIv04m9LQvQQF4RERn8FGAGCbPJxOiMYRgRE7satDK1iIgMbgowg8iYggyMgJtKfxXBSCje5YiI\niMSMAswg0jEOJkKEiqbKeJcjIiISMwowg8joTgN5tbCjiIgMZgowg4jbaSfd3L4ytQbyiojIYKYA\nM8iMzR6GEbaws14DeUVEZPBSgBlkCj3pRPxp1LbW0hxqjnc5IiIiMaEAM8h0Xpm61FsR52pERERi\nQwFmkBmR68LU3DEjr8bBiIjI4KQAM8jYrGYKkocBsKtR42BERGRwUoAZhMbm5WME7exqUA+MiIgM\nTgowg9BYTxqRpjR8IS+Nrb54lyMiItLnFGAGoc4rU6/88Cle272OEm8ZESMS58pERET6hjXeBUjf\ny0lPJsl/DDTXsotSdnlLeWnnazityYzPGMv4zGM5LvNYspOz4l2qiIjIEVGAGYRMJhNjsj3858Mk\n7vz2NCpbS9ha9wlb6j5hY82HbKz5EIBsRyYTMo9lQuY4xmeMwWlzxrlyERGRnlGAGaQKC9z8Z8c+\nVj7/KccXZjF1xBzmn3wxjeF6ttV9wta6T9hWv4P1le+xvvI9TJgY4Rq+P9Acy+i0kdjM+niIiEhi\n0l+oQeoLE/PYtGMfu/d42b3Hx1/+ARaziVEFLiaMyOPUY8Zz5fhU9rbsYWvddrbWfcIubyklvjLW\nlqzDbrYxNqOQ4zLae2gKUvIwmUzxflsiIiIAmAzDMOJdRG/V1MTuzpqcHFdMj9/fAi0hPq1oYGtp\nA9tKGyjZ4yOyv8nNJhMj812MH5HOhBHpHJOfTHlz++WmrXWfsCdQHT2O2+5ifEb72JnxmWNJT0rr\n9/cy2NpmsFC7JC61TeJS2/RMTo7rkPsUYD5nsH+omltD7KhobA80ZfXsrvIRjrR/BEwmGJHnYsKI\ndMYfk0FuLpQEdrdfcqr/BF9bU/Q4BSl57ZebMo5lbHohDmtSzGsf7G0zUKldEpfaJnGpbXombgFm\n+/btfPe73+UrX/kKV111FVVVVdx6662EQiGsViv33XcfOTk5vPjiizz11FOYzWYWLlzIpZde2u1x\nFWD6TmtbmE8rG9lWWs+20gZ2Vno/CzTAMbmpjB+Rwbhj0nBntVIS2MnWuk/4tGEXwUgQAIvJwui0\nEUzIGMeEzGMZ6R6O2dT3d+gPtbYZKNQuiUttk7jUNj0TlwATCAT41re+xahRoxg/fjxXXXUVt9xy\nC2eeeSbz5s3jf//3f6moqGDJkiVcfPHFPPPMM9hsNhYsWMDq1atJT08/5LEVYGKnLRhmR6U3Gmh2\nVHoJhT+bP2Z4TgrjR2QwZngqSemNlAZ2s7XuE8p8FRi0f5SSrcmMyxjDhIz2AcE5yVl9Mn5mqLdN\nolK7JC61TeJS2/RMdwEmZoN47XY7K1euZOXKldFtP/nJT0hKar/UkJGRwUcffcSmTZuYPHkyLld7\nkSeeeCLFxcXMnj07VqVJN+w2C8eNzOC4kRkABENhdlZ62VbawLayBnZUNFJe4+dvH7Q/35OdyfgR\nc5kxLAmzq47S/T00m2o2s6lmMwBZjozo7drjMsaQakuJ19sTEZFBImYBxmq1YrV2PbzT2T7PSDgc\n5ve//z3XXnsttbW1ZGZmRp+TmZlJTU1NrMqSXrJZLYwfkcH4Ee2BJhSOsKvKy9bSBraX1vNJRSOV\nxX4obn9+fuZwxo+YxOkeE5GUGkoDu9hWv4N3K9/n3cr3MWHiGJeHCZnjmJBxLIXpo3S7toiI9Fq/\n/+UIh8PcfPPNnHLKKcyYMYOXXnqpy/6eXNHKyHBitVpiVWK3XVYCBflpnDrtGKA90Hxa3sCHn9ay\neec+tuzax9//XQX/3v/c7PGcVDiD3OFttDmq2dn4Kdv27aTUV8FfS97EbrFxXM6xnJB3HJPzJjAy\nfVi3l5vUNolJ7ZK41DaJS21zdPo9wNx6662MHDmSJUuWAJCbm0ttbW10f3V1NVOnTu32GPX1gZjV\np+uSvZfltHHWCQWcdUIB4UiEkj1NbCtrH0PzSXkDr79fBu+3Pzc7bRKTR3yBtLwmWh17KPHvYtOe\nj9m052MAXLZUxmeOZULmOI7LPLbL7dpqm8SkdklcapvEpbbpmbiMgTmYF198EZvNxvXXXx/dNmXK\nFG677Ta8Xi8Wi4Xi4mJ+9KMf9WdZ0ocsZjOFHjeFHjfnfWEkkYhBabWvfQxNaQPbyxr4vw9r4UOA\ndLLcp3D8yCRSchpotu1ht38XG/b+mw1727tw8p250bWbTk2fEtf3JiIiiSNmdyFt3ryZ5cuXU1FR\ngdVqJS8vj3379pGUlERqaioAY8aM4ac//SmvvfYav/nNbzCZTFx11VV88Ytf7PbYugtp4IpEDMpr\nmqKDgreV1uNvCUX3p7vsjBwJydn1+C1VlPpLaNt/u7bNbOXYjDGckD2R47OOI8Nx6DvVpP/odyZx\nqW0Sl9qmZzSRXS/oQ9W/IoZBZY0/Gma2lTXgCwSj+90uC8NHBknKrKfRUk6Vvyq67xjXMCZnT2Ry\n9nEck9r92BmJHf3OJC61TeJS2/SMAkwv6EMVX4ZhULkvwPb9YWZraQNef1t0f3pGmJyRXsIpe6gO\nlRM2wu3bk9L2h5mJjMsYozub+pF+ZxKX2iZxqW16RgGmF/ShSiyGYbCnLtA+S/AeH//eXkNT8/4e\nGkuQ7OFNOHP24bWW0xppAcBusTMxcxyTsycyKWsCLntqHN/B4KffmcSltklcapueSZhBvCK9ZTKZ\nKMhKoSArhUtzXOyt9lJe3cTHu+v5uKSO7WVJ1JZkAIVY3A1kDGsg4trDv2s28++azZgwMTptJCfs\n753Jc+boUpOIyCCgACMDitlkYkSeixF5Loq+MIJQOMKOikY+3l3PlpJ6dm71EjFGY3L4sWfW4szb\nx87GEnY27ub5Ha+Qk5zF5OyJnJA9kcK0UVjMsZtPSEREYkcBRgY0q8UcnSn4YtpX295W1sDHu+vY\nUlJPxUY/WNuwpNdgz6xhn1HLurJ3WFf2Dk5rMpOyJjA5eyITs8aRbE2O99sREZEeUoCRQSU5ycrU\nsdlMHZsNQGNTK1tK6qOXnOo+CWB212FJr6Y5s4Z/7d3Iv/ZuxGwyMy59TPSupqzkzMOcSURE4kkB\nRga1tNQkTpmUzymT8jEMg+r6Zj4uqW/vodlSR4u5Hkt6NZaMarYan7C1/hPWfPICBc58puRMZHLO\nREa4hmM2meP9VkREpBMFGBkyTCYTeZlO8jKdzJo2LDpL8Jbd7YFm+849RFL3YsmooTJSTVVgD6+V\nrMNpSWFKzkSm5E5ifMZY7BZ7vN+KiMiQpwAjQ5bZbGJUvptR+W7OO2UkwVCYTyu8fLy7jo9KqykL\n7MacXo0/vYb/2/Mv/m/PvzBjpdA1mumeE5icPZG0JC3GJiISD5oH5nN0b37i6u+2CbQE2VbawEe7\n9/Hhnh3Um0uxpNdgdjZFn5NpyWNq7iROOWYKnpT8IXmLtn5nEpfaJnGpbXpG88CIHAGnw8a0cTlM\nG5cDTKDe18qWkjo2lpSw3bud1uRK9rmqWVe1l3VV67AbqRSmHMvMkVM4IW8cVs0GLCISM/ovrEgP\nZbiSOPX4Ak49vgDD+AJ76gL8e1cVH1R+RFVwF62uarYGNrJ1y0ZMH1nJNo9gctZEZo2dSmaKO97l\ni4gMKgowIkfgsxmCx3IeYwlHIuysauTdnR+xtXErXksZNUk7WbdvJ3+rfRlHWw6jnGOZccwJTBs5\nCqtFdzWJiBwNBRiRPmAxmzl2WAbHDjsNOI3WthDv7d7B+xX/obx1By32araFq9m2+x88sTUFV7iA\nZIuTFGsKqfZU3EmpZDhcZDndZKW4cTntpCTbcDqsmIfguBoRkcNRgBGJgSS7lTPGjeeMceMBqGqs\n460dG/mobgv19gqazJ/SBNQARIDm/V/1YBhAyI4RtGOE7FgjDmw4SDI7SbY4SbWm4EpKJS0phYxk\nN5lOFy6nndRkG6nJNlKSberhEZFBTwFGpB8UpGVy+YlzgDm0hYNUNe1ln99Lrd9LQ4uPhhYfvrYm\n/KEAzWE/rZZmgpYWIuYmDKBt/5cPqO44aGv7l1FvgqANI9QeeIygHUskCbspGYfZidPiJNWWimt/\nL096cmqXwNPxZbeZh+RdVCIyMCnAiPQzu8XGyLThjEw7/HPDkTBNQT++tia8bU3UB7zsC7SHnsZW\nH01tfgLhAM2mAG22ZsKmz27xDu7/8gF7O2002kxQa4uGHSNkh6AdUziJJFMyDouTFKsTlz2VtCQX\nbkfKQQNPqtNGcpL+EyIi8aH/+ogkMIvZQlqSm7Sk/XcxZXX//FAkRFPQT1ObH1+wiaY2Pw0tPuoC\nXhpbfXhbm2gK+mm2BGiJBAjR1PX1QNP+r47QY4RMUGfHCNkwgkkYIRsEk9qDT8iGw+zEbnbgsCTj\ntDpItTlJcSSTkmTDmWQlOcmK07H/387fO6wk262Yzer1EZHeU4ARGUSsZivpSWmkJ/Wge4fPAo+v\nzU/T/sDjCzbhbW2iobm9l8fX5scfaqI5HCBoNB1wjDBdhvAA+8fxhG0YXmv7vyEbhK3tIShshZAN\nI2yDkBWbKYkks4Nkm4Nkq5MUWzKpSUkkO/YHnk4h6GCByGbVpS+RoUgBRmQI623gCUZC+DsFHl9b\nE9hD1DQ0EAg14w8209QWwN8WIBBqpiXUQkukmZDh7fa4HWN8Gvc/NsLm9uDjs2E0dISgg4UhG+aI\nDYfFgcPiwGl14rQ7SHHYPuvxSbJ2CUOdw0+yeoEGhIgRIWJENDmkdKFPg4j0mO0ggacnU6KHI2EC\noWaaQ80EQs0Egp993xxsad8WaiYQDOAPNu8PQC00h5ppjfgx6H7FkzDg3/8V7f0JWSHQtReoS+/P\n/hBE2Ird5CDZ6iDZmkyyzY7dZiHJZiHJbiHJZv7s8f4vu8382ff2zvvM+/e3Px7qwcgwDIKREC3h\nFppDLbSE9v8bbo0+9rcF8AdbCATb9zUHm2kJt9IabqE13EZrpIWg0QZAiiWVXGcOHlcuec4c8pw5\n5DqzyXJkYjFb4vxupb8pwIhIzFnMFlz2VFz21F6/1jAMWsOt+wNQC4Fge7iJBqLOYSjUjL+tvSco\nEArQHGomZAR7dJ6W/V91ERMYZohYMCLt/9JswfDv/77z9i7fmzH2b+v43oIVm9mK3WLDbrFjs9hI\nsthJstpwWO04bHYcNutnocjeOSQdGIg6hyqrpW8vnUUiBm2hMMFQhGAoQkswSFNry/4etWYC+0NG\nS6iZ5nDL/pDR/tVmtFTDNjcAAA54SURBVBKMtBE0Wvn/7d15bFTlv8fx95k5M3M6004XugkVlJrf\nJSyCLL9cENRE0BtNILJYRKp/mRjiHxJcCIpoMCYlMTEKwQ0ThRgq4IJRcYliSAA1QREbESX8uLJ0\nn2mnM52eWc79Y0ptFbyIlunA55WcDGebfk+mgQ/P88zzJLFJYZMiQcpIgJH+y7X0hdCUCSkfTjIA\nGER8MaKpYxyLHBtwvYGLQrOIMn8pVwYrqQyUUe7PbEFvvroYL1EKMCIypBmGgWVaWKZ1Qfcn08lM\n8DlL2Mnsx4kle1t8Et30pGzstI2dTGCnEyRSCRLpbhLnGYR+z6HvG+9nP59yQdINtgsn4j5nGCL9\nu1DluHEbJh7Dg+ky8bo8eFyZoORze/CZXnyWSaQ7ip3OBAzb6SHp2L0hI0HKsEkbCRxXAlxJcCcx\n3GdeU3/tQY3M5qTcmdatlAkpCydlYqQ9uBwPbseD2/Fi4sU0vHh6N6/Lwmf6sFw+fKaFZXrx+tx4\nTBdeM/MK0NYRpykcoTHaQrvdTsqMYORFcVlRQlaEcLKdnzuPDCjLxEuhp5jyvFKqCiupClZQ4S+j\nLK8Uy/T91Y9ThhAFGBG5pJku84Jbf/pzHIekkyKRsnuDTZJEOoGdtkmkEtjp5G/n0gnsVOY1c27g\nn+NJm55kAjtlY6f6XZtOkHRsUk6SNOcXIFK927kCEv/PY/fmDnBcfQHDTQAz7cVj+HoDhg+vy4fP\n5cuESbePPNPC78nD77Xwm3nk+/IIePPweUy8pguv6cJj/vMtRWc4jkOkO0FLqJuWcDdNoRiNHWEa\nu1pot9voNjpwWTHSVpRWq5m2RBM/djYMeA8vforMEsrySqkqrOCq4iuoDJSpSypHKMCIiJwHwzDw\nGJkuIf9F+HlpJ90XbPoHnP77dqp/K1FmvydlE0/YxJM2Xp+JO2US8FoEPHn4PXlYpkWeeSaIZMb+\nWKaFJ8cGyBqGQdDvJej3Uj3ij4PQ7USKlo44LeFumtuj/NrRSmO0mZDdRle6A3xdxK0ozZygOXKC\nhghwovdmx8Ai+Fu4CVYwethwRgQr1CU1hOTWb6yIyGXCZbiwTB8WF97NcT4DrC9VXo+bEaUBRpQG\ngFJgVN+5tOMQjvTQEu7mVKiT/w03ZVpuetqIOmFSngjdVoy40UFj1zEOdQGnMvcaaQ+WE6TQLKHc\nX8qIgnKqhw3n6pIrsDwX1s0pF0YBRkRELisuw6AkaFEStPivkcX0DzcAsXiS5lCME+3tHA830hht\npq2nja50mIQ7Qsxqp9tpozH6M99HgcbMfUYyjzwnSNAspiyvjBEFFVSXXsHo0kosj+eiP+elTgFG\nRESkH79lctUVQa66IshMrhpwLplK09oR41hrM/8JN3K6q5n2nja60iFsd4SYt4kYTTTGD3MoDrSA\nkzZwJQJYTiFBMzOgeOSwSvJdASoLhlEWLKDAr0VY/yoFGBERkfNkul1UluRTWZLPdEYPOOc4Dm1d\nUX5pOcXxUCbcnGm5sc1Out0n6eYkTQk41NjvvpQbx7ZwJfPwOn4sI598s4Cgt5CSvCJK/YWU5RdS\nmO/LjPsJePF5NMhYAUZEROQfYBgGpQX5lBb8i//mXwPOOY5DqDvCLy2n+E/oNJFUhOaudrqSEeJ0\nYfuipPOifbNSd9I77Kb3gNPuwrF9OLaFk7Bwp/KwCOB35xP0BCm2ihjmL6QwYFEYyIScAr+HwoCX\nPJ95SQ48VoAREREZZIZhUOIP8u9RQf49asxZB1jbqQThng7au8OcjrTT0hWirTtMuKeDrmQnMVcX\nti8MRmZm6jPzC4WA44CTAFp8OCetTNCxfTgJCyOZh9+VT74nSLEvSJHfTzDgJej3ZF7PbL2rzufK\nDNIKMCIiIkOA1+2h3F9Kub+UMedYeT6VTtFpRwj3dBDu6SQUD9MSDdEaywSdTrOTmDdCum9lsQwb\naO/dnKQHp8vCabd+a9WxLZyED2yLgBkkaOVRGPD1BZv+r4W9rTvBgDer43YUYERERHKE2+Wm2Cqi\n2Co65zWO49CViBLu6STcEx4QdkLxDtrjYTrtTuz02b9inwTaU25az7TitFk4p63fgk5v6CHpJWB5\nmDqmnHv/Z8wgPfG5KcCIiIhcQgzD6Jt9+sqC4ee8rjsZp6Ong1BvwAnHOwjbHZnXnszWlWg/9w9y\nXJC0+NVVDSjAiIiIyEWQWYHdojJQcc5rEqkEHXZnb8AJE7YzQSfU09EXfoYXZqcbSQFGREREzsrj\n9lCaN4zSvHMMyskizZojIiIiOUcBRkRERHKOAoyIiIjkHAUYERERyTkKMCIiIpJzFGBEREQk5yjA\niIiISM5RgBEREZGcowAjIiIiOUcBRkRERHLOoAaYI0eOMHv2bLZs2dJ37I033mDcuHFEo9G+Yzt3\n7mTBggUsWrSIbdu2DWZJIiIicgkYtLWQYrEYa9euZfr06X3H3n33Xdra2igvLx9w3YYNG9i+fTse\nj4eFCxcyZ84ciorOvVS4iIiIXN4GrQXG6/XyyiuvDAgrs2fPZvny5RiG0Xfs4MGDTJgwgYKCAizL\nYvLkyRw4cGCwyhIREZFLwKC1wJimiWkOfPv8/Pw/XNfa2kpJSUnffklJCS0tLX/63mVlBf9MkVl6\nf7lw+myGJn0uQ5c+m6FLn83fM+QG8TqOk+0SREREZIjLeoApLy+ntbW1b7+5uXlAt5OIiIjI72U9\nwEycOJFDhw7R2dlJNBrlwIEDTJ06NdtliYiIyBBmOIPUZ/PDDz9QV1fHyZMnMU2TiooKZsyYwd69\ne/nuu++YMGECkyZN4pFHHmHXrl1s2rQJwzBYunQpc+fOHYySRERE5BIxaAFGREREZLBkvQtJRERE\n5K9SgOnnmWeeoaamhsWLF/P9999nuxzpZ926ddTU1LBgwQI++eSTbJcj/cTjcWbPns3bb7+d7VKk\nn507dzJ37lzmz5/P7t27s12OANFolAceeIDa2loWL17Mnj17sl1SThu0eWByzddff83x48epr6/n\n6NGjrFq1ivr6+myXJcD+/fv5+eefqa+vJxQKcccdd3DLLbdkuyzptXHjRgoLC7NdhvQTCoXYsGED\nO3bsIBaL8cILL3DTTTdlu6zL3jvvvMPVV1/NihUraGpq4t5772XXrl3ZLitnKcD02rdvH7Nnzwag\nurqajo4Ourq6zjr5nlxc06ZN49prrwUgGAzS3d1NKpXC7XZnuTI5evQov/zyi/5xHGL27dvH9OnT\nyc/PJz8/n7Vr12a7JAGKi4v56aefAOjs7KS4uDjLFeU2dSH1am1tHfDLdD4zAsvF4Xa78fv9AGzf\nvp0bbrhB4WWIqKurY+XKldkuQ37nxIkTxONx7r//fpYsWcK+ffuyXZIAt99+O6dOnWLOnDksXbqU\nRx99NNsl5TS1wJyDvpw19Hz22Wds376d1157LdulCJnFWSdNmsSVV16Z7VLkLMLhMOvXr+fUqVPc\nc889fPHFFwPWoZOL77333mP48OFs2rSJw4cPs2rVKo0d+xsUYHqdbUbgsrKyLFYk/e3Zs4cXX3yR\nV199lYICrR8yFOzevZtff/2V3bt309jYiNfrpbKykhkzZmS7tMvesGHDuO666zBNk5EjRxIIBGhv\nb2fYsGHZLu2yduDAAWbOnAnAmDFjaG5uVnf436AupF7XX389H3/8MQANDQ2Ul5dr/MsQEYlEWLdu\nHS+99BJFRUXZLkd6Pffcc+zYsYO33nqLRYsWsWzZMoWXIWLmzJns37+fdDpNKBQiFotpvMUQMGrU\nKA4ePAjAyZMnCQQCCi9/g1pgek2ePJlx48axePFiDMNgzZo12S5Jen344YeEQiEefPDBvmN1dXUM\nHz48i1WJDF0VFRXceuut3HnnnQA8/vjjuFz6/2q21dTUsGrVKpYuXUoymeTJJ5/Mdkk5TTPxioiI\nSM5RJBcREZGcowAjIiIiOUcBRkRERHKOAoyIiIjkHAUYERERyTkKMCIyqE6cOMH48eOpra3tW4V3\nxYoVdHZ2nvd71NbWkkqlzvv6u+66i6+++upCyhWRHKEAIyKDrqSkhM2bN7N582a2bt1KeXk5Gzdu\nPO/7N2/erAm/RGQATWQnIhfdtGnTqK+v5/Dhw9TV1ZFMJkkkEjzxxBOMHTuW2tpaxowZw48//sjr\nr7/O2LFjaWhowLZtVq9eTWNjI8lkknnz5rFkyRK6u7tZvnw5oVCIUaNG0dPTA0BTUxMPPfQQAPF4\nnJqaGhYuXJjNRxeRf4gCjIhcVKlUik8//ZQpU6bw8MMPs2HDBkaOHPmHxe38fj9btmwZcO/mzZsJ\nBoM8++yzxONxbrvtNmbNmsXevXuxLIv6+nqam5u5+eabAfjoo48YPXo0Tz31FD09PWzbtu2iP6+I\nDA4FGBEZdO3t7dTW1gKQTqeZOnUqCxYs4Pnnn+exxx7ru66rq4t0Og1klvf4vYMHDzJ//nwALMti\n/PjxNDQ0cOTIEaZMmQJkFmYdPXo0ALNmzeLNN99k5cqV3HjjjdTU1Azqc4rIxaMAIyKD7swYmP4i\nkQgej+cPx8/weDx/OGYYxoB9x3EwDAPHcQas9XMmBFVXV/PBBx/wzTffsGvXLl5//XW2bt36dx9H\nRIYADeIVkawoKCigqqqKL7/8EoBjx46xfv36P71n4sSJ7NmzB4BYLEZDQwPjxo2jurqab7/9FoDT\np09z7NgxAN5//30OHTrEjBkzWLNmDadPnyaZTA7iU4nIxaIWGBHJmrq6Op5++mlefvllkskkK1eu\n/NPra2trWb16NXfffTe2bbNs2TKqqqqYN28en3/+OUuWLKGqqooJEyYAcM0117BmzRq8Xi+O43Df\nffdhmvprT+RSoNWoRUREJOeoC0lERERyjgKMiIiI5BwFGBEREck5CjAiIiKScxRgREREJOcowIiI\niEjOUYARERGRnKMAIyIiIjnn/wArtMTCOBoM9gAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RidI9YhKOiY2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Make Better Use of Latitude\n",
+        "\n",
+        "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n",
+        "\n",
+        "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hfGUKj2IR_F1",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "4d337638-500b-4148-dac6-b49eced50289"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f92b2ef59e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Ivm6vroDbUaH4HSJuRyUYeixrPeBscVZbYLUIkudcW1OBpqtdsLDJy0Xp2nqn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\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6N0p91k2iFCP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Try creating some synthetic features that do a better job with latitude.**\n",
+        "\n",
+        "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n",
+        "\n",
+        "Or you could break the space into 10 different buckets.  `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n",
+        "\n",
+        "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n",
+        "\n",
+        "What's the best validation performance you can get?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wduJ2B28yMFl",
+        "colab_type": "code",
+        "cellView": "form",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "119dd6de-b1f9-4974-fd3c-c30efa381793"
+      },
+      "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)\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=1000,\n",
+        "    batch_size=10,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 234.79\n",
+            "  period 01 : 232.74\n",
+            "  period 02 : 230.68\n",
+            "  period 03 : 228.64\n",
+            "  period 04 : 226.59\n",
+            "  period 05 : 224.54\n",
+            "  period 06 : 222.50\n",
+            "  period 07 : 220.47\n",
+            "  period 08 : 218.43\n",
+            "  period 09 : 216.40\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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8Wkku6iXZqJdkYx5pYCxws0FVWtnA8t3ZHD1bAsDw3kFXFv96dZxb9l+qK2FV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\nRK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pZa8miwu6_tQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PzABdyjq7IZU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n",
+        "\n",
+        "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xdVF8siZ7Lup",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def select_and_transform_features(source_df):\n",
+        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "  return selected_examples\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U4iAdY6t7Pkh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "92546bfd-2bf4-4824-eb8e-27bd8858f823"
+      },
+      "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": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 226.63\n",
+            "  period 01 : 216.49\n",
+            "  period 02 : 206.42\n",
+            "  period 03 : 196.46\n",
+            "  period 04 : 186.62\n",
+            "  period 05 : 176.90\n",
+            "  period 06 : 167.34\n",
+            "  period 07 : 157.97\n",
+            "  period 08 : 148.82\n",
+            "  period 09 : 139.91\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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HL2GgtW+ntP1S6axsRPskF/WSbNRLsrkwHVrAbN68mRdeeIFXXnkFb+/WoO65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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TIHg6p1Zi1Ha",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/first_steps_with_tensorflow.ipynb b/first_steps_with_tensorflow.ipynb
new file mode 100644
index 0000000..ba296e9
--- /dev/null
+++ b/first_steps_with_tensorflow.ipynb
@@ -0,0 +1,2000 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "first_steps_with_tensorflow.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/first_steps_with_tensorflow.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": "91af14fe-62fc-4979-a2f8-6a18f59d1019"
+      },
+      "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>1688</th>\n",
+              "      <td>-117.2</td>\n",
+              "      <td>32.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>2539.0</td>\n",
+              "      <td>616.0</td>\n",
+              "      <td>964.0</td>\n",
+              "      <td>526.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>275.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11613</th>\n",
+              "      <td>-121.3</td>\n",
+              "      <td>37.9</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>371.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>171.0</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>55.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10576</th>\n",
+              "      <td>-120.5</td>\n",
+              "      <td>34.6</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2253.0</td>\n",
+              "      <td>382.0</td>\n",
+              "      <td>1197.0</td>\n",
+              "      <td>384.0</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>134.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4772</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>34.6</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>1537.0</td>\n",
+              "      <td>416.0</td>\n",
+              "      <td>1239.0</td>\n",
+              "      <td>397.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>99.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4750</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>2259.0</td>\n",
+              "      <td>383.0</td>\n",
+              "      <td>1378.0</td>\n",
+              "      <td>386.0</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>287.0</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>9224</th>\n",
+              "      <td>-119.1</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>3471.0</td>\n",
+              "      <td>510.0</td>\n",
+              "      <td>2002.0</td>\n",
+              "      <td>555.0</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>257.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>34.8</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>812.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>375.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>48.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9747</th>\n",
+              "      <td>-119.7</td>\n",
+              "      <td>37.1</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1280.0</td>\n",
+              "      <td>254.0</td>\n",
+              "      <td>707.0</td>\n",
+              "      <td>267.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>106.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16104</th>\n",
+              "      <td>-122.5</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>2005.0</td>\n",
+              "      <td>359.0</td>\n",
+              "      <td>847.0</td>\n",
+              "      <td>356.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4987</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1088.0</td>\n",
+              "      <td>231.0</td>\n",
+              "      <td>617.0</td>\n",
+              "      <td>211.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>193.1</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",
+              "1688      -117.2      32.8                18.0       2539.0           616.0   \n",
+              "11613     -121.3      37.9                28.0        371.0            71.0   \n",
+              "10576     -120.5      34.6                27.0       2253.0           382.0   \n",
+              "4772      -118.1      34.6                31.0       1537.0           416.0   \n",
+              "4750      -118.1      33.9                17.0       2259.0           383.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "9224      -119.1      34.2                23.0       3471.0           510.0   \n",
+              "7         -114.6      34.8                41.0        812.0           168.0   \n",
+              "9747      -119.7      37.1                17.0       1280.0           254.0   \n",
+              "16104     -122.5      37.8                52.0       2005.0           359.0   \n",
+              "4987      -118.1      33.9                36.0       1088.0           231.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "1688        964.0       526.0            3.4               275.0  \n",
+              "11613       171.0        70.0            1.0                55.7  \n",
+              "10576      1197.0       384.0            3.3               134.7  \n",
+              "4772       1239.0       397.0            2.0                99.2  \n",
+              "4750       1378.0       386.0            5.9               287.0  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "9224       2002.0       555.0            5.3               257.5  \n",
+              "7           375.0       158.0            1.7                48.5  \n",
+              "9747        707.0       267.0            3.5               106.3  \n",
+              "16104       847.0       356.0            4.1               500.0  \n",
+              "4987        617.0       211.0            3.9               193.1  \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": "cd585ed9-70a8-4756-f553-9c4742d75fb0"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "f1cbc2de-f904-4cbc-9bdd-afeea5f4329b"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an input function for predictions.\n",
+        "# Note: Since we're making just one prediction for each example, we don't \n",
+        "# need to repeat or shuffle the data here.\n",
+        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+        "\n",
+        "# Call predict() on the linear_regressor to make predictions.\n",
+        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "\n",
+        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "\n",
+        "# Print Mean Squared Error and Root Mean Squared Error.\n",
+        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Squared Error (on training data): 56367.025\n",
+            "Root Mean Squared Error (on training data): 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AKWstXXPzOVz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Is this a good model? How would you judge how large this error is?\n",
+        "\n",
+        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+        "\n",
+        "Let's compare the RMSE to the difference of the min and max of our targets:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7UwqGbbxP53O",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "772d0f20-ecf6-4513-9461-2d558cc2a5ff"
+      },
+      "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": "5c245b56-48e2-4124-8df6-9e5b46eace37"
+      },
+      "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": "bf4feecd-2ad8-45d7-d477-2c476afbf7e7"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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oDuQXX3wxLr74YgCA1+vF8uXLVWtUsgv3w1np9VIBMNoTtox6nWwhmVgFFI4+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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "t0lRt4USU81L",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This initial line looks way off.  See if you can look back at the summary stats and see the same information encoded there.\n",
+        "\n",
+        "Together, these initial sanity checks suggest we may be able to find a much better line."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AZWF67uv0HTG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Tweak the Model Hyperparameters\n",
+        "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
+        "\n",
+        "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
+        "\n",
+        "For each period, we'll compute and graph training loss.  This may help you judge when a model is converged, or if it needs more iterations.\n",
+        "\n",
+        "We'll also plot the feature weight and bias term values learned by the model over time.  This is another way to see how things converge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wgSMeD5UU81N",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
+        "  \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label]\n",
+        "\n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Output a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kg8A4ArBU81Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1:  Achieve an RMSE of 180 or Below\n",
+        "\n",
+        "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
+        "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "b72c9c80-3c74-4c18-d94b-1ecdbdfaaa5e"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=400,\n",
+        "    batch_size=4\n",
+        ")"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 227.96\n",
+            "  period 01 : 218.81\n",
+            "  period 02 : 210.16\n",
+            "  period 03 : 202.07\n",
+            "  period 04 : 194.62\n",
+            "  period 05 : 188.50\n",
+            "  period 06 : 183.02\n",
+            "  period 07 : 178.47\n",
+            "  period 08 : 174.38\n",
+            "  period 09 : 171.74\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>97.8</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>80.7</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>54.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>78.7</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>116.6</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1403.7</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          97.8    207.3\n",
+              "std           80.7    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           54.1    119.4\n",
+              "50%           78.7    180.4\n",
+              "75%          116.6    265.0\n",
+              "max         1403.7    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 171.74\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lUpSUlNRanHnEiBFYt24dwsLC7thmyXOFsrIyzJgxw1QAEQAuXryI1NRUhISEICgoCElJ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\niIhIUCgpISIiIiIiIiJB8f8BSXCFoYFkQbYAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "38c4c224-33d0-4f81-c2e3-e9038600412d"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.42\n",
+            "  period 02 : 204.04\n",
+            "  period 03 : 194.62\n",
+            "  period 04 : 186.92\n",
+            "  period 05 : 180.27\n",
+            "  period 06 : 175.22\n",
+            "  period 07 : 171.57\n",
+            "  period 08 : 168.96\n",
+            "  period 09 : 167.30\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>116.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>96.4</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>64.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>94.0</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>139.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1676.8</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         116.9    207.3\n",
+              "std           96.4    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           64.6    119.4\n",
+              "50%           94.0    180.4\n",
+              "75%          139.3    265.0\n",
+              "max         1676.8    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 167.30\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TkPnSe+Qs/JLgrkl0eu9faE1evHF3udD/9CW6o3twxbbGMeIG0NdDYkBV3dM1\nrEWgM0FE6/pJhNSCxaFhzykz5Q4tUUFOusbb0Decf54umMulsvYXO2u3OtAA4wYbJSERgCaN6MCR\nzCJ+3pdFUqsIhvdp6e+QhBBCCL+R6RsBrKFMT6gYPdGQEhLwvwKdlWkoU0xE05D90VecfP5NjK0S\nSPpoPvqwEO81rrrQb1mG7shuXDGJOEZOA0M9JNxUFxSfcCck9Ob/jpBoGAmJIouWHSeCKHdoaRnu\noHuLxpGQKCxx8caXFtb84iAiRMPMiUEM79swRqWI86PXablzQndCggx8vC6dY6elQrsQQoimS5IS\nAaw20xOaunMLdJoZ3T+xwUwxEY1fwervOPrPZ9BHRdD541cwxsd4r3FVRf/L1+gObccVlYBj1HQw\nmr3XflVcLijMAFsJGIIhoo27lkQDcLpEz65MMw4XJMXY6BRjR9sIBhLs+8PJC5+UcyTTRY8OOuZc\nH0y7Fo0g09KERYWZue2KriiKymtf7aHc6vB3SEIIIYRfNIyrSHFBZHpCzRr6FBPRuJX8sotDdz6E\n1mQk6cOXCOrQxnuNqyq6bSvRpf+CK7I5jtEzwFgPxSVdChQeB6cFjKEQ3hI0/s9vqyr8kW/geKER\nvValW7yVyODALwLpdKqs+NHO5l0O9Dq4driJgT30UoOgkejRPppxg9qy4sejvPf1fmZd00N+t0II\nIZoc/19Jigsm0xNqr6FOMRGNlyX9COk3zUF1OOn4zjxC+nb3XuOqim7HGvS//4QrPA7H6JvAFOy9\n9quiOKDgqDshYQ6H8MQGkZBQXPBblonjhUaCDC76trQ0ioRETqGL+Z9b2LzLQXykhrunBDGop0Fu\nWhuZCUPacVHrCHYezGXN1gx/hyOEEELUOxkpEeAa+goYQjRF9swsDlz/N5TCYtq99AQRIwd7tX3d\n7m/R7/seV1gMjuSbwNzMq+1XSrFD4TF3YiIoEkKaQwO4ObY6New9ZaLUriPCrNCtuZXGkHvc/ruD\nLzbYsDngkq56JgwzYTL4v7+F92m1Gv56ZTee+GAraRsP0yEhnI6J4f4OSwghhKg3kpQIcDI9QYiG\nxVlYzIEb/ob9VBaJD80idvJ4r7av+3UD+j3f4QqNwpF8MwSFerX9Sjlt7oSEywnBMdAstkEkJIqt\nWvaeNmFXtLQIddApNvDrR9jsKl9utLHtdycmA9yYaqJPUsMoICp8JzzExB1XdeO5T3byxtK9PH7z\nxYQFSxFTIYQQTYP/x90Kr5DpCUL4n8tiJf2mOVgOHCH+1utocdcMr7av27sJ/e71qM0icCTfAsFh\nXm2/Ug6Le8qGywkh8RAS1yBlJmuUAAAgAElEQVQSEtmlOnZlmrErGjpE20hqBAmJkzkK//60nG2/\nO2kVr2XO9cGSkGhCOreO5JrL2lNQYuOd5ftwuVR/hySEEELUCxkpIYQQXqAqCofveoTSX3YRdUUy\nrefO8ercf92+H9HvXIsaHI49+RZoVg/Du+1lUJThXv4ztIV72oafqSocKzBwtMCITqPSo7mN6Gb1\ns/yxr6iqyg+/Oli22Y7iguF9DYwZaESvC/AsizhvYwa04eCJIn49nMeKH49y5ZB2/g5JCCGE8DkZ\nKSGEEHWkqipHH5pHwaqNhA25mPbz56LReu+fV+2BLei3r0QNCsWefDOE1kNywFbiXmVDdUFYYoNI\nSCgu2J9t4miBEZPeRZ+WloBPSJRZVD742spX39kxG+EvV5q5YohJEhJNlFaj4S/juxIdZmLp93+w\n72i+v0MSQgghfE6SEkIIUUeZ/36XnIVfEtw1iU7vPY/W5L254Nr0rRh+WYFqDnHXkAiL9lrbVbIW\nuUdIAIS3BnM9TBOpgc2pYVemmexSPWEmhX4tLYSYAnt4+5GTCi98Us5vRxQ6Juq4b2owXdrKAMam\nLiTIwJ0TeqDVanh72W8UlJy77LcQQgjRmEhSQggh6iD7o684+a+3MLZKIOmj+ehCQ7zWtvbwDvRb\nlqOagnEk34QaXvkSwF5Vng/FJ91LfUa2AZP3zudCldo07DhppsSmIy7ESa8EK8YAvnd3uVTW/mLn\n9S8tlJSpjBlo5K8TzISHyFeycGufEMaUkR0pLnfw1tK9KK7AX+JWCCGEqEoAX9YJIYR/FazayNF/\nPoM+KoLOH7+CMT7Ga21rj+xG/+MSMJpxjL4JNSLea21XSlUpzzkJpadBo4OINmAw+/aYtZBbpmNf\nlgmXqqFdlJ3WEY6GUGfzghWVuvhotY3DJxUiQjTcmGqmXYIUKBbnGtUvkfQTRWz7PZsvvzvCpBGy\n1LcQQojGSZISQghxAUp+2cWhmQ+jNRlJWvgSQR3aeK1t7bG96H/8Agwmd0IiqoXX2q6UqkJZNmXl\neaA1QERr0Jt8e8xahJRRpOdInhGtBrrFW4kNCez6Efv+cPLJWivlVujRQcfkUWaCzQGcYRE+pdFo\nuHnMRWRklbByy3E6JUbQu5P3Ep9CCCFEQyFjRYUQ4jyVHzhM+k1zwOmk4zvzCOnT3Wtta4/vQ7/5\nc9AbcYyajhqd4LW2K6WqUHIKyvPQGc0Q2dbvCQmXCgdyjBzJM2HUqfRpGdgJCaeisnSTjfeWW7E7\n4JrhJmaMlYSEqFmQSc/Mq3tg0Gt57+t95BZa/B2SEEII4XWSlBBCiPNgz8wifepslMJi2r3wKBEj\nB3utbe2JA+g3fwY6PY6R01BjW3mt7Uqpqrt+hLUQ9GYi2nUFncG3x6yBQ4HdmWZOlxgIMSn0S7QS\nagrc+fS5hS5e+dzCpl0OYiM13D05iME9DV5dLlY0bq3iQrgxOYkyq5PXl+zF4QzcvwchhBCiMjJ9\nQwghaslZWMyBG/6G/VQWiQ/NImbSeK+1rck8hP67T0CjxTHyRtQ4700HqZTqcq+wYS8DQzCEt0Kr\nNwBW3x63GmV2DXtOmbE6tcQ0c9IlzoYugFPnOw44SFtvw+aAi7vquXqYCZNBkhHi/A3tlUD6iUJ+\n2HOaxesPcuPlnf0dkhBCCOE1kpQQQohacFmspN80B8uBI8T/5Xpa3DXDa21rTh3BsPEjQINjxA2o\n8e281nalXAoUHQeHBYwhEJ7oXm3Dj/LLdfyWZUJxaWgdYaddVOAWtLQ5VL76zsbWfU5MBph6uYl+\nF/l3BIoIfDde3pmjp0tYv+MkSa0iuKSLj4vfCiGEEPUkgJ9BCSFE/VCdTg7f9Qilv+wi6spkWj9x\nr9eG32uyjmLYsAhUFcfwqagtOnil3Sq5nFB4zJ2QMIVBeCu/JyROFun59ZQJlwsuirPSPjpwExKZ\nOQr//rScrfucJMZpmXN9sCQkhFeYDDpmTuiOyajjg5W/cyqvzN8hCSGEEF4hIyWEEKIaqqpy9KF5\nFKzaSNiQi2n/8lw0Wu/cxGtyjmNYvxBcCs5h16O27OSVdqukONwJCcUO5ggIbYE/7/5dKhzKNZJZ\nbMCgU+ne3Eq4OTDny6uqyg+/Olj+vR2nAsP6GBg7yIheF6DZlQbsueeeY/v27TidTv7617/So0cP\nHnzwQZxOJ3q9nueff57Y2FiWLVvGggUL0Gq1TJ48mUmTJvk79DprEd2Mm8dcxJtLf+P1JXt5ZHp/\nTAZZUlYIIURgk6SEEEJUI/Pf75Kz6CuCuyXR6b3n0ZqMXmlXk3cSw7cfguLEOXQyrlYXeaXdKjlt\n7oSEywnB0dAszq8JCYcC+7JMFFj0NDO66NHcitmg+i2euii3qixeZ2XvEYVgM8wYa6ZrO/l69YWf\nf/6ZgwcPsnjxYgoKCrj66qu59NJLmTx5MmPHjuWjjz7igw8+YNasWbz22mukpaVhMBiYOHEiycnJ\nRERE+PsU6uySLvGkZxSyfsdJFq05wK3juvo7JCGEEKJO5KpJCCGqkL3oS07+6y1MrVuS9NF8dKEh\nXmlXk5+JYd1/wGnHOXgirjbdvNJulRwWKDwOquJORjSL8e3xamBxuAtalju0RAU76RpvQx+gkwn/\nyFRYtMpKYalKh5Y6bkgxER4SoCcTAC6++GJ69uwJQFhYGBaLhccffxyTyb2MbWRkJL/99hu7d++m\nR48ehIaGAtC3b1927NjByJEj/Ra7N00Z2YkjmcX8sOc0SYkRDO3l46WDhRBCCB+SKychhKhEwaqN\nHH3gWfRRESR9NB9jnHdu5DUFpzGsWwB2G85B1+Bq19Mr7VbJXu4eIaEqENrc7wmJQouW7SeCKHdo\nSQx30KN5YCYkXC6VdVvtvP6FhaIyldQBRu642iwJCR/T6XQEBwcDkJaWxmWXXUZwcDA6nQ5FUfj4\n44+54ooryM3NJSoqyrNfVFQUOTk5/grb6wx6LXdO6E6wSc+itelkZJf6OyQhhBDigslICSGE+JOS\nX3ZxaObDaE1Gkha+RFAH7yzPqSnKxrDuP2hs5TgGTMDVvrdX2q2SrdS97CcqhLUEc7hvj1eDU8V6\n0nPc01+SYm0khDn9Gs+FKip18fEaG4dOKISHaLgxxUz7ljKvvz6tW7eOtLQ03n//fQAUReH+++9n\nwIABDBw4kOXLl5+1varWbmpQZGQwer33f5exsaE+afO+G/rx5PtbeGvZb/z73mEEm6WoamV80f+i\n9qT//Uf63r+k/2tPkhJCCHGG8gOHSZ9xLziddFzwb0L6dPdKu5riXAxrP0BjLcNx6RW4OvXzSrtV\nshZD8QlA415hw+S/L0ZVhSN5BjKKjOi1Kt2aW4kMCsyClvuPOvlkjZUyK3Rrr2PKKDPNgqSYZX3a\nvHkzb775Ju+++65nesaDDz5ImzZtmDVrFgBxcXHk5uZ69snOzqZ375qTgAUF5V6PNzY2lJycEq+3\nC9AurhljLm3Nyi3HeX7hNu68qpvXVgZqLHzZ/6Jm0v/+I33vX9L/56ouSSPjTIUQ4r9sJ0+TPnU2\nSlEJ7V54lIgRg7zTcEm+OyFhKcXZfyyupEu8025VLAXuhIRGCxGt/ZqQcLpg72kTGUVGggwu+ra0\nBGRCwqmoLNts491lVqx2uHqYkZvHSUKivpWUlPDcc8/x1ltveYpWLlu2DIPBwOzZsz3b9erViz17\n9lBcXExZWRk7duygf//+/grbp64Z1p6kxHC2/Z7Nt9tP+DscIYQQ4rzJSAkfsTkUikpthIeYZLku\nIQKAo6CI9BtmYz+VRauH/0bMpPHeabi0EOPaD9CUF+Psm4LSZaB32q1KeS6UZoNG505IGIJ8e7xq\nWB0a9pw2UWbXERmk0DXeSiD+c5hb6GLRKisZ2S5iIzRMG2OmZWwAnkgj8M0331BQUMA999zjeS0z\nM5OwsDCmTZsGQIcOHXjiiSe47777uPXWW9FoNNx1112eURWNjU6r5a9XdeeJD35h8fpDtEsIo0OC\nf6dqCSGEEOdDkhJeprhcLF5/iJ3pOeQX24gKM9EnKZYpIzui0wb2wBRJtIjGymWxsnX63VjSjxD/\nl+tpPnO6dxouK8K49n00ZYU4e49C6TbEO+1WRlWhLMedlNDqIaIN6E2+O14Niqxa9p424VC0JIQ5\n6BhjRxuAgwp2pjv4/FsbNgf076LnmmEmTMYAPJFGYsqUKUyZMqVW26amppKamurjiBqGyFATt1/Z\njRc/3cWbS/by+M2XEBIk9SWEEEIEBklKeNni9YdYt+1/wyfzim2en6eOTvJXWHXSmBMtQqhOJ4dm\nPkzhD9uJujKZ1k/c65052eUl7ikbpQU4ew5H6TG87m1WRVWh9LR72obO6B4hoTP67ng1yCrR8XuO\nCVWFjjE2WoY5CbRp7jaHypLvbPyyz4nRANcnm+jfRW7yRMPVrW0UVw1px5Lv/+DdFfuYPbEn2kD7\nwxNCCNEkyR2lF9kcCjvTK19ybGd6LjaHUs8ReUdFoiWv2IbK/xIti9cf8ndoQtSJqqocfWgehau/\nI3rkQNq/PBeNNxJtllIM6z5AW5KHs9tQlJ4j695mVVQVijPdCQm9CSLa+i0hoarwR76B/dlmtBro\n0cJGYnjgJSRO5Sq8/Gk5v+xz0jJWy5zrgiUhIQLC+MFt6dYuil8P57Hy52P+DkcIIYSoFUlKeFFR\nqY38Ylul7xWUWCkqrfy9hqyxJlqEAMh88R1yFn1FcLck+n3+KlqTF27mrWUY1v0HbVEOzi6DUPok\n47O7ctXlXvLTVgT6oP8mJPwzAE5xwb4sE8cKjJj17oKW0cGB9e+Dqqp8+0sZLy22kFWgcllvA7Mn\nBREbKV+VIjBoNRpuu6IrkaEmvtx0hAPHC/wdkhBCCFEjudLyovAQE1Fhlc/hjgw1Ex7iv/ndF6ox\nJlqEAMhe9CUnX3gbU+uWJH00H0NYSN0btVncCYnCLJTOl6L0S/VdQsKlQOFxsJeCoRlEtgGtf2q9\n2JwadmWaySnTE25W6JtooZlR9UssF6rcqrLgGysLlhdjNMAt481cdZkJvT7AhnmIJi8s2MgdV3VD\ng4Y3l/5GUZnd3yEJIYQQ1ZKkhBeZDDr6JMVW+l6fpJgqi0PaHArZBeUNctRBY0y0CFGwciNHH3gW\nfVQEnT9+BWNcTN0btVsxfLsAbcFplE79cV481ocJCScUHgNHuXu5z4hW7uU//aDEpmX7CTMlNh3x\noQ56JVgxBlgd3D9OKbz4STl7Dit0bmvkvuuD6dZeSi6JwNUpMYKJwztQVGbn7WW/4XIFVpJQCCFE\n0yJXXV42ZWRHwD21oaDESmSomT5JMZ7XzxQIBSQrEi1nFu+sUF2iRYiGqmTLLg7d9TBak5GkRS9j\nbt+67o06bBjWf4g27yRKhz44L73Cd0kCxeEeIaHYwBwBoS18l/yoQU6pjv3ZJlwqtI+y0yrCEVD1\nI1yqyoZtDlb9bEcFLr/UyNSxUeTllfo7NCHqLOWSVhw8UcjOg7ks+f4Prrmsvb9DEkIIISolSQkv\n02m1TB2dxLXDOtS4fGagrNRxPokWIRqy8gOHSb/pXnA66bjg34T07lb3Rh12DOsXos3JQGnbE+eA\nCb5LSDjt7hESLgcERUFIvF8SEqoKxwsN/JFvRKtR6dbcRmyzhjfSqzrFZS4+XmPjYIZCeDMNN6SY\n6ZCoQxuI65YKUQmNRsOt47rwxAdbWfHjUTolhtOjfbS/wxJCCCHOIUkJHzEZdMRFBlf5fk0FJK8d\n1qHBjEI4n0SLEA2V7eRp0qfORikqof38uUSMGFT3Rp0ODBs/Qpt9DKVNN5yDrwFfjXJyWt0jJFxO\naBYLwTF+SUi4VDiQbSSr1IBJ56J7CxuhJle9x1EXvx9z8skaG6UWla5tdUxJNhMSJMkI0fgEmw3M\nvLo7Ty/czjvL9/HEzRcTFWb2d1hCCCHEWRrGHIEmKBALSFYkWiQhIQKNs6CI9BtmYz+VRauH/0bM\nxHF1b1RxYNj4MdrTR1BadcE5ZJLvCk06yqHgqDshERLvTkr4ISFhV2B3ppmsUgOhJoW+idaASkg4\nFZXl39t4Z6kVi01lwmVGbrlCEhKicWvbPIzrRydRanHwxpK9OJXA+ZsVQgjRNEhSwk+aagHJhlzU\nUzROLouV9JvmYEk/Qvxt19N85vS6N6o40X/3KdpTh1BaJuEcOtl3CQl7qXvKhuqC0AQI9s/w6zK7\nhh0ngiiy6ogNcdI7wYpJHzjF8/KKXLyWZmHjDgcxERpmTw5iaG8jmkAqgiHEBRreO4EBXeM5nFlM\n2sbD/g5HCCGEOItM3/CTplZAMhCKeorGR3U6OTTzYUq37ibqqstp/fi9db8JdSnoN3+G7mQ6rhYd\ncQ67DnQ++qfUVgxFJ93/H54IpjDfHKcGeeU69mWZUFwa2kTaaRsZWAUtd6Y7SFtvw2qHfhfpuWa4\nCbMxgE5AiDrSaDRMT+3MsawS1mzNoFNiOP06x/k7LCGEEAKQpIRfNaUCkoFS1FM0HqqqcvSheRSu\n/o6wIZfQ/qUn0NQ1AeZS0H+fhi5jP674djiGTwWdwTsB/5mlEEoy3dM0wluBMcQ3x6mGqsLJIj2H\n8oxoNdAlzkp8aOCMcrI7VJZusvHzb06MBrg+2UT/Lj76fQnRwJmNemZO6M6TH27j/W/2kxgXQnw1\nta+EEEKI+iJJCT/ydwFJq91JdkG5z48bSEU9ReNx8oW3yVn0FcHdO9PpvefQmox1a9DlQv/jl+iO\n7cUV1wbHiBtB76Mb3PJ8KD3tXsUjojUY6v/GwaXCoVwjmcUGDDoXPZrbCDMHzlz0U3kKC1fayMp3\nkRCjZdoYM3GRMipLNG0tY0OYntKZd1fs542v9vLw9H4Y9PL9K4QQwr8kKdEA1LRSh7dVTKX49XAe\nOQUWn0+lKCq1kVdDUc/6PH/R+GUv/ILMF9/B1LolSYteRhdax1EGqgv9z0vR/fErrthWOEZOA0Md\nkxyVHkeF8lwoywGt3p2Q0Nd/pXyHAr9lmSm06GhmVOjRwoY5QOpHqKrKz3udLNlkw6nAkF4Gxg82\nYtDLdA0hAAZ1b0F6RhGbdmfy8bqDzEi9yN8hCSGEaOIkKdFE2ByKZzTGF98drrepFIrLxeqtGWg1\n7ievf9aYi3oK/yhYuZGjD85DHx1J549fwRgXU7cGVRX9luXoDu/AFd0Sx8jpYPDBZ1ZVoTQLLPmg\nNUBEG9D7IPFRg3K7hj2nzVgcWqKDnXSJt6EPkAEGFpvKZ99a+fWQQrAZpqWa6d5BvuaE+LMbkjtx\n9FQx3+3KJCkxgoHdm/s7JCGEEE2YXK01cn8uMBkZaqTcVvmccF9MpVi8/hAbdpys8v3GWNRT+E/J\nll0cuuthtGYTSQtfwty+dd0aVFX0W79Gd3AbrsjmOEZNB6MPRi6oqrt+hLUIdCb3CAlf1aqoRnaR\nyo6TQThdGlpF2GkfFTgFLY+dUli4ykpBiUr7BC1TU8xEhgZINkWIembQ67jz6u7833+2smD177SO\nD6FlbP3XrRFCCCFAlgRt9CoKTOYV21CB/BI7VnvlSYmKqRTeUl0tCa0GRvRJaJRFPYV/lB84TPpN\n94LTScd35hHSu1vdGlRVdNtXoTuwBVdEHI7RN4HJB9OMVBcUn3AnJPRmiGzjl4REZrGeTb+rKC7o\nHGujQ3RgJCRcqsr6bXZeTbNQWKKSfImBO64JkoSEEDWIjwzmlrFdsDtcvL5kL1a7098hCSGEaKLk\nqq0Rqy4pUBlvT6UoKrWRX0UtCRVIuaS1LAcqvMJ28jTpU2ejFJXQ7sXHiBgxqG4Nqiq6XevQ7/8R\nV1gMjtE3g7mZd4I96zguKMwAW4m7mGVEG3ctiXqk/regZXqOCYMOeiVYaREWGDcnJeUu3lli5esf\n7YQEa7jjGjOpA0zotAGQTRGiAejXOY7k/q04lVfOh6sOoKqBUTtGCCFE4yLTNwLQmfUhqpv6UF1S\noDLenkoRHmIiKsxUaZHLKKklIbzEWVDEgal/w34qi1aPzCZm4rg6t6n7dQP6vZtwhUbjSL4Fgnww\nrNmlQOFxcFrcy32GJ7pX26hHThfsyzKRX64n2OBiWDcdlpLAWGHjwHEnn6yxUVKu0qWtjutGmwkJ\nlmSEEOdr0ogOHMks4ud9WSS1imB4n5b+DkkIIUQTI0mJAPLn+hA1rZpRXVLAbNQR1sxIbqGFyFAz\nfZJivD6VwmTQ0Scp9qyimhWkloTwBpfFSvpNc7Ae/IP4266n+Z3T6tymbs936H/dgBoSiSP5ZggO\n9UKkf6I4oegYOG1gCoewBOp7roTFoWHvaTNldi2RQU66xdsIMYdiKanXMM6boqis/NnOhu0OdFq4\ncqiRy3ob0ATCXBMhGiC9TsudE7rzxAdb+XhdOu1ahNGmuQ/+3RNCCCGqIEmJAFJRH6JCTatmVJcU\nGNKzBX+9theHj+bVOOKiLioSHTvTcykosfosASKaHtXp5NCdD1G6dTdRV11O68fvrfONqW7fD+h3\nrUNtFo49+RZoFu6laM+g2N0jJBQ7BEVCSPN6T0gUWbXsPWXG4dLQMsxBhxg7gTDjIa/IxUerrRw7\n7SI6XMO0MWZaxUlyU4i6igoz85fxXXnp89289tUenrj5YoLN9V/bRgghRNMkSYkAUV19iOpWzagu\nKWA26omL9EHhvjPotFqmjk7i2mEdajXlRIjaUFWVow/Oo3DNJsKGXEL7l55AU8f6JPadm9BvX4Ua\nHOZOSIREeCnaMzhtUHgMXE4IjoFmsfWekDhdouNAtgkV6BRjo2V4YNSP2H3QyWffWrHaoU9nPROH\nmzCbAiCT4iN5BXb+s/gkWTk2nn24M9pAyCqJBq1nh2jGD2rDih+P8d7X+5l1TQ8ZgSSEEKJeSFIi\nQFRXH6Ji1YzKEgwNJSlgMuh8ngARTcfJF94m56OvCO7emU7vPYfWZKxTe9r0X7BuWY4aFOKeshEa\n5aVIz+CwuEdIqAqExENwtPePUQ1VhT/yDRwvNKLTqnSLtxIV3PDrRzicKks32fhprxOjHqaMNnFx\nF32TvVlSFJVv1ufw8ZeZWG0uenULRUoTCm+ZMKQ9h04UsfNgLmu2ZpBySR2XVRZCCCFqQZISAaK6\n+hC1WTVDkgKische+AWZL76DqXVLkha9jC60bkUotYe2Y9iyHE1QCPbRN6OGxXgp0jPYy6Aow73a\nRmgL97SNeqS4YH+2idwyPWa9ix4trDQzNvxb2dN5CgtX2jid76JFjJZpqWbio5ruij0H/yjjzQ+P\nc+SYhZBmOu66vjUjh0TLKAnhNVqthr9e2Y0nPthK2sbDdEgIp2OiD6axCSGEEGdould3AaaiPkRl\nals00uZQyC4ox+ZQvB2eEPWiYOVGjj44D310JJ0/fgVjXN0SCNoju9D/tBTVFEzwxJmoEXFeivQM\ntpL/jpBwQVhivSckbE4NO0+ayS3TE25W6JdoafAJCVVV+Xmvg5cWWzid72JwTwN3Tw5qsgmJsnKF\ntxdl8M+nDnDkmIURg6N49f91ZfRlMZKQEF4XHmLijqu64VJV3li6l+Jyu79DEkII0cj5dKSE1Wpl\n/PjxzJw5k4EDB3L//fejKAqxsbE8//zzGI1Gli1bxoIFC9BqtUyePJlJkyb5MqSANmVkRxSXyq70\nXArLbETVsmhkVat2zJrcp54iF6LuSrbs4tDMh9CaTSQtehlz+7oNK9Ye3YP+xy/BaMIxega62ATI\n8fLSE9YiKD4JaCC8NZh8sLRoNUpsWvacMmFXtDQPdZAU2/ALWlpsKp+vt7H7oJMgE9yQYqZHh6Y5\nqE9VVX7YWsD7n5ygoMhJyxYm7pjWmu4XycoIwrc6t47kmsva88V3R3hn2W/cM7lXpat8CSGEEN7g\n0yu9N954g/Bw97C/+fPnM3XqVMaMGcOLL75IWloaEyZM4LXXXiMtLQ2DwcDEiRNJTk4mIsIHBeYC\nXEVi4ddDuRSU2ogIMdKzQ1SVy4GeqapVO4KDjEwY3NbHkQtRd+W/HyL9pntBUej4wQuE9Opap/a0\nx39D/30a6I04Rs1AjUrwUqRnsORDyWnQaN0JCWP9Tp/KLtXxe7YJlwodom0khjvru6bmeTt2WmHR\nKiv5xSptW2i5MdVMZGjTvBE6lWXl7UUZ7PqtBKNBw9SrWzBhTDwGfdPsD1H/xgxow6ETRew+nMfn\nGw5z3ahO/g5JCCFEI+Wzq5vDhw9z6NAhhg8fDsCWLVsYNWoUACNGjOCnn35i9+7d9OjRg9DQUMxm\nM3379mXHjh2+CimgVSQWKmpKFJba2bAzk8XrD1W7X3Wrdvy895RM5RANnu3kaQ7cMBulqIR2Lz5G\nxPCBdWpPm/E7+s2fg06PY+R01JhEL0V6hrLc/yYkdBDRtl4TEqoKxwoM7MsyowG6N7fRKqJhJyRc\nqsqG7XZeTbNQUKwy+mIDM68NapIJCYfDxWfLTnH3o/vZ9VsJfbqH8dKTXZl0RQtJSIh6pdVouP3K\nbrSIDmbN1gw2/5rp75CEEEI0Uj67wpk3bx4PPPCA52eLxYLR6K6QHx0dTU5ODrm5uURF/a/KfVRU\nFDk5ld9AN2U1LQdaXWKhulU7cgstFJVW/l6gkDoZjZuzoIgDU/+G41Q2rR6ZTczEcXVqT3PyIPpN\nn4JGi2PkNNQ4L1eWV1UozYKybNDqIbItGMzePUY1Kgpa/pFvxKR30aelhZhmDftvo6TcxbtLraz4\nwU5IkIa/Xm1mzEATuoY+z8QH9uwv4d7H9/PJklOENNPz9zva8ei9HWgRV30hYyF8JcikZ/bEnjQz\n61m4+gCHThT5OyQhhBCNkE+mbyxZsoTevXvTqlWrSt9X1cqLrFX1+p9FRgaj19d9WcvY2MCYl3sq\nt4z8kqqXA9UZDcTGNKv0/dDwIGIjg8gusJzzXkxEEB3aRmM2Bt58bUVx8f7y3/h57ylyCi3ERgQx\noHsLbrmiGzrd+eXaAiJoyK4AACAASURBVOVz4GsNrR8Ui5Ut1/4d68E/aHf3TXR5bGadloF0Hk+n\n/LuPQaMl+Orb0LdOOmebuvSBqqqUnjqKtTwPndFMeJuL0Bnr72bSalf5MV0lrxSiQmBwkg6z8fxr\nWNTn52DvYRtvpRVSVOqiZycTt18TTlhI/S9Z/Gf1/bdQUGjn1fePsHpDFhoNTBzfkr/c2JaQZoH3\nb7NofOIjg7lzQndeXLybV7/aw2Mz+hMVVn/JViGEEI2fT654Nm7cSEZGBhs3buT06dMYjUaCg4Ox\nWq2YzWaysrKIi4sjLi6O3Nxcz37Z2dn07t27xvYLCsrrHGNsbCg53i5q5yOKQyEqtOrlQBW7o9pz\n6dkh+qyaEhUGdG9BSZGFwOiFs328Lv2sc8ousLBs8xHKLXamjj73ZrMqgfQ58KWG1g+q08nBv9xP\n4U87iZqQQsw/ZpKbW3rB7Wmy/sDw7UJQVRwjpmILanFOUcs69YGqugta2opBb0YJa01+kR2on6r1\npf+fvfsOjKrO9///PNPTeyMFQm8JVcVCL4KFIk2poqtgXXf9XXct6zbv7nf1rldX3cuuClIFhRUQ\nBQGxYAGlCKGHnt77ZNo55/fHCMtKyiSZmnwe/xCSmTOfmUySOa95f95vq0RWoQmrQ0N8qINecVZq\nqmjxz7a3ngeyrPLJPhu799uRNHDnLQZGDNJhrTdTcm1+6lXe/FlQFJVde8pYtSGP2jqZbp2DWbIg\nle7pIdSb66lv+5+6VvO3kFLwrb5dorl7bHfW7srmtY1Z/HreYJemfgmCIAiCKzwSSrzyyitXPn7t\ntddITk7m0KFDfPLJJ0yZMoUdO3YwfPhwBgwYwHPPPUd1dTVarZaDBw/yzDPPeGJJAcVql6mqtRIR\nasSo114ZB9pQsODKONDL0zkOnS6losZC1I9TO+67sx/l5XUurcGfNLedZfrIbn63ZsF1qqpy4em/\nULnjS8KHX0/XV36H1Iau71LxRfS7V4Oq4Bh5D2onNzdrUxWoygVbLeiDnE0tNd57/pXWaTlRZERW\nJbpE2+gcaffr/hHl1Qqrt1u4WKgQEy4xb5KJtISO9/N6IcfM0pU5nDpbR5BJwwNzU7h1dFyH3LYi\nBIaxQ1LILanly8MFLPvoBEum9GtT9ZogCIIgXOa12tDHHnuMX/3qV6xfv55OnToxdepU9Ho9Tz75\nJPfffz+SJPHII48QFtZx351pbHTn7DHdGw0WmhsHCqDVaJgzrifTR3b7j6ChoW0OTa3BX8aBNdUn\no6LGQlWtlfgo7046ENwn73/+ScmaDwju34seb72IxqBv9bGk0lz0u1eB7MAxYjZKSi83rhRQZKjK\nAbsZDCEQkeqctuEFqgq5VTrOlhnQSNA3wUJ8qH/3jzhyxsF7n1qot8LAnjpmjjZiMnaskxqLVWbd\n5gI+3FGMosDN10Vy390pREcZfL00QWiSJEnMm9CLgjIz358sJiU+lDtv6uLrZQmCIAjtgMdDicce\ne+zKx8uXL7/m6xMnTmTixImeXkZAaGx0J8CccT0bDBZawqjXNnuy3twa/EFEqJHo8Ma3s0SEiqZw\ngap45Qby//dNjJ2T6bXmb2jDWt4T4TKpLB/9pyvAYcNxy0yUtLaNEb2G4oDKS+CwgDEcwpPxVomC\nokJ2iYGCGj0GrUL/RCvhJsUrt90adofKlj1WvslyoNfBrLFGru+r63Dvsn53qJK31uZSUmYjIc7A\ng/NSGZwR4etlCYLLdFoNj0zL4I8rvueDL8+REhvCoJ5xvl6WIAiCEOD8461vweUJG5eDBU9sT2jL\nlA9vurydpSGubGcR/FP5ts+48MyL6GKi6LX2dfRxMa0+llRRiH7XO2Cz4rhpOkqXDPctFEC2Q8UF\nZyBhivRqIGGX4Ui+iYIaPaEGmcEpFr8OJIrKFV5dX883WQ6SYjT84u5gbuin71CBREmZjT+/dpY/\nv3aOiko7M+5I5NU/9hWBhBCQwkMMPDY9E4Newz8/PE5ucev7/QiCIAgCeHH7htA0f9iS4A9rcFVb\ntrMI/qdm3yHOPvwsGpORnqtfxZTe8OQeV0iVxeh3Lkey1WO/cRpK1wFuXCngsDorJBQ7BMdASLzX\nAok6m8TRQhP1dg2xIQ76xFtp4bAZr1FVle+OO/jgCyt2B9yUoWPycCN6XccJIxwOlY92FbNucwEW\nq0K/XqEsnp9KaqcgXy9NENokLSGMn93el79vOsrfNh7hNwuHEhYstiAJgiAIrSNCCT/hD1sS/GEN\nrmqsT4YQeMwnz3D63l+CLNNj+cuEDmj9NgupqsQZSFjN2G+YjNJ9sBtXCtgtUHkRVNkZRgTHeC2Q\nKDdrOFZkQlYk0iJtpEf7b0NLi1Xl/c+s/HDaQZAR5kwwkdm9Y/25OXW2jqUrLnEht57wUB0PzEtl\n9E3RHapCRGjfhvaOZ/LNXdjy9QX+/sFRnrx7IDp/TUkFQRAEv9axXiV6WFsmVrRmwoa7J2S0dcqH\nL7jSJ6MxVrtMQWkdsl32y/vWEVhzCzk193Hkqhq6vvYHIkYNa/3BqsucgYSlFvt1t6P0vM59CwWw\nmaHqknPaRmgiBEe79/hNyKvSkV1qQAJ6x1tJDHN47bZb6lKRzOptFsqqVTonapg30UR0eMc5Uamt\nc7BqYz47vyhFVWHciBjmz0gmPFT8uRXan8m3pJNXWseBUyW8uyub+be6uZmwIAiC0CGIV0lu4K6J\nFa5uSfDkhIyOsC3iPx6/GivRYf43YaQjcFRUcWruY9gLikn9zc+JnX5b6w9WW4Fh53Kk+hocQyai\n9G5DuNEQa61zygaqs3+EyTu9ABQVzpYZyKvSo9eo9E+0EBHkn/0jFFXly0N2PvrGhqrA2KF6br3B\ngFbbMSoDVFXli73lvLM+j6pqB6nJJpbMT6Nvz9Y3axUEf6eRJH52e1+KKw7w2aE8UuJCGD04xdfL\nEgRBEAKMCCXcwF0TK1zdkuDJCRkdYVtEIEwYae9ks4XTC3+BJfs8CQ/OIXHJvNYfrK7KGUiYq3AM\nGo/c92b3LRTAUg3VuYDkHPlp9M7YYocMx4qMVNTrCNYrZCRZCNKrXrntlqoxK6zbaeXkRZmwYIk5\nE4z0TOs4f17yCiz8Y3UOWSdqMBgkFszsxJ3jE9B1oP4ZQsdlNGh5bHoGf1yxn7W7skmKCaF35yhf\nL0sQBEEIIOJt4TbyxMSKpiZseGtChienfPhSoEwYac9Uh4OzDz9D7f4jRE+9lbTnn2j9PntzNfqd\ny5BqK3AMGIPcf4R7F1tf4QwkJA1EpnktkKi3SxzMC6KiXkd0sIPBKfV+G0hk5zh4+d16Tl6U6ZWm\n5ck5QR0mkLDZFd7dlM8Tvz1B1okahmSG89oLfZk2KVEEEkKHEhsRxCPTnFOO/r7pKMWV9T5ekSAI\nghBIOsYrRw/y9sSKQJqQ4Y/E4+dbqqpy4df/j8odXxI+/Hq6vvI7pNZumamvRb9zOZqachz9RyBn\njHLrWjGXQW0RSFpnIKH3zsSEynoNRwtNOBSJlAg73WJsftnQUlZUduyz8en3diQN3HGzgZGD9Wj8\ncbEe8MOxav65KoeCYisxUXp+NieVGwZHiEaWQofVMzWS+bf24p1tJ3lt4xGemTeEIKN4mSkIgiA0\nT/y1aIWrG0x6e2KFJ2/P3Y0z/VEgTRjxFm82/Mz7n39SsnYTwRm96fH2S2gM+tYdyFLnDCSqS3H0\nvRl54Dj3TcFQVagrAXMpaHQQ2Rl03nleFFTrOF3iHKvXM85Kp3D/bGhZUaOweruFCwUK0eES8yaa\n6JzYPn9n/FRFlZ3l63LZs68CjQR3TojnnilJBAV1jPsvCE0ZMaATOcW1fHogl7e2HueRuzI6TFAp\nCIIgtJ4IJVqgsQaTA3vE8umBvGsu74mJFZ6YkOHOxpn+HmwE4oQRT/F2w8/ilRvI/983MXZOptfq\nV9GGhrTuQFYz+l3voKkqxtF7GPLgW90bSNQWOrdtaPXOQEJrcM+xm7nZc+V6cioN6DQq/RIsRAX7\nZ0PLrLMO1u+yUG+FAT10zBxjJMjY/k86ZEVlx+elrN6Yj7lepkd6MEsWpNG1s6isEoSr3T22O/ml\ndRzKLmXTnnPcNaKbr5ckCIIg+DkRSrRAYw0SxwxJZtzQFK9NrHD3hAx3NH705EQQd+sIE0Zc4c2G\nn+Uf7+bC039BFxNFr7Wvo4+Lad2BbPXod61AU1GI3PM65KG3uS2QUFUVqvPBWgVao3PLhraVlRwt\n4FDgRJGRMrOOIL1CRqKFYIP/9Y+wO1Q+/MrG10fs6HUwc4yRG/rpOsR2hXMXzSxdeYns82aCg7Qs\nnp/K+JGxaDXt/74LQktpNRoemtqfF1bsZ+s3F0mJC+X6Pgm+XpYgCILgx0Qo4aKmGiQezi7jhQdu\n8NrECndOyGiu8eP0kd1cOnYgTbS4+vHTGvTINrtPKiR8WVXiru+7K2r2HeLsI8+hCTLRc/WrmNJT\nW3cgmwX9p6vQlOcjdxuM4/o73FghoVCdk+0MJHRBzkBC4/nvicUhkVVgpM6mJTJIpl+CBX8s1ikq\nV1i13UJBqUJitIb5k4wkxvjhQt2svl7m3U0FfLSrGEWFEcOiuHd2ClERng+rBCGQhQbpeWxGJv+9\ncj/LPjpBfFQQXRLDfb0sQRAEwU+JUMJFrjZI9GaTxMsTMtrCHY0fvXmC605GvZa42BBKSmq8erv+\nUFXirYaf5pNnOH3vL0GW6bH8ZUIH9G3dgexW9J+tRlOag9x1AI5hU5wTMdxBkaEqB5vdDPoQ59hP\nL3wfqi0ajhYasckaksLt9Ii14W9vvKuqyvcnHHzwuRWbA27M0DFluBF9O58soaoqew9W8vbaXMoq\n7CQlGFk8L5UB/cRJlSC4Kjk2hAcn9+O1DUd4bWMWzy8c2iH7NgmCIAjN86+6ej92uUFiQ9zVINFq\nlymuMHt1LKU77pcrJ7jCv12uKimrtqLy76qS9bvPeG0NXnk+5xZyau7jyFU1pL/yOyJGDWvdgRw2\n9J+tQVN8Eblzfxw3TnNfaKA4oPIi2M0YwqIg0juBRFGNlh/yTdhkie4xVnr6YSBhsaqs2WFl/S4r\nGg0smGRixmhTuw8kCoos/PerZ3nxjfNU1TiYPTmRV/7QRwQSfuTFF19k9uzZTJ8+nR07dgCwcuVK\n+vXrR11d3ZXLbdmyhenTpzNz5kzef/99Xy23QxvYPZbpo7pRUWPl9Q+ysDv8s1eOIAiC4FuiUsJF\nnmyQ6Mt3zt1xv8REC9f5S1WJpxt+OiqqODX3MewFxaQ+/wSxd01q3YFkO/rP16IpOo+c1hfHLTPc\nt61CtkPlJZCtYIogPLUHpaW17jl2I1QVLlTouVhhQCupZCRaiQnxXgjpqpwimVXbLZRVqaQlaJg3\n0URMRPvOsB0OlS07inhvSyFWm0JmnzAenJ9KcqLJ10sTrrJ3716ys7NZv349FRUVTJs2DbPZTFlZ\nGfHx8VcuZzabeeONN9iwYQN6vZ4ZM2Ywfvx4IiMjfbj6jmnSDWnkFtey93gRKz85yX239ekQvWgE\nQRAE14lQogU81SDR1/0YZo/pjqqqfJ1ViMXmPEEyGTQoqoqsKM0GI2Kiheu8tW3CFZ56PstmC6cX\n/AJL9nkSF88lacm8Vh7Ige6LdWgKziKn9MJxy0w3BhI2qLgIih2CoiE0weMvkmUFTpUYKa7VYdIp\nZCRZCPGzhpaqqvLFIRsffW1DVmDMED0ThxnQatv3CcTx07UsXXWJnDwLUZF6HlqYxohhUeLEyQ9d\nd911ZGZmAhAeHk59fT1jx44lLCyMDz/88MrlDh8+TEZGBmFhYQAMHjyYgwcPMmbMGJ+suyOTJIl7\nJ/WmqMLM11mFpMaFMuH6NF8vSxAEQfAjIpRoAXc2mLzMHe+ct7VholajQZKkK4EEgMWmsPtAHhpJ\ncikYERMtXONPVSWeaPipOhycfehpag8cIWbaRFJ/8/PWHUiR0X25Hm3eaZROPXCMuBu0bvp15bA4\nKyQUB4TEQXCs+xpmNsLqkDhaaKTGqiXcJNM/0YLBz7K6WrPKytUVHD5tIzRIYs4EI706t+8/EdW1\nDla9n8euPWVIEtw6KpafL+6Jtd7i66UJjdBqtQQHO4PbDRs2MGLEiCvBw9VKS0uJjo6+8v/o6GhK\nShr+Wyt4nkGv5dG7MvnDiu9Z/9kZOsWG0L9rK6cwCYIgCO1O+37F6SHuaDB5WVveOXfXtg93BCOe\nCGzaI3+sKnFXw09VVbnw6/9H5c49hA+/nvT//S1Sa7YfKTK6Pe+jzT2JktgV+8h73BdI2OudgYQq\nQ2gCBHv+RXGtVUNWoRGrQ0NCqJ1e8f7XP+JMroM1n1iprlPpmarlnglGwkPa73YNVVX57Oty3nkv\nl5pamS6pQSxZkEavbiGEh+opEaGE39u1axcbNmxg2bJlLl1eVV2rSoqKCkanc//v4bi4a4OTjiYu\nLozf3HcDT//9a/6x5Rh/fWIkyXGhXrttwXfE4+874rH3LfH4u06EEl7206qGtrxz7q5tH+7cUuDO\nwOZqvhyf6W7ttaok76V/ULJ2E8EZvenx9ktoDK0Ym6go6L7eiPbSMZSELthHzwWdm8Yv2uqgKgdU\nBcI6QZDn95aX1mk5XmREUSXSo22kRdo9XZTRIrKisvM7G7u+syNpYPaEMIb2UtD40yLdLCevnqWr\ncjh+uhaTUcO9s5O5Y1x8u9+i0p7s2bOHpUuX8tZbbzVYJQEQHx9PaWnplf8XFxczcODAZo9dUWF2\n2zovi4sL8/qUJ38VHaxn4cRevLX1BL/957f8ZsEQgk2eHbErHn/fEo+/74jH3rfE43+tpkIaEUp4\nSVNVDa1559ydDRP9aUvBT/nD+Ex3a49VJUUrNpD/ylsYOyfTa/WraENDWn4QVUH37Sa0F7JQ4tKw\nj54HOoN7Fmitgaoff8bCU8Dk2UkKqgo5lXrOlevRSNAvwUJcqH81tKyoUVjziYXz+QrR4RLzbjUx\nNDO03f4BtVoV3t9awKbtRcgy3DAogp/NTSU22k3PMcErampqePHFF3nnnXeabFo5YMAAnnvuOaqr\nq9FqtRw8eJBnnnnGiysVGnNT/yRyS+rYvu8SSzcf44mZA9D4W/mYIAiC4FUilPCSpqoaWvPOubur\nG/xtS8Flvm4C6kmeqirxtvKPd3Pxmb+gi42m19rX0ce1YkuEqqDbuwXtuUMoMSnYx8wHvZvCsPpK\nqMl39o2ISAWDZ8uFFRVOlxgorNFj0CpkJFkJM/rXGLyjZx2s22Wh3gqZ3bXMGmsiyNh+TwoOHKni\nzdU5FJXaiIsx8LM5KVw/SExhCEQff/wxFRUVPPHEE1c+d8MNN7Bv3z5KSkp44IEHGDhwIE899RRP\nPvkk999/P5Ik8cgjjzRaVSF434yR3cgvrePI2TLe//wMs8f08PWSBEEQBB8SoYQXuFLV0NJ3zttS\n3XB5K0RYRNCVz/njlgJ/GZ8pNK5670HOPvIcmiATvVa/iik9teUHUVV0332E9swBlOhO2McuAIOb\nxjCay6G2ECQNRKaB3rMhkE2GY4Umqixawowy/ROtGHX+M2HD7lDZ+rWNrw7b0Wlhxhgjw/rp2u2U\nibIKG2+/m8u3+yvRaGDqxHhmT0nCZBS/NwLV7NmzmT179jWff/TRR6/53MSJE5k4caI3liW0kEYj\n8eCd/fjvVfv55LscUuJCuTkjydfLEgRBEHxEhBJe4GpVQ0veOW9NdcNPt0LERQWR2S3mylYIf9tS\n4E/jM4VrmU+cIfveX4Is02P5y4Rk9mn5QVQV7f5taE9/hxKVgH3cQjAGNX89F46LuRTqSpxjRCM7\ng85NQUcj6mwSWQUmLA4NcSEOesdb0frRDqPiCoVV2yzklyokRGuYP8lIUkz7PDmXFZVtn5aw9oN8\n6i0KvbuHsGRBGp1T3PDcEgTBLYJNOh6fnskfV+xnxfaTJEQH0z05wtfLEgRBEHxAhBJe4KmeDS2t\nbvjpVojiivprtkL405YCf+510dFZcws5Ne9x5Opaur7+RyJGDWv5QVQV7aEd6E5+ixIRh33svWB0\nw3NPVaG2COrLQaP/MZDwbN+AcrOWY0VGZEWic5SNLlH+1dBy/wk7Gz+3YrPDsH46powwYtD70QLd\n6Mz5Ov5v5SXOXawnNETLw/emMfaWGLFnXRD8UEJ0MA9N7c/L7/3A6//K4vmFQ4kO92yALAiCIPgf\nEUp4gad6NrSkuiEQt0L4c6+LjsxeXsmpOY9iLygm9fkniL1rUquOoz28G92xr1DCY7CPXwRBbuj1\noKpQUwCWStAanIGE1rOd3XOrdJwpNSBJ0CfeQkKY/zS0tNhU/vWZlQOnHJgMMH+ikYE9Pft4+Eqd\nWWbtB/ls212CqsKom6JZOCuZyPD2eX8Fob3olx7N3WN68O6n2bz2ryx+PXew+PsuCILQwYhQwks8\n2bPBleqGQN0K4Y+9Ljoy2Wwhe+EvsZy5QOLieSQtmdeq42iPfI4u63PUsGjs4++DIDc0oFMVqM5z\nTtrQmZw9JDSe+xWnqHCm1EB+tR69VqV/ooUIk/80tMwtllm1zUJplUpagoZ5E03ERPjRfhI3UVWV\nr7+vYNm7uVRUOUhONLJ4fhoZfURTQ0EIFOOGppBbUsueIwUs//gEiyf3a7e9bgRBEIRriVDCS1rT\ns+FyQ0p39HcI1K0Q/tjroqNSHQ7OPvQ0tQeOEDNtIqm/ebxVx9Ee24Pu8KeoIZHYxi+CYDeM51QV\nqMoBW52zmWVEqrOXhIfYZTheZKKiXkuIQSEj0YJJ7x8NLVVVZc9hO1u/siErMHqInknDDGi17e8F\nfkGxlTdX53DoaDUGvcScaUlMnZiAXt/+whdBaM8kSWLehF4UlJv57kQxKXGh3HFTF18vSxAEQfAS\nEUp4mStVDT9tSBkdbmRQz7grDSlbE1YE+lYIf+p10RGpqsqFX/2Zyp17CB9xA+n/+1skTctP/LQn\nvkF3cAdqcDi28fdBiBvGMioyVF0Ce71z3GdEinPahoeY7c6GlvV2DTHBDvokWNH5yTlwbb3K+p0W\njl+QCQ2SuGeCkd6d29+vebtdYdP2IjZsLcRmVxnYL4wH56eRFO+f4aogCM3T6zQ8Mi2DP674nn99\neY7k2BAG9Yzz9bIEQRAEL2h/r1bbgZ82pCyrtrJrfy6KqqKRpEbDiub8dCtEbOS/p28ILefOShZ/\nl/fSPyh5dzPBGb3p8daLaAwt36evObUP3f5tqEFhzkAiLKrtC1McUHkRHFYwhkN4Mp7sMFlRr+FY\noQmHIpEaYaNrjP80tDybK7PmEwtVdSo9UrXMmWAkPMRP0hI3OnqyhqWrLpFXYCUqQsfj96Ry03WR\notRbENqBiBADj92VyZ/XHOCfW4/z7PwhpMS5od+QIAiC4NdEKOFnmmpI+U1WIRbbv5voXQ4r4N/T\nM5ry060Q3brEUFNV756FdyDNVbK0N0UrNpD/ylsYu6TQa/WraENDWnwMTfZ+9N9tRTWFOJtahse0\nfWGyDSovOf8NioLQRI8GEgXVOk6XOKd49IqzkhTu8NhttYSiqOz8zsbO7+1IwG03Ghg9VI+mnZ2k\nV1Xbeee9PD7/phxJgtvGxjFnWidCgtt3ICgIHU3nxDDuv70v/7fpKH/bcITn772O0CDRsFYQBKE9\nE6GEn2mqIeXVgcTVWjo94/JWCJNBR02rV9pxNVbJAq6FQ4Gk/OPdXHzmL+hio+m19nX0cS0PEzRn\nD6HbuwXVGIx93CLUCDeU4zqszgoJxQHBMRAS77FAQlXhbJmB3Co9Oo2zoWVkkH80tKysUVjziYVz\n+QpRYRJzJ5pIT2pfJ+mKovLpV2WsfD+P2jqZrp2DeGhBGt3TWx6OCYIQGK7rHU/ezV3Y8vUF/v5B\nFr+cPRCdtv2F/oIgCIKTCCX8TFMNKRvjjukZHWkrQlsE4mjV1qree5CzjzyHJjiIXqtfxdQlpcXH\n0Jw/gu7bD8Bgwj7uXtSohLYvzF7vrJBQZWcYERLb9mM2wqHAiSIjZWYdwXqF/kkWgv2koeWxcw7W\n7bJgtkBmNy0zx5oINrWv6oiLufUsXXmJk2fqCDJpuP+eFCaNjUOraV/3UxCEa02+JZ3ckjoOni7h\n3U+zmT+hl6+XJAiCIHiICCX8TFMNKU0GbYPVElFhRmx2GatdbvEJcUfbitBWgTpataXMJ86Qfe8v\nQZbp8c7LhGT2afExNBePovt6I+iM2MctRI1OavvCbHXOKRuqAmFJzm0bHmKxS2QVmqizaYgKkumb\nYMEf8iaHQ2XrNzb2/GBHp4Xpo43c2F/XrnoqWKwy6zcXsGVHMYoCNw2N5L57UoiJMvh6aYIgeIlG\nkvjZHX340yoznx3MIyUulNGDkn29LEEQBMEDRCjhh37akDIqzMSgnrGoqsqnB/KuuXydxc5vl33f\nqkChI21FcIdAHa3aEtbcQk7NfQy5upaur/+RiJHDWnwMTc4JdHveB50e+9gFqDFueCFprYGqXEB1\nNrQ0RbT9mI2osmg4WmjCLkt0CrfTPdaGP7w5X1KpsGqbhbwShYQoiXmTTHSK9YOkxI2+/6GSN9fk\nUlJmIyHWwAPzUhmS6bnvtSAI/stk0PH49Ez+sGI/a3eeplNMML3SPBdGC4IgCL4hQokW8NYWh582\npLx8e7KiIEnSlbDCoHdWTlhszv3tLQ0ULDZHh9mK4C6BPlq1OfbySk7NeRR7YQmpv32C2LsmtfgY\nmrzT6L5cD1od9jHzUeNS274wSxVU5wESRKSCMaztx2xEUY2WkyVGVBV6xFpJjvCPhpYHTtrZ+JkV\nqx2u76tj6kgjRr0fJCVuUlpu4601Oew7VIVOKzH99gRm3pGE0SgqtgShI4uNDOKRaf35n3U/8MYH\nR/nNwqHERQb5elmCIAiCG4lQwgW+2uJwuSHlZVeHFSWV9bzy3g8NbudwNVCoqO4YWxHcrbFKlkAf\nrSqbLWQv/CWW/UjdAgAAIABJREFUMxdIXDyPpMXzWnwMKf8Mus/fBUmDffQ81PjObV9YfQXUFICk\ncQYSBs80OFRVlfPlei5WGNBqVPolWokObri5rDdZbSr/+tzK/pMOjHqYN9HIoJ7tpxO9LKts3VXM\nuk0FWKwKfXuGsmR+KqnJ4qRDEASnXmlRzJ3Qk5XbT/HaxiM8PW8IQUbxElYQBKG9EL/RXeBvWxyM\nei0GnYaKGluDX3c1UIgKb/9bEcD9FS6NVbIEMsXh4OySp6k9cISYuyaR+pvHW3wMqfA8+s/XAGAf\nNQc1Mb3tC6srhbpikLQQmQZ6z5yoygrszVbJrTBg0ilkJFkIMfi+oWVusczq7RZKKlVSEzTMu9VE\nbGT7qRw4dbaOpSsvcSGnnrBQLQ/M7czom6PbVX8MQRDcY9TAZHKLa9l9MI+3th7nkbsy2t3oY0EQ\nhI5KhBLN8OW0haZOpt3R28Bk0LXrrQiernD5aSVLoFJVlaMP/5bKXXsIH3ED6S8/j9TCx0cqvoj+\ns9WgqjhGzUHt1MaqEVV1hhHmMtDoILIz6DwTklkdEkcLjdRYIcIk0y/RgsHHT31VVfnqiJ0P99iQ\nFRg1WM+kGw3otO3jBXhtnYNVG/PZ+UUpqgpjb4lhwaxkwkPFnyRXORwqX3xbTmW1nem3J/p6OYLg\nFXeP7UF+aR2HskvZvOc800Z09fWSBEEQBDcQrwCb4YtpC66cTLurt0F73YoA/lfh4q/yXlpK/vIN\nBGf2ocdbL6IxtGxrgFSSg373KpAdOEbejZLcxsdWVaG20LltQ2twBhJaz2xXqLFqyCowYpM1dImD\ntDCLzxta1tWrrN9l4dh5mRAT3DPBRJ8u7eNXtaqqfLm3guXrc6mqdpDaycSSBWn07Rnq66UFDIdD\n5fNvy9jwYSFFpTbCQ3VMnZQgxqQKHYJOq+HhaRn8ccX3fPjNBZLjQri+jxtGTQuCIAg+1T5e6XqQ\nL6YtuHoy7Y5AoT1uRQDfVrgEkqJ33if/lbcJ7pZGr1WvoA1tWb8GqSwP/acrwWHHMXwWSmrLR4f+\nB1V1NrS0VjsrIyI7OyslPKCkVsuJYiOKCl2jbQzuaqS01CM35bKzeTJrPrFQVavSPUXLnAlGIkLb\nx3aNvEIL/1yVw5ETNRgMEvOmd2LyrfHode3j/nmaLKt8/k05728toKjEhk4ncdvYOO66TQQSQscS\nGqTn8emZvLDqAMs+OkFCVDCdEz3X/FgQBEHwPBFKNMPb0xZacjLdXKDQkl4K7WUrwmW+qHAJNOUf\nfcrFZ19EFxvN9R+9jTm8ZWPWpPIC9LtWgMOK4+YZKJ37tW1BquIc+WmrdfaOiEgDjfuDI1WFS5V6\nzpcb0Egq/ROtxIbISJLJ7bflKkVR2fW9nR3f2ZCASTcaGDNEj6YdnGza7Ar/+qiQjR8X4XCoDMkM\n54G5qSTEtY+eNZ4myypf7C3n/Q8LKSy2otNJTBwdy/TbE4mNNvh6eYLgE8lxoSy+sx+vbTzC3zYe\n4fl7ryMiRPw8CIIgBCoRSrjAm1scyqstDVZlQOMn0z8NFHw1LcSf+KLCJZBUf3uAs488hyY4iF6r\nXyWkWxrmkhqXry9VFKHf9Q7YLDhumoaSntm2BSkyVOWA3eycrhGR6py24WaKCqeKDRTV6jHqFDIS\nrYQaFbffTktU1Sqs+cTK2TyZyFCJeRNNpHdqH1U8h49V84/VORQUWYmJ0nP/nBSGDY4UjSxdIMsq\nX/4YRhQUW9FpRRghCFcb2COWu0Z2ZeMX53jjX1n81z2DROWVIAhCgBKhhAu8ucVh14FrKzIuc/Vk\nui29FNw9qcJXvF3hEkjMJ86QvehJUFV6vPUiIZkt23IhVZWg37UcyWrGPmwKSrdBbVuQ4oDKS+Cw\ngDEcwpPBAyetNgccLTJRbdESZpTpn2jFqPPthI3j5x28u9OC2QIZ3bTMGmsi2BT4J+wVVXbeWZ/L\nl3sr0Ehw54R47pmSRFBQx/25c5Usq+zZV857HxZSUOQMI24d5Qwj4mJEGCEIV7ttWGdyS+rYd7yI\nVZ+cYtFtvUXoKQiCEIBaFEqcPn2aS5cuMW7cOKqrqwkPD/fUuvySp7c4WO0yR840vqk9s1t0syfT\nre2l4Ep1RaAFFu25iWdrWXMLODX3MeTqWrq+/gIRI4e16PpSdRn6ncuRLHXYr78DpcfQti1ItkPl\nRZBtYIqEsCSPBBK1VomjhSYsDg3xoQ56xVnR+vANNYdD5aNvbHz5gx2dFu4aZeSmDF3Av5hWFJUd\nX5SyakM+5nqZ7unBPLQgja6dO/ZWKVfIispX+yp4b0sB+UVWtFqYMDKW6bcnEB/bsSu7BKExkiSx\naFJvCsvNfJVVQEp8KBOuS/X1sgRBEIQWcjmUeOedd9i6dSs2m41x48bx97//nfDwcB5++GFPrq9D\naaoPAsC4oc3/oW1tL4Wmqitmj+kekNtB2msTz9ayl1dyas5j2AtLSP3tE8TeNbFlB6ipQL9zGVJ9\nDY6hk1B63dC2BTlszkBCsUNQNIQmeCSQKKvTcrzIiKxKdImy0TnK7ombcVlppcKq7RZyixXioiQW\nTDTRKS7wn5fnL5lZuvISp8+ZCQ7Ssnh+KuNHxoomjM2QFZWvv3OGEXmFzjBi/IgYZtyRKMIIQXCB\nQa/lsbsy+OOK/azfnU2n2GD6p8f4elmCIAhCC7gcSmzdupX33nuPhQsXAvDUU09x9913i1DCjZrq\ngxATbiI6vPlGfK3ppdBcdYWsqHx2MO/K5wJttGZ7a+LZGrLZwumFv8By5gKJS+aTtHheyw5QV4lh\n5zIkczWOwROQ+9zUtgU5LD8GEjKExEFwrNsDCVWF3CodZ8sMaCTom2AhPlR262201IGTdjZ+ZsVq\nh+v66pg20ohRH9gn7fX1Mu9uLuCjncUoKgy/IYpFd6cQFeGZMa7thayofPN9Be9tKSS3wIJWC+NG\nxDDj9kTRBFQQWig63MSjd2Xwl7UHWbrpGM8tHEpidMf+uy8IghBIXA4lQkJC0Fz1rrhGo/mP/wtt\n544+CK05RlPVFeXVFn443fCWEjFaMzCoDgdnlzxN3YEsYu6aROpzj7XsAOZqDDuXI9VV4hgwFrnf\n8LYtyG529pBQFQhNhODoth2vAYoK2SUGCmr0GLQK/ROthJt819DSalf54HMr359wYNTDnAlGhvQO\n7JN2VVXZe7CSt9fmUlZhJyneyIPzUxnYr2Nt62spRVH5Zr8zjMjJt6DRwNhbnJURifEijBCE1uqW\nHMHCib15+6MT/G3DEZ5bMJRgk2idJgiCEAhc/m2dlpbG66+/TnV1NTt27ODjjz+mW7dunlxbh+SO\nPggtPUZT1RURoQYqa8VozUClqirnn/oTlbv2ED5yGOkvP4/UkjCxvsa5ZaOmHEfGKOTMUW1bkK0W\nKnMAFcI7OftIuJldhmOFJiotWkINMv2TrJh82NAyv0Rm5XYLJRUqKfEa5k80ERsZ2IFucamVN9fk\nsP9wNTqdxKzJiUy/PRGDPrDvlycpisq3+ytZv6XgShgx5scwIkmEEYLgFjdnJJFXUsf27y7xjy3H\n+PmMNk6GEgRBELzC5VDi+eefZ+XKlSQkJLBlyxaGDBnC3LlzPbm2DskdfRBaeowmqyt6xHLkbJkY\nrRmg8l5aSum6LQRn9qHHm39BY2jBu/OWOvQ7l6OpLsPR7xbkAWPathhLNVTnApJz5KcxrG3Ha4DZ\nJpFVaKLeriE2xEGfeN81tFRVla+P2PnwKxsOGUYO0nPbTQZ02sDdruFwqGzZUcT6LQXYbCoZfcJY\nPC+V5KTmt5Z1VIrirChZv7mAS3k/hhE3RzvDiATxuAmCu80Y1Y3c0lqyzpWx4fOzPDK7jROiBEEQ\nBI9zOZTQarUsWrSIRYsWeXI9wo/c0QehJcdoqrpCqz0jRmsGoKJ33if/lbcxdkmh1+pX0YaGuH5l\nqxn9ruVoqkpw9L4RedCEtvV8qK+EmnyQNM5AwtCCtbiowqzhWJEJhyKRFmkjPdp3DS3NFpX1uywc\nPScTYoKFt5nomx7YZcTHT9eydNUlcvIshIfpeGhhMiOHRQf8xBBPURSVfQedlREXcy1oJBh1UzSz\n7hRhhCB4kkYjsWRyP15YeYDt312id9cYMrtE+XpZgiAIQhNcfpXct2/f/3jxKUkSYWFh7Nu3zyML\nE7yrqeoKMVoz8JRv3cXFZ19EFxtNr7Wvo49tQd8Gaz36Xe+gqShC7nk98tBJbQskzGVQWwSSFiLT\nQB/U+mM1Ir9Kx+lSAxLQO85KYrjD7bfhqnP5Mmu2W6isVemWrGXurUYiQgN3W0N1rYNV7+exa08Z\nABNGxTJ/eidCQwI7ZPEURVHZd6iS9zYXciG33hlG3BjNjDsTSU4UYYQgeEOwSc/jMzJ5YcV+Xnvv\nB34+I5P+XcVEDkEQBH/l8qvKkydPXvnYZrPx7bffcurUKY8sqr2z2mW/HVHZUHWFGK0ZWKq/PcDZ\nR3+DJjiIXqv/hqlLiutXtlnQf7oSTXkBcvchOK6/vfWBhKpCXQmYS0GjcwYSOveelCkqnC0zkFel\nR69R6ZdoITLINw0tFUXl0/12PtlnA2DiMANjh+rRBOhITFVV+eybclasz6O61kHnFBNLFqTRu3uo\nr5fml1RV5btDVazbXMCFHGcYMWJYFLPuTBLbWwTBBxKjg3l8RiZ/Xf8Db3xwlKfmDCI9STTiFQRB\n8EeteqvLYDAwcuRIli1bxoMPPujuNbVbsqKwfvcZDp0uobzaSnS4kUE945xbJAJgkokYren/zCfO\nkL3oSVBVerz1IiGZvV2/st2KfvcqNGW5yF0H4Rg22bndojVU1VkdUV8OGj1EdQatoXXHaoRDhuPF\nRsrNOoL1ChlJFoL0vmloWVWrsHaHlTO5MhGhEvNuNdE1OXCDu5z8ev6xKodjp2oxGjTcOyuZ28fF\no9MFZsDiSaqq8t0PVazfXMD5S/VIP4YRM+9MIkWEEYLgUz1TI/mveUP584rv+N/3DvPM/CFiVKgg\nCIIfcjmU2LBhw3/8v7CwkKKiIrcvqD1bv/s/ezOUVVuv/H/OuJ6+Wla75M/VKJ5izS3g1NzHkKtr\n6fbGC0SMHObydVW7Ff3u1WhKLiF3ycRx49S2BRI1+WCpAq3RWSGhde/4y3q7RFaBCbNdQ3SQg74J\nVnQ++jafuODg3R0W6izQr6uW2WNNhAQF5sm71Sqz5l/5bNpWhENWuX5QBD+bk0pcjHsDpfZAVVW+\n/zGMOPdjGDH8hihm3plIaif3b1ESBKF1bsxIYsGtvVix/RR/XfcDz8wfQlSYaNItCILgT1wOJQ4c\nOPAf/w8NDeWVV15x+4LaK6td5tDpkga/duh0KdNHduswJ8+eFOjVKK1lL6/k1D2PYi8sIe13vyBm\n2kTXr+ywY968Ck3xBeS0fjhuvgta+1ipClTlga0GdEHOQELj3ud1Zb2GY4Um7IpEcoSdbjE2fLFD\nwiGrfPyNjS8O2dFqYNpIAzdn6gO28ePBrCrefvc4+YUWYqP1/GxuKjcMcv/I1kCnqir7D1ezfnMB\nZy+akSS45fooZt2ZSGqyCCMEwR+NHJhMVZ2NTXvO87/vHebXcwcTbBJ9cQRBEPyFy7+R//znP3ty\nHe1eVa2V8gbGagJU1FioqrW6vDWiI1YBuKojVqPIZgunF/4Cy9mLJC6ZT+KDLRjVKzvQf/Eucn42\nckpvHMNntj5EUBSoygF7HeiDISKt9eFGIwprdJwqNqACPWKtJEf4pqFlaaXC6u0WcooV4iIl5k8y\nkRwXmD+L5RU2lq3L5evvK9FqYMrEeGZPTiLIFJj3x1NUVeXAEWcYceaCM4y4+bpIZk1OIk2EEYLg\n9+68qQtVdTY+O5jHaxuP8MvZA9D7qsROEARB+A/NhhIjR45s8p2/zz//3J3rabciQo1EhxspayCY\niAozERHacCnh1QGETiv5tArA38OQjliNojocnF3yNHUHsoiZPonU5x5z/cqyA92X69DkZ6NL74v1\nxrYEEjJUXgJHPRjCICK59ds/GqCqcL5cz6VKAzqNSt8EC9HBvmloefCUnQ27rVjtMLSPjrtGGjEa\nAq86QlZUtu8uYc2/8qm3KPTqFsLTP+9NRKhv+nL4K1VVOZhVzbrNBZw5bwbgpqHOMKJziggjBCFQ\nSJLE3HE9qamzsf9UCf/ccpyHpvYP2GbEgiAI7UmzocTatWsb/Vp1dXWjX6uvr+fXv/41ZWVlWK1W\nHn74YXr37s1TTz2FLMvExcXx0ksvYTAY2LJlCytWrECj0TBr1ixmzpzZunvjx4x6LYN6xv3Hu/iX\nDeoZe83JckPbEIJNenKKa69cxltVAIGyJcKd1SiBQFVVzj/1Jyp37SF85DDS//o8kqvfD0VG99X7\naHNPoSR1I+jORdRVWFq3ENnuDCRkK5giIKxT20aI/vTwCpwoNlJapyNIr5CRaCHY4P0TZ6tdZdMX\nVr477sCghzkTjAzp7d5eGd5y5nwdS1fmcPaimdAQLQ8tTGPc8BgSEkIpKanx9fL8gqqqHDrqrIw4\nfc4ZRtw4JJLZU0QYIQiBSqOReODOvtTWH+bA6RJW7zzN/Ak9A3bbnSAIQnvRbCiRnJx85eMzZ85Q\nUVEBOMeCvvDCC2zbtq3B63322Wf079+fBx54gLy8PO677z4GDx7MnDlzmDRpEi+//DIbNmxg6tSp\nvPHGG2zYsAG9Xs+MGTMYP348kZHtby/z7DHdAee79hU1FqLCTAzqGXvl81draBtCQ1UWl4/nySqA\nQNkS0dpqlECV9+L/UbpuCyED+tLjrRfRGFw8QVZkdF9vRHvpOEpCOvZRc5B0eqAVoYRsg4qLoNgh\nKBpCE9waSFgcEkcLjNTatESaZPolWvBFsUt+qcyqbRaKK1SS4zTMn2giLsp/AjlX1Zll1n6Qz7bd\nJagqjLoxmoWzk4kMD8xwxRMuhxHrNhdw+mwdAMOGRDJ7ciJdUttPqCkIHZVep+XRuzL5y9qDfH4o\nj8gQA5NvSff1sgRBEDo0l3tKvPDCC3z99deUlpaSlpZGTk4O9913X6OXv+222658XFBQQEJCAvv2\n7eP3v/89AKNHj2bZsmWkp6eTkZFBWFgYAIMHD+bgwYOMGTOmtffJb2k1GuaM68n0kd2a3AbR1DaE\nhniyCiCQtkS0tBolkBUtf4/8V5dh7JJCz1WvoA1x8XuvKOi+/QDthSyU+M7YR88FXSsnKzgszgoJ\nxQHBsRAS59ZAotqi4WihEZusISnMTo847ze0VFWVb7McbN5jxSHDiIF6br/JEHCjMVVV5ZvvK3n7\n3VwqquwkJxpZPD+NjD5hvl6a31BVlcPHatj48RmOnnRWAd4wOILZk5NITxNhhCC0J8EmHb+YNYA/\nrTrApq/OEx5iYNSg5OavKAiCIHiEy6FEVlYW27ZtY/78+axatYqjR4+yc+fOZq939913U1hYyNKl\nS1m0aBEGg/MEKCYmhpKSEkpLS4mOjr5y+ejoaEpKmj4hj4oKRueG5kRxcb57QZ5y1ccWm4OKaitR\n4UZMBh0FpXWU1zRcFdGQ2MggunWJwWRoeSfp5h6DptZSUWNBa9ATFxvS4tv1lEdnDSI4yMDeowWU\nVtYTGxnEsP5J3HdnP7Taht/Z9uXzoDUKNm7n4nMvYYiP4cbtywnplubS9VRVwbJjPfZzh9EmdiZs\n+kNIRtOVr7fkcbDX11J18RKq4iAkMY3gmKQW34+m5JSp/JCvoqgwoLNEj0QDkuT5SperH4O6eoW3\nN1Wx/7iV0GCJB6ZFMqi3qYlr+6e8gnpeXprNvoMVGPQSP5vbhTnTUzHo28fPQ1s5p2lUsmztBbJO\nOMOI4TfEsOiezvTs1rEeC0HoSCJDjTw5eyB/Wn2AVTtOERZsYEivOF8vSxAEoUNy+Sz2cphgt9tR\nVZX+/fvzl7/8pdnrrVu3jhMnTvBf//VfqOq/94Ff/fHVGvv81SoqzC6uunFxcWE+3zvdWK+GqcPT\niQ5reBtCQzK7xVBTVU9L740rj4FslxtdS1SYCdlmJze/0q8aYE69uQuTrk/9jzWVl9c1eFl/eB60\nRPU3+zm14P9DExxEj5WvYg6PwuzK+lUV3Xcfoj39PUp0J6wj52KutgN2oIWPg63OOWVDVSCsE3VK\nKHVuegxVFS5W6LlQYUArqWQkWonSyZSWuuXwTbr6MThfILNmu4WKGpWunTTMvdVEZJidkhK75xfi\nJna7wqbtRWzYWojNrjKgXxiL56WSlGCiqrJ9/Dy0haqqZJ2oYd3mAk5kOx+P6wZGsGRhN6IjnJfp\nKI9FQzpaOCV0TAnRwTwxcwAvrj3EP7Yc48nZA+iVFuXrZQmCIHQ4LocS6enprFmzhqFDh7Jo0SLS\n09OpqWn8BdvRo0eJiYkhKSmJPn36IMsyISEhWCwWTCYTRUVFxMfHEx8fT+lVZxzFxcUMHDiwbfcq\nQDTVq6GxbQip8aGYLY5me1K4i1GvJbN7LJ8dzLvmawN7xLDxi7N+2QDTqNe2q6aWAObj2WQvehJU\nlR5vv0RIZm/XrqiqaPd/7AwkohKxj1sIhlY26rPWQFUuoEJ4CpjCW3ecBsgKnCoxUlyrw6hzNrQM\nNXq3oaWiqOw+YOeTvTZUYMINBsZfpw+47uxHT9WwdOUl8gqsREXoeOyeFG6+Lko0c+PHMOJkLes3\nF3D8tLNx8HUDnds0unUJ7lDBjCAIkJ4UziN39efV94/wt41ZPD13MCnxob5eliAIQoficijxhz/8\ngcrKSsLDw9m6dSvl5eUsXry40cvv37+fvLw8nn32WUpLSzGbzQwfPpxPPvmEKVOmsGPHDoYPH86A\nAQN47rnnqK6uRqvVcvDgQZ555hm33Dl/1lyvht/ff/2Vj38aQDhk1SuVCZcrOQ5nO9epkUBRIebH\n8EFRVT4NgAaY7YE1t4BT8x5Hrqmj2xsvEDHiBteuqKpoD36C7uRelIh47OPuBWMrwxpLJVTnAxJE\npIHRfS/arA6Jo4VGaqxawo0y/RMttGI3UptU1sj8c7OF7ByZiBCJubea6Jbi+8qflqiqtrPi/Tw+\n+7ocSYJJY+KYe1cSIcFefjD91NGTNby76d9hxJDMcO6ekkT3dP/ZgiYIgvf1T4/hvtv78OaHx/nr\nez/w7LwhxEaKKTuCIAje4vIr1VmzZjFlyhRuv/12Jk+e3Ozl7777bp599lnmzJmDxWLh+eefp3//\n/vzqV79i/fr1dOrUialTp6LX63nyySe5//77kSSJRx555ErTy/asufGVtWZbo00xtRq8UgXw00oO\n5cc3rTO7xTB9ZDeee3Nvg9fztwaYgc5eXsmpex7FXlhC2u9+Qcy0ia5dUVXR/rAL3fGvUcJjsY9f\nBKZWnnyZy6G2ECQNRKaB3n3Pv1qrRFahCatDQ3yog15xVhpp/+ExJy84WPdpKTV1Cn27aJk93kRo\nUOBUFSiKyqdflbHy/Txq62S6pgWxZGEaPcTJNuCsHFm/uYCjJ/8dRsyanETPruLxEQTB6cZ+idTU\n2Vi3+wx/fe8wz8wbTFhwKxtBC4IgCC3icijxq1/9im3btjFt2jR69+7NlClTGDNmzJVeEz9lMpn4\n61//es3nly9ffs3nJk6cyMSJLp5otRNNja806LUY9FqKK8xEhBp9sg2hqUqOI2fLGT3I3GSo4o5p\nIFa77Fe9KnxBNtdzeuEvsJy9SOJD80l8cK7L19VmfY7u6JcoYdHOQCKoFZUNqgrmUqgrAY0WIjuD\nzn3NHkvrtBwvMqKoEunRNtIi7e4c4NEsh6yy7Vsbnx+0o9PC1BEGbhmgD6htDhdz61m68hInz9Rh\nMmq4754UbhsTh1YbOPfBU46fruXdTflXwojBGeHMnpxEz24ijBAE4VoTrk+jqs7Gtn2XeOX9Izx1\nzyCMho75+kMQBMGbXA4lhgwZwpAhQ3j22Wf57rvv2LJlC7/73e/Yu7fhd8sDmTdOhpsaX2mxyTzz\nz2+x2hS39Wlo6X1qrpIDSWo0VIkKMxER2vpJCY01APWHXhXepDocnFnyNHUHsoiZPonUZx9z+bra\no1+iO7wbNTQK+/j7ILgVvR9UFeqKwVwGGr2zQkLnngkYqgo5VTrOlRnQSNAvwUJcqOyWY7uqrEph\n9XYLl4oUYiMlHr8nhhC9xatraAuLVea9LYVs2VGELMONQyK5f04KMVHinb3jp2tZt7mArBPO3hCD\n+ocze0oSvUQYIQhCM2aM6kZVnY1vjhbyxqYsHp+eic7b5XuCIAgdTIs2GldXV7Nr1y62b99OTk4O\ns2fP9tS6fMLbJ8NTh3flqyP5WGzKNV+7/Lm29mlo6j41palKjqgwE3GRQY2GKoN6xrYpzGmqAWhH\n6VWhqirnn/oTVbu+ImLUjaS//FskF5+D2uNfozu0EzU4Atv4RRAS0ZoFQE2Bs4+E1uCskNDqW36c\nBigqnC4xUFijx6BVyEiyEma89mfAkw6dtrNhtxWLDYb01nHXKCOpnfSUlARGKPH9D1W8uSaHkjIb\n8bEGHpibytABrfg+tzMnsp0NLA8fd4YRA/uFMXtKEr27i6Z1giC4RpIk7p3UmxqznaxzZSz/+CT3\n39EHTQBV0AmCIAQal0OJ+++/n+zsbMaPH8+SJUsYPHiwJ9flE94+Ga4127A2EEg0pLV9Gpq6Tz+/\nZ0ij12uqkuNy6HA52GioGWdrNdcAtKP0qsh78f8oXbeFkAF96f7mX9DoXftR1Zzci+7AdtSgMGwT\n7oPQVow2U1WozgNrtXOrRmQaaNzTKNEuw9FCE1UWLaFGmYxEK0ad9yZs2Owqm760su+YA4Me7hlv\nZGgf94Qt3lBabuOttTnsO1iFVgvTb09g5h1JGI0d+128k2eclRGHjznDiAH9wpg9OYk+PUQYIQhC\ny+m0Gh6e2p+X1h3i22OFRIQamDXac5POBEEQOjqXzzQWLFjALbfcglZ77Qnhm2++yQMPPODWhXmb\nL06Gm6oAb+MVAAAgAElEQVRG+KnW9Glo7j5ZbI4rl2toa0dzoYNWo2m0GWdrNbdtxB29Kvxd0bL1\n5L+6DGN6Kj1XvYI2xLX7qzn9PfrvP0I1hTp7SIRFt/zGVQWqcsBW52xmGZHq7CXhBnU2iawCExaH\nhtgQB33ivdvQsqBUZtV2K0XlCp1iNSyYZCIuKjBO5mVZ5aNPi3n3gwIsVoU+PUJYsiCNtOSO3R3+\n1Nk61m3K54cfw4jMPs7KiL49RRjRnr344oscOHAAh8PB4sWLycjI4KmnnkKWZeLi4njppZcwGAxs\n2bKFFStWoNFomDVrFjNnzvT10oUAYjRo+fmMTP68+iDb910iIsTArden+XpZgiAI7ZLLocTIkSMb\n/dqePXsCPpTwxclwU9UIP9WaPg0llfVN3qfSyno2fnq60e0qroYORr3WbY9Nc9tG2tKrIhCUb93F\nxd/8D7rYaHqtfQ19rGvBgubMQfT7tqAaQ7CPX4QaEdfyG1dkqLoE9nowhEJEinPahhuUm7UcKzIi\nKxJpkTbSo73X0FJVVfYedbDpSysOGW4ZoOeOmw3odYFRinv6bB3/t/ISF3LqCQvV8rM5nRl9czQa\nTWCs3xNOn61j3eYCDh2tBiCjTxh3izCiQ9i7dy/Z2dmsX7+eiooKpk2bxo033sicOXOYNGkSL7/8\nMhs2bGDq1Km88cYbbNiwAb1ez4wZMxg/fjyRkZG+vgtCAAkLNvDL2QP406oDrN99hvAQAzf2S/T1\nsgRBENodt9Rkq6r3yq89xVcnw/+uRihpsmKiJX0aLveROHiqmMa+M1FhJj7cc86l7SruDB2a48q2\nkZ9qL1M6qr/Zz9lHf4MmOIheq/+GqXOKS9fTnPsB3bebUA1B2MffixoZ3+LbVhx2qLwIDgsYwyE8\nGXelBnlVOrJLDUhA73gLiWHea2hZb1V571MLR87IBJtg/kQT/bu5ZyuKp9XWOVi9MZ8dX5SiqjD2\nlhgWzEwmPCww1u8Jp8/VsX5zAQeznGFE/96hzJ6SRP9e7X+MtOB03XXXkZmZCUB4eDj19fXs27eP\n3//+9wCMHj2aZcuWkZ6eTkZGxpUR44MHD+bgwYOMGTPGZ2sXAlNsRBC/nDWQP685yLKPThAWpKd/\n1xhfL0sQBKFdccur20Aan9eY1pwMu8PlagRZVvjsUP41XzcZtNySmdSiPg0/7SPRkMxu0ew/UdTg\n19y5XeXqwABwKTxwtVdFWxuTWu0yBaV1yHbZ52GG+Xg22YueBFWlx9svEZLZ26XraS5kofvmX2Aw\nYh93L2pUK97Bke1Unj/nDCSCoiA00S2BhKLCmVID+dV69FqV/okWIkzea2h5sUBm1XYLFTUqXTtp\nmHOriagw/9+uoaoqe/ZVsGxdLlXVDlI7mVg8P5V+HfjEO/u8M4w4cMQZRvTrFcrdU5Lo37vjPiYd\nlVarJTjYGZJv2LCBESNG8NVXX10ZTx4TE0NJSQmlpaVER/+70iw6OpqSkoa3MwpCc1LiQ/n5jEz+\nZ90PvPHBUZ6aM4j0pFZMtRIEQRAa1HHfcmvA1SfD5TUWIkOMDGxj48afaugkPcio48jZsgYvH2LS\nMX1kN5enfzTVRwIg5scT99GDkvn8h2tDEHDPdpWrA4OyaismgwaQ/n/2zjswqjpr/5/pk0nPJCEh\nJBCCNOkoIkUgIKKCoFKk2XYta1nbVt+V93VX17KK6/qTtReiKIiKBZAWUEJTOoj0khBSJ2WS6TP3\n/v4YiZTMZCaZZJL4/fyVzG3n3plM7nnuOc/B4fQ0KB4E2jbSWGPS88SMGgcJ0eEdOeo4XcSh2Q/i\nqbGQteBpYq+6IqDtlPkHUOctBbUW19jbkI0dgz+42wFVp/BIbjAYITI5JIKEywMHSnRU2tREaiX6\nptjRa1qmokqSZTbscLFyixNZhquHaLh6iBZVG2h3KCy280ZOAXt/qkGrVTDn5o7ccE0yGnXrF1Oa\ng2MnrXz8xRm27/GKEb27e8WIvr2EGPFrZ+3atSxdupR33nmH8ePH173uq3Iz0IrO+HgDanXoReqk\nJPGZDSehuP5JSdEoNWqeff97Xl66l+cfHElakmgZCwTx+Q8f4tqHF3H9A0eIEuegUiqZkd0Nj0di\n15FyKmsd7D1ajkqpaHLC6i9Jj4vSUVnry/vBEZRA4M8bQwE8NLUfnZKjcbg8JMVFUFppu2i9ULSr\nXCgYnDv2NFDxwF/bSFOMSVvTyFGXqYpDMx/AVVJOxpOPYpxyTUDbKU8fQr1xCajUuLJvRU4MrNXj\n/IPboCofZA+RyelYCM0Xp83lNbS0upQkGNz07uCgpXJqs0Xio9UODhd4iIlUMPsaHd06tf6vOadL\n4vMVJSxdXozbLTOobwx3z0mnQ1L79lDxxbFTVhZ/UcQPu6sB6HVJJLdM6UjfnlHtojJP0DQ2btzI\na6+9xltvvUV0dDQGgwG73Y5er6ekpITk5GSSk5MpLy+v26a0tJQBAwY0uO/KSmvI401KiqasrCbk\n+xUERiivf7eUKOZc04OF3xzib//dxONzBxPXzr2umor4/IcPce3Di7j+F+NPpAlJqtClS5dQ7KZV\nsDj3KOt3naGq1gn8krAuzj3a5P2u3X66zjfC7pSwOz3I4FOQgOAFgrPeGPWREKMn6eckX6dRMbRP\nar3rNbVdpaFqjbPsOlyOw9U4f4FAjEmDja0p8TQGj9XG4dsexn7sFCm/m0vKXbMC2k5x5gjqbz8C\nhRJX9lzk5Ea4gTstXg8J2QPRqRiSGlFlUQ9VNiU7TkdgdSnpFOuib0rLCRKHTrl5cZGNwwUeenVR\n8dgsQ5sQJPb8aObheT/x8RdFxESp+dN9mfzt4axfpSBx/JSVZ145xh+ePMgPu6vp2S2S/3usG0//\npTv9ekULQUJATU0Nzz//PK+//nqdaeWwYcNYtWoVAKtXr2bkyJH079+fffv2YTabsVgs7Ny5k8su\nuyycoQvaCaMHpDFlZCbl1XbmL96D1e4Od0gCgUDQ5gn4jr2wsJDnnnuOyspKcnJyWLJkCUOGDKFL\nly78/e9/b84YW4zmGgsaaJJeH8EKBMF4Y9w56VKsNmfI21X8CQbn0pQ2kcYak7aWkaOSy83Re/+K\nZed+jFOvI/1/HgxoO0XRcTQbFoFCgWvMbOQOXYI/uKPWO/YT2WtoqY8Nfh/1UGRWc7jM29fdPclB\nx5iWuVHzeGRWbnWyfocLlRJuGKnlqgGaVp/AVlW7eHfxab7bWolSARPHJTHzxo4YItquWWtjOZHv\nrYzYtstbGdEjK5JbpqTSv7cQIgTns2LFCiorK3n44YfrXnv22Wf529/+xuLFi+nYsSNTpkxBo9Hw\n2GOP8Zvf/AaFQsH9999fZ3opEDSVScO6UG1xsn5nIa98updHZ/RH0wxtPwKBQPBrIWBR4oknnmD2\n7Nm8++67AGRmZvLEE0+Qk5PTbMG1NM2VsAaapAPERWkxW5w+zR0DIVCjSJWqedpV/AkG59KUNpHG\nGpO2hpGjsixz8k9PU702j9jRV5L54jwUAVxrRclJNOs/AFnGNXo2cmpW8Ae3V4O5EFBAbDromn6T\nLstw3KShoFqLWilzaYqd+IiWMbQ0VUt88I2d/BIJY6yCudfqSU9u3TeGkiSz+ttycpaewWrz0K2L\ngXtvyyCrc8tMuGlNnCyw8vEXRWzb6RUjumdFMnNyKv0vFWKEoH5mzJjBjBkzLnr97L3JuUyYMIEJ\nEya0RFiCXxkKhYLZ47pjtjjZcaiMN748wO+m9PlVj2oWCASCphCwKOFyuRg7dizvvfce4B3L1d5o\nroQ10CTdGKNn3u2XYXO4mzTeMlCjSPilXeUsF/orNGbcpj/B4FyaOuLTl/gyZWRXSiut9W4frikr\n53L6uQWUL/6KyP696fbmc7hQUO0j3rMoyvLR5OaA5ME9aiZy2iXBH9hWCTVFoFB6BQltZBPPBNwS\n/FSiw2RVE6HxGloatC1jaLnniJsl6+zYnTCwh5qpo3Xoda37hvBEvpXXFuZz+LgVQ4SSu2anc82Y\nxDZhwhlKThZYWfJlMVt2VAHQvauBGZNTGdgnRogRAoGgTaBUKrh7Um/mW/ew43AZH6w5zNzx3cV3\nmEAgEDSCoBquzWZz3ZftkSNHcDgCe/rfVmiuhDWYJD3aoCXaoG3Uceo7rr/KDrvT7bOtZOehMjyS\nzN6j5Y0at3neJBOzHZ3We+2cLk/IRnxeKL5EGTQs23iC/317m9/tA60kaQ5K3llM0X/eRZeZTtb7\n81m85XSD56soP41m3ULwuHFfNR0pPbBxoedhKQdLKShUEJcBmogmn4vdpWBfsQ6LU0V8hIfeHey0\nxGRVl1vmi+8cbNnvRquGGeN0XN5L3apvBG12Dx8vK+LrtaVIEowYEs8dt3QiIU4T7tBalFOnbSz+\nsogt271iRLdMA7dMTmVQXyFGCH7h5MmT7cqrStB+0ahVPHhzP55btJMNuwqJi9Ryw4jMcIclEAgE\nbY6ARYn777+f6dOnU1ZWxqRJk6isrORf//pXc8YWFporYW1Mkh4MjaloKK+y+azeqKhxsH5nYd3v\nwU6oqK9aA7goxnPj/vTbY42ainFWfFm09nBA258bm0qrweN0tUiFRMVXazn1xAtokoz0WPQKn+2t\naDBeRcUZNOveB7cT94hpSBmXBndQWfaKEVYTKNUQ1xnUTW9RqbYr2V+sw+VR0jHGRbdEJy3xsL/Y\n5CFnpYPiComOiUrmTNDTIaH1jsuUZZnvd1Xz5ocFmCpdpCTruHtOOgP7/Lrm2+cX2lj8RRGbz4oR\nXQzcMkWIEb9m7rjjjvNaLhYsWMB9990HwLx581i4cGG4QhMIgsKgV/PI9P78M2cHy/JOEBOlZfSA\ntHCHJRAIBG2KgEWJoUOHsmzZMg4fPoxWqyUzMxOdrv25wwfT+tDU/cLFSXqwBFtdcC5fbTzuc5lS\nAVI9Vfh5e4uYMrIrBl1gH50LqzXO/nxh3PHRWqyO+idfBGIyanW4ydt7pt5lvrbXaVQkJUa2yLge\n8+btHHvwCZSRBrp/8DKKjqnsWr7Vb7z62jI0a98HpwP38JuQuvQN7qCyDLXF3rYNldZbIaFqehVO\nSY2Kg2U6ZBm6JTpIi3HT3HmlLMts+9HNsu8cuNwwvJ+GSSO0aNStN6EtLXfw1qLT/LC7GrVKwbRJ\nKdx8fQo6besVUUJNQaGNJV8Vs+mHSmQZsjp72zQu6y/EiF87bvf5Rrhbt26tEyVkuWVawASCUBEX\npePRGQP4Z84OclYdIjpCy+AeSeEOSyAQCNoMAYsS+/fvp6ysjDFjxvDSSy+xe/duHnzwwXY7Yquh\n1odQ7bepxzg7avQsgVYXOFwetv9U4nN5fYIEgN3p4aM1h/nNxN6NC/hnLoy7osbpc91ATEY/WnMY\nu7N+c8WWnKpRH9YDRzhyx2Mgy1zy1vNE9u1JaaXVr6mqpaiQ6K2LUDisuK6cgtR1QHAHlWWvoaXD\n7K2MiOvsrZRoArIMJys1nKrUolLK9E5xYDQ0/whVm0Pmk1wHe464idDB7Gv09M1qvaM+3W6Zr9aU\nsPiLYhxOiT49o7hnbgadUvXhDq3FKDhjY8mXv4gRXTtHcMvkVC7rHyvECAHARZ+Dc4UI8RkRtEVS\nEgw8Mr0/zy/axetf/shjM/rTIyM+3GEJBAJBmyDgR3ZPPfUUmZmZbN++nX379vHEE0/wn//8pzlj\nEzRAQyNMHS7fCWN1rYOyKpvP5bGRvnvdD+ZX+t13QwQ7IrUhk1GHy8PB/Eqfy+OidC0yVaM+HAVn\nODT7QTw1Frq+/CSxV10B/GJ+Wh/dYzx02PYRCocF1xU3IHUbHNxBZck78tNhBnUExHVpsiDhkeBA\niY5TlVr0aolBabYWESROFXuY/5GVPUfcdElV8tgsQ6sWJH46UstjT/7Ewk/OoNMpeei3nfn7Hy/5\n1QgSp4vszH/9BA898RN531eSmR7BXx/sygvzenL5gDiRbAp8Ij4bgvZAZmoM99/UB1mW+c+n+zhd\nWhvukAQCgaBNEPDdvU6no0uXLixevJjp06fTrVs3lI0cGSkIDU0ZYRobpSMpLoLSyouFCWOMnm6d\nYtl2oP5KisoaR5MqD4IZkQoNm4w2tL+eneNbxDPiQlymKg7NehBXSTkZTz6Kcco1dct8mZ8mq2w8\nGrMXpd2K6/LrkboHOeVG8ngFCZfVO10jNt07baMJONwK9hfrqHGoiNV7uDTFjraZL6cky2zY6WLl\nFieyBOMu1zD+Cm2rnVJhrnWTs7SQtd+ZALj6KiNzp6YRHdV6BZRQUlhkZ8lXReRtq0SSoUu6tzJi\nyEBRGSGon+rqarZs2VL3u9lsZuvWrciyjNlsDmNkAkHT6JNp5M7re/HmVweYv2Q3j88dTGJs082l\nBQKBoD0T8B2zzWZj5cqVrF27lvvvv5+qqipx4xBmmjLCVKdRMbRPKl/W4yvhHauZyZ6j5didFz8N\nb8p41Ibi1mtVROrVVNY4AjYAbWh/s65uxPjMJuKx2jh828PYj50i9b5bSblr1kXrXGiq2jVG5o+x\n+4j0WHEPnoDUc2hwB5XcUJUPbjvooiEmrcmCRI1Dyb4iHU6Pkg7RLnokNb+hZY1V4qPVDg7le4iJ\nVDBrvI5L0ltnci/LMus3V/D+4kLMtW46d9Jz760Z9OwWFe7QWoTCYjuffFXMxq0VXjGiUwQzfhYj\nlK1UQBK0DmJiYliwYEHd79HR0bz66qt1PwsEbZkrL02hxuLk49yjzF+8h7/OGRSyyWoCgUDQHgn4\nTv/RRx9l4cKFPPLII0RFRfHKK69w++23N2NoAl8TNc59vSkjTO+cdClWm7PeSSMqpZIR/VJDPh4V\n/I9IHdEvNWiT0Yb2Z9C17NhFyeXm6D1/wbJzP8ap19Hp8QfqXe9c89PasjJStn6I0mLBPWAcnt7D\ngzuoxwVVp8DjBH0cRKfSVPfJsloVP5XqkGTomuAkPc7V7IaWh/PdLFrtoMYq07OziplX64kytM7k\ntuCMjddzCvjxUC06rZLbpqcxcVwy6lZsvhkqzpTY+eTLYr77WYzo3EnPjMmpXDEwTogRgoDIyckJ\ndwgCQbMyfkgGVRYn32zL59+f7OVPMwfWTV4TCAQCwfkELEoMGTKEIUOGACBJEvfff3+zBfVrwZfo\n4GuixtTRXVm64fh5rw+4JJHswWnsOWIKeoSpSqXk5lFZXNXPm8AmxUWcN6pzzMA0PJLM3qPB7TuQ\n8aT+Rq+qlMqgW0Oaa5RrsMiyzMk/PU31uk3Ejr6SzBfnoWigzUnnshL1/ccoLVW4+43B03dUcAd1\nO72ChOSCiASI6tAkQUKWIb9Kw4kKLUqFzKUpDpIim9c/wuORWbXNSe52F0ol3DBCy8iBGpStsPTf\n4ZRY+nUxy1aW4PbIXD4glrtmp5NkbP9PwYpK7HzydTHfbqlAkiAjzStGDB0kxAhBcNTW1rJ06dK6\nhxsff/wxH330EZ07d2bevHkkJiaGN0CBIARMHZ2F2eJk8/5iFizbz4M390WtEq3PAoFAcCEBixK9\ne/c+rzdYoVAQHR3Ntm3bmiWw9kxDYzx9TdQ4lF9FwTmmSSazg3U7Chl3WSeeuuuKoKoLPJLEm8v2\nsWlPYYPCR78sI+MuSychRu9338GMJw316NXmGuUaLKefW0D54q+IHNCbbm8+h1LTwJ+YrRbNmndQ\n1phw97kKT78xwR3Qbfe2bEhuiEwCQ2KTBAlJhkOlWkpqNehUEn1SHUTr6p9qEioqzBIffGPnVLGE\nMUbBnGv1ZHRonU+Tdu0383pOPiVlThITNPx2djpXDIwLd1jNTlGpg6VfFbHhZzEiPU3PjBtSuXKw\nECMEjWPevHmkpaUBcOLECebPn8+///1v8vPzefrpp3nppZfCHKFA0HSUCgW3X9uTGquLfcdNvLvi\nIL+Z2KtVCu4CgUAQTgIWJQ4ePFj3s8vlYvPmzRw6dKhZgmrv+BvjefOoLJ+TKQrL6ndx3nW4nJtH\nZQVVXRCM8LF+1xlUKqXfEaMNnZevbUM9erW5RrkGQvHbH1P0n3fRZabTPedlVJENxGG3oFn7Lkpz\nOe7ew/EMGBecoOCyegUJWYKoFDAkNCl+pwf2F+sx21VE6zz0SXGgU/uYDRsi9h51s3itHbsTBnRX\nM22MDr2u9d2sVVQ6eefj02z6oQqlEiZPSGbGDalE6FuneBIqiksdfPJ1MRs2m7xiRMefxYjLhBgh\naBoFBQXMnz8fgFWrVjFhwgSGDRvGsGHDWL58eZijEwhCh1ql5L4pfXj+o11s+bGY2Cgt08e0bBWn\nQCAQtHYa5R6n0WgYNWoU77zzDnfffXeoY2rXNDTG86p+qT4nSUg+8sOGJm0EE0NDwoev6oOGzsvf\ntu2Biq/Wkj/vRTRJRnosegWNsYHZ5A4rmrXvoawqxd1jKJ5B1wQnSDhrvVM2ZBmiO0JE057WV1tl\ndp6OwO5WkhTppmeyg+asMHW5Zb7Y6GDLPjcaNUwfq2NIb3Wrm9TgkWRWrS/jw8/OYLVJdM+K5N65\n6WRmhEf4ailKyhws/bqY9ZtNeDyQlqpjxg2pDLs8vtVOQBG0LQyGX/6Gvv/+e6ZOnVr3e2v7HhAI\nmopOq+Lhaf145oOdfLMtn9hILdcMyQh3WAKBQNBqCFiUWLp06Xm/FxcXU1JS/8hIgW8aGuOJQuFz\nkoRSUb8w4W8aRn3+Dv5iaKzw0ZTxpG0d86btHHvwCZSRBrp/8DL6zp38b+C0o1m3EGVlMZ5LLsdz\n+XXBCRIOM1QXen+OTfdO2mgCJouKn07KuD1KOsc76RLfvIaWJRUSOSvtFJkkUo1K5l6rp0NC6+ux\nPXbSyn/fz+fYKSuRBhW/uzWDcVcZ23WFQFGJndcXnmL9pnPEiEmpDBsixAhBaPF4PJhMJiwWC7t2\n7apr17BYLNhsF4+qFgjaOtEGLY/O6M8/c3awOPcoMZFarrw0JdxhCQQCQasgYFFix44d5/0eFRXF\nv//975AH1N5paIxnUlyEz0kSaUlR57VWnKW+aRj+/B38xdAY4SOQ8/K3bSDGmK0V64+HOXLnYyDL\nXPL2v4js29P/Bi6HV5AwFeLJGoT7ionBCRK2Kqg5490mNgO0kY2OXZahsFrNUZMWpQJ6JdvpEN18\nhpayLPP9ATeff+vA5YZhfTXcMFKLppVNq7DaPCz6/Awr15UhyTDqygRun55GXGzLTnFpSUrLvZUR\nuZsq8Hhk0lJ0TL8hleFCjGiVOBwSW3dWsX6TCbtD4um/dm9z79Ndd93Fddddh91u54EHHiA2Nha7\n3c6sWbOYPn16uMMTCJqFxNgIHp0+gGc+3Mk7y38iOkJDn67GcIclEAgEYSdgUeKZZ54BoKqqCoVC\nQWxsbLMF1RporkTZ3/jKs+KCr0kSv5hQNjxhoiF/h1AIHxdeo2DHkwZjjNkacRSc4dCc3+OpsZC1\n4GliRw7xv4HLiSY3B2V5AZ7M/riHTgZFEOdpNUFtCShUEJcBmohGxy7JcKRcS5FZg0YlMbKnEsne\nfIKEzSGzdL2D3YfdROhg1ng9/bo1qnus2ZBlmU0/VPL2otNUVrvo2EHHPbdm0K9X0ypRWjNlJidL\nlxeTu9GE2yPTqWMEN1+fzMgrEtpcktvekWWZQ8csrMszsen7Smx2rwHt5QNiaYtv1ahRo8jLy8Ph\ncBAVFQWAXq/nj3/8IyNGjAhzdAJB89EpOYrf39yXFxfv4dXP9/OnWQPJTI0Jd1gCgUAQVgLOCnbu\n3Mmf/vQnLBYLsiwTFxfHv/71L/r27duc8bU4LZEoNzS+0t8kiUAmTATi7zAjuxuGCC2b9pwJWvjw\nN7JUkmU27yvG7vQmuHqtClmW8UjSRdevMcaYrQWXqYpDMx/AVVJOxt8fwzjlGv8buJ1o1n+AsvQU\nns59cA+7EQL9PMkyWMvBUgZKtVeQUOsbH7sHfizRU2VTEan10DfFgTE6ijJ7o3fpl/wSDx+stGMy\ny3ROUTJngp6EmNYlOhWXOnju1ZNs3VGBRq3glimp3HRtBzSa1hVnqCivcLL062LW/SxGpCbrmH5D\nCjdO7ExlRf2+MoLwUF7h5NstFeTmmThT4q1ES0zQMHFcMmOGJ5DaofHfBeHkzJkzdT+bzea6n7t2\n7cqZM2fo2LFjOMISCFqEHhnx3Dv5Ul79fB8vLdnD43MHk5LQPltcBQKBIBACFiVefPFFFixYQPfu\n3mTxwIEDPP3003z44YfNFlw4aIlE+azoMGlYF06X1tIpOYpog/ai9XxNkmhowkSg/g53TenLtUPS\ngxY+/F0jpUJRJ0gA2J0e1u0oRKFQnHf92rIxpsdq4/BtD2M/nk/qfbeS8tuZDWzgQrPhI5QlJ/Ck\n98I9YiooAzw3WfZWR9gqQKmBuM6gvvizEihWp4J9xXpsLiVGg5teHRyomynvlmSZb3e5WLHZiSzB\n2Ms0XHOFFpWq9TzWdbklvvimlE++KsLpkunfO5q756bTsY0meg1RXuHk0+XFrP3OK0akJOuYNimF\nUUMTUKkUqFvRe/NrxumS+H5XFbl5Fez50Ywkg1aj4Kqh8WQPN9KnV3Sbr2TJzs4mMzOTpKQkwFsJ\nchaFQsHChQvDFZpA0CIM6p7E3Gt6sPCbQ8xfvJvH5w4mzk+rq0AgELRnAhYllEplnSAB0Lt3b1Sq\n1pk0NpaWSpSDrcbw10pS37Jg/B2CFT78XaOdh8p82iNceP0aEk7Kqmxo1cpW5zMhudwcvecvWHbu\nxzjtejr9z4P+N/C4UX/7Mcqio3jSeuAeOT04QaLmDNirQaXzVkioGu9rUGlT8mOxHrekID3OSdeE\n5jO0rLFKfLzGwcFTHqINCmaN19E9o3W1a+w/VMPrCws4XWQnLkbNXx+6hP699O3S+d9U6eTT5SWs\n+a4ct1umQ5KW6ZNSGXVlQqsSiX7NyLLMkRNW1m8ysXFbJRarV9ztkRVJ9nAjw4fEE2loPd+FTeW5\n512ZxQoAACAASURBVJ7jiy++wGKxcP311zNx4kQSEpo21lggaGuMHpCGudbJsrwTzF+8h7/MHoRB\n37r+VwoEAkFLEJQosXr1aoYNGwbAd9991+5EiepaR72JPIR2gkSg1Rj+xIuz+6lvWWP8HQLFv5hQ\n/+veZedfP3/CiVaj4t9LdlNZ42xVPhOyLHPyj09TvW4TsWOGkfnCE/4TWMmDeuMSVIWHkTp2wz1q\nBqgC/JOTJTAXgqPG26oRl+Ft3WgkZ8xqjpR5Kyx6JDlIjXE3el8NcaTAzaLVDswWmR4ZKmaO1xFt\naD1tENVmF+9/Usj6TRUoFDBhTCJzbu5Il87xlJXVhDu8kFJR6eTTFSWs/vZnMSJRy7SfxQh1KzMY\n/bVSWe1iw+YK1m8yUXDG20OVEKfhmtGJjBlupFNq+6zamTx5MpMnT6aoqIjPP/+c2bNnk5aWxuTJ\nk7n66qvR69vneQsEFzJpeBeqLU7W7yrklU/38uiM/mjU7ev+WiAQCBoi4CznySef5B//+Af/8z//\ng0KhYMCAATz55JPNGVuL4pEkVv1Q0OjpE4ESTDWGP/EC8CtsNORb0Vj8V2HoUCgIuELDl3Bid3rq\nWkBak8/E6WcXUL7kKyIH9KbbG8+i1Pj585E8qPM+QVXwE1JKV1yjZgVe5SBJUF0ALgtoDN6xn4FW\nV1yALMMxk5bT1RrUSpk+KXbiIqRG7as+zq3UUauUrN7mZN0PLhRKmDhCy6iBGpStpPJAkmRy80y8\n/0khtRYPmRkR3Ds3g+5ZjZ9g0lqpqHTy2coSVm8ox+WWSU7UMm1iCqOHGYUY0QpwuSR+2FNNbp6J\nXfvNSBKo1QqGXx7HmOFGBlwa86upYElNTeW+++7jvvvu45NPPuGpp57iySefZPv27eEOTSBoERQK\nBbOv7o7Z6mTHoTLe+OoAv5vcp12PnxYIBIILCViU6NKlC2+//XZzxhJWFuceZf3OQp/Lm1phcJZA\nqzH8ixdl5/Xfnr/sF2EjEFPMYPEnJgzq4e0NDrRC42LhRIfF7sLuvDhpDrfPRPHbH1P0yrvoumbQ\nPedlVJF+KmYkCfXmz1Cd+hEpuTOu0bNBHagg4YGqfHDbQBsNsWnBTeg4B7cEB0p0VFjVGDQSfVPt\nRGjq/9wEy4VVPPHRURi0WVhsOhJiFMydoCcjpfU86Tl12sbrOfn8dMSCXqfkzls6cd3YpHaX+FVU\nufh8RTGrvy3H6ZJJMmqZNimFMUKMCDuyLHM830ZunonvtlZQa/EKr926GMgeYWTEkHiio359Zdtm\ns5kvv/ySzz77DI/Hwz333MPEiRPDHZZA0KIolQruntSb+dY97DhUxodrDjNnfPd22U4oEAgE9RHw\nHdCWLVtYuHAhNTU15yXE7cHo0p8AoFTAqAEdm1xhAMFVY5RV2Xy2SVTUOPChSVzUJtGQKWZj8FeF\n4ZEkDuVXUVhWiyR7r19aUhRTR3e9aD8XThlxujz87zs/BHReLYnpyzXkz3sRTbKRnoteQWOM972y\nLKHeugzVib1ISem4sueCJkBjSo8bqk6BxwG6WIjpSGNNH2wuBfuL9VicSuIj3FzawUEoq0HPreLR\nqOLxuDOxeNTEx9h4dGYiEbrWcSNld3hY8mUxX64uweOBoYPj+M3MTiQmNN4stDVSWe3i8xUlrNpQ\nVidGTJ2YwpjhCWiay8lUEBBVZhffbfVOzzh12tueERujZvI1yYwZbqRzp8aP9m3L5OXl8emnn7J/\n/37Gjx/Ps88+e55vlUDwa0OjVvHgzf14btFO1u8qJDZKyw3DM8MdlkAgELQIQbVv3HfffaSkpDRn\nPGHBn0+CDFwzJCMkfgaBVGOoVQoWrT3MzkOl+HqmnRCtQ5ZlKmqcFy0LVZuJP/yNLF2ce5SC0l9G\nCkoyFJTWsnTDcZ/tF2eFE4fLE7BBZ0th3rSd47+fhzLSQPecl9FlpPleWZZRb/sK1bFdSMY0XNm3\ngibAmD3OnwUJF0TEQ1RKowWJaruS/UV6XJKCtBgXWYlOQlkF+ouIpyBCk4Fe0wFZ9mBxnEBhqUGp\nTADCXyWxfU81b3xQQJnJSZJRy12z07l8QGy4wwopVdUuPl9ZwjcbynA6ZRITNEydmEL2CKMQI8KI\n2y2zY281uZtM7NhbjccDapWCoYPjyB6ewMA+sb/6ypXf/va3dOnShUGDBlFRUcG777573vJnnnkm\nTJEJBOHDoFfzyPT+/DNnB8s2niAmUsvoAX7uOwQCgaCdELAokZaWxg033NCcsYQNfz4JCSFIhh0u\nD2WV1oCqMS70kaiPgd2Da5NoLi6swmjq9JLmNOhsDNYfD3PkzsdAlun+zgtE9u0J+JiGIsuof1iO\n6sh2pIRUXGNvA22ARm1uh1eQkNxgSITIpEYLEsU1Kg6V6pCBSxIdpMWG3tCyutZBVY2CaP2lqJUG\nPJKVWscxJNlGVS1hq2g5S3mFk7c/Os3WHVWoVHDTdR2YPikVna79JOlVZhfLVpawcr1XjDDGa5g6\nI4WxI4xoNO3nPNsaJwus5OZV8O3WCsw13r+9zIwIsocbuWpoAjHRv772DF+cHflZWVlJfPz51Wen\nT/v/HygQtGfionQ8OmMA/8zZQc6qQxh0aob06hDusAQCgaBZafAOqaCgAIDLLruMxYsXM2TIENTq\nXzZLT09vvuhaiOZKhs/tu/flIwG/VGO4PbLPpB7AeMH0DQi9kWVTaGjMZyDJanMZdAaLo+AMh2Y/\niKfGQtZ//0nMiMt9T0MZk4V212pUh7YhxXXwChK6AEuyXTavh4TsgagOYDA2Kl5ZhhMVGvKrtKiU\nMpd2sJNgCJ2h5S/HkTl6WkVMxKWACoerBKsrH36u6wlXRQuAxyOzfF0pH31ehN0h0euSSO69NYOM\ntPZTHl9ldrHsmxK+yS3H4ZSEGNEKMNe62bi1gtxNJo6fsgEQE6Vm4rgkskcYycwIn0DXmlEqlTzy\nyCM4HA4SEhJ4/fXX6dy5Mx988AFvvPEGN910U7hDFAjCRkqCgUem9+dfH+3i9S9+pMbqYuzgTuEO\nSyAQCJqNBkWJ2267DYVCUecj8frrr9ctUygUrFu3rvmia0GaIxkOpOoBfqnG8JfUK4CHpvajU3J0\n3WvNYWTZFPxP5ggsWfXXGtJSuExVHJr5AK5SExl/fwzj5PGAr2koBQw276Sf5Uek2CRc424HfYDT\nHJwW75QNWYLoVG/bRiPwSPBTqY5yixq92mtoGakNjaHludgdMks3ONh1yI1KqaDadgSXp/K8dcJR\n0QJw+LiF1xbmcyLfRlSkilsnpzBhTDIRuvbxZLra7OKLVaWsWFeGwymREKfh1mlpXH2VECPCgccj\ns2u/mdw8Ez/srsbtkVEq4fIBsWQPNzK4f4xon2mAl156iffee4+srCzWrVvHvHnzkCSJ2NhYPvnk\nk3CHJxCEnczUGP48axAvLdnNh2sOU21xcOPIrsL8UiAQtEsavGPPzc1tcCfLli1jypQpIQkoXIQ6\nGfbXynAhZxM5v20kMXqS6qkyaA4jy8YSyoqT5jqvelsvzsFjtXH41oewH88n9f7bSPntzLrt6ns/\nb4o+ST/LSTzRCbjH3QERUQEGUgPVpwEZYjqBPqZx5+NWsK9IR61TRazeQ58UO82hCRSUeMj5xo6p\nWqZzipKZ4/Ws2R7JrsO2sFa0WKxuPvj0DKs2lCPL0KWrGkV0DV/vNrH5+Im6yqLGesI09Hlpbsw1\nbpZ9U8LK3DLsjrNiREfGXZWIVogRLU5BoY3cTSa+3VJBZbW3PSMjTU/2CCOjhiYQFxvglB0BSqWS\nrKwsAMaOHcszzzzDn//8Z66++uowRyYQtB46p0Tz+NzBzF+8h683n6K61smtE3qExOdMIBAIWhMh\neYz42WeftXlR4iyhSob9VT2A1zIg4YJErqU8Fc4mWtGxESFPulpL+8WF+Gq9eGD6wLp1JJebo/f8\nBcuuHzFOu55Ojz9Qt6y+93Ny1ElujjlJiVuPZ8hMjIZoAsJeDeZCQAGxGaALUMi4gBqHkn1FOpwe\nJSnRLronhdbQEkCSZTbucrF8sxOPBNmDNUwYqkWlUoS1okWWZfK2VfLOx6epMrvplKqnW28FewqK\nwFtB/3Mli/dvyZfJqi98tuo0QeAIBnOtmy++KWHFOq8YER+rYfZNHRk/WogRLU2txU3e95Xk5pk4\ncsIKQFSkimuzkxg7wkjXzhHiyWUjuPCapaamCkFCIKiH5HgDf507mH8v2cPGvUXUWF3cM/nSsFfH\nCgQCQSgJiSgh+5pP+SvGv3mmjoen9ycpLuKifypNSeobrAK4INHS69TIsoTdKZ3nV9GUpKs1tF/U\nR/2tF6cxRGiZMrwLsixz8o9PUb1uE7FjhpH5whPn3TRf+H5eF5XP9NgTlLn1LLAP5bGkpMACsVZA\nbTEolF5BQts4Aay0VsXBUh2SDFlGB51i3Y31xvRJrVXm47V2fjrpISpCwazxOnp0Pv8rIxyVOmdK\n7LzxQQF7fqxBq1Ew5+aOTMg28n/vfl/v+oGYrF6Ir88LBC9wBENNrZsvVpWwfO1ZMULNrJs6Mn5U\nIjqtECNaCo8ks/dADbl5JrbtrMLlllEqYHC/GMYMNzJkQKxomwkxQtgRCHwTG6nlT7MG8urn+9h9\ntJwXP97N76f2IypCVGcJBIL2QUhECXEzcTE6jYp+WUbW7zpz0bJBPZLolFT/0/HGJPWBPtW9MNGy\nOX6ZzBDqpKs1tZX4a6XZur+Ia4ekU/rCa5Qv+ZrIAb3p9sazKDUXJ99nq1iuiTzN7NhjmNw6ni4f\nQP+B6Q0nvLIMVhNYSkGhgrjOoAlwOscFu8mv0nCiQotKIdMnxUFipCfo/TTE0QI3H652YLbIdE9X\nMXO8jpjI8CZhLpfEZytL+PTrYlxumYF9Yrh7TjopyTpKK61NNlk9S1OnyDSGmlo3X64uZfnaUmx2\nibgYNbNu9FZGCDGi5SgstvPZyjJWrC3GVOkCIC1Vx9if2zMS4rVhjrD9sGvXLkaPHl33u8lkYvTo\n0ciyjEKhYMOGDWGLTSBojUTo1Dw8rT9vL/+JbQdKePbDnTw6vT8JMcHfSwgEAkFro324wLUyzooE\ne4+ZAO/IT0n2Kt2DAqx6CCapD+SpbqAeF82VdIUTf6005VU2Cl5fRMUr76LrmkH3nJdRRdZ/3Wdk\nd6OH5RDDzEeo9GhZYL+C/gM7N/x+yrJXjLCaQKmBuAxQBz+hwiPBoTIdpbVqdGqJvil2onShrVLy\nSDJrvney9nsXCiVcP1zL6EEalGEWHvceMPN6TgFnShwkxGn4zaxOXDk4rk4QDYXJ6llCMUUmUGot\nbr5cVcrXP4sRsTFqZkxOZcLopHY1wrQ1Y7V5yPu+kvWbTBw8agHAEKFi/OhExg43cklXgxDem4Fv\nvvkm3CEIBG0OtUrJXZN6E2PQsmZ7AU/n7ODR6f1J8/GgSyAQCNoKQpRoBi4UCaSf88Zqi5O9x0yo\nVEdD1pse6FPdhjwuztKYp8rVtQ4idGpsDneradc4F38Ja//Cn6j49D00yUZ6LnoFjdH3BAzNsV0M\nM3+PpIvEPnQWD6WmBVYhUVME9ipQab0VEqrgyy2dbthfrMfsUBGj8xpaakP811tZI/HhKjsnzkgk\nxCiYc42ezqnhfS+rql28u/g0322tRKmA68clMevGjhgizo8rlH4soRQ4fGGxeisjvl5TitUmEROt\nZsYNqUwYI8SIlkCSZPYfrGFdnomtO6twOmUUChhwaTSTr+1Er246UaHSzKSlpYU7BIGgTaJUKLhl\nbDfiorR8suEYz364k99P7cclneLCHZpAIBA0mpCkNVFRQqE9S0MVCaFukwj0qa6/ROtcAk26zlaD\n7DxUSkWNs64aJFTeFKHEV8LaseAoQ77KQRlpoPsH/0GX4fsmWXl8N+qtXyDrDLivvoOE+A4NH1iW\nvYaWDjOo9d4KCWXwf3K1DgX7ivU43EqSo9z0SHKgCvGl3fGTnTc+tWJzQP9uaqaN1RGhC9/TYUmS\nWfNdOTlLz2CxeujWxcC9t2aQ1cW3WBYqk9XmNJy1WN18tbqUr9aUYbV5iIlWc9v0VCaMSUSva11i\nXnukqNTB+k0mNmyuoMzkBCA1WUf2CCOjhyWQmKAlKSmasrKaMEcqEAgEvlEoFFw7tDMxkVreXXGQ\nFz7ezb2TL2XgJQH6WwkEAkErI+AMqaysjBUrVlBdXX2eseVDDz3EggULmiW4tkigFQmhapPw/1RX\nR63NidMtkRQX4TPROpeeGfUr7ReaaPqqBmkpQ8BguTBh7WIpZ9zKHJRKBd3feYHIPj18bqs8sRf1\n5s9Aq8c17nbkgAQJyTvy01kLmgivqaUy+Pe63KLipxIdHllBlwQnneNcITW0dLllvspzsmlvLWoV\nTM3WMfRSdVjL1U/kW3ktp4DDxyxE6JXcNbsT14xJQtXAaJFQmqyGeoqMxerh6zWlfLm61CtGRKm5\ndVpHrs1OEmJEM2Oze9j8QxW5m0wcOFwLgF6nZNxII9kjjPTsFinaMwQCQZtkeN9Uog1aFizbx//7\nbB+3TejJVf07hjssgUAgCJqARYl77rmHHj16iJLLBgi0IiFUven+nupW1Tp4auFOAPRaFcP6dCB7\ncBp7jpiorLGj06qRZRmH04NOqwJkNu0v5mB+ZV21A3CRiWa/bonsOeLfn6K1eVOcm7CWHzpJyezn\ncDvsDPhwPpoRl/vcTnnqR9SbPgW1Fte425ATUhs+mOSB6nxw2UAbBbGdvNM2gkCW4XS1mmMmLUoF\n9O5gJzkqtIaWpZUSOSvtnCmXSEtSM3O8hlRj+N4vm93D4i+K+GpNKZIEI4bEc8eMtKDNBUNhshoq\ngcNq+0WMsFg9REepmDvVK0ZE6FvH30Z7RJJkDhypJTfPxJbtVdgdEgB9e0WTPTyBoYPjhBgkEAja\nBf2yjPxx5kBe/mQv7608SHWtg4nDugixVSAQtCkCFiUMBgPPPPNMc8bSLvAnEpxLML3pDY36vPCp\nrlajwu704JF+Wcfu9JC78wzjLuvEU3ddQXWtg6wuRsrLa8lZdYjN+4vr1j232gG4yERz/c7CBmMO\ntSHgWRq6Fg2hNJsx3ftH3GUmMv7+BzpOu85nqbay4CDqjUtApcY19jZkYwCCnOSGqnxw20EXAzFp\nBFvaIMlwpExLUY0GrUqiT4qDGL3U8IYBIssy2w+6+WyDA6cLhl6q5jc3JWKurg3ZMYJl284q3vyw\nAFOliw5JWu6ek86gvrFhi+csjRU4rDYPy9d6xYhai4eoSBVzbu7IddlJRESIZLi5KC13sH5zBevz\nTJSUe9szOiRqmXKtkTHDEkhObLofiEAgELQ2sjrG8tc5g5i/eA+fbzxBtcXJrHHdUTZQYSgQCASt\nhYBFif79+3Ps2DGysrKaM552wbkigclsr3edc3vTfSXagY76PPepblmVjfkf78TurD+2nYfLuHlU\nFsnxBvQ/OyUeyq+sf91DZT7z6bMeEr4IlSHgWQK9Fn73YbVx+NaHsR/PJ/X+20j57S0+11UWHkb9\n3cegVOHKnouclB7AAVxQdQo8TtDHQXRq0IKEywM/FuupsquI0nrok+pArw7dhA27U+az9Q52HHKj\n18LcCToGdNeg04bnxqW03MFbi07zw+5q1CoF0yamcPPElDZrMmizefj6AjFi9k0duX6sECOaC4dD\nYsuOSnI3VbDvJ6/AqNMqGTM8gewRRnpfEiVuzAUCQbsn1RjJ43MH89KS3eTuLMRscXLXpN5o1OJ/\nj0AgaP0ELEps3LiR9957j/j4eNRqtZgl7odzRYIKs521O06z96jpot70hhLtQEZ9notOo0KrVlJl\ncfuMrcLsIGfVIe64rifQkFGm7xYUf4IENN0Q8EKCvRYXIrncHL37z1h2/Yhx2vV0evwBn+sqio6h\n3vARKBS4sucgd+jScIBuh7dCQnKBwQiRyUELEhangv3FemwuJYmRbnolh9bQ8nSph5yVdsqrZTI6\nKJkzQY8xNjzJv9st89WaUhZ/UYTDKdGnZxR3z0knvWNEWOJpKjabhxW5ZSz7pqROjJh1YyrXj0u+\naFKIoOnIsszBoxZy80xs+qESm91bSdS7exTZw40MuyxOiEACgeBXR3y0jr/MHsR/Pt3H9kNl1Nr2\n8MBN/TDoxbA9gUDQugn4W+q///3vRa+ZzeaQBtPe0GlUpBojmTu+B44xF1dDLFp72GeiffOorIBG\nfV5IhE6NAvCnGWzeX4xBr+ahmYMbNMpUKKh3mTFGR78sI3uOmqiocdQ7fSNUBDr21BeyLHPyj09R\nnbuZ2OxhZL7whM9eS0XJCTTrPwRkXKPnIKd0bThAl91bISF7vGJEZGIgp3UeFVYlP5bo8UgKMuKc\nZCaEztBSlmU27nHxdZ4TjwRjBmu4dqgWlSo8T48PHq3ltYX5nDptJyZKzT1z0xk9LKFN9r/a7B5W\nrCvji1Ul1NR6iDSomDnFK0ZEGkRSHGrKK5xs2FxB7iYTRSXe76XEBA0TxyUzZngCqR30YY5QIBAI\nwotBr+GxGf1548sD7DhcxnOLdvLI9P7EhbB6VSAQCEJNwKJEWloaR48epbLSW+rvdDp56qmnWLly\nZbMF157QaVTERunqhAnAb6J9Vb/UgEZ9XojN4fYrSJx7DLvT7dcDY1AP72ip+kcjJjFrXHemZ3vF\nlgidGpvD3aSJB74IdOypL04/8yrlS74mckBvur3xHEpN/R97RekpNLkfgCzhHjUTuWMAworT6jW1\nlCWISgFDQkDndC6F1WqOlGtRAD2THaRE+650CZZam8ziNXYOnPQQFaFg5ngdPTuH54lJTa2bnKWF\nrPnOBMDVVxmZOzWN6Ki29wTHZvew8ufKiJpaD4YIFbdMSWWiECNCjsMp8f1O7/SMPQdqkGXQahRc\nNTSe7OFG+vaKFu0ZAoFAcA4atYrfTenDB2sOs2FXIf/M2cGjMwaQkhBany+BQCAIFQFnA0899RSb\nNm2ivLycjIwMCgoKuPPOO5sztnZDfW0aPTLi/SbaK7aeQqHwTmG4EH9+DbFROowBTP+oMNupNDtQ\nE9j4Q1/LzjUCjDYENyUhUPxXc/j3rih+62OK/t976Lpm0D3nZVSG+tsDFGUFaHJzwOPGPeoWpE6+\nR4TW4ahFri4AZNyRqWgM8YGeEuCtLDlm0lJYrUGjlOmTYic2InSGlsdOe/hglR2zReaSdBWzxuuI\niWz5dg1Zlvl2SwXvLi7EXOMmI03Pvbdm0OuSqBaPpanYHR5W5pazbGUJ5lq3V4yYnMrEq5OINLQ9\ncaW1IssyR45bWbfJRN62Sqw27+SZHlmRZI8wMvzyeCH+CAQCgR+USgVzx3cnLlLLsrwT/DNnB49M\n709maky4QxMIBIKLCPguet++faxcuZK5c+eSk5PD/v37WbNmTXPG1m6ozw9h8/5i9FrvlIwL0WpU\nbD1Q6nN//vwaAp3+odOqiI/RUVNta3D8YUOjEZs6EaMh/J2Tv2th+nIN+f/7IppkIz0XvYLGWL9o\noDCdQbNuIbiduEdOR0rv1WBMHlsVVJ9BkmUWrKvitLkyKONNtwd+LNFRaVNj0Ej0TbUToQmNoaVH\nkln7vZM1P7hQANddqWXMZRqUYWiPOF1k5/WcfPYfrEWnVXLrtDQmXZ2MWt22nmw7HBIr15fx+coS\nzDVuDBFKpt+Qwg3jk4UYEUIqqlx8u8VEbl4Fp4u8JsHGeA3XZicyZpiRtFTRniEQCASBolAouGFE\nJjFRWnJWHeL5Rbu4/8Y+9OlqDHdoAoFAcB4B301rtd6n4C6XC1mW6dOnD88991yzBdZe8OeH4Jv6\nk1OlAkYNTGvQr+GXyoeyBismzsXf+MP6loViIkagBFLNcS7mvB84/vt5KCMNdP/gP+gy6h/n6Skr\nRLP2PXA5cI+4Galzn4aDsVWiMBfhcEu8vLaSw8UugICNN20uBfuK9FhdShIMbnp3cKAO0eWqqpH4\ncJWd42ck4qMVzJ6gJzO15Z8oO5wSn35dzOcrS3B7ZC4fEMtvZ3VqcyMZHQ6JbzZ4xYhqs5sIvZJp\nk7xiRFSkECNCgcsl8f3uatZvMrFrnxlJBo1awYgh8WSPMNKvdzQq0Z4hEAgEjWb0gDRiDFpe++JH\nXl66lzuv68WVfVLCHZZAIBDUEfBddWZmJh9++CGXXXYZd9xxB5mZmdTU1Pjd5vnnn2fHjh243W7u\nuece+vbty5/+9Cc8Hg9JSUn861//QqvV8uWXX/L++++jVCqZPn0606ZNa/KJNZVQPf3354fgcHoY\n3ieFg/lVdYl2z4w4Nu0vrnd9WYZrLk9vMOE/W/lwVb9U5r3zQ73rOF2euvaNxtLUiRjB0FA1x7lY\n9h/i8J1/AKD7uy8S2af+VgxFVQnWte+C04572I1Imf0bDsRqgtoSbE6JF76p4JTpfP+Hhow3q2xK\n9hfrcUsKOsW6yDI6Q2Zo+eNxNx+vtWO1Q78sFdPG6jHoWz6Z27XfzBsfFFBc6sAYr+Gu2ekMGRjb\npowsHU6JVRvK+HxFCVVnxYiJKUwan9wmPTBaG7Isc/yUjXV5JjZuq6DW4q0YuyTTQPYIIyOGxAvR\nRyAQCELIoO5J/OGWAfxn6V7e/PoA1RYnE67ICHdYAoFAAAQhSjz55JNUV1cTExPD8uXLMZlM3HPP\nPT7X37p1K0eOHGHx4sVUVlZy4403cuWVVzJr1iyuvfZa5s+fz9KlS5kyZQqvvvoqS5cuRaPRMHXq\nVK6++mri4uJCcoLBEuqn//78EBJi9My5pgdOl4fTpbV0So5Cq1FxML/S5zQMf/4JF5IUb/DpLxEf\nra9r32gMTZ2I0Vj8VXMAOPILOTzn90gWK1n//Scxwy+rdz1FdRmaNe8h2y24h05Gyhro/8CyDJYy\nsJbjQcUzy8s4U3Vx640/480is5rDZd6Ko+5JDjrGhMbQ0u2W+Xqzk427XahVcPMYHVf2Ube4ySGl\nhwAAIABJREFUCFBR5eLdj0+T930lSiXcMD6ZW6akEqFvO73/DqfE6g3lfL6ymMpqN3qdkpuv78AN\n13QgRogRTaaq2sW3WyvIzTORX+htz4iLUTN5QjLZw41kpLXNkbACgUDQFuieHsdf5gxi/uLdLFl/\nlGqLg2ljuoWlvVMgEAjOpcG77AMHDtC7d2+2bt1a91piYiKJiYmcOHGClJT6y78uv/xy+vXrB0BM\nTAw2m41t27bx5JNPAjBmzBjeeecdMjMz6du3L9HR0QAMGjSInTt3kp2d3eSTawyNffrvq7LCnx/C\ngEuMfPrtsYsEkP6XJJK7o/Ci9a0ON59+e8ynQHJhDA15Mei1ai6sdQm0QqSpEzGCJZC4XKZKDs56\nEFepiYx//AHjDVfXvzOzCc2ad1HYa9Fn34wjbYD/g8sy1BaDrRJUGtyR6TikcuBiUaI+401ZhuMV\nGgqqtKiVMpd2sBNvCI2hZVmlRM43dgrLJDrEK5h7rZ7UxJYVATySzKr1ZXz42RmsNonuXQ3ce2sG\nmRltx+Xb4ZT45MvTLFyST2W1S4gRIcTlltixx0zuJhM791Xj8YBapeDKwXGMGW5kUN+YsI2nFQgE\ngl8bnZKieHzuYF5asodV3xdQbXFy53W9UKta3ghbIBAIztLg3fayZcvo3bs3CxYsuGiZQqHgyiuv\nrHc7lUqFweBNSpYuXcpVV11FXl5enTeF0WikrKyM8vJyEhJ+GaOYkJBAWVmwHgyhoTFP/wOprPDl\nhyDJMuvqEUDGDk5j3GWdyNtbdJ4Rpt3pqVcg8RdDoF4MwVaINGUiRjAEGpfHauPwrQ/jOJ5P6gO3\nk/KbW+rfYW0l2jXvorDV4B58LdoBI6HMTxuSLIP5DDiqQaWDuM7oVOqAjTfdEvxUosNkVROhkeib\nYsegDY2h5fafXHy6wYHTBUN6q5kySodO07LJ3bFTVl57P5+jJ60YIlTcMzed8aMS28yIRqdLYs23\n5Xy6vKROjLjpug5MvqYDMdFCjGgKJ/Kt5OaZ+G5rJeZab1VQ14wIskcYGXlFgri+AoFAECYSYyP4\n65zBvPzJHrb+WEKN1cX9N/ZBrxXfywKBIDw0+O3z+OOPA5CTk9OoA6xdu5alS5fyzjvvMH78+LrX\n5fpmXfp5/Vzi4w2o1U1/GpyUFH3e70XlFipqfD/9V2k1JCVGnvf6m8v21VtZYYjQcteUvnWvPzRz\nMHanm0qzg/gYb8J+//O59R5r3/EK5j88ij3HTNidF7dX7D1m4p6bI+r+eTQUw4XHPvefztlrEOh5\nnMvw/ml8ufF4Pa93pFPH0LTfBBKX5HKx/c5Hsez6kU5zb6Tf/L/U27og1VRi+eI9ZGs1uhETiRky\nDrj4c3AWWZIwnz6C01GNOiKK2M49UKq81+6B6QMxRGjZur+I8iobiXERDO2Typ2TLkX189MGq0Mm\n75BMtRWSY+DK7iq06qaPwbQ7JN7/2sym3Q70OgX3TYtlaL+ml737ug71YbG6efODk3y2vBBJgvGj\nk3ngziwS4ptnLGyocTglvl5dRM4n+ZRXONHrlMy6OZ2ZN3YiPrZtnENzEczn4EKqql2s+baEFetK\nOHK8FoC4GA3Tb0jj2nEpXJLZNsbANuUaCJqfw4cPc99993H77bczZ84cjh07xrx581AoFHTp0oX/\n+7//Q61Wt0q/KoGgtRAVoeEPtwzkv1/sZ+8xE88v2sXD0/oTE/nr/h8oEAjCQ4OixNy5c/32pi9c\nuNDnso0bN/Laa6/x1ltvER0djcFgwG63o9frKSkpITk5meTkZMrLy+u2KS0tZcAA/+X0lZXWhsJu\nkKSkaMoueELucXlIiPb99N/jdJ23jcPlYdOei9ssADbtOcO1Q9IvqqxQAzXVNk6X1lBaWb+fQ3mV\njT0/FVPuZ/mxkyaS4w1BxXD22GfP4Ow1qLE6+W5X/SNEfZ0HwKQrM7DanBdVYUy6MuOia9sYAjk3\nrVrJiUeepPyb74jNHkbKU3+mvLz24g2sZrSr30ZRU4G7fzaOzCugrKbezwEAkgeqC8BlBU0k7shO\nmCrOfz+mDO/CtUPSz2srqaiwAGC2K9lfrMPpUZIa4+KSRCfVlU2+JJwu9ZDzjZ3yKpn0DkrmXKMn\nMc7d5Ovt8zpcgCzLbNlRxduLTlNR5SK1g45756bTr3cMHreDsrLAp72EA5dLYu1GE58uL8ZU6UKn\nVTJlQjJTJnSgW1YCZWU1rf4cmpNAPwfn4vHI7NxXTe6mCrbvrsbtkVGpYMjAWLKHGxnULwaNWgnI\nIfleaG4acw3aI61VmLFarfzjH/84r0rzhRde4O6772bUqFG8+uqrrFy5krFjx7YqvyqBoDWi06p4\n4Ka+vP/NQTbtK+afH+zgsRkDSIoT/j4CgaBlaVCUuO+++wBvxYNCoWDo0KFIksTmzZuJiPD9pVVT\nU8Pzzz/Pe++9V3cTMGzYMFatWsXkyZNZvXo1I0eOpH///vztb3/DbDajUqnYuXNnXXVGS9OQB8OF\niXmF2e5z5KYvX4Vz2xF8ER+tp1NyVEDtEU3xdvB4JBatPcyOg2VU1TqD3kcwEzEaQyDn5vjvO5Qv\n+ZrIgZfS7Y3nUGrq+Ujbar0eEjUVuPuMwtNvjP8DS26oyge3HXTREJMGivp7Lesz3iypUXGoTIck\nQ5bRQadYd5MnbMiyTN5eF19tdOKRYPQgDddeqUXdgr34xaUO3vywgJ37zKjVCm6ZnMqN13VAq2n9\nfagul8S6PBNLv/aKEVqtgsk/ixFxMZpwh9cmyS+0kZtn4tstFVSZve0ZnTvpyR5h5KqhCeK6CpoF\nrVbLm2++yZtvvln32qlTp+o8rEaOHMmiRYtITExsVX5VAkFrRa1Scud1vYiL0rF8yyn+mbODR6b3\nJ6ND6xQmBQJB+6RBUeLs04i3336bt956q+718ePH87vf/c7nditWrKCyspKHH3647rVnn32Wv/3t\nbyxevJj/z957h0d1n2n/nzNd0ozaqDckBJJAohebZkAU44LBTmxsDK5xmrObvJv27u5v4yS72Wyy\nm+TdlI2dbIJjbGxcMU5cAAswvRqQAElUIQkJSaM2o9G0c87vj6GojkZdwPdzXb4uo5k55ztHZzTn\nuc/93E9SUhIrV65Er9fz7W9/m2effRZJknj++eevX0QMB8FmMABsO9K1uwC6z1XoGKTZFVOyYrCE\nGoISSPqT7fDnD072uJZg8iF6mojRV3p6b563NlH125cxjU4j/c+/xOZWidDL7YURVwv6revQNNfh\nGz8XefKiwDuVvX5BQnaDKQIsSQSrKKgqXGzQU9ZgQCupTEhwYw3rHIbZW1paVTZuc3Hygow5ROLR\nJUbGpQ9d36fXp7D5kxre3FyFx6sycZyFL69NJTnBNGRr6Cten0LBVTGirt4vRjywNI4H74knMkIU\nzb3F0eJj14EGCvbYOHvB71gzh2m5d1Es+XOtjE4LualGvwpuPnQ6HTpd+79/WVlZ7Ny5k5UrV7Jr\n1y7q6ur6nFc1UO2hHRmpzpPbBXH8e+arX5xMUryF/32/iJ+//jn//PRMJo6JHZBti+M/fIhjP7yI\n4x88QVc21dXVXLhwgYyMDAAuXbpEeXl5t89ftWoVq1at6vTzdevWdfrZsmXLWLZsWbBLGVR6uvt/\nbQpEiFHHibN13W5nYmZ0J9dAoCBNgGiLkanZsdcFkGAEkt66O9quZX9RVbdrCWYbg02g9zav/gyX\nf/9bdHFWip//B9a/U9w5CNPrQr/tZTRNNfhyZiFPXRpYYPB5oLEMFC+ERIM5PmhBQlaguNZIrUOH\nSaeQl+DCbOx/oOW5SpnXPnHR5FAZk6Jl9VIjEeahcyacLLHz4ivlVFS5iAjX8fzTKcy7I2rEF55e\nn8L23fW8/bdqam0eDHqJ5VfFiCghRvQKWVE5frKZgt02DnzehM+nopFg2sRw8udamTEpAv1N4JYR\n3Lp8//vf54c//CHvvvsuM2fO7DKbKpi8KhiY9tCOiJag4UUc/+CZlROHVs3ljx+c4oU/7OO55bnM\nyInr1zbF8R8+xLEfXsTx70wgkSZoUeJb3/oWTz31FG63G41Gg0ajGbY2i6Gg493/jlMgIs1GGhzd\n954vnp7a6WeB2hEkCb71yCRSYm8EwflklcXTUlg+O51Wt6/b9ojeuDuuUdvYSm03mRUAkWYD03Pi\nAm5jKOjqvc32XSHxly+iMYdy/u+/w8dlN1pPrgVhGhQPj3l3oWmoRs6aiTz9nh4ECZffIaH4ICwW\nQmOCFiTcPomiaiN2t5Zwk0xevIu+BFi3HXuq12rYdsjLloMeJOCeWQbyp+mHbKpFs93HX96qpGC3\nDUmCZQtjePyhJMxhIzuZ2+dTKdjjd0ZcFyOWxLHynniiI4UY0Rsqq1wU7LGxY2899Y1eAFIS/e0Z\n82dFi+MpGDEkJiby0ksvAf4sq5qamj7lVQkEApg5Lh5ziJ7fvlvIi5uKaF6SxaJpKcO9LIFAcIsT\ndIWxePFiFi9eTGNjI6qqEhUVNZjrGnF0bLsIJEhYw01Eh3e2tgdqR4i2mK4HC3U1BnPimBgWT0sh\nOtzUSZjoTbbDtW0fLamhu/tGkWYD33goj+RYS5fjQIeSju/NUFbG2Uf+ERVI/8PPeeOkD2h/PEMk\nH3Muf4xG24Q8Zhq+mfcFFhi8Tr8goSp+d0SoNej12d0aiqqMuGUN8WYv2XEeeqsbdPx9R1nCCDOO\nweE0EmmWWLPMREbS0LhVFMVf1P/lzUocLTIZaSF8dW0aWZlhPb94GPH5VHbstfHWX6upqfOg10nc\ntziWh+5NEMVzL3C0+Niyo46CPTZKzvlDW0NDtNy9IIb8uVbGZoSOeJeM4Pbj17/+NRMnTmTBggW8\n++67rFixYkTlVQkENxvj06P5/uqp/OrNY7y2tZSmFjcPzhst/v4LBIJBI2hRorKykp/97Gc0NDSw\nfv163nrrLWbMmEF6evogLm9k0FPbRUeutTy0vfNt1GuDbrXoKIDYmt1sP1rJ9qOVWNu2J3QQDILJ\ndggm06LV5eMnrxxt3woxDOJEx+MXYW/g1FPfQmlxkvn7f0eeOIH6ffvbvcYo+fiu9QTp2iZaUvLQ\n3flAtyGVAB5Hk79lQ1X9+REhwSez17VoOXXFiKJKZER7SIv09inQsu3vRK+JRPZl4JD1RFpa+fZj\nMYSahuYi4FJlKy+tL+dUqQOTUcPTjyZz36I4tEMYptlbfD6VHftsvP1BNVeuiRGLYnno3vibZjzp\ncCMrKoWn7WzfY2P/0SY8HgVJgsm5FvLnWpk5JRKjQbRnCEYGRUVF/OxnP6OyshKdTscnn3zCd77z\nHf71X/+V3/zmN0yfPp0FCxYAjKi8KoHgZmNUgoV/WjuNX248zl/3ltHk8PDEsuxhv1klEAhuTYIW\nJf7lX/6Fxx9//HomRHp6Ov/yL//C+vXrB21xI4VAbRfgdxY0t3iut008MGcU//vXUxSX1dNg97Qr\n7lflj0FVVfYUVuPy+EMQTQYNiqoiKwo+WQ0ogFxrTwBYvTirV++jJ3FFq5GQFRW3T+l2Xx2FgsGg\nK6fItAQj437xE7w1NtL+9TtYH1iC2yu3c54YJJnvWAvJNjZxxJvI2NkPoQsgSOBupqm2ElQgIgWM\n4UGtT1WhvFHP+Xo9Ggly413EmvsWaHnjdyIRok/FpE9AVRWcnotIzma02mhgcF0SbrfCi385z+vv\nlSPLcOe0SJ59LIWY6JFb1Muyyo699bz11yqu1HrQ6STuvSpGWIUYERRVV1wU7Klnx14bdfVX2zOS\nQlgwK4r5s6JH9O9fcPuSl5fX5XXH22+/3elnIymvSiC4GYmLCuUf107j/715nF0nqrA7vXxlRe6w\nZY0JBIJbl6BFCa/Xy6JFi3j55ZcBmDFjxmCtacQRqO3CGm7i/z4+hZqGVhJjwvhwfxnf+/3+64ID\n3CjuZUVl7dJsJElq97jLo1BwpBKNJLF4WkpAAeQan5fW8YX5mb36YuhJXJGVrhs6Pi+tZeW8DDbt\nutBOKBgsF0VHN0dTXTPmP/4B95VyEv/uaRKefRRoH4SpR+YfogsZb2zkYGsspzLyyTMGsO23NoL9\nMmg0fkHCYO7+uW1QVCitNVBt12PQKkxIdGMxKn1+r00ON412sBjHo9OGISuttLjPIquteB0EHOk6\nEBw+3sQfXyunps5DrNXAc4+nMmNyxKDtr7/IssrO/fW89UE11TVudDqJZQtj+MJ9CaKIDoLWVpk9\nhxso2G3j9Bl/e0aIScPiu6wsmmtl7p0J1NU5hnmVAoFAIBgpRIQZ+N7qKfzuvUKOna3jF28c4++/\nOBFziGiNFAgEA0evUuuam5uv95OdOXMGt7vn4vlWIFDbRahJx3+8dpT6ZjdGgwaXp/sCdefnlciK\nQtE5W5ePf15ax/LZ6d0KIG1psLt6XbAGElck6DZjwtbsZsPWM+wtqm73s944NoJ1WHR0c2hkmaUf\nvUrclXIuTpzJxG9/pd22VuWPQaPKTK/YSq6ugUJvHKczFvPIogBrctaDoxokDZGjcmhs6XH5AHhk\nOFltosmlxWKUyUtwY9T1b8LGhctawkPyAC1uXy1OTxngP4eCGcfaV+rqPfzp9Qr2H2lEq4XHv5DK\n/YujMRlH5t0PWVb57KoYUVXjRqcVYkSwKIrKqVIHn+62se9wI+6rf6MmjrOwcG40s6ZGYTT6hUXR\nLywQCASCjoQYdXzr4Un86W+nOXDqCv/x2lH+4ZFJXeanCQQCQV8IWpR4/vnneeSRR6itrWX58uU0\nNDTwn//5n4O5thFFV1MgQk06ymtu3FUMJEiA/y77Z8e6H8NZ3+yiosbBxDExbD9aGXBbfSlYA4kr\ngUprSYLTF7sXUgI5NrpqxQjksGjn5lBV5n/6NmllJZSl57B13oPYt5RSfKnhRlvH2Gge1x1Bp6ul\nNXY0oxasJsvUzXFRVXDWQUstaHQQmYY+1AItPY/rafFIFFaZcPk0xIb5yIlzo+2HQcTtUXlvp5tD\np31oNBLNrefwyu2P8WCMY5VllQ8/rWXDe5dxuRVyxoTx1SfSmD4lbkSOLZJllV0H6nnzg2qqrvjF\niKULYvjifQnEWoUYEYgrtW527K1n+x4bV+r8E2riYw3kz7GyYHY0cTGDI3gJBAKB4NZDp9Xw3PLx\nhIca2Hq4nJ+sP8I/PDKJ5NjgnKYCgUAQiKBFiYyMDB588EG8Xi/FxcXMnz+fI0eOMGvWrMFc34ih\n4xSIEKOOH798qE/b0kh+gaIjkgT/9cYxoiwGUuPMOF3ebh0TfS1YO4srRsamRHC6rIGmFm+Xr1FV\naHB0/VhPjo2uQjsDOSzaujnu2PsR2cVHuBKfytZ71qAPMbCnjVujobmVcRe2ogupRUnIRLPwcYy6\nbuyEqgqOK9BaDxo9RI4CXXBFbb1Ty8krRmRFYlSUh/SovgVaXqOyVmb9xy5qG1RS4jSsXmri06Mh\nfF5qCnqka18oPd/CS69c4vylVsxhWp5/LI38udYhGzPaG2RFZfeBBt7cXMXlK260Wlg6P4Yv3Bcv\niukAuNwy+w43UrDHRlGxXzA1GTXkz4kmf66VcWPNI/L3LRAIBIKRj0aSeHTRGCItBt7afo7/eO0o\nf//FiYxNCT4kXCAQCLoiaFHiueeeIzc3l/j4eMaM8RdLPp9v0BY2Urk24aKmwRlU9kNXdBPdcP3n\n9XYP9XYPC6cksXh6KtsOl3PiXP2AFKzXxJV770zjrR0XKL5Yx4FTNegDnAnRFsP1dXUkkGMjULBm\ndw6La26OK//7BlOO7KAxMoaPHngGn97Q7mSVUPla1GlmhtRyxhdN/NxVgQUJexW4GlE0Buo1cVhU\nLcGUthVNOs7WGZAkGBfnIt7St0BL/zJU9pzw8sFuDz4Z5k/Rc+9sAzqtFPRI177Q4vTx6juX+WRH\nHaoKC+dE8+TDyUSEj7x+UFlR2XPQL0ZUVvvFiCV3WVl+dxxGk0qEuVcdZ7cFqqpy+kwLBbtt7DnU\ngMvtd2zlZpvJn2Nl1vRIQkwjsy1HIBAIBDcXkiRxzx2jCA81sO7DYv7rjWN8dUUuU8bGDvfSBALB\nTUzQV/iRkZH89Kc/Hcy13FQEymfoCWu4kYmZVk6cq6e+2YXUjXPixLl6Hskfy9q7c7A7PVTUOEiJ\nM2MJDd623jHL4Vo7xe4TVe3CNj0B9KWp2XEAPY4y7UigYM1ADosljguc3/UBreZwPnzwS4TFW5ma\nFsm+qy4JCZUvRxUzO7SGEncE/1mfywsulbiQLnakKtBcCW47Nif8elsNFXWXrreRfOORKV2uT1Hh\nbJ2By8169FqVvAQXEaa+B1o6XSobt7koOi8TZoIn7zUxPqP9xy+Yka69QVVVdh9sYN0bFTQ0+UhO\nNPLVtWnk5Yy8sXiyorL3YAMbP6iissqNRgOL51l58N44dhSW8+v3jg56wOrNRl29h+17bGzfU09V\njf9zFms18MDd0SyYbSUxTjhKBAKBQDA4zJmQiCXUwP9sKuS37xby5LIc7pqUNNzLEggENylBixJL\nlixh8+bNTJkyBa32RhGalHR7/gEKlM9gMmjxeGX0Og1ub+dCdkpWLKsXZ+H2ypSU1fPfbxd2uY8G\nu4v6ZhfbP6/s9dSL7rIcFFWl4EjgvIqO72XlvIzrwkPbTI2eHBuBhJvuHBbNuw9x8ZsvoDOHMunN\n3zEubdT155VcaqC+2cWzkSXcFVrNWU84/2mbSKjF3LVbQ1WgsRy8LVxxSPzovWpcXr/6c62NJDTE\nwMo56e1e5pXh1BUTDa1awgwKExJcmPR9D7Q8f1nmtY9dNDpUMpO1PH63kQjz4BbUVVdcvPRqOcdP\n2jHoJVY/mMjKe+LR6wa/kO/N2FhFUdl7uIGN71dTUeVCo4FFc6188f4EEuKMbNhW2qv2n1sdt0fh\nwFF/e8aJU3ZUFQwGifmz/O0ZedmiPUMgEAgEQ8PETCvffWwK//3WCV7+qJgmh5v7Z6eL0GSBQNBr\nghYlSkpK+OCDD4iMvNE3JkkSO3bsGIx13RR0FX45JSuGlfMycDi9mEP1N8Zo2t1EW24ICrKi8M7O\ncxwtqek2ZDLKYmLbkYp2oZfBFmXdZTmYDL0rSj1eGYfTS2iUvtctBoGEm64cFi2FxZQ+8x2QJMb+\n+ReETxrX/jVjY0g5W8DCsCoueMz8rG4iraqOOV25NRQZGi+BrxVZH8b/21JxXZBoy/6iKu6ZmXr9\n9U6vP9Cy1avBGupjXLybvtbxiqLy6WEvnxzwt70su9PAoun6QS0avV6Fdz+6wjt/rcbrU5mSF85z\na1KH5K55b0JNFUVl3+FGNm6uovyyX4zIvypGXFtrX9p/bkVUVaX0vJOC3TZ2H6zH2eoXOnPGhJE/\n18rs6VGEhd76x0EgEAgEI4/MpAj+cc1UfrnxOO/tukBTi4fVi7OEQC4QCHpF0KLE8ePHOXToEAaD\nSLy/Rsfwy7aFeqhRj6z4iwdVVVFVUBQFp8uH2yuzadeFLov1tuSOjuJYaV2XjwUqygIVcz1NCOlI\nR0dDb1sMuhNuOjos3JcqKV3zTZQWJ2Ne/Cnhc6a335CqsibiLHrzZSplCz+zTSLEYmF2V24N2QdN\nZeBzgzECmxxJTeO5LtdX19h6vY2koVXDyWoTPkUiNcLDaGvfAy2bHAobtrg5WyETYZZYc7eJ0cmD\nWzieOG3npVcucfmKm6gIPc8+lsLsGZFDdscimFBTRVHZd+SqGFHpQiP5My4evj+BxPj2o8X62v5z\nq1Df4GHHvnoK9tiorPIfB2uUnnvyY1k4x0pyghjFJhAIBILhJ9Eaxj+tncav3jxGwdFKmls8PLd8\nPHqdEMwFAkFwBC1K5OXl4Xa7hSjRBd0V6h2LtAaHl71F1RwpqQlY7EZbDISFGDh+pq7biRiBirJA\nxVxv6e9YykDCzTW8tgaKV/8d3lobaf/6HaKXL26/EVVFe3QLupL9KBGxhC98in/26bp2a8gev0NC\n9kBIFJgTiPAp3baRxESGEGE2UtWso7TWf25nx7pJDO97iOvpiz5e3+KixQW5o7WsWmQiLGTwhIHG\nZi8vb6xk5756JAnuWxTLYw8mDend855cDQ/OG82xQjsbN1dRVuEXIxbMjubh5QkkxXddXPel/edm\nx+NVOPR5EwV7bBwrakZRQa+TmDszivy5ViaOt6AVd58EAoFAMMKIshj5v49P5dfvFHK4pBZH63G+\n8dDE4V6WQCC4SQhalLhy5Qr5+flkZma2y5R47bXXBmVhNzuBirSuciba4mj1dTnpoi2BirJAxZzJ\noG0XcHkNo15DXFQoTpeXBrt7wMdSdifcyC1OStd+E/f5S0Q8t4aoJx7u9Bzt8U/RndqNEh6Dd8nT\nGEIsxHW1E58bGstA8UFoDITFgiQFbCO5My+R8qYQKpr06DT+QMvIkL4FWvpklQ/3etj5uRetBh6c\nb2DORP2gORVa3T7+9mkNmz6spcUpM3pUCF97Io0xGWGDsr9AdCeEqSpUX5b53o9LqKhy+8WIWdF8\ncXlCj3f6e9v+c7OiqirnLjr5dLeN3QcbcLT4P59jM0LJn2tl7swozGFi6ohAIBAIRjahJj3fXjWJ\nP2w+xZHSWn624Sg//srs4V6WQCC4CQj6SverX/3qYK7jlqM/bgWPr+eiOFBRFqiYmzMhAUmS2rRT\nGMlJi+KxJVmEGnW9CinsL4rXx5nnvk/LsVNcmDiTLaYJRP9xf7scAu2J7egKd6JaovEueRq3LpSm\nBmfn9Xlb/Q4JVQZzHG59FE2Nrdef11UbydTsOHKyc6loglC9Ql6ii9A+BlrWNSq8+rGL8hqF2EiJ\ntfeYSI4dnOMnKwovvVPCrs8cuFq0SBqVydOM/ONXsjAMk1WyoxCmquBt0eOymZDdWlokN3fdGcUj\nyxNJTgy+7SDY9p+bkYYmL5/tq+fTPTbKK10AREXoWLksjvw5VlKTuxonIxDcoL7Ri889ktkvAAAg\nAElEQVSnEBdz67mGBALBzYlep+VrK/N4dWspOz6v5P/8aifPLR/PuFFRw700gUAwgpFUVe37WIFh\norbW3u9txMZaBmQ73eH2yvx/f9zfp5GhgYgyG5mW05vpG52LOa1Gg9srozXokT3eYbnjrKoq57/1\nQ2xv/Y2y9Bw+ue9JlDYOnMXTU1ibUI3u862oYZG4ljzNGwfqug5R9LVCUzmoCoo5gTf2dvO8q++7\nyeHGZDJRUhdGi0dDVIjM+HgXfT0MR0u8vF3gxu2F6eN0PDTfiNEwSO4Il8wPf3OK0tMeQEJv9hAa\n14pGp7J4ekqfJ1IMxOdhw7ZSth6qwNuiuypG6ACVtFF6vvvlLFJ6IUZ0ZCjEssH+mwDg9SkcPt7E\n9j31HDnRhKKATisxY0oE+XOsTMkLR6sdvvaMoTgGI52RfAwamrwUFdspKnFwsthOZbUbo0HDa7+b\nNODnTWzsyBsdPBQMxu9+JJ9TtwPi+A8PqqpScLSSNz49g6KqfHFBJstmponJHEOIOPeHF3H8OxPo\n2kJ4ggeJQG6FvhJpNvDDZ2ZgCe0518MnqyyelsLy2em0un2dijmjXktsTNiwfVgq/v232N76G7bk\nUWy9Z007QQIg4sIhdFXFqKEROBY+yfqd1ewpqr7++LUQxSSLwoIMFVAhPIU3dlcHDFs06rUYQ8wU\nVpvwyhKZ8ZAc5s846C1ur8qmnW4OnvJh1MPqpUam5ej7dDyC4cDnjfzx1XJsDV40eoXQuFb0YTey\nL4ZzIoWqqoyOjoU6Jy0NCqBijpaZM8vCcw9mBRTQgqG3AasjjQuX/O0Zn+2vx+7wt2dkjgolf240\nc++IJtws/hQLOtPY5KWoxE5RsYOiEvv1wFOAEJOGaRPDmT09aliFLIFAIOgKSZJYNC2FSdnx/PvL\nB3hr+znOVzbzzH3jCDGK7zyBQNAe8VdhELlmMd99oqrLHIfeMj0nrkdBItBYxpFC9f++TtXv/oIu\nPZW/LnkKn779e1oSVsFDpjP4jGY2hS1g54biLjM2ZmaYmDdKRkWDFJGGWxPS4wjJRpeB4lojqgpj\nY9xMzgihtuuXBORyncz6j1zUNKikxGpYc4+J2Mj+Fd7dUVPn5n83VHDoWBNaLZiiXZiiXUgddjdU\nEynauhYMOg2Hjzez8f0qzpU5kSSYNT2SpQujGDcm/JbJfegLTc1ePjvQQMFuGxfLWwEIt+hYvjSO\n/DnRpKfevCKLYHBobPZyssThd0MUO6iocl1/zGTUMHVCOHk5ZnKzLWSOChVihEAgGPGMy4jmhadm\n8OL7JzlSWktFXQvfeDCP5FjzcC9NIBCMIIQoMUDYnR4qahykxJmvCwfXJk+snDea17eWUnjeRrOz\n62kagbCGB99HH8xYxuHEtukTLv3gF+jjYxjz6q8J/fgSrW1aXBaGXuapyDM0KwY+CVvApmPNXW5n\nfnYIa2eH4/KquEPiiTKaaWpwBhwheaZGS53LhFajkpvgJjq090KRqqrsLfSxeZcbnwx3TdZz32wD\nOt3AFwc+n8pft9XwxqYq3B6F3GwzzzyWzO//egxbF4dlsCdStBW8bE1uTGoIrvoQGur9GShzZkTy\nyAOJpN3GWQg+n8rRQv/0jCPHm/HJKlot3DElgoVzrUybEDEo54rg5qSp2cvJUsd1J8S1bBHwixBT\n8vwiRF62hdGjQsW5IxAIbkoizEa+89hk3tlxno8PXuLfXjnC0/fmMHNc/HAvTSAQjBCEKNFPPD4f\nP3nlKJW1DhQVNBIkx5r55yemYtD5D2+oUcez94/H1tTK936/j96EeKyYM4pld6YHdce5p7GM/bX2\n97evv2nXQc5/8wW0ljCyX/01oaNTmZLVel00mRdaxTORJTTLerbELWXXxa7Hct47MYwvTrfQ3Crz\npz1Ovv5wBND91BGtVkv+7GnUucIw6RQmJLoIM/Q+SsXpUnnzUxeF52RCTfDEPSZyRw/OR6j4rIMX\nX7lEWYWLcLOOL69NZeHsaCRJGraJFBsLzrL1UAW+Fh2tNjMNbh2gkJKm4ztfGsuolNtXjCiraKVg\nt42d++tpavaft+kpIeTPtTLvzigiwwevrUdw89Bs93Gy9Go7RrGdS21ECKNBw+RcC3k5/v8yhQgh\nEAhuIbQaDY/kj2F0Ujh/+vA0L75/knOVzTy8MBOddnCcpgKB4OZBiBL95CevHKW8xnH934oK5TUO\nfvLKUX70zMx2z7VGhJASZ273/J6Ijw4LutAMNPGjP9b+QC0hwWYFtBQWc+bZ74IkMXbdLwjN9bs2\nrrk/NOdPsNZUTCt6CuKXMn1mHptOHOi0nS9ON3PvRDM2h8wvPq4nLyvx+vHpKscjxGRk4ZyZxERH\nEmGSyU1wYehD3X7hssxrn7hosKuMTtLw+N0mIi0D/yVqd/h49Z3LbNlZB8Diu6ys/WJyu8yB4ZhI\n4fL42H3Ihr3cjOzyr0Vv9hBidWGKNZAQ33POya2G3eFj19X2jHNlTgDMYVruWxRL/lwrGWkhItDr\nNqfZ4eNUieNqLoSdsoobIoTBIDEp10JetoW8HDOZ6aHodeLCXCAQ3NpMz4kjOTaM375byNbD5Vys\nbuZrK/OIHESnp0AgGPkIUaIf2J0eKmu7Fhgqax3YnZ5OGRD//MRU/u0vR6iobQlqH2NT/C6AYFwK\n3TkFoH/W/v62hLjKKihd802UFidjXvwp4bOnX39Mq9GwZqwHXfUJVK0RNf9J7olPxe2V270XSYI1\ns8JZmBNKdZOPP+xyMC4zvlMh3rZgl7RG8ufOxGQyEW/2kh3n6XWgpaKoFBzx8sl+Dyqw9A4DS2bo\n0fQlGTMAqqqyc1896zZW0mz3kZps4qtr0xif1bnn8lpb0BfmZw76RApVVTl20s76dyqoKvOfP3qz\nB5PVhc7ob9sYqiyLkYAsqxw72UzBbhsHjzXh86loNDB9Ujj5c6xMnxSBXi8Ky9sVu8PHqVLH9QkZ\nZRWtXJtvZTBITBznFyDyciyMyRAihEAguD1JtIbx/z0xnXUfFXO4uIYfrTvE11bmkZUaOdxLEwgE\nw4QQJfpBRY2/ZaMrFNX/+Lj06HY/N+h0fOOhCfzfl/YHtQ+PT2HDttKgXAqBJn50Ze13eXzUNDgD\nFrX9bQlxVNVSuup5fLU2Rv3bdwlbtrDdPjXlp9Htegt0BnyLnkQfm9rpvWgl+NL8CO4YHUKZzcvv\nPm3E5pCxu2xotWfbHYtrBfuCGdmcqQtBBUZHe0iN9NLbm9bNLQobtrg5Uy4TESbx+DITmckDX/xX\nVrl4cf0lioodGAwSTzycxPIl8T1atwdzIoWqqhw/aeeN96soOecX0MIiZTThTnSm9lkcg51lMRKo\nqHJRsNvGjr31NDT5c2FSk0zkz7Vy153RREeK9ozbEUfLNRHC74a4WN5GhNBL/laMbL8IMTYjVAhW\nAoFAcJUQo46vrchlS1I4b20/x883fM4j+WNYMj1FuAwFgtsQIUr0g5Q4MxqJLoUJjeR/vCsizEas\n3Tga2mLUa9h6uJwdn1++/rOeXArBWPuvtWOcOGejtqE1oNDR15YQWVF488Mion74Q6IvV3J6zhL+\nRgYtf9hHg91DdLiR5WkeFjftBK0Ob/5a1KuCRNv3opVUJsa4GJeo52yNl19tqafVo3Z7LFQVLjXq\nuVBvQCOp5Ma7iQnrfaBl8UUfr29142hVGZ+h5dHFJsJCBvZL0u1ReOdv1bz30RV8PpVpE8P58ppU\n4mKGr8BXVZUTp/xiRPFZvxgxc0oEqx5IZP+ZCrYd7jxCdrCzLIaLFqeP3QcbKNhTT+k1YSZUy7KF\nMeTPtTImPVRcON1mtDjbiBDFdi60ESH0OoncqwJEXraZrNFhQoQQCASCAEiSxN0z00hPsPD790/y\nxqdnOH+5iafuycFkECWKQHA7IT7x/cASaiA5tuuMiORYc7fjOwM5Gtri9irsP3mly8e6cykEY+3v\nTTtGX1tCNm4pxvBv/0H05UsUj5/OzqmLoc1xSnJXMb++EFmSUBatQY0b1WkbWlRWTTWCV8aFiT/v\nabguSHR1LPQ6LSU1Bq449Bh1ChMS3JivthgEi09W+Wifhx1HvWg1sPIuA3Mn6Qe8+DxW1MxLr5ZT\nXePGGqXnS6tTuWNqxLAVuX4xopk33q/i9Bl/AT5jcgSrViSSOcovOo1KHfosi6FGVlQKT9kp2GPj\nwNFGPF4VjQRT8sLJnxvNzCmRGEShedvQ4pQ5VergZIk/nPLCJed1EVqnkxifZWZCjoXcHL8IIc4N\ngUAg6D3ZaVG88NQMfv9+EQdP11Be4+AbD00g0Ro23EsTCARDhBAlgqS7TId/fmJqt9M3AtHW0VDf\n7Op2IofL0/Vd/o4uhY7r687a39t2jN62hPjX7EP3q9+QdqmUsvQcduZ/gba9E+MNDfyDtRCAF51T\necKaRidpQ/FB4yXwucAYTrMcxZWGi90eC1uzh2pXFM0uLRajTF6CG6OudxM2bE0K6z92UX5FISZS\nYu0yEylxA+sAqG/0su6NCnYfbEAjwfKlcTy2IpGQkOFxGqiqSmGxg3c/PMfxk02APx/h0RVJZKa3\nP3+GMstiqLl8xcW7H9Xx4bYqbA3+9oykeCP5c60smB2NNer2C/K8HXG2yuw7bGP3gRpOFjs4X9Ze\nhMgZayYvxy9EjB0dhtEgRAiBQCAYCKIsRr732BTe3O6/cfbjvxzm2XvHMT0nbriXJhAIhgAhSvRA\nT5MnDDodP3pmJnanh4oaBylx3Tsk2tK2wDtf2cR/vXGsV6NCr7kUejsZoy/tGL2d9nDxx/9NeuEh\nrsSnsfWeNaiaG4VrtqGRb1tPIKHyK9sECj1mVnTcp+yFxjKQPWCKBEsiET6lW8dGaqKVMns0bllD\nnNlHdqyb3k6X+rzUy9sFblwemJaj46EFRkyGgXMtyIrKlh11vPpOJc5WhbEZoXz1iTRGjxq+cMii\nYjuvb6riVKnfwTJtYjiPrkhkTEbgOxODmWUxlDQ0e9i+t46DR5opOeefnhFi0rDkLiv5c61kZ4aJ\n9oxbnNZWmVNnHJwscVBYbOf8xTYihNYvQlxrycjOFCKEQCAQDCY6rf/aODMpgnUfneZ/NhWxbGYa\nX1gwOuhpbwKB4OZEiBI9EGyrgyXU0CnUMhiMei2jkyO6Lbi1GpC76EC45lLYsK20V5Mx+tKO0Zs7\n5NV/3EDzn1+n2RrHRw88jU9/Q6AZY2jiu9YT6CSV/67P47jbijW8wz59br9DQvFCqBXC4kCSunVs\nJCfEMW/WdNyyhvQoD6Oiehdo6faovPmpiwMnfRj08NgSI9PHDWxo4bkyJy++comzF5yEhmj5ytpU\nlsyPQTvAEzyCpajEzsb3qygqviFGfOXJTGKjbv0CXFFUTpxu5uV3yrh0yYuqSIBKXLyOL68eQ15W\nCEajuPC5VWl1yZw+48+EOFli5+xFJ8rVv686rURWZhh3TLMyOtVAdqZZnAsCgUAwDNwxPp6U2DB+\n+14RHx+8xMXqZr6yIo+IMOFaFAhuVYQoEYD+Tp7oDTlpUewpqu70c1mB1DgzTpevk0uhL+vrSztG\n29cGukNu2/QJl174Jfr4GOq/98+4ym4IHxn6Zr5vPY5BUvhN/XiOumI679PrgqYyUGS/GBFqbdf2\n0dGxMTUvi3HZWWg1kBPnIs7cu0DLqjqZX75eR2Wtj6QYDU/cYyI2auCKkNZWmdc3VfG3bTUoKtx1\nZxRPrUohKmJ4JjWcKnXw+qbL18WIKXl+Z0RWZhixsRZqazuHWN4qXKl1U7DHxvY99dTaPABo9ArG\nKA+GcA9evUp5cwTTjOnDu1DBgNLqkik520Lh1RGdZy+0XBchtFrIGh1Gbra/HSN7TBgmo/aW/ywI\nBALBzUByrJkfPDmdP/3tNEdLa/nRuoN8/cEJjEmOGO6lCQSCQUCIEgHo6+SJYGnbehFoEofT5eMH\nT02n1e1r51KwNTl7vT63V2bhlGRkReXkhXrqGlsHJLCwaddBzn/zBbSWMLJf+w0TczLxFZzl89I6\nLK21/GPsCUzI/KV1Ekfc0VjDO+zT6/Q7JFQFzAkQ2tl1cs2x8eBdmZTU6Gl0h2DQKuQluAk3BR9o\nqaoq+4t8bPrMjU+GeZP03D/H0OMIzl5t/0gjf3q9AluDl8R4I19Zk8qk3PAB2X5vOVXq4I33qyg8\n7S+0puSFs2pFItmZt3aAlMsts/dwIwW7bZws8QsxRqMGi9WHGtKKLkRu56rZX1TFPTNTb5mcjNsR\nl1um+GwLRcX+YMqzF1uQr2qVGg2MyQhjQo6ZvGwLOWP9IoRAIBAIRiYhRh3PP5jHxwcu8fbOc/zs\ntaM8umgs+VOTRXulQHCLIUSJAPR18kSwbNhayvY24z67o8HuotXt6yQw9GZ9XWVP3JGXyJzceKLD\nTf0qxFoKiznz7HdBkhi77heEjh8L+NtHHp5kJuTTHWi8XnyzH+ILaRNZ3LEFxO2ApnJAhfBkMHWv\ngntlKKkNo9GtxWyQyUt0Y+pFoGWrW+XNbS5OnJMJNcHzq6JIi/H2+b135Eqtmz++Vs6RE83odBKr\nHkjgofsShiWV//QZBxvfr+L4Kb8YMTnXwqoVieSM6XpU7a2AqqqcPtPCp7tt7D3UgMvtF6vycsws\nnGNlTKaRH718sMv8lrrG1n4LjYKhxe1WKD7roKjEP6Lz7AUnPtn/29VoYEx6KLnZFiaMs5AzJowQ\nkxAhBAKB4GZCkiTuuXMU6QkWXtx8kte2lnLuchNP3p2D0SD+pgsEtwpClGhDVxMs+trqEAhZUdiw\n7Qw7j/UsSED3Akhv1tdVNsaHey/i8fhYvTir2+kiPeEqq6B0zTdRWpyMeemnhM+efv0xqamWsB2v\nIHlb8d65EiVzCkZoX/S5mqG5ApAgIhWMlm735fRIFFabaPVqiAnzkRPnRteLWv9ilcyrH7tosKuM\nTtKw+m4TWaNN1Nb2X5Tw+hQ2f1LDmx9U4fGoTBxn4ctrU0lOMPV7272l+KzfGXH8pF+MmDTeL0aM\nG3vrihG1Ng879too2FNPdY1fpIu1GlhxdzQLZltJiPN/ftxeuVshLyYypN9Co2BwcXsUSs61UHTa\nTlGJnTPn24gQEmSmh5KXYyE328z4seZhm2ojEAgEgoFlXHo0Lzw1g//ZVMT+k1f8Y0MfnEB8tLiR\nIBDcCghRgsATNnozeSLYwn5jwVm2H60Men2BBJCV8zJwunwUlzXQ6HB3ub7A2RO1yLLCiXO2oKZ3\ntMVbV0/J6r/DW2tj1L99l+j7F19/TGq2od+6DsnVgnfmcpSx0zpvoLUB7FUgafyChKH7doIGp4aT\nV0z4FInUSA+jo4MPtFRUle1HvHy8z4OqwpKZepbMNAxY0OSpUgcvvnKJ8ssuIsJ1fP3JFO66M2rI\nrYUl51p4Y9Nljl0VIyaO84sR47NuTTHC7VbYf7SR7XtsnDhtR1XBYJBYMCua/LlWcrPNaDr8jgMJ\neXfmJYrWjRGG26NQeq6FohJ/O0bp+RZ8vhsixOhRoeTl+KdjjBtrJlSIEAKBQHDLEh1u4vurp/JG\nwRm2H63kx385xJfuG8+UrNjhXppAIOgnQpSg5wkbPU2e6M1YzkACwTU0EqhAdAABpKt9zspN4LEl\nWYQa2/9aA2Vj2Jrd7VpIeprecX3/LU5K134L94VyEv/+aeKfWXXjQXs9+q1/Rmq145t+L0r2zM4b\ncNrAcQUkLUSmgT6k231dbtJRWmdAArJj3SSG+7p9bkeaWxQ2bHFzplwmPEzi8buNjEkZmNO+2e7j\nL29VUrDbhiTB3QtiWPOFJMxhQ/uxKj3XwhvvV/F5UTMAE8ZZePQWFSNUVaXkXAsFu23sOdSAs9Xf\nnjFubBj5c6zMnhHVY2HandD4zPJc6utbBv09CLrH470qQlwNpiw914K3jQiRkdZehAgLvb1FiL46\n3AQCgeBmRa/TsHZpNplJ4bzycQm/ebeQ+2aN4sF5ozvdiBAIBDcPt70oEewEi0CTJ4IdGwqBBYJr\nzJ+cxN0z0wJeaHa1zz1F1YSYdJ32GSh7QiOB0kWDfaDpIorXx9nnvk/L8VPEPPoAKd//+o0HHY0Y\ntq5Dcjbjm3o38rhZ7V+sqtBSC846VElHvTYOMwa6Ms0rKpyzGahs0qPTqOQluIgMCT7QsqTMx4Yt\nbhytKuPStTy6xIQ5pP9fWKqqUrC7nr+8VYHdIZOeGsJXn0gb8uDI0vMtbHy/iqOFfjEiL8fMqhWJ\n5GV33wLTlrYFzUjH1uBhx956tu+xUVntP4+tUXruXRTHwjnRJMUH3ybT3YhbrVaMfxxqvF6F0vMt\nFBU7KCqxU3L2hgghSZCRFkJetoW8HDPjs8yEhd72X1lA74RwgUAguBWZnZdIapyF371byN/2lXGh\nqpkvP5BLeKgYGyoQ3Izc9ld4/Z2w0duxnD0JBPMnJ7F6SVbAC0u708OR4uD3Gciy3pUgAd2/d1VR\nuPDtH9O0Yx8Ri+eS8fN/ut6m4GlqIGTbn5GcjfgmL0LOndt+o6rqd0e01mN3w28+tXGuuqLLC2qf\nDKdqjNQ7dYTqFSYkugjRBxdoKcsqH+33sP2IF60GVswzMG+yfkDaKS5VtvLS+nJOlTowGTU8tSqZ\n+xfHodUOnTp/5oJfjDhywi9GjM8y89jKRPJyghMjuipo5kxKZvmstBFV0Hi8Cgc/b6Rgdz3HTzaj\nqGDQS8y7I4r8OVYmjLf0qwWnpxG3goHH61U4c8FJUbGdwmI7peda8HhviBDpqSHk5VjIy/aLEEPt\nOrpZ6I0QLhAIBLcqqXFmfvDUdP73r6c5draOH798iK+vnMDopOGZdiYQCPrObX/F198JG70VNQIJ\nBPOnJLN2aTZur4ytydnJKSErChu2lnK4pBa7s+twxu7EhFX5Y1BUlb2F1bg8/hl5IUYtiqzi9nV2\nH3T33iv+/bfY3v6QsGkTGPPifyDpdMiKwnufHGfxlY+xaJ185M6k+koSq3KVG0WuqoL9MriaaGyF\nH22qoemq9b7jBXWrV6KwyoTTqyE6xMf4eDe6IJ3JtiaFVz92cemKgjVCYu09JlLj+m9rdrsV3vpr\nFZs+voIswx1TIvjS46nERA+dIn/uopM33r/M4eM3xIhHVySSl2PuleDSVUGzedd5nK2eYS9oVFXl\nzAUn2/fY2HWggRan/1zNGh1K/lwrc2dGibvlNxFen8KZ805OXs2EKD7nwOO5IS6mp4aQl20mb5yF\n8WPNWMz9/93e6i0NvRXCBQKB4FYm1KTnG1+YwIf7ynjvs/P8x2tHWL04i/mTk8TYUIHgJuK2v7rv\n74SNvoga3fW0f3HBaDZsK+3Skgvw45cPU17jCLie7vap1WjQSNJ1QQKg1S13et41unrv1X/cQNX/\nvIIpcxRZL/8KbagJWVH4r3W7eUbaQ7zeyWZ7GhubU6GuzV07VYGmSvDYUbQmfrml+rog0ZbPS+tY\nckcWpXUheBWJ5AgvmVYPwd4MP37Gx5ufunB5YEq2ji8uMGIy9v8L6ciJJv7wajk1dR5irQa+tDqF\nmVMi+73dYDlX5mTj+1UcOtYE+PMTHl2ZxIReihEwcguahibv9faM8ssuAKIi9Cy9J4aFc6JJTeo+\nc0QwcvD6FM5ddFJ42s7JEgenz3YQIVJCyM0xk5dtYXy2mfABECGucbu0NPTX3ScQCAS3GhpJ4v7Z\n6aQnWvjD5lO88kkJ5yqbWHt3NgYh0goENwW3vSgB3YsEXQVMdqQvokZ3Pe0btpV2a8mVFbVHQSLQ\nPoMJ2AQwGbTMnZjY6b3bNn3CpRd+iT4+huzXf4veGomsKPx83R6ekfaSonfyoSOFjc2jAX+h/Hlp\nHV+4KwNjSyV4naAPw6ZaqbRd7HLfkVExnKoJRQXGxrhJjggu0NLjVXl/l5v9RT4MOli12MiMcbp+\nK+S2Bg9/er2CfYcb0Whg5bI4Vq1IxGQcmi+482VONm6u4uDnfjEiZ0wYj65IZOJ4S5/f20gqaLw+\nhcPHmijYY+NoYTOKAjqdxOzpkeTPtTI5N3xI22IEvcfnUzl7sYWTJQ4Ki+0Un2nB7bkhOKYlm5iQ\nYyE3x0xuloVwy+B95dwuLQ39dfcJBALBrUpehpUfPDWd/3mviD1F1ZTXOPj6QxOIixQ3NgSCkY4Q\nJeheJIDgrMB9FTXa9rT3PLYzcJ5ClNnItJzYbvcZTMAmQJhJxxfmZ7a7s9j02QHOf/MFtJYwsl/7\nDcaURADe3lLEk+wlTd/CFkcyrzWN4ZogAeB2u9E0lYHqAaMFwpMJ96ldXlBPycthwrixaCSV3AQX\n0aHBBVpW22TWf+Smul4hKUbDmmUm4qP7d1dUllU+LKhlw7uXcbkVcsaE8dUn0hiVMjRfahcu+Z0R\nB66KEdmZYTy6MpFJ/RAjrjHcBY2qqly41ErBbhufHajH7vC7dTJHXW3PuCNqQO+eCwYWn0/lfJmT\nwuKrTogzDlzuG5/V1GQTedkWJlwNpowI1w/JukaqA2gw6K+7TyAQCG5lYiJC+Mc1U9mw7Qw7j13m\nx+sO8eUHxjMxM2a4lyYQCAIgrv7b0FYk6I0VOJCoESyBRIN6uxs1gCZhCdXzw2dmYAmQOByoGG23\nr2Z3u7vlLYXFnHn2uyBJjF33C0LHjwXA3dLCXVVbSDc4+LQliVeaxtJWkIgM0fDde63oVQ+YIsCS\nBJKEUU+7C2qdVsucmVMYlZKIz+ti5miFUEPPgZaqqnLgpI9Nn7nx+mDORD3L5xrQ6/pXtJ+50MKL\nr1zifFkr5jAtX38sjUVzrUMyZupiuZM33q/iwFG/GJGVGcZjKxKZlNt/MeIaw1XQNDV7+Wx/AwW7\nbVysaAUgIlzHA0vjyJ9rHTLBR9A7ZFnlXJk/E6LwdBciRJKJ3Gz/iM7cbDORQyRCdGQkOYCGgv64\n+wQCgeBWR6/T8uSyHEYnhbP+k1L+31sneGBOOg/MyRBjQwWCEYoQJbqhL1bg/qCNTzQAACAASURB\nVKT5BxINoi1GVFWl3u7p8rXTs2MDChLX1jZxTAzbj1YGfp5Be/1uuausgtI130RxtjLmpZ8SPnu6\n/0leN8bt60nXNbGzJYF1jVmobQSJWIuWb98dRVy4FkKiwRzvj9a/yrUL59NldqZMmkR0VATuVjvz\ns8Gk79nl0OpWeavAzfEzPkKM8PjdJiZk9u9UbnHKvPbuZT7eXouqwoLZ0Tz5SPKQFFkXy528ubma\nfUcaARibEcqjKxOZkhc+KCFNXRU0cyYlsXxW2oDux+dTOVLYxPbdNg6faEKWQauFO6ZGkD/HytQJ\nEej6KSIJBhafrHLmgn9E58kSO6dKHbS6bogQyYnGq06IqyJExPCIEB0ZbgfQUDMQQrhAIBDc6syb\nmERanIXfvVfI5j0XOX/ZPzbUHDIyvrsEAsENhCjRBYGswEdLarlrUhKxkSEDehFo1GsJNem7vKgO\nNenJTovs8u52apyZ1Uu6Fkk6tp4snpbSoyhxDW9dPSWr/w5vrY1RP/ke0fcvvvqAB33Bq2gaKjns\nTeSPjdntBImkSB3fWRZFZKgWJcSKxhzXTpAA/wX1/XNzGJVpxKtoiAvzkDNaE1SgZVm1zKsfu6hv\nVklP9LdrRFn63q6hqiq7DtSz7o0KGpp8JCcY+craNCaMC268Zn8oq2hl4+Yq9h32ixFjMkJ5dEUi\nUycMjhhxja4KmpSkSGpr7QOy/bKKVj7dbWPnvnqa7f5ckPTUEPLnWrnrjqghs/QLekZWVC5eaqWw\n2E5RsZ3isy3XJ54AJCcYyb06ojMvx0LUCBEhOnK7tjSIsbYCgUAQmFEJFn7w1Az++MEpCs/b+NG6\nQzz/UB7pCWJsqEAwkhCiRBf01Erxwp8ODniyu9sr09LatROipdXLynmjAf/d7Xq7i8gwI5OzYli9\neGyn/XfXerJy3misPbRweLwyDVcasD33D7gvlJP0zWeIf/oR/4M+L/odr6GpuYg8Kpci92TUmsvX\nX5sRo+f/LI3CbNKghMWhCeu6f6/GoaW4xoiiQqbVTUqEr6Nu0QlFVdlx1MtH+zyoCiyeoWfpHQa0\n/bDhVV1x8dPfXODg5w0Y9BKrH0xk5bJ49EG4NfrDpcpWNr5fxd5rYkS63xkx2GJERwayoGl2+Nh9\noJ5Pd9s4X+Zvz7CYtdy3OJZFc61kpInCaSQgKyoXy1spuipCnCp14GwzCSclKYQ5M8LIyzaTm20m\nOmroRt72F9HSIBAIBIKuMIfo+ebDE/lgz0U2777Av68/ytqlWcyblDTcSxMIBFcRokQX9JS/oDLw\nye5NDjcN3bRnNDrcOJyeoO26gVpPurubeI3oUB3133mBluOniHn0AZK/9zX/A7IX/c4NaKrPI6eO\nwzf3YR5BQpU0fF5aR2yYwt8vjsKgk1DMCWhCozttW1WhrEHPxQYDWkllQoIba1j3Y0mvYXcqbNji\npvSSTHiYxOqlRsam9v3U9XoV3vvoCm//tRqvT2VyroUvr00jMW5wLd7lla28+UE1ew41oKr+cMdV\nKxKZPmloxYiBQpZVPi9qpmCPjUPHmvD5VDQamDE5goVzopk+KQK97tYZxXgzolwTIUrsFBU7OFXq\naOeESIwzMnuG+Xo7Rk6WdcAcM0ONaGkQCAQCQXdoJIkVczPISAznjx+cZN1HxZy73MTjS7LQ68R3\nhUAw3AhRogsCWYE7MlDJ7sH2RPd0d9vtlTlaUtPlY0dLavnxl2YCsPtEFS5PB0FAVVhU8DaOAweI\nXDyPjJ//k79Yln3odm5Ec/kscnIWvnmPgEaLFr8g8/CseHQtlYCEFJ4Mps6WOFmBklojNQ4dRp3C\nhAQXZmPPgZYll3y8vsWN3amSM0rLY0tMmEP7XsAXnrbz0vpLVFa7iYrQ8a2vjGVCtmlQRYHyy628\nufmGGDF6VAiPrkhk+qSIm1KMKL/sn56xc189DU3+9ozUZBOL5li5a1b0iLX43w4oikpZRStFxQ6K\nrmZCOFpufM4T4ozMmhZJbo6ZvGwLMdE3jxMiWERLg0AgEAi6Y2KmlR88NYPfvVfIZ8erKLvi4PkH\n84iJEIHbAsFwIkSJbmhrBa5vdtFd+TxQye4D1RPd5HB3G4hZb3fjcHpZvTiLlfNG8+6uCxwvraG+\n2Y3RoGXazg+JOrQXW3I651Y9Q6ZGg1aR0e16E21lCUriGHzzHwVtm9PG1Yi+5TIgQWQqGMyd9uv2\nSRRVG7G7tYQbZfISXBh6OPNkWeXj/R62H/Gi0cADcw3Mm6JH08civrHZy182VrJjXz2SBPcuimX1\ng0mkjxq4LIWOVFS5eHNzFbsPXhUj0kJYtSKRGZP7J0YEM6Z2oHG0+Nh90D8948wFJwBhoVqWLYxh\n0VwrmemhN6XAcrOjKCqXKq+KEMV2TnYQIeJjDNwxJZK8HH8mxK0oQggEAoFA0BtiI0P4pzXTeHVL\nKbsLq/jRukN85YFc8kZbh3tpAsFtixAlusEnqyyelsLy2ek0Odz899snunQxRJqN3Sa797Z4HIie\n6BCjDo0EShcqikYCrUaipsFJhNnI/3lsKhWXG3n1kxLs699kwqEdNETFsvneJ3EX1iLrSngq5ATa\n8tMo8Rl4F6wGbZu74M56cFSDpIHINNB3FmYcbonCahNun4Y4s4/sWDfaHhz99c0Kr37soqxawRou\nseYeE2nxfSu+FUVl2y4b69+uxNEiM3pUCF97Io0xGWF92l4wVFa5ePODKnYfaEBR/SGPj65IZOaU\n/okRvRlTOxDIisqJU3YKdts4cLQRr09FI8HUCeHkz7EyY0oEhkHO3xC0R1FUyi+7/JkQJf4JGXbH\nDREiLsbAzMkR10d0xsXcWlMnBAKBQCAYCAx6LU/fm0NmcjivbS3lV28eZ8W8DO6fnd7nG2ACgaDv\nCFGiA90VfpPHxvDpkc6TK5xuH+/sPNeuMOxr8TgQPdGtbl+XggT4hYp/X3+URod/TXMmJbN0egot\nH25j9q6/0hJm4W8rvoQ7JAwJleyLn6INvYIcOwrfwjWguypIqCo466ClFjRaiBwFOlOn/dW1aDl1\nxYiiSmREe0iL9PYYaHnirI+N21y4PDA5S8fDC42YjH37crhY7uTFV8opOddCiEnDs4+lcM+i2H6F\nYwaistrFWx9Us2t/vV+MSPE7I2ZOiRiQudh9GVPbFyqrXWzfY2PH3npsDV7AP4Uhf66V+bOisd5E\n4Yc3O6raRoQodnCyxEGzw3f98VirgemTIsjLtpCXI0QIgUAgEAiCRZIk5k9OJi3ewv+8V8imXRc4\nf7mZ55aPJ8wkWlEFgqFEiBId6K7wy5+WzOLpKZ2yGFweuVNh2N/isT890RFmY8AJGw0O9/U1bd51\nHteeg8zY/Bpug4m/rfgSjvAoJFS+FFnC3NArlLrDOaCZxSP6q4WoqoLjCrTWg0Z/VZBoX6SqKpQ3\n6ThvM6CRIDfeRaw5cKCl16fy/i43+wp96HXwyCIjM8fr+uQscLll3ni/ig+21KAoMHt6JM88ljJo\nxfTlKy7e2lzNZ1fFiFEpJlY9kMgdUyMHRIyAwGNqByLXpMXpY+tndRTstlF8tgWA0BANS+fHkD/X\nStZo0Z4xFKiqSsVlF0UlDgqL7X4Rwn5DhIiJ1rNgdnQbEcIgfi8CgUAgEPSDjMRwfvDUDP6w+SQn\nztn48cuHeP7BCaTFD/54eIFA4EeIEm0IVPgdP1PH11bmcqSktnNAJDcKQ///D3zxGGwrSG9COmNq\nKkh99yWQ4OP7n6Q+JhFQeTqylAVhVZzzWPi5bRKhniZWLJQx6jRgrwJXI2gNfkFC215JVlQorTVQ\nbddj0CpMSHRjMSpdL+Aq1TaF9R+7qLYpJFo1rL3HRPz/z96dh8V5nvce/86+D8zCsCOBkEACtO+g\nDe3e5N2xLDuLkyZtk/a0SdOenDRtkzQ5adM0bdMet3bsJF5iO943SZaEJFuSZe0SSAK0L4htZlhm\nmH3mPX8MDIsAIRkBsp7Pdfm6EMMM77y8Y3h+89z3bb2xsoB9h1t55qXLNLtCpNrVfG1DNrOmJt3Q\nY11LfWOAP7zXwM5P3MRikJOp5ZF16cwfxjCiy2Bjam+0r0ksJlFV7aFit5u9B1sJhmLIZDCtyER5\nqY15M5PRqEV5xs0kSRKX6wMcr/EmSjLa2rtDCJtFxZIF1nhPiAITqSkihBAEgNraWv7kT/6EL33p\nS2zYsIH9+/fzi1/8AqVSiV6v55/+6Z9ISkrimWeeYdOmTchkMr75zW+yZMmS0T50QRDGIJNezV88\nPJ23dp3lvT0X+MfnD/LE6gJKS9JH+9AE4bYgQokeBlv4udqD/Ph3hwa8b9fCEBjwMdw3sHi8kVKQ\nvr0pzAY1rd7ezS/NrS7uePtZlKEQ+x76EvXpEwCJJ5JOsdxwhfMhIz9zTsMvKQl6ArR5AjgUbgh6\n4qUayTkg7335hKNQ1aClLaDAqIlSkhZEoxx4woYkSXx6PMJbHwUJR2BhiYp7FqlRKa9/0dXsCvHM\nS5fYd7gNpULGA3em8tBd6Wg0w7+orm8K8tq79ezoDCOyM+M7IxbMGv4wostQp7MMRUNTkIrO8oxm\nV/y6yErXsXh+MksX2kixifKMm0WSJK40BBO7IKqqPbT2CCGsySoWz7dQXGiiuNBEmgghBOEqPp+P\nH/3oRyxYsCDxuZ/+9Kf8/Oc/Jy8vj6eeeopXXnmFtWvX8sEHH/Dyyy/j9XpZv349ZWVlKBRi/J8g\nCFeTy2Xcv3gCeelJPP3eCX79/kn2Vzfx6PKJpFrFVCdBuJlEKNHDYAu/a+m5MBzoMSQJ/uONSv72\ni7NQKwc/9V07Izbvv8T2Q929LIZSCtK3N4VOo+SHv9mfOCatz8udbz+D3u/lk+X3cTR9MiCx3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73kge1rDq2Xvi9Z1negUIrvZg4t/9LeyH8tjXGhmq9Xm58+1n0Pu97Cm/j/P5JSy1tPIV3QmC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\nSgghhBBCCCGEEMIr/j+1q5tLqwHbsAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "26767997-ade2-4668-b223-c905e873cd0c"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate = 0.00002,\n",
+        "    steps = 800,\n",
+        "    batch_size = 8,\n",
+        "    input_feature = \"population\"\n",
+        ")"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 227.94\n",
+            "  period 01 : 219.13\n",
+            "  period 02 : 210.71\n",
+            "  period 03 : 203.01\n",
+            "  period 04 : 196.59\n",
+            "  period 05 : 190.92\n",
+            "  period 06 : 186.43\n",
+            "  period 07 : 182.39\n",
+            "  period 08 : 179.87\n",
+            "  period 09 : 177.69\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>104.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>84.3</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>58.0</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>85.7</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>126.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2619.1</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         104.9    207.3\n",
+              "std           84.3    116.0\n",
+              "min            0.2     15.0\n",
+              "25%           58.0    119.4\n",
+              "50%           85.7    180.4\n",
+              "75%          126.3    265.0\n",
+              "max         2619.1    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 177.69\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Yuydiv2asagkJN7sVcn9zJST0ARDeRrWEhKhZ44ggpt/YBYBXPtvD2Zz6ac4t\nhBBCXKokKeFj8ousVU69mVtoIb+o5pk96mMdQgjhSc4yG4enP8rZxR8SGNeWTqveJjCurXoBKU50\nOzdg+PYT0OqwD5qEo3N/0GjUiwmw5GVB7hFXYiIw3JWQkP4RPi++ZThTk+Mptth5KWU3RaU2tUMS\nQgghfJYkJXxMaHAAESGVn3CGm02EBtd8Mlof6xBCCE9xFBWTPmUmOSvWE9w7gY6fLcYY21i9gMos\n6DcvRf/LFpzmCGzD7sDZPF69eOD32TVOUXjyMKBxza5hbiqza/iRfl1jGXZVS87mlPDqZ3uwO5xq\nhySEEEL4JDm78TH1McWjTBMphPBVtsxs9o29i4KtPxJ2bX86fPQK+nAVSxEKczCsewPdiQM4m7TD\nNuxOlFCVp6W2WyHHVa6hNwVJuYYfGzOgHT3iotl/PI/31h/AD9t4CSGEEB7nsSlBxYWrjykeZZpI\nIYSvsRw9wYGJ07EePUH0xFG0fvYhNHr1voY0pw9j2LIMTVkp9g59cPRMAq3KSdvSPNfsGigQGE5Y\n6/ZkZRerG5O4YFqNhtuv78SzH6SxdfdpmkY2IvnKlmqHJYQQQvgUSUr4IJ1Wy8ShcYwZ0O6Cp3is\nj3UIIUR9Kd69nwOTZ2DPyiF21l9pdv+daNTq16AoaA9sR5+6FjQabH1G4WzfU51Y3DH9eXaNZmAK\nQaOVAY3+LsCoY8bYrjy55Cc++foQjcMDqxzNKIQQQjREcrbjwwIMOmLCgy4qmVAf6xBCiIuRv2U7\n+8bcgT07l1ZPP0jzB+5SLyHhsKP/4XMMP30BAUHYEv+ifkLiD+Ua6E0Q3hZMIerGJOpVuDmAmWMT\nMBi0vL7qV46dkanihBBCiHKSlBBCCOEx2SvWkz5lJorNRvvXn6HxrePUC6a0CMPGd9Ad2oEzIpay\n4XehxKg8lL40D3KOgKN8do3WoDeqG5PwiFZNzNwx4nJsNicLPt1NbqHMhCWEEEKAJCWEEEJ4yJk3\nP+Tw3f9EawogfulCIq4fqlosmpzTGNe8hvbcMRytOmNLmgaNVGweqTih4CQUnnJNOxrSXGbXaAB6\nxEUzdmA7cgutLPh0N1abQ+2QhBBCCNXJ2Y8QQoh6pSgKGU+/zPF/vYAhJpKOyxcTcnUv1eLRHvsF\nw7rFaErysXcbir3feHVHI9gtrtERlnxXuUaElGs0JMlXtuSark05dqaQN1ftxSkzcgghhGjgpNGl\nH7PaHNLEUgjhU5w2O0fvf4qsj1djatuS+A9fJqBFrDrBKE50u75Gv2czit6IbeBEnC06qhMLgKK4\nEhHu2TUiIDhGRkc0MBqNhqlfnnRMAAAgAElEQVRJ8WTllbIjPZPPthxhzIB2aoclhBBCqEaSEn7I\n4XSybNMhdqZnklNgJSIkgO5x0UwY3B6ddGoXQqjEUVLKoTsfIv+rbTTqfjlx776EITJcnWBsVvTb\nPkWXsQ8lOBzboEkoYY3ViQXA6YSi066kxB9m1xANk16n5e7RXfjPu6l88f0xmkQE0bdLU7XDEkII\nIVQhV7B+aNmmQ2xMPUF2gRUFyC6wsjH1BMs2HVI7NCFEA2XLzmP/+L+R/9U2QgddTYePF6mXkCjM\nxbDuDXQZ+3A2aetqaKlmQsJugVwp1xAVBQcamDkugUYmPe+s3c+B47lqhySEEEKoQpISfsZqc7Az\nPbPS53amZ0nTLCGE11lPnGbfqGkUp/1C5NjhXPbOPHSNglSJRXPmCMa1r6HNO4c9/ipsQ6ZCgDqx\noChQmuua7tNR5irXCG8DOpldQ7g0iQji7tFdAHh5+R7O5paoHJEQQgjhfZKU8DP5RVZyCiqfRiy3\n0EJ+kUwxJoTwnpJ9h9h7w1+wHD5Gk79Noe1Lj6M1qFMZqD2wHcPGJWCzYrtqJI4rrgOtSv12nA4o\nOOXqH6HRQGgLMDdx/VuIP+jYKpwpSfEUW+zM/2Q3xRab2iEJIYQQXiVJCT8TGhxAREhApc+Fm02E\nBlf+nBBC1LeCH9LYN/qv2M5k0vLxv9Py0Zlo1Ohr47Cj/2Elhh9XgzEQ29BbcV6m3mwfrnKN38Ca\nD/pAV7lGgFm9eITP658QS/IVLTmTU8Krn/2C3eFUOyQhhBDCayQp4WcCDDq6x0VX+lz3uCiZhUMI\n4RU5a7/mwM3TcZZaaPvyUzS5Y5I6gViKMWxcgu7gTzjDm7j6RzRurU4slZZrtJZyDVErYwe2o/tl\nUew7lssHG9JRZKpQIYQQDYQkJfzQhMHtGdqrOZEhJrQaiAwxMbRXcyYMbq92aEKIBuDcuykcuv1B\nNHo9ce++RNSNyarEock5jXHNa2jPHcXR8nJsSbdDcJgqsbjKNU5KuYa4YFqthttHdKJlTDDf/HyK\nDT9lqB2SEEII4RUyJagf0mm1TBwax5gB7cgvshIaHOBzIySsNofPxiaEuDCKonDyhTc4NW8x+shw\n4j9YQKOuHVWJRXvsV/TbPkXjsGFPGIyjy0D1EgA2CxSccI2O0AdCaDMZHSEuiMmoZ8bYrjz5birL\nNh0iJjyIbpdFqR2WEEII4VGSlPBjAQYdMeEqdZWvgsPpZNmmQ+xMzySnwEpESADd46KZMLg9OjVq\nzYUQ9UJxODj68LNkvv8ZAa2aEb/0ZUxtWqgQiBPd7s3od3+NojdiG3AzzpadvB8HuMo1LLlQeBZQ\nICgSGsXI6AhxUSJCTMwY05XnPkjj9ZW/8vDkHrRsLD1JhBBCXLrkKlHUq2WbDrEx9QTZBVYUILvA\nysbUEyzbdEjt0IQQF8hZauHQ7Q+S+f5nBF0eR8fP31InIWGzot+yzJWQaBSGLfl29RIS7nKNM6DR\nuso1ghtLQkLUizZNQ7h9RCesNgcLPt1NnsysJYQQ4hImSQlRb6w2BzvTMyt9bmd6Flabo1brOJdb\nUqvXCiE8z55fyIGJ95K7bjMh1/Sm4/I3MMaoMJy8KBfD+sXoju/F2bi1q6FleBPvxwFgK/19do0C\nMMjsGsIzesbHMGZAW3IKrCz8dLd8LwohhLhkSfmGqDf5RVZyCiq/m5NbaCG/yFpluUl1ZR9CCHWU\nnT7HgUn3Urr/MBEjEmm74Am0Ad7vlaA5exTDNx+isZbgiLsCe+/hoFWhV0357BpFUq4hvGP4Va04\nk13Ctl/O8Nbqvdw1qjNa+bwJIYS4xMhICT/jyyMJQoMDiAgJqPS5cLOJ0ODKnwMp+xDC15QePMre\nG/5C6f7DNJ52E+0W/UeVhIQ2/ScMG/4HZRZsV47AfuUIdRISToermWWRlGsI79FoNNwyrANxLcJI\nPZDJiq1H1A5JCCGEqHcyUsJP+EMDyQCDju5x0WxMPXHec93joqqchaOmsg9Lmb1e4xRCVK9oxx4O\nTJ2FIzef5g9Pp+n0W9B4++Lb6UD/0xp06T+iBARhG3ATSuM23o2hnK0U8k+A0+Yq1whpDjqDOrGI\nBkev03LP6M78590drP7uGE0igri6c1O1wxJCCCHqjW9czfoxb41c8JeRBBMGt2dor+ZEhpjQaiAy\nxMTQXs2rLcOoqewjt4rnhBD1L2/jt+wfdxeOgiLavPgYsffe6v2EhKUYw8Yl6NJ/xBnWmLJhd6mT\nkFAUKMmB3KOuhERQJIS1loSE8DpzkJGZ47oSFKDnnbX7Sc/IUzskIYQQot7ISIkL5M2RCzWNJBgz\noF2VoxC8TafVMnFoHGMGtCO/yEpocECNsZWXfWRXknwIN5sIDwmgML/UUyELIX6X+dFKfrv/P2gN\neuL+9wJhQ6/xegya3DMYNi9FU5SLo0VH7H3HgKHq0i+PcTqg8BRYC0Gjg5BmEBDs/TiE+F3TyEbc\nPboz85bt4uXle5hzSy9iwgLVDksIIYS4aDJS4gJ5c+RCbRpI/pnavScCDDpiwoNqlSwpL/uoTPe4\nKExGyZ0J4UmKonBq4Tv8dt+/0YUEE//xIlUSEtrjezGsW4ymKBd710HYB9ykTkLCVgo5R1wJCUPQ\n77NrSEJCqK9T6wgmJ8VRVGpj/ie7KLHY1A5JCCGEuGhytXcBvD1yoaaRBH9sIOkPvScqU17esTM9\ni9xCC+FmE93jomT2DSE8THE6Of7YPM6+9RHG2MbEf/gygZd5uVRCUdDt+Qb9rq9QdAZs/SfgbNXZ\nuzH8HgelOb/PrgEERUGjaGlmKXzKwG7NOJNdwpc/ZbBoxS/MHJeAXue73+9CCCFETSQpcQEuZurL\nC1GXBpLlIzjKlY/gAJg4NK7eYqpvF1L2IYS4OE5rGUdmPEbOqg0EdmhH/AcLMTaN8W4QtjL03y9H\nd+xXlEah2AZOQolQoYmflGsIPzJ+UHvO5Zby86Eslm48yJRr47zf+0UIIYSoJ5JavwAXM/XlhapN\nA8maRnD44jSif1aXsg8hxIWzFRSRPmUmOas2YL6yOx2XL/Z+QqI4D8P6xeiO/YozppWroaUaCQkp\n1xB+RqvVcMcNnWgRE8zmnScrvWkhhBBC+AsZKXEBLnTqy4tRm5EE3h7BIYTwT2Xnsvjh1vso+Hkv\n4ckDaffKU2gDTV6NQXPuGIbNH6KxFuO4rBf23teBzstfSZdouUaZHY7mGgkPdBAd7PvJaHFhTEY9\nM8d25cklqXy06SAx4YEktI9SOywhhBCizmSkxAW6kKkv60N1IwnUGMFxodRuxClEQ2X5LYN9N0yj\n4Oe9RE8eTfvFz3k9IaE9mIphw/+grBTbFddjv/IG7ycknA7IP+FKSGh0ENYSgmP8OiGhKHC2UMeP\nGUGcKjBQYJWv+EtdRIiJe8d0Ra/T8trKXzlxrkjtkIQQQog6k5ESF8gXeyCoMYKjrvy1EacQl4Ki\nXXtJnzwTe3Yulz06nbC7bvFuHbrTgS51HfoDP6AYA7H1vwmlaVvvbb+crQTyT4LT5irXCGkGOoP3\n46hHVruG9Ewj2SV6tBqF9lFWmoXY1Q5LeEHb2BD+en0nFq34hfkpu5gztZdP3YQQQgghaiJJiYtU\nPnLBV/j6LBb+2ohTCH+X/80PHJx2P06LldbPPkTc7NvIzCz0XgDWEgxblqE9cwRnWAy2gZPAHOG9\n7cMlWa6hKHCmUM+hbCMOp4awQAfx0VYCDYraoQkv6t0hhjP92/LZliMsXL6HB27ujtEHbkQIIYQQ\ntSFJiUuML47gKOftqVSFEC5Zy9fx298fB62W9m88S8TwwV7dvibvLIbNS9EU5uBo3gH7NWPB4OU7\nuU4HFJyEsiLQ/j67htG/m1labBoOZAaQW6pDp1GIi7bS1Gz35xyLuAjX92nFmewSvv/1DG+v2ccd\nN1yOVj4MQggh/IAkJS5RvjaCA6QRpxBqOPPGBxx//EV0IcFc9s48Qq7q4dXtazP2o9+WgsZmxd5l\nAI6EwaDxcqmWrcTVP8JpB0Oj38s1/PfrT1HgVIGeI9lGHIqGiCA7cdFlmPQyOqIh02g03DqsA1n5\npfy47xxNIoIY1U+F8ighhBCijqSIX3iNPzXiFMLfKU4nx5+cz/HHX8TQJJqOn73p3YSEoqDb8w36\nzUvB6cTWbzyObkO9m5BQFCjOgtyjroREo2hXQ0s/TkiU2DT8fMrEwawANBroEGOlSxOrJCQEAAa9\nluk3diE6zMTKbUf5/tczaockhBBC1EiSEgLwzmwY5Y04K+MrjTiFuBQ4bXaOzHqcM4vew9SuFZ1W\nvk1QRy/2lbGXof/2E/Q/b4SgEGxJf8XZuov3tg+uJER+BhSfA60ewlr5df8IRYGMPD2pGYHkW3RE\nNbLTu0UpTaRcQ/yJOcjIzLEJBAbo+d+afRw8kad2SEIIIUS1/Pd2kagX3p4Nw9cbcQrh7xzFJRy6\n4yHyv/6ORj06E7fkJQyRYd4LoDgfw+alaHNO4YxuiW3AzRDo5d4Nfy7XCG3mSkz4qeIyDQfOBVBg\n1WHQKnSIsRDdyCHJCFGl2KhG3D2qMy9+vIuXl+9hztReRIcFqh2WEEIIUSn/PUsT9cLbs2H4ciNO\nIfydLTuP9KkzKd75K6FD+tL+9WfRBXnvQkSTeRzD5g/RWIpwtO+J/YrrvVsqoShQku0aHQGukRFB\nUX47OsKpwL6TCr9mBKKgISbYTvsoK0b5kylq4fI2EUy6No731h9gfspuHpnckyCTnPYJIYTwPVK+\nUc+8UQZRX2qaDcPTpRwx4UGSkBCinlgzTrFv5F8o3vkrUeOv57K3X/BqQkJ7KA3Dl2+DtQRb7+uw\nXzXSuwmJS6xco8iqJe2EiV8yFAw6hcubWOjUWBISom4GdW/G0F7NOZVVzGuf/4LD6VQ7JCGEEOI8\nkjKvJ94ug6gPMhuGEJeGkr0HOTDpXmxns2g6/VaaP3wPGm9djDsd6NK+RL/vOxRjILb+E1CatvPO\ntsuVlUDB7+Uaxt9n1/DTcg2nAsdyDRzPNaCgoVUUNA8upaHlb+fOncuOHTuw2+3ceeeddOnShYcf\nfhi73Y5er+f5558nOjqalStXsmTJErRaLePHj2fcuHFqh+5zbhp8GedyS9l9OJsPNx5k8rXxaock\nhBBCVOCfZ20+yNtlEPWhfDaM7EoSEzIbhhD+oeC7VA7eNhtHYTEt/z2bJn+92Xsbt5Zi2LoM7enD\nOEOjsQ2cBCGR3tv+JVauUWDRciAzgOIyLQE6J3HRVjq0DiKz8gFtl6wffviBgwcPsmzZMnJzcxk9\nejRXXnkl48ePZ/jw4XzwwQf873//Y/r06bzyyiukpKRgMBgYO3YsiYmJhIV5sYeKH9BqNdx5w+U8\n8/4ONqWdpGlkI4b0bK52WEIIIYSbb97C9zNqlkFcDJkNQwj/lvPFVxyYeC9Oi5V2r/7HqwkJTf45\nDGtfQ3v6MI5m8diS7/BuQsJph/zjl0S5hsMJh7MNpJ00UVympWmIjd4tS4ls5JvfHZ7Wu3dv5s+f\nD0BISAilpaU89thjJCUlARAeHk5eXh67du2iS5cumM1mTCYTPXr0IC0tTc3QfVZggJ4ZY7sS0sjI\n0o3p7D6crXZIQgghhJskJepBbcogfNWEwe0Z2qs5kSEmtBqIDDExtFdzmQ1DCB93dkkKh+54CI3B\nQNx784kcleS1bWtPpmNY+wbawhzsnftjHzgRjCavbZ+yYsg54vq/sRFEtHX93w/lW7SknggkI8+I\nSa+Q0LSU+Ogy9A3421mn0xEU5CodTElJoX///gQFBaHT6XA4HCxdupQRI0aQlZVFRESEe7mIiAgy\nG9qwkjqICg3k3jFd0Gm1vPb5L5zILFI7JCGEEAKQ8o164c9lEDIbhhD+RVEUTj7/OqdeehN9VATx\n7y+gUdcO3to4ur3fokvbADodtmvG4WzT1Tvb/n37lGRB8e8Xno1iICjSb0dHHMkxcjLf9TXcLNRG\nm4iGnYz4s40bN5KSksLbb78NgMPh4IEHHuCqq66iT58+rFq1qsLrFUWp1XrDw4PQ6z3zPRcdbfbI\neutLdLSZ+5wa5r6fysvL9/DCzAGEmX33HOVC+PoxaAjkGKhPjoH65BjUjSQlfme1OS74ory8DOKP\nPSXK+UsZRPlsGEII36XY7Rx9+DkyP/iMgNbNiV/6MqbWXqoNt9vQ/7AC3W+7UYJCsA2ciBLZzDvb\nht/LNU6CrdhVrhHSHIz++Tcrt1TLgXMBWOxaAg1O4qOthAXKrAh/tHXrVl577TXefPNNzGbXid3D\nDz9Mq1atmD59OgAxMTFkZWW5lzl37hzdunWrcd25uSUeiTk62kxmZqFH1l2fOjQPYVS/NqzY+huP\nL/6OB27ujsFDSRpv85djcCmTY6A+OQbqk2NQueoSNQ0+KVFfs2aUlzvsTM8it9BCuNlE97goKYMQ\nQtQLZ6mFQ397hLwvtxDUpQPx78/HEO2lHg4lBRg2L0WbfRJnVAtsA26GIC/eASgrhoKTv8+uEQwh\nsX45u4bdCUeyjZwqMAAKLcLKaB1uQyejIyooLCxk7ty5vPPOO+6mlStXrsRgMDBjxgz36xISEpgz\nZw4FBQXodDrS0tJ45JFH1Arbr4y4ujVnckr44dezvL1mP3eM6OS9GXuEEEKIP/G/s7p6Vl+zZkgZ\nhBDCU+y5+aTfeh9FP+0ipN8VXPbW8+iCvdNDQZOZgeGbD9GUFuJo1x37lTeAzktfHZdQuUZ2iY70\nTCNWu5Ygg5MOMVZCTDI6ojJr1qwhNzeXWbNmuR87deoUISEhTJkyBYB27drx+OOPM3v2bKZNm4ZG\no+Gee+5xj6oQ1dNoNNw2rANZeRa27z1Lk4ggRl7TRu2whBBCNFANOilhKbNXO2vGmAHtLqiUQ8og\nhBD1pezUWQ5MvJfS9CNEjEqi7UuPozUavLJt7eGd6H9YCYoDe89hODr28V5C4M/lGqHNweB/f1tt\nDjicbeRMoQENCq3Cy2gVbkPrf3kVr5kwYQITJkyo1WuTk5NJTk72cESXJoNex/Qbu/DUu6l8/u1v\nNI4I5KpOTdQOSwghRAPUoAeN5hb476wZQohLX2n6Efbe8BdK04/Q+Pabaffyk95JSDid6Hasw/Dd\nctDrsQ2egqPT1d5LSJTPrmErdpVrRLT1y4REVrGOnzICOVNoINjooEdzC20iJCEhfEdIIyMzx3Yl\nMEDH21/s48DxXLVDEkII0QA16KREeIhr1oxKn/PxWTOEEJe2wp92sXfUXyk7dZYW/7yXlo/fh6YO\nfW4uWFkphq/fR793G86QKGzD7kSJvczz2wVXuUZxJuQdc42UCG4MoS38rn9EmQP2ng3glzMmbA4N\nbSLK6NHcgjlAyjWE72kWHczdo7ugKLDw0z2czCpWOyQhhBANTINOSpiMerrHRVf6nL/MmiH8i9Xm\n4FxuCVabQ+1QhA/L/XIL+yfcjaOwmLbzH6fpPbd4pQmdJj8Tw9rX0Z46iCP2MldCIiTK49sFwGF3\nJSOKM0FrgPDWftc/QlHgXJGOn44Hca5IjznAQa8WpVKuIXze5a0juG14B0qsdl76+GdyC2WkqBBC\nCO/xr9tPHiCzZlxaLmZqV0+qr1lexKUvc+kKfnvwGbRGA5ctmUfY4L5e2a7m5EEMWz9GY7Ngv/wa\nHN0SwVufzbKi32fXcPw+u0Yz0PrO729tWO0aDmYZySrWo9UotIu00jzU7k85FdHAXd25KTkFVpZv\nOcKLH+/i4ck9CAxo8KeJQgghvKDBf9vIrBmXBl+/6K+vWV7EpUtRFE4veJsTzy1CHx5K3HvzCe7R\n2RsbRrfvO3Rp60Gjw9Z3DM623Ty/3d+3TXGma4YNcJVrBEb43eiIs0V6DmUZsTs1hJocxEdbCTIq\naocmRJ1d16cVOYVWNu88ySuf7WHWuAT0MmetEEIID/PoN43FYmHo0KEsX76c06dPM2XKFCZOnMjM\nmTMpKysDXHOPjxkzhnHjxvHJJ594Mpxqlc+aIQkJ32Mps9dY8lB+0Z9dYEXh/y/6l2065L1Aq2C1\nOaqd5UVKOYTicHDsn3M58dwijM2b0vHzt7yTkHDY0H+3HP2OdWAKxpY0zXsJCYfNVa5RkuW35RoW\nu4Y9ZwLYfy4ApwKXRVnpFmuRhITwWxqNhsmJcXRrH8Xeo7n8b81+FEU+z0IIITzLoyMlFi1aRGho\nKAALFixg4sSJDBs2jHnz5pGSksKoUaN45ZVXSElJwWAwMHbsWBITEwkLC/NkWMJPlI9+2H04m8zc\n0ipHP9R00X8hU7vWp/yimmd5kWlkGy6ntYzD9z5K7uqvCOzYnvgPFmJsUnmvm3rdblE+hi/fRpt1\nAmdkc2wDb4agEI9vF3CVa+SfBMUBRjOExPpVuYaiwOlCPYezjTicGsIDHcRFWwk0yMWb8H9arYY7\nR17O8x/u5PtfzxAREsCYAe3UDksIIcQlzGMjJQ4fPsyhQ4cYOHAgANu3b2fIkCEADBo0iO+//55d\nu3bRpUsXzGYzJpOJHj16kJaW5qmQRD3yRsPG8tEP53JLqx39UJuL/j/ydrPJ0GCZ5UVUzl5QxIFJ\n95K7+ivMV/Wg4/LFXklIaLJOUPzBPLRZJ3C0TcCW9BfvJCQUBYrOQd5xV0IiuDGENverhESpTcOu\n0ybSM12/t/HRVro2tUhCQlxSAgw6ZoztSkx4IF98f4yv007UvJAQQghxgTw2UuK5557j0UcfZcWK\nFQCUlpZiNBoBiIyMJDMzk6ysLCIiItzLREREkJlZ+R1v4T3VNYv0Vu+Guox+KL/oz64kMfHHi361\n+k4EGHR0j4uu0FOinMzy0nCVnc0ifdIMSvamEz58EO1efgqtyfMJKu2RXei/X4GiOLD3SMLRqa93\nSiYcNlczS1uJq1wjtDkYAj2/3XqiKHCyQM+RbCNORUNEkJ246DJMeklGiEtTSJCR+8Yn8PR7O3h/\nQzphwQFVzlgmhBBCXAyPJCVWrFhBt27daNGiRaXPV1WfWNu6xfDwIPT6+rmQi44218t6fI2lzE5u\ngZXwkABMxtodZofDydurfuWHX06TmVdKdFggV3Vuyl9GXI7u90ZXi1fsqbRhY1CgkdtHdam3+E9n\nFZNTxZRkuYUWdEYD0VGN3I/1TWjGyq1Hzntt34RYmseGeTX2ykwf352gQCM//HKarLxSoirZt5eK\nS/V3qj4Vpf/GntHTKD16kpZ33ETnBf9Co/NsckpxOrF+u5qy1E1gNBF43V8wtOnk0W2WKyvKo+DE\nbygOO0ZzOOZmbdHq/KfPcmGpQuoRhaxCMOigV2sNLaMMaDRGr2xffqeEWmLCg5g5LoHnlqbx+spf\nuf/m7rRrFqp2WEIIIS4xHjkr3Lx5MxkZGWzevJkzZ85gNBoJCgrCYrFgMpk4e/YsMTExxMTEkJWV\n5V7u3LlzdOtWc5O13NySeokzOtpMZmZhvazLV1zMaIClG9MrXLSfyy1l5dYjlJSWMXFoHFabg227\nTla67LZdpxh2RYt6u+vvsDmIMFc9+sFRZqtw7Eb0aUlJadl5U7uO6NOSzMxCr8ZelVF9WzPsihYV\nRqHk5BR7dJvedin+TtW3op9/JX3yTOw5eTS7/y4az5pGVk79/E2rUpkF/befoDuZjjMkEvvASYS0\naev5Y1Vhdg0NBDehzBROdk6pZ7dbTxQFMvL1HM1xjY6IamTnsqgyAlD4w1eXR/ni75QkSRqWNk1D\n+NvIziz8dA/zU3bzyJSeNImQPkhCCCHqj0eSEi+99JL73wsXLqRZs2bs3LmT9evXM3LkSL788kv6\n9etHQkICc+bMoaCgAJ1OR1paGo888ognQmowLnTqydqUS3izYWNdSx5qmtrVV5pNls/yIhqmvM3f\nc+ivD+C0WGk99xFiJt/o8W1qCrLRf/0+2oIsnLHtsV0zHgK8UDbh5+UaxWUa9p8LoNCqw6BV6BBj\nISZYZsoRDVNC+yimJsfzztr9zFv2M/+c2ovQRt4ZKSSEEOLS57Xxs/feey8PPvggy5YtIzY2llGj\nRmEwGJg9ezbTpk1Do9Fwzz33YDbLHZgLdTGzUNTmor22vRvqy4TB7QHYfTibrLxS9+iH8scrU9VF\nv7djF+LPsj5dw29/fwL0ei5bPJfwYQM9vk3NqUMYti5DU2bB3vFqHD2u9U5TSWshFJxyNbMMMIPZ\nf2bXcCqQkWfgaI4BBQ0xwXbaR1kx+kf4QnhM/4RYcgosrNx2lPmf7OKBid1rXR4qhBBCVMfj3yb3\n3nuv+9//+9//zns+OTmZ5ORkT4fRIOQUWCq96IaaRwPU5qLd2w0by0c/3DkmkMNHsyttvFlb0mxS\nqOn0a++T8e+X0IWaiXvnRcxX1lymdlEUBd3+79HtWAcaLbarb8TZrrtnt/n7dik+ByXZlJdrEBju\nnUaa9aDQquXAOSNFZTqMOidx0VaiGsnoCCHKjbymDTkFVr7dc5rXPv+Ve8d08WijaCGEEA2DpLg9\nqLpZLDxhY2pGlc/VNBqgthft5aMU/ty7obrRCxfLZNTXS8mDGrGLhk1xOsl4cgFnXn8fQ9MY4j9Y\nQFAHD3/eHHb021ehO5yGEhiMbcBElOjKmw7X73ZtUHACbKWgM0CI/5RrOBU4lmvgeK5rdEQTs412\nkWVIrlKIijQaDVOT48krtrL7cDbvrT/ALckd0PhJ4lEIIYRvkqSEB6gx9aTV5mD34ewqn+/aPrLG\nxEhtLtpr6t3gy/w5duF/nGU2frvv32QvX4upfWvil75MQPMmnt1oaSGGbz5Em5mBMyIW28CJ0MgL\nnfIrlGuEgLmp35RrFFi07D8XQIlNS4DeSXy0lYggGR0hRFX0Oi13j+rMcx/sZMuu00SYTdxwTRu1\nwxJCCOHHJCnhARfabPJiVNcTAmBoz+Y1rqMuF+3+3LDRn2MX/sFRXMLBvz5AwTc/0KhnF+KWvIgh\nIsyj29Rkn8SweSmakhBlSb0AACAASURBVAIcrbti7zMK9AaPbvO8cg1zEzD5R7mGwwlHcw1k5BkA\nDbEhNtpGlqGXkehC1Mhk1DNrXFf+894OVnz7G+HmAPolxKodlhBCCD8lp1/1rKZmk1abZ+7AlfeE\nqEyEOYCIEFOt11V+0S6jCISoO1tWDvvH3kXBNz8QNrQfHZYt8nhCQvvbbgzr34SSQuzdE7FfM9bz\nCQmHDXKPuhISOiOEt4HACL9ISOSVakk9EUhGnhGTXiEhtpS46EsnIXH8rIO3VpXywy82tUMRl7DQ\n4AD+Pj6BRiY9S9YdqHa0phBCCFGdS+QUzHfUZhYLTyjvCVGZEqudT785jMPp9Mi2hRAu1uMn2Tty\nGsW79hI1YQSXvf08uqDaJwTrTHGi27kBw7efgFaHfdAkHJ37ez4xYC2EnCNgL3WVa4S3AYMH32c9\nsTvhYJaRn0+ZKLVpaB5qo3eLUsIDL42/jflFTj7cYGH+slL2/uagxKKoHZK4xDWNbMTMsQnodBoW\nrfiFo2cK1A5JCCGEH5LyjXqm5tST5b0fvt19GkvZ/4/IsJQ5PF4+IkRDV/zLAdInz8B2LpumM26j\n+YN3e7b5W5kF/bYUdCcO4DRHYB84CSUsxnPbA1e5RtFZKM3BVa7RFExhfjE6IrdEy4HMACx2LYEG\nJx1irISaLo1khM2u8M1OG1+lllFmg9goLSP7G2nfXL7ihee1bx7KHSMu59XP9vDSJ7v555SeRIf5\nR5NbIYQQvkFGStSz6kYseHrqSZ1Wy5gB7WhkqvxE1JPlI0I0ZAXbUtl34x3YMnNo+eQ/aPHQPZ5N\nSBTmYFj3hish0aQdtmF3ej4h4ShzlWuU5vyhXMP3+0fYHXAg08iu04FY7BpahpXRq3npJZGQUBSF\nXQftPPdeCWu/L8Oo1zBucAB/vylQEhLCq3rGRzMxMY6C4jLmfbyLwpIytUMSQgjhR+SsxQPUnHqy\nNuUj0uRRiPqTs2ojh+99FIB2i54m8oZEj25Pc/owhi3L0JSVYu/QB0fPJM/PdGEthIKToDj9anaN\n7GId6ZlGrA4tjYyumTVCLoFkBMCJ/2PvvgOjKPPHj79ndmY3ddMLhBYICdKbqChNRVFBUJB63llO\nva/l7HqnJ987f3p3iO1sd98rnMqBqLEhiCgKgkiREopIEjoEQno2bdvM/P7YwKGGsMnuZneT5/VX\n6jxPstnNzGc+pUTjo7UODhzXMckwdqjK5eebibSEdpBIaL8uG9aFCpudFZuO8NJ7O3l45hDMojeV\nIAiC4AURlAiAM6dYlFbWgySREh8ZsHGgZwpm+YggdDQnF7zN4SeeRY6OInvBs1gvOT9wixkGcv4m\nlC0rQJJwXTQFPWtY4NZrXDMcyzVcGuwrM3OyVkXCoEeCk24JLuTQ3rZXbHU6KzY4+XaPGwPo19PE\ntZdYSI4XiY9C8E0d24vKWgcbvzvJ/y39jruuG4DcHp54giAIQkCJoESAaLrOe1/tZ3tBKRU2B4lW\nC0OyU5hxaVZAgxOnykfOHEl6SqDLRwShozAMg6Jn/srxvyxATUki+z9/IXpAn8AtqLlRNi/DtG8r\nRkQ0rjGzMFK7B2498JRrVB8Dt91TrhHXBZTQb2ZZWmuioMyMS5OJMWv0SXUQYwn/ho8ut8G6PBer\nvnXicEGnJJlrR5vJ7ir+jQuhQ5Ykbrn6PKprnWwvLGPxqgLmjM8ObDmbIAiCEPbE2UyAvP3lvh8E\nBsptjjZrNhnM8hFBaO8Mt5uDj/yRsiVLsWR2JWfxy0R07xK4BRtqUdcuQS45jJ7YGdfY2RAdF7j1\nABw2sB1vLNeIayzXCO078U4NCkstlNYpSJJBZqKTrvHhnx1hGAY797n5+GsHFTaD6AiYdImFEf0U\nTOH+wwntkmKSueu6Afx50Va+3FZEojWCqy8McBBVEARBCGsiKBEADpfG9oLSJj+3vaCMqWN6Bbzh\n5anykepaB3ExFpEhIQh+oNXb2f8/v6Xq83VED+pL9sIXUZMTA7aeVHECdfUipPpqtO79cY+8DhRz\nwNYLx3INw4CSWhP7yiy4dAmrRSMn1UG0OfyzI46Xavzz4wq+P+hElmHMEJXxI0TfCCH0RUUo3HfD\nIJ5euJXcNftJiLVwUb/0YG9LEARBCFEiKBEAodJs0qKaRFNLQfATV0UVhb94gNqtO7GOuZDe/3wG\nU3Tgnl/y4d0o699H0ly4B1+O1n90YIMDYViu4XBLFJSaKa9XkCWDXkkOusS5QzmG4pWaep1PNzjZ\n9J2nb0TfTE/fiJSE0M5WEYQzJVojeGD6IP74n20sWP49cdFm+vYIXBBXEARBCF8iKBEAotmkILQv\njqJi8mffg73wIEnXX0Xm83ORzWpgFjN0TDtWo+xag6GYcY2djd71vMCsdYrdBjWN5RoRcRAT2uUa\nhgHFNQr7y824dYm4CE92RJQa3tkRbs1g3Q4XqzY7sTshLVHm55PiSY8T4xWF8JSREsM91w/g+Xfy\nePWDXfxmzjC6psYEe1uCIAhCiAnds84wdqrZZFNEs0lBCC/1+fvZc+0t2AsPkn7HHHq+9IfABSRc\nDpSvlngCEjEJuCbcHtiAhKFDTTHYjnmu9GM7gzUjpAMSdpfErhMW8kstGAb0TnYwuLM9rAMShmGw\ne7+b+f+pZ9nXnlKN68aYeXB2JAOyRBBbCG99uidw6zV9aXBovPBOHhU2e7C3JAiCIIQYkSkRIKHc\nbNLh0kSvCUHwQs2mPApuuh+tuoauT9xLp/+5MYCLVaKu+Q9yVQl6WiauMTPBErjyEM1ph8pDjeUa\nFojLCOlyDcOAEzZPdoRmSCREuslJcRIRxsEIgBNlGh+tc1J4VEOWYNQglSsuMBMVEeY1KIJwhgv6\nplFZ4+Cd1ft44Z0d/PZnQ4mKCFBwVxAEQQg7IigRIKHYbFLTdd7+cl+bjykVhHBU+eka9t35OLjd\n9HzpDyRPuyZga0nFB1HXLkFy1KPlXIB7+FUgB/D1wm6jsuwE6JqnkWVsOkih+xrQ4JLIL7FQZTdh\nkg1ykh2kx4Z374jaeoOVmxxs2O3GMKBPdxPXjrKQlhi6j4Mg+OLKEV2pqLGzassxXn5vFw/MGIyq\niL93QRAEQQQlAi6Umk0Gc0ypIISTkkUfcOjRPyFHWMh64wXix40M2Fpy/maUb5eDJOG6cDJ67+EB\nWwtDb5yuUYkhyZ5yjcj4wK3nI8OAomqFAxVmdEMiKcpNdooTixK+2RFuzWD9ThefbfL0jUhNkLh2\nlIXzeoh/x0L7JkkSMy/tTWWNg635pfxz2R7umNwPOZyji4IgCIJfiLOgDqK1Y0pFqYfQkRiGwfEX\n/0XR/L+hJMaTvfBFYob0D8ximhtlyyeYCr7FsETjGjMTI61HYNYCcDs9vSMayzUSemRTWaMFbj0f\n1Tsl9pZasNlNKLJBToqd1BgtbLMjDMPg+0MaS9c5KK0yiLTAlNFmRg5QMZnC9IcShBaSZYnbJ/Xl\n2bo8vt1bQqLVwoxLewd7W4IgCEKQiaBEB9HSMaWi1EPoaAxN4/Dv5lPyRi7mrp3JWfwykb26B2Yx\nex3qV0uQSw6hJ6TjGjsHYgKYsWCvhpoTjdM1POUaSkQU1NQEbs1W0g04VqVysFLFMCRSot30TnZg\nDuP/VsXlOh+tc1BwxNM34uKBKldeYCY6UgQjhI5HVUzcM3Ugf/rPVlZuPkpibATjz+8a7G0JgiAI\nQRTGp3lCS7R0TKko9RA6Et3uYP89T1C5/Eui+maTveglzGnJAVlLqixGXb0Iqa4KrVs/3COvB9Uc\nkLXOLNdAksDa2ROUCFG1Don8Ugs1DhOqySA72U5KTOhmc5xLXYPByk1ONuxyoRuQ3c3E5FFm0pNE\n1pnQscVEqtw/fRBPv7mVJV8UkhBrYXif1GBvSxAEQQgSEZToIE6NKT0z0HDKj8eUtrbUI1hEiYng\nC3d1DYW3PEjNhm3EjhxG7wXPoVhjArKWfOQ7lPXvI7mduAddijZgLAGrR3A7wFZ0xnSNLqCE5nhJ\n3YAjlSqHK1UMJNJi3GQlOwjXp7OmGXyzy8XKTU4aHJAcLzF5lIXzepiQwrX+RBD8LDkukvunD+JP\ni7bx94/3YI02k901dIOmgiAIQuCIoEQH4u2Y0paWegSLKDHxr44Y3HEWl5L/s1/TsKeQhImX0eul\nJ5EjAnDhbuiYdq5B2bkaQzHjGjMLvVtf/69zShPlGqE6XaPGIbO3xEyd04TZpJOd4iA5OnyzI/Ye\ncrN0nYOTlQYRZrh2lJmLB6ooom+EIPxEt7RY7r5uAC++u4OX39vJb382jM7J0cHeliAIgtDGRFCi\nA/F2TGlLSz2CRZSY+EdHDe407DtE/ux7cB47QepNN9D9/z2EZApAMMblQPnmfUxH9mBEx+MaNwcj\nId3/64AnCFFTDPYqTxDCmgERcYFZy0e6AYcqVI5UqYBEp1gXPZOcYZsdcbJC5+OvHXx/yNOM86IB\nChMusBATJYIRgtCcfpmJ3HRVH/61/HteeCePx24cTkJsaJxnCIIgCG1DBCU6oHONKW1JqUewhFuJ\nSSjriMGd2m27KbjxXtyV1XR59H/o9OtbApNWX1uJumYRcuVJ9LQeuEbPhIgA3QV0Oxqnazg8ZRrW\n0C3XsNll9pZYqHfJWBSdnBQ7iVF6sLfVKvV2g882O1m/04WuQ1YXE5NHm+mcLF5/BMFbFw/oREWN\ngw/WHuDFd3fwmzlDibSIU1RBEISOQrziC03yttQjWMKlxCTUdcTgTtWX69l326PoDic95v+O1DlT\nArKOdPIQ6ldvITnq0bJH4D7/apAD9Lv8QblGAsSmhWS5hqbDwQozx6oVQKKz1ZMdoYTeVs9J0w02\nNPaNqLdDUpzEtZdY6NdT9I0QhNaYeFF3Km121uQd57UPdnHvDYNQTGH44iAIgiC0mAhKdACt6RXg\nbalHsIRLiUmo62jBnbJ3l3Hwwf8HikLvf80n4coxAVlHLvgWZfMyAFwjJqHnjAjIOuFUrlHVIJNf\naqHBJROperIj4iPDMzsi/4ibpWudFFfoWFSYeLGZUYNUFEUEIwShtSRJYs4V2VTVOsnbV8brK/Zy\n6zXniSCfIAhCByCCEu1Ya3sF/DiIEYoXpeFQYhIOOkpwxzAMiv+6kKNPvYQp3kr2688TO2Kw/xfS\nNZRvP8FUsBnDEoVrzEyMtEz/rwOeMo3qY6CFdrmGW4cD5WaO21TAoEuci8xEJ+F4A7S0SmfpOgd7\nDmpIwIX9FCZcZCY2Kgx/GEEIQSZZ5o7J/Zj/1na+2V1MQqyFqWN6BXtbgiAIQoCJoEQ71tJeAeHW\n8DDUS0zCQUcI7hi6zpEnX+Tk3xdj7pRG9uKXiMoJwEmuvQ517dvIJw+ix6fhGjsHYhP8vw54MiNq\nToBhQGQCxIRmuUZFvSc7wuGWiVJ1clIdxEWEX3ZEg8Pg881Ovt7hQtOhV4bM5NEWMlLC//khCKHG\nopr49bSB/HHhVpZvOEyiNYJxQzKCvS1BEAQhgERQop1qTa+AcGt4GOolJuGiPQd3dKeLA/f9nooP\nVxKZ3ZPsRS9hyfD/5Aupshh1zWKk2kq0rufhvngqqIEZLfrDco3OIVmu4dZgf7mZEzWe7Ihu8U66\nJ7jCLjtC1w02fedmxQYHdXZItEpMusTCgF6ib4QgBJI1yswD0wfx9MKt/OezfOJjzAzpnRLsbQmC\nIAgBIoIS7ciZZRct7RXQkiBGa3pUNLVHfwUQQrXEJFy01+COVltH4S8fwbZ2EzHDB5L9xgsoCf6/\ngJeP7EFZ/x6S24l74Di0gWMDk7Xwg3KNiMZyDbP/1/FReZ2J/FIzTk0m2qzRJ9VJrCX8siMKj7r5\naJ2TE2WevhFXjzQzerCKKvpGCEKbSE2I4r4bBjFv8Tb+76PveHjWEHplhF4QVhAEQfCdCEq0A02V\nXQzMSiYh1kxFjfMnX99UrwBvghhJcRGtLu84c4/lNkfjXY9kZo/PDsnSkI6oPQV3XGUV5P/sXup3\nfk/8+FH0+uufMEVF+HcRw8C06yuUHV9gmFRco2egd+/v3zVOaWgs1yB0yzVcGuwrs3CyVkHCoEeC\nk24JLuQwu4Yvq9L5+GsHuw94+kaM6Ktw1UVmrNGh9fsWhI4gs5OVX03uz8vv7eQvuTt5/MZhpCW2\nj/9TgiAIwn+JoEQ70FTZxeptRXRNjWkyKNFUrwBvGh76Ut7x4++tqnWyevtx9hXZmHvTcBGY8FEg\nMlDClf3QMfJn343j0DFSZk2mx7zfIil+fqlzOVE2fIDp8G6M6DhcY+dgJHby7xrQWK5xwjPyU5Ih\nNgMirP5fx0eltSYKyiy4NIlYi0ZOioMYixHsbbWI3WHw+bdO1uV5+kZkdvb0jeia2rGfT4IQbIOz\nkvn5lTm88Wk+L7yzg8duHIY1OvSyxARBEITWE0GJMNdc2UW93cW4IZ3Zub/inL0CztXwEGhxjwpv\n9ni0pJbFqwq58YqcJj8vNC/cmpMGWt2uveTP+TXusgo633crGQ//yv+1/3VVqGsWI1ecQE/tjmv0\nTIiM8e8aAG47VBeFdLmG0w2FZRZK6xQkyaBnopMu8eGVHaHrBpv3uFmxwUltg0FCrMTEi80M6q2I\nvhGCECLGDM6gwubg428O8ZfcHTwyaygWswgYCoIgtBciKBHmmi+7cHDliG5Mv7S3V3fRm2t4WF5t\nb1GPih/vsakMjFPyCsqYPi6rw9/hb41wa04aSNXrNlN468PodfV0f/oR0m6e7vc1pJLDqF+9hWSv\nQ8sajnvENWAKwMvoD8o1EiEmNaTKNQwDSmpNFJZZcOsS1giNPikOoszhlR2xv0jjw68cHC/TMatw\n1UVmxgwRfSMEIRRNGZVJRY2d9buK+etHu7ln6oAOGXwXBEFoj0RQIkDaKp3em7ILb3sFNNfw0Jt1\nmttjfIyZqtqflpIAVNU5mg1qCE1rzYSV9qr8w5UcuPd/QZLI+r8/kTjxcr+vIRduRdn8MRgGrhET\n0bNHgL/vpIdBuYbDLVFQaqa8XkGWDLKSHGTEuf3+qwik8mqdZesd7NynATC8j8LVI83ExYgLHEEI\nVZIk8YsJfaiudbJzfzkLVxbwiwk5IqNJEAShHRBBCT9r63T6c5VdtOaitKkghi/rWFQTQ3ons3r7\n8SY/n3iOoIbQtJZOWGmviv+5hCNzn8UUG03vBc9hvXi4fxfQNUxbPkXJ34hhjsQ1eiZGp57+XQMa\nyzWOgeb0lGvEdQFT6JRrGAYU1yjsKzej6RLxERo5qQ4i1fDJjrA7Db7c4uSr7S7cGvTo5Okb0S2t\nYwTvBCHcKSaZ/5nSn2cWb2ftjuMkWi1ce3FmsLclCIIg+EgEJfwsGOn0zZVdNKel2RytXQdg9vhs\n9hXZOFpS+5PPtTZ40tH5kr3SHhiGwbE/vcqJV15HTU0iZ9HLRPXz83PMUY+69m3k4gPocam4xs2B\n2ET/rmEYYK+CmmL+W66R5v8sDB/YXRL5pWYqGxRMkkF2soNO1vDJjtANgy3fu/nkGyc19QZxMZ6+\nEUOyRd8IQQg3kRaF+24YyNMLt/LhuoMkxFoYNbBzsLclCIIg+EAEJfzg1MV9pEUJSjp9c2UXTWlt\nNkdL1/nx9869aTiLVxWSV1BGVZ2DxBYENYSfCkSWTLjQXW4OPfw0Ze98jKVnN/osfhlLtwy/riFV\nnURdsxippgKtSx/cl0wD1c+BHr2xXMPRWK5hzQBL6JRrGAYctykcKDejGRIJkW5yUpxEhFF2xIHj\nGh+tdXCsREdV4MoLzIwdqmJWRTBCEMJVXIyF+6cP4o8Lt/LGinwSYiz075kU7G0JgiAIrSSCEj74\n8cV9XDN9EwKZTn9mxoM3x/c1m8PbHhU/ZpJlbrwih+njssT4Sj/xJXslXGn1Dey74zdUf7Ge6MF9\nyV74F9SkBL+uIR/di7I+F8nlwN1/DNrgS/3faDLEyzXqXRL5JRaq7SYU2aBPsoO02PDJjqiw6Sxb\n72RHoRuAoTkK14w0Ex8r+kYIQnvQKSmaX08byLNL8nj1g938Zs5QUlJig70tQRAEoRVEUMIHP764\nP1tAAgKTTt9cxoNbM5q88D9Xc8RJI3vQ4HAHNGDQ2qCG8FO+ZK+EI1dFFQU/v4+6bbuJG3sRWf+Y\nhynaj39LhoFp91pMeV+AScE1ajp6jwH+O37jGqFcrmEYcKxa4WCFGd2QSIpyk53ixKKER3aEw2nw\n5VYna7Z5+kZ0S5OZMtpC907t93khCB1V7y7x3D6pH699sIsX3t3Bc53jEM90QRCE8COCEq3U3MV9\nUwKRTn+2jIf8I1XU211NlmY0N56z3GbnfxdsprrWGfAGnYJ/dYRAj+PYCfJn3Y19/2GSpl1N5nNz\nkVU/voS5nSgbPsR0aBdGlBXX2DkYSX6uU9a1xnINW2O5RhewhM6dvTqnJzvC5jChygZ9Uu2kRGuh\nEi9plm4YbNvrZvk3Tmx1Btboxr4ROQpyOPwAgiC0yrCcFGaPz2bR5wX87m/f8PDMwSRaI4K9LUEQ\nBKEFRFCilZqbfACQEGOhus4RsHT65oIiZzaTPLM0Y8alWaz89iiyBPpZbnqeyvZoiwadguCt+u/3\nkT/nHlzFpaT/z410ffweJH8Gy+qqUdcsRq44jp7SDdeYWRAZ47/jA7jsYDtVrhEJcRkhU66hG3C0\nSuVQpYphSKTEuOmd7MAcJrccD53w9I04clJHMcH4ESrjhpmxiL4RYeuZZ55h69atuN1u7rjjDq64\n4grefPNN5s2bx+bNm4mOjgZg6dKlvPHGG8iyzPTp07nhhhuCvHMhGC4b1oU6u4sP1x1k/lvbeXTO\nUOLbebNnQRCE9qRFQYmCggKOHDnC5Zdfjs1mw2oNnYZsba25yQdJ1gjm3jTc5zKI5qZjnCso8mPb\nC8rQdIPV24patIdANugUBG/YNm6j8KYH0Gy1dP3f++h0x8/8enyp9AjqmreQ7LVovYbivmASmPwY\nrw3xco1ah8TeUgu1DhOqSSc72UFKjBbsbXmlskZn+TdOtud7+kYMzlaYeLGZhA7eN6LohJ1PV5cy\n4LxYRgyJD/Z2Wmzjxo0UFhby9ttvU1lZyXXXXUd9fT3l5eWkpqae/rr6+npeffVVcnNzUVWVadOm\nMX78eOLjw+9nFnw3aWQPVLPCu18U8uySPB6ZPQRrVGgEfgVBEITmeX3m/frrr7Ns2TKcTieXX345\nr732GlarlTvvvDOQ+wtZ55p8EBtlJraV/wy9mY7RXFCkKRU2O3kFZU1+rrnMiUA26BSEc6lYsZr9\ndz4OmkbPV/4fyddf5dfjy/u2oWxaCoaBe/jVaH0u9G+w4AflGiawdg6Zcg1dNzhUoXK4UsVAIi3W\nRVaSk3CIPzpdBqu3uVi91YnLDV1SPX0jMjuHweYD6OCRet5bXsw3W6owDDCZpLAMSpx//vkMHDgQ\nAKvVSkNDA5dddhmxsbF8/PHHp79ux44dDBgwgNhYz3Nq6NChbNu2jUsvvTQo+xaCS5IkbrzqPKpt\ndj779ijPLcnj4VlDiIlUg701QRAE4Ry8DkosW7aMd955h1/84hcAPPLII8ycObPDBiUgcJMPvJmO\n0VxQpCmeySBNBzAMIP4sk0MC0aBTELxRsvA9Dv12HnKEhd7/fp64sRf67+C6hmnbZyjff4NhjsQ1\negZGp17+Oz6AqwFsRWeUa3QBU2icHNc4ZLbvNqiuN2Mx6WSnOEiKDv3sCMMw2F7gZtl6J9W1nr4R\nU8eZGdanY/eNKNhfR+7yYr7NqwagZ7dIpk1M54Kh4ReQADCZTERFeQLhubm5jB49+nTg4UxlZWUk\nJiaefj8xMZHSUu97PQntjyRJzLg0C5dbZ/X2Ip5/O4+HZg4hKkJUKwuCIIQyr1+lo6Ojkc+o4ZZl\n+Qfvd0SBmHxwrukYZ5ZSnBkUqaixI3H2jIeBWUl8d6CiycyKxNgIBvZKZPX24z/5XCAadApCcwzD\n4Pjz/6Doub+jJCWQ/Z+/EDOor/8WcDSgrnsb+cR+9LgUXGPngNWP8+0NAxoqofYkYEBUEkSnhkS5\nhqbD4UqVI1We4Egnq4teiU6UMHiKHynW+HCtg8PFnr4Rlw1XuWy4GYs5+L/XYDAMg+/ya8ldVsyO\nPTUA9MmKZtrEdIYOsCKFwN+br1atWkVubi4LFizw6usNw7sJMQkJUSgB+qMXIymDLzXVyn2zh6Go\nJj7ffIRXP9zNH26/iEiLCEy0FfE8CD7xGASfeAxaxutX6G7duvHKK69gs9n47LPP+OSTT+jVy893\nFsOUPyYfnOof4XRpZ+0V8eNSijODIgeKqpm/JO+sx7/y/G6YFdNZy01mXJqFySSfzvqIj7HQp3sC\nU0Zl+vRzCUJLGJrGocfmUbrwfSzdMshZ/DIRPbv57fhSdQnK6kXINRVoGdm4L7kBzH7s0h7C5RrV\ndpm9JRYaXDIRis6I3jKy8+xjjENFda2nb8TWvZ6+EYOyFK652ExSXMcMihuGwbZdNnKXFbN3Xx0A\nA8+L5YZJ6fTLiWkXwQiAdevW8be//Y1//vOfTWZJAKSmplJW9t+yxJKSEgYPHnzOY1dW1vttn2dK\nSYmltLQmIMcWvHPmYzBjbC9qah1s3HOSuX9bz703DBI3WdqAeB4En3gMgk88Bk1rLlDjdVBi7ty5\nvPnmm6SlpbF06VKGDRvGnDlz/LLBjqyp/hEWs4zdqf/ka89WSmFRTfTMiCOpmcabidaIZstNTgU4\npozqyVufF7D3SCUbdheTf6RSjAYV2oRud7D/rt9RuWI1Uf2yyV70EubUZL8dXy4qQFn3DpLLgbvf\nKLTBl4M//6ZdDY3TNVygRnrGfYZAuYamw8EKM8eqPS/3GXEuMhOdpMXFEsqZ7i63wZptLr7c4sTp\nhowUmcmjLfTK6JgXFbpusGl7FbnLijlwuAGA4YOsTJvYiZxe0UHenX/V1NTwzDPP8PrrrzfbtHLQ\noEH87ne/w2az72ZuqQAAIABJREFUYTKZ2LZtG4899lgb7lQIZbIscevE83BpOlvzS3nl/V38euoA\n1HBIDRMEQehgvA5KmEwmbr75Zm6++eZA7qfDaap/xNk0V0pxrsabp77vXOUmH647wPrdxT/YjxgN\nKgSau7qGwpseoGbTdqyXnE/vf83HFOunkZyGgWnP15i2fQ4mE65LbkDPHOifYzceP1TLNSobZPJL\nLNjdMpGqTk6Kg/jInwY8Q4lhGOQVulm+3klljUFMpMSUMWbOP09BloP/O21rbs3gqw0VvLe8mKPH\n7UgSjBwez7SJ6WR2a58NiD/55BMqKyu57777Tn/sggsuYNOmTZSWlnLbbbcxePBgHnnkER588EFu\nvfVWJEnirrvuOmtWhdAxmWSZO67tx2sf7CZvXxmvfbCbu64fgGISN1kEQRBCiddBib59+/4gLVSS\nJGJjY9m0aVNANtYRNNc/IsJsIsqiUFXr8LqBpjeNN5sbM9qSfhaC4C/OEyXkz7mHhr37SZw0np4v\n/QHZ4qcxbm4XysYPMR3ciRFlxTV2NkZShn+ODY3lGsfBURNS5RpuHQ6UmzluUwGDrvFOeiS4CPXz\n8KMlGh+tdXDwuI5JhnHDVC4fbibC0vGCES63zppvKvho5R6KTtiRZRg7MpGp16TTpZMfS45C0IwZ\nM5gxY8ZPPn733Xf/5GMTJkxgwoQJbbEtIUwpJpn/mdKPl97bxY795fzf0u/41eR+IvtTEAQhhHgd\nlNi7d+/pt51OJxs2bCA/Pz8gm+ooqmsdZ+0f4XRpPHbjMMyK7FUDzVPBhqljejWZCeHNmNHm9iNG\ngwqB0FB4iPzZd+MsKibtlhl0e/JBJH+dKNbbUNcsRi4vQk/uimvMLIjyY8DA1QDVx0B3gRoF1oyQ\nKNeoqDeRX2rG4ZaJUnX6pDqwRoR2doStTueTDU627HFjAAN6mZh4sYXk+I530eBw6qxaW8YHK05S\nXulCVSSuGJvM9VelkZYiJiEJQmuoiom7rx/AX97dwdb8Uv617Ht+ObFvh8y+EgRBCEWtakVsNpsZ\nM2YMCxYs4Pbbb/f3njqMuBgLiWfpA5EQG0FKfOQ5gxHeBBvAuzGj59qPGA0q+FPt1l3k//w+tMpq\nuvz2LjrdfZPfmvRJpUdRv3oLqaEGrdcQ3BdM8l/A4HS5RmOZU1QyRKcEvVzDpcH+cjPFNZ7siO4J\nTronuAjlc26X22DtdhdfbHHicEGnZJkpo8xkde14XfIbGjQ+XVPKRytLqLa5MZslJl2Ryi2ze4Ie\n+g1JBSHUWVQTv542kOff3sHGPSdRTDI3Xd2nQ48TFgRBCBVen/nl5ub+4P3i4mJOnjzp9w11JN72\ngWiON8EGb8sy/LEfQfBG1aqv2Xf7o+guN5nPzyVl5rV+O7a8fzvKxqVgaLiHXYV23kX+Cxj8pFwj\nAyx+6n3hg7I6EwWlZpyaTIxZIyfVSawldLMjDMNg5z6NZesdVNg8fSMmjTJzQd+O1zeiptbN8lUl\nLP+ilNo6jahImanXpDFpfCpxVpWUJAulpSIoIQj+EGFWuO+GQTy7ZDtf7zqBqsj87IrsdjO1RhAE\nIVx5HZTYunXrD96PiYnhxRdf9PuGOhpv+kCcjbfBhpaUZfiyH0HwRunbH3PwoaeQVYXeC54lYfwo\n/xxY1zFt/wxlz3oMNQLX6NkYnXv759gQkuUaTg32lVkoqVWQMMhMdNI1PrSzI46VaCxd52B/kadv\nxJghKuNHmInsYH0jqqpdLP2shBVflmJ36MREm5h9XSeuviyF6KiOlykiCG0lKkLhgRmDmf/WdlZv\nL0Ixycy8LEsEJgRBEILI6zOfP/3pT4HcR4d1ahRncxMxzsbbYENLyjJ82Y8gNMcwDE688gbH/vQK\npoQ4st94gdjhfpqC4WxAXfcu8vFCdGsy7nFzMKx+GidqGNBQ0Thdg5Ap1yipNVFYasGlS8RaNPqk\nOog2G0HdU3Nq6nVWbHCy+TtP34h+mSYmjbKQ0gZ9I5pr8NvWyiqcfLjiJJ+vLcPpMkiIU5g5uRNX\njE0mMkK81gpCW4iJVHlw5mCeWbydz7ccxazKXD+6pwhMCIIgBMk5gxJjxoxp9kV6zZo1/txPh2VR\nTS1uIultsKE1ZRmt2Y8gnI2h6xz53+c5+a8lmDunkfPWK0T2zvTLsaXqUpQ1i5Bt5Wide+MeNR3M\nfppOoGtgOw7OxnKNuAwwB7dcw+GWKCwzU1anIEsGPZMcdI1zBztGclZut8HaHS5Wbfb0jUhPlLl2\ntJmcboHPBvC2505bOFHi4P1PilmzvgK3ZpCSZOa6q9K4bFQSZrXjNfQUhGCzRpl5aOZg5i3axvIN\nh1EVmWsv9s//JUEQBKFlznlWuHjx4rN+zmaznfVzDQ0N/OY3v6G8vByHw8Gdd95Jnz59eOSRR9A0\njZSUFObPn4/ZbGbp0qW88cYbyLLM9OnTueGGG1r303QwLQk2iLIMIVh0h5MD9/4vFUs/JzKnJzmL\nXsbcOc0vx5aKClHXvYPksuPuewnakPHgr4vNECvXMAw4WWtiX5kFty4RF6GRk+IgKkSzIwzDYPcB\njY+/dlBebRAVAVMvtnBBfwVTG9WXeNNzJ9COFjWQu7yYrzdVohvQKc3C1KvTGXNRIooSopEkQegg\n4mMsPDxrCH9etI0P1x1ENclcdWH3YG9LEAShwzlnUCIjI+P02/v27aOyshLwjAV96qmnWLFiRZPf\nt3r1avr3789tt91GUVERt9xyC0OHDmX27NlcddVVPP/88+Tm5jJlyhReffVVcnNzUVWVadOmMX78\neOLj4/30I7Zv3gYbRFmGEAxaTS2Ftz6C7evNxIwYTPbrz6PEW30/sGFg+v4bTNtWgmTCdfFU9J6D\nfT9u47FDrVzD7pYoKDVTUe/JjshKdpBhDd3siONlGh+tdbLvmIYsw+jBnr4RURFtt2Fve+4Eyv7D\n9eQuK2bj1ioAumVEMG1iOiPPT2izoIwgCOeWaI3gkVlD+NOibby7Zj+KIjN+eNdgb0sQBKFD8Tp/\n9qmnnmL9+vWUlZXRrVs3jh49yi233HLWr7/66qtPv33ixAnS0tLYtGkTf/jDHwAYN24cCxYsIDMz\nkwEDBhAbGwvA0KFD2bZtG5deemlrf6YOpaXBBlGWIbQVZ0kZBT+7l/rd+cRfOYas155GjvRDWYXm\nQtm4FNOBPIzIWFxjZ2Mkd/H9uNBYrlEEzlqQG6drBLFcwzDgRI3C/nIzmi4RH+nJjohUQzM7orbe\n4NONDjZ+58Yw4LweJiZdYiEtse3LE1rS4Nefvi+sJXdZMdt2eTIJszKjmDYxnfMHxXW4ySKCEC6S\n4yN5pDFj4q1VhaiKzNjBGef+RkEQBMEvvA5K7Nq1ixUrVnDjjTeycOFCdu/ezeeff37O75s5cybF\nxcX87W9/4+abb8ZsNgOQlJREaWkpZWVlJCYmnv76xMRESkubvrt1SkJCFIrinztcKSmxfjlOKPDT\nZVlIak+PU3uXkhJL3b7D7L7+NuoPHKXbL2fQ7+W5yIrvPQT02moalr6BVnwYOa0bUZNvRY6J88Ou\nwVVfg+3YQXSXEzXaijWjF7Jq9suxW6PObrDloEFJNSgmGJYpkZmqIEn+KyHx1/PK7Tb4fFMdH66u\no8Fh0DlFYfZVVgb2tpz7mwMkNi6SlIRISiobfvK55PhIevVIIsLsn74WhmGwZUcVb75zmO27qgEY\n3C+On0/vxvlDEnxunide/wQh8NISo3ho1hCeWbyNhZ/mo5pkLh7QKdjbEgRB6BC8PiM7FUxwuVwY\nhkH//v2ZN2/eOb9vyZIlfP/99zz88MMYxn/v7p359pnO9vEzVVbWe7nr5qWkxFJaWuOXYwmB63Av\nHifftdX0gZSUWA59sZn8Ob/GXV5J5/tvI+2h2ylv4sKwpaSyY6hrFiM11KBlDsJx4WQaGmRo8PFv\n48flGtEpuKKSKa9yAE3faQ8kw4DjNk92hG5IJEa5yU5xEiEblJX5bx1/PK8Mw2DPQY2lXzsoqzKI\ntMCUMWZG9lcxmZyUljr9tNvWGdgrqcmeOwN7JVFT3YCvryqeYEQ1ucuKKTjg+b80pL+VaRPT6Zvt\nybApK6v1aY1QfP0TQRKhvcpIjubBxnGhCz75HlWRGXGef3ogCYIgCGfndVAiMzOTRYsWMXz4cG6+\n+WYyMzOpqTn7idLu3btJSkqiU6dOnHfeeWiaRnR0NHa7nYiICE6ePElqaiqpqamUnXGmXVJSwuDB\nfqoNF9pEKHW4F36orR+b0lXr+X7a3ej1DfT4829I/fk0vxxXPrADZcOHoGu4h16J1vdi//R40N2N\n0zVqQVYayzWifT9uK9U7JfJLLVTbTSiyQXaKg7SY0OwdUVzu6RtRcFRDluCSQSpXXtC2fSPOJVAN\nfjXdYOOWKnKXFXPomCfgdsGQOKZNTCcrM3h/P4Ig+K5bWiwPzBjMs0u28/elezDJMsNyUoK9LUEQ\nhHbN66DEk08+SVVVFVarlWXLllFRUcEdd9xx1q/fsmULRUVFPP7445SVlVFfX8+oUaNYuXIlkydP\n5rPPPmPUqFEMGjSI3/3ud9hsNkwmE9u2beOxxx7zyw8neAT6LnkodLgXmtaWj035B59y4L4/gCyR\n9Y95JF7th74wuo4pbxXKd+swVAvusbPQM/y0b1d943QNN6jRnnGfcuDHVDbFMOBYtcLBCk92RHK0\nm97JTixK6PWOqG0wWLnRycbdLnQDcrqZuHaUhfSk0AtA+rvBr9ttsHZTBe8vL6ao2IEswagLEph6\nTTrdu0T6ceeCIARTZicr908fzHNL8vjbR7u5Z+oABvZKDva2BEEQ2i2vz8CnT5/O5MmTueaaa7j2\n2mvP+fUzZ87k8ccfZ/bs2djtdubOnUv//v159NFHefvtt+ncuTNTpkxBVVUefPBBbr31ViRJ4q67\n7jrd9FLwzZl3ycttDuJjzAzpnczs8dl+u0se7A73wtm15WNT/PdFHPn9CyjWGLL+/RzWi4b5flCn\nHeXrdzEVFaDHJuEeNwcjzg93qwwD6suhrsTzfnSKZ8JGkNIR6pwSe0ss1DhMqLJBn1Q7KdFayGVH\naJrB+l0uPtvkpMEBKQkSk0dZ6NPd5HPPhEDztcGv06Xz5dflfLDiJCVlTkwmuHxUEtddnUbnND80\nbxUEIeRkZcRx3w0DeeGdHbzy/m7uvWEg/XoknvsbBUEQhBbzOijx6KOPsmLFCq677jr69OnD5MmT\nufTSS0/3mvixiIgInnvuuZ98/N///vdPPjZhwgQmTJjQgm0LZ3NmVsR7X+3/wV3yqlonq7cfZ1+R\njbk3DfdLYCJYHe6Fc2uLx8YwDI49/TInXnsTNS2ZCz/5F45Ovncsl2zlKKv/g2wrQ++cheuS6WDx\nw53oECrX0A04WqVyqELFQCI1xk1WsgNzCMbwvj/k5qN1DkorPX0jJo8yc/FAFZMptIMRvrI7ND77\nqowPV5RQWe1CVSSuviyFKRPSSEkKXhNUQRDaRk63BO6ZOpC/5O7k5dyd3D99EDndEoK9LUEQhHbH\n66DEsGHDGDZsGI8//jibN29m6dKl/P73v2fjxo2B3J/gpR/3DkiINVPv0Jr82qMltSxeVciNV+T4\nvG5cjIVEq4XyJi5+E2IjiIsJXvf9ji7Qj43ucnPwof9H+bvLiejVnZy3XsE6MNvnpnzS8X2o695G\nctpxnzcSbegVnvGcvnLWgy00yjVqHDL5JWZqnSbMJp3sFAfJ0U0/X4PpZIXO0nUO9h72ZG6MHKBy\n5YVmYiLbdzCirl5jxZelfPxZCbZaNxEWmSkTUrn2yjQS4vw3/UQQhNDXLzORO6/rz6vv7+LF3J08\nNGMwvTL8M/VJEARB8GjRGbnNZmPVqlV8+umnHD16lBkzZgRqX0IL/bh3QEVN813v8wrKmD4u6yfp\n+y3tP2FRTQzJTmmyw/2Q7GRRuhFEgXxstPoG9t3+KNVffkP00P5kv/EialK8L9sFw8C0dyOmrStA\nknGNvA6911Dfjtl43FAp19ANOFypcqTSkx2RHuuiV5KTUHua1NsNVm5y8s1OT9+I3l1NTB5tplNS\niG3Uz2w1bpZ9XsLyL0qpb9CIjjIx/dp0rrk8FWtMcAJYgiAE3+CsZH41uR9//fA7nn9nB4/MGkL3\ndFFqLAiC4C9en2XdeuutFBYWMn78eH71q18xdKgfLhbCTFuNVWyp5noHnE1VneMH6fu+TGkIVId7\nwXeBeGxc5VUU/Pxe6rZ/R9ylI8n6+zxMUT6WVmhulE0fY9q/DSMyBteYWRgp3Xw7JjSWaxSBsy7o\n5Ro2u0x+qYU6p4xF0clJcZAYFVrZEZpusGGXi5WbnNTbITlOYtIoC/0yQ79vhC8qqlx89OlJVq4p\nw+HUscYq/GxqZ666NIWoyNB5rRcEIXiG5aTyy0k6/1i6h2eXbOfR2UPpkhoT7G0JgiC0C14HJX7+\n859zySWXYDL99ATtH//4B7fddptfNxZKQn3kZXO9A84m8Ufp+75MafB3h/v2IhSCWP5+bBxHj5M/\n627sB46QPH0iPeb/Dln18Q5yQw3qV28hlx5FT+yMa+xsiPZDaqyzzhOQ0N2eQIQ1OOUamg6HKlWO\nVqmARGeri55JTpTgv3T8QP5hNx+tc3KyQifCDJMuMXPJIBWlHfeNKClz8MGKk3yxrhyX2yAxXmXO\n1M5cMToZiyXEHiBBEILuwr7puN0GCz75nvmNgYnOyWIMsCAIgq+8PkMfM2bMWT+3bt26dh2UCPWR\nl831DjDJnouiHzszfd9fUxp87XDfXoRiEMsfj039nkLy59yD62QZne76BV0eu9vnu+dSeRHqmsVI\n9Ta0HgNwX3QdKD7W7BsG1JdBXePfdHQqRCUFpVyjukFmb6mFBpdMhKKTk2onIbKJJ2QQlVTqfLzO\nwZ5Dnr4RF/ZXmHChmdio9ntRXlRs5/3lxXy1sQJNg7RkM9dfnc64ixNR1fb7cwuC4LtLBnbCpeks\nXJnP/CXb+c2coaSJcx9BEASf+OW2oWEY/jhMSAqHkZfN9Q5QTBKabiBJnmu1pDMukE8REzT8K9SD\nWK1h27CVwpseQKupo9sfHiD9ttk+H1M+uBNlwwegabiHjEfrN8r3wMFPyjW6gLnt/3Y1HQ5UmCmq\n9rzEdolzkZnoxBRC17t1DTofrXXw9U4Xug5ZXUxMHmWmc0r7zXI6dLSe95afZP23lRgGZHSyMO2a\ndEZdkNjuJ4kIguA/44Zk4HLrLPmikGff2s6jc4aSHOeHCVGCIAgdlF+CEu251jhcLth/3DvArJqw\nOzUcLk/A6FTcaGCvpJ9cGIsJGv4TDkGslqpY/gX7734CdJ1erz1N0pQrfTugoWPK+wJl91oM1YJ7\n9Ez0Lr5PgvlhuUYMWDsHpVyjst7TO8LulolUdfqkOoiLaF12RCBKgDTdYNNuNys3l1Bbb5Bk9fSN\n6N+z/faNKDhQR+6yYr7NqwYgs1sk0yamc+HQeGS5ff7MgiAE1hXnd8Wt6eSu2c/8tzylHInWiGBv\nSxAEISyJduLnEC4X7Gf2DiitauDFd/KwO3/aRG/n/gocLu0HFzhigob/hEsQy1sn38jl8GPzkKMi\n6f2v+cSNvsC3AzrtKOtzMR3Lx4hNxDV2DkZ8qm/HDJFyDbcO+8vNnLCpgEHXeCc9Elytyo4IVAlQ\nwVE3H611UlyuE2GRuOZiM6MHqShK+7ww/y6/hneXFbPjO8+Y2pxe0UybmM6wgdZ2G4ARBKHtXH1h\nd5wujaXrDzF/SR6/mT0kZM4LBUEQwokISpxDuF2wW1QTZkWm8iwjQStsdg4UVdMzI+4Hew/2BI1Q\naArpD+ESxDoXwzAomv9/HH/xnyjJieT85y9EDzzPt4PWVKCu/g9ydSl6ei9co6eDxccAje6G6iJw\nBbdco7zeREGJGYcmE232TNawtjI7AvxfAlRWpbN0nYPvDmpIwIi+Cj+bmIjLXt/qPYYqwzDYvttG\n7rJivi+sA2DAebHcMDGd/n1iRDBCEAS/mnxJJi5NZ8XGIzy7JI9HZg8hNsoc7G0JgiCEFb8EJXr0\n6OGPw4SsYF+wt1RzF8aSBM8uyfvJnddgTdAIxaaQvgi3IFZTDLebQ7+dR+miD7B0zyDnrVeJ6NHF\np2NKJ/ajrn0bydmAu89FaMOuBNnH30UIlGu4NE92RHGNioRB9wQn3RNc+FIR4M8SoAaHwapvnazL\nc6Hp0LOzzOTRFrqkmoiPNVFqb/0+Q42uG2zeXk3usmL2H/YEW4YNtDJtYjp9ssTYPkEQAkOSJKaN\n6YXLrbNqyzGeW5LHw7OHEB3hY9NmQRCEDsTrM/iioiLmzZtHZWUlCxcu5J133mHEiBH06NGDJ598\nMpB7DLpwG3mpmCSiItQmgxJ6Y2+Js9159ecEDW+yH9pjU8hwC2KdSW+ws+/Ox6la+RVRA/qQ85+/\noKYktf6AhoGcvwllywqQJFwXTUHPGubbJn9crhGTCpFtX65RVmeioNSMU5OJMWv0SXUSY/F9soY/\nSoB03WDzHjcrNjipbTBItEpMvNjCwKz21zdC0wzWf1tJ7vJijhbZkSS4aHg8065Jp2f38CmVEgQh\nfEmSxKzLeuN266zJO87zb+/goZmDibSIhGRBEARveP1q+cQTTzBnzhz+/e9/A5CZmckTTzzBwoUL\nA7a5UBMuIy8Xf17A0ZJar742EM0Xvc1+aI9NISH8glinuKtsFNz0ALWb87BeMoLeC+ZjivFh/rrm\nRtm8DNO+rRgR0bjGzMJI7e7bJrXG6RqnyjXiuoDats9Jpwb7yiyU1CpIGGQmOuka71t2xJl8LQHa\nd8zTN+J4mY5ZhasuMjNmiIrazvpGuNw6X31TwXufnKS4xIEsw9iLErn+mjS6dhZd8AVBaFuSJPGz\nK3NwaTrrdxXzwrs7eGD6ICLMIjAhCIJwLl6/UrpcLi677DJef/11AM4///xA7anD8rWvgqbrLF5V\nyFd5x73+nkA0X/Q2+6G9NYX8sXAJYgE4j58kf849NOQfIHHyFfR88ffIFh9qYhtqUdcuQS45jJ7Y\nCdfY2RAd7+Mm68B2DHStsVwjw/cSkBYwDCitM1FYasGlS1gtGjmpDqLN/h2J3NoSoPJqnY+/drBr\nv6fB7fnnKVw90ow1OvzKoJrjcOp8sa6MD1acpKzChaJIXDEmmeuuSiM9NTx6tgiC0D7JksTNV52H\ny62z+fsSXsrdyX03DMIcBjcmBEEQgqlF4VubzXY69bewsBCHo+kLSuG/vAk0+Kuvwttf7mP1tqIW\n7c/fzRdbkv3QXppChruGwoPkz7ob5/GTpP1yFt1+fz+SD/08pIoTqGsWIdVVo3Xvj3vkdaD4EOAw\nDE+pRn2Z5/2YNIhMbNNyDYdborDMTFmdgiwZ9Epy0CXOHbAttKQEyO709I1Yu93TN6JHJ5kpoy10\nTWtfJ8ENDRqfrilj6cqTVNncmM0Sk8anMnlCKkkJoqlcuDl06FC770cldEyyLPHLiX1xawbbCkp5\n5f1d3DN1IKrSvgLEgiAI/uR1UOKuu+5i+vTplJaWMmnSJCorK5k/f34g9xbWWhJo8EdfheaCAc3x\nd/PFlmQ/tIemkOGu5tsdFPzifrQqG10eu5tOd/3Cp54DroI81E8XIWku3IMvR+s/2rfggeb2ZEe4\n6kFWIS6jTcs1DANO1irsKzPj1iXiIjzZEVGqf7MjfsybEiBdN/j2e0/fiJp6g4RYz4jPwb2VdtU3\norbOzfIvSln2eQm1dRqRETJTr0lj4vhU4q2ikVwou/nmm0+XfAK89tpr3HnnnQDMnTuXN998M1hb\nE4SAUkwyv5rcj1fe38XO/eX89cPd3Hldf5TWzIgWBEHoALwOSlx44YV8+OGHFBQUYDabyczMxGIR\nd7LPxttAgzeZBcDpC5NTb0daFBoc7tMXK80FA84kS2AAiQFqvtjS7IdwbgoZ7io/W8v+X/0W3eUm\n88XfkzJ9YusPZuiYdqymYdcaUMy4xs5G7+rjCFFnrWfcp6GBObZxukbbBarsbomCUjMV9Z7siN7J\nDjpbA5cd0ZSzlQAdKNL4cK2DolIdswITLjQzdmj76htRZXPx8WclrPiylAa7Tky0iVlTOnH1ZSnE\nRIsa7XDgdrt/8P7GjRtPByUMI7CBPUEINsUkc9d1/flL7k7y9pXx94/3cMe1fcNyspggCEKgeX1m\nt3v3bkpLSxk3bhwvvPACeXl53HPPPQwfPjyQ+wtLLSlhOFdmwcKV+eQfqaTc5iDC7PlHZnfqyJJn\nkkZirJmhOalMGZV51mDAmQwDHpo5mJ4ZcU1mIvja16Kl2Q+h3hTS199HqCp96yMOPvJHZLNK9uvP\nEX/ZJa0/mMuBsv49TEe/R4pLwjlqFkZCWuuPF+RyDcOAEzUK+8vMaIZEQqRGdoqDyABnR3ijwqaz\n7GsnO/Z5LvaG9VG4+iIz8bHt5yS3rMLJR5+e5LO1ZTidBvFWhenXduLKsclERrSf52BH8OOMnTMD\nEe0pm0cQzkZVTNwzdSAvvLODLXtLUE0St17TF9lfnZEFQRDaCa+DEk899RR//vOf2bJlC7t27eKJ\nJ57gySefFOmXTWhJCUNzmQVm1cQ3u4tPv293/nfc4KnRnhU1ztMBgLMFA86UaI1oMiDhr74W0Lrs\nh1BrCunP30coMQyDEy8t4Ni8v2JKiCPnzReJGTag9QesqURdswi56iR6WibW63+JvdaHsZiaq3G6\nxqlyjS6gtt0khQaXRH6phaoGEybZICfZQXps22ZHNMXhNPhyq5M121y4NeieLjN5tIXu6e3nIr24\nxMH7nxSzen0Fbs0gOVHluqvSuWxUEhZz+D7ngqGi0sn6LVX0yYqmd6YPE3T8TAQihI7Iopq4d9pA\nnn87jw3fnURVZH4+oQ+yeD4IgiCc5nVQwmKx0KNHD95++22mT59OVlYWchhfnPnD2e6it6SEobnM\nAk+hhXeZbqacAAAgAElEQVS2F5Txh1tHnH673GZv8uvO1qvBH30tTgn17Adv+PP3ESoMTePw3Oco\n+fc7mDPSyVn8CpG9e7T6eFLxQdS1S5Ac9Wg5F+AefhVyZDTU1rTugI5aT0DC0MASC7FtV65hGFBk\nUzhQbkY3JJKi3GSnOLEowc2O0A2DrXvdfPKNE1udQVy0p2/EkByl3ZzQHi1q4L1PTrJuUwW6Dp1S\nLVx/TRpjLkoUjeFaQNMMtu6sZtW6crbuqEY3YPzopKAGJaqrq9mwYcPp9202Gxs3bsQwDGw2W9D2\nJQhtLdKicP/0QcxfksfaHSdQTDJzxmeLQJ0gCEIjr4MSDQ0NrFixglWrVnHXXXdRVVXVYU8qznUX\nvaUlDE1lFvTpFs/6M7IkzqWyxk5tvfN0MKDCZmfVlqPs3F9xzmyFlpSbtESoZT94K1C/j2DSHU4O\n/HouFR+vIrJPL3IWvYy5U2qrjyfnb0b5djkArguuRc/2YURwkMs16p2e7IhquwlFNshJsZMaowU9\nO+LgcY2P1jo4WqKjKnDFCJWxw8xY1PZxEnvgcD25y4rZuK0Kw4BuGRFMuyadkecnYDK1j5+xLZwo\ncfDFujK+/LqCymoXAFmZUYwflcyYixKDujer1cprr712+v3Y2FheffXV028LQkcSFaHy4IzBPLN4\nG19uK0JVZKaPyxKBCUEQBFoQlHjggQd48803uf/++4mJieHll1/mpptuCuDWQpc3d9FbUsLQVGYB\nwN7GXhLeODMDw6Ka6JQUzY1X9vGqJ0JLyk06gvb2+3Dbaim89SFq1m8h9sKh9P73cyhxrbwg0Nwo\nWz7BVPAthiUK15hZGGk9Wr+5IJZr6AYcq1I5VKmiGxIp0W56JzswB7mHYmWNzrL1TvIKPH0jhmQr\nXHOxmYR20jdi775acpcVs3WnJ6id1SOKaRPTOX9wnKiz9pLTpbNpaxWfrS1j995aAKKjTFx9WQqX\nj0ois1tovD4tXLgw2FsQhJASE6ny0MwhzFu8jZWbj6IqJq4f3TPY2xIEQQg6r0+/R4wYwYgRnvIA\nXde56667ArapUObtXfTWlDD8OLNgUO9kvtxa5NW+zlaW4U22QksnZrR37en34TxZRsGcX1O/p4CE\nq8bR69WnkCNauX97HepXS5BLDqEnpOMaOwdi4lu/uSCWa9Q5JfaWWKhxmFBNBn2SPdkRweRwGaxu\n7BvhckPXNE/fiMxO4ZWV0xTDMNi1t5Z3Pz5x+iK6b3YM0yamM7hfrLhT6KXDxxr4fG0ZX22ooLbO\n8/fav08Ml49K5sJh8SHXe6O2tpbc3NzTNzCWLFnCW2+9Rffu3Zk7dy7JycnB3aAgBIE12nw6MLHs\nm0OoJolJF2cGe1uCIAhB5XVQom/fvj84cZQkidjYWDZt2hSQjYWqlt5F96WEobnT9P9O37AwNCfF\npxGaLS03CYa2nIIRDr8Pb9gPHGHvrLtxHj1Oyo3X0+OPjyKZWrd3qbIYdfUipLoqtG59cY+cCqq5\ndRszDKgrgfpyQIKYdIhMaJNyDd2AI1UqhytUDCRSY9xkJTswB/Eh1Q2D7flulq93Ul1nYI2WmDbO\nzNA+4d83wjAMtuywkbu8mIL9dQAM7hfLtInp9MsR6fveqK9389lXZaxaW0bhwXoA4q0K11+dxmWj\nkuicFhHkHZ7d3LlzycjIAODgwYM8//zzvPjiixw5coSnn36aF154Icg7FITgSIi18PDMIfx50TY+\nWHcQVTEx4YJuwd6WIAhC0HgdlNi7d+/pt10uF9988w35+fkB2VQoa6u76A6XRl5hWZOfi48x8/iN\nw9B0wy8X6Q6XxrghGWia7lUPirak6Tr/+HAX63cUtekUjNZMEAkltXnfUfCze3FXVJHx0B10vv+X\nrb4bLR/5DmX9+0huJ+5Bl6INGANSK3/3mgtsx8DV0OblGjUOmfwSM7VOE2aTTnaKg+To4GZHHC7W\n+PArB0dO6igmuPx8lUuHmbGYwzsYoekGG7dW8d7yYg4eaQBgxJA4pk1MD6lpEKHKMAwKDtSzam0Z\n67+tpMHuGQM9bKCV8aOTGTYwDkUJ/b+Ro0eP8vzzzwOwcuVKJkyYwMiRIxk5ciTLly8P8u4EIbiS\n4iJ4ePYQ5i3axjur96EqMpcN6xLsbQmCIARFq6qnVVVlzJgxLFiwgNtvv93fewppbXUXvbmMDFud\nE003fpCBcbZMguYyDDRdZ/GqQvIKyqiq9VzwD8xK5vJhXUi0RoRERkCwpmCE8wSRqjUb2PfLR9Dt\nDnrM+y2pN05t3YEMHdP/Z+/MA6I6z/3/ObMzwLDMDIK4L2BcwD1xATdI1GjUxCSNNultenPTJu1N\n+mubLtf2Nm16s3VJ0yZN29TstqlkMzGLkqigURMBRaPivoHAzDAwbLOe8/tjIkFkhgEZGOB8/tI5\nc97znDnMmfM+7/f5PqXbUZVuQ1Jp8My7A3HY+K4H5qoHR8WX5RoGiE3pkXINUYIzNWrO1aoBgeRY\nD6ONbnrzctY1iGze5aaozO8bkTlWxbI5GhINkSW/7yxer0Th3hreeL+S8osuFALMnZnA6mXJDB/S\nc61d+yqOei87dtewtdDK+XJ/B6WUJB2rliSwYI4RU2IX1Um9hF7/1W/UZ599xurVq1v+L5fsyMhA\nUnwUP7rDr5h4besx1CoF2ZmDezssGRkZmR4n5KREXl7eZf+vrKykqqqq2wPqC/TEKnqoioxAnUBW\nzx9F3vZTATuE+ESRX724j/PVDS3j2hwuthWXo1QIEdH2MhK6YPS1DiLWNz/g9IO/BKWSsX9/goQl\n87s2kMeF6tM3UZ47jBQdj2fBWqSE5K6N1YvlGg6ngqPVWpo8CrQqkXSzi0R976kj3B6J7cUethW5\ncXthiNnvGzEqtW8kvALh8Yh8ssvGW+9XUWV1o1TCwrlGbl46iNTkyC0viAREUeLgkXryC23sKa7F\n65VQKQXmzIgnN9vEwuzB2GwNHQ8Ugfh8Pmw2G42NjZSUlLSUazQ2NtLc3NzL0cnIRAbJiXp+9LXJ\nPL6hhBc/OIqj0c2Ns4bLiTsZGZkBRchJiaKiosv+HxMTw1NPPdXtAfUFemIVPVRFRiAlQdm52isS\nDq0VBhu2Hrtse2sipe1lf+uCEW4uPvcq53/1FMq4WNJe/D2x107p2kANdtTbX0Nhr0IcNAJP9tdA\n10XJfetyDaUGDKk9Uq7hE/3qiPN1fnXEYIOHUUY3ql4SIkiSxP7jXt7b6aa2QSJWL7Bqvobp1/Rt\n3winy8fWHTbe/rCKmloPapXA4gUmVi0ZRJKp7xjC9gY2u5tPdtr4uNBGldUNwNDBOnKyjcyfZcQQ\n6/957ssdSe655x6WLl2K0+nku9/9LnFxcTidTtasWcNtt93W2+HJyEQMqeYYHlozhac2HuDNglNU\n1zZz1w3pqJR9Wz0nIyMjEyohJyUeffRRAGpraxEEgbi4uLAF1VcI9yp6W0VGfIyWccMTWJnld2kO\npiQotwROOCyfPYKSAH4VADURMuHvT10wwokkipz/9dNU/vVV1ClJpL/2NPpxXVPtCFVnUO/4J4Kr\nCV/aTLwzlna9xKKXyjVqmxWUWbQ0exToVCLjkpzER4lhP24gzlX5eKfAxZmLIkoFLJymZtEMDbo+\n7BvR2OTjw20WNn1UjaPBi06rYMUNSdx0wyAS49W9HV7E4vVKFB2sI7/ASnGpA1ECrUbBorlGcrKN\npI+O7lero/PmzWPnzp24XC5iYmIA0Ol0/OhHP2Lu3Lm9HJ2MTGQxxBzDurum88e8UnaWXsRW5+T+\nVRPR6+R7qoyMTP8n5KREcXExDz30EI2NjUiSRHx8PE8++SSTJk0KZ3wDmkuKjJVZo/jn1mMcPWdn\n96FKys7ZmZJmZsGU1IBKAlFqf0x7vZML1Q3UNrgDHjc+WhsRE/7+0gUjnIgeL6f/38PY3vgA3ZgR\npG/4M9ohXSuzUBz7HNVn7wHgmbkcMX1m14JqW64RmwK6+LCXa3hFOG3TUO5QAxJD4jyMTHTTWwtN\ndQ0i7+92s++I3zciY7SSZXO1GOP67sqXo8HLe1ur2ZxvoanZhz5Kya3Lk1mWm4QhpksWRQOCi1VO\n8gttbNtlw17n/3sYM1JPbpaJudcmoI/qn/eyioqKln87HI6Wf48aNYqKigoGD5Zr52VkWhMfo+Un\na6byt3e/oOS4lf97tZgHV2dgipc9eWRkZPo3IT9F/u53v+PZZ58lLc3vNXD48GF+85vf8Nprr4Ut\nOBk/bxeeYtehypb/XyrF8IkSWo0Spzv0GvmEWB1DkmLQaRQ43e2vHk+OoAn/7QvHoI/SsOtARZ/s\nghFOfI1NnLjnx9Rt3030tEmkvfQH1InxnR9I9KHa9wHKsr1IWj2e7K8hJXetZ7rP7QL7GfBeKtcY\nAurwewrYm/zqCKdXgV4tkp7kIk7XO+oIj1diR4mHj/e5cXtgsEnBimwNY4b03Um7vc7DOx9V8dE2\nK06XiCFGxddvGcziBWai9ZFxr4g03B6RPUW1bC2wcuioX7kWrVdy4yIzi7KMjBzW/0vPFi5cyMiR\nIzGbzYC/jOkSgiDw8ssv91ZoMjIRi1aj5P5Vk3j9kxNs3XeeR14p4r9vyWDUYENvhyYjIyMTNkJ+\nSlYoFC0JCYDx48ejVMoPo+EmWIlG6QkrktT+xEup8NfVt2VKmgmAALuhVMAt80Z3KdZwoFQouGfl\nJJbMHNrnumCEE4/NzrE7H6Bx/2HicuYy5rnHUOq7MPl3NqIueB1F1WnE+EF45q+F2ISuBeWqx36q\nAnw9V67h9cFJm4aL9X51xLB4N8MTPL2ijpAkidITPt7d6cJeLxETJbAiS8PM8ao+6wtgsbl564Mq\n8guseLwSifFq1qwaTO48Izqt/D1sjzPnm8gvsLFjTw0Njf6E8cRxMeRmm7h2ajxaTd9VynSWxx9/\nnHfeeYfGxkZuvPFGli1bRmJiYm+HJSMT8SgUAnfkjCUpIYoN+cd4YkMx9yyfwLR0c2+HJiMjIxMW\nOpWU2LJlC7NnzwagoKBATkr0AMHMHmvqXUgByjR8IsyZmMzRc7UtCoPMsUYkSeJ/13+Gy9t+VkKS\noKHJjV4bWau6fa0LRjhxnSvn6Jrv4Tp1DtPtyxnxxP+gUHf+egn2KtTbX0NosOMbeg3eObeAugtl\nO5IEDVXQXIMk9Fy5hq1RSZlFg9unIFrjY1ySm1ht76gjLlT7fSNOVfh9I+ZPVZMzQ0OUtm8mIyqq\nnLy5uYrtu234fJBk0nDz0kEsnGNErR44k+pQaW72UfiZnfwCK8dPNwGQEKfi5qWDWJRlZPCggdmB\nZMWKFaxYsYKLFy/y1ltvsXbtWlJTU1mxYgW5ubnodAPzc5GRCZVF04ZgjNPx13e+4Nm3DnLrgjHc\nMHNov/KekZGRkQEQJCnQtPZyzpw5w69//WtKS0sRBIHJkyezbt06hg0bFu4Yr8Biqe+Wcczm2G4b\nK1y4PD7W/X1Pu2aPRoMWSZKoqb/SH8Jo0PHIPdcCtCgM3thxsl1/hvb26001gsvju0wV0ReuU0/R\neKiMY1//bzzVNlK+902G/OS+Lj2cKM4fQbUzD8HrxpsxH1/GAhC6MNn0uaGuvKVcI2FEOvb68Lbc\n9PjghFVDVYMaAYnhCR6GJXjoDTGCo1Hkg91uPj/sRQImjFJy01wtpvjIn7i39706e6GZvPcq+fRz\nO6IEqSlablmaTNa1iahU8kNwayRJouxkI/kFNnZ9bsfpElEIMDXDQE62iWmT4rrlM4vE+5/ZHNvl\nfTdu3Mhvf/tbfD4f+/bt68aoQidcn2ckXquBRn+9Bmcr6/lj3gFqG9wsmJLKmtyxKBWR+TvTX69B\nX0K+Br2PfA3aJ9jzQ8jLqyNGjOAf//hHtwQkEzrBzR79Mr6OjCCTEvRBy0AC7dedtE00tIdPFHn9\nkxOUHLNQ43CRaNAyJc3Md2/rYmvLfoZj1z6O3/0DfA1NDPv1D0n+1tc6P4gkoTy4A9WBj5GUajzZ\ntyMOn9i1gFz14Cj31wJp4yA2GZVOD/XhuwlbGpQcs2rw+BTEan2km13EaEPKq3YrHq9E4X4P+Z+7\ncXkgxajgpmwNaUMjS2EUKsdPN5L3XiWfldQBMGJoFKuXJXPdtHiUfbT0JFw46r1s320jv8DG+Qon\nAINMGm5eamThXCPGBE0vRxh5OBwONm3axJtvvonP5+Pee+9l2bJlvR2WjEyfYXhyLOvums5TG0vZ\nVlKOtc7Jt1dMICrCVK0yMjIyXSXku9nu3bt5+eWXqa+vv8ysSja6DD9tW4O2Z/YYbBsELwMBSIjR\nMm2cudsNJAMlGm5fOOaKLP/rn5y4LMFyydBTH6Vh5ZwR3RpXX6Pm3XxOfu/nIEmM/sv/Ybwpt/OD\neNyodr+F8uwhpOg4PPPXIiWmdH6cVuUaPdVdw+2F41YtlkYVgiAxKtHNkPieV0dIksTBk37fiBqH\nRLQOls3Vcu0EVZ+cvH9RVk/ee5Xs/8KfSEobpWf1shSmZxpkeXArRFHi4JF6thZY2VtSh9croVIJ\nzJ2ZQE6WkUnXxPZZ35BwsnPnTt544w0OHTrE9ddfz2OPPXaZN5WMjEzoJBp0/PTrU/nLO4c4eMrG\no68W8+CtGSQa5DIoGRmZvk/I5RuLFy/mvvvuIzn58naDM2d2sW3gVTCQyjdaE0xt0JESIVgZSHyM\nhofvnkmsvvtX+DbkH2tXyZEzfQhrcr56OA0WX1JCFA/fPXPAGlxWvfBvzq57EkW0nrT1v8Uwd0bn\nB2msRb19A4qai4hJw/Fkfw2iYjo/js8NdRfA6/R314gbAqqvHoi6+zslSVDdoOS4VYtXFDBofaQn\nuYjW9Lw6osLi4+0CNyfLfSgUkJWpJndm3/ONkCSJ/V/U885HFg584VdGTBwXw63Lkpl0TaycjGiF\ntcbNtl028gttVFv9ZXJDB+vIzTYxb1Yihtjwr1JG4u9UqOUb48aNY8SIEWRmZqJoR2r+6KOPdndo\nISGXb/RfBsI18Ikir209zvaScuJjNDywOpPhyV0vqepuBsI1iHTka9D7yNegfbqlfCM1NZWbbrqp\nWwIaqIRSwhCMYGaPHRlBBisDmT4uKSwJiWAlIyXHrNwyb3TL5xBMyWGtbaauwTXgjC4lSaL8ib9Q\n8cf1qM1G0l79I9GTxnV6HKH6LOod/0RwNuIbMx3vzBtB2YXJlMsBjgp/uYYuDmJSIIw1rS6vwDGL\nBluTCoUgMcboIjXOG27/zCuobxL5cLebvV/4fSPGj1ByU5YWc0Jk1vMGQhQlPt9fR957lZw44zdj\nnJZhYPWyZMaN6UKCqp/i9UoUldaxtcBKyUEHogRajYJFc43kZBtJHx0tJ25C5FLLT7vdTkLC5V19\nLlwI7m8kIyPTPkqFgjuvTyMpPoqN207w2GvFfHvFBDLHmHo7NBkZGZku0+HM5Pz58wBMnz6d119/\nnZkzZ6JSfbXb0KFDwxddP6G9EoaMMSZypg0h0aDrMQVAKGUg3YXL4+NUeV27ygcAe73zskRDXIyW\nRIO23feb4qOIi+l8V4irTQL1JpLXy5kfP4rln++gHTmU9A1/Qjd8SKfHURwvQvXZuyBJeGYuQ0yb\n2fkyC0mEhupW5RqD/UmJME3MJAkq61WctGnwigLxOr86Ikrds+oIr0+i8ICH/M/cON0wKFHBiiwN\n6cP7Vg2vT5T49DM7eZsrOVfuRBBg1rR4/vPro0iM6+3oIoeLVU7yC21s22XDXucFYOxIPTnZJubO\nTEAf1bfuIZFw/1MoFHz/+9/H5XKRmJjIX//6V4YPH86rr77K3/72N26++eZeiUtGpq8jCAKLrx2G\nOV7H3989zNNvlLImJ41F0zr/nCAjIyMTCXT4dP2Nb3wDQRBafCT++te/tmwTBIGPP/44fNH1E9rz\nSthWXM624nKMQTwWuhulQsGanDRumTc6bA+rbRMwCgHEduaSCbG6yxINwZQc101M6VScnfGxiER8\nTU5Ofuen1G4tRJ9xDemv/hG1KbFzg4g+lPs+RFW2B0kThSf7a0gpo7oQTPByje7G6REos2iwN6tQ\nChJjTS4GG3pWHSFJEl+c8vtGWOsk9DpYNU/DrEnqPuUb4fGK7Nhdw5vvV3GxyoVCAfNmJXLL0kEM\nTY2SpYWAyy2yp6iW/EIrh442ABATreTGHDM5WUZGDO176qxIuv/94Q9/4MUXX2T06NF8/PHH/OIX\nv0AUReLi4ti4cWOPxiIj0x+Zlp5EQqyOp/MO8NrWY1hqm7ltwRjZ40ZGRqbP0WFS4pNPPulwkLff\nfpuVK1d2S0B9nbarUx11vbhk5ghc5rHQlWOFSkelHldD2wRMIMeS9rp8BFJy3L18AjU1jV2O4Wo+\n457Ga6/j2De+T8O+UgzZ1zL2+SdQxkR3bhBXE+qC11FUnkKMS8KzYC3EdjKpAeB0QH2rco3YlK61\nDQ0BSYIKh4pTNg0+SSAhyku62Y2uh9URF60+3il0c/y8D4Xg9424/loNel3fecBzuUU+LrTx9odV\nWGxuVEqB3Gwjq5Ymk5LUecVRf+TM+Sa2FtjYsbuGxiZ/C9uJ42LIzTZx3bR4NOrIT14GIpLufwqF\ngtGjRwOwaNEiHn30UX784x+Tm9sFo14ZGZl2GTXYwLq7pvOHjQfY8vl5LLXN/NfyCWg1fUvdJSMj\nM7DpFh3ym2++OeCTEoFWpxZMSQ3a9eISbT0WunKs3lYCBEvAKAT/xDPRELhkJJCSQ6kM/Zw642MR\nabjKKylb8z2cx09jXLWYkX/4XxQadafGEGqrUG/fgFBfg2/IOLxzV4O6kxNRSfyyu4adlnKNqPjO\njdEJmj0CZdVaap1KVAqJdJOL5NieVUc0NEl8tNfF7kNeJAnGDff7RgxK7DuT02anj4+2W9n0URX2\nOi8ajcCyHDMrFg/ClCi3qWxq9rFzr52thVZOnPZ7aiTEqVh84yAWzTWSMqjvO9hH2v2vrfdGSkqK\nnJCQkQkDpvgo/ufOaTzz1iFKjlt5fEMxD6zO6FLpq4yMjExv0C1JiRAbePRrAq1O+UQpoFdCa9p6\nLHTlWNC7SoBgZpUS8MOvTWZUalyHD8VXo+QIFkNnPuOepqnsJGVrvofnYjWD/msNw37xIEInE0yK\n80dR7cpD8LjwTpyHb/LCzisbvG5wXCrX0H5ZrhGehxpJgvI6FadqNIiSgFHvJc3sRqvqufuJ1yex\nq9TDlr1+34ikBIGbsrRcM6Lv+EY0NnnZnG/h3a3VNDT60GkV3Lx0EMuvTyLe0LmkVn9DkiTKTjay\ntcDGrs/suNwiCgGmZxrIzTYxdVIcKlXfUcF0RKTf/2SDUBmZ8KHXqfn+bZm8/GEZOw9e5JGX9/HA\nrZkMMctGxjIyMpFPtzx5D/QHjWCrU6UnbGSMNrKtpCLoGG09FgIdx2JviqiVsNYEM6tMjNWFlJAI\nZwyhfMa9Qf1n+zn2je/jq6tn6M8fIOU7d3ZuAElC+UUhypJ8UKrwZN2GOGJS5wO5rFwjHmKTw1au\n0egWKLNocVxSR5idJMX4ekwdIUkSR8742FTowlIrEaWFldkaZk9So1T2jftZncPDu1uref9jC81O\nkZhoJV9bkcLSRWZiY/pOUiUcOOq9bN9tI7/AxvkKJwCDTBoWZRlZONeIMaF/Kkci7f5XUlLC/Pnz\nW/5vs9mYP38+kiQhCALbt28PuO8TTzxBUVERXq+Xe++9l0mTJvHQQw/h8/kwm808+eSTaDQaNm3a\nxEsvvYRCoeC2227j1ltvDf+JychEKCqlgm8uHUdSQhRvFpzi0VeLuG/lJCaM7EIJp4yMjEwPMrCf\nXLuJjlancqYPRalUUHLMis3hbPd97XksXKJ1uUYwxUWNw8mp8roemfzDlZ4Wwcwqg51fdxIJMXQG\n+0c7OPGdn4HXy6inH8a0+sbODeB1o9r9NsozB5H0Bjzz1yIZB3dujB4s1xAluFCr5rRdjSQJmKO9\njDW50PTgnajSJvJOoYtj5/y+EXMy1NxwrYboqL6RjLDZ3bzzYTUf7bDgdkvEGVTcujyFxfNNRPWx\nDhHdiShKlB6pJ7/Ayt7iOrw+CZVKYO7MBHKzjUwcF9vvzd8i7f734Ycfdmm/PXv2cPz4cV5//XXs\ndjurVq1i1qxZrFmzhiVLlvD73/+evLw8Vq5cyTPPPENeXh5qtZrVq1eTm5tLfHz4ys1kZCIdQRBY\nNnsEpngd6zcf4Q//PsBdi9PJzuzks4GMjIxMDyInJbqBuBgtWo0Sp9t3xTaNWkmiQdfilVDjcJK/\n7zylJ2uCtuVsPeF/Y8fJdh8y2yII8Nt/7Q+7x0QwT4ueajsazOSzJ1ufXg3Vr73NmR//HwqthjEv\n/YH4BbM7N0BjHertG1DUVCCah+GZdwdEdVKm6XWBo7xHyjUaXAJHLVoaXErUSpE0kwtzzJXfmXDR\n2Czx0V43uw96ECVIG6pkRbaGZGPfmMhXVrt464MqPtllw+uVMCaoufnWQSzKMqHV9B3vi+7GWuPm\nk502Pt5po9rqBmBoqo7cLBPzZidiGGCqkUi6/6WmpnZpvxkzZpCRkQGAwWCgubmZvXv38vDDDwOw\nYMEC1q9fz8iRI5k0aRKxsbEATJ06leLiYhYuXNg9JyAj04e5bnwyibE6/vzmQV784CjV9mZunjcK\nxQBXN8vIyEQm3fK0FhMj16v5XROCo1UrSTFGc+cN4wJOqtub8Dc0e0KK4FLrzXB7THTkadHarDJK\nq6LZ5cXrk+iEX2VAQjH57InWp1eDJElUPPUPyp98DlViPGmvPEXMlImdGkOwnEO9/Z8IzgZ8o6fi\nvXY5KDv5dXbWQf3FsJdriBKcs6s5a1cjITAoxsMYk5ueuiQ+n8SnBz18tNdNswtM8QIrsrRcM0LZ\nJ0rPzlc08+bmKgr21iCKkJyk5Zalg5g3OxG1amAmI7xeiaLSOrYWWCk56ECUQKdVkJNlJCfbRNoo\nfQQKCuAAACAASURBVJ+4tuEg0u9/oaBUKtHr/d4XeXl5ZGdns3PnTjQaf9mN0WjEYrFgtVpJTPxK\nlp6YmIjFErjb1SUSEvSoVOH5TMzm2LCMKxM68jX4CrM5lpFDE3j4+T28v+csjmYPD94xNez3BPka\n9D7yNeh95GvQOUKexVgsFt5//33q6uouM7Z84IEHePbZZ8MSXF+hrsGF0y22u83l9rVrLhbIzLG9\nCX9HKISvEhKtCYfHRKju7iqlQH7RhW7vENIZk89wtj7tKpLPx9l1v6X6pY1ohqSQ/s8/EzV6eKfG\nUJwsRrVnE0gi3ulL8Y27jk6ZMbQu1xAEMKT6W36GgXqXgqPVGhrdSjRKkXSzC2N0z6kjjp7xsqnQ\nRZVdQqeBm+ZqmJOpRtUHfCNOn2si771KdhfVIkn+1f/VNyYzZ0ZCn/G96G4qqpzkF9jYtstGrcML\nQNooPTnZJubOSBjQ5StticT7X2fJz88nLy+P9evXc/3117e8HshcO1TTbbu9qVvia4vZHIvFUh+W\nsWVCQ74GV6IGfrJ2Kn96o5SdByq4aG3ge7dkYNCHx1tHvga9j3wNeh/5GrRPsERNyEmJe++9l/T0\n9C7LMfszcTFajAHMxbQaJTH60Bzwg0342yMxVstdi9P548bSdreHw209VHf3cHQIibR2d51FdLo4\n+b2fY9/8CVHjx5L+6tNoks2dGMCHsngLqiOfImmi8GTdhjS4k5Jsr+vL7hqusJZr+EQ4a1dzrlYN\nCKTEehhtdBOmxckrqKoReXeniyNn/OaZsyapWHytlhh95E/mj55oIO+9SopKHQCMHq5n9bJkZk6J\n6/eeCO3hcovsLrKTX2Dji7IGAGKildyYYyYny8iIoX174i3TPoWFhTz33HM8//zzxMbGotfrcTqd\n6HQ6qqqqSEpKIikpCavV2rJPdXU1kydP7sWoZWQik5goNT/82hReeP8Iew5X8ZuX9/HgrZmkGKN7\nOzQZGRkZoBNJCb1ez6OPPhrOWPoswczFnG4fbxeeDmkiHmzC3x5T082kD0sIq9t62zKT4O7uWuJi\ntB0kDyxdTh5Eeru7YHgdDRz/5v+jfncxsbOmMvaF36MydKLsydWMuvDfKC6eQIwz452/Fslg7FwQ\nPVSuUedUUFatpcmjQKcSSTM7SdS3ryTqbpqcEls+c7Or1IMowpghft+IwabITVaBf4X30NEGNr5X\nycEj/sz6NWOjWb0smSkTDQOyFOH0uSbyC23s2F1DY5NfXTPpmlhys4xcOy0ejXpglq4MBOrr63ni\niSd48cUXW0wrZ8+ezUcffcSKFSvYsmULWVlZZGZmsm7dOhwOB0qlkuLiYn72s5/1cvQyMpGJWqXg\nnuXjMcdH8e6nZ/i/V4r47s2TSB+W0NuhycjIyISelMjMzOTkyZOMHj06nPH0WVZmjWJnaUW7ZRyh\nruIHm/C3ZfbE5JZSiK66rQcziwzm3RDoeI1OD2/sOMmCKakBkwc2h4tXPirjm0vHdbqMI9La3YWK\nu9JC2df/m+bDx0m4cSGj//RrFLrQYxXqqlFt24Ci3oYvNQ3v3FtBows9AEmE+kpw1vqTEGEq1/CJ\ncOCsyLGLOkAg1eBhpNFNT9ge+ESJPYe8fLjHRZMTjHECN83VMmFUZPtGSJJEUamDvPcqKTvZCEDm\nhFhuXZbMhPSBV4vY1OyjcG8N+QU2TpzxS+wT4tQsvtHEoiwTKUmR+R2X6V7ef/997HY7Dz74YMtr\njz32GOvWreP1119n8ODBrFy5ErVazQ9+8AO+9a1vIQgC999/f4vppYyMzJUIgsCq7FGY46N46cOj\n/PZf+/nm0nHMnpjS26HJyMgMcEJOShQWFvLiiy+SkJCASqUKqc/4QKKhyY0rgK9EqKv4wRQXrTEa\ntNx5Q3rLpL6zbuuhmEUGK7+4NO7O0ouXdRxxukXy913A5xODJlc+PVSJXqfqdBlHpLW7C4XmE2co\nW/M93BcukvSNWxn+yA8RlKHHqSg/hqrw3wgeF94JWfgm50BnkjmtyzVUWjCEp1yjtlnB0WotTi9E\nqSXSzU7io3pGHVF2zsumAjeVNSJaNSyboyErU41KFbnJCFGU2FNcyxvvVXLqXDMAMybHsXpZMmmj\nBpacVpIkyk42srXAxq7P7LjcIgrB/3nkZBmZlhE3YD00Biq33347t99++xWvv/DCC1e8tnjxYhYv\nXtwTYcnI9BvmZqRgNGj581uHeP69I1Tbm1kxd2REJ/FlZGT6NyEnJf7yl79c8ZrD4ejWYPoy3bWK\n/1WCwRJwUj8lzXzZBLyzbusd+T2E4t1wy7zRFJdVt9sGtfRkDRljTGwrLg8YQ1c9ICKp3V1HNJQc\n4tjXH8BrryP1oW8z+IFvhf6DL0koD+9EWbwVlEo8c1cjjszsXADOOqivAEmCqASIGdTt5RpeEU7Z\nNFQ41IBEWgoM0jV3S6eVjrDUimwqdHH4tA8BuG6CisWzNMTqI1fW7/NJFO6t4Y3NVVy46EQQYO7M\nBG65cdCA80aoc3jYvtuvirhw0QnAIJOGnGwTC+YkYkwIjwmbjIyMjAxcMyKR/7lzGk9tPMCmXWew\n1DbzH0uuGbBdnWRkZHqXkJMSqampnDhxArvdDoDb7eaRRx7hgw8+CFtwfYnuXsW/5CKuVQsIggK3\nx9fhBLw9t/W2JRqhJBxC8W7w/9sd8D0504bgdvvYdagy6Did9YDoK+3uaj/ZxYl7fozocjPiyXUk\nrV0Z+s5eD6o9b6M8XYqkN+CZvwbJ2AmD2SvKNQaHpVyjpklJmUWDy6tArxYZl+Ri9NBoQujId1U0\nuyS2fuZm5wEPPhFGpypYka0l1Rx5fweX8HhEtu2q4c0PKqmyuFEqYeGcRG5emkxqSidKcfo4oihR\nerierQVWPiupw+uTUKkE5s5MIDfbyMRxsQPSzFNGRkamNxhsimbdXdN5+o1Sdn9Rhc3h4rs3TyIm\nKjSDdhkZGZnuIuSkxCOPPMKuXbuwWq0MGzaM8+fPc/fdd4cztj5Hd6zit1UxuDwS4GP2xGTuvCG9\nwwn4pSREjF7D24WnrijRCOb3cClREKrqI9h7Eg06vn5DOkfO1lDTTvKi9TjBvC0CEcnt7qwb3+P0\nD34NKhVjn3+ChMXzQ9+5yYF6+wYUtnJE01A88+4Affs10u1+bl4X1F0AnwtUui/LNbp3xdnjg5M2\nDZX1fnXE8AQ3wxM8hHsuKYoSe7/w8sFuF41OSDQILJ+rZdLoyPWNcLlEthRYeefDKmx2D2qVwOIF\nJlYtGUSSaeD4I1hr3Hyy00Z+oQ2LzX8/GJqqIzfbxLxZiRhiQv4pkpGRkZHpRgzRGh66Ywp/f+8w\nRWUWfvNKEd+/NSNin7FkZGT6JyE/CR48eJAPPviAO++8k1deeYVDhw6xdevWcMbW57jaVfxgKoay\nc7VB923rE6HVKC4z3bxUouETpQ4TDlq1ksyxJj4purL8InOsseWcQlGGTE1PCvgelVJgQ/6xoN4W\nfQlJkqj8yyucf+RplHGxpL30B2Jnht6eTrCcR73jnwjN9fhGTcF73XJQXrlaEcgT5GuzzSgaK8Na\nrmFtVHLMosHtUxCt8TEuyU2sNvzeEcfPe3mn0M1Fq983YulsDdmT1agj1DeiqdnHB59Y2LSlGke9\nF61GwU3XJ7HihiQSB0hZgtcrse9AHfmFVkoOOhAl0GkV5GQbyc0yMXaUPmKTSTIyMjIDCY1ayXdW\nTiRv+0k+3HuOR14u4r9vyWDMkO5XWcrIyMi0R8hJCY3G/yDt8XiQJImJEyfy+OOPhy2wvkxXV/Gv\npuVlW4VFe11AAEpP2MgYbWRbScUV21onEwJNFS697hNFRElC1yr5odMomTMp+TJlSDD1SEfeFn0J\nSRQ596unqPrbBjQpg0jb8DT69NA71ShOlqDaswkkH95pS/BdMwsCTNjafm71jS6G6hwoGrytumsY\nrvqcWuPxwXGrluoGFQISIxLdDIsPvzrCWivy7k4Xh075fSNmjFexdJYGQ3RkJq0cDV4251ezOd9C\nY5MPfZSCW5clsyw3CUPswFADVFQ5yS+wsW2XjVqHF4C0UXpysk3MnZFAVFTkltnIyMjIDFQUgsBt\nC8aQFB/Fq1uO8cQ/S/jPZdcw85pBvR2ajIzMACDkp+SRI0fy2muvMX36dL75zW8ycuRI6uvrg+7z\nxBNPUFRUhNfr5d5772XSpEk89NBD+Hw+zGYzTz75JBqNhk2bNvHSSy+hUCi47bbbuPXWW6/6xPoK\nrSX4wcomDNEaorTtX65gCou22Oud5EwfilKpCFhm4vL42H/c2u7++4/bWD3fxxs7Tl6hpHC6fQiC\ncJnKIZB6JBRvi0j0imgP0e3h9PcfxvbWh+jGjiR9w5/QpiaHuLOIsmQLqsO7kNQ6PNlrkAaPDfj2\ntp/b4Hgl314Qz5AENRfsXszDx6LVRV3tKV2GpUHJMasWj08gVusj3ewiRit16zHa4nRJ5O9zU1Di\n940YOdjvGzE0KTL/Jux1HjZ9VMWH26w4XSKxMUrW3jyYJQvNROsjM+buxOUW2V1kJ7/AxhdlDQDE\nRCtZlmMmJ9vE8CHd+zcpIyMjIxMe5k9JxRSn49m3D/HcO19gqW1m6XXDZWWbjIxMWAk5KfHwww9T\nV1eHwWBg8+bN2Gw27r333oDv37NnD8ePH+f111/HbrezatUqZs2axZo1a1iyZAm///3vycvLY+XK\nlTzzzDPk5eWhVqtZvXo1ubm5xMfHd8sJRiqBJPiByiZqG9z86sXPryhvcHl8nCqvC9ipoy2X/B6C\nlZl0pNiw1DZ3OqHQVj1yNaqQSMLX0Mjx/3wIR8FeYqZnkPbSH1AlhCh3dDejLtyIouI4osGEd8Fa\nJIMp6C6tP7fZY3TcOcuAVq0g/4tG8vbV86v/HENSN/kmur1+dYSlUYVCkBhldDEkzhtWdYQoSnx2\n2MsHu900NEskxAosm6Mhc6wqIh+ILDY3b39YRX6BFbdHIiFOzR2rUrh+ngmdtv8nI06fa2JrgY0d\nu2toavZ34pl0TSy52UaunRqPRh2ZihYZGRkZmcBMHGXkp1/3d+Z4Y8cpqu3N3HlDOqqeaK0lIyMz\nIOkwKXH48GHGjx/Pnj17Wl4zmUyYTCZOnz5NcnL7K8IzZswgIyMDAIPBQHNzM3v37uXhhx8GYMGC\nBaxfv56RI0cyadIkYmP9Zn5Tp06luLiYhQsXXvXJRTKBShcWTUslZ/oQSo5ZsTmcl+3TurzhUvnD\npaSGQgAxhMXr1iUagcpMOjK6RJKuOqEQ/BjakFuo9iYeaw1lX3+AptIjxOdmMfovj6LUh5YREBxW\nVNteQ+GwIg4eiyfrVtB0vJocF6NlUIKWpRN1zB0bRZNb5JmP7RSddWE0hN56NhiSBNUNSo5btXhF\nAYPOxzizC70mvOqIk+U+3ilwUW4R0ahg8XUa5k+NTN+Iiionb26uYvtuGz4fmI0abl46iIVzjf1+\nIt7U7KNgj7+V58mzTQAkxKlZstDEoiwTKUmR/92VkZGRkQnO0KQY1t01nT/mHaCw9CI2h5P7Vk5C\nrxsYpYgyMjI9S4d3lrfffpvx48fz7LPPXrFNEARmzZrV7n5KpRK93j8xzcvLIzs7m507d7Z4UxiN\nRiwWC1arlcTExJb9EhMTsYS7p2AvE6x0Yf9xG4/ccy3LZ4/gl+s/x95w5aS95JgVn0+8zBdCCjBf\n1GmUIbUTbU1H7U3NCfqQunN0dAy9Tt3uGHqduttKN7rS2SMUnGcvULbme7hOn8d8xwpGPP5TBFVo\nP9RCxXHUBf9G8Djxjp+Lb0ouhGjsqRU8/HhJAnE6OG3x8Nz2Wiz1/hXqrrSebYvLK3DMosHW5FdH\njDG5SDV4A9lbdAu2OpH3drkoPeE/j+njVCydrSEuJvIm92cvNPPG5kp2fWZHlGDwIC23LEsm+9pE\nVBGYPOkuJEni6IlG8gus7Pq8FpdbRKGAGZPjyM02MnVSHEpl/z1/GRkZmYFIQqyWn6ydyt82HWb/\nCSuPvlrEA7dmYIqTS/JkZGS6lw5nUT/72c8AeOWVV7p0gPz8fPLy8li/fj3XX399y+tSgFl0oNdb\nk5CgR6Xqngmm2dx+u8VwctHaSE19YKWBUqMmSqOmtjHwew6ctLW7TaEASQRzQhTXTUxh7Q3p1DV6\nSDBo0WlCmzTXNbjImjoUpUpJSVk11tpmTPH+8e5ePgGlUsGczFQ2FZ66Yt85mYMZMrjj0hun24vT\n7Q24LTYu6rJ4O3udfD6R9e9+wZ5DF7HUNmNuE//VUFdymAMr78FVZWXMz75D2i8fCKm0QJIk3MXb\ncRVsAoUS3eK1aMbPCOmYkiThqrVSbzlDnA5O2lWs/7QOW4OPpISrPzdJkjhjgQMXJDw+SDLAtFEK\nYrrgTxHqtWp2ibxX0MCHnzbh8cKYoWrWLjUwekjkdac4eryel/59lsI9/u/d6BHR3HXbMObPNvfp\nyXhH18pe5+bDT6p4b0slZy/4VRGDk3Usvz6FJQsHYTLKqoieoCu/U/UNXqJ0ClSqyEvuycjI9B10\nGhXfvXkS//r4OPlFF3jk5SIeWJ3ByJTuNdSWkZEZ2HQ4S73zzjuDTrhefvnlgNsKCwt57rnneP75\n54mNjUWv1+N0OtHpdFRVVZGUlERSUhJW61emitXV1UyeHLyNot3e1FHYIWE2x2KxBDfrDAc+j4/E\n2MBKA5/bAxDwPXHR2oDlE5IEP/zaZEalxqFVK6mxN1HX4GoZM5hqwO318puXiym3NCBK/k4bySY9\nD39rBqY4vX+8mkYAls8aRlOz+wqzzOWzhoX0mVbbm7DUOtvdZql1cuSEhSHmGKBr12lD/rHLlB7V\n9mY2FZ6iqdl9VZ09HDs/59jdP0RsbGL4bx4i8Zu3YbU2dLyjz4Nqz7soT5UgRcXimb8Gl2kIhHJe\noojPUYHS7UASFAhxQxidZOAXoy5XgVy6Np3F6REos2ixNytRChJpZjcpsV6a66G5k1+PUK6VKEns\nO+Ll/U/d1DdJxMX4fSOmpKkQBBcWS2j+KD3B4WMN5L1XSckhB+DvIrF6WTLTM+MQBIGamhCufYQS\n6FqJokTp4Xq2Flj5rKQOr09CpRLIujaBnGwTE9NjUCgEJNGNxeLuhcgHFqHe/0RR4sSZJopK6yg6\n4ODk2SYWLzBx753DwhKTjIzMwEGhEFiTm4Y5IYp/fXycx18r5r9umsDUNHNvhyYjI9NP6DApcd99\n9wF+xYMgCFx33XWIosinn35KVFTgVdT6+nqeeOIJXnzxxRbTytmzZ/PRRx+xYsUKtmzZQlZWFpmZ\nmaxbtw6Hw4FSqaS4uLhFndFf6ag84lLCINB7JqeZKD1hbTdhkRirY1RqHCqlwIb8Y5Qcs2BzuNBp\nFICAy+1rMdVsbZgJ8MhLRVywfDWxlYCL1iYee7WEn905jUSDriW2QF01QiUuRntZO9G2PPXv/UxN\nT7qsI0ioxwlXZw/bO1s49d+/AEFgzHOPkrg8J7Qdm+pR79iAwnoB0ZiKZ/4a0Ie2wuBzN9FQ6VdH\nnLK4+dfnzYwYouD2hTFdbj17CUmCCoeKUzYNPkkgUe8lzexGpwqfd8SpCr9vxIVqEbUKrr9Ww4Kp\najTqyFEbSJLEgcP1bHy3ksPH/EmHieNiWH1jMhnjYyPScLM7sNa4+XinjY8LbVhs/mTDsFQdudkm\nsmclYoiR64gjjcYmL/sP1VN0sI7igw7qvmzBqlT6DUfnzkzo5QhlZGT6E7nTh2KOi+K5TYd45s2D\n3L5wDLkzhvbb30UZGZmeo8OnzEueEf/4xz94/vnnW16//vrr+c53vhNwv/fffx+73c6DDz7Y8tpj\njz3GunXreP311xk8eDArV65ErVbzgx/8gG9961sIgsD999/fYnrZn7k02Q7UlrOj9ygVQtCkRlul\nQOvJf2vDzDU5afhEkVe2lF2WkGhNo9PL//x9L8Z2khlXNzEO/CNWU+8mf98FREkiRq9l14Hyy7qU\ntE2otCYcnT0qn/8X5/73dyhj9Ixd/zsMc6aHtJ9gvYB6+waE5np8IzPxXrcCVOqOd5QkcNYi1V0k\nTgdbDjWycV89PhFOVH517bpKk0egrFpLnVOJSiExzuxiUEz4vCNqHCKbd7nZf9w/aZqa7veNSIiN\nHGm5KErsO1DHxvcqOXHar8aaOsnA6mXJXDM2ppejCw9er8TnB2rJL7BRcsiBJIFOqyAn20hutomx\nI/Xyw2YEIUkSFyqc7Ct1UFRax9ETDfj8VizEG1QsnGtkeoaBzAkG9FH9v/uLjIxMzzN5rImfrJ3K\nHzeW8q9PTlBd28wdOWMDPpPJyMjIhELIS1+VlZWcPn2akSNHAnDu3DnOnz8f8P233347t99++xWv\nv/DCC1e8tnjxYhYvXhxqKP2CUJQGwd7TOmFR43ASF6Nhylh/wiKYUqA1l1QDb+w4ScH+ix2+v20y\n42qoa3Dhcvs6fN+nBytxtnpfKDF01D2kMx0qJEniwqPPcPHPL6JOMpL26tNET0wPaV/F6QOodr8N\nPh/eqTfgGz+HkGb9ogj1F8FVh9sr8mxBHfvPXX4u+45Ws3z2CGL1nfNfkCS4UKfidI0GURIwRXsZ\na3KjDZM6wuWR+GSfm+3FHrw+GDZIwcpsLcNTwjdh6qy5qU+U+PRzO29sruTsBX9J0XXT4ll9YzKj\nR0R+W9quUF7pZONmC5u3XmxZXU8bHU1ulpE5MxKIkie0EYPL5fOXZHyZiKi2+lUsggBjRuiZlhnH\n9Iw4Rg6LQhHOfr0yMjIyXzIi2dDSmeOT4nKsdU7uvWkCUVpZUScjI9M1Qr57PPjgg/zHf/wHLpcL\nhUKBQqHo92UWPUEoSoP23qNUKLh94Rh8osT+Y1ZqG1yUnrShVJ5gwZTUgEqB1tTUO6mwNISUwGhN\nyTEL2RkpmBP0Xe72ECxx0BpngMRFsDKMUMtjOkL0eDnzo99g/fe7aEcNY9yGP6EdlhrCjiLK/fmo\nvihEUmvxzrsDMTXEJI7XCXUXwOfGI2j45dvlWBuu/AxqG9z8cv3nTBsXXDXSmka3Xx3hcClRKyTG\nJTkxR/uuSh0RKAEgShLFR71s/tSNo1HCEC1w42wNU8epUIRp5d0nipe1ye1IVeP1SuzYXcOb71dS\nUeVvq5t9XQK33JjMsNT+5yzuconsLrKztcDWUpYSE61keW4Si7KMDB/S/865r2KtcbPvQB1FpXUc\nPNKA60ulmz5Kwezp8UzLjGPqJAPxhhBUVzIyMjJhwBin46dfn8azbx+i9KSNx18r5oFbM2XPGRkZ\nmS4hSKG0u2hFbW0tkiSRkNB7tardZU7ZW0aX3UXbEo1LLJiaGtBzoi3x0RpqG7tmVtdeOUdnCBR/\nKCgE+L//ui5gQuerCWp7pS8dx+prcnLi2z+hLn8n0ZPHk/bKH1EbQ/ibdztR7dyIsvwYYqwR74K1\nSHEhGEF9Wa5BfSUgQVQiLq2Jdc/v7fA65kwfElS5IkpwvlbNmRo1EgJJMV7GmFxormIxPFAC4Lu3\nTaH4CwfvFLg4VyWiUsKCaWoWTNOgDbNvRKC/p7afj9sj8nGhjbc+qMJic6NSCsyfk8jNSwaRMkgX\n1hh7g9Pnmtiyw0rBHjtNzf4EV8Y1sdy8bAjXjNGiUcuS297G55MoO9n4pSKirkWxAzBiqJ7MCTFM\nz4hj3JiYiGg929cnHeH63e/rzxT9Afka9Dxen8hrW4+xY38FCbFafvGf1xGnldV2vYn8Peh95GvQ\nPsGeH0JWSpSXl/P4449jt9t55ZVX2LhxIzNmzGDEiBHdEWPE01lJeLjHDFaiUXrCSsZoI9tKKjoc\np6sJCbj6co7WJSg2R/udOHQaZbtqiY7KMK7GiNNTU8uxb3yfxqKDxM2fxZi/P44yumMZv+Cwodr2\nKgqHFTFlDJ6s20Abwuqz6PuyXMMBggIMQ0Abi5bAZqetCaYaaXApOFqtocGtRKMUGWt2YY7uuGym\nI17/5MRlcdkcLj4uquZc5VksNf6J/eSxKm6coyHREP5JbyjmpqIPtmy38s5HVdjrvGjUAjcuMrNy\nySBMiZHXhvRqaGzyUbi3hvwCGyfP+v0xEuPVLF1kZtFcI8lJWvkHs5dxNHgpOegvySg55KCh0f+9\nVKsEpkw0MD3TwLSMOCaON8nXSUZGJmJRKRXcdUM6SQlRbNx2kof+VMgdOWOZlzlY9iSSkZEJmZCT\nEj//+c9Zu3ZtiyfEiBEj+PnPf84rr7wStuAigc5KwntqzLoGV8AVdJvDRc70oSiVihbPCa1GiSRJ\nuDztd7u4GoJNioMlXlonDmocTvL3naf0ZM1lygZJkvi4qPyKcUMtw+isEafrQiVla76L88QZjLcs\nYeTvfoFC07FEWqg4gbrwdQS3E+81s/FNvR4UISRBPE5w+Ms1UEVB3BBQfnW8S4mbfUerqW1oP4HU\nnnmnKMFZu5pzdr86IjnWw2ijm+7Ip12ZAFCgUyWjU6dgqVGSahZYNU/HyME9t1ISzNzUVutiw1vl\nbNtpp77Bh06rYNWSQdx0fRLxcf1H/i5JEkeON5JfaGXX53bcbgmFAmZMjiM328TUSQaUSvkBsbeQ\nJIkz55u/LMtwcPxUI+KXOkVjgprZMxKYnmFg0jWx6ORVRhkZmT6EIAgsuXY4qaZo/rH5CC9/WMbx\n87XceUM6Oo3sMyEjI9MxId8pPB4PixYt4sUXXwRgxowZ4YopomhvRfhqzR67Y8worQqFQMtDbWsU\nAsREqS9TCsToNbz60VH2HK4OOKZGpcDtvTJpodMocXl8BCr0aW9S3JnEi1atJMUYzZ03jLsiieET\nRaL1WnYdqAjYpaS7aDpygrK138NTaSH523cydN33EDpKEkkSyqN7UBZ9AIICz+xViKOndnwwSQKn\nHeqrAAn0RohOusII81LiZvnsEfxy/efYGzo273Q4FZRZtDS6FWiVImlJLoz6q1dHXKJ1AkCtGsWF\n5gAAIABJREFUNKJXD0Gh0CJKbprdZ1l7w2gGJfbspKo9jxLRK+Cq1eKu07LphJWYaCW335TMjTlJ\nxPaj9pa1Dg87Pq1ha6GV8ov+8x9k1pCbbWLB7EQSE/qXCqQv0ez0UXqknqID/padNrsH8N+j08dE\nMy0jjmkZBoYPiZJXFGVkZPo8GaNNPPX9+fzmhb3s/qKKM5X13LdqEqmm6N4OTUZGJsLp1JO5w+Fo\neXA6fvw4LlfHngV9mVAk4ZdW6zsqxbi0PUqrCnnMYDS7vO0mJMCfqGh2eYnVa1qUAhvyjwVNSBgN\nOv7nrmm8sf0kR8/Zsde7WhIAK7NGUeNw8tS/91NTf+VqfXulFF1NvLRVNigVCu5ZOYklM4d2e/lM\na+r3lnDsG9/H52hg6C8eJOXbX2/ZFvDa+ryo9r6L8mQxki4Gz/w7kMzDOj7YZeUaSjAMBm3wGu1Y\nvYZp44Kbd/pEOGNXc75WDQikGPzqCFU3V0/ExWhJiI3H405BpYxFkkSaPRU4PRUkJWiJj+15X4bW\n5qaiV8BZo8VVpwVJQKsTuP3mFBbPN/ebrhI+UaL0cD1bC6x8XlKH1yehVglkX5dATpaJCekxcieG\nXuJitYuiL00qD5U14PX6b9Qx0Uqyr0tgekYckyca+lViTEZGRuYSSYl6frJ2Kv/+5AT5RRf49Uuf\n843F45g1Ibm3Q5ORkYlgQn4quv/++7ntttuwWCwsX74cu93Ok08+Gc7Yep1gkvBL6gBjnC6oIqCt\nYiAuiLFke4qDQMTFaEmM1bSbJEiM1V6WJAilReiUNBPxMVq+tWx8u5NwvTmGqelJIXW06EwyJ1Q6\nXYbRCb8O+wfbOXHfz8DnY9Sff43p5iVAB2oPVyPqHf9EYTmPmDgYz/w1EB3XcWCeZnCU+8s11FF+\n/whlaCUErT042qpG6pwKjlZrafYo0KlE0s1OEvTdX6pT1yCy+VMPki8NlRLc3hqaPecQJf/f4XUT\nU8KSNAqFBRlDKfrMyanTHpAEVGqJiRkafnT3OPRR/WMCaK1x83GhjY932rDY/J/5sFQdudkm5s1K\nlCe6vYDHK3LkeGNLIqK88qvfjBFDo5iWYWB6ZhxjR0WjlBNFMjIyAwCVUsGa3DTShsaz/v0j/P3d\nwxw7X8uanLGoVf1jcUBGRqZ7CfkJduTIkaxatQqPx8PRo0eZN28eRUVFzJo1K5zx9SrB2lZeUgd0\npAhouz2YsWRCrI4orYpqe1OHk2mtWsnkNDOftOO3MLlNkiBYcgVg9sTky8ohAiUAbl84BlGS+PRg\nZYv5pO5LrwqfKLaUZXSUzLHYm9ColUHPsXVSoTN01q+j+pU3OPPTx1HotIx54XfEz//q7znQtTV6\nbCxv2onQ5MA3YhLeWatA1UFiQZKg2Q4Nwcs1gtGeeadKqeRUjYbyOv9XOTXOw6hEN8puVkd4vBLb\niz18ss+N2wuDTQIx0RZOlFfQ7HFjNPgTJHcvn0BNTWP3HrwDyi86eeP9SnbsrkEUYZBZy/XzE7lh\nvpnoqL7vGeHxiuw7UEd+gY2SQw4kCXRaBbnZRnKyTYwdqZel/z2Mvc5DcanfpHL/Fw6anf4EoFaj\nYMbkOKZnxDE1w9DvDFRlZGRkOsP0cUkMTYrhmbcOsWN/BacvOrhv5cROLTLJyMgMDEJOStxzzz1M\nmDCBQYMGMWaMfwLr9XrDFlgk0FoS3pYpaSaAoIqA5bNHdKhQaI1Oq+RXL34esvlloGlI29eDJVeM\nBi133pAeksGmUqFAIQiXdcNwun18XFSOIAgtZRnBjqdRK/ljXmnAc2wvqTAnM5Xls4aFFGOoZSOS\nJFHx+79T/ru/oUqMJ+3VPxIzeULL9kBqj+uiqlhsOQqCiHdKLr4JWR0nFrpQrhGMS0kje7OCsgot\nTq+CKLVIutlFfFT3qiMkSWL/cS+bd7mx10vERAmsnKdhxjUqFIoRuDyXl9UouzsbEoTT55rIe6+S\n3UW1SBIMHazjlhuTmTszoV8YOpZfdJJfaGXbpzXUOfz32vTR0eRkG5kzI4Eonbza1FOIosTJs01f\nqiEcnDjT1LJtkFnDwjlxTMuMY0J6jNxiVUZGRqYVgxL1rLtrGhvyj1Fw4CIPv7iPu5dew7T0ENql\ny8jIDBhCTkrEx8fz6KOPhjOWiCSYZN5W5wyqCLhQ3RBUodCWcstXK8wdeTC4PD72H7e2O87+4zZW\nz/ehVStbFAcZY0xsK26vi4U5qCKjrWIhlLKMYMkcp9vXktRo7xzbSypsKjxFU7O7QxPQUMtGJJ+P\nMz97HMsrb6Idlkr6hj+hG3W5F0RbtYeAxGrDaVbGnqVZVFI3czWGcZlB4wH85Rp1F0D0dLpcIxBe\nEU7ZNFQ41IDE0Hg3IxI83a6OOF/t450CF6crRJQKWDBNTc50DTrtVxP+zpbVdAdlJxvJe+8i+w44\nABg1PIrVy5K5dkp8n/dRcLlEPt1nJ7/QxuFjDYDfi2B5bhKLsowMHxJCi1mZbqGxyceBw44Wk8ra\nLxNDSiVMHBfD9Ax/IiI1WSsrVWRkZGSCoFEr+Y8l1zB2SDyvfFTGM28d5PoZQ1k9fzSqHlzMkJGR\niVxCTkrk5uayadMmpkyZglL51SR28ODBYQksUmhPMn9pEt9ReceQpJiA29sSqJPGpck0cNnxOyqR\nqHE42VZS3qI4SIjVMDQphian5zITy0BdLNpTLKQPS+jQY+PSBHX1/FGUnaul3NKAKPnVGwqFgK+d\nk2x9jlfjRRGKB4gpSsnJ+9dh/2Ab+vFppG14Gk2S6Yr3t762UYKX7yQcYVqUlUpvFOtd0/ju6IkB\n4wC6pVwDrvTGqGlSUmbR4PIq0KtFxiW5MOi6Vx3haBR5f7ebfYe9SMCk0UqWzdFiiu+9BwdJkvii\nrIGN71ZSeqQegHFjolm9LJmpkwxhnRR2xp+kq5w628TWAisFe+w0NfuTdpnjY8nJNnLtlHjU8up7\n2JEkiQsXnRR9WZZx5HgDvi9FYfEGFQvnGpmeYSBzggF9PzFMlZGRkelJ5kxKYXhyLM++dYgtn5/n\nZEUd31kxkURDzxtky8jIRBYhJyXKysp49913iY+Pb3lNEAS2b98ejrgijvZWhDsq74jVawJub0ug\nTho1DievflTG0XP2y0oeVmaNCpoQyd93nm0lFV+NU++mpt7NgimDuWHmsA4nWO0pFj49VIlOo7ys\nfKP1MVv7P+RtP8X56oaW/0vQbkICvkoY+M+346RHoEliR0miGJ+bsjt+RP3eEmLnTGfsP36LyhDT\n7vEuXdvSkuP8P+NBhqobOeRM4OmaCcyaNjL45FT0QX0FuOq/LNdIBW37xwlE26RQUmI0WTMz0cca\nEZAYnuBmeIKH7hQGeLwSBSUePt7nxuWBFJOClVkaxgztPfNESZIoPugg771Kjp7wK4kyx8eyelky\nE9JjwpqM6Kw/SWdpbPJRuLeGrQVWTp1tBiAxXs3SRWYWzTWSnNQ5PxWZzuP2iBw6Wu9PRByoo8r6\nlefPmJF6vxoiw8Co4fo+r8KRkZGRiQSGmGP4+Tem89KHR/nsSDW/fOFz/mv5eCaOMvZ2aDIyMr1I\nyLONAwcO/H/2zjyurftM91/tC1pAYrUxNmDwjhe82+Al4DiJHbuJk6ZpZqZpb3s7mencz73tZ2Zu\nb6dN2s79TG9nudPeTmcmzaRpm6TtOGlWO4lx7OAl3rAN3rHBBozNIrFIQkLL0bl/CDAYSQiMg5ff\n95/E6Ej8ztGROO9znvd5OXr0KFqtCO4aTLz2jmiPJ5t0JBk0QxwLRdPtVF9sjzpJQ6dVceB0y8C/\nB7c8xBI8iqbbqbkUvbWjpq6DJ9cXjNiyMZosjMhaboRrjvb5gwWNuKKCUcNrFbUxi8R4ItHiVCV1\nn/86vnOXsG0uI+8n30epi38uf2G2ki+0HEcvB/jQk81OeQ4ritNjukuAm9o1jBFBYgztGoNFoeys\nDJYXF2E06An4vazIV2DWjZ87QpZlTtVJvLvfT4crkhuxuUTLstnqCSvEwmGZwye62P5uC/WNkYJ9\nyQIr2x7JpDD/s5l3PtaxtvGQZZlzF3vYVeng4LFOAgEZpRKWLrRSVpLKonmWeyIP407G0RGgqiaS\nDVFz1o0/EPksGQ1KVixOjoRUzrOQbL37Q1IFAoHgTsSgU/NfH53DjCnJvL77Iv/0+2o2rZzGltW5\nQgAWCO5TEhYl5s6di9/vv+9FiZvv0sdr74DY7R/DX0eRkKOinxO1Dl74ypKB/x8siKxbOJm9UfIj\nILGxo90ef8yWE39AYtXcTM43dkUVYfqfP5osjcGCRjznyVv7LscsEvuP79aSPGDoMVlq8lPw4x/i\na24h/dknmfr9b6JQxXE6yDLKC4dRH9sJCgW+xY8yO2MOK+K5S2QZfB197RqAMRWS0kbdrgE3RB2d\nVsvShXPIzclGkiSOnzpHy/Vm1hQsBcbHPn61TeKdfX7qmiO5EWsWaihfqsWgm5iLAkmS2X+kkzfe\nb6HpWi8KBaxakszjj2SSm/PZZVeM91jbLleQvQc7qKh0DIyMzEzXUVZiZ90qO7ZkUQDfLqSwTG1d\nT0SIqHZx5apv4LHJWbo+N4SVWQUm1GpxMSwQCASfBQqFgnWLspmWZeHnb53m3YNXuNTczdcenYM1\n6f6uNQSC+5GERYnW1lbWr19Pfn7+kEyJV1999bYs7E5jJCv3SIF/Nz9+87+jOS5m5CTz6SCXxGA6\n3b14vMGYgsdIo0zj7eeHR5tiZlzotCq+UF6ASqmM2Wcfr41Cr1WRpFcPybXYWpI3MAY12nFYNX8S\nGxZn872XDkdd8/6a68Pelxe+sgSPN4i6tpYrX/4bAp3dZP/1c2R949n4ln8phPrIe6guVSHrkwiu\n+QLK9Kmkx37GuLRrDKbL7cdssbF+zTwMeh3tzk4OHj1Jt9uDUsGIolIiuL1hdn4a4MiZSG7EnFwV\nm0t0pE1QbkQwGGbPwQ7e3NFCa3sApRLWrbLx2MOZZGd99r2mieSTjPQeSGGZ6jMuKiqdHDnZhSSB\nRq2gdHkKZSWpzJlhEneEbhNuT4gTpyPZEMdPufD0RFrONGoFC+daKC6yUFxkFS0yAoFAMMHkZln4\n3rNLeOm9c5y85OD5l4/w9UfnMCMnZaKXJhAIPkMSFiW+/vWv38513PGMZOVOJAwv3jbRHBUAFxo7\nRxQXdBoVVpOO9i4fyDI2qwGjXhP1eYNdCbH2M9qUjn56AxJv7bvM02WFMYuyeG0Uq4uyBvbRZNTw\n1r7LfO+lw8OEnsHHIXtSMmdqW2MWibEmejysaOPSV/+KcDBE7j/8DWlf2BJzvwDwedBU/hZlWwNh\nWxbBtU9DUnL854xTu0Y//pCC1t5kSlekEZIkjp48w/mL9fTrQyOJSiMRCslUVgepOBLJjci0KXm0\nVMuMnInJjfD7w+yqdPDWB604O4Oo1QoeXJvK5x7KICNt4grGkfJJ4r0H7c4AH+93snu/k3ZnpCVr\narae8tJUSpfbMJsmLqPjXkWWZa40+Th+ysWx6m5q63oGRFV7ioaVi1MoLrJQNNuMXidCKgUCgeBO\nIkmv4RuPz+ODI428sbeeH79+ksfW5LFxWQ5KMd1IILgvSPjqeOnSpbdzHXc08a3c7UhSmJo6Z8ww\nvNEE5t3soIjXzqDTqJDCYX67+yIHTrUMFOYqJUhRIgempJvi5iEkmgWRiH09XtaGSqkkPcXIaxW1\ncYWewcchXpEYjY7/fJ/aHb9FoVFT8NKPSdlQGnd7Rcd1NHtfRdHTjTR1LqGVnwN1HPvgOLZr9L9c\nq0fNJYeWUFiB3+dmx96juD09Q7YbSVSK/foyp+sjuRHObhmjHh5bpWP5XDWqCbhb7/VJfLCnnXc+\naqPbFUKnVbJ5QzpbH0zHljLxts2RQmxvfg+CoTDHTnazq9LJyTMuZBn0OiUb1qRSVmpn+jSjGBs5\nzvT6JWrOugemZTg7g0BkklFhfhKL50dCKqdmG8SxFwgEgjschULBQ8umkj/Jyr++fZrte+u42NTF\nVzbNxmQQLY4Cwb2OuGWXAPGs3E6Xf8iUi2hheLcSmDdSkObvPr7E7qqhzoZoggSAtzdESJJRKaO7\nNhLNgkjEvj5S1sZoe/bjFYlDkGUWVO1l6cGdKC1mZvz6/2JeMj/uU5QNp1EfeBOFFCS04AGkuWvi\niwthCVzXINDXrmGdDNqxt2v0hhTUtmvp8KpRKmQKUv1kmMDZksKJWilmdkeiXHNIvF0Z4NJVCaUS\nShdEciOM+s++UHN7Qrxf0cb7u9vx9EgYDUq2bcpkU1kaVsudddEx0mcP4Or1Xir2OdhzoAOXOwTA\njPwkykrtrFqSgkEv7sqPJy1t/oGQytPn3QRDETuEKUlF6fIUiousLJhrwSLcKAKBQHBXUjglmeef\nXcq/v3uG6jonL7x8lOc+N5fcLMtEL00gENxGxJVbAsS7Sx8re6G/sI78/9gD8+IV9/6gRNWFxKdc\ndLp76XD1sudEc1TXRqJuhNG0EMTK2hhLz/62tXlcaOyiud1DWI4ce4VikAgjh1m57z2KTu7Ha05m\n4Rs/xzynIPbi5DCq6j2oT+1FVmsJrn2a8JRZ8Xco6IXu5nFp15BluO5WU+fUIoUVpBgkCtP8GDQy\nEF/USQSPV+aDQ34OnQkhyzBrmooHl6vRaYKoVGHGKywzEbq6g7zzURs7P26n1x/GbFLx9OeyePiB\nNJKMd+bXUMyQWn+YymNOKvY5OVsbGXtrNqnYvCGdshI7OZMNE7zye4dQSObcRQ9VNd0cq+mm+fqN\n74xp2QaK50eyIQrzksTUEoFAILhHsCRp+R9PLuDdg1d4Z/9l/vevq3jqgQLWL5osnG8CwT3KnVkN\n3GHEu0sfTZCAG4U1cMuBef1rGLydFA7z6w8v0OlOfMpFillPxbGmuM6ORNwIY20hGIzJqEWnVdIb\nGG7riCV6bN9bT1ObZ+DfYRn6wxaUoRDrKn5HQW01HbYMur/7HazxBImgH/WBN1A1nUM2pRBc+0Xk\nlIzY249zu4YvqOBCu44unwqVUmZGmp9Mc2jYy40UoBqNkCRzoDrIR0cC9AYgI0XBptVaTtZd5ifb\nR24hGk8cHQHe2tnKrkoHgaBMilXNU1uy2LA29a5xEfS/B3UNXioqHVQe6sDri5y38+eYKS9JZelC\nKxrNxISE3mt0dQcj2RA13VSfcQ0ca51WyZIF1oGQylTbxLf5CAQCgeD2oFQq2LI6l+mTrfzbO2d4\ndVcttU1dfOmhmRh0onwRCO41xKc6QaJZuYum26m+2E6HOzBse61GhcmoRaVUjDkwLx6/+/gSB2NM\n5ohF0XQ7NZccUR/rd21sLcljf821qGKBUgFrFk4eUwvBzby1rz7q74Dooke8dg+zHKRsxytkXKml\nfUoe7m//NU9sXhD7l7s70ex9FWVXK+GMXIJrngJdnMI/LIGrGQIeUKrAkg3apBH3MRqyDM0uNfVO\nLWFZgc0YYlqyj97eXgKh0bshhr62zNnLEu/s9+PokjHoYOsaLSvnavjdnotjbiEaC9fb/Ly5o4W9\nBzoISTJpdi2PPZzB+tV2tHdR8d7jDVF5qJOKSgf1jZFRkrZkDY88kM4DJfYJDeO8nSQS3DtehMMy\n9Q1eqmoiQsSly96BxzJStaxdGREi5s4031XnjkAgEAhunTm5Nl748lJ+/vZpjp5vo7HNw3Nb5zIl\nfextswKB4M5DiBJRiHZBHsvKrVIqojoLIlMq6uO6D0bjOPAHpYHpGlaTLqFASr1WRSAoDfTCr1s4\nmb0xJmsMdnb4Y4gFsgwPLpky5jvr/cfVoFPHXL9eq2JrSe6wn8dq9zB43Tz49kuktl/DsH4163/+\ntxjMsQUDRctlNJW/ReH3Is1YRmjxQxGhIRZD2jWS+to1xvax8QYi7ojuXhVqpUxBqo89R87z6wQC\nUEeixRnJjahtklAqYPV8DRuWakkyKEad33Er1Df08IvfXGb/4U7CMmRl6Nj2SCaly22o1XeH5VKW\nZc5d7GFXpYODxzoJBGSUSli20EpZaSoL51ru2VaB0YTy3gpen8TJMy6qqiMjO7tckTwOlQrmzjRR\nXBQRIrKz9MKqKxAIBPc5KWYdf/mFhbxZWc8Hhxv54a+O8cyGQkqKJk300gQCwTghRIlBjOWCfGtJ\nLvtrrg9MvhhMf8GXSGBevDXdPF1Dp1biD8VIswS0aiWr52exacVUrju8ZKebMBu1+INSQq6NWNvY\nLGNzdnj9IV7fVcv5xk46XH6sJi1dnuHuEoBAUMLjDWLUDc1piJZ3Yely8Mhbv8Dq6sD2hS3k/+h/\nolDHPqWVF46gPvo+AMFljxIuXBJ70bIMXif0tEX+nZQWadkYQ4Eky3C1W83ljog7IjUpREFqgDf2\nXrhl94LHJ/PhoQCHTgcJyzAjR8WjJToy7TfO13j5HR3uXuqbu8mbbL0lYaKuwcv291o4VNUFREZg\nbtuUyYrFKRMy3WMsdHUH2XOwg4pKB9daI8crM11HWYmddavs2JLvrCDO28GthPLGQ5Zlmlv8VFVH\nsiHOXfQg9X1lWi1q1q+ysajIyoI5FpKMd0dbj0AgEAg+O9QqJU+um05BtpWX3jvHyzvOc7Gpmy9u\nKLztjj6BQHD7EaLEIOJdkH9+/fSogsW6hZPxRxEkYGhmxFhDC6NN14gnSCQnafnus0vYcaiBv/1V\n1TBxJRHXRqxtZuQkJ7TmfvpFnpvbQWIJEhC7pUWnUbGgIHXgWKS2XeXht/8Do8+Dc8vnWPL33459\nR1UKoT62A1XtUWSdkeCaLyBnTIu98HCob7qGB5TqiDtijO0aPQEF59t0uP0qNEqZmem9pJukW3Yv\nSJLMgVNBPjocwOeHtGQFj5bomDVNNew4xAswVQA//u1J7GO4I+4PSlSd6uSjPZ1Un3EDMKvQzNYH\n01g834ryLhAjpLBM9RkXFZVOjpzsQpJAo1ZQujyF8tJU5sww3Td36sfbURMIhjlzwTMgRLS23/jc\nT59mjGRDzLeSP9V4V5wrAoFAIJh4Fhak8b1nTfzLW6fZf+o6V1pcPPe5eWTaRpe/JRAI7iyEKNHH\nSBfkUlhmz6DWh37BQgrLCWdGjDa0MN6aYrF4Vjo7DjXEFVf69ymWa2PwNh2uXnTaSCHy6ekWLjR2\nJly83izyJEK8lpb+TNHsxlo2vP8rNMEglWs/R+Zj22IXjr09aCp/i7L1CuGUDIJrnwFTHHEl6IXu\nqxFhQpMUGfepHP3HJCxDU5eGKx0aZBSkm0JMT/XTdyjjuxdcvbR3eslON0d9/NyVEG/v89PeGcmN\n2FKiZWWRBnWMloJEglpHc0c8JEn89PULHDnSQ68nskNp6Uq+/sVcNqybjMPhifv8O4E2h5+P9zvZ\nvd+JoyMIRKY5lK+xU7rchinp/vtqHMtEnJtxdAQ43pcNUXPWPdAKZtArWVGcTHGRlUVFFlKs977r\nRCAQCAS3h7RkA99+ppjffnyRPcebeeGXR3n2oZksnRUnsFwgENzR3H9X3jHocPXGHIXZ4erlZG30\ngMiaS06K8u1DJlr0c6tTKuIVCf0km7S4egID4sLWkly+99KRqNv23+0cybUxOD/jNx9e4MCgQM1E\ni9dEBZUUk47uHv+ILS3+oET1RQfTL5xg3a7fA/DRw89wefo8Wi86eWKtNGw/FJ0taPa8iqKnCyln\nNqGVj4MmRmL/OLZruP1KLrRp8QRUaFVhCtP8pCYNddPEcy/IwD9vrxkm/rR2hHlnn5/zDRIKBayc\np+bB5TpMhpHXOERocveiIP4o22jnrSzLHKvu5l9fvUKHMzJSVG0MYrD3EjJInG818qAie8S1jJVb\nDV8MhsIcPdlNRaWTk2dcyDLodUo2rEmlvNRO/jTjfeOKiEa8czKWg0kKy1ys7+FYdTdVNS6uNPkG\nHpucqYtkQ8y3MqsgCY1ahFQKBAKBYHzQqJX80YYZFGRbeWXnBf717TNcbOrmyfXTxd8bgeAuRIgS\nfVRUxb6jH8lAiH0HsWzxFFQq5ZgyI+IRr0gAsJl1/M9nFtHW6RvIjWjr9CZ0tzNR18b5xs6oPx/J\nzt3t8cdc9+D1/9nn5qJRK0nrW1Msuj1+Jn9Swcp97+LX6vlg059wPTt/2H71o2w8g/rAmyhCAULz\n1yPNWwOKGH+kRtGuEa8wDsvQ0KmhsTPijsg0B8m3B4i2W/HcCzBU/Nm6uoCPDgc4UBPJjSiYomJL\nqZYse+KF+WChqb65mx//9mTU7aIdSyks8+mxTt54r5UrVyNFp8YUQG/zo9bfEFtO1DroDYQSXlOi\n3Gr44tXrvVTsc7DnQAcud2R9M6cnUVaSysolyXfNaNLbTbxzcrDA6vaEOHk64oY4cdqF2xM5B9Rq\nBQvmmAeEiKz0e3MyiUAgEAjuHJbPzmRqhpl/+cNpdh+/Sv31bv50y1xSkw0TvTSBQDAKhChBpNCM\nNSoToCjfxpnLnTHvINos+jFnRsRjpMI1yaDh7149PqRQ21qSO24jSMdq55bCYT482oRSEf1ufD9e\nf4gf/qpqxCJTlmV6fvoiK/e9S0+Smfe3/Bc6UrOi75ccRlWzF3XNHmS1luCapwjnzIm9iIAXXH3t\nGtq+6RpR2jVGKoxdvUrOt+nwBpXo1GFmpPmxGaNnjfTTL1odv9BOhzv6cT5+XuZsXQ8+P6RaFWwu\n0TEnd3huRKLoNCryJluxJ3COhEIylYc6eOP9Fq61+lEqYOlCC+c7rqLSDc816XT30unyj/uXyljC\nF/3+MAeORUZ5nrvYA4DZpOLRDemUldiZMllcrEQjWnvXggI7ywom88b7LRyr7qa2rmfgc21P0VBe\nmkzxfCtFs8xC4BEIBALBZ06WPYnv/PFifv3RBQ6ebuGFXx7lK5tms2B66kQvTSAQJIgQJRi5TeLB\npVPRatQj3kEcbWZEInx+/XRkWR4yfUOvVZFq1dPUdqN3f3ChNh4jSGFsdm6IFJF7YowDOZfqAAAg\nAElEQVQeBVApQQozsD/xisxwMET1l/+a9t+8RSAzi7c2/gluiy36fgUDqA++garxLHJSMsF1X0RO\nyYy+iGHtGulgtMds14hZGCsULFswm6YuDaBgkiVInj3ASM7BfsfF42vyKZ0/ie+9dITB+o1aacGo\nzUEOG5HCsHm1ltVFmnEZqznSHXEFCj7Y086bO1ppdwZQqxSUldh57OEMbDYN33mxNeY5kWLR4e72\nDXtsrIw2fLHuipddlQ72He7A64sIJ/PnmCkvSWXpQisajbB0xqPfUbNpRS6HT3RQe6mXvR+62P76\nBQCUCijMTxoY2TltiuG+bnkRCAQCwZ2BTqviK4/MonBKMq/uquUn22t4aHkOj5XmjetIa4FAcHsQ\nogTxi2+7JeKEuJWxnreCSqnki+Uz2LZ2Ou1dPpBlrCYd3//l0ajbn6ht54WvLBuXtSZq5x5MvCJS\nqYBFM9Kpv9YdVQS6uciUvD4ufe2v6P74IEkL5zD/lX+i8aQj+n55utDsfRVlZwvhjGkES58CfYyJ\nGeEQuJoh0JPQdI1Y+5Rut2FJK6CpS4teHWZGei8phvDAc6K5ZqI5Lubm2dBplfQGwigVegzaKWhV\nKciyDAon33p6Mnbr+H5Uo53P8/LsGEMWvv6XZ+jsDqLVKHj4gTS2bswgzX4jiyPeOaHXqnGP4zoT\ncesk6bRUHoq4IuobI4KIPUXDI2XpPLDaTkaaaCNIhNZ2P1U13RyrdnH6vJtgKCKTmZJUlCxLobjI\nysK5Fixm8WdDIBAIBHceCoWC0vmTmJZp5l/eOs3OQ43UXe3mv26ZS4pZXAsIBHcy4uqSxIvv29Gi\nMZo1ZqeZkMJhXt5xPmZeg9Pl57VdtTz78MxxWetoxZh4RaQMrFswiarzbVEfH9wSEnR2UfvH/42e\nE2dI21hKzv/7W1RGA0+X2Ybtl6L1CppPXkfh9yIVLiG05BFQxtjfQE9EkBihXSPePqlVKhbOm8nM\n6bkA2HRe5kyS+xwg8ds8ojkuPjl5HQUqDJocdOp0FAolQcmFL9DAumL7uAsSMDRjoqXdy6dH3ezc\n4cDluYZep2TrxnQefTAj6pSEz1KgiyUYyjLoFQZ+85+tHK7qIhCUUSph2UIr5WtSWTDXgkqMmYxL\nKBTm9Hk3x2q6qap2cfV678BjU7P1fW4IKzPyk1DFmOwiEAgEAsGdRk6Gme99aQkv7zjHsQvtvPDy\nEb726BxmT7ON/GSBQDAhCFGij0QLrdvRojEafvfxJQ4OmoYRjYOnWzDq1TxdVnjLax1cvPYLAQDO\n7t6oYkc814nNrCc73TRiS4i/6RoXnv4GvXUN2J94hMWv/Ahn142CafB7oKw9ivrIewAEl24mPGNp\n9B2RZfA6oKfP8TBCu0asfcpMs7Ni8XzMpiS6XG7OnD3Ht56ciapPBImXf/D4mvyojgutOg2DJhul\nQoMU7sUXaCIodaLXKtlakjvi+saKyx3i3V1t7NjdjtcnkWRU8eSjmTxSlo7FFPurIdo5cbsEupsF\nw3BIQcClxd+tpSuooplOstJ1lJXaWbfKLkZNjkCXK8jxUy6qqrupOefG0xNpodJqFSyebxkQIgY7\nYwQCgUAguNsw6NT86da5VFRd5fcfX+IffnuSLSW5bFo5DaVoOxQI7jiEKNHHZ1lojZVEx2xCpI2j\nvxXiVkcpQqQ4tFv1I05BGMl1YjZq4z4uXaznwhe/QbDVQdZzf0z2//oGSo0G6B26cVhCfWwnqguH\nkXVGgqVPIWfGKOCHtWtkgzZxsUanUbFoRgZuKZnC/KmEw2FOnbtI9dla1i+aNHBMR8o/KC3KGuq4\n6MuNUCmNyLKEN9CEP9QCfekSvYEwHm8Qo258C+2OzgBvf9jGh3sd+ANhLGY1f7RtEhvXpWE0JH5+\nfFYC3ba1+Vy/FuLEyR56upSAAqUKSpenUL4mlTmFJpFrEINwWKa+wUtVnxBx6YoXuS+8JCtdT8ky\nG8VFFubONKPTip5bgUAgENw7KBQKyhdPIW+ShZ+/dZq39l3m0tVu/svm2ViMQnwXCO4khChxExPt\nhIjHSIGcg3G6/Ly84xxqlZILjZ1jGqV4M4lOQRjJdRLr8YcNLs499i0kl4ecF/4HmV99euA1hwgr\nUi+ayt+hbL1MODmd4NpnwJwSfdFD2jVMYJk0YrvGzTh7VEzNn0dAUuJyu9l/5ARyyM/6RZOGOGlG\nyj9AocBm0dHpBoMmB606khvhD7XhCzQjExzyHKUiovSPF20OP3/Y2UrFPiehkIw9RcMzj0+ivDQV\nne7OK0jbHH5273eye58TZ2cQUJGdpaNsTSoPrLJjShJfX9Hw+iSqz7g4VuPixKluOrsjY1CVSphd\naKK4yMriIgsL56fhcHhGeDWBQCAQCO5u8idZef7ZpfzivbPU1Dl54eWj/OmWuUzPtk700gQCQR/i\nqv4uoL8gN+jUMVsfonHk3NDshkRGKcZbQ6JTEEZynUR7vGfXJ1z86ncgHCb/Zz/E/rmNQCSj4cW3\nTnGgupkOl585yQH+3FKNTvIgTZlFaNXjoIkSXnQL7Rr9BCW45NDS6tGgQGZaSoCMKTLLp84adetK\nilmP2aAnzZqPFDQOyo1oRJK9UX9/WAafP4T5FtX85uu9vLmjhU8OdSBJkJGm5bGHM1m30nbHTaMI\nhsIcPdnNrk8cVJ91I8tg0CvZsDaV8hI7+dOMwhVxE7Isc63FH8mGqHFxrtZDSIrYISxmNetW2Sgu\nsrJgjpkk442vfHEcBQKBQHC/YDJo+IttRew81MCblfX86LXjbFubz4YlU8TfQ4HgDkCIEncw0UIT\njXpNwqJELI6db2PzymmjKnYTmYJws8NkJNdJ/+Otr2yn4ds/Qmk0UPCL/4N1zfKBbQa7M4r17fyp\n4RwGSeKEqYjZax4HRZSiOhyC7mYI9rVrWLNBMzr3S3uPiovtWgKSEpNWYma6H5NOBlQYYrR+xGtd\nyU7L4R9f9+PxmdBpQwSlJry+VpJNOnp6lfiD4WHPsZl1MceuJsKVJi/b32vh4LEuZBmys/Q8vimD\nkqW2Oy64sOmaj937nOw50IHLE7mzP3N6EuWlqaxckoxed2e1Uk00wWCYMxc8A0JES9uNz2b+VCPF\nffkQ06cZUYrAT4FAIBAIUCoUPLJiGvmTrPzbO2f43ceXuHi1my8/PBOjXmRSCQQTiRAlbpHxyGuI\n9XpvfFI3rF3C6fIzJd2EtzfU1/qgw+eX8PpDCf+OLk+A5//jKMUzb7Ry+IMS7Z1eUChISzaM2gUQ\nq3iOd3xkWab57/+da//0Imp7CjNe/QlJRbOGPDfizpDZam7gCctlesNK/tk5hzr3ZH4YkhkWt3CL\n7RoBCS46dLR71CgUMrm2AFOSgyRa193cmpKcZEevyaHxugatRuahFVrWLEwiLJvp9uRGfZ/7WTQj\nbUznVG1dD9vfb+HoyW4AcnMMPLEpk2WLku+oArXXL3HwaBe7Kh2cv9QDgMWk5tEN6ZSV2Jky2TDB\nK7yzcHYGqKpxUVXTTc1ZN73+iJCl1ylZXpxMcZGFRfOs2JLFhZVAIBAIBLGYOTWF559dwr+9c4bj\nte00tbl5bus8pmaaJ3ppAsF9ixAlxshIox9v9fVSzFq8finqtt7eEH/9xYW0dfpITzHwd68eH5Uo\nAdDpibRyyLKMDBw8dZ3eQF+Ro1Wxal4mTz1QkHCA5c3F80jHRw6FuPLtH9H+mz+gmzqZGa/9P/S5\nU4a8RrfHT4/byzdSzrPc2IYjpOMfO+bREDSj9N/kzri5XcOUAQZbwu0asgxtHhWXHDqCYQUWncSM\ndD9JWnkUR/VGa8q6hbm8s89PbSP4/LB4lpqHV2ixmvrPjRsukvEYsSnLMmdqPWx/t4Xqs24ACvKM\nPLjexqrFNvTaO+OjLssy9Q0+dlU62He4A68vjEIBC+aYKStNZekC6x3XUjJRSGGZi/U9A0LE5Ubf\nwGOTMnQUz49kQ8wqNKFRi2MmEAgEAkGiWE06vvnUAt7ef5n3Djbwt7+u4umyAtYsmCTaOQSCCeDO\nqFTuQhINfRzr63W4AzG3dbp6+d+/Pk6Xx0+ySUenZ+ztHAdOtdAbGCp+9AYkdlc1o1AoRhVgGW9/\nBh+fp1blcOm5/0XXh59gnDuDwt/8M9r01GGvkazw8XzGSaaoXJz3W/nnjrm4wpGWkyHuDCkErqsQ\n9IJSA9bJo2rX8IcUXHRocfSoUSpk8u1+sq2h0cRPDNAbkNl9NMAnJ4JIYZiWpWRrqY4pGbEdD7cy\n+UWWZU6cdvGf77YMuA3mzTJhmxTiapeT1yqv8cHJWxPMxgNPT4jKQ51U7HMMFNf2FA2PlEVcEemp\nY29TuZdwe0KcPOOiqsbFiVOugVYWtVrB/DnmgZDKrAz9BK9UIBAIBIK7G5VSyWOl+UyfnMyL757h\nVx9eoLapiy+UFdxynpdAIBgdQpQYA6MJfbz5edGKztGM+uynX4i4FUECGCZIDGbwWFFIvHiOtz+n\nqxs599Mf0XO0GsvqpRS89H9QmU3DtlO0NWD65HXMqh729GTxclchEjcK6gF3RsDT164h9bVrTAZl\nYsdelqHVreaSU0sorMCqj7gjjJrRuSMAwrLM0bMhdn4awO2VSTYp2LRay4ICdcKK+2gmv4TDMkdO\ndLP9vRbqGiJBmYvnW9i2KYuqy81UHGsZ2PZWBbOxIssyZ2s97Kp08umxTgJBGZUKli2yUl6ayoK5\nFlR3UDvJRCDLMo3NvRyr7qaqppsLl3oI951+tmQN5aV2iousFM02Y9CLXA1BbGRZpt0ZoL7BR32D\nl4ZmHyXLUli91DbRSxMIBII7mqJ8O88/u5R/ffs0h862cqreyeNr8ymdPwmlcE0IBJ8JQpQYA6MN\nfRyplaHD1XvL4ZW3gw63f0wBlrGOT5K7i9J3XqLH2Yrt0XLy/vkFlLrhSrTyYhXqI++CLBNY/DBt\nzkySa64PdWesywdPW6RlA2K2a8Q69ltKC6hz6OnwqVEpZApS/UyyjM0dUd8s8Xaln6vtYbRq2Lhc\ny5qFGrSa8f9DJkkyB452sv39Fpqae1EoYOXiZLZtyiQ3x4g/KPGLD0cvmI0nXd1B9hx0sqvSyfXW\nyHmQlaGjvNTO2pV2Uqz3d+aB3x+m5pybqppujp9y0e6MuKIUCijMS6K4yMLi+VamTTEIC6kgKuGw\nzPVWP/WNXuobvBEhotGLp2eoyJw7ReSyCAQCQSLYrXr++plF7K5q5q199fzqgwvsq77GMxtmkJtl\nmejlCQT3PEKUGAOjDX0cqdWjomp4TkM/KiVYk7R0eQJY+v47nug0CvzB6M6A/ukPow3zjHZ8kjta\neeStlzB7ukh99klyf/AtFDe3EoQlVFUfoD5/CFlrIFj6FHJWHl9NM/PQspwba1CGwdWUULtGtGPf\n0KHicIMBpVJFiiHEjLQA+jG4IzpcYd7bH6D6UsRiXzxDzcMrtSSbx79FIhgKs/dgB2/uaKWlzY9S\nCWtX2nj8kUyys25Y+ccyJWU8kMIyJ0+7qDzcyIEjDiQJtBoFa1bYKC+1M7vQdF8X2G0OP8eqI9kQ\np8+7CfR95pKMKlYvTaF4voVFc61YzOIrWTCUUCjMlSYv9Y2+PgHCy+VG30DQaT9Z6TqKZpnJm2ok\nf6qR3BwDVsv9LQAKBALBaFAplWxYMoWls9L5/ceXOHS2lR++cow1CyfzWGkeJoP4ThUIbhfiCngM\njCb0caRWj80rp1FzyRHzd0lhKMxJ4eFlOVhNOr7/y6NRxRCbWYcMdLoTc1ykmHQUz0wjLMt8XNUc\ndZsFBam88UndqMM8bz4+GdcbeOjdl9H3enE88RRLfvjN4QWq34um8vcoW+oIW9MJrvsimG1DXjM9\nxRhp1+hoBlkCrblvukZ0oeTmY29KMrKiuIisjDSCwSAFqQGyk8Ojdkf4AzIfVwXYezxISIKcjEhu\nxNSs8Xcg+ANhKiodvPVBK46OIGq1gg1rU3nsoQwy0obnMIx1SspYaXP42b3fye59TpydQQCmTTFQ\nXppK6fIUTEn351dMKCRzvs5DVXVkZGfTtd6Bx3Im6yPZEPOtzMhPuuPGswomjkAwTMPVPvGhT4Ro\nvOobELEAlAqYPElPfo6R3KkG8qYayZ1iJMl477X31NbW8txzz/GlL32JZ555hrq6Or773e+iUCiY\nNm0azz//PGq1mnfeeYdXXnkFpVLJk08+yRNPPDHRSxcIBHcxySYdX3t0DqXzJ/GbXbXsPdHMsfNt\nbFubz+qiLNHSIRDcBu7PimEcSDT0caQ711fbPDEf7+fQmVYuNnWxsDCNBQWp7I4iIiyakQYQVSi5\nmWSTlue/vASzUYsUjtxtOzgo8LJ/+oYM7B5jmGf/cWh5fy/L//AySkmi9av/lYe/95VhgoSiqw3N\n3ldRuDuQsmcSWr0NNDcVz7IcmawxQrvGYAYf+5nTc1k4byYatZqmay0cOV7D/D9eiEKRuGsgLMtU\nnQ+x42AAV4+MNUnBI6u0LJyhHvc/UD6fxAd723nnwza6XCG0WgWby9PZsjEde0rs8KXRTkkZC8Fg\nmCMnu6modFB91o0sg0Gv5MG1qTyxJQebRb4vXRHdriDHT0XcECdOu/H6Ip8nrVYx0JKxaJ5FhHoK\ngMhn/HJTvwDh5XKDj8ZrPsKDDBBqtYK8qUnkTNb1uR+MTMs2oNPd+9NWvF4vP/jBD1ixYsXAz/7+\n7/+er33ta6xZs4af/exn7Ny5kwceeICf/exnbN++HY1Gw7Zt2ygvLyc5OXkCVy8QCO4F+keHVhy7\nytsHLvPLnecHWjrE+FCBYHwRosQYSTT0caQ719npJlLM2rjTNuCGILC+eDJli7PjiiH9j2k1qqhB\nlotnpg+kCquUSp4pn8ETa6fT3ukFhYK05Egf8ndePBR1LYlkE6iUSsod57m8/SUUGjXT/v3vWL5x\nzbDtlE3nUR/YjiLoJzR3DdKC9aAYesEtBQPQ1TCoXSMbNCP3SltNOqZkpjB71mzSU230+gN8eqyK\nK03XsFtG5xq4fD2SG9HUGkatgvKlGtYVa9GNc26E2xNix+523qtow9MjYdArefyRDDaVp5OcoBV7\nPEaMRqPpmo+KSid7D3YMTIWYOT2J8tJUVi5JRq9TkZZmpr3dfUu/524hHJa53OTrc0N0c/GyF7nv\nhnaaXUvp8hQWz7cyd6YZnfbeLyIFsXF7Qlxu9FLXcKMF43qbf+B8AdBplRTmJZGbYyRvqoH8qUay\nJ+mZlGW9bz5Tg9Fqtbz44ou8+OKLAz9raGigqKgIgJKSEl577TVSU1OZN28eZnOkQFi0aBHHjx9n\n/fr1E7JugUBwb6FWKdm4LIdlszP43ccXOXKuje+/cpR1CyfzudI8kvSipUMgGA+EKHGLjBT6ONKd\na7NRS5JhZFGin+qLTn741WUxxZB+oaS9y4cUDlN58ho1dR0jFqc6jYrs9Buq73VnT8zwzZGyCWRZ\n5vpPX+bq3/0LqhQrM371fzEVz7t5I1Rn9qE6UQEqNcGSJwlPmzf8xfweOutqI2M/dWYwx27XGExY\nhlaPnjWrVqJQKrnSdI0jJ07R648c50RdA53uMO8fCHCiNtT3PDWPrNKSMs65EV2uIO982MbOj9vp\n9YcxJan4wtYsHn4gbdQtELcyYvRmev0SB492savSMTBy1GJSs+XBdB4osTNl0v0VpOfzSZw866Kq\n2sXxUy46uyMtK0olzC40DYzszJ6kvy/dIgLo6Ar25T54qesLoewPM+3HaFAxZ4aJvBwjeVMjIsSk\nTP19P41mMGq1GrV66HdfYWEhn3zyCVu3bmXfvn04HA4cDgc2241WP5vNRnt7/GlWKSlG1Orb0+6S\nlibunk404j2YeO7F9yAtzczf5KVSXdvOv/6hho+PN1NV286XHpnD+sVTUN5h39/34ntwtyHeg9Eh\nRInPgHh3rv1BCW9vMOHXGiwIRBMFpHB4WA5E0fRUyoqzsVn0CRenFceaYj4WL5tAliQavvsPtL38\ne7STM5nx2k8xFOQO3SgUQP3pW6iunEI2WgiufRrZPvmmF7rRriErFGDKBENK3HaNfjx+BRfadbj9\nKrTqMK3X6zlzto5AIIDdkphrwB+U2dOXGxEMwZR0JVvW6Mgd59wIR0eAtz5oZVelg0BAJsWq5qkt\nWWxYm3rLIyBHM2J0MLIsU3fFy659TvYd6sDXG8ndWDjXQlmpnSULrGjU98+d/+aWXqpquqmqdnG2\n1kNIitzetpjVrF1pY3GRlQVzzSQZxdfp/UT/CM5+4eFy3ySMzu7QkO0sZjUL51rI68t/yMsxkpGm\nFaLVGPirv/ornn/+ed58802WLl2KLA8PKI72s5vp7PTejuXdV06xOxXxHkw89/p7MClFz3f/ZDG7\njjbx9oHL/PPvTvD+gXqeKS8kJ+POKELv9ffgbkC8B9GJJ9SIq+jPgJAkU1aczeaV0/D5Q0PuXDu7\nvSNmSgxmpLDCaNMm9hxvRqVUjJgD0Y8/KFFT54z5eNF0e1RxI+wPUP8X36Xj3QoMM/OZ8epP0Wal\nD92opxvN3tdQdlwjnJZDcM1TYLjpBJWC4GoeaNdInjaDLs/QpPlohGVo7NTQ0KlBRkGGKcj01ACa\n3AweWZKakGsgLMucuBDi/QMBuntkLEkKtq3Tsmjm+OZGXG/z84cdLew50EFIkkmza/ncQxk8UGJH\nq5mYgt/TE6LyUAe7Kp1cafIBYE/RsHlDOg+stt83WQjBYJgztTdCKq+33fh85k019LkhrOTnGsWd\n7fuEgRGcDV7qGm+IEDeP4Ey1aVi60DogPuRNNWBL1ggBYpzIysri3/7t3wDYt28fbW1tpKen43Dc\nCItua2tjwYIFE7VEgUBwH6BWKXlo+VSWzc7gt7svcuxCOy/88igPLMpma0keRr0orwSC0SI+NbcR\nKRzmdx9fijq9oh+rSUeySUenJzFhIl7bwUiTPkbKgegnXjgnQFlx9rCfhVweLn7lW7gPHMO8bCFT\nX/wxXSot1qA08DsV7Y1o9r6OoteDlL+I0LLNoLrpFPR7IoKELA20a2gMSeCJrza6/UrOt2npCajQ\nqsIUpvlJTbpRMCTiGmhokXjrEz+NfbkRZUs0rC/WotOOX0HR1OzjjR2t7DvUQViGrAwdjz+cSemK\nlAlxH8iyzJlaDxWVTj491kkgKKNSwfLiZMpK7CyYa7kvCu+OzgBVp1xUVXdTfdY9MG5Rr1OybFFE\nhFg0z4ItTsio4N4gFJK5et1HfV/+Q12DlytNUUZwZuiYP9vc134RESHESNfby09+8hOKiopYu3Yt\nb775Jlu2bGH+/Pl85zvfweVyoVKpOH78ON/+9rcneqkCgeA+wGbR89zn5nH6spNXd12kouoqR863\n8eS6fFbMyRSCtEAwCsQV1G0kmmuh4thVfL0hnnlwBjqNCp1GxYLCVPYcjz6WU6mIdDLYEmg7GGnS\nR7wciMHEDec06YbNaQ60Oaj94l/gPVNL8sa1VG37Ev/x+zNDhJinp7rRHHkX5DChxQ8jzVw+tBVD\nlqGnDbxOIPF2jbAMVzo0NHZpAAVZ5iB59gCjiVDo9kRyI6ouRGzX8wvUbFqlxWYZP5GgvsHL9vda\nOHS8C1mOjIXctimTlUtSJqTo7+wOsueAk4p9Tq63Rt7nrAwd5aV21q60k2K9t4ObpLDMpcvegZDK\n+kbfwGNZGToWF1kpLrIwu9CEZoKcK4Lbjz8QGcF5uc/9UN/gpeGqj2Bo6AjO7En6IfkPuTlGjIZ7\nbwTnncTp06f50Y9+RHNzM2q1mg8//JBvfetb/OAHP+CnP/0pixcvZu3atQB885vf5CtfiUx2+rM/\n+7OB0EuBQCD4LJiba+f7X07ho6ONvHvgCr947xyVJyNTOrLTTRO9PIHgrkCIEreJeK6FA6dbONfQ\nwaIZ6Xx+/XSeLivg4tUurrb1DNu2dMEkNi7NSSiscKRJH6OZNjEzJ4UDp1uG/bzT4+f7vzw64PgI\nXrnKhae/gb+xmbQ/eoxPH3iMiuPXbmzv8pFxcQ+661eRtXqCJZ9HnnSTsCIFwXUVgj5QacCS2HQN\nV6+S8206vEElOnWYGWm92Iwjt3n0EwjK7D0eZE9VgEAIstOUbCnVkTd5/IqN85c8/Oe7LRw/5QJg\neq6RbZsyWTLf+pmHIklhmZOnXeyqdHCsuhtJAq1GwdoVNspK7cwuNN3Tqr6nJ8SJ0y6O10RCKvsn\niKhVCubPNlNcZKV4voVJGfoJXqngdtA/grOuL4SyvsFL07XeYSM4p042DMl/mHqfjOC805g7dy6/\n/vWvh/18+/btw362ceNGNm7c+FksSyAQCKKiUSt5ZMW0vpaOSxyvbef5l49StjibLatzMehEySUQ\nxEN8Qm4TI7VAdLgDAy6Kp8sKKZySHFWUUCoVCYcVjjTpA6Ct0xtT4BjcbuJ0+VEpQYpS4/c7PnT1\n9eT+9B8IOTuZ/M2vYf/Glznxi8MD2xkVQb5hO0ORvpMWKQlT+ZfR2G7KmPC7wXWtr13DAuasEadr\nSGG43KHlarcaUDDZEiTXHiDR7gdZljl5McR7+wN0eWTMRgVb12hZMks9LkKBLMucOufmP99r4fR5\nDxCZzPDEpkzmzzF/5oV/m8NPxT4nH+934uyMhKrm5hgoL02ldHnKPRvQKMsyjc19IZU1Ls5f8gwU\noClWDWUldoqLrMyfbcYg7nrfU7g8IS43eKkf5ICINYJzcP5D9iT9fRXiKhAIBILxJdVq4M8fm0dN\nnZPXdtXy0dEmDp9t5fPrp7NsdsY9ffNHILgV7s1q5A4gnmthMCdqHWxeOY3qi46oj1dfdPLEWinh\nqRnRJn0sKLATlmW+8+KhYdkWKuWNC/Cb202iCRL9ZDfUkvXzXxGSQkz70f8k/Y8ep63zRmjnJHUP\n/8N+iiy1jxO9dn7eOZu/UZgYkCRubtcwZ4J+5HaNLp+SC+06fEElBk3EHZFsSI4GNTgAACAASURB\nVNwd0dgq8XalnyvXw6iUsL5YwwNLtOjHITdClmWOVbvY/t51ausj6e4L51rYtimT2YUm/EGJ9i7f\nLY3oTJRgMMyRE93s2ueg5qwbWQajQcmDa1MpX5NK/tTRT+W4G/AHwpw65x4QIvpHMSoUUJCXxOIi\nC8VFVnJzDOLC4B5AlmU6u4LUN0aEh/oGL/WNw0dwJhkjIzjz+/MfphrJytDdF3kpAoFAIPjsKcq3\nM2vqUnYebuT9Txv493fPUll9jS+WFzI5TbR0CAQ3I0SJ20Q818JgOt29XG3zjEsWBIBKqeTpskIe\nX5M/MG3ijU/q2B0l2wIYmMgRr93kZgrOH2dtxe+RFUrS/un7pD8Rsc32CzHZ/mv8ue0MRqXEO+4c\nfu/Kw2Yx3GgfkYLQfRVCPlBp+9o14lvmQ2God2q55tIAMtnWILm2AKoEb2p2e8Ls+DTAsXMRy/68\nfBWbV+uwW2/9rqgUljlU1cX291oGplYsW2jl8U2ZFOQmIYXDvFZRGzXwdLAoNB40NfvYtc/J3oNO\n3J5I0OesgiTKSlNZuTgZve7ecwS0OfxU1bioqunm1Dk3gWDkdniSUcXqpSkUF1lYONeC1XJv52Tc\n68iyTJsjMCA89IsQXa6hIzitFjWL5lnIzTEMiBDpqWIEp0AgEAg+WzRqFY+uymXFnExer7jIyUsO\nnn/5KOWLp7B51TTR0iEQDEJ8Gm4jN1wL7TEdEylmPdnppnHLguinf9pEohM5Rmo36afoeCUr97+H\nX6vnwOe/yn/fWn7jd6qV/NGkNopdNYRQ8rOO2Rz0ZQCDpoaMoV2jtVvmaJMBf0iJURNmRrofqz4x\nd0QwJPPJiSC7jwUIBGFSqpItpVqmZ9/6qR8Kyew73MEb77fQ3OJHoYCVS5J5cnMWU7NvZGLECjwF\nEh7TGo9ev8SBI11U7HNw/lKkBchiVrNlYzplJalkZ91bGQmSJHP+koeqGhfHarppau4deGzKZP1A\nSOXM6SZUKlGI3o1Ig0ZwDhYherxDR3Cm2bUs6xvBmZtjJH+qgRQxglMgEAgEdxBpyQb+YlsRJy85\neG1XLR8caeTQ2RaeeqCAJTPTxd8sgQAhStxWBrsWfv3hBQ5GCY5cWJiK2aiN6aooyrfdktU/0Ykc\nI7abyGGWH9jBguOV9CRZeH/LV5hZsvDG2kJB1IfeZom7mh6VgZ+7F1Ddq8fePzVkXT64W8DXQaRd\nIwv0yXHbNUIS1Dm1XHfLgIKc5ADTbEEScVzLskzNJYl39/vpdMuYDAq2lGhZOvvWcyOCwTAfH3Dy\n5o5W2hwBFAowp4ZQmry0yT4OnA+RPSnighivMa3R9u/SFS8VlU72He7A1xtGoYi0i5SV2lmywHpP\n9cZ3u4KcOO2iqsbFidOugeJUq1FQ3NeSUVxkIT119AKeYGIJhWSarvWN4OwLoIw1gnPhXEskhDLH\nSO5UIxaT+BMmEAgEgruDBdNTmT01hR2HGthxqJF/ffsMn5y8xjMbCsmyJ0308gSCCUVc0X0G6DQq\nnn14Jka9ekjWw+ARn/3/PX6hnQ63H6UiMu6yps7JaxW1Y7b6JzqRI167iVKSWFvxnxReOE5nShrv\nb/kvhFJTebq8ILKB14Vm72sonc2EU7NRr3mar2mMA+0jOqUE3Y2jatdw9qi40K4lICmxGmG6rRez\nLjF3xNW2SG5E/bVIbsTaRRrKlmgx6G5NjOj1S3z0iYO3P2ijoyuIRq0gv0BDe8iBShNpGbjZBTFe\nY1r78fSEqDzUwa5K50CrSKpNw6Mb0lm/2n7PFOWyLFPf6BsY2XnxsncgpDDNrqVkWQrFRVbmzTSL\nyQh3Ef0jOAccEA0+Gpp9hAaP4FRCdpZ+IPshf6qRaVMMYgSnQCAQCO56tBoVW0vyWDk3k9cqLlJT\n5+S7Lx1hw9IpbF45Db1WlGaC+5PbeubX1tby3HPP8aUvfYlnnnmG69ev85d/+ZdIkkRaWho//vGP\n0Wq1vPPOO7zyyisolUqefPJJnnjiidu5rAkhWtbD4Dvk/Y9LYZk9x5sJ912j36rVf6SJHIPXMDgk\ns8PVi1IJil4/G3b8mpzGWloyc9i5+Vn8hiTKirIw6jQo2pvQfPI6Cp8bKW8hoeWbQaVBB5Fi2++G\nrmaQwwm1awQluOTQ0epRo0BmWkqA4gIdTufIgoSrJ8zOTwMcPRtCBubkqXh0tY7U5FsrWnu8Ejs/\nbufdj9pweULodUq2bExn4/pU/uH3Vahc8rDn9LsgxmNMqyzLnLngYVelg0+PdREMyahUsLw4mfJS\nO/PnWO6JwD6fT6L67I2Qys7uyKQQpRJmFZhYPN/ConlWcibrhdXxLsDrkyKjNwflP1y9PnwE57Rs\nA7k5hgERYmq2AZ1WCE0CgUAguHdJTzHy37YVcfKig9cqLrLzUCOHzrTyhQcKKJ6RJq5zBPcdt02U\n8Hq9/OAHP2DFihUDP/vJT37C008/zUMPPcQ//uM/sn37drZu3crPfvYztm/fjkajYdu2bZSXl5Oc\nnHy7ljah9Gc9RMMflKi5FH0KR9X5djavnIbZqB3174w2kWOwS6Ofm9tNjh+5xMPvvkx6axMN02ay\n66FnUCcZKCvK4vPrp6OsP4n607dBlggVb0SatfJGO4Ysg6d1VO0a7R4VtQ4tQUmJWScxI82PSSej\nVI4QghmSqTwZpOJoAH8QMu2R3IjCKbd2ervcId7b1cb7u9vx+iSSjCqefDSTR8rSsZjUQ6aN3Mxg\nF0SiotCw1+gO8vF+J7v3Obne1jfVJENHWWkq61baSLbe/cGN11p7qaqOhFSeqfUM3DG3mNSsXWGj\neL6FBXMsmJLEnYM7GZc7NNB6cbnRR12Dl+utQz8bel1kBGd+X/5D3lQDUyYZUKvFhZdAIBAI7j8U\nCgULC9OYnWvj/U8b+OBwA//y1mnm5Nr4YnkhmbZ7c1KaQBCN23alr9VqefHFF3nxxRcHfnb48GFe\neOEFANatW8d//Md/kJuby7x58zCbzQAsWrSI48ePs379+tu1tDuWuFZ/j5/vvnSYJbMyRt3KMZJL\nIxpN1ZfYuv1fSO5ycH7WYirXP05YpcKiU/N4SS7aEx+hPnsAWaMnWPI08uSCG0+WAn3TNXoj7RrW\nbFDHFhYCIbjo0NHeo0ahkMlJ7iVJ4Uaj1AGx1ynLMqfqJN7b78fpkknSw6bVOpbNUd+Sc6CjK8g7\nH7by4V4Hvf4wFrOaZx6fxEPr04ZYyBN1QSQqCkEkxPHEaRcVlQ6OVncTDkdyE9autFFemsqsgqS7\nWj0PhsKcvXAjpHJw4ZqXY4hkQ8y3Mj3XeE+4P+41+kdw1g3Kf6hv8OLoCA7ZLsmoYt4sM3lTDeTn\nRBwQmWIEp0AgEAgEw9BpVDxWmsequZm8uquW05c7+JtfHGbjshw2rZiGTivaFwX3PrdNlFCr1ajV\nQ1/e5/Oh1Ubu9Nvtdtrb23E4HNhstoFtbDYb7e3xR1OmpBhRq8fnA5qWZh6X1xkPzFYDqcl62rt6\noz7e3ROk4thVjAYtX906b0y/IzuBbeo+OcHal/+JJK+b44vXcWTFxgGHQ6DHg3H/66ivXUSZko5h\ny1dQ2TIGnut3deBuvowcltBZ7ZizclGoor9XsizT5IQTzTKBENhMcOXyJXbuaqC9y0dasoHlc7P4\n8uY5w96nxutBXt3p4tzlyFjQB1cmsXWtiSTD2G3fLW29vPpGE+/vuk4gKJNq0/K1P57Coxuy0Ouj\n78Oq+ZN5Z199lJ9PInvSDbfPf/tCMb2BEJ0uPykW3bCewWstPt6vaGFHRQvtzgAABXkmNm/IpHxN\nBua7KNDv5vfK0eHn0LEODh7r4OjJTny+SEilQa+kZLmdlYvtrFhsI9V+b+Rh3E3E+/6TZZlrrb1c\nrPNwoc5DbZ2b2noPnV1DBQhbsoblxTYK803MyDdRmG8mM113V4tndxp30t8pgUAgENweMmxG/vuT\n8zle287ruy/y/qcNHDrTwlMPFLKoMFX8XRXc00xYpSPLw/vw4/18MJ2d3nFZQ1qamfZ297i81niR\nyCSGA9XXeGjplFuayhEL18Fj1D77TYxeL/tLH+X0gtUDj2WqvXzLfgr1NS/hSQX4S57AJxmg3R21\nXcOvTcbfEf298ocU1LZrcXrVKBUy0+0BKo+dHdLm0NbpGyj4t66aBoDbG+aDTwMcPhPJjZg9TcWj\nJTrSUhR4PT14PaPf5+aWXt7c0connzqRJMhI1fLYw5msW2VDo1HidntxxzhNNq/IwesLDHNBbF6R\nE/XcUgPubh9uIlM8jpzoZlelg+qzkW2NBiUb16VSVppK/tSIba/X56PXN/r9mgjS0sy0trq4dNnL\nsZpISGV9w43FZ6XreGCVjeL5VuYUmtBoIiKSHA7Q3h6YqGXflwz+/pPCMtdaeqlv8HG50UtdXxtG\n1BGci6zk9bkf8qYasSXf3EYUxOEIIhgf7sS/U0IkEQgEgtuDQqGgeEY6c3PtvPfpFT443MjP/nCK\neXl2ni4vIGMU4egCwd3EZypKGI1Gent70ev1tLa2kp6eTnp6Og7HjRyFtrY2FixY8Fkua1T4g1LC\nLRBjeW2Pb+SL+bFMbUiEjvcqqPvzvwFZpvW5P+e0esrAY/N0Tr5hO0uSMsSppNkUrvs8fkmmu9OL\n1aBA572eULuGLEOLW80lpxYprCBZLzEj3Y+SUMzRmYdOX6e8OJsjZ8NUHAnQG4AMm5ItJVpmTB37\nKfz/2Xvv6LjO+8z/c6f3wRT0RhSisAAEwSIWkBKLJKrbkmxF6zR7s7tO7BNvnP0l63iz2Z93k8hx\n1snGG3vXcuwkjmzZsiNZXSwSewVIgqRIgChE7xjMAJg+9+4fdzAASLAXkNT7OYcHZYaDd+bOz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YtDyPN/a1sbexj7/7xSkW5jl5/qFSSnOd871EgSCFECXmifQ08w01elypnnR0PMKfvnwYfzAG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\nMYWSEiIiIiIiIiJiiv8FzGZmz2SWeIcAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ci1ISxxrZ7v0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SjdQQCduZ7BV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "cd8d1a67-0048-4f8e-be34-21d856f6f718"
+      },
+      "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": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.86\n",
+            "  period 01 : 214.84\n",
+            "  period 02 : 205.05\n",
+            "  period 03 : 196.43\n",
+            "  period 04 : 189.39\n",
+            "  period 05 : 184.02\n",
+            "  period 06 : 180.76\n",
+            "  period 07 : 178.42\n",
+            "  period 08 : 177.12\n",
+            "  period 09 : 176.12\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>117.8</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>94.6</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>65.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>96.2</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>141.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2940.0</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         117.8    207.3\n",
+              "std           94.6    116.0\n",
+              "min            0.2     15.0\n",
+              "25%           65.1    119.4\n",
+              "50%           96.2    180.4\n",
+              "75%          141.8    265.0\n",
+              "max         2940.0    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 176.12\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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sgAhw8uRJ9u3bx6BBg+jfvz9JSUnk5uZis9lYuXKl83lhYWHOBomnT5929laqTV59+/Yl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\nIKFpGDd/gKFgD2q3nrhH/8z3FQ4N5U2BhMEC8dl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rJvTm9Y/288KyXfz6hmFSX0IIIUS7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\nCSxZsoRu3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297RvopIy5JptLmudpiSK+oauHBJUn8Z/VR9hy53GZmmJLNTub832LPzafT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\ng89sMDFv9um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MVonE3CWk3IUEDaXs8buRJKh1x3jlrQh7jiTJcUo8drcZT7YGRVH57xf7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\nnmUAAAAASUVORK5CYII=\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..8b543c4
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,1902 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "improving_neural_net_performance.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/improving_neural_net_performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F9wILrhH4m9-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "cellView": "both",
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "eV16J6oUY-HN"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Improving Neural Net Performance"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "0Rwl1iXIKxkm"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n",
+        "\n",
+        "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "lBPTONWzKxkn"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, we'll load the data."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "VtYVuONUKxko",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "B8qC-jTIKxkr",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ah6LjMIJ2spZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "c54487ab-f347-4742-db7a-63f00f597b13"
+      },
+      "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/html": [
+              "<div>\n",
+              "<style scoped>\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>2637.3</td>\n",
+              "      <td>538.2</td>\n",
+              "      <td>1421.8</td>\n",
+              "      <td>499.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2129.9</td>\n",
+              "      <td>413.9</td>\n",
+              "      <td>1106.0</td>\n",
+              "      <td>377.0</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>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1463.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2131.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1162.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3143.0</td>\n",
+              "      <td>650.2</td>\n",
+              "      <td>1720.0</td>\n",
+              "      <td>605.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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       2637.3           538.2   \n",
+              "std         2.1        2.0                12.6       2129.9           413.9   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2131.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3143.0           650.2   \n",
+              "max        42.0     -114.5                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1421.8       499.4            3.9               2.0  \n",
+              "std        1106.0       377.0            1.9               1.2  \n",
+              "min           3.0         1.0            0.5               0.1  \n",
+              "25%         790.0       282.0            2.6               1.5  \n",
+              "50%        1162.0       409.0            3.5               2.0  \n",
+              "75%        1720.0       605.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2659.0</td>\n",
+              "      <td>542.4</td>\n",
+              "      <td>1448.3</td>\n",
+              "      <td>505.5</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2295.7</td>\n",
+              "      <td>439.2</td>\n",
+              "      <td>1242.4</td>\n",
+              "      <td>402.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1458.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>789.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2122.0</td>\n",
+              "      <td>432.0</td>\n",
+              "      <td>1180.5</td>\n",
+              "      <td>409.5</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>3162.5</td>\n",
+              "      <td>645.0</td>\n",
+              "      <td>1722.2</td>\n",
+              "      <td>606.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</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>41.3</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       35.6     -119.6                28.7       2659.0           542.4   \n",
+              "std         2.1        2.0                12.6       2295.7           439.2   \n",
+              "min        32.5     -124.3                 1.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1458.0           296.0   \n",
+              "50%        34.3     -118.5                29.0       2122.0           432.0   \n",
+              "75%        37.7     -118.0                37.0       3162.5           645.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1448.3       505.5            3.9               2.0  \n",
+              "std        1242.4       402.0            1.9               1.0  \n",
+              "min           8.0         3.0            0.5               0.0  \n",
+              "25%         789.0       282.0            2.6               1.5  \n",
+              "50%        1180.5       409.5            3.5               1.9  \n",
+              "75%        1722.2       606.2            4.8               2.3  \n",
+              "max       35682.0      5189.0           15.0              41.3  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.6\n",
+              "std                 116.3\n",
+              "min                  15.0\n",
+              "25%                 120.0\n",
+              "50%                 180.1\n",
+              "75%                 265.9\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>206.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>263.6</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                206.5\n",
+              "std                 115.2\n",
+              "min                  15.0\n",
+              "25%                 118.7\n",
+              "50%                 180.9\n",
+              "75%                 263.6\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "NqIbXxx222ea"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Train the Neural Network\n",
+        "\n",
+        "Next, we'll train the neural network."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "6k3xYlSg27VB",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "De9jwyy4wTUT",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural network model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "W-51R3yIKxk4",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    my_optimizer,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A tuple `(estimator, training_losses, validation_losses)`:\n",
+        "      estimator: the trained `DNNRegressor` object.\n",
+        "      training_losses: a `list` containing the training loss values taken during training.\n",
+        "      validation_losses: a `list` containing the validation loss values taken during training.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor, training_rmse, validation_rmse"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "KueReMZ9Kxk7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "outputId": "dc9488ad-023e-445b-fb70-17b293b8f7ca"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 158.06\n",
+            "  period 01 : 149.86\n",
+            "  period 02 : 137.48\n",
+            "  period 03 : 120.75\n",
+            "  period 04 : 110.31\n",
+            "  period 05 : 105.58\n",
+            "  period 06 : 102.44\n",
+            "  period 07 : 101.42\n",
+            "  period 08 : 101.56\n",
+            "  period 09 : 100.25\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   100.25\n",
+            "Final RMSE (on validation data): 99.29\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ldPzcnV3fxWQy0TugO481Gk6ukctne+ew4cxmndwrIiLllgqMDXlUcGTiw03p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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "flxmFt0KKxk9"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Linear Scaling\n",
+        "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Dws5rIQjKxk-",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def linear_scale(series):\n",
+        "  min_val = series.min()\n",
+        "  max_val = series.max()\n",
+        "  scale = (max_val - min_val) / 2.0\n",
+        "  return series.apply(lambda x:((x - min_val) / scale) - 1.0)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MVmuHI76N2Sz"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Normalize the Features Using Linear Scaling\n",
+        "\n",
+        "**Normalize the inputs to the scale -1, 1.**\n",
+        "\n",
+        "**Spend about 5 minutes training and evaluating on the newly normalized data.  How well can you do?**\n",
+        "\n",
+        "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n",
+        "\n",
+        "Sanity check your normalized data.  (What would happen if you forgot to normalize one feature?)\n"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "yD948ZgAM6Cx",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "02b773d9-0fd2-4de1-b9d4-4345fa713603"
+      },
+      "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=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 229.79\n",
+            "  period 01 : 204.09\n",
+            "  period 02 : 154.93\n",
+            "  period 03 : 117.41\n",
+            "  period 04 : 113.31\n",
+            "  period 05 : 108.99\n",
+            "  period 06 : 103.77\n",
+            "  period 07 : 97.66\n",
+            "  period 08 : 90.73\n",
+            "  period 09 : 84.32\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   84.32\n",
+            "Final RMSE (on validation data): 84.12\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Fpg6xmq3M6jyNLgEdOJhxhJzG3zKgWxCnknN55sMfyCtUiRERkYZBBaaOOVdi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\nRK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "jFfc3saSxg6t"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Ax_IIQVRx4gr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n",
+        "\n",
+        "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "D-bJBXrJx-U_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "4ac58898-e769-4ac9-91b3-dd4e63be0416"
+      },
+      "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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 208.09\n",
+            "  period 01 : 117.08\n",
+            "  period 02 : 105.80\n",
+            "  period 03 : 88.14\n",
+            "  period 04 : 77.63\n",
+            "  period 05 : 75.16\n",
+            "  period 06 : 73.54\n",
+            "  period 07 : 73.28\n",
+            "  period 08 : 71.95\n",
+            "  period 09 : 71.36\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   71.36\n",
+            "Final RMSE (on validation data): 69.71\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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WGBi/3FZA14MRERFxpBotMEFBQRXrW3bs2EFERAS9evVi7dq1FBUVkZKSQmpq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\nhjyqbptmrbEAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MrwtdStNJ6ZQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Optimizer\n",
+        "\n",
+        "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+        "\n",
+        "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate.  This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training.  You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`.  Note that you may need to use a larger learning rate with Adagrad.\n",
+        "\n",
+        "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad.  To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "61GSlDvF7-7q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 911
+        },
+        "outputId": "1c549fd7-8714-44d5-89e4-50776ec2965c"
+      },
+      "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)\n",
+        "\n",
+        "_, 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)\n",
+        "\n",
+        "plt.ylabel(\"RMSE\")\n",
+        "plt.xlabel(\"Periods\")\n",
+        "plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "plt.plot(adagrad_training_losses, label='Adagrad training')\n",
+        "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n",
+        "plt.plot(adam_training_losses, label='Adam training')\n",
+        "plt.plot(adam_validation_losses, label='Adam validation')\n",
+        "_ = plt.legend()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 85.43\n",
+            "  period 01 : 73.64\n",
+            "  period 02 : 73.46\n",
+            "  period 03 : 74.77\n",
+            "  period 04 : 70.71\n",
+            "  period 05 : 70.26\n",
+            "  period 06 : 68.77\n",
+            "  period 07 : 67.89\n",
+            "  period 08 : 67.89\n",
+            "  period 09 : 68.77\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   68.77\n",
+            "Final RMSE (on validation data): 67.81\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 210.77\n",
+            "  period 01 : 121.06\n",
+            "  period 02 : 110.03\n",
+            "  period 03 : 96.50\n",
+            "  period 04 : 80.59\n",
+            "  period 05 : 72.59\n",
+            "  period 06 : 71.36\n",
+            "  period 07 : 70.85\n",
+            "  period 08 : 70.87\n",
+            "  period 09 : 70.25\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   70.25\n",
+            "Final RMSE (on validation data): 68.40\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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73fo9e35jy5bNaDRq6tSpz9Sp77N48QLCwy/w9derKSgowNbWlsGDX2LFiiWc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\nKcSIiIhILP0CB22TQV26LoIAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "FSPZIiYgyh93"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "X1QcIeiKyni4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, let's try Adagrad."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ntn4jJxnypGZ",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "5JUsCdRRyso3"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's try Adam."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "lZB8k0upyuY8",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "twYgC8FGyxm6"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's print a graph of loss metrics side by side."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8RHIUEfqyzW0",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.ylabel(\"RMSE\")\n",
+        "plt.xlabel(\"Periods\")\n",
+        "plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "plt.plot(adagrad_training_losses, label='Adagrad training')\n",
+        "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n",
+        "plt.plot(adam_training_losses, label='Adam training')\n",
+        "plt.plot(adam_validation_losses, label='Adam validation')\n",
+        "_ = plt.legend()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "UySPl7CAQ28C"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Explore Alternate Normalization Methods\n",
+        "\n",
+        "**Try alternate normalizations for various features to further improve performance.**\n",
+        "\n",
+        "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n",
+        "\n",
+        "For example, many features have a median of `-0.8` or so, rather than `0.0`."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "QWmm_6CGKxlH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 715
+        },
+        "outputId": "6dd796b9-54e6-48e5-c8ae-60263916a92e"
+      },
+      "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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cOnWKhx9+mNOnT3PdddeRlJTE3LlzSUlJobKy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\na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+            "text/plain": [
+              "<Figure size 1296x864 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Xx9jgEMHKxlJ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We might be able to do better by choosing additional ways to transform these features.\n",
+        "\n",
+        "For example, a log scaling might help some features.  Or clipping extreme values may make the remainder of the scale more informative."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "baKZa6MEKxlK",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def log_normalize(series):\n",
+        "  return series.apply(lambda x:math.log(x+1.0))\n",
+        "\n",
+        "def clip(series, clip_to_min, clip_to_max):\n",
+        "  return series.apply(lambda x:(\n",
+        "    min(max(x, clip_to_min), clip_to_max)))\n",
+        "\n",
+        "def z_score_normalize(series):\n",
+        "  mean = series.mean()\n",
+        "  std_dv = series.std()\n",
+        "  return series.apply(lambda x:(x - mean) / std_dv)\n",
+        "\n",
+        "def binary_threshold(series, threshold):\n",
+        "  return series.apply(lambda x:(1 if x > threshold else 0))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-wCCq_ClKxlO"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n",
+        "\n",
+        "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8ToG-mLfMO9P",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "e0e5b4e3-579a-4ec1-ca91-d545ea4136fa"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  processed_features = pd.DataFrame()\n",
+        "\n",
+        "  processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n",
+        "  \n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "\n",
+        "  processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n",
+        "\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 215.53\n",
+            "  period 01 : 139.79\n",
+            "  period 02 : 113.89\n",
+            "  period 03 : 112.06\n",
+            "  period 04 : 109.84\n",
+            "  period 05 : 106.89\n",
+            "  period 06 : 102.93\n",
+            "  period 07 : 98.05\n",
+            "  period 08 : 92.69\n",
+            "  period 09 : 87.29\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   87.29\n",
+            "Final RMSE (on validation data): 86.69\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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gsDLIjn3aGlpERCTcFGBOgUOrkSyJOdrUTkREpBEowJwCab5Du/LmkLlZ+8GI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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "GhFtWjQRzD2l"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "OMoIsUMmzK9b"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "These are only a few ways in which we could think about the data. Other transformations may work even better!\n",
+        "\n",
+        "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n",
+        "\n",
+        "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n",
+        "\n",
+        "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XDEYkPquzYCH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "3c987496-f934-4186-9d07-5bcaba39f30e"
+      },
+      "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 : 84.94\n",
+            "  period 01 : 75.36\n",
+            "  period 02 : 75.72\n",
+            "  period 03 : 72.24\n",
+            "  period 04 : 71.95\n",
+            "  period 05 : 71.84\n",
+            "  period 06 : 70.89\n",
+            "  period 07 : 71.53\n",
+            "  period 08 : 70.30\n",
+            "  period 09 : 69.70\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.70\n",
+            "Final RMSE (on validation data): 68.49\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ujVdf4HPa1NLGjRsZNGgQADExMRw9epTy8nJuv/12xo8fz5VXXnnSPlFRUQwd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\n/wE8kf6ybe6qUQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "b7atJTbzU9Ca"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Use only Latitude and Longitude Features\n",
+        "\n",
+        "**Train a NN model that uses only latitude and longitude as features.**\n",
+        "\n",
+        "Real estate people are fond of saying that location is the only important feature in housing price.\n",
+        "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n",
+        "\n",
+        "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n",
+        "\n",
+        "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5McjahpamOc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "8be30608-da75-4ec2-c036-52f8c90e0c3c"
+      },
+      "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=1000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10, 5, 5, 5],\n",
+        "    training_examples=lll_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=lll_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 103.96\n",
+            "  period 01 : 101.61\n",
+            "  period 02 : 100.03\n",
+            "  period 03 : 99.11\n",
+            "  period 04 : 98.32\n",
+            "  period 05 : 97.89\n",
+            "  period 06 : 97.77\n",
+            "  period 07 : 97.55\n",
+            "  period 08 : 97.38\n",
+            "  period 09 : 97.55\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   97.55\n",
+            "Final RMSE (on validation data): 95.78\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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3332DxMSERz4+c+bbdVVSndNpgImMjMTgwYOxevVqAEBSUhJCQkIQHByMGTNmoLS0tNLz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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "P8BLQ7T71JWd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "1hwaFCE71OPZ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "It's a good idea to keep latitude and longitude normalized:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "djKtt4mz1ZEc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "1586ec36-00bf-407b-92bb-d51f951d9a5d"
+      },
+      "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": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 112.13\n",
+            "  period 01 : 106.69\n",
+            "  period 02 : 104.46\n",
+            "  period 03 : 103.46\n",
+            "  period 04 : 102.37\n",
+            "  period 05 : 101.41\n",
+            "  period 06 : 101.24\n",
+            "  period 07 : 100.97\n",
+            "  period 08 : 101.06\n",
+            "  period 09 : 101.13\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   101.13\n",
+            "Final RMSE (on validation data): 99.44\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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kGf03akBAAM6dO4fy8nJUVVUhISEBgwYNMnZY7TLs5sq8WY1X5lVYWSBsoAeK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\nNMf8AAAAAElFTkSuQmCC\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..189e469
--- /dev/null
+++ b/intro_to_neural_nets.ipynb
@@ -0,0 +1,1254 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_neural_nets.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/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": "CI_oHuOR2rWa",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "eV16J6oUY-HN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_wIcUFLSKNdx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n",
+        "  * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_ZZ7f7prKNdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n",
+        "\n",
+        "One important set of nonlinearities was around latitude and longitude, but there may be others.\n",
+        "\n",
+        "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "J2kqX6VZTHUy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's load and prepare the data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AGOM1TUiKNdz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2I8E2qhyKNd4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pQzcj2B1T5dA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "567d5e09-d340-4127-f0e9-22ab0a6c6f7d"
+      },
+      "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/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2646.0</td>\n",
+              "      <td>540.0</td>\n",
+              "      <td>1430.3</td>\n",
+              "      <td>501.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2163.3</td>\n",
+              "      <td>422.4</td>\n",
+              "      <td>1164.0</td>\n",
+              "      <td>384.9</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>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>1458.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>789.0</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>2129.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1165.0</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>3158.0</td>\n",
+              "      <td>650.0</td>\n",
+              "      <td>1720.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.7       2646.0           540.0   \n",
+              "std         2.1        2.0                12.6       2163.3           422.4   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1458.0           296.0   \n",
+              "50%        34.3     -118.5                29.0       2129.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3158.0           650.0   \n",
+              "max        42.0     -114.3                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1430.3       501.4            3.9               2.0  \n",
+              "std        1164.0       384.9            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         789.0       281.0            2.6               1.5  \n",
+              "50%        1165.0       408.0            3.5               1.9  \n",
+              "75%        1720.0       606.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.4</td>\n",
+              "      <td>2638.2</td>\n",
+              "      <td>537.9</td>\n",
+              "      <td>1427.8</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>12.5</td>\n",
+              "      <td>2219.6</td>\n",
+              "      <td>419.3</td>\n",
+              "      <td>1108.2</td>\n",
+              "      <td>383.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1469.0</td>\n",
+              "      <td>297.8</td>\n",
+              "      <td>791.8</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2123.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1174.0</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>3121.0</td>\n",
+              "      <td>646.2</td>\n",
+              "      <td>1723.2</td>\n",
+              "      <td>605.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>16122.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.4       2638.2           537.9   \n",
+              "std         2.1        2.0                12.5       2219.6           419.3   \n",
+              "min        32.5     -124.3                 2.0         12.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1469.0           297.8   \n",
+              "50%        34.2     -118.5                28.0       2123.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3121.0           646.2   \n",
+              "max        42.0     -114.6                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1427.8       500.7            3.9               2.0  \n",
+              "std        1108.2       383.5            1.9               1.2  \n",
+              "min           8.0         3.0            0.5               0.1  \n",
+              "25%         791.8       282.0            2.6               1.5  \n",
+              "50%        1174.0       410.0            3.5               1.9  \n",
+              "75%        1723.2       605.0            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              52.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.6\n",
+              "std                 116.4\n",
+              "min                  15.0\n",
+              "25%                 119.4\n",
+              "50%                 180.4\n",
+              "75%                 265.6\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>206.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>17.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>263.6</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                206.5\n",
+              "std                 115.0\n",
+              "min                  17.5\n",
+              "25%                 119.8\n",
+              "50%                 180.4\n",
+              "75%                 263.6\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RWq0xecNKNeG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Building a Neural Network\n",
+        "\n",
+        "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+        "\n",
+        "Use **`hidden_units`** to define the structure of the NN.  The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it.  For example, consider the following assignment:\n",
+        "\n",
+        "`hidden_units=[3,10]`\n",
+        "\n",
+        "The preceding assignment specifies a neural net with two hidden layers:\n",
+        "\n",
+        "* The first hidden layer contains 3 nodes.\n",
+        "* The second hidden layer contains 10 nodes.\n",
+        "\n",
+        "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+        "\n",
+        "By default, all hidden layers will use ReLu activation and will be fully connected."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ni0S6zHcTb04",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zvCqgNdzpaFg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural net regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                             \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U52Ychv9KNeH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `DNNRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2QhdcCy-Y8QR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train a NN Model\n",
+        "\n",
+        "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+        "\n",
+        "Run the following block to train a NN model.  \n",
+        "\n",
+        "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good.  We'll aim to beat that.\n",
+        "\n",
+        "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+        "\n",
+        "Overfitting is a real potential hazard for NNs.  You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+        "\n",
+        "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+        "\n",
+        "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rXmtSW1yKNeK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "outputId": "306e0b02-b6be-409e-b416-3c2d6142e04b"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.002,\n",
+        "    steps=1500,\n",
+        "    batch_size=150,\n",
+        "    hidden_units=[10, 8],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 158.23\n",
+            "  period 01 : 146.86\n",
+            "  period 02 : 142.60\n",
+            "  period 03 : 124.23\n",
+            "  period 04 : 121.28\n",
+            "  period 05 : 120.42\n",
+            "  period 06 : 117.62\n",
+            "  period 07 : 116.22\n",
+            "  period 08 : 114.49\n",
+            "  period 09 : 116.34\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   116.34\n",
+            "Final RMSE (on validation data): 114.10\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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BPcI4lV5EQkqhjasVERFHpwAjTcbD2Z07u9/K/b3uxM3Jlc+Pf8Mb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\nftMAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "O2q5RRCKqYaU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "j2Yd5VfrqcC3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IjkpSqmxqnSM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "e5167b2d-897d-4e51-db06-be95485216a9"
+      },
+      "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 : 159.51\n",
+            "  period 01 : 153.69\n",
+            "  period 02 : 146.55\n",
+            "  period 03 : 139.72\n",
+            "  period 04 : 133.58\n",
+            "  period 05 : 122.52\n",
+            "  period 06 : 115.36\n",
+            "  period 07 : 106.31\n",
+            "  period 08 : 112.93\n",
+            "  period 09 : 107.65\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   107.65\n",
+            "Final RMSE (on validation data): 108.70\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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fU+rZm2q63EqjjdkIyUWbSTbaS7IpH2lgKkBbdypFUQhPiWRz1A6S81Ix0zdl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\nAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c6diezCSeH4Y",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Evaluate on Test Data\n",
+        "\n",
+        "**Confirm that your validation performance results hold up on test data.**\n",
+        "\n",
+        "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n",
+        "\n",
+        "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "0d719aed-f68f-4ba9-9421-1a2b3db2971f"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\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): 107.13\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vvT2jDWjrKew",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FyDh7Qy6rQb0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+        "\n",
+        "Note that we don't have to randomize the test data, since we will use all records."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vhb0CtdvrWZx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ea1b83b0-030a-4e81-c02f-94789666f8f7"
+      },
+      "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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 107.13\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9Om8sSHq4PzY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..1382779
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1888 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_pandas.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/intro_to_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "rHLcriKWLRe4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to pandas"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "QvJBqX8_Bctk"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n",
+        "  * Access and manipulate data within a `DataFrame` and `Series`\n",
+        "  * Import CSV data into a *pandas* `DataFrame`\n",
+        "  * Reindex a `DataFrame` to shuffle data"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TIFJ83ZTBctl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n",
+        "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "s_JOISVgmn9v"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Basic Concepts\n",
+        "\n",
+        "The following line imports the *pandas* API and prints the API version:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "aSRYu62xUi3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f631213a-3793-44c2-c29c-7a0b3f55e6fc"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import pandas as pd\n",
+        "pd.__version__"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'0.22.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "daQreKXIUslr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The primary data structures in *pandas* are implemented as two classes:\n",
+        "\n",
+        "  * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n",
+        "  * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
+        "\n",
+        "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fjnAk1xcU0yc"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to create a `Series` is to construct a `Series` object. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "DFZ42Uq7UFDj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "f8062802-00b4-4e3b-9bb7-66dff93a2f7b"
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+      ],
+      "execution_count": 3,
+      "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": 3
+        }
+      ]
+    },
+    {
+      "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": "5fc54d56-0203-4ceb-8bba-51c0a8a8954c"
+      },
+      "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": 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>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": 4
+        }
+      ]
+    },
+    {
+      "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": "01a460c1-e37d-4c27-c2be-923117093dc4"
+      },
+      "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": 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>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": 5
+        }
+      ]
+    },
+    {
+      "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": "00ba6cf9-32dd-4943-8c5d-b1abd9dbd8b7"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.head()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>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": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "w9-Es5Y6laGd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "nqndFVXVlbPN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 396
+        },
+        "outputId": "4ea87b1f-4134-4f1d-d1e5-f18fa43e4cc6"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.hist('housing_median_age')"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fa125c06588>]],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wQNnZ2ZKkvLw89evXryGOCQCAn4Uaz6g//PBDFRcXa8aMGb5to0aN0owZM9SmTRuFhIRo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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "XtYZ7114n3b-"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Accessing Data\n",
+        "\n",
+        "You can access `DataFrame` data using familiar Python dict/list operations:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "_TFm7-looBFF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        },
+        "outputId": "face4019-85a6-4353-e287-0ac1686c5ae8"
+      },
+      "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": 8,
+      "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": 8
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "V5L6xacLoxyv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "1d01b3fe-3bf2-40b1-f4c2-dc2c3f413f11"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities['City name'][1]))\n",
+        "cities['City name'][1]"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'str'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'San Jose'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "gcYX1tBPugZl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 128
+        },
+        "outputId": "51e50856-01aa-4fb6-88b8-b4c0a99afe69"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities[0:2]))\n",
+        "cities[0:2]"
+      ],
+      "execution_count": 10,
+      "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": 10
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "65g1ZdGVjXsQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "RM1iaD-ka3Y1"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Manipulating Data\n",
+        "\n",
+        "You may apply Python's basic arithmetic operations to `Series`. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XWmyCFJ5bOv-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "1cbdc769-a786-4a38-8e05-f3017abf6be5"
+      },
+      "cell_type": "code",
+      "source": [
+        "population / 1000."
+      ],
+      "execution_count": 11,
+      "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": 11
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TQzIVnbnmWGM"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "ko6pLK6JmkYP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "e22b1f1c-3347-4a79-eb8f-4ed7df40af2c"
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.log(population)"
+      ],
+      "execution_count": 12,
+      "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": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "xmxFuQmurr6d"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n",
+        "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n",
+        "\n",
+        "The example below creates a new `Series` that indicates whether `population` is over one million:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Fc1DvPAbstjI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "b8066882-7e16-4b9a-ee3d-dba7e93066be"
+      },
+      "cell_type": "code",
+      "source": [
+        "population.apply(lambda val: val > 1000000)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "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": "09c910fe-7529-47a6-a60f-2441c0899c0c"
+      },
+      "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": 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",
+              "    </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": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "6qh63m-ayb-c"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #1\n",
+        "\n",
+        "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
+        "\n",
+        "  * The city is named after a saint.\n",
+        "  * The city has an area greater than 50 square miles.\n",
+        "\n",
+        "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
+        "\n",
+        "**Hint:** \"San\" in Spanish means \"saint.\""
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "zCOn8ftSyddH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "6f41f907-425e-4266-8678-6f2510d9e54a"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Wide and Saint Name?'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name : name.startswith('San'))\n",
+        "cities"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Wide and Saint Name?</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Wide and Saint Name?  \n",
+              "0                 False  \n",
+              "1                  True  \n",
+              "2                 False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "YHIWvc9Ms-Ll"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5OlrqtdtCIb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "0de29896-7d3c-4581-ca3c-dfdeb84c6502"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Wide and Saint Name?</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Wide and Saint Name?  Is wide and has saint name  \n",
+              "0                 False                       False  \n",
+              "1                  True                        True  \n",
+              "2                 False                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "f-xAOJeMiXFB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Indexes\n",
+        "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n",
+        "\n",
+        "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "2684gsWNinq9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "651bbe5f-c8e8-4b2f-d27a-5ee5cfb52b3d"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names.index"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "F_qPe2TBjfWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "c2e9cde9-8cab-4190-d3c3-15d75d326f67"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.index"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "hp2oWY9Slo_h"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "sN0zUzSAj-U1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "5d040a10-a36b-416c-ae83-20506d5206f9"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([2, 0, 1])"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Wide and Saint Name?</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "\n",
+              "   Wide and Saint Name?  Is wide and has saint name  \n",
+              "2                 False                       False  \n",
+              "0                 False                       False  \n",
+              "1                  True                        True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-GQFz8NZuS06"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
+        "Try running the following cell multiple times!"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "mF8GC0k8uYhz",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "9eba0058-f523-4ce6-adc0-02d3b532b156"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex(np.random.permutation(cities.index))"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Wide and Saint Name?</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "\n",
+              "   Wide and Saint Name?  Is wide and has saint name  \n",
+              "1                  True                        True  \n",
+              "2                 False                       False  \n",
+              "0                 False                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fSso35fQmGKb"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8UngIdVhz8C0"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #2\n",
+        "\n",
+        "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "PN55GrDX0jzO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "outputId": "3cab8e50-9fae-4baa-ce77-296ba90f56e8"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([6,3,7,1])"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Wide and Saint Name?</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785.0</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.17776</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  City name  Population  Area square miles  Population density  \\\n",
+              "6       NaN         NaN                NaN                 NaN   \n",
+              "3       NaN         NaN                NaN                 NaN   \n",
+              "7       NaN         NaN                NaN                 NaN   \n",
+              "1  San Jose   1015785.0             176.53          5754.17776   \n",
+              "\n",
+              "  Wide and Saint Name? Is wide and has saint name  \n",
+              "6                  NaN                        NaN  \n",
+              "3                  NaN                        NaN  \n",
+              "7                  NaN                        NaN  \n",
+              "1                 True                       True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TJffr5_Jwqvd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8oSvi2QWwuDH"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "yBdkucKCwy4x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "outputId": "63e24eec-7b6d-444e-faaf-0320dca3e79a"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Wide and Saint Name?</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469.0</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199.0</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco    852469.0              46.87        18187.945381   \n",
+              "4            NaN         NaN                NaN                 NaN   \n",
+              "5            NaN         NaN                NaN                 NaN   \n",
+              "2     Sacramento    485199.0              97.92         4955.055147   \n",
+              "\n",
+              "  Wide and Saint Name? Is wide and has saint name  \n",
+              "0                False                      False  \n",
+              "4                  NaN                        NaN  \n",
+              "5                  NaN                        NaN  \n",
+              "2                False                      False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "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..894893a
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1708 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "logistic_regression.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sPm_6-TTqniO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Logistic Regression"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LEAHZv4rIYHX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n",
+        "  * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CnkCZqdIIYHY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9pltCyy2K3dd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Frame the Problem as Binary Classification\n",
+        "\n",
+        "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n",
+        "\n",
+        "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "67IJwZX1Vvjt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and prepare the input features and targets."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fOlbcJ4EIYHd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "lTB73MNeIYHf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kPSqspaqIYHg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FwOYWmXqWA6D",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "f08d785b-206b-49a6-9f0a-26fc7958d933"
+      },
+      "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/html": [
+              "<div>\n",
+              "<style scoped>\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>2652.9</td>\n",
+              "      <td>541.4</td>\n",
+              "      <td>1429.3</td>\n",
+              "      <td>503.3</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2203.6</td>\n",
+              "      <td>426.0</td>\n",
+              "      <td>1174.2</td>\n",
+              "      <td>389.9</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>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>1462.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>786.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2141.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1163.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3157.2</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1721.0</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>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>"
+            ],
+            "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       2652.9           541.4   \n",
+              "std         2.1        2.0                12.6       2203.6           426.0   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1462.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2141.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3157.2           651.0   \n",
+              "max        41.9     -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       1429.3       503.3            3.9               2.0  \n",
+              "std        1174.2       389.9            1.9               1.1  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         786.0       281.0            2.6               1.5  \n",
+              "50%        1163.0       409.0            3.6               1.9  \n",
+              "75%        1721.0       607.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              52.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2621.4</td>\n",
+              "      <td>534.6</td>\n",
+              "      <td>1430.1</td>\n",
+              "      <td>496.2</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>2122.1</td>\n",
+              "      <td>410.5</td>\n",
+              "      <td>1082.1</td>\n",
+              "      <td>371.4</td>\n",
+              "      <td>1.8</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>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.8</td>\n",
+              "      <td>796.0</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>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2103.5</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1174.0</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>3127.2</td>\n",
+              "      <td>646.0</td>\n",
+              "      <td>1720.0</td>\n",
+              "      <td>600.0</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>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>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.7     -119.6                28.7       2621.4           534.6   \n",
+              "std         2.1        2.0                12.5       2122.1           410.5   \n",
+              "min        32.5     -124.3                 2.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1462.0           297.8   \n",
+              "50%        34.3     -118.5                29.0       2103.5           433.0   \n",
+              "75%        37.7     -118.0                37.0       3127.2           646.0   \n",
+              "max        42.0     -114.6                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1430.1       496.2            3.8               2.0  \n",
+              "std        1082.1       371.4            1.8               1.3  \n",
+              "min          13.0         1.0            0.5               0.1  \n",
+              "25%         796.0       283.0            2.6               1.5  \n",
+              "50%        1174.0       407.0            3.5               1.9  \n",
+              "75%        1720.0       600.0            4.7               2.3  \n",
+              "max       15507.0      5050.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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_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>"
+            ],
+            "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"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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_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>"
+            ],
+            "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"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "uon1LB3A31VN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## How Would Linear Regression Fare?\n",
+        "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n",
+        "\n",
+        "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "smmUYRDtWOV_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "B5OwSrr1yIKD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "SE2-hq8PIYHz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_regressor_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    \n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "TDBD8xeeIYH2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "04e9cb22-baaf-4fc5-b20f-f3cf54155ade"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_linear_regressor_model(\n",
+        "    learning_rate=0.0001,\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.51\n",
+            "  period 01 : 0.51\n",
+            "  period 02 : 0.54\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.59\n",
+            "  period 06 : 0.59\n",
+            "  period 07 : 0.62\n",
+            "  period 08 : 0.64\n",
+            "  period 09 : 0.63\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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IIS5ksTCzd+9e0tLSiIuL46WXXuKll16q8/wrr7zCPffcw6pVq9BoNGRnZ5ufW758OR4e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\n17fVAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JjBZ_q7aD9gh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Can We Calculate LogLoss for These Predictions?\n",
+        "\n",
+        "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n",
+        "\n",
+        "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability.  For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n",
+        "\n",
+        "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily.  Remember, `LogLoss` is defined as:\n",
+        "\n",
+        "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n",
+        "\n",
+        "\n",
+        "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n",
+        "\n",
+        "Given the predictions and the targets, can we calculate `LogLoss`?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dPpJUV862FYI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to display the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kXFQ5uig2RoP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "outputId": "436d019d-cec4-49aa-a1c0-5f4e75cd1a8a"
+      },
+      "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": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rYpy336F9wBg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n",
+        "\n",
+        "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n",
+        "\n",
+        "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JElcb--E9wBm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VM0wmnFUIYH9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "2ec6bd28-56b5-4450-a8dd-dbd90334a7ce"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.60\n",
+            "  period 01 : 0.61\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.57\n",
+            "  period 06 : 0.55\n",
+            "  period 07 : 0.54\n",
+            "  period 08 : 0.54\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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E8N8t59gZn8FdE3sYtX0hhBAdi8yNbYa31otwn1C87D07TCHzs3DPPvTzCONC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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i2e3TlyL57Qs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see the solution.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5YxXd2hn6MuF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)  \n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UPM_T1FXsTaL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "def0cca4-a478-4c5c-9826-902bd5447424"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.62\n",
+            "  period 01 : 0.60\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.55\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.54\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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xMg8Hd/4Y9QcG+fUntSyd1+LfJasiR+lYbW5ob386+7uyLzGPjNwLSscRQgjR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\nRDzGAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-Xo83_aR6s_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n",
+        "\n",
+        "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n",
+        "\n",
+        "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DKSQ87VVIYIA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "7574a8b9-beba-4f37-d6fd-4a8232545c55"
+      },
+      "cell_type": "code",
+      "source": [
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AUC on the validation set: 0.73\n",
+            "Accuracy on the validation set: 0.76\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": "b62f9dd0-1183-4909-acf1-594669fb4844"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "# Get just the probabilities for the positive class.\n",
+        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
+        "\n",
+        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
+        "    validation_targets, validation_probabilities)\n",
+        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
+        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
+        "_ = plt.legend(loc=2)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lpFFQX4i/ux8PJs/mlpABapcluoiEsBCdoKqumd0nitm095xj28tPjCZE9veK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\nxCT/CzmTCYstEyn2AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PIdhwfgzIYII",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+        "\n",
+        "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+        "\n",
+        "**Verify if all metrics improve at the same time.**"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XKIqjsqcCaxO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "6ca74e87-b137-4f83-b841-8148e2178a73"
+      },
+      "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=1000,\n",
+        "    batch_size=100,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\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": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.57\n",
+            "  period 01 : 0.54\n",
+            "  period 02 : 0.54\n",
+            "  period 03 : 0.54\n",
+            "  period 04 : 0.53\n",
+            "  period 05 : 0.52\n",
+            "  period 06 : 0.52\n",
+            "  period 07 : 0.52\n",
+            "  period 08 : 0.52\n",
+            "  period 09 : 0.52\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.78\n",
+            "Accuracy on the validation set: 0.79\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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JthzMh95gELscIiKiXscwY4GYANPozLb8XTY3OuPm4ohJEb7QVTXiUFbpjTcg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\nRBTTGGaIiIgopv0/EESJa+GKBLcAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wCugvl0JdWYL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VHosS1g2aetf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One possible solution that works is to just train for longer, as long as we don't overfit. \n",
+        "\n",
+        "We can do this by increasing the number the steps, the batch size, or both.\n",
+        "\n",
+        "All metrics improve at the same time, so our loss metric is a good proxy\n",
+        "for both AUC and accuracy.\n",
+        "\n",
+        "Notice how it takes many, many more iterations just to squeeze a few more \n",
+        "units of AUC. This commonly happens. But often even this small gain is worth \n",
+        "the costs."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dWgTEYMddaA-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "f561b01a-e225-4e0b-d133-69509f783e59"
+      },
+      "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": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.50\n",
+            "  period 01 : 0.49\n",
+            "  period 02 : 0.48\n",
+            "  period 03 : 0.48\n",
+            "  period 04 : 0.48\n",
+            "  period 05 : 0.48\n",
+            "  period 06 : 0.48\n",
+            "  period 07 : 0.48\n",
+            "  period 08 : 0.48\n",
+            "  period 09 : 0.47\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.82\n",
+            "Accuracy on the validation set: 0.81\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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pqJTtOJ17vsU6qURAQ+P93+5tiHoQZvZ9pN3Hx42bgGPHDuOxx57AkSOHMG7cBHh4eGLc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\njIiIiKS1/wfxPY4GLWIQ/AAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7hlfw_jNwv6r",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb
new file mode 100644
index 0000000..3589ae9
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1204 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "sparsity_and_l1_regularization.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/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": "FP5ZbKRnzEG7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Sparsity and L1 Regularization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "g8ue2FyFIjnQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Calculate the size of a model\n",
+        "  * Apply L1 regularization to reduce the size of a model by increasing sparsity"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ME_WXE7cIjnS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n",
+        "\n",
+        "L1 regularization is a good way to increase sparsity.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fHRzeWkRLrHF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and create feature definitions."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pb7rSrLKIjnS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3V7q8jk0IjnW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pAG3tmgwIjnY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "deb91baa-8d6a-44c3-d88d-519f8e91fa7a"
+      },
+      "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/html": [
+              "<div>\n",
+              "<style scoped>\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>2652.6</td>\n",
+              "      <td>541.8</td>\n",
+              "      <td>1433.6</td>\n",
+              "      <td>502.8</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>12.5</td>\n",
+              "      <td>2226.9</td>\n",
+              "      <td>430.6</td>\n",
+              "      <td>1140.2</td>\n",
+              "      <td>391.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1453.0</td>\n",
+              "      <td>296.0</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>2131.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1170.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3160.2</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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       2652.6           541.8   \n",
+              "std         2.1        2.0                12.5       2226.9           430.6   \n",
+              "min        32.5     -124.3                 2.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2131.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3160.2           652.0   \n",
+              "max        42.0     -114.5                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1433.6       502.8            3.9               2.0  \n",
+              "std        1140.2       391.5            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%        1170.0       410.0            3.5               1.9  \n",
+              "75%        1721.0       607.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              52.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2622.2</td>\n",
+              "      <td>533.6</td>\n",
+              "      <td>1419.9</td>\n",
+              "      <td>497.5</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>12.7</td>\n",
+              "      <td>2062.9</td>\n",
+              "      <td>398.9</td>\n",
+              "      <td>1166.1</td>\n",
+              "      <td>367.3</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>1.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.0</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>1480.8</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>793.0</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2116.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1159.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3128.2</td>\n",
+              "      <td>641.0</td>\n",
+              "      <td>1720.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>27700.0</td>\n",
+              "      <td>4819.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>4769.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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       2622.2           533.6   \n",
+              "std         2.1        2.0                12.7       2062.9           398.9   \n",
+              "min        32.6     -124.3                 1.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1480.8           300.0   \n",
+              "50%        34.2     -118.5                29.0       2116.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3128.2           641.0   \n",
+              "max        41.9     -114.3                52.0      27700.0          4819.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1419.9       497.5            3.9               2.0  \n",
+              "std        1166.1       367.3            1.9               1.1  \n",
+              "min           9.0         3.0            0.5               0.2  \n",
+              "25%         793.0       284.0            2.6               1.5  \n",
+              "50%        1159.0       408.0            3.6               1.9  \n",
+              "75%        1720.2       603.0            4.8               2.3  \n",
+              "max       35682.0      4769.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gHkniRI1Ijna",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bLzK72jkNJPf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_buckets(feature_values, num_buckets):\n",
+        "  quantiles = feature_values.quantile(\n",
+        "    [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n",
+        "  return [quantiles[q] for q in quantiles.keys()]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "al2YQpKyIjnd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"households\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"longitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"latitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"housing_median_age\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"housing_median_age\"], 10))\n",
+        "  bucketized_total_rooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_rooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n",
+        "  bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_bedrooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n",
+        "  bucketized_population = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"population\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"median_income\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"rooms_per_person\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"rooms_per_person\"], 10))\n",
+        "\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "    set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n",
+        "\n",
+        "  feature_columns = set([\n",
+        "    long_x_lat,\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_total_rooms,\n",
+        "    bucketized_total_bedrooms,\n",
+        "    bucketized_population,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hSBwMrsrE21n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Calculate the Model Size\n",
+        "\n",
+        "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "e6GfTI0CFhB8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def model_size(estimator):\n",
+        "  variables = estimator.get_variable_names()\n",
+        "  size = 0\n",
+        "  for variable in variables:\n",
+        "    if not any(x in variable \n",
+        "               for x in ['global_step',\n",
+        "                         'centered_bias_weight',\n",
+        "                         'bias_weight',\n",
+        "                         'Ftrl']\n",
+        "              ):\n",
+        "      size += np.count_nonzero(estimator.get_variable_value(variable))\n",
+        "  return size"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XabdAaj67GfF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Reduce the Model Size\n",
+        "\n",
+        "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n",
+        "\n",
+        "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n",
+        "\n",
+        "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G79hGRe7qqej",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1: Find a good regularization coefficient.\n",
+        "\n",
+        "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n",
+        "\n",
+        "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n",
+        "\n",
+        "Again, the model will train on the entire data set, so expect it to run slower than normal."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1Fcdm0hpIjnl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    regularization_strength,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    regularization_strength: A `float` that indicates the strength of the L1\n",
+        "       regularization. A value of `0.0` means no regularization.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 7\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on validation data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9H1CKHSzIjno",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 707
+        },
+        "outputId": "864a1a86-b891-4489-9b5a-3cad90756ea6"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    # TWEAK THE REGULARIZATION VALUE BELOW\n",
+        "    regularization_strength=0.05,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\n",
+            "  period 01 : 0.28\n",
+            "  period 02 : 0.27\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.25\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.24\n",
+            "Model training finished.\n",
+            "Model size: 766\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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BmDHj2LHjC37+85/wz3++QkFBQZ37nTmTTP/+tVceBg0aYn3ey8uLhQt/xaOPPkxKytcU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\nIiKSNJYZIiIikjSWGSIiIpK0/w8VYMzS3homOAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yjUCX5LAkxAX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hgGhy-okmkWL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n",
+        "stronger regularization gives us smaller models, but can affect the classification loss."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_rV8YQWZIjns",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 588
+        },
+        "outputId": "b2acb41f-16b8-4b1f-c9b0-e09815eb795f"
+      },
+      "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 : 0.25\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.24\n",
+            "Model training finished.\n",
+            "Model size: 755\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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cXe3Jyrpmsv3Xh61WIXhgZ7YdOc/Kb2OZGuSNp6U3vdr5EXMlnu/jfiDQEGDW\nGm/UHDJraSQz9SQz9SQz9SQz9UyZWV1NkskuMxkMBrKzs2teZ2Zm4urqCkBeXh7Hjh0DwNramqCg\nIKKiouoc05bdP8wTZ3srth5O43JOUfWTgbs/gFaj5ZvEbymtuG7uEoUQQgizMVkzM2zYMCIiIgCI\njY3FYDDUXC6qqKjg+eefp6ioCICYmBi8vLzqHNOWWVtqmTm2O5VVRr74PgGj0YirbTuCu4wk73o+\nW1N3mLtEIYQQwmxMdpkpMDAQf39/QkNDURSFRYsWER4ejr29PcHBwcybN49Zs2ah1Wrx8/NjzJgx\nKIpyyxhRLdDXlQCfdkQn53DkzBUG93JnXNdRHM04zq4L+xnc4S462LmZu0whhBCiySnGFv44WVNe\nz2xu10sz80p46eMj2FppWTJnMLbWWmKy4/h39Kf4OvnwVP+5Zp8M3NwyawkkM/UkM/UkM/UkM/Va\n3ZwZ0fgMTjZMGtKV/KIy1u8/B0Cf9r3o074nCXnJHM88ZeYKhRBCiKYnzUwLE3J3V9xcbNkVdZG0\njOrud1r3yeg0WsITv6WkotTMFQohhBBNS5qZFkan1fDIOF+MRvgs4ixVRiPtbVwY13UU+WXX2JKy\n3dwlCiGEEE1KmpkWyN/ThUE9DaRcLmDfyXQAgruMpL21C3suHiS9MMPMFQohhBBNR5qZFmrG6O5Y\nW1rwzd5kCorK0FnoeMh3MlXGKtYkrK/XAweFEEKI1kCamRbK2d6KKUHeFJVW8PXuJAB6t+9J3/b+\nJOWlcOzKCTNXKIQQQjQNaWZasNGBHeli0HPwdAZnz+cC8GD3+9BpdIQnfUdJRYmZKxRCCCFMT5qZ\nFsxCoyEsxA8F+OL7BCoqq2hn40KI52iulRWy+ZxMBhZCCNH6STPTwvl4OBLUz4NL2UVsj7wAwJgu\nIzDYtGfPxYNcvJZu5gqFEEII05JmphV4cIQPehsdGw+kcLWgFJ1Gy0O+kzFiZE3CBpkMLIQQolWT\nZqYV0NvomD6qG2XlVXy1IxGAXu386Ofah3P5qRzJOG7mCoUQQgjTkWamlRjWxx3fTo5EJWRxKikb\ngAe7T8JSo2N90maKy2UysBBCiNZJmplWQlEUHhnvh0ZR+HJ7AtfLK3GxdmaC51gKy4v4LiXC3CUK\nIYQQJiHNTCvSyVXPuIGdyc4vZfOhNABGd7kHN1tX9l08xIVrl8xcoRBCCNH4pJlpZe4f7omzvRXb\njqRxOacIrUbLdN8HqicDn11PlbHK3CUKIYQQjUqamVbG2lLLzLHdqag08sX3CRiNRnq4dCfQEEBK\nwXkOX5bJwEIIIVoXaWZaoUBYx3saAAAgAElEQVRfVwJ82nEmLZejZzIBmNptEpYWlmxM3kJRebGZ\nKxRCCCEajzQzrZCiKMwM9kWn1bB6ZyLFpRU4Wzsx8cfJwN+ek8nAQgghWg9pZlopg5MNk4Z0Jb+o\njA37zwEwuvM9uNu5ceDSYc4XXDRzhUIIIUTjkGamFQu5uytuzjbsjLpIWsY1LDQWzPjxycCrE2Qy\nsBBCiNZBmplWTKfV8Mh4P4xG+CziLFVGI77O3bjLrR9pBRc4lH7M3CUKIYQQv5o0M62cv6cLg3oa\nSLlcwL5T1YtOTul2L1YWlmxM3kpheZGZKxRCCCF+HWlm2oAZo7tjbWnBN3uSKSgqw8nKkXu9xlFU\nUcym5G3mLk8IIYT4VaSZaQOc7a2YEuRNUWkFX+9JAmBkp2F42LnzQ/pRUgvOm7lCIYQQouGkmWkj\nRgd2pItBz8GYDBIu5GGhsZAnAwshhGgVpJlpIyw0GsJC/FCAzyPOUlFZRXdnbwa6BXL+2iUOph8x\nd4lCCCFEg0gz04b4eDgS1M+DS9lFbI+8AFRPBra2sGZT8jaulRWauUIhhBBCPWlm2pgHR/igt9Gx\n8UAKVwtKcbSyZ5L3OIorStiUvNXc5QkhhBCqmbSZWbp0KTNmzCA0NJTo6Oibth0+fJjp06cTGhrK\nggULqKqqoqqqipdeeonQ0FDCwsJITk42ZXltkt5Gx/RR3Sgrr2LVjkQAgjoOoaO+Az9cPsa5/DQz\nVyiEEEKoY7Jm5ujRo6SlpbFmzRqWLFnCkiVLbtr+8ssv88EHH7B69WqKiorYv38/O3fu5Nq1a6xe\nvZolS5bw1ltvmaq8Nm1oH3e6d3LkeEIW0cnZNZOBAZkMLIQQosUxWTNz6NAhxo4dC4CPjw/5+fkU\nFv48JyM8PBx3d3cAXFxcyM3NJTU1lYCAAAC6dOlCeno6lZWVpiqxzdIoCmHj/dAoCl98n0BZeSXd\nnLy4230AFwvT2X/psLlLFEIIIepNa6odZ2dn4+/vX/PaxcWFrKws9Ho9QM3/Z2ZmcvDgQebPn090\ndDQrV67k0UcfJS0tjQsXLpCbm0v79u1v+z3OzrZotRam+hm4utqbbN/m5Opqz+QRPqzfk8TuU5d5\nZEJPfnf3dGK2xPFdSgTBPYfgaO3Q4H0LdSQz9SQz9SQz9SQz9cyRmcmamV8yGo23vJeTk8Pjjz/O\nokWLcHZ2ZsSIEURFRfGb3/wGPz8/vL29ax13o9zcYlOVjKurPVlZ10y2f3MLDvRgz/ELfLM7kQAv\nZzq0s2OS13jWJmzg4yNrmdVrhup9tvbMTEEyU08yU08yU08yU8+UmdXVJJnsMpPBYCA7O7vmdWZm\nJq6urjWvCwsLmTNnDk8//TTDhw+vef+ZZ55h9erVvPLKKxQUFNCuXTtTldjmWVtqmTm2OxWVRr74\nPgGj0cg9HQfTWe/BkYzjJOWlmLtEIYQQ4o5M1swMGzaMiIgIAGJjYzEYDDWXlgDeeOMNHn30UYKC\ngmrei4+PZ8GCBQDs27ePXr16odHI3eOmFOjrSoBPO86k5XL0TCYaRcN0vykArE3YQGWVzFkSQgjR\nvJnsMlNgYCD+/v6EhoaiKAqLFi0iPDwce3t7hg8fzoYNG0hLS2PdunUATJo0iYceegij0ci0adOw\nsrLi73//u6nKEz9SFIWZwb6cSTvC6p2J9PFuh7djV4Z0GMihy8fYd+kQozoPv/OOhBBCCDNRjHea\nlNLMmfJ6Zlu6XrrpYAob9qcwdkAnZgb7cq2skFcPv02V0cjLg/+Co1X9JgO3pcwai2SmnmSmnmSm\nnmSmXqubMyNalgl3d8XN2YadURdJy7iGvaWe+31CKK0sZX3SFnOXJ4QQQtyWNDMCAJ1WwyPj/TAa\n4fPvz1JlNDLM42662Hfk2JUoEnPPmbtEIYQQolbSzIga/p4uDOpp4Fx6AftOpaNRNMzwm4KCIpOB\nhRBCNFvSzIibzBjdHWtLC77Zk0xBURmeDl0Y6jGQ9KIM9l48aO7yhBBCiFtIMyNu4mxvxZQgb4pK\nK/h6TxIA9/tMwE5ry+aU7eRdzzdzhUIIIcTNpJkRtxgd2JEuBj0HYzJIuJCHXmfHZJ8JlFZeZ33S\nZnOXJ4QQQtxEmhlxCwuNhrDxfihUTwauqKxiiMdAujp0JvLKSRJyk8xdohBCCFFDmhlRK5+OjgT1\n8+BSVhE7Ii9WTwb2fQAFhTVnZTKwEEKI5kOaGXFbD47wQW+jY+OBFK4WlNLVoTPDOw4moziTXRf2\nm7s8IYQQApBmRtRBb6Nj+qhuXC+vZNWORADu8x6PXmfHltQd5JbmmblCIYQQQpoZcQdD+7jTvZMj\nxxOyiE7Oxk5ny2SfiZRVlhGe9J25yxNCCCGkmRF10ygKYeP90CgKX3yfQFl5JYM7DMDLoStRmdHE\nX000d4lCCCHaOGlmxB11ctUzbmBnsvNL2Xwo7ccnAz9Q82Tg8qoKc5cohBCiDZNmRtTL/cM9cba3\nYuuRNDKuFtPZviNBnYZwpTiL3edlMrAQQgjzkWZG1Iu1pZaZY7tTUWnki+/PYjQameRVPRl4a+oO\nrpbmmrtEIYQQbZQ0M6LeAn1d6ePdjrjUXI6eycRWZ8OUbvdSVlXON4kyGVgIIYR5SDMj6k1RFH4z\nzhedVsPqnYkUl1YwyD0Qb0dPTmbFEJdz1twlCiGEaIOkmRGqGJxsuHdIV/KLytiw/xwaRUOo3xQ0\nioavEzZSXllu7hKFEEK0MdLMCNUm3N0VN2cbdkZdJC3jGh31HRjRcSiZJdmsOf0tRqPR3CUKIYRo\nQ6SZEarptBoeGe+H0Vi9EGWV0ci93sE4WTmyKX47/4r+H/nXr5m7TCGEEG1EvZuZwsJCALKzs4mM\njKSqqspkRYnmz9/ThUE9DZxLL2DfqXRstDY8d9eT9HXvSVzOWZYeXcaprNPmLlMIIUQbUK9m5rXX\nXmPr1q3k5eURGhrK559/zuLFi01cmmjuZozujrWlBd/sSaaguAwnK0cWBD3JQ90nU1p5nRUxn/Hl\nmXWUVlw3d6lCCCFasXo1M3FxcTz00ENs3bqVKVOm8P7775OWlmbq2kQz52xvxZR7vCkqreDr3UkA\naBQNIzsP4693PUUnvQc/XD7K3469R0q+HC9CCCFMo17NzE8TOvfs2cPo0aMBKCsrM11VosUYPaAj\nXQx6DsZkkHDh51W0PfTu/OWuJwnuMpKckqssi1rO5pTtVFZVmrFaIYQQrVG9mhkvLy8mTpxIUVER\nPXv2ZMOGDTg6Opq6NtECWGg0hI33Q6F6MnBF5c9zqXQaLQ90m8hT/efiaOnAlpTtLItaTmZxtvkK\nFkII0epYLK7H5JdRo0Zx11138dhjj2FhYUFlZSXTpk3DysqqCUqsW3Gx6c4Q2dlZmXT/rYWLgzV5\nhdeJOXcVrYUGb3f7m7a3s3FhcIe7yL2eR9zVsxy6fAx7Szs66zuiKIqZqm4+5DhTTzJTTzJTTzJT\nz5SZ2dndvueo15mZM2fOkJGRgaWlJe+++y5vvfUWCQkJjVagaPkeHOGDg50lX26L5/OIm8/QANjq\nbHjMfyazez2MhaLhq/hvWBHzGdfKCs1UsRBCiNaiXs3M66+/jpeXF5GRkcTExPDSSy/xwQcf3HHc\n0qVLmTFjBqGhoURHR9+07fDhw0yfPp3Q0FAWLFhAVVUVRUVFPPnkk4SFhREaGsr+/bIac0uht9Gx\n4JFAPDs4sPvEJd78Morca7fexTTQvT8LBz1DdydvorNjWXJ0GbE58WaoWAghRGtRr2bGysoKT09P\ndu7cyfTp0+nWrRsaTd1Djx49SlpaGmvWrGHJkiUsWbLkpu0vv/wyH3zwAatXr6aoqIj9+/ezfv16\nvLy8+Pzzz3n//fdvGSOaNzdnW97+0z0M7uVGcnoBr3xylLPnb11N28Xamaf6z2VKt3spLi/hX6f+\nx5qzGyirlNO5Qggh1KtXM1NSUsLWrVvZsWMHw4cPJy8vj4KCgjrHHDp0iLFjxwLg4+NDfn5+zYP3\nAMLDw3F3dwfAxcWF3NxcnJ2dycurviOmoKAAZ2fnBv0oYT7WVlrm3NeLh8d2p6i0grdXneT7o+dv\nWeJAo2gY22UEz931J9zt3Nh36QfeOPYB569dNFPlQgghWqp6NTPPPvss3377Lc8++yx6vZ7PP/+c\n2bNn1zkmOzv7pmbExcWFrKysmtd6vR6AzMxMDh48yIgRI7j33ntJT08nODiYRx55hL/+9a8N+EnC\n3BRFIfiuzjz3cH/sbXWs3pXEfzbFUlpWcctnO9t78Ne7nmJUp+FcKc7k7ch/8n3qbqqM8oRpIYQQ\n9aOtz4cGDx5MQEAAKSkpxMXF8fvf/x4bGxtVX1Tb4oM5OTk8/vjjLFq0CGdnZzZu3IiHhwf//e9/\niY+PZ+HChYSHh9e5X2dnW7RaC1W1qOHqan/nD4mb/JSZq6s9Pbu58sbKYxw9k0lGbgkvzB6Eh6v+\nljF/dP8NQzP6868jn7Hx3FYSriUy7+7ZGOzaNXX5ZiHHmXqSmXqSmXqSmXrmyKxezcyOHTtYvHgx\n7u7uVFVVkZ2dzWuvvcaIESNuO8ZgMJCd/fPzRDIzM3F1da15XVhYyJw5c3j66acZPnw4AFFRUTV/\n3aNHDzIzM6msrMTC4vbNSm5ucX1+QoO4utqTlSULJqpRW2bPPBTAml1J7Dx+kaff3cPvJ/Wif3fX\nW8Z6WHTm+YFPsyr+G05mneYvW19nht8DDHTr36pv4ZbjTD3JTD3JTD3JTD1TZlZXk1Svy0wff/wx\nmzZtYt26dYSHh/P111+zfPnyOscMGzaMiIgIAGJjYzEYDDWXlgDeeOMNHn30UYKCgmre69q1K6dO\nnQLg0qVL2NnZ1dnIiJZBa6HhN8G+zJnUi8pKI//4JobwfclUVd16tk6vs+P3vcN4pOd0jFSxMm41\nn8R+RXG56ZpWIYQQLVu9zszodDpcXFxqXru5uaHT6eocExgYiL+/P6GhoSiKwqJFiwgPD8fe3p7h\nw4ezYcMG0tLSWLduHQCTJk1ixowZLFy4kEceeYSKigpZzLKVGdLbnY6udny4Pobvfkgj9fI15t7v\nj97m5mNJURSGdLiL7k5erIxbzfHMUyTnpzKr5wz8XLqZqXohhBDNlWKsbTLLLzz++OMMGjSIoUOH\nAnDgwAEiIyP597//bfIC78SUpwDlFKN69cmsqLScj76NIzo5h/aO1syb0oeu7rWfPqysquT7tD1s\nSd1OlbGKMZ2DuM8nBJ2mXn14iyDHmXqSmXqSmXqSmXrmusxUr+UMhgwZQkREBF9++SU7d+7Ezs6O\nhQsXqp4EbAqynEHzUp/MLLUWDOrlhqIonEjM5ofTGTjrrejiduuBqlE0dHf2plc7X5Jyz3E65wzR\nWbH4OHniYNk6JubJcaaeZKaeZKaeZKaeuZYzqNeZmdokJyfj4+PT4KIai5yZaV7UZnYqKZuPvo2j\n+HoFI/t35OEx3dFpa5/Kdb2yjPCk7zhw6TBajZbJPhMY2WkYGqVeU7+aLTnO1JPM1JPM1JPM1GvW\nE4Br88orrzR0qBA1+nZrz8uz76KTq549Jy7x5ldRXC0orfWzVhaWPOw3lccDZmNtYcU3id/y4cn/\nknc9v4mrFkII0Zw0uJlp4AkdIW5hcLblhVkDGOzvxrn0Al799Bjxabcug/CTPu178cLdz9K7XQ/i\ncxNZcmQZUZnRt/28EEKI1q3BzUxrfu6HaHpWOgvmTOrFzB+XQfj76pNE1LIMwk8cLO15POAxQv2m\nUF5VwX9Pf8FncWsoqaj9rI4QQojWq85bQn66bbo2Ny5NIERjUBSFsXd1poubPcs3nGbNriRSLhcw\ne0IPrC1vPVQVReGejkPwdfLh07jVHMk4TlLeOWb1CqWbk5cZfoEQQghzqLOZOX78+G239evXr9GL\nEQLAt7MTix4byPINpzl6JpNLWUXMm9oHdxfbWj/vZmfgLwPmsSV1BxGpu3gv6t+M6zqKiV5j0bai\nW7iFEELUrsF3MzUXcjdT89KYmVVUVrF2VxI7jl/ExsqC39/bi/6+ty6DcKPkvFRWxq0mp/QqXew7\n8mivh3G3MzRKPaYix5l6kpl6kpl6kpl65rqbqV7NzMyZM2+ZI2NhYYGXlxdPPPEEbm5uv77KBpJm\npnkxRWaHYjNYuTWesooq7h3SlSn3eKPR3H7OVklFKesSNnE4IxKdRsfUbpO4p+PgZjvPS44z9SQz\n9SQz9SQz9Zr1Q/MuX75MRUUFDz74IIGBgeTk5ODr64u7uzv/+9//mDx5cmPWq4o8NK95MUVmnQ16\n+nZrT1zKVU4mZZOcXkCATzssdbWv26XTaOnr6k8HOzfics5yMiuG89cu4ufSDSuL2z90yVzkOFNP\nMlNPMlNPMlPPXA/Nq9fdTMePH+edd95h3LhxjB07ljfeeIPY2Fhmz55NeXl5oxUqxO10Nuh5afZd\nBPi0IzblKq98coy0jLq7/0BDAC/c/Sw9nLtzOieeJUeWEZ0V20QVCyGEaCr1amZycnK4evVqzetr\n166Rnp5OQUEB167JKTjRNOysdTw1LYAHhntxtaCUJZ8fZ390ep1jnKwcmdfvd0zrfj+lldf5T8xK\nvor/huuV8l9bQgjRWtTrVo9Zs2YxYcIEOnbsiKIoXLx4kT/84Q/s3r2bGTNmmLpGIWpoFIX7h3vh\n2cGeFZvi+GRLPCnpBTw81ve2yyBoFA2jOg/Hz7kbn8at4mD6ERJzk3nUPxRPhy5N/AuEEEI0tnrf\nzVRYWEhqaipVVVV06dIFJycnU9dWLzIBuHlpyswyc4v5Z/hpLmYV4u3hwBMP9MbFwbrOMeVVFXx7\nbhu7zu9HURQmeo5lXNdRWGhqn3/TFOQ4U08yU08yU08yU69ZTwAuKipi5cqVfPfdd0RGRpKTk0Pv\n3r3Ras3/DA+ZANy8NGVmdjY6hvZx52pBKTHnrnIoNgNPdwdcnW6/mruFoqGniy8+jl7E5yYSnR1H\n/NUkujv5YKer/Tk2pibHmXqSmXqSmXqSmXrNegLwSy+9RGFhIaGhoUyfPp3s7GxefPHFRitQiIay\n0lnw+0m9+E2wL8WlFbyz+iTbjtx+GYSf+Ll044VBzzDA0JeUgjT+duxdfkg/JmuOCSFEC1SvUyvZ\n2dksW7as5vWoUaMICwszWVFCqKEoCmMGdKKLm55/bTjN2t1JnLtcwG8n1r4Mwk9sdbb8tvdv6JPR\ni9Vn1/Nl/NfE5pzhYb8H0VvaNeEvEEII8WvU68xMSUkJJSUlNa+Li4u5fv26yYoSoiG6d3Ji0eyB\ndO/kSGR8Jq9/dpzLOUV3HDfQvT8LBz1DNycvTmadZunRZcTlnG2CioUQQjSGes2Z0Wg0zJ8/n8jI\nSLZs2cJ7773HnDlz6NGjRxOUWDeZM9O8mDsza0stQ/zdKSmr4FRSDj+czqBDOzs6tKv7TIutzoa7\n3QdgZWHJ6Zx4jmQcp7i8mO5OPiafHGzuzFoiyUw9yUw9yUy9Zj1nZtq0aaxatYoHHniAKVOmsHr1\napKSkhqtQCEak9ZCw8yxvsy9rxdVVUb+GR7DN3uTqaqqez6MRtEQ3HUkz931JO62BvZcPMibkR9w\n4dqlJqpcCCFEQ9T7dqQOHTrQoUOHmtfR0dEmKUiIxjLY352Orno+DI9h86E0Ui8XMPd+f+xtLesc\n19m+I38dOJ8NyVvYe/Egb0f+k/u8xzOmSxAapV79vxBCiCbU4H8zy10foiXobNDz8uy76OvTjtjU\nXF79NJLUjII7jrO00DHddzJP9P0ddjpbNiRv4YMTK7hamtsEVQshhFCjwc1Mc12BWIhfsrXW8acb\nlkFY+nkU+0/VvQzCT/zb+fHCoGfp296fxLxzLD36LscyTpi4YiGEEGrUeZlpxIgRtTYtRqOR3Fz5\nL1TRcvy8DIIDKzbF8snWeM5dLmBmHcsg/ERvacecPrM4dDmSrxM38mncKk7nnGGG7xRsdbd/QJ8Q\nQoimUWcz89VXXzVVHUI0iQCfdrz82EA+DI9h78l0zl8pZN6UOy+DoCgKQz0G0t3Jm5Vxq4i8cpLk\nvFRm9ZqOr3O3JqpeCCFEbeq9NlNzJWszNS8tJbPr5ZV8tu0sh2IzsLfV8fjk3vTs6lyvsZVVlUSk\n7WJr6k6MRiNjugQxyXs8Ok3DlvdoKZk1J5KZepKZepKZeuZam0luzRBtUvUyCD1rlkH4++oT9VoG\nAcBCY8FEr2CeDXyC9jYu7Di/l7cj/0F6YUYTVC6EEOKXpJkRbdZPyyD838z+ONhZsnZ3Ess3xlJy\nvaJe470cu/D8wKcZ5jGIS4WXeTPyA3ZfOECVscrElQshhLiRSZe9Xrp0KadOnUJRFBYuXEhAQEDN\ntsOHD7Ns2TI0Gg1eXl4sWbKEb775hk2bNtV85vTp05w4IXeOCNPq3smJxbMHsnzDaSLjM7mUVciT\nU/vc8anBANZaK2b2mIZ/u558Fb+OdYmbiM2J55GeD+Fk5dgE1QshhKjXcgYNcfToUXbv3s3KlSvp\n378/ixcv5qGHHqrZ/tvf/pYVK1Ywe/ZsNm3ahJ2dHRMnTmTq1KlMnTqVTp06odVqGTlyZJ3fI8sZ\nNC8tNTNrSy2Df1wGITq5ehkEdxc7PNrXb8FJdzsDg9wHkFGcSdzVsxy5fJz2Nu3oYOd2x7EtNTNz\nkszUk8zUk8zUa9bLGTTEoUOHGDt2LAA+Pj7k5+dTWFhYsz08PBx3d3cAXFxcbrnV+8MPP+SJJ54w\nVXlC3KJmGYT7e1FlNPLh+votg/ATRyt7/hjwGDN8p1BWVc7Hpz/n87i1lFSUmrhyIYRo20zWzGRn\nZ+Ps/PPdIS4uLmRlZdW81uv1AGRmZnLw4EFGjBhRsy06OpoOHTrg6upqqvKEuK3Bvdx5MewuDE42\nbD6UxrtrT3Ktnv+loSgKQZ2G8PzA+XS278jhjEj+dvQ9kvNSTVu0EEK0YSadM3Oj2u4SycnJ4fHH\nH2fRokU3NT7r1q1jypQp9dqvs7MtWq3pVjWu61YwUbvWkJmrqz3ve7dn2VfHORZ3hdc/P87CRwfR\nrbNTvce/2fl5vo7dzIYzEbx7YjlTeoYwzf9etLWswt0aMmtqkpl6kpl6kpl65sjMZM2MwWAgOzu7\n5nVmZuZNZ1oKCwuZM2cOTz/9NMOHD79p7JEjR3jxxRfr9T25ucWNU3At5BkD6rW2zP5wXy86trNl\n4/4UnvvHfsLG+XJPX496jx/bYTSeNl6sjFtNeNxWjl84zaP+objZ/vzPQmvLrClIZupJZupJZuq1\nuufMDBs2jIiICABiY2MxGAw1l5YA3njjDR599FGCgoJuGnflyhXs7OywtKx7ZWMhmoJGUbh/mBfz\nH+qLlU7DJ1vjWbktnvKK+t9+3c3Ji4WDnuZu9wGkXbvAG0ff48Clw7JYqxBCNBKTnZkJDAzE39+f\n0NBQFEVh0aJFhIeHY29vz/Dhw9mwYQNpaWmsW7cOgEmTJjFjxgyysrJwcXExVVlCNEiATztemj2Q\nf6lcBuEnNlobZvWagX+7Hqw+G86qs+HEZJ/hkZ4P4YqcxhZCiF9DljOog5xiVK+1Z3a9vJLPI87y\nw+kfl0G435+enuqa79zSPD4/s5azuUnY6/T87q4Z+Fh3R6PIMyzrq7UfZ6YgmaknmalnrstMJnvO\nTFOR58w0L609M62Fhv7d2+NgZ8mJhGwOns7AUqehW0fHWleYr42N1pqB7v2x1VpzOucMP1w4zqms\n09hb6nGzda33ftqy1n6cmYJkpp5kpp65njMjzUwd5EBWry1kpigKXh0c6NXVhehzOUQlZJOeXURv\n73botPU7u6IoCl6OXRng1hejtpLYrASiMk8RnR2LvU6PQZqaOrWF46yxSWbqSWbqSTPTQNLMNC9t\nKTMXB2uG9HIjJb2AmJSrnEjMopenM/a29Z+8bqezY5Tv3fS070lJRSlnryZxXJqaO2pLx1ljkczU\nk8zUk2amgaSZaV7aWmY/LYNQWlbJqZplEGzrvQwCVGemlGvp59qbAW79KC4v5WzuDU2NpT0G2/bS\n1NygrR1njUEyU08yU0+amQaSZqZ5aYuZaTQKfbzb4eZiw4nEbA7HXqG8oooeXZzr1YDcmJleZ0c/\nQ28GGPpSXPFzUxOTHYeDzKmp0RaPs19LMlNPMlNPmpkGkmameWnLmXVy1dOvW3tiU69yMimbpEv5\n9PFuh5Wu7idU15aZ3rK6qQk09KWkouSGMzVxOFjat/mmpi0fZw0lmaknmaknzUwDSTPTvLT1zBzs\nLBna25307GJizl3l2JkrdO/khLP97f8hrCuz6qamD4GGvhRXFP/Y1JwkJjsO+zbc1LT146whJDP1\nJDP1pJlpIGlmmhfJDHRaCwb2NGChUTiRWH37tqPekq7utT8joT6Z6S3t6G/oQ6AhgOKaMzUnf7z8\nZN/mJgrLcaaeZKaeZKaeNDMNJM1M8yKZVVMUBb8uznh7OHAqKZtj8ZnkXruOv5cLFpqbmw41mekt\n9TVNTVF5MWdzk4nMPElMzhkcrRww2LSNicJynKknmaknmaknzUwDSTPTvEhmN3NztmVgDwMJ5/OI\nPpdDbEoOfbzbYWP180oiDcmsuqkJoH9NU5NE5JW209TIcaaeZKaeZKaeNDMNJM1M8yKZ3crOWsfQ\n3u7kXrtO9Lmr/HA6A093e1ydbKq3/4rM7H9savq59qH4hqbmdCtvauQ4U08yU08yU0+amQaSZqZ5\nkcxqZ/HjMgiOdpY182gstdXLIDRGZjc2NUXlRdWXn66c5HROPE5WDri2sqZGjjP1JDP1JDP1pJlp\nIGlmmhfJ7PZqlkHwdCEmuXoZhEvZRQzyd6e8rKJRvsPeUk/gj01NYXkRZ3MTibxykticszi2oqZG\njjP1JDP1JDP1pJlpIGlmmhfJ7M5cHKwZ7O9OyuUCTp+7ys5j51EUhU4GPVqLxlk5++empjeF5cXE\nt7KmRo4z9SQz9SQz9fclZdkAACAASURBVKSZaSBpZpoXyax+rC0tGOzvhqJAwoU8TiZms/9UOqDQ\nuRGbGgdL+5+bmrKin5uaq2dxsnLE1aZdi2xq5DhTTzJTTzJTz1zNjGI0Go0m+dYmkpV1zWT7dnW1\nN+n+WyPJTD0rWytWbYtjR+RFSssq0dvoCLm7C6MDO2Jtqb3zDlS4VHiZLSk7OJkVA4Dn/7d378FR\n1nm+x9/d6XQ6SSfp7qQ7Cbl2boQk3FRASAacEZwVUM/quCCzaJ212GJdR91dp8rDLLBzHC2ZmrUs\n0ePuOrt1Rqf2mFFZC0ccxRlwWAw3RyIkQMj9Sq6dG7l39/mjQ5NAQJ42nX46+b6qLMi1f/nUk+Ez\nz/N8f090Kuvt68iz5ARVqZHjTDnJTDnJTDl/Zma1Tr1XF8iZmZuSVq6cZKacxRRBmjWSNUuSCNVp\nqWzq5euqTj4/3YzT5SbFZiRUN31nam6PX8wSawF9I/2cd1zkZOtXnOuqwBQWQ1yQnKmR40w5yUw5\nyUw5OTPjIzkzoy6SmXLXZjYwNMpnpxr59GQDA8NjRBp0rFuWwtrbk4kwhE7razf2NfNx7Wecbj8L\ngH38TM0ClZ+pkeNMOclMOclMuUCdmZEycxNyICsnmSl3o8wGh8f4/ZeNfHKinstDY4SH6Vh3RzLr\nlqUQOc2lpmG81JR6S00aG+zryLVkq7LUyHGmnGSmnGSmnJQZH0mZURfJTLlvymxweIxDXzXxu+P1\n9A+OYtCHsPaOZO5ZlooxfLpLTRMf13xGaUcZABkxaay3ryPXrK5SI8eZcpKZcpKZclJmfCRlRl0k\nM+VuNbPhEed4qamjd2CUMH0Id9+WzD3LU4iO0E/rmhr6mjhQ8xlfe0tNOhvs65hvzlJFqZHjTDnJ\nTDnJTDkpMz6SMqMukplySjMbHnXy+VdNfHy8np7LI4SFhvDd25L4s+WpREdOb6mp72vk45rfq67U\nyHGmnGSmnGSmnJQZH0mZURfJTDlfMxsZdfLH0mYOHKuju38EvU7LXUuTuHdFKjHGG9/174v6vkYO\n1HzGmY5yADJj0lkfwFIjx5lykplykplyUmZ8JGVGXSQz5b5tZqNjTo583cJHJXU4+oYJ1WlZs3ge\n996ZhjlqmktNbyMHag9ypuMcAJkxdjbY15FjzpzRUiPHmXKSmXKSmXJSZnwkZUZdJDPlpiuz0TEX\nR8+08FFJLZ29w+hCtKxenMj6O9OwRBu+/UInqO9t5KOag5zt9JSaLJOn1GSbZqbUyHGmnGSmnGSm\nnJQZH0mZURfJTLnpzmzM6eKLs5f47Re1dPQMoQvRULRoHhvuTCM2ZnpLTV1vAwdqDnK28zxwpdTc\nQ445c1pf51pynCknmSknmSknZcZHUmbURTJTzl+ZjTldHCtr5bdf1NLWPUiIVkPhwkQ2rEzDagqf\n1te6ttRkmzJYP375yR/kOFNOMlNOMlNOyoyPpMyoi2SmnL8zc7pcHC9v5cMv6mjtGiBEq2FlQQIb\nV6ZhM0dM62vV9tZzoOYzyiaUmg32dWRPc6mR40w5yUw5yUy5WVlmXnzxRUpLS9FoNOzYsYNFixZ5\nP3bs2DFefvlltFotdrudF154Aa1Wy/79+/nlL3+JTqfjqaee4q677rrpa0iZURfJTLmZyszlcnPi\nXCsfflFLS+cAWo2GO/Pj2bgqnQTL9Jaamp56DtQepLzzAgA5pkzW29eRbc6Ylu8vx5lykplykply\ngSoz0/tI3glOnDhBXV0dxcXFVFVVsWPHDoqLi70f37VrF2+99RYJCQk89dRTHDlyhEWLFvH666/z\n/vvvMzAwwN69e7+xzAghbo1Wq+HO/ASWL4jn1IU2PjxayxdnL1FSdokVefFsXJnOvLjIaXkte0wq\nf7v4cU+pqTlIedcFKr6qmvZSI4QQ4McyU1JSwtq1awHIzMykp6eH/v5+jEYjAPv27fP+3WKx4HA4\nKCkpYeXKlRiNRoxGI88//7y/lifEnKXVali+IJ47cm386UI7+4/WcqysleNlrSxbYOO+VekkWY3T\n8lr2mFT+dsnj1PTUcaDms6ulxpzFBvs6skz2aXkdIcTc5rcy09HRQX5+vvdti8VCe3u7t8Bc+bOt\nrY2jR4/y9NNP8+677zI0NMT27dvp7e3lRz/6EStXrvTXEoWY07QaDXfk2rhtvpXTFzvYf7SGE+fa\nOHmujdtzPaUmxTZdpSaNv13yONU9dRyoOci5rgoqHJXMN2exXkqNEOJb8luZudZUt+Z0dnayfft2\ndu/ejdlsBqC7u5vXXnuN5uZmHn30UQ4dOnTTfSvM5gh0uhC/rftm1+jE1CQz5QKd2fdt0dyzys7J\n8lb+38ELnDrfxqnzbaxcmMjmdfPJSIqZltexWgtYkVXAhY4q3iv7iNJL57jgqGRh/Hwezr+PXOut\n3ygc6MyCkWSmnGSmXCAy81uZsdlsdHR0eN9ua2vDarV63+7v72fbtm0888wzFBUVARAbG8vSpUvR\n6XSkpqYSGRlJV1cXsbGxN3wdh2PAXz+C3PzlA8lMOTVlZrdF8r+2LOVMdRf7j9ZQcqaFkjMtLMmK\n4/6idNIToqfldSzY+Ou8/0l1Ui0fVR/kTOsFzrReINeczXr7OjJN6Tf9ejVlFiwkM+UkM+UCdQOw\n1i+vCBQWFvLJJ58AUFZWhs1m815aAnjppZd47LHHWL16tfd9RUVFHDt2DJfLhcPhYGBgwHvGRggx\nMzQaDYsyY/nJ1tv5+02LyUqK4XRlB//7/57ilXdLqW7unbbXyohJ50dLt/H3tz1Brjmb846LvPyn\n/8Per96kuqd22l5HCDG7+XU0+xe/+AWnTp1Co9Gwe/duysvLiYqKoqioiGXLlrF06VLv527cuJFN\nmzbxzjvv8N577wHwN3/zN9x99903fQ0ZzVYXyUw5tWfmdrs5V+dg/3/XUNHYA0BBhoX7C+1kTdPl\npysqu2v4uOYzzjsuArDAksN6+zoyYtImfZ7aM1MjyUw5yUy5WbnPzEyQMqMukplywZTZ+ToH+4/W\ncL6+G4C8dDP3F9rJSTFN6+tUdtdwoOYgFxyVwPWlJpgyUwvJTDnJTLlZt8+MEGL2yU0zk5tmpqKh\nm/1HayivdVBe6yA31cQDRXbmp07PZeEsk52nlv41ld01fDQ+/XSuq4IFlhw22NdhtRZMy+sIIWYH\nOTNzE9LKlZPMlAvmzCobe9h/tIazNV0A5KSYeKAwndw087Q+Pfuio5oDtZ9RMX6mJjvWToE5j8Vx\nBVgjbjwgIK4K5uMsUCQz5eQyk4+kzKiLZKbcbMisqrmHD4/W8nVVJwBZyTHcX5hOfrplmktNFb+r\n/QMXuiu92z0kGRNZHJfPEttC5kUmTOvrzSaz4TibaZKZclJmfCRlRl0kM+VmU2Y1Lb18eLSW05We\nbRky50VzX6GdhRnTW2rCouDQhROcbj/Lha6LjLmdAMQZLCy2FbDEWkB6dCpajd8GNoPObDrOZopk\nppyUGR9JmVEXyUy52ZhZ3aU+Pvyilj9VtANgT4zivkI7izNjp6XUTMxscGyIss7zlLaf5WzneUac\nIwDE6KNYaM1nibWAHFMmIVr/ba4ZDGbjceZvkplyUmZ8JGVGXSQz5WZzZg1t/Xz4RS1fnm/DDaTG\nG7m/0M7S7LhvVWpulNmoc5Tzjoucbj/LmY5yLo96NtUM14WzMG4BS6wFLLDkoA/R+/zawWo2H2f+\nIpkpJ2XGR1Jm1EUyU24uZNbU7ik1J895Sk2Kzch9q9K5bb4VrQ+l5lYyc7qcVPXUcrr9LKXtZ+ke\n9uyRE6oNJT92PoutBRTELiAiNNyXHynozIXjbLpJZspJmfGRlBl1kcyUm0uZNXdc5rcltRwvb8Xt\nhiRrJPetSueO+Ta02lsvNUozc7vd1Pc1eotN64Dn8pdWo2W+OYvF1gIWxeUTEzZ7n8Mzl46z6SKZ\nKSdlxkdSZtRFMlNuLmZ2qWuA335Ry7GyVlxuN4mxEdxXmM7y3PhbKjXfNrNLl1u9xaa+rwkADRrs\nMWksHr/PJi58do18z8Xj7NuSzJSTMuMjKTPqIpkpN5cza3UM8FFJHV+cuYTL7SbBEsF9q9JZnmcj\nRHvjSaTpzKxz0EFph6fYVHXX4ubqyPcSawGLrQWzYuR7Lh9nvpLMlJMy4yMpM+oimSknmUF79yAf\nldRx9EwLTpcbmzmcjSvTWVkQP2Wp8VdmfSP9fN1RNj7yXYlzfOTbGh7LYqtn5DstOiUoR77lOFNO\nMlNOyoyPpMyoi2SmnGR2VUfPIAeO1XOktBmny43VZGDDynRWFSSgC7laIGYis8GxQco6znO6o4yy\nSSPf0Sy25rPYWkC2KSNoRr7lOFNOMlNOyoyPpMyoi2SmnGR2va7eIQ4cq+OPpc2MOd3ERhvYsCqN\nooWJ6EK0M57ZiHOUC46LnG4bH/ke84x8R+jCWRiXx2LvyHfojK1JKTnOlJPMlJMy4yMpM+oimSkn\nmd2Yo2+Yj4/V8XlpM6NjLizRYWy4M43/8b0ceroHArImp8tJZXfN+H02Zd6Rb702lLzYXBZb81U5\n8i3HmXKSmXJSZnwkZUZdJDPlJLNv1t0/zO+O13P4qyZGxlxEGnQszbGyIi+eBalmRWPd08nldnlG\nvts8NxC3DXoe4xCiCSHHnMkSawGLrPlE6wM/8i3HmXKSmXJSZnwkZUZdJDPlJLNb13t5hIOnGjhe\n3kpHzxAA0ZF6luXaWJEXT+a86IBNHbndblout1LaXkZp+xka+psBz8h3RkyadzIqNtwSkPXJcaac\nZKaclBkfSZlRF8lMOclMudhYIyWnGzle3srJ8230D44CEBdjYPmCeFbkxZNsjQzoOHXnYBel7Wc5\n3V5Gdc/Vke8U4zwWjxebxMj4GVujHGfKSWbKSZnxkZQZdZHMlJPMlJuY2ZjTxbk6B8fLW/lTRTtD\nI55x6nlxkaxY4DljYzNHBHK59I70caa93DPy7bg68m0Lj/MWm7ToZL+OfMtxppxkppyUGR9JmVEX\nyUw5yUy5G2U2Murk66pOjpe3UlrVyZjTBXie2r1iQTzLFsRjjgqb6eVOMjg2yNkOz1O+yzrPM+Ly\nnFUyhcWwKM6z+3CWyT7tI99ynCknmSknZcZHUmbURTJTTjJT7lYyGxga46uL7Rwvb6W81oHL7UYD\nzE81sTwvnjvm2zCGB3aUesQ5yvmuCu9TvgfGBgGI1EWMj3znkztNI99ynCknmSknZcZHUmbURTJT\nTjJTTmlmvQMjnDrfxvHyVi42ekapQ7Qa8u0WVuTFszQ7DoNe56/l3hKny8nF7urxG4jP0jPSC4A+\nRE++ZT5LrAXkx+USrvNt5FuOM+UkM+WkzPhIyoy6SGbKSWbKfZvMOnuGOHG+lePlrdS39gOg12lZ\nnBXHirx4FmbEEqoL7OMKXG4Xdb2N4zcQn6F9sBPwjHzPt2R5Rr7j8onSG2/5e8pxppxkppyUGR9J\nmVEXyUw5yUy56cqspfMyx8tbOX6ujdYuzyZ84WE6bp8f+D1srrgy8n26/Qyl7WU0Thj5zjSle24g\njisgNtx80+8jx5lykplyUmZ8JGVGXSQz5SQz5aY7M7fbTX1r/3ixacXRNwyoZw+biTq8I99nqemp\nuzryHZXk3csmIcJ23VrlOFNOMlNOyoyPpMyoi2SmnGSmnD8zc7ndVDb2qHoPmyt6hvv4usNzj02F\no8o78h0fYfU+5Ts1KhmNRiPHmQ8kM+WkzPhIyoy6SGbKSWbKzVRmwbCHzRUDo4Oc7TxHaXsZ5deM\nfC+2FlCYsZRYbBh0hgCvNHjI76ZyUmZ8JGVGXSQz5SQz5QKRWTDsYXPFiHOEc10XKb1m5Fur0ZIS\nlUSOKZNscwYZMemES7m5IfndVE7KjI+kzKiLZKacZKZcoDMLhj1srrgy8t043EBp8zlqextwuT1F\nTIOG1Khkss0ZZJsyyDSl+zz6PRsF+jgLRrOyzLz44ouUlpai0WjYsWMHixYt8n7s2LFjvPzyy2i1\nWux2Oy+88AInT57k6aefJjs7G4CcnBx27tx509eQMqMukplykplyasosGPawgauZDTtHqO6ppdJR\nTUV3NXW9Dd57bTRoSIlK8pabLJN9TpcbNR1nwSJQZcZvv2EnTpygrq6O4uJiqqqq2LFjB8XFxd6P\n79q1i7feeouEhASeeuopjhw5gsFgYPny5bz66qv+WpYQQkyr6Ag937stme/dljxpD5uvqzr5uqpT\ndXvYhIXoWWDJYYElB/BckqruqeNidzUXHVXU9jZQ39fI7+v/OF5u5pFlyiDHnElmjJ2I0LlbboR6\n+a3MlJSUsHbtWgAyMzPp6emhv78fo9GzydO+ffu8f7dYLDgcDhITE/21HCGE8LvYGAP3rkjj3hVp\nk/awOXne8194mI7bczx72OSmmQjRBrbYgGeH4VxLNrkWzxnxEecINT31XOyu4mJ3NbU99dT3NfGH\nhiNo0JBsTCTbnOk9cxMRqo4boMXc5rfLTDt37mTNmjXeQrNlyxZeeOEF7Hb7pM9ra2vjhz/8Ib/5\nzW+oqKjgpz/9KampqfT09PDkk09SWFh409cZG3Oi003vA9mEEGK6uN1uqpp6+ONXTRz5qpGOniEA\nTMYwihbPY/XSZHLTzaoY9Z7KyNgIFZ01lLdXUN52kYrOGsZcY4DnslSaKYk8Ww551mzyrNkYwyID\nvGIxF83YhdypOlNnZyfbt29n9+7dmM1m0tPTefLJJ7n33ntpaGjg0Ucf5dNPP0Wv19/w+zocA35b\ns1wvVU4yU04yUy7YMosJC+G+O1PZsCJl0h42vz1aw2+P1hAbbWB5no0VC+JJsRn9Umy+TWbx2nnE\nx8/ju/F3MeIcpba33ntZqqa3ntruRg5U/AENGuYZE8gxZZJl9py5MYYGb7kJtuNMDWbdPTM2m42O\njg7v221tbVitVu/b/f39bNu2jWeeeYaioiIA4uPjWb9+PQCpqanExcXR2tpKSkqKv5YphBAzRqvR\nkJNiIifFxCNrsyftYfPxsXo+Plbv3cNmeV488SrZw2YifUgoOeZMcsyZYF/H6KRyU01Nbx1N/S0c\navxvAJKMiZ57bkwZZJkyMOqDt9wI9fJbmSksLGTv3r1s3ryZsrIybDab9x4ZgJdeeonHHnuM1atX\ne9+3f/9+2tvbefzxx2lvb6ezs5P4+Hh/LVEIIQJGF6JlYUYsCzNir+5hc66V0spO/utIDf91pEaV\ne9hcKzQk1HMPjTkT7DDqGqOut4GLjioququp6amlqb+FzxuPAjAvMmF8WiqTLJNd0cMyhbgRv45m\n/+IXv+DUqVNoNBp2795NeXk5UVFRFBUVsWzZMpYuXer93I0bN7JhwwaeffZZent7GR0d5cknn2TN\nmjU3fQ0ZzVYXyUw5yUy52ZzZ4PAYf6po5/i5Vsprpm8Pm0BldrXcVFPZXU1VTy2j47sTAyRGxpM9\nvolftilDVeVmNh9n/jIr95mZCVJm1EUyU04yU26uZNY7MMKX43vYVHzLPWzUktmYa4y63kbPtJSj\nmuqeWu+jFwASIuO9l6SyzRlE62/8D5i/qSWzYCJlxkdSZtRFMlNOMlNuLmY2cQ+b+tZ+AEV72Kg1\nszHXGPV9jVx0VHOxu5qq7prJ5SbCRpb5yj03mcSEzVy5UWtmaiZlxkdSZtRFMlNOMlNurmc2cQ+b\n1i7PROc37WETLJk5XU5vuanorqKqp5YR54j34/ERVrJNGd69bmLCov22lmDJTE2kzPhIyoy6SGbK\nSWbKSWYebreb+tb+8WLTiqNvGIDoiFCW5cazIi+ezKRoNBpN0GbmKTdN3stSVT01DE8oN7aIOLJN\nmZ4zN+YMTGEx0/bawZpZIEmZ8ZGUGXWRzJSTzJSTzK7ncrsn7WHTP+i5VHNlD5u7l6dhMujQatW5\nOd+tcrqcNPQ3TbosNeQc9n7cFh5HtjnD+wiGb1Nu5DhTTsqMj6TMqItkppxkppxkdnNjTtekPWyG\nRjwPkowI07EgzUye3UJ+uhmbCvexUcrpctLY3+zdxK+yu5Yh55D349bw2EmXpcwG0y1/bznOlJMy\n4yMpM+oimSknmSknmd26kVEnZ6o7qWzp48tzrd7HKQDExRjIt1vIT7eQm2b2aeRbbVxuF419zVRM\nuCw1ODbhZzZYvMUm25yBxWC+4feS40w5KTM+kjKjLpKZcpKZcpKZclZrFG1tvbR1D1Je00VZrYNz\ndQ4Gh688ZwnSE6PIS/eUm8ykmIA/4Xs6uNwuz5kbRzUXu6uo7J5cbmINFrLNGZ5HMJgyiA2/Wm7k\nOFNOyoyPpMyoi2SmnGSmnGSm3FSZOV0uai/1ectNVVMPTpfnnwR9qJb5KWby083kpVtIskaq9mGY\nSrjcLpr6W7w7FHvKzaD347EGs2d3YnMGi1Ky0AzqCdeFz4qffSZImfGRlBl1kcyUk8yUk8yUu5XM\nhkbGuFDfTVltF+W1Dpo7Lns/FhOpJ2+82OSlW1T7eAWlPOXmkueszfhNxQMTyg2AIcSAxWAiNtyM\nxWDx/H3Cn5GhEVJ2xkmZ8ZGUGXWRzJSTzJSTzJTzJTNH3zDltV3ectN7+epIdFJcpOeSlN3M/BQz\nYfqQ6V5yQLjcLpr7L1HZXUOfu4dGRytdQw46h7omjYRPpA/RYzGYiTWYJ/1pMZiJDTcTFeqfJ6Gr\n0ax7arYQQojgZo4Ko3BhIoULE3G73TS2X6aspovy2i4qGro5eKqBg6caCNFqyEqKGZ+SspCeEBW0\nI+BajZbkqHkkR82b9A+z2+1mYGxwvNg46Brsomuo2/P38fddutw65fcM1eq85WZS6Qn3/Bmtj0Kr\nCf77kwJJyowQQohvpNFoSLEZSbEZ+bMVqYyOuahs6vGcuanxlJsLDd381x+riTToyE0zj99MPDtG\nwDUaDZGhEUSGRpASlTTl5wyODXoKjrfodNE1oey0DrRP+XU6TQhmg2mKszqeS1mmsBgpO99AyowQ\nQgjFQnVaFqSZWZBm5qE1mfQPjnKuzuE9c/PlhXa+vOD5x3s2joBPJVwXTpIxnCRj4pQfHxob9pYb\n7xke75keBxcclVN+nVajxRwWM152PAXHEm4h1mDCYrBgDoshRDs7LvP5SsqMEEKIb80YHsqyXBvL\ncm243e7rRsA/P93M56ebZ+0I+K0w6MKYZ0xgnjFhyo+POEcmXbrylp5BB11DXZ6NAam+7us0aDBd\nKTvh19+3YzaYCNXO7n/uZ/dPJ4QQYsZpNBrizRHEmyP47m3JnhHwlj7PjcQ1XVQ191LT0sdHJXWT\nR8DtFpLiZscIuC/0IXoSIm0kRNqm/PiocxTHcPfVS1iDDjqHuuka8lzWqu6ppaqn5rqv06AhWh/l\nLTrX3qxsNpjRhwT32TIpM0IIIfwqRKslMymGzKQY7i+0XzcCfqa6kzPVnQDEGPXkpVnISzeTb7dg\nMs6OEfDpEBoSii3Cii3COuXHx1xjdA/3TDib45h0lqe2t4HqnropvzZKb5w0bm65cg9PuAWLwUxY\niN6fP9q3JmVGCCHEjDLodSzOimNxVhxw/Qh4SdklSsouAbN3BNwfdFodceGxxIXHwhRPaXC6nPSM\n9HqLzrX37TT0NVHbWz/l9zaGRo4XHMt101gWg5lwncHPP93NSZkRQggRUHNxBDwQQrQh3vIxFZfb\nRe9I3zVnda5OZjVfbqW+r2nKr43QhRNrMLN5yf2k6zP8+WNMScqMEEII1bh+BNxJZWMPZbUOb7m5\ndgQ8P91Cnt2CzRQe6OUHNa1GiyksBlNYDJmkX/dxl9tF38jl8YLjKTvey1iDDtoHu2jubSU9TsqM\nEEII4RWqC2FBuoUF6Rbg5iPgVpPBOyU1m0fAA0Wr0RITFkVMWBT2mLQpPydQu3NLmRFCCBE0bnkE\nXAPpCVdHwLOSY9CFzI0R8LlIyowQQoigJCPg4gopM0IIIWaFa0fAB4fHuNDQTXlNF+V1U4+A59s9\nj12QEfDgJmVGCCHErBQepmNJVhxLbmUE3BrpuZE43cL8FJOMgAcZKTNCCCHmhG8aAf+0vYFPT14d\nAV+UYyXWqCfZaiTeEk6IVu65USspM0IIIeacm42Al00YAb9CF6JlXlwEKVYjyTbPfylWI9GR6t4Z\nd66QMiOEEGLOmzgC/oPxEfC+ESdnK9ppaO+nsa2fpo7L1Lf2T/q66Eg9KdZIT8GxespRYmzknHl4\nplpImRFCCCGuYQwPxZ5qITHm6jb9LpebVscAje2XaWjzFJzG9v7xszkO7+dpNRoSYyPGC04kKeNF\nxxwVJhNUfuLXMvPiiy9SWlqKRqNhx44dLFq0yPuxY8eO8fLLL6PVarHb7bzwwgtox69HDg0NsXHj\nRp544gkefPBBfy5RCCGEuCVarYbE2EgSYyNZlnv1ydYDQ2M0dXjKTUP75fE/PWdyjk/4+kiDjuTx\ny1RXCk5SXKTcbDwN/FZmTpw4QV1dHcXFxVRVVbFjxw6Ki4u9H9+1axdvvfUWCQkJPPXUUxw5coQ1\na9YA8MYbbxATE+OvpQkhhBDTJsKgIzvZRHayyfs+l9tNZ8+Qt9hcKTrX3oujAWzmcG/J8VyqiiTO\nFI5WzuLcMr+VmZKSEtauXQtAZmYmPT099Pf3YzQaAdi3b5/37xaLBYfDc4quqqqKyspK7rrrLn8t\nTQghhPArrUaD1RSO1RTO0hyr9/3Do06aOy5PLjlt/XxZ0c6XFe3ezwsLDSH5mntxkq2RRBjkEQ1T\n8VuZ6ejoID8/3/u2xWKhvb3dW2Cu/NnW1sbRo0d5+umnAdizZw87d+7kgw8+uKXXMZsj0On8d4rO\nao3y2/eerSQz5SQz5SQz5SQz5fyRWfI8E8snvO12u+nqHaK2pZfa5l7Pny291F7qo6q5d/J6zOGk\nJ0aTnhiNPTGG9HnRzIuLJERFj2oIxHE2YzcAu93u697X2dnJ9u3b2b17N2azmQ8++IAlS5aQkpJy\ny9/X4RiYzmVOdsSw9QAACflJREFUEqgHZgUzyUw5yUw5yUw5yUy5mc4sNTaC1NgIVi9MAGDM6aKl\nc+CaS1X9nCxv5WR5q/frdCFakuIiSbZFXh0dD9DYuD8zu1lJ8luZsdlsdHR0eN9ua2vDar16qq2/\nv59t27bxzDPPUFRUBMDhw4dpaGjg8OHDXLp0Cb1eT0JCAqtWrfLXMoUQQghV0oVovXvhrJzw/t6B\nEZquudm4ueMyda2TS8RcGhv3W5kpLCxk7969bN68mbKyMmw2m/fSEsBLL73EY489xurVq73ve+WV\nV7x/37t3L0lJSVJkhBBCiAmiI/REj++Jc4XT5aLNMegZGW/vp7HNMz4+V8bG/VZmbrvtNvLz89m8\neTMajYbdu3ezb98+oqKiKCoq4oMPPqCuro733nsPgI0bN7Jp0yZ/LUcIIYSYtUK0Wu/Y+PIF8d73\nz5WxcY17qptZgog/r2fKNWblJDPlJDPlJDPlJDPlZmtmNxobb+saYGIh8I6Njz+64cpjHOJiDDcc\nG59198wIIYQQQn2+aWx84u7GDW39fHmhnS8vTBgb14eQHKeusXEpM0IIIYQgLDQEe2I09sRo7/vc\nbjfd/SMT7sXxnM2Zamw8NjqMx+9fSG5y9LXf2u+kzAghhBBiShqNBnNUGOaoMBZlxnrfPzrmoqXz\nsqfgjN+L09J5GUffECBlRgghhBAqF6rTkhofRWr85PtYAnWf0ewbNhdCCCHEnCJlRgghhBBBTcqM\nEEIIIYKalBkhhBBCBDUpM0IIIYQIalJmhBBCCBHUpMwIIYQQIqhJmRFCCCFEUJMyI4QQQoigJmVG\nCCGEEEFNyowQQgghgpqUGSGEEEIENSkzQgghhAhqGrfb7Q70IoQQQgghfCVnZoQQQggR1KTMCCGE\nECKoSZkRQgghRFCTMiOEEEKIoCZlRgghhBBBTcqMEEIIIYKalJkpvPjii2zatInNmzfz9ddfB3o5\nQaOiooK1a9fy61//OtBLCRo///nP2bRpEw899BCffvppoJejaoODgzz99NP85V/+JQ8//DCHDh0K\n9JKCxtDQEGvXrmXfvn2BXorqHT9+nDvvvJOtW7eydetWnn/++UAvKSjs37+f+++/nwcffJDDhw/P\n+OvrZvwVVe7EiRPU1dVRXFxMVVUVO3bsoLi4ONDLUr2BgQGef/55Vq5cGeilBI1jx45x8eJFiouL\ncTgc/Pmf/zn33HNPoJelWocOHaKgoIBt27bR1NTEX/3VX/Hd73430MsKCm+88QYxMTGBXkbQWL58\nOa+++mqglxE0HA4Hr7/+Ou+//z4DAwPs3buXu+66a0bXIGXmGiUlJaxduxaAzMxMenp66O/vx2g0\nBnhl6qbX63nzzTd58803A72UoLFs2TIWLVoEQHR0NIODgzidTkJCQgK8MnVav3699+8tLS3Ex8cH\ncDXBo6qqisrKyhn/x0XMHSUlJaxcuRKj0YjRaAzI2Sy5zHSNjo4OzGaz922LxUJ7e3sAVxQcdDod\nBoMh0MsIKiEhIURERADw3nvvsXr1aikyt2Dz5s08++yz7NixI9BLCQp79uzhueeeC/QygkplZSXb\nt2/nkUce4ejRo4Fejuo1NjYyNDTE9u3b2bJlCyUlJTO+Bjkz8w3kaQ/C3z777DPee+89/uM//iPQ\nSwkK77zzDufOnePHP/4x+/fvR6PRBHpJqvXBBx+wZMkSUlJSAr2UoJGens6TTz7JvffeS0NDA48+\n+iiffvoper0+0EtTte7ubl577TWam5t59NFHOXTo0Iz+bkqZuYbNZqOjo8P7dltbG1arNYArErPZ\nkSNH+Jd/+Rd++ctfEhUVFejlqNrZs2eJjY0lMTGRBQsW4HQ66erqIjY2NtBLU63Dhw/T0NDA4cOH\nuXTpEnq9noSEBFatWhXopalWfHy895JmamoqcXFxtLa2SiG8idjYWJYuXYpOpyM1NZXIyMgZ/92U\ny0zXKCws5JNPPgGgrKwMm80m98sIv+jr6+PnP/85//qv/4rJZAr0clTv1KlT3rNXHR0dDAwMTLok\nLK73yiuv8P777/Ob3/yGhx9+mCeeeEKKzDfYv38///7v/w5Ae3s7nZ2dcn/WNygqKuLYsWO4XC4c\nDkdAfjflzMw1brvtNvLz89m8eTMajYbdu3cHeklB4ezZs+zZs4empiZ0Oh2ffPIJe/fulX+kb+LA\ngQM4HA6eeeYZ7/v27NnDvHnzArgq9dq8eTM/+clP2LJlC0NDQ+zatQutVv7/mJhe3/ve93j22Wf5\n/e9/z+joKP/0T/8kl5i+QXx8PN///vf5i7/4CwD+8R//ccZ/NzVuuSlECCGEEEFM/m+NEEIIIYKa\nlBkhhBBCBDUpM0IIIYQIalJmhBBCCBHUpMwIIYQQIqhJmRFCzJjGxkYKCgq8TyTevHkz//AP/0Bv\nb+8tf4+tW7fidDpv+fMfeeQRjh8/7styhRBBQsqMEGJGWSwW3n77bd5++23eeecdbDYbb7zxxi1/\n/dtvvy3PsBJCTCKb5gkhAmrZsmUUFxdz/vx59uzZw9jYGKOjo+zatYu8vDy2bt1Kbm4u586d41e/\n+hV5eXmUlZUxMjLCzp07uXTpEmNjYzzwwANs2bKFwcFB/u7v/g6Hw0FaWhrDw8MAtLa28uyzzwIw\nNDTEpk2b+MEPfhDIH10IMU2kzAghAsbpdHLw4EFuv/12fvzjH/P666+TmprK+fPn2bFjB/v27QMg\nIiKCX//615O+9u233yY6Opp//ud/ZmhoiPXr1/Od73yHL774AoPBQHFxMW1tbdx9990AfPzxx2Rk\nZPDTn/6U4eFh3n333Rn/eYUQ/iFlRggxo7q6uti6dSsALpeLO+64g4ceeohXX32Vn/zkJ97P6+/v\nx+VyAZ7HjFyrtLSUBx98EACDwUBBQQFlZWVUVFRw++23A54Hx2ZkZADwne98h//8z//kueeeY82a\nNWzatMmvP6cQYuZImRFCzKgr98xM1NfXR2ho6HXvvyI0NPS692k0mklvu91uNBoNbrd70nNhrhSi\nzMxMPvroI06ePMnvfvc7fvWrX/HOO+982x9HCKECcgOwECLgoqKiSE5O5vPPPwegpqaG11577aZf\ns3jxYo4cOQLAwMAAZWVl5Ofnk5mZyVdffQVAS0sLNTU1AHz44YecOXOGVatWsXv3blpaWhgbG/Pj\nTyWEmClyZkYIoQp79uzhZz/7Gf/2b//G2NgYzz333E0/f+vWrezcuZMf/vCHjIyM8MQTT5CcnMwD\nDzzAH/7wB7Zs2UJycjILFy4EICsri927d6PX63G73Wzbtg2dTv4nUIjZQJ6aLYQQQoigJpeZhBBC\nCBHUpMwIIYQQIqhJmRFCCCFEUJMyI4QQQoigJmVGCCGEEEFNyowQQgghgpqUGSGEEEIENSkzQggh\nhAhq/x+6zJrCOKaU/QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FcUSnCm11Nlx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb
new file mode 100644
index 0000000..f6f4d91
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1330 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "synthetic_features_and_outliers.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/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": "64c90f26-5611-4c23-ec2f-8a141a49e38f"
+      },
+      "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": 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>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>7430</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2260.0</td>\n",
+              "      <td>692.0</td>\n",
+              "      <td>1603.0</td>\n",
+              "      <td>673.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>223.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8220</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1973.0</td>\n",
+              "      <td>332.0</td>\n",
+              "      <td>1257.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16913</th>\n",
+              "      <td>-124.1</td>\n",
+              "      <td>40.1</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1319.0</td>\n",
+              "      <td>267.0</td>\n",
+              "      <td>393.0</td>\n",
+              "      <td>163.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>135.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3612</th>\n",
+              "      <td>-117.9</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>3442.0</td>\n",
+              "      <td>1526.0</td>\n",
+              "      <td>1427.0</td>\n",
+              "      <td>977.0</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>106.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>499</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>32.9</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>5211.0</td>\n",
+              "      <td>949.0</td>\n",
+              "      <td>3025.0</td>\n",
+              "      <td>948.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>134.2</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>7429</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>3156.0</td>\n",
+              "      <td>857.0</td>\n",
+              "      <td>2394.0</td>\n",
+              "      <td>787.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>191.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8244</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>338.0</td>\n",
+              "      <td>821.0</td>\n",
+              "      <td>341.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>362.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10316</th>\n",
+              "      <td>-120.1</td>\n",
+              "      <td>39.2</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>1746.0</td>\n",
+              "      <td>306.0</td>\n",
+              "      <td>251.0</td>\n",
+              "      <td>104.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>146.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10273</th>\n",
+              "      <td>-120.0</td>\n",
+              "      <td>38.9</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>1669.0</td>\n",
+              "      <td>422.0</td>\n",
+              "      <td>589.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>100.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3233</th>\n",
+              "      <td>-117.9</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2535.0</td>\n",
+              "      <td>490.0</td>\n",
+              "      <td>1327.0</td>\n",
+              "      <td>466.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>180.6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "7430      -118.3      33.9                25.0       2260.0           692.0   \n",
+              "8220      -118.4      34.1                35.0       1973.0           332.0   \n",
+              "16913     -124.1      40.1                17.0       1319.0           267.0   \n",
+              "3612      -117.9      33.6                20.0       3442.0          1526.0   \n",
+              "499       -117.0      32.9                32.0       5211.0           949.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "7429      -118.3      33.9                26.0       3156.0           857.0   \n",
+              "8244      -118.4      34.0                44.0       1462.0           338.0   \n",
+              "10316     -120.1      39.2                19.0       1746.0           306.0   \n",
+              "10273     -120.0      38.9                24.0       1669.0           422.0   \n",
+              "3233      -117.9      34.1                23.0       2535.0           490.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "7430       1603.0       673.0            2.1               223.3  \n",
+              "8220       1257.0       296.0            9.0               500.0  \n",
+              "16913       393.0       163.0            2.6               135.6  \n",
+              "3612       1427.0       977.0            3.2               106.3  \n",
+              "499        3025.0       948.0            4.1               134.2  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "7429       2394.0       787.0            3.0               191.9  \n",
+              "8244        821.0       341.0            2.6               362.2  \n",
+              "10316       251.0       104.0            4.8               146.9  \n",
+              "10273       589.0       281.0            3.0               100.8  \n",
+              "3233       1327.0       466.0            3.6               180.6  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6kNgrwCO_ms",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll set up our input function, and define the function for model training:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5RpTJER9XDub",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VgQPftrpHbG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
+        "    after training the model.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
+        "\n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "    \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "      metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "    \n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Create a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
+        "  \n",
+        "  return calibration_data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FJ6xUNVRm-do",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Try a Synthetic Feature\n",
+        "\n",
+        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
+        "\n",
+        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
+        "\n",
+        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
+        "\n",
+        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
+        "the final RMSE should be.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "isONN2XK32Wo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5ihcVutnnu1D",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1074
+        },
+        "outputId": "dbca6cb6-c969-472e-88f4-cf475eec184f"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.00005,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.51\n",
+            "  period 01 : 237.49\n",
+            "  period 02 : 237.46\n",
+            "  period 03 : 237.44\n",
+            "  period 04 : 237.41\n",
+            "  period 05 : 237.39\n",
+            "  period 06 : 237.36\n",
+            "  period 07 : 237.34\n",
+            "  period 08 : 237.31\n",
+            "  period 09 : 237.29\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.3    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.1     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.3    265.0\n",
+              "max            6.2    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.29\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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D+Oyzz7Bq1SrIZDLs3bsXQHVaQkJCAjIzM43H7tu3D/369TN5nfj4eISEhAAA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\nAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i5Ul3zf5QYvW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Leaz2oYMQcBf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "bf8a65c9-675c-4d65-b445-3d160e1c02cd"
+      },
+      "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": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.74\n",
+            "  period 01 : 189.65\n",
+            "  period 02 : 168.95\n",
+            "  period 03 : 152.27\n",
+            "  period 04 : 140.46\n",
+            "  period 05 : 133.89\n",
+            "  period 06 : 131.02\n",
+            "  period 07 : 130.73\n",
+            "  period 08 : 130.83\n",
+            "  period 09 : 131.45\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>192.8</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>88.8</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>43.8</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>158.0</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>189.8</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>216.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4235.1</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         192.8    207.3\n",
+              "std           88.8    116.0\n",
+              "min           43.8     15.0\n",
+              "25%          158.0    119.4\n",
+              "50%          189.8    180.4\n",
+              "75%          216.9    265.0\n",
+              "max         4235.1    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 131.45\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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LWfD+SUrL7NzQM5D772qOt5cMaWuowhp5cWPnCL7dn8n2n7IY2Lmpp0MSQggh\n6ryGf8XegCzfcpTEpAzyis0oyrn6Dsu3HL3gOhcbgmHUaxjbv8UF173YjBoXS2aIy2cvKyfthX+j\nMuhpPmt65UbFgXb3GlSKgq3nTc5Clopy3rCNxlBNMqrMouJEvg69xkErNw/bWLPdwqlcB72v09Il\n5uKzubjbb6llLFxyEm8vNU9Pa0WAv2fjqQ1rNmXz7sfpBPhrefGxGElI1CFWq4MPlmUwZ8ExzGYH\n99/ZnEf+ES0JiWvATX2j0WvVrN5+HMtl9mgUQgghrmWSlKgnLlXf4UJDOS42BMNitVNafvEpIicN\nbs3Q7pEE+xtRqyDY38jQ7pEym0YNObXgA6xZ2TS5PwFjdGSlNvWRfahzM7BHdUCJ+P39NhWAtQIM\n/mCoWsRQUeBwtgEFFTGhFtyZN/op1caOg1bCg9SMudGzvWayc828vDAVu11hxv0tada06pCWhuaL\ndadZsiyDwAAdsx+PISqy4R9zfXEmx8wDT/zI6k3ZNG1sYO4zsQwfGCLDNa4RjXwNDOkeSWGphS37\nMz0djhBCCFHnyfCNeuJy6jucLVZ5vj87BEOjVjN5aAzjB7SiqNRMgK9BekjUENOxNE6/8xH6iHCa\nTPlrpTZHeQna5E0oOoNzClBw1pAozQaVGnyrL6CWUaSlxKwhzNdGiI/7ntAVlDhYnmhCq4GEEQb0\nOs/dbFVU2HnpjVSKim3cc3szunTw91gsteWz1Vl8uiqL4EAdLzzWhohwo6dDEr/bua+AN5ekUV5h\nZ2CfIO5NaIaXUf5mXmtG9o5ia/Ip1u06yYDOEXgZ5HJLCCGEuBDpKVFPnE0uVOdiyYWaGoJh0GkI\nk+k9a9TJ515Hsdpo/tzDaLwr31Sav1+NymLC3mkIeP9+k116xjnrhk+YcyjHH5RbVRzP16NTK7QO\ncV8RUrtD4eMNJirMMG6AgSbBnvtM2B0Kry8+TlqmiRGDQxk5pPrPekOhKAr/++IUn67KIixEz0tP\nxEhCoo6wWB2897905i46jt2u8NS0WKb+PUoSEtcoH6OOEb2aU1phZeOeNE+HI4QQQtRpkpSoJ/5M\nckGGYNQ9BZu3UfTNDvxv6EHg6CGV2lRnjmP9dS+OoAjssT2dC80lYC4GrRd4BVbZnqJASrYBh6Ki\nTagZvRvvgzbttnAiy0GnNlp6XefZp38ffpbJvoPFdL7Oj7tvi7z0CvWYoih8uCKTlWtP0zjMwOzH\nYwgPlWKzdcGpMyaefOk31n2TQ7OmRuY9E8vIoY1luMY1blj3Zvj76Nm4N53icvfW9xFCCCHqM+lP\nWI+cTSIkp+RSUGIi0M9Il5h4waCZAAAgAElEQVSQSyYXZAhG3eIwmUl79jXQaGj+4ozKNy52G9rd\nawAVtl43gVoDDgeUnHa2+zeBam50ThVrKTRpCPa2EerGYRsp6Ta+2WslyF/FLYMNHr3p2rQ1lzWb\nsolsYmTG/S3ReHgqUndSFIX3P83g68QcmjY28MKjbQgK1F96ReF223bl89aHaZjMDobeGMzfb2uG\nwSD5fgEGvYab+kbzv80pfP3DSW4b2sbTIQkhhBB1kiQl6pHzkwsavQ67xXpFyYWzQzCEZ51e/DHm\nk5mE3zsZ79hWldo0h35AXZSDrlM/zCG/P/kvywaHFbyDQVu1q77JquJYnh6NWiEm1FJdzqJGlJQ7\n+GSjGZUa7og34mXwXBLg4K/FvPu/NPx8NTw9rRU+3g03yeZwKCz+OJ1NW3Np3tTIrBltaBTQ8GcW\nqevMFgfvf5LO5u/zMBrUPHxvNDf2DvJ0WKKOGdA5go170vg2OYPhPZoRHCDDrYQQQog/ksc59ZBB\np6FJiI/0dqiHzBmnOfXGEnShwTR95N7KjaUFaA5uRTH6YOw3yrnMWgEV+c4aEj5Vh+8oCqTk6LEr\nKloHWzBoFbfE7VAUlm02U1KuMLKPnqjGnvvsZWaZmPvWcVQqFU9MaUXjsIY7hMHuUFj03zQ2bc2l\nRXMvXnwsRhISdUD6qQoee/Ewm7/PI7qZF68911YSEqJaWo2aMTe0wGZX+GrHcU+HI4QQQtRJkpQQ\nohalv/hvHCYzkU9NQevvW6lNu/drVHYrtm7xqIzezoxDSZaz0a+Jc9aNPzhTqiW/Qkugl43Gfja3\nxf1dspXDJ+20jdIwoKvnboqLS2289EYqZeV2HrirOe1jfC+9Uj1ltyss+M8JtmzPo3W0N7NmtMHf\nTzq3edqWHXk8+sJvpGWaiB8UwqszY2naWJ5+iwvrc11jIkJ82PFTFll5ZZ4ORwghhKhzJCkhRC0p\n3r6X/DWJ+HS7npBbRlVqU6cfQpPxG47wFjhadHIurMgHmwkMAaCvevNttqk4mqtHo1KIdeOwjZOn\n7az7wYKft4pbhxlQe6iOhNXmYN5bx8jKNjN+VDiD+gV7JI7aYLMpzF98nO93FRDbyofnZ7TBz1cS\nEp5kMttZ8P4JFr5/Eo0GZtzfgn8kNEevk69RcXFqtYpx/VuiKPDlNuktIYQQQvyRXOUKUQscVhsn\nZ84DlYrolx5DpT7vRsZqQbv3axS1xlncUqXCbjVDWQ6oNOAXXmV7igJHcvXYHCrahJgx6twzbKPC\n7Jz+U3HA5DgDft6euQFTFIV3P0rn58Ol9O7WiMnjIjwSR22wWh289s5x9iQX0T7Gl5nTWuHlJUO1\nPOlkRgXz3j5GZpaZ1tHeTL+vRYMeNiRqXteYEFo08SfpcDYnT5cQ1djP0yEJIYQQdYY84hGiFmT/\n9zMqUo4RevtYfDq2q9Sm+elbVGVF2Nv3QwkIBUWhNOsEKA7wDQN11dxhTpmG3DItAUY7Ef7uGbah\nKAorvjGTX6wwpIeOmGaey2Gu3phN4rY8WkZ5Me3vUajVDXOmDYvVwauLjrEnuYjr2/nxzMOSkPAk\nRVHY/H0uj714mMwsMzcNC2POkzGSkBBXTKVSMX5ASwA+/y7Vw9EIIYQQdYv0lBDCzaw5eWS+thhN\nI38iH3+wUpuq4AyaX39A8WmE/foBzoXmEiwlhaDzBmOjKtuz2OFIrgG1SiE21Oy2YRu7f7Fx4KiN\n6CZqhvfy3PSTe5IL+XBFJkGNdDw1tRVGQ8O8STebHby8MJUDv5bQpYM/j09piUEveWNPqaiw8/bS\nNLbtLsDHW8Mj90XRq0vV30chLlf76CDaRQXy8/F8fksrILZ5oKdDEkIIIeoEueIVws3SX3oTe0kZ\nkY/ehy74vJsaRUG7Zw0qxYGt52jQ6sFhh9LToFL9XtyyasbhaK4Bq11FdJAFb717hm2czrPz5Xdm\nvAzO6T81HuqZcDytnH+9ewKdTsVTU1sRHOi55Ig7VZjszH7jKAd+LaFH5wCe/KckJDzp2Mlyps86\nzLbdzpoe859vKwkJUSPGD3BOA73yu1QUxT1/v4UQQoj6RnpKCOFGpft+IvezNXi3jyEs4eZKbepj\nyaizT2Jv1g5HZKxzYVk2OGx4hzalXFW1i3humYbsUi1+BjuRAe4ZtmGxKixdb8ZmdyYkAv08c3Nc\nUGRlzoJUTGYHjz3YglbR3h6Jw93KK+y8+K+jHD5aRp9ujXj4H9HotJKQ8ARFUVi/JZcPlmdgsymM\nGxHO5HERaLUNc7iQqH0tI/zpGhPK/pQcDhzNo3ObEE+HJIQQQnicJCWEcBPFbufE03MBiHrpUVTa\n837dzOVo921E0eqx9fh9Jg5rOVQUgEaPd0gE5X+YOs5mh5QcPSqcwzbc1Xnhq+/NnMl30K+jjutb\neeZPhNni4OUFqeTmW7ljfAR9ujXMbs6lZTZemH+UI8fLuaFnIA/dE41GIzfAnlBWbmPRB2ns3FeI\nv6+WqX+PolvHAE+HJRqgcTe2JDklhy++T6Vj62CPzWgkhBBC1BWSlBDCTXKWrab84CGCx8Xj16tL\npTbt/k2ozOXYusaBT4BzOo2SLGejX5PKs3P8LjVPj8WuJjrQgq/BPd1+k1Os7PrFRkSImptu8MxQ\nCYdDYeH7JzhyvJyBfYO4eWTV2UcagqJiK8/NO8KxtAoG9g1iyt+iPDZM5lqXcqyM1985TnauhfYx\nvjzyj+gGO1RIeF7TEB/6dGjMDz+fZvevZ+hzXWNPhySEEEJ4lCQlhHADW0ERGXPeRO3jTbNnplVq\nU2WnoTm6D0ejMOzt+jgXlueBzewsbKn3qbK9gnI1WSU6fPR2mgda3RJzXpGDlVvM6HWQMMKIzkNd\n1pevzmLH3kLatfHhgbuao2qATxELi63M/vdvHEurYOiNwdx/Z/MGO6NIXaYoCms2Z/PRilPYHQq3\njG7MpDFNpLeKcLuxN7Rg969nWLXtGD3ahqHVyJAtIYQQ1y75FhTCDTLmvYOtoIimD92NvnHouQaH\nHe2e1QDYev0fqDVgt0BZDqg04Fu1V4DdAb/lGACFtmEWtwzbsNkVPtpgwmSB8QMNhAV65k/D97vy\n+Wz1acJD9DwxpRU6XcP7E5VfaOWZV4+QeqKM+EEhkpDwkJJSGy8vPMYHyzLx9dHw3COtmXxzhCQk\nRK0IaeTFwM5NySk0se1glqfDEUIIITxKekoIUcPKf0khe+nnGFs2J/yeyZXaNId3oS44g71VV5Sw\nqPOGbSjg19iZpPiDY/l6TDY1zRtZ8DM43BLz+p0W0s846NZWS/d2Orfs41IOHy3lzSUn8fZS8/S0\nVvj7Nbw/T7n5Fp6dd4SsM2YmjWnKpP8La5A9Qeq6w0dLef2d4+TmW7m+nR8P3xtNYIBnPvfi2jW6\nXzTbfjrF6h3H6duhMQZdw5zuWAghhLiUhvcYUggPUhSFkzPngcNB89mPotafd6NTVoTmwBYUvRe2\nrsOdy8zFYClzDtkw+FfZXlGFmswiLV46B1FuGrZx6ISNrfuthDRScfPAqjN+1IbT2SZeefMYdofC\njPtb0qypl0ficKfsXDMzX00h64yZm0eGM+XuVpKQqGUOh8IX607z9Csp5BdYuW1sE56b3loSEsIj\nAnz0DOvejKJSC1v2ZXg6HCGEEMJjGt6jSCE8KO/LjZTsTqZR3AAaDexTqU2btA6VzYK191gw+oDD\nDiWnARX4NoE/3KDaHXA4x5kkaBtqxh1DjotKHXy6yYRGDQnxRoz62r9JLq+w88zc3ygqtnHP7c3o\n0qFqcqa+y8o289y8I+TkWbh1TBMm/l9jSUjUsqJiK2/85yTJPxcT1EjHw/+IpkOsn6fDEte4Eb2a\nszU5k3W7TjKgcwTeRkmQCSGEuPZIT4lrmNlqJ7ugHLPV7ulQGgR7aRnps99AZdATNeuRSm3qzBQ0\nab/iCG2Oo/XvM3GUngHFDj6hoK1a6f9kgY4Kq5qmATYCvGp+2IbDofDJJjNlJripv57IsNrvOmx3\nKMxffJxjJ8sYOSSUkUNCL71SPZOZZeKZV1PIybNwx/gIJo1pIgmJWvbzbyU88vxhkn8upksHf+Y/\n31YSEqJO8DbqGNE7ijKTjQ170jwdjhBCCOER0lPiGmR3OFi+5SjJKTnkF5sJ8jfQJSaUSYNbo6lm\nKkpxeU79+32sp3OIePgeDM2bnmuwWdHuWYuiUmPrdROo1M4hG6ZC0BjAO7jKtgpKFdIKdRi1DloG\nWdwS7zdJVo5m2LmuhYYbOnrm6dyHn2Wy72AxPbsG8rdbIz0SgzulZVbw3LwjFBbb+MukpoyJa5jT\nm9ZVdofC52tPs/yrLFDBnbdEMCYuXAqLijplSLdINu9NZ/PeDIZ0a0aAj0xHK4QQ4toid6DXoOVb\njpKYlEFesRkFyCs2k5iUwfItRz0dWr1VcfQEp9/7BH1kE5o8eFelNs3P36EqLcDerg9KYGNQHL8X\ntwT8qw7bcCiw95gCqIh107CNY5l2Nu62EOCrYtJQo0ee3G/cmsOaTdk0izDywmPtG9ysB8fTynnm\nVWdC4p7bIyUhUcsKiqy88PpRPl2VRVCgjpeeiGHciMaSkBB1jkGn4aZ+0Zitdtb+cMLT4QghhBC1\nTpIS1xiz1U5ySk61bckpuTKU4yooikLas6+jWG00f/5hNN5GV5uqKAfNL9tRvP2xdxzkXFie55wG\n1CsQdN5VtpdWoKOoHJr4WQn0rvlhG+UmhY83mgC4I86Ij1ft36Qd/LWYdz9Ox99Xy1NTW+Hr07A6\nbaWeKOfZeUcoKbNx/53NGTkkzNMhXVMO/FLMw88d4uChEnp0DmD+8+1o29rX02EJcUE3doogJMDI\n1uRMcosqPB2OEEIIUaskKXGNKSo1k19srratoMREUWn1beLCCjd+R9HWnfj370ngiEHnGhQF7Z61\nqBx2bD1Ggs4ANjOU5YJaCz5Vb1RLzSpOFujw0kOr4JoftqEoCssSTRSVKsT10tOyae3XkcjMMjH3\nreOo1Soen9KSxmGemfHDXX5LLePZeUcoK7cz5a9RDB8Y4umQrhl2u8L/vjjFrPlHKSuz87dbI3ny\nny3x821YSS/R8Gg1asb1b4ndofDV9uOeDkcIIYSoVZKUqEdqojBlgK+BIP/qbwID/YwE+DasG0R3\nc1SYSHv+X6i0GqJmP1ppGIT6xEHUp49hbxqDo1l7UJTfh20o4NsY1JUTAg4FfssxoKCiawsVWjfk\nC7YftPLLMTutIzUM6V77dSSKS2289EYqZeV2HrirOe1jGtbT619TSnn+tSOYzHYeuieawTdUrRci\n3CM338Kz846wcu1pwoL1zHkqhpuGh0lRUVFv9GofTtNQH374+TSZuWWeDkcIIYSoNfL4qB6orjBl\nv05NualP8ysuTGnQaegSE0piUtU50bvEhGDQ1f6T8/os6+2PMKdl0vgfd+DVpsW5BksF2qQNKBot\nth6jnHUjKgrBWg56XzBUrfyfUaSlxKwhzNdGRKCenOpH2Vy1jGw7a7ZZ8DHC5OGGWh9bb7U5mPfW\nMbKyzYwfFc6gfg3rhv2nQyW89EYqNruD6fe1oG/3QE+HdM3Yd7CIN/5zgpJSO326NeLBvzbHx1u+\n3kT9olaruPnGliz8/CdWfX+MB2++3tMhCSGEELVCrtrqgbOFKc/KKzazetsxyissTB4ac8XbmzS4\nNeCsIVFQYiLQz0iXmBDXcnF5zBlZnHrzv+jCgmn6yN8rtWl//AaVqRRb56HgFwQOm3MKUJUK/KoW\ntyy3qDiRr0enVmgdYgZqtvq6yaLw0QYTdgfcNtxIgG/tdpJSFIXFS9P5+XApvbs1YvK4iFrdv7v9\n+HMxLy9MxeGARx9oSa8ujTwd0jXBZlP43xeZrNqQjVar4t47mhE/KER6R4h6q3PrEFo19WdfSg7H\ns4pp0cTf0yEJIYQQbidJiTruUoUpxw9odcW9GzRqNZOHxjB+QCuKSs0E+Bqkh8RVSJv1LxSTmWZz\nn0Ljd24YgiovE/Vve3D4h2Bv38+5sPQMKHbwDQdN5WETyu/DNhyKirZhJvRuOBVfbjWTW6gwsKuO\ndtG1/2v/1cZsvtmeR6sob6b9PapBzYCQdKCIVxcdQwU88c+WdOsY4OmQrgnZuWZeX3yClNQymoQZ\nmHF/C1pGVS0cK0R9olKpGH9jK+Z+mszn36Uy49Yung5JCCGEcDtJStRxl1OYMsDXcFXJBYNOQ1ig\nXMRfjaLvd1Pw9RZ8u3ckePzIcw0OB9pdq1GhYO11E2i0YCkFUxFojeAVVGVbp4q1FJk0hPjYCPWp\n+dlPkg5ZSTpso1m4mhF9arYHxuXYk1zI0hWZBDXS8eTUlhgNDScBtmtfIa+/cxy1Bp76Zys6XSdP\nNWvD7uRC3lxyktIyO/17BXL/nc3x8mo4nytxbWsbFch10YH8cqKAQyfyaRdd9XtDCCGEaEgkKVHH\nnS1MmVdNYiLQz8DGPWkcTM1z1ZroEhPKpMGtr7jWhLh8DquNk8+8BioVUS89Vrm45ZG9qPNPYW/R\nCaVxS1AcUHLa2VjNsA2TVcWxPD1atUKbEMsfm/+07AIHn281Y9RDQrwRraZ2eygcTyvnX++eQK9T\n89S0VgQH1n5SxF127Clg/rvH0evUPP1QKzrEVq0TImqW1eZg6WeZrE3MQa9T8cBfmjO0f7AM1xAN\nzs0DWvHLiSRWfneMmVGB8hkXQgjRoMmdax13tjBldbyNOr5NPkVesRkFZ62JxKQMlm85WrtBXmPO\nLFmG6chxwhJuxuf6tucaKkrQJm9G0RuxdYt3LivLBbvF2UNC51VpO85hG3rsiorWwRYMWqVG47Ta\nFD5ab8JihQmDDQQH1O6ve36hlTkLUjGZHUy7J4pWDahr/dadecxffByjQc1z01tLQqIWnM4289Sc\nFNYm5hDZxMjcZ9oy7EapHyEaphZN/OkWG8rxrGKSj+R6OhwhhBDCrSQpUQ9MGtyaod0jCfY3olZB\nsL+RkX2jKS2vfljH/t9y/tS0oeLCLGdyyXz9PTSBATR97P5KbdqkDaisZmydh4GXL9hMUJ4Lah34\nhFXZ1ukSLQUVWoK8bIT72Wo81rU7LJzKddDrOi1dYmp3+k+zxcHLC1PJzbdyx/gI+nRrODNRJG7L\nZcF/TuLlpeH5GW1o27phTWtaF+3YW8D0WYc4eqKcwf2CmPdsLFGRXpdeUYh6bFz/lqhU8OX3x3A4\najZpLYQQQtQlMnyjHqiuMKVGr2PdDyeq/fn8EjM5BeXodRopYlnD0ucsxFFaRvQrT6ALOjfDgior\nFc2JgziCm+Jo093ZDaIky9no1xj+MJzGbFORmqdHo1KICa35YRs/p9rYfsBKeJCasTcaanbjl+Bw\nKCx8/wRHj5czqF8QN48Mr9X9u9OGb3NY/FE6fr4anp/eRgorupnF6uCDZRls+DYXg17N1LujGtxU\nskJcSESID/06NGH7T1ns+vU0fTs08XRIQgghhFtIUqIeOb8wpd6oRa2CCz08+deKgxSWOOtMdGwd\nwtBukQT5GyVB8SeU7D1A3oqv8e4QS+jt48412G1o96xBUamw9fo/ZwKiogCsFWDwc/53HkWBlBw9\nNoeKNiFmjLqafQJWUOJgWaIJrQYSRhjQ62q3e/vy1Vns2FtI+xhf7r+zeYPpXr92czbvf5pBgL+W\nWTPayJN6N8s8beK1t49zIr2CqEgjM+5vSWQTo6fDEqJWjbmhBbt+Pc2qbcfp2S4crUY6uAohhGh4\nJClRT5WbbBdMSAAUlDiHduQVm/l2fybf7s8kWAphXjXFbufk03MBiJr9KCrNueSO5pftqIvzsMX2\nRgmOALvVOQWoSg2+jatsK6dMQ165lgCjnQj/mh22YXcofLzBRIXZWUeiSXDtJqG+35XPZ6tPEx6q\n5/EHW6LTNYzP2Zfrz7B0RSaBATpmPdqaZhGSkHCn73bm887SNExmB8MHhPC32yIx6BvGZ0mIKxEc\nYGRgl6YkJmXw3Y+nGNIt0tMhCSGEEDVOrvLqqUB/A8H+V9YtXwphXr2cT1ZR/vNvBE8YiV/Pzuca\nSvLR/Pwdipcv9s5DnMtKzzhn3fAJA03lWg4WOxzJMaBWKcSGmWt82Mam3RZOZDno1FpL7+tqN+d4\n+Ggpby45ibeXmqentsLfr2HkPD9bncXSFZkEB+qY/UQbSUi4kdnsYNEHJ/n3eycAeOQf0dx/V3NJ\nSIhr2ug+0Rh0Gtb8cAKzRepFCSGEaHjkSq+eMuq1F5yV41KSU3KlEOYVsOYXkv7KW6h9fWj29NRz\nDYqCbs9aVHYbtm4jQG8EcwmYi0HrBV5VizsezTVgdahoEWTBu4aHbRxJt/HNXitB/ipuGWKo1WET\n2blmXnnzGHaHwoz7W9Ksaf2/cVcUhU++OMWnq7IIDdbz0hMxRITL8AF3Sc+s4NHZh0nclkfL5l68\n/nxb+vcK8nRYwkPmzp3LpEmTGD9+PJs2bSIrK4uEhAQmT57MtGnTsFgsAKxevZrx48dzyy23sGLF\nCg9H7R7+PnqG9WhGcZmFxH3png5HCCGEqHEN41HmNWrS4NaAM8lQUGKika+BcrMN0yWepBSUmCgq\nNbvqU4iLy5z3DvaCIpo9+xD68BDXcnXaL6hPHcHRuBWO6OudvSNKTjsb/Zvwx24QuWUasku1+Bns\nRAbU7LCNknIH/9toRqWGO+KNeBlqLyFRXmHnpTdSKSq2ce8dzejSwb/W9u0uiqKwdEUmqzZk0zjM\nwAuPtiE0WO/psBokRVHYsj2fd/+XhsWiMHJIKHdNbIq+gQz9EVdu165dHDlyhOXLl1NQUMC4cePo\n06cPkydPZsSIEcyfP5+VK1cyduxYFi1axMqVK9HpdEyYMIFhw4bRqFGjS++knonv2Zxv92ewflca\nA7s0xcdYuzMqCSGEEO4kSYl6rLpZOT7/LpXEpIyLrhfoZyTAt3ZnZKivyn46TPbSzzG2jib8b5PO\nNVjNaJPWo6g12HqNdiYgSrPBYQXvYNBWfqJutTuLW6pQaFvDwzYcisKyzWZKyhVG99MT1bj26kjY\nHQrzFx8nLdPEyCGhjBh8db136hJFUVjyaQZrE3No2tjArEfbEBwoCQl3qDDZefejdLbuzMfbS8ND\nDzSnT/eGM32suDo9evSgY8eOAPj7+1NRUcHu3buZNWsWAIMGDWLJkiW0aNGC66+/Hj8/ZzHhrl27\nsn//fgYPHuyx2N3F26hlZJ8oVnybyvpdaUwY2MrTIQkhhBA1Rh5FNQBnZ+Uw6DRMGtyaod0jCfa/\ncDfzLjEhMgvHZVAUhZMz54GiEPXiDNT6c0+mNAe2oCovxt6hP4p/CFhNUJ4Hah34VL0xT83TY7Gr\niQ6y4qOv2WEb3yVbOXzSTmxzDQO61u7Tsw+XZ7LvYDFdOvjzt1vrfwE2h0Nh8UfprE3MoVmEkRcf\nj5GEhJucSC/n0RcOs3VnPm1aeDP/+baSkBAAaDQavL2dPflWrlzJjTfeSEVFBXq983cxODiYnJwc\ncnNzCQo6N8QnKCiInJwcj8RcG4Z0jaSRr57EpHSKSs2eDkcIIYSoMdJTooE5v/dEfrGJxKR0Dqbm\nU1BiItDPSJeYENewD3FxeV+sp3TvAQJHDiJgQG/XclV+FprDu1D8grB3uNE5x2fJKWejfxPnrBvn\nyS9Xc7pEh6/eTrNG1hqNMe20nXU/WPDzVnHbcAPqWqwjsXFrDms2Z9Mswsj0+1qg0dTvqT/tDoW3\n/5vGN9vziG7mxfPTWxPgL12ka5qiKGz6Lpf3P8nAalMYExfG7eMj0GklRy4qS0xMZOXKlSxZsoTh\nw4e7litK9YndCy3/o8BAb7Ra9yTmQ0P9Lv1Df8Lk+Ha8tfIAicmnuO/mjm7dV33l7nMgLk3OgefJ\nOfA8OQdXRpISdZjZancNy7jSng0GnYYmwT4kxLX9U9u5VtlLSkl/8Q1URgPNn3v4XIPiQLtnDSrF\ngaXnaOfsGuV5YDOBIQD0vpW2Y3PAbzkGQCE2zIK6Bu/bK8wKH20woThgcpwBP+/au6k78Esx736c\njr+vlqentcLHu35/rux2hYVLTvLdznxaRXnz3PTW+PnKn8eaVl5h5+0P09i+pwBfHw2PPhBFj84N\nb/y/+PO2bdvGO++8w3/+8x/8/Pzw9vbGZDJhNBo5c+YMYWFhhIWFkZub61onOzubzp07X2SrTgUF\n5W6JOTTUj5ycErds+6zOLQIJC/Riw84T3Hh9Y0Ib1f+iwjWpNs6BuDg5B54n58Dz5BxU72KJGnk0\nVQfZHQ4+SUxh5nu7eHLxLma+t4tPElOwOxxXtb3zh3dcKbPVTnZB+TU3W0fmv97Hmp1HxJS/YGgW\n4VquProfdU469qjrUCLagN0KZTmg0oBfeJXtHM/TY7apad7Iip/h6s5fdRRFYcUWM/nFCkN66Ihp\nVns30JlZJua9fRy1WsXjU1oSHlq/65PYbAr/evc43+3MJ6aVD7MelYSEOxw+WsL0WYfZvqeAtq19\n+NesdpKQENUqKSlh7ty5LF682FW0sm/fvmzcuBGATZs20b9/fzp16sRPP/1EcXExZWVl7N+/n+7d\nu3sydLfTatSM7d8Cu0Nh1bbjng5HCCGEqBFy5V0HLd9ytFKxyrxis+v12aKWfgHufTpidzhYvuUo\nySk55BebCfI30CUmlEmDW6NRN+xcVsWRE5z5zyfom0XQ5P6Ecw2mMrT7N6Fo9di6j3QuKzntnHXD\nrwmoK/86FVaoySzW4a1zEBVYs8M2dv9i48ARG9FN1AzvVXs1D4pLbbz0Ripl5Xam/T2K9jG+l16p\nDrPaHLz+znF27y+ifYwvM6e1wsurfvf6qGsURWHdNzl8+FkmVpvC+FHh3DomAq22fg/3Ee6zbt06\nCgoKeOihh1zLXnnlFWbOnMny5cuJiIhg7Nix6HQ6pk+fzt13341KpeLBBx90Fb1syHq2C2fdzjR2\n/XKaEb2bExlav/8OCxGFYd4AACAASURBVCGEEJKUqGPMVjvJKdUX6tp+MMuVJAgN9KJjq2C3JQku\nlhiZPDSmxvdXVyiKwsln5qHY7EQ9/whqr3MFQ7X7N6KyVGDrPgK8/cFcDJYS0HmDsfITX3ulYRtm\nNDV4ik7n2fnyOzNeBuf0n5qaHBNyEVabg7mLjpGVbWb8qHAG9g2ulf26i8XqPJ59B4u5vp0fT01t\nidEgCYmaVFpm480PTrJ7fxGNAnRMvTuqQUwZK9xr0qRJTJo0qcryDz74oMqy+Ph44uPjayOsOkOt\nUnHzgJYsWHmQL78/xj/HS20JIYQQ9VvDfuRdDxWVmskvrr6qtsliJ6/YjAJkF1SQmJTB8i1HazyG\niyVG/p+9+w5zqsweOP5Nz5RM770xICpdEAEposIq2FFRXMu6uoiyK4q7Vmw/1y52xV17oegioKAg\noogU6YLAML3PZDIl03KT3Ht/f0QHUMrAJEwC7+d59lnnZm7uCdPynnvec7bk1Z3QWzkalq3C/v16\nwkcNJWLcyI7jmppidAVbUCITkHsOAUX2VEmg8VRJ/K7BZHGDgXaXlpRwN+Fm723bcLpU3l8q4Zbh\nyrFmIi3H50dYVVXeeK+MnXtaGDowgsmXJB35JD8mSQpPvFjQMTnkvunZIiHhZXkFrdw5azfrNzdx\nWq9Q3pk9UCQkBMFL+mZHk5Mczpa9dRRUNHV3OIIgCILQJSIp4WfCQ01EhXV+j74vkgSHS4w0NDtO\n2FFkcpuD0oeeQ2PQk/bIDDS/JRoUGf36xahocA+ZCFqdp4+E4obgaNAf+PWyO7SUNRow6xUyo5xe\njfHz1RLV9QrD+hg4Pfv4FTp9/lUt3/xgIzs9mOl/yUB7nKozfMEhyTw2O5+tO5sZ1DeMf96ehcko\nfhV6i6KofL6shnv/vYe6eieTJiYw664exEQHdu8RQfAnGo2Gy0ZmAfDpdwWdnjwiCIIgCP5IvBP3\nMyaDjv65sZ3+fF8kCQ6XGIm0mAkPPTEXF1WvvouzvIqEmycTlJPRcVy360e0TbUoPQaixqaCqx3a\n60FnhJCYA55DUX/btqHx+raNrXku1u1wkxSjZcLw49dHYsOWRt6bX0F0pIF778jCZArcXxtt7TKP\nPJfPjt0tnDkwgpm3ZWE0BO7r8Tf2Fjf/92IB78yrICxUz6y7enD1xUnHbYuRIJxMeqZFclpWFLtL\nG/mlpKG7wxEEQRCEYybejfuhK8fkMHZQCtFhZrQaiLKYMBsPXlruiyTB4RIj/XNjTsixolJpBVWv\nvochPoakv9+074GWRnTbvkU1heDufy6oKjRXeh6zJILmwB+hkgYDrU4tiWEuIoO8t23D1qQwf6WE\n0QBTxpsxHKcmgUWlbTz/ZjFGg5Z/3ZFNVOTxS4Z4W2ubm4ef3cuuva0MHxzJjFsyMejFr0Bv+SWv\nhTsf2sWm7Xb69rbw3KxT6HPKid90UBC602VnZwPwmaiWEARBEAKYaHTph3RaLZPH5nZM2ggPNfHp\ndwUHNJ78ja+SBFeOyQE820Mamh1EWsz0z43pOH6iKZ31PKpDIvWZ+9GFhnQc1//0BRrZhWvIBDAF\nQ2sduCVPY0tjyAHP0SJpKG0wYNIpZEd7b9uGW1b5YJkDhxOuOtdEXOTxWUjXN7p4fHYBDknhntuy\nyE4PPi7X9QV7iychUVjSzqihUUy7KV3cvfcSRVH539IaPvpfJahwzaVJXPqn+IDe4iMIgSI9wcIZ\nveL4aXctm/OsDOwZ190hCYIgCMJRE0kJP2Yy6IiL9CwEf58kiInYN33DFw6WGDkRKyQAmlato2HZ\nKkIH9yP6kn1d3LVlu9GV70aJy0DJ6gey09NLQqOD0PgDnkNRYbfVhIqG3FgJb96AX7rWSWmNwsBe\nes44xeC9Jz4MyanwxEsF2BpcTLk8iTMHRhz5JD/VaHfx8DP5FJe3M3ZENLf+OU0kJLykscnF7LeK\n2bqzmehIA3fekhnwY2IFIdBccnYWm/ZY+ez7Qvr3iBUJQUEQBCHgiKREgPh9kiA7I5rmpnafX3f/\nxMiJSHG6KHngadBqyXh85r7mli4n+p++QNVocQ+Z4DnWXAWoYEnwNLvcT3mjgRZJR3yoi+gQ7zUe\n3VXsZtVmFzERGi4ddXx6eSiKyotvFZNf1MboYVFcMj7+yCf5qfpGF7Oe2UtZpYNxo2O4+ZpU8Ybd\nS7bvauaFN4toaHIzsE8Yd9yUQZhF/EkRhOMtISqYYacnsHp7FT/uqGZ4n8TuDkkQBEEQjop4Bxlg\nfksSmI16mrs7mBNAzVsf4ygoIe7PVxB8am7Hcd3Pq9C0NuI+dQRqRBw4msDZCoYQMB041rDNqaGo\nwYBBp5AT471tG00tCh9/7UCnhSnjzJiNx2cx/cnnVfy4sZHeuaH87bq0fYmaAGNrcPLgU3uprJGY\ncG4cN1yVHLCvxZ/Iisr8RVXMW1yNVgt/npTMxPPiRLJHELrRRcMzWbuzhs9/KGRI73jRL0cQBEEI\nKD79q+VwOBg7diyfffYZVVVVTJkyhcmTJzN9+nScTs/ibdGiRVx22WVcccUVzJ8/35fhCF4iuWRq\nG9q8Por0eHNWW6l4/i30keGkzLy147imsRbdL2tQQyKQ+4wCRYbmakDza3PLfYsv9bdtG6qG3Bgn\n3trhoigqH30t0eqACcONpMQdn60z362tZ/7iauJjjdxzWxaGAJ1MUVsncd+/86iskbhkfLxISHhJ\nfYOTWc/sZe6iamKijDz+z55cPE70jxCE7hYVZmbMgGRsdolVWyu6OxxBEARBOCo+rZR47bXXCA8P\nB+DFF19k8uTJjB8/nueee44FCxZw8cUX88orr7BgwQIMBgOXX3455557LhERgbt//UQmKwpzV+az\nJc9KvV0iKsxE/9xYrhyTg04beIvXssdeRGltI+2he9FHer5PUVX0GxajURVcZ1wAeiPYK0GVISTO\n8/F+Kux67A4dsSFuYkO9l6RZ8n0L+eUyp2bqGN73+PSR2J3fwstvlxAcpOO+6dkBW4pfXSvx4NN7\nsdqcTJqYwFUXJYqEhBds3WHn+TnF2JvdDOkfzrQb0wkNCczvEUE4Ef1paDrfbatkyY/FjOiTiNko\nfj4FQRCEwOCzlWRBQQH5+fmMGjUKgPXr13POOecAMHr0aNauXcu2bds4/fTTsVgsmM1mBgwYwObN\nm30V0gnhtyoFh9N93K89d2U+KzaWY7NLqIDNLrFiYzlzV+Yf91i6qnn9VmyfLSW4zynEXn1Rx3Ft\n4Va0NcXIKb1QUnuBsw0cjaAzQXD0Ac/R7tJQaDOi16r0iJG8Flthpcxn37YQHqrhyrHm47Kgrq2T\neOKlQhRF5e6/ZZKaFOTza/pCRbWD+5/Mw2pzMvmSRK6+OEkkJLpIllU++LSCh5/Lp61N5qarU7hn\nWpZISAiCnwkLNnL+Gak0t7lYfpBpXYIgCILgr3z2rvLJJ5/kgQceYOHChQC0t7djNHruMkdHR2O1\nWqmrqyMqKqrjnKioKKxW6xGfOzIyGL3+j+XssbEWL0Xvf2RZ4b+Ld7JuRxXWxnZiI4I487REbpxw\nKjqd76sUHE432wtsB31se4GNWy4L8vldGW99fVVZZvdDzwDQ75VZRCZ4KnPU9lZatnyNqjcSPu5K\nNKEhNBQWIQMRadkYgvddX1VVvt+toqgwKEtDspdia2lT+Hi5FVWFaVdGkZFmPPJJXdTa5ubJh/dg\nb3Zz5605nDs62efXPJiufn2LSlt56Ol8bA0upt6QxeRLU70Ume/4+++sGquDh5/bxc+77CQlmHlk\nZm969Tj2mP399XrbyfZ6he53/uA0Vm6uYNn6Ukb3TyY06PhU2gmCIAhCV/hkFblw4UL69etHaurB\nFwWqqh7V8d9raGj7w7HYWAtW64nb+vGjFXms2O/OR21DO4tWF9LW7mTy2NzDnOkdtQ1tWBsOPu2j\nrrGdgmKbT6d0ePPrW/POfOzbdxMz6ULc2dkdz6tftwhdewvuAedhk/RQXwxSOwRF0tgKtO67fpVd\nT22TiahgN0GqRCdyaUekqipvf+Ggvknh0jGhRIVIWK3eq8A4GFlReeLFAgpLWrngnFhGDA7rlp+j\nrn59i8vaeOiZfOzNbv4yOYVzR0T4/e8Df/+dtXFbE7PfKqalVeasQRFMvT6dkGCOOWZ/f73ediK/\nXpFs8V9BJj0XDE1n7sp8lq4r4YrRvhkbLgiCIAje5JOkxKpVqygrK2PVqlVUV1djNBoJDg7G4XBg\nNpupqakhLi6OuLg46urqOs6rra2lX79+vggpoEkumS15B1/1bsmr47KR2Zi81WHxEMJDTUSFmbDZ\n/7hIjrSYCQ89PuMqu8pla6T8qdfQWUJIuXdax3GNtQzt3o0o4XHIp5wFbgla60Cr9/SS2I/k1pBv\nM6LTquTGOvHW7oA1213sLJTJSdExcWQoNluLd574MN6dW8Gm7Xb6nxbGDVel+Px6vlBQ0sasZ/bS\n0irzt+vSOG9UTHeHFNBcboUPP63k869qMeg13HpdKueNjBHbYAQhQIzun8zXP5WxYlM5YwelEmkJ\njL/PgiAIwsnLJ3X/L7zwAp9++inz5s3jiiuuYOrUqZx11ll89dVXAHz99deMGDGCvn378vPPP2O3\n22ltbWXz5s0MGjTIFyEFtKYWifqDJAMAGpodNLX49m46eEaR9s+NPehj/XNjfJ4U8ZbyJ19BbrST\nPOOvGON+XbwqMvr1i9Cg4h4yATRaaK4CVAhNAO2+16aqkGc1IisasqOdmPWdq+45kgqrzKLVTkLM\nMPk803GZZrDsWyuLl9eSmmRmxq2Z6HSBt+jMK2zloaf30tomM+2GdJGQ6KLaOon7/53H51/VkhRv\n4sn7e3L+qFiRkBCEAGI06Jg4LAOXW2Hxj8XdHY4gCIIgHNFx61R2++23c8899zB37lySkpK4+OKL\nMRgMzJgxg5tuugmNRsNtt92GxSLKQn/PX6oUrhzjKQPdkldHQ7ODSIuZ/rkxHcf9Xev2XVg/XEhQ\nbhZxN1zZcVy3Zz3ahmrk7P6o8RnQ3giuNjCGgunA78faFh22Nj0RQTKJFu80G5WcKu8vdSArcPV5\nZsJDfd8jZNtOO3M+LCMsVM9907MJCQ6MpNL+du1t4dHn85Ekhel/yWDk0KgjnyQc0tpNDbzydimt\nbTIjh0Zxy5RUgsyB930hCAIM75PIsvWlrN5WybjBqT7dXikIgiAIXeXzpMTtt9/e8d9vv/32Hx4f\nN24c48aN83UYAe23KoUVB+mm3dkqBckl09QiER5qOuaqBp1Wy+SxuVw2MrvLz3W8qYpCyX1Pg6qS\n/tjdaA2/fuu32dFt/QbVGIR7wPmguKGlBtCAJYH992Y43bC3zoRWo9IzVvLato3PVklYG1VG9jdw\nSobv84TlVQ6eerUIrVbDP2/PIj428Ep7d+xu5vHZBbjcCnfemsmwMyK7O6SA5XIpvDOvgi+/sWI0\naph2QzpjhkeJ6ghBCGA6rZZLzs7i9c93snB1EX+deGp3hyQIgiAIhyRmugWI31cpxEQE0Sc7+ohV\nCrKiMHdlPlvyrNTbJaLCTPTPjeXKMTnotMd2R95k0AXcXZe6BV/Ssmk7kReeQ9jwMzqO6zd+icbt\nxHXmRWAOAXsFqDKExoPuwMkXe20m3IqGnGiJIIN3tm1s3OVi4243qfFa/nSW7ydt2FvcPD67gLZ2\nmel/SeeUHqE+v6a3bd1p54mXClBkuHtqFkP6R3R3SAGrqsbBM68XUVjSTmqSmbv+lklacmCOgxUE\n4UCDesWRtraE9b/UMP7MdFLjAu/3vSAIgnByEEmJAPH7KoXsjGiamw4+DWN/c1fmH1BhYbNLHR8f\nj6kd/sBtb6H88ZfQmk2kPfiPjuOayr3oSnaixKai5AwAZys4mkBvhqADtwJYW3VYW/SEmWSSw72z\nbcPaoPDpKgmzEaaMM6P3cU8Hl1vhqVcKqa6VuOyCeEadFe3T6/nCpu1NPPlyIQD/vD2LgX3Cuzmi\nwPXDhnpefaeUdofCOcOjufmaVEwm328dEgTh+NBqNFw6MpsX5m/jf98Xcsflfbo7JEEQBEE4KPEO\nNMD8VqVgNh45n9Tc5mTT7kNP7ZBcsrfD80uVz8/BZbWReMcNmFISPAfdLgwblqBqtLgHT/Aca67y\n/L8l8YBtGy4Z9lqNaDQqPeO8s23D7VZ5b6kDpwsuH2MiOty3P4qqqvLGe2Xs3NPC0IERTL4kyafX\n84X1mxv590uFaLRw7/RskZA4RpJT4bV3S3n29WJUFabfnM60G9NFQkIQTkCnZ0WRmxLO1vw68sub\nujscQRAEQTgo8S70BCQrCh+tyOOh/26g4RCTOY7X1I7u1p5XSM1/PsGUnkzirVM6jut2fo+muR65\n15moUYme8Z+y01MhYTiwfL3AZsQpa8mIdBFi9M62jcVrnFTWKQw5VU//XINXnvNwFi6r5ZsfbGSn\nBzP9LxnHZbqHN635qYGnXytEr9fwwN9z6HdqWHeHFJDKqxzc89huvv6ujoyUIJ55sBejhgZexYwg\nCJ2j+bVaAuDT7wpQVe/8DRMEQRAEbxLbNwLMbw0rLeFBBz0eHmri0+8KDtoUc3/Hc2pHd1FVlZL7\nn0F1y6Q9PAOt2fN6NfY6dDtWowaHIfcdA24J2upAq4eQuAOeo75NR3WzgVCjTGqEyytx7Shw88M2\nF/GRGi4+2/dfg/VbGnl/QQXRkQbuvSMr4O6If7e2nhffKsZk0vLAP3ICsg+GP1j1o4033i/DISmc\nPyqGG65KwWQMrO8FQRCOXm5qBH2yo9leYGNnUT2nZYlEpCAIguBfRFLCj+2faNDrNAc0rIyN9DS6\nvHxUFgtWFR7QyLLVceTFc2endhwuJn+fvNHwxTfYf9hA+JiziDh3hOegqqJfvwSNIuMaNB70Rmgs\n9jxmSYT9mn+6FdhjNaJBpVecE28UFzQ0K3yywoFeB1P+ZMZo8G3FQlFpG8+/UYzRoOVfd2QTFen7\nZpre9M1qG6+8U0KQWcdDd+aQmx3S3SEFHIckM+eDMlauqSfIrOWuWzMZNlhMKxGEk8mlZ2exvcDG\np98V0jszCq2YriMIgiD4EZGU8EMHm5gRbDZQVtvS8Tm1De2s2FjOntLGA47b7IffkhEZamJgr9gj\nTu3oTExdneLhS3Kbg9KHX0BjNJD+yF0d4w21xT+jrS5ASeqBknYqOBrB1Q4mi+d/+ym0GZHcWtIj\nnYSalK7HpKh8+JWDdsnTRyIx2rdJnfpGF4/PLkByKtxzWxbZ6YE1MeWrVVZef6+M0BAds+7qEXDx\n+4PSinaeea2IskoH2enBzPhbJolxJ3aFlCAIf5QWb2HwKXFs2FXLpj1WzugVd+STBEEQBOE4EUkJ\nP3SwiRmHSjZUWFsOevxgIkKNzLrxDCzBR3+3PNCmeFS9/A7OimoSp12POSvNc9DpQL9pKapOj2vw\nhaDI0FIDGi2EJhxwfmO7lkq7gWCDQnqkd7ZtLN/gpKhSoW+OnjNP9e2PniQpPPFSAbYGF1MuT+LM\ngYE1NvOLFbW89VE5YRY9D9+VQ0aqSEgcDVVV+Wa1jTkfleF0qlw4NpbrrkjGYPC/BKIgCMfHJSOy\n2Ljbyv++L2RAboxf3lAQBEEQTk7iL5KfkVwyW/IOPjHjYJSj6Fk1qFfcMSUkDheTP07xcJSUU/Xa\nexgS40iafmPHcd3Wb9C0tyCfdjZYoqClGlTF00dCt6/ZpKzAHqsJUOkVJ3ll28beMjcrNriICtNw\nxTmmjsoNX1AUlRf/U0x+URtjhkVxyfh4n13LFxYuq+Gtj8qJDNfz2MweIiFxlNrbZV6YU8wr75Ri\n0Gv557QsbpqcKhISgnCSi48KZkTfRKrr21jzc3V3hyMIgiAIHcS7VD/T1CJRf4QtGPs71ILZbNQR\nZTGh1UB0mJmxg1KOestGZ2LyxykepQ89hyo5SXtgOroQz4JWY6tEl7ceJSwa+dQRIDWDZAd9EAQd\nuL++qN5Iu0tLSribMHPXt200tyl8+JWERgvXjjMTZPLtXt5PPq/ix42N9M4N5dY/p/k0AeJt8xdX\n8e48T1POR+/JJTU56MgnCR2KStuY8chuvl/XQG52CM/N6sWQAYFVJSMIgu9MHJaJQa9l0ZoiXG7/\nuqEgCIIgnLzE9g0/Ex5qIirMdMTeEL9Jjg09oKfEb4b3SeSykdleaUp5uJj8bYpH48o1NH79PZah\nA4i66DzPQUVBv34RGlXFNXiCp5ll8693icISYb9Fu92hpbxJT5BBITPK2eV4FFXlk+USzW0qFw4z\nkp7g2z4S362tZ/7iauJjjdxzWxYGfWDkHVVV5eOFlcxbVE1stJFH7u5Bguh90GmqqrLs2zre/qQc\nl1vl4nFxXHNpMnp94CSkBEHwvUiLiXMGpLBsQynfbqnkvDNSuzskQRAEQRCVEv7GZNDRPzf2oI+l\nxoUSHWZGq4G4yCDGDkrhvusGMHZQSsfx/asiTAYdcZHBf0hISC6Z2oa2Tm+7OFxMxzrFwxcUyUnJ\ng8+CVkv6o3fva265dyNaWwVyxumoidnQagXFBcHRoDfvO1+F3bUmQEPPWAmdF346vt/iYneJTM80\nHSMHGI58Qhfszm/h5bdLCA7Scd/0bMIsgZFzVFWV198tYt4iTzLlsXtEQuJotLbJPP1aEW9+UIbZ\nrOW+6dn8eVKKSEgIgnBQfxqajtmoY8mPxbRL7u4ORxAEQRBEpYQ/+m2bxZa8OhqaHURazPTPjeHK\nMTm4ZZWmFonsjGiam9oBT6PJzlRFdGWCxuFi8hfVcz5CKiwl/sYrCe7dw3OwvQX9luWoBhPuQePB\n5YA2G2gNEHJgoqWkwUCbS0tSmIuIoK5v2yitlvniRyeWYA1Xn2fy6Qi22jqJJ14qRFFU7p6aSWpS\nYGx7UFWVtz+pYPHyWpLiTTwyswfRATa2tDvtLWrl2deKqKlzckqPEO68JZOYKPHvJwjCoYUGGRg3\nOI2FPxSx/KcyJg7P7O6QBEEQhJOcSEr4IZ1We8hEg04LcZHBmI16mvc757eqiMPpygSNw8XkD5xV\ntVS+8B/0UREk33VLx3H9pmVoXA5cZ1wA5lBoKPI8YEn0TN34VYukpbTBgEmvkBXd9W0b7ZLK+8sc\nqApMPs+EJdh3RUlt7TKPzS7A3uzmlimp9Ds1zGfX8iZFUZnzYRnLvq0jIzWYB+/MJjLct9UkJwpV\nVVmy3Mp78yuQFZXLL0zgqosS0elEdYQgCEd27hmpfLO5nGUbShk9IPmYmmALgiAIgreI7Rt+7FDb\nL46FtyZoeDMmbyp9dDZKWzup905DH+FZlGuqC9EVbUOJSkLJHQztDeB2gCkMTKEd53q2bRhR0dAz\n1klX2zCoqsr8lRL1dpUxgwzkpvku9yfLKs++XkRZhYMLzoll3OiDb7PxN7Ki8tq7pZ6EREoQLz3R\nVyQkOqm5xc0TLxXy30/KCQnR8eCdOVxzaZJISAiC0GlBJj0XDM3A4ZT5cl1Jd4cjCIIgnOREUuIk\nYW1sD6gJGkfDvm4z9Qu/IqRfb2Kumug5KLvRr1+Migb3mRNBlaG11lMdYUk44PyyRgMtTh0JFhdR\nwV3vRr5+p5tte91kJGo5/0zf3n16Z245m3+20/+0MG64KsWn1/IWWVZ5+T8lrFhtIys9iIdn9iAy\nXNyl64zd+S3MeHg3P21t4rReoTw365SAqYwRBMG/jO6fRFSYiW82VVBvd3R3OIIgCMJJTCQlTnCy\novDRijxemLcV9RCf428TNI6G6nZTct9TAKQ/NhPNr70xdL+sQWuvQ+k5GDU62TNtQ1UgNB60+yoX\nWp0aihsMGHUK2V7YtlFtk1n4vUSQyTP+U3eoma1esOxbK0tWWElNNjPj1syAuFPudqu8MKeYVWvr\nyc0K5pG7exAWKnaRHYmiqPxvaTX3/TsPW72Tqy5OZNZdPYiKENUlgiAcG4Nex0XDMnHLCovWFHd3\nOIIgCMJJTKwGTnC/7yNxMP40QeNo1b73Ke278om5aiKhA07zHGxuQPfzKlRzKO5+54BkB2czGILB\nHNFxrqrCnloTqqqhR6xEV/8JnC6V95dKuNxwzflmIi2+y/lt22lnzodlhIXque+ObEKC/f/r53Ir\nPPt6Ees3N3FKjxDu/3sOwUH+H3d3a7K7ePE/JWz+2U5kuIE7b8ngtF6W7g5LEIQTwFmnJ7BsQyk/\nbK9i/JA04qMO35tKEARBEHxBVEqcwA7XRwIgOszUMT40ELlsDZQ//Tq6sFBS753mOaiq6H9agkZ2\n4x40DvRGT5UE/Nrccl81QUWTHrukIzbETWxI17dtfL5aorpeYVgfA6dn+y7fV17l4KlXi9BqNfzz\n9iziY/2/ysXpUnjqlULWb/ZsO3jgHyIh0Rk79zRz56zdHVt0nnu4l0hICILgNTqtlktGZKGoKv9b\nXdjd4QiCIAgnKVEpEWAkl0xTi4Ql/MgjH5tapEP2kdAA0y/vQ0pc4C5wyp94BbmpmbRH7sIQEwWA\ntmwXuoo8lIQslIw+0FIDihuCY0C/b/He7tJQWG9Er1XpEdP1fhpb81ys2+EmKUbLhOG+649gb3Hz\n+OwC2tplpt+czik9Qo98UjeTnAr/fqmArTub6XuqhX9Ny8ZkEvnQw5EVlc++qOaThVWggWsvS+KS\n8fFofbgdSBCEk9OAnrFkJFjYsKuWsQObyEkJ7+6QBEEQhJOMSEoECFlRmLsyny15VurtErGRQfTJ\njubKMTnotAdf4IWHmogKM2E7SGIiKsxM7BFGiPqzlq07sX78OUG9som//nLPQZeE/qcvULU63IMv\n9EzaaK8HnRFCYjrOVVXYYzWhqBp6xjowdvGnwNakMH+lhNEAU8abMeh9s3B0uRWefLmQ6lqJyy9M\nYNTQaJ9cx5sckszjswvYsbuFgX3CmHlbFkaDSEgcTkOTixfeLGb7rmaiIw3MuDUzIJJPgiAEJq1G\nw9Vje/DEB5v5SF6UIgAAIABJREFUcHkeD/x5kEiACoIgCMeVWB0EiN96Q9jsEipQ29DOio3lzF2Z\n/4fPlVwytQ1tAPTPPfiIyEDuI6Eqiqe5paqS/tjdaPSerIJu+7do2uzIpw5HDYuB5irPCZZEz9SN\nX1U162ls1xEd7CYutGvbNtyyygfLHDiccOkoE3GRvvmRUlWVN94r45e8FoYOiuDqixN9ch1vam+X\nefR5T0JiyIBw7pkmEhJHsv0XO3c+tIvtu5oZ1DeM5x4+RSQkBEHwuR4pEQw9NZ6SmmZWb6/s7nAE\nQRCEk4yolAgAh+sNsXmPlctGZmMy6P5QTREVZqJfjxjGDExm214bDc0OIi1m+ufGBGwfCYC6eUto\n3bKTqInnEnbWIAA0DdXodq1FDY1EPm0ktNs8lRLmCDCGdJzrcGsoqDOi06rkxjr3bzFxTJaudVJa\nozCwp55BvXz347RwWS3f/GAjJyOY6Tdl+P1drNY2N488X0BeQSvDB0cy/S8Z6H1UQXIikGWVuYuq\nWLCkGq0Wrr8ymYnnxaHp6jeoIAhCJ10+KofNe+v49LtCBvWKI8QspvsIgiAIx4dISgSAw/WGqG+W\neHfpbiYMy2DFpnK+3VzR8ZjNLvHNpgrGDkrhsZuH0NQiER5qCtgKCQB3UzNlj7+ENshM2oN/9xxU\nFfTrF6NRFVyDLwSNCi1W0OggNK7jXFWFPKsRWdXQM0bCpD/UkNTO2V3sZtVmFzERGi4dbfLZAnL9\n5kbeX1BBdKSBf92e5ff9GJpb3Dz8bD4FJW2MGhrFtBvTA2JcaXexNTh57o1ifslrIS7GyIxbM8nN\nCjnyiYIgCF4UaTEx8awM5q8qYOHqIq45N7e7QxIEQRBOEiIpEQAO1xsCYN0vNaz7pYZD3Tzfkmfl\n7D6JxEYGB3RCAqDi2Tdx2xpI+edUjEnxAGjzt6C1liKn9UZJ6gFNZYAKlnjQ7vsWr2nRUd+mJzJI\nJsHi7lIc9laFj5dL6LQwZZwZs9E3i+7Ckjaef7MYo0HLvXdkExXpuyaa3tBkdzHr2XyKy9o5Z3g0\nf7s+DZ2fV3V0p80/NzF7Tgn2FjdnDoxg2g1phASLX8uCIHSPsYNS+X5bJd9urmBk3yRS4sT2MUEQ\nBMH3/PuWqwCAyaA7ZG+I/SmHuPFvs0s8+N+fuH/OOj5akYesKF6O8Pho251PzdvzMGWmknDLtZ6D\njlb0m79C1RtxD/oTSHZwtoAhBEz7Oog73ZBfZ0KrUcmNlbq0bUNRVD78SqKlXWXCcCMpcb5J9NQ3\nOPm/FwtwuhT+8dcMstL9uzFpQ5OLB57eS3FZO+NGxzBVJCQOye1Wee2dQh59voA2h8zN16Qwc2qm\nSEgIgtCtDHotV4/tgaKqfLQiD1XtWkWhIAiCIHSGeAccIK4ck0Obw82PO6qP+TlsdokVG8sBmDw2\nsMoyVVWl5P6nQZZJf3gGWpOnYkC/+Ws0znbcA8dBUCjU5wOaX5tb7lsQ760z4VY05MRIBBm69iZr\n5SYX+eUyvTN1DO/rmz23DofMEy8VYmtwcd0VSQwZEOGT63iLrcHJQ0/vpaJa4sKxsdx4dYroh3AI\nVpuT594oYnd+K4lxJmb8LZNsP084CYJw8uiTHUPf7Gi2FdjYuMfKGb3ijnySIAiCIHSBqJQIEDqt\nlinn9yQ6zNTl59qSV4fk6trUieOtasFSmn/cRMTYEUSMHQ6AprYEXcFmlMh45F5nQkstKLJn/Kd+\n3zYHa4sOa6ueMLNMcljXtm0UVsp8tc5JeKiGq8aafbLwVhSVx17YTX5xG2OGR3PxuHivX8ObrDYn\n9z/pSUhcMj5eJCQOY8OWRu6ctYvd+a2cc3YszzzUSyQkBEHwO1eN7YFep2Huyr1IzsB6vyAIgiAE\nHpGUCDA90yK7/BwNzQ6aWg7en8IfyW3t7Jr5JBqjgbSH7/QcVGT06xcB4B48EdwSOBpAZ4LgmI5z\nXTLk1RnRaFR6dXHbRptD5cNlDlTg2vPNhAT5ZuH9ycIqVq2po3duKLdel+rXC/zqWon7/p1Hda3E\nFRMSmHJ5kl/H211cboX/flLOEy8V4nQq/O3Pacy66xSCgwK7x4vgn+zNblausdHuEItJ4djERwZz\n/uA06u0SX64r6e5wBEEQhBOc2L4RAH4/6tNs1OJyq8iHaiJxBJEWM+GhXa+4OF4qX/wvjvJqEu+4\nAXNmKgC6XWvRNtYi5wxEjU2F+kLPJ4cduG0jv86IS9aSFeUk2Hjs2zZUVWXuCgeNLSrjzjSSleyb\nxeR3a+uZv6SapAQz99yWhUHvv3nDyhoHDz61F1uDi8mXJHLFhMTuDskvVddKPPt6EfnFbSQnmrj7\nb1mkpwSJ5I3gdU12F59/VcvSlVYckoJBp2HEmVHdHZYQoC4Yms6an6tYur6U4X0SiY0I6u6QBEEQ\nhBOUSEoEgLkr8zt6QQA4nF1rVNmvR3TATOFwFJVR/foHmFMSSLrjRs/B1iZ0279FNQXjHnAetNWB\nLEFQJBj2lcLbWnXUtBgINcmkRLi6FMea7S52FMpkJ+s4Z5Bv+kjs2tvCy2+XEByk46kHTyPE7L8N\nScsq23no6b00NLm57opkLhnv31tMusuPGxt45e0S2toVRp0VxV+vTSXIHBg/e0LgaLS7+HxZDcu+\nrcMhKUSGG5h8aRJnDe56ZZ1w8jIb9UwancObi3/hk2/2cvtlfbo7JEEQBOEEJZISfk5yyWzJs3r1\nOeUA6qZd8tCzqE4Xpzx1D7pgz10a/U9foHE7cZ1xAeh00FTnGf0Zsq8Zl1uBPKsRDZ5tG10ZAlFh\nlVm02kmIGa4534TWBxMlausk/v1yIYqicvfUTDJSQ7Bam71+HW8oKW/nwaf3Ym92c+PVKUw4VzRB\n+z2nS+HtT8pZ9m0dJqOW229KZ8yw6O4OSzjBNNpdLFxWw7KVdUhOhagIA9delsTYs2MwGf23ykoI\nHEN6x/Ptlgq27K1jR5GN0zLF7zFBEATB+0RSws81tUjU273b/2HdjhquHN3D76slGlf8QNOKH7AM\nG0Ti5eOpq2tBW74HXdkulLh0lKy+0FQGqBCaANp9r6fAZkSStaRHOgk1HXsSRnKqvL/UgazA1eeZ\nCQ/1/hv9tnaZx2YXYG92c8uUVPqdGub1a3hLYUkbs57dS3OLzC1TUhk3+sijak82lTUOnnmtiKLS\ndtKSzdx1ayapyaLsWfCehiYXC5fWsGyVFadTJTrSwHVXeJIRRoNIRgjeo9FouObcXB5+5yc+XrGX\nh2+MRK8T32OCIAiCd4mkhJ8LDzURFWbC5sXEhMMpY21oIyXO4rXn9DbFIVHy4DOg05H+2N2e/fdu\nJ/oNS1A1WtyDJ4DUDK42MIaCad9raWjXUmU3EGJUSI/s2raNz76TsDaqjOxv4JQM7/+4yLLKs68X\nUVbh4IKxsX69yM8rbOWR5/Jpa5e57YY0xo6IOfJJJ5nV6+p59d1SHJLC2LOj+cvVqZhM4g284B0N\nTS7+t7SGr/ZLRlw2KYFzRkSLZITgM2nxFkb2S2bVlgq+2VTO+YPTujskQRAE4QQjkhJ+zmTQ0T83\n9oCeEoeSEhtCubW1c0/s5032qt/8EKm4nPi/XE1wz2wAdD9/h6a1EXfv4ajh0WArADRgSeh4PbIC\ne2pNgErPLm7b2LjLxcZdblLjtPzpLOORTzgG78wtZ/PPdvqfFsYNV6b45BresDu/hUeey0eSFO74\nSzqjhooS3v1JksJbH5ex4nsbZpOWf/w1g7NFg0HBS+obXfzvy2q+/q4Op0slJsrAZRckcM7waAwi\nGSEcB5eencVPu2r4/IcizuwdH1DNsgVBEAT/J5ISAeDKMTkAbMmro6HZQaTFRHioicZmicYWiUiL\nmf65MUwYlsG8b/LZVVJPfbPzkM9nNur8uou2VFFN5ez/oo+JInnGXwGQbdXoflmDGhyO3GcUtNSA\nKkNoPOj2JQyK6o043FpSI5yEdaFRpLVB4dNVEiYDXDvOjF7n/STOsm+tLFlhJTXZzIxbM9H54Bre\nsGNPM4+/UIDTpXDnLZkME83zDlBW2c4zrxVRWuEgMy2Iu/6WSVK8ubvDEk4AtgYn//uyhq+/q8Pl\nVomNNnLZBfGMGSaSEcLxFRpk4JKzs/jg6zwWfFfATRf07u6QBEEQhBOISEoEAJ1Wy+SxuVw2Mpum\nFonwUBMpSRGUVzbS1CIRGmxk4epCHnn7J+rtEpEWI2edloBBr+W7rZV/eL5hpyf4dT+Jskdmo7Q7\nSH98JvpwC6gqjm8WoFFkXGf8CVQ3OJpAb4agfXejmxxaypv0BBkUMrqwbcPtVnlvqQOnC64dZyIm\nwvtv/rfutDPnwzLCLHruuyObkGD//Hps22nn/14qQJFh5tQshgyI6O6Q/MrKNTbefL8Myakwfkws\n11+ZLMrohS6zNTj57Msalu+XjLj8wgRGD4vy6zHBwoltVL9kvttayZqfqxnVP5nspPDuDkkQBEE4\nQYikRAAxGXTERQb/4eOPVuQdsL2jvtnJjzuqGTMwmbGDUtiSZ6W+WSLKYqJ/bmxH5YU/sq/ZSP3i\n5YQMOI2YSRcCoC3ahlyej5zcEyWlJzQUeT7ZkniQbRsaesY66EofriVrnFTWKQw5VU//XO+P/yyv\ncvD0q0VotRr+OS2L+Fj/LIPdtL2JJ18uBOCeaVkM6ivegP6m3SHz5gdlrPqxnuAgHTOnZjJ0kKgg\nEbqmrt7Jp19Us2K1DbdbJS7Gk4wYdZZIRgjdT6vVMHlsD578aAsfLc/jvusGofXzraCCIAhCYBBJ\niQB3uJGh2/baeOzmIQdUWPhzhYTiclPywNOg0XiaW2q1ILWj37QM9Abcgy+ANhvITk+FhGHfFpSS\nBgNtLi3JYS4igo5928aOQjert7mIj9Rw8dneTxbYW9w8PruAtnaZ6Tenc0qPUK9fwxs2bGnk6deK\n0GrgX7dn0+80/50IcrwVl7XxzOtFVFRJ5GQGM+OWTBLi/DOxJASG3ycj4mOMXD4hgVFDo9HrxaJP\n8B890yIZfEocG3bVsmZ7FSP6JnV3SIIgCMIJQCQlAtzhRoY2NDtoapGIiww+oMLCX9W+O5/23QXE\nTr6Y0H6nAqDfugKNoxXT8AuRzMFQXwBaPYTsm1LRLGkpbTRg0itkRh+6l8aRNDQrzF3hQK+DKePN\nGA3eXQy43ApPvlxIda3kufvpp80if9zYwHNvFKHXablvejann+K/U1qOJ1VVWf6djf98XIbTpTLh\nvDimXJ4k7mALx8xq8yQjvlltwy2rJMSZuPyCBEYOjRLJCMFvTRqdw9b8OhZ8V8DAnrEEm71fUSgI\ngiCcXERS4jiSXLLXKxYONzI00mIOmA7Zrrp6Kp55A124hZR/3QaApq4cbd5PKOGxGAaMhMJdnk+2\nJILW8++nqLCn1shv2zaOdX0oKyoffuWgzQGXjzaRGOPdihJVVXn9vTJ+yWth6KAIrr440avP7y3f\nr6tn9lvFmIxa7v97Dr1z/bOS43hra5d57d1SftjQQGiIjhm3pjO4v+ivIRyb2jqJT7+oYeUPnmRE\nYpyJyyckMPLMKL9teCsIv4kKM3Ph0Aw++76Qz38o5uqxPbo7JEEQBCHAHVVSIi8vj9LSUsaOHYvd\nbicsTJR0d4asKMxdme/p7WCXiArb19tBp+3aXdbDjQztnxvj19s19lf2fy8j21tIf+xuDNGRoCjo\n1y9Gg4pryAQkewO42sFk8fzvt/MaDbQ4dSRYXEQFH/u2jeUbnBRVKvTJ0XHmad7P1S1c5lmA5GQE\nM/2mDLRdmVXqIyt/sPHy2yUEmXU8dGcOudkh3R2SXygoaePZ14qoqpXomR3CjFsziY32zYhY4cRW\nY5VY8EU1366xIcuQGG9i0oQERgwRyQghsJw/OJXV2yv5ZlM5Z/dLIjlG/L0QBEEQjl2nV1/vvPMO\nS5Yswel0MnbsWF599VXCwsKYOnWqL+M7IcxdmX9A0sBmlzo+njw2t8vP/8eRoZ4Rof7c0HJ/LZt3\nUPfJIoJOySHuussA0OZtQFtfiZzVDzUmldaaAtBoITSh47xWp4biegNGnUJ2F7Zt5Je5WbHBRVSY\nhknnmNF4uXHX+s2NvL+gkuhIA/+6IxuTyf/K/b9eVcdr75USGqJj1oweZGf4/3YfX1NVlaUrrbw9\ntwK3W+WS8fFMviRJlNULR626VuLTL6r59kdPMiIp3sQVExMYMVgkI4TAZNDruPqcXF78dDsfLc/j\nrqv6ef1vpyAIgnDy6HRSYsmSJcybN48///nPAMycOZOrrrpKJCWO4HCNKLfk1XHZyOwuVzMcbGRo\noFRIqIpCyX1PAZD++Ew0ej20NaPfugLVGIR7wPnQUo2qyJ6EhM6zd1VVYXetCRUNubESx/pyW9pU\nPvxaQqOFa883E2Ty7puqwpI2nn+zGKNBy713ZBMV4X97b7/8ppY5H5YTFqpn1l05ZKaJhERrm5uX\n3y5l3aZGwkL1TL85nQGni+kjwtGpqpVYsKSaVT/aUBRITjAxaWIiwwZHovPDailBOBp9c6I5LSuK\nHYX1bM6zMrBnXHeHJAiCIASoTiclQkJC0O631UCr1R7wsXBwnW1E6Q2/HxkaCKwfL6J12y9EXXw+\nYWcOAEC/aSkal4RryETQApIdfVAI7qB9IxfLm/Q0SzriQt3EhMjHdG1FVfl4uQN7q8oFw4ykJ3o3\nkVPf4OT/XizA6VK457YsstL972vz+bIa3plXQUSYnofv7kFactCRTzrB5RW28uzrRdTWOemdG8qd\nt2QQHSm2awidV1XjYM6HFSz7tgZFgZREM5MmJHCWSEYIJxCNRsPV5/TgweINfPJNPqdnRWMMkBsi\ngiAIgn/pdFIiLS2Nl19+Gbvdztdff82XX35Jdna2L2M7Ifh7I0pfNN/sLHejnfInXkYbHETaA9MB\n0FTmoyv+GSUmBSWnP9QXAWBJyqKh2ZN8aHNpKKo3YtCq5MQcPOHTGd9vcbG7RKZnmo5RA7xbwSBJ\nCk+8VIitwcV1VyQxZID/NUVcsKSaDz+rJCrCwCN39yA50dzdIXUrVVVZ9HUt7y+oQFFg0sQEJk1I\nFOX1QqdV1jiYv7ia79fVoyiQmmRm0sQEhg4SyQjhxJQYHcK5Z6SybH0py9aXMnF4ZneHJAiCIASg\nTiclHnzwQd577z3i4+NZtGgRAwcO5JprrvFlbCcEf21E2Sa5+Gj5XnaX1NPQ7PRq883OqnjmDdz1\njaTcOw1jYhzILvQbFqNqNLiHTIA2GyguCI5Gbw6G5mZUFfJqTSiqhl5xDozH+M9XWiPzxY9OLMEa\nrj7PhNaLe2EVRWX2f4rJL25jzPBoLh4X77Xn9gZVVZn7eRVzF1UTG23k4bt7kBgXGFNafMXe4ual\n/xSzcZudiDA9//hrBn16i0a+QudUVDmYv6Sa1evqUVRITTbzl2syOS3X7JdNbQXBmyaclcHaHdV8\nsa6Es05PICZcVNwJgiAIR6fTSQmdTscNN9zADTfc4Mt4Tkj+1Ijyt0kgq7dVIrn2TavwdvPNI2n7\nZS8178zHlJVGws2TAdDtWI22uR53r6GolihoKAStAUJiO86rtOtpdOiIDnYTe4zbNtollQ+WOlAV\nmHyeCUuwd5MwnyysYu3GRnrnhnLrdal+1fxLVVU++LSSz76sIT7GyCMzexAXc3InJHbtbeHZ14uw\nNbjo29vC32/OICLc/3p/CP6nvMrB/MVV/LC+AUWF9BQzkyYmcuaACOLjw7Bam7s7REHwuSCTnitG\nZ/PWkl3MW5nP1EtO7+6QBEEQhADT6aRE7969D1hcaTQaLBYL69ev90lgJxJ/akT58Td7Wbmp4pCP\ne6v55uGoqkrJ/U+DopD+6F1oTUaw29DtWI0aZEHuMxqaKz2fbEn0TN0AHC4NhTYjeq1KbqyTY1nr\nq6rKgpUSNrvKOYMM5KZ5d/znqrU25i+pJiHOxD23ZWHQ+0/fFVVVeXtuBYu/riUx3sQjd/cgJurk\n7ZWgKCr/W1rDR/+rBBUmX5LIpRckiDJ74YjKKtuZv7iaHzY0oKqQkRLEpIsSGNI/QlRGCCelM09N\n4NstFWzcY+WX4np6Z0R1d0iCIAhCAOn0imz37t0d/+10Olm7di179uzxSVAnqu5uRCm5ZH78ueqw\nn+Pt5psHU7/wK5rXbSbivLOJGH0WqCqGDUvQKG5cg8aD3A5uB5jCwBQKeBbUeVYjsqqhZ4yESa8e\n07XX73Szda+bjEQt5w/x7oJ8194WXnm7lOAgHfdNzybM4t2ER1coispbH5WzdKWV1CQzs+7q4ZeT\nQI6XRruL2XOK2bqzmagIA3feksGpPS3dHZbg58oq2pm3uJo1P3mSEZlpQUyakMjg/uEiGSGc1LQa\nDdecm8uj72zk4xV7eeiGM9Dr/CcpLwiCIPi3Y1o1GY1GRo4cyX//+1/++te/ejsmwUesDW04nMph\nP8fXzTfl1jZKH52NxmQk7eE7AdCW7EBblY+SmIOS0tOzbUOjBUtCx3kldVDfricyyE2CxX1M1662\nySz8XiLIBNecb/ZqA8Maq8S/Xy5EUVRmTs0kxY+aRiqKymvvlbLiexvpKZ6ERETYyZuQ2LG7mefe\nKKahycWA08O446Z0wk/ifw/hyErK25m/uIofNzaiqpCVFsSkixIZ3C/cr7ZnCUJ3ykgIY0TfJL7f\nVsm3Wyo4d1Bqd4ckCIIgBIhOJyUWLFhwwMfV1dXU1NR4PSDh2HRqikYn3jz7uvlm5ez/4qq2kvT3\nv2BOTwGnA/3GpahaPa7BF0JrDaiKZ9uG1vPtKbk1bC1X0WlUeh7jtg2XW+X9pRIutychERXmvTs4\nbe0yj79YgL3ZzS1TUul7qv80SJQVlVfeLuHbNfVkpQfx0IwehIX6TwXH8SQrKgsWVzNvURVo4Lor\nkrno/Dhxh1s4pJLyduYt8iQjALLSg7hyYiJniGSEIBzUpSOz2Li7loWrixhySjxhISfvFkFBEASh\n8zq9Otm0adMBH4eGhvLCCy94PSDh6PzWuHJLnpV6u3TYKRqxEUGYjToczoM3iBzdP8mnzTfbC0qo\nfuMDjMkJJE67HgDdtpVo2ptx9xkNJiM01YAhCMyeEZqqCnvrjLhk6BHjxGw4tm0bn38vUV2vMKyP\ngdOzvbcol2WVZ18voqzCwQVjYxk3OvbIJx0nsqwy+61iVq9voEdmMA/NyCEk+ORMSNQ3unhhTjE/\n72omNtrInbdk0CsntLvDEvxUcVkb8xZVs3aTJxmRkxHMpImJDOobJpIRgnAYYcFGLh6RyUcr9vLZ\n9wVcP/6U7g5JEARBCACdXqE88cQTvoxDOEZzV+YfMG70cFM0TAYdw05P4JuDNLoc1T+JKef38lmc\nqqpS+tCzqC43abP+gS7YjKa+Et2edSiWKOTeZ0FTqeeTLUkdVR3WVh11rXpiLJAUdmzbNrbtdbN2\nh5vEGC0Thnv3rs3bc8vZ/LOdAaeHccOVKV597q5wuRWef6OYtZsa6ZUTwgP/yCE4qHuaq3a3rTvt\nvDCnmCa7mzP6hXP7jelYTtJqEeHwikrbmLuoivWbmwDIyQzmqosSGXC6SEYIQmeNHpDMd1srWb2t\nipH9kslM9J/qQUEQBME/HfGd+ciRIw/7ZmzVqlXejEc4CpJLZkue9aCPHWqKxlXn9ECj0XgqK5ol\noiz7Kit8qXH5appW/kjY8MFE/mkMqAr69YvRqCquwRPA0QiKG4JjQO/paeGUYW+dCa1GZVCWFkfL\n0V/X1qQw7xsHRj1MGWfGoPfewmLZt1a+WGElNdnMjFszvdqjoitcLoWnXyvip61NnNYrlHvvyCbI\nfPIlJGRZ5ZPPq/j0i2p0Wg03Xp3ChWNjxeJS+IPCkjbmLapi/RZPMiI3y1MZIZIR3SsvL4+pU6dy\n/fXXc+211/LTTz/x3HPPodfrCQ4O5qmnniI8PJy33nqLZcuWodFomDZtGiNHjuzu0E9qOq2Wyefm\n8vTHW/hoeR7/mjIQrfg5EgRBEA7jiEmJjz766JCP2e12rwYjHJ2mFol6u3TQxw41RaM7xpMqDonS\nh55Fo9eR/thdaDQatHkb0daVI6efhhqbDA1FoDNCSEzHefl1JlyyhqxoCUtQ0FEnJWRZ5YNlDhxO\nuOpcE/FR3usjsXWnnTkflhFm0XP/9Gy/qUKQnApPvlzIlh12+p5q4V/TsjGZTr4O6HX1Tp5/s5hf\n8lqIjzVy162Z5GSGdHdYgp8pKGlj7udV/LT112REdghXXZRIv1MtIhnRzdra2nj00UcZOnRox7En\nnniCZ555hqysLF5//XXmzp3L+PHj+fLLL/nkk09oaWlh8uTJDB8+HJ3OP34nn6xOSY9kUK84Nu6u\nZe2OaoadntjdIQmCIAh+7IhJieTk5I7/zs/Pp6GhAfCMBX3sscdYunSp76ITDis81ERUmAnbQRIT\nR5qicTzHk1a9/j5SSQUJt1xDUG4WtLeg3/I1qsGEe+A4aP51TKkl0TN1A6hr1VHbosdikkkJP7Zt\nG0vXOSmtURjYU8+gXt4r1y+vcvD0q0VotRr+dXsWcTG+m1ZyNBySzP+9WMjPu5oZ2CeMmbdlYTSc\nfAmJTdubmP1WMc0tMkMHRXDb9emEBIsFirBPQbFnm8ZvyYheOSFcOTGRviIZ4TeMRiNz5sxhzpw5\nHcciIyNpbPT0+WhqaiIrK4v169czYsQIjEYjUVFRJCcnk5+fT8+ePbsrdOFXk0Znsz2/jvmrChiQ\nG0uQSWybEwRBEA6u038hHnvsMdasWUNdXR1paWmUlZVx4403+jI24QhMBh39c2MP6CnxG19P0egs\nqbyKqhffxhAbTfKdNwOg3/wVGqcD96A/gcYNbgeYw8HouZPtliHPakSDSs9YiWMZjrC7xM23m1zE\nhGu4dLTJawsNe7Obx2cX0NYu8/eb/adZYnu7zGOzC/glr4XB/cO569ZMDCdZQsLtVvngswo+X1aL\nQa/hlimvwli8AAAgAElEQVSpnD8qRiwyhQ57i1qZ+3kVm7Z7qvx65XgqI/r0FskIf6PX69HrD3yL\ncu+993LttdcSFhZGeHg4M2bM4K233iIqKqrjc6KiorBarSIp4QdiwoP405npLPyhiMVripnk422i\ngiAIQuDqdFLi559/ZunSpUyZMoX333+fHTt2sHz58kN+fnt7O//85z+x2WxIksTUqVPp1asXM2fO\nRJZlYmNjefrppzEajSxatIh3330XrVbLpEmTuOKKK7zy4k4Gv/WC2JJXR0Ozg0iLmf65MT7vEdFZ\npY+8gOKQyHjyX+gsoWhqitAVbkWJSkTO6e/ZtqHRQWh8xzkFNiNOWUtGpJNQ09FP27C3Knz8tYRO\nC1PGmzEb/5+9+46Oqtz6OP6dPum9EdJICL1X6R2UqihwUezdq16s770qClbseu0FC3IFBAtKlSoi\niECQJqSShPQyyaRNPef9YyBSQjKBJJPyfNZirZSZM3syAeb8zrP30zAnG1abxOJ3U8nNN3PdlFBG\nXuFf952aQEWlnWffSOZESgVD+vsy/84Y1A04O6MlyC8089oHaSSmVhIWouPRe2KIiWyalUBC85eY\nUsGKNTkcOOwII7rGezJ7Wig9uogwoiV59tlneeedd+jXrx+LFy+usb1Uluv+P8PPzx21unFC+6Ag\nr0Y5bkt1w5Ru/HYsj5/3ZTJtVBwRIY3/8xGvgeuJ18D1xGvgeuI1qB+nQwmt1rFrgdVqRZZlunfv\nzuLFiy96+23bttG9e3fuuOMOsrKyuPXWW+nbty9z587lyiuv5PXXX2fVqlXMmDGDd999l1WrVqHR\naLj22msZP348vr6+l//s2gBXzIhwVunOvRh+2oJnv54EzLwK7DbUv/+IjALbwKlQkQ/IjkBC6fhV\nNFQqySnT4KG1E+lnrfdjSpLMso1myqtkpo/Q0j64YX4WsizzwZeZHEssZ0h/X+bMaB79sWXlNha9\nnkzyyUpGDPbjgduim83Azaby+4ES/rsknYpKOyMG+3H3vEjcmsmMD8G1TqQ4VkYkHHGEEd06eTJ7\nWhjdO3uKMKIFOnHiBP369QNgyJAh/PjjjwwePJi0tLTq2+Tl5REcHFzrcQyGykapLyjIi4KCskY5\ndkt23chY3v3uMO99c5D5s3o16t898Rq4nngNXE+8Bq4nXoOa1RbUOB1KxMTEsGzZMvr3788tt9xC\nTEwMZWUX/2FfddVV1R/n5OQQEhLC77//zsKFCwEYPXo0S5YsISYmhh49euDl5Siyb9++HDhwgDFj\nxjhbWptnttqrAwmAtOxSyiqtxLTzxsu9YbfAdJZktZH+5CugUBD1/GMolEpUR35FWVqAPX4Aspc3\nGLNA4+Fo3QBsEpwo0AEynYMtl9S2sXW/leRTdrrGqBjeS9Ngz+f7DXls/bWIuGh3HrgtGuWlFNfA\njGU2nnktibSMKsYM9efeW6JQNYO6morVKvHFN1ms3VyAVqvgvpsjGTs8QJxsChxPLmfFDzkcPOr4\nP6p75zNhhLhq0ZIFBgaSnJxMXFwchw8fJioqisGDB/PZZ59x//33YzAYyM/PJy6ueawUFBz6xgfS\nLdqPI2nFHEwupE/HIFeXJAiCIDQzTocSixYtoqSkBG9vb3766SeKi4u566676rzfnDlzyM3N5YMP\nPuCWW26pXnEREBBAQUEBhYWFNfaD1uZiSy/b2jIZf38Plvx4lD1Hcsg3VKHXKjFbJM5evBoT5sWr\nD4xAq23aAVOpb36OKSmNyDtmEz12AFJpEeWHt6Nw98R79FRKTiUjKRT4RcWh1ukBSDgpYbJB53bQ\nof2FOyXU9fomplvY+Hs5ft5K7psTiJd7w8xU+GV3IUtXZRMcqOPVhT0J9G+awZa1Pd9ig4VnXjtO\nWkYV0yeF8fA9HZtFUHI56vP3NyunigWvHONEcjnREe4serwrHaJa3u4abe3frMZ+voeOlfLZ1+n8\ncdAxkLlfT19u+UcUvbu7ZuVdW3t9G9KRI0dYvHgxWVlZqNVqNm7cyMKFC3nyySfRaDT4+Pjwwgsv\n4O3tzaxZs7jhhhtQKBQ888wzKJVta55Oc6dQKPjHuHieXrKX5VuS6B7jj6aR2mcEQRCElsnpM9VZ\ns2Yxffp0Jk+ezLRp05x+gOXLl/PXX3/x6KOPntPrebG+T2f6QWtaetlal8mcvQri7LaMoCAv3lmZ\ncM6QS5NFuuD+aTll/OuNHSy8dWCT1AtgyS8kcdHbqHy9CXjwDgoKylBvW4nKZsUyaBqmvFywWcEj\nCIPRClgprVKSnKvHTSMTpKvi/Fyqrte30iTzzvJKZBn+MV6HqaICU8XlP5fU9EoWvpqIVqPk8fti\nkO0WCgosl3/gOtT2fIsNFha8mkRWjpnJ44K46bpQiorquV9qM1Ofv7+79hp49/N0qkwSY4YFcMf1\n7dHrpBb397+1/pt1MY35fI8lOlZGHPrLcfyeXbyYPT2MrvGOQbSu+Dm35te3KcKW7t27s3Tp0gu+\nvnz58gu+Nm/ePObNm9foNQmXrl2gB2P7tWfTH5ls2JvJ1CHRri5JEARBaEacDiUef/xx1q9fz9VX\nX03nzp2ZPn06Y8aMqV75cL4jR44QEBBAWFgYXbp0wW634+HhgclkQq/XV/d9BgcHU1hYWH2//Px8\nevfuffnPrIWzSxIrtiaTkFhAsdGMv7eOPvFBzB4Th0qpxGSxkZBY+4qSM07ll1NWaWmyVo5TL7yD\nvayCqBceR+PvizLzL1SnTiCFxCC17wgl6aDSgXsgAHYJjhc4Vh90DnIMqKwPWZZZsdlESbnMxEFa\nYsMb5gpMscHCC2+nYLFKPH5fBzpEuX5wYmGxhQUvJ5GTb2bGpGBuvC68zbQrmC0Sny0/xcbtheh1\nSh68PYpRQwJcXZbgQkdPlLFiTS6HT4cRvbp5MXtaGF06No9dcQRB+Nu0oTHsOZrL2t0nGdo9FH9v\nvatLEgRBEJoJp0//+vXrx5NPPsnWrVu5+eab2blzJyNGjLjo7fft28eSJUsAKCwspLKykiFDhrBx\n40YANm3axPDhw+nVqxeHDx/GaDRSUVHBgQMH6N+//2U+rZZvxdZkNu87RZHRjAwUGc1s3neKFVuT\nATAYzRQZzU4dS8YRTDSFsn2HKFz5E+7d4gmedw1YLaj/WIusVGEbOAXKch039AqD0yfT6QYNVVYl\n4T42fNwuXO1Rl12HrBxJtRMbrmLcgIaZI2E2S7zwdipFBivzrg1nUF/XD17NKzDzxEuJ5Jze/aMt\nBRJZOSb+77kTbNxeSHR7N15Z0FkEEm3YkeNlPPVyIk8uTuLwX2X07ubFi/+J55mHO4pAQhCaKXe9\nmpmjYrFYJVZuS3Z1OYIgCEIzUq9BA0ajkc2bN7NhwwYyMzOZPXv2RW87Z84cnnjiCebOnYvJZGLB\nggV0796dxx9/nBUrVtCuXTtmzJiBRqPh4Ycf5rbbbkOhUHDfffdVD71sq8xW+0VXQSQkFjJzZCxe\n3lqUCpCc2DFTAbQPbvw36rLdTvoTLwMQ9dxjKFQqVH9uRlFRiq37CGSNAixm0PuB1rHqoMysJKNE\ng14t0cG//m0RWQV21uy04KGH6yfqGmSugiTJvPXpSVLSKxkzLIAZk2qf5N4UcvJMLHglicJiK/+Y\nEcasac1j94+msH13ER9+mYnJLDFhVCC3zmmPTit6xtsaWZY5cryc5T/kcCzREbL26e7N7OlhdIpt\nefNEBKEtGtojjO0J2ez9K5/RfQx0ivRzdUmCIAhCM+B0KHHbbbeRlJTE+PHjufvuu+nbt2+tt9fr\n9bz22msXfP2zzz674GuTJk1i0qRJzpbS6pWWmym+yCoIQ5mJ0nIzKq3GqUACINTfHW0TbBNa8PUP\nVB4+TsDMK/Ea1BuFIQ/Vsd+QPXyxdxkCxgzH1p+ejpN8SYbj+VpAQacgU73bNswWmaUbTNglmDNe\nj49nw5yofv19Drv3ldA13pO7b4xw+WqEUzkmFrychKHUyo3XtePqK0NdWk9TMZslPl6WyZZfi3DT\nK3n47miGDfSv+45CqyLLMof/crRpnAkj+vX0ZtbUMOJFGCEILYpSoeD68fE89+U+lv2cxNO39Ecl\nBpMKgiC0eU6HEjfeeCPDhg1Dpbrw5Pbjjz/mjjvuaNDC2iqz1Y7FasffW1dje4aflx4fTx1+3jr8\nvbQUl9W+ukCpgJziSp74aDedo/yZO74j7rqG2yrzDJuhlFMvvovSw52IJx8EWUa990cUsoR1wGSo\nKgRk8AwBpeN3KMOgocKiIszLip97/ds2vt1hpsAgM7KPhq4xDbO7yPbdRaz6KZfQYB2P/7MDGrVr\n3yyln6ri6VeTKDXauHVOe6ZOcP2qjaaQkVXFq++nkZltokOUG4/cHUNYiOg/bktkWebQsTKW/5DD\n8WTH1Np+Pb2ZNS2M+A4ijBCElqpDO2+G9Qjj18M5bE/IZmy/9q4uSRAEQXAxp8/kRo4cedHv7dy5\nU4QSl+n8wZYXW57eJz4QnUaFXqumb6fgc3bfOJtGrcRqk6pXUxSXWfjtSC4HEgsY1jOsemBmQzn1\n8gfYDKVEPPkA2pBAlCkHUOanY4/oghQYCmXZoPUEnTcA5WYF6QYNWpVEbED92zb2/WVl3182IoKV\nXDWkYQZ4/pVUzrufZeDupuKJB2Px9mzabVTPl5ZRydOvJlFWbueueRFMGt3693aXZZktvxbx8bJM\nLBaZyWODuGlWOBqNuJLWVsiyzJ9Hy1ix5u8wYkBvH2ZNDSUuRoQRgtAazBwVy/7EfL7fmcrALsFN\nNohbEARBaJ4a5KzLmW08hdqdGWx5xpntPfVaFRarHT8vPX3iA5k9Jq76Nmc+TkgspMhoOud4VlvN\nKw9MFnv148wdF98gtVccOUH+0tXoY6MIuf0fYK5EvX8jslqLrd9EKM8DFOAVCgoFkgwnCnTIKIgP\nMlPf7coLDBKrt5vRaeCGSXrUqstvr8grMPPSf1ORJJnH7o2hfZhrr8r/lWhkwStJVFTaue/mSMaN\nCHRpPU2hqsrOB0sz+GWPAQ93FfPviGJwP9cPGBWahizLHDzqWBmRmPJ3GDF7Whix0a7f+UYQhIbj\n46Fl+tAYlm9N5rudadw4sZOrSxIEQRBcqEFCCVf33Ld0tQ22dNep+c+8fgT5uqE7by6ESqlk7rh4\npg6J5pklf2Aod243Dvh7YOb5x6wvWZZJf/IVkCSinn0UpVaDevdaFOZKbH0ngmwG2e6YI6FyXAk5\nVaqmzKwi2NNGoIe9Xo9nsznmSFiscMMkHYG+l38FvaLSzvNvpWAst3HXvAh6dfO+7GNejuPJ5Tz3\nZgpVVXYeuK1tbHuZlFbOE88fJzvPTMcYdx65J4bgQJ2ryxKagCzLHDhsZOWaHBJTKwEY1MeHWdPC\nmsU2vIIgNI4x/dqz489sdiRkMbJXO6JC2/aQc0EQhLbMtevTBaD2wZYl5Wa0amWt4UGV2UZJPQIJ\n+HtgZrDf5b3pL/puA+V7D+I3aRQ+owajyM9AlbwfyTcYe2xPMJ4CtR7cHCfWlRYFJ4u1aFQyHQPr\nVzPAT7ssZBVIDOyqpk/85c/GsNtlXvvAMbtgyrggl7dIHD1RxnNvpmC1Ssy/q/UPdpRlmY3bC/ls\n+SksVpnpE4O5fmY7l8/yEBqfLMvsP2RkxZocktMcYcTgfr7MmhpKTKQIIwShtVOrHBdWXltxkGWb\nE/n39X3FRS5BEIQ2SoQSzYCPp67OwZaXev+Lcea4dbGXV5D57Fso9Doin5kPkh313jUA2AZOhYp8\nxw29wkChQD7dtiHJCjoHmqjvIo0jqTZ2/mklxE/BjJENcxX9sxWnSDhipG8Pb26e49phW4eOGXn+\n7RQkOyx6vCtdO7buwY4VlXbe/yKdXX+U4O2l5pF7ohjQ28fVZQmNTJZl9v3pWBmRfNIRRlzRz5dZ\n00KJjhBhhCC0Jd1i/OkbH8SBxAL2HMvjim5tY3cpQRAE4VwNEkpER0c3xGHaLJ1GRZ/4oBqHVp4Z\nbHkxZqud0nIzPeMC2XYg64Lvq5Rgr2G8RF3HdUbWG59gzSuk3UN3oIsMR3VsF0pDHvbYvsge7lBZ\nCG7+oHEDINuoptSkItDDRlA92zaKSu2s2GxCrYJ5V+rRaS7/asr6rQWs3VxARLieh++OQaV03RWa\nA4dLWfxOKpIMj93XgZFDgigoKHNZPY0tOa2CVz9II6/AQuc4D57/T3eUWF1dltCIZFlmb0IJK9fk\nkpLuCCOG9Pdl1rQwotq7ubg6QRBcZfaYOA6nFrFyWzK94wJx04nrZYIgCG2N0//yZ2VlsXjxYgwG\nA0uXLmXlypUMHDiQ6OhoFi1a1Jg1tglnD600lJlqHGx5Nrtd4n+bE6t36/Dz0hIR7EmlyYqhzFx9\n/6lDY1i5JYnjGYZzvn6x4zqrKvkkeZ98jTaiHe3uuwkqSlH9uRVZ64at1yioyAGlGjwc7RAmq4LU\nIi1qpUzHQAv1WaFpl2Q++qaEShNcO1pHWODlhSkAB48a+eR/mXh7qXnywVjc3S7/mJfqj4MlvPxe\nGkoF/OeBWPp0d+1Mi8YkyzJrNxfwxcos7JLMzMkh/GNGO0KC9BQUiFCiNZJlmb0HS/l2bSKJqeUo\nFDB0gC/XTRVhhCAIEOTrxpWDIlmz6yRrd6dz7ahYV5ckCIIgNDGnQ4mnnnqK66+/ns8++wyAmJgY\nnnrqKZYuXdpoxbUlZ4ZWzhwZS2m5GR9P3TkrGc6siDjz9SU/Hj1nZUVxmYXiMguj+7Rj4sDIc+5/\n25SuF9y/pmM6S5Zl0p96FdlqI/KZ+Sjd9Kh3fIfCZsE6eDpYSh039AoDpep024YWu6ygc6AZnbp+\nu7X8vNfCiXQrPeNUDO5++VdQMrOreOW9NJRKBf++v4NLByru3mfgtQ/TUKuU/OfBWHp2ab2Dvsor\nbLyzJJ3fE0rx9lIz/45oerfiAKatkySZvQmlrPwxh7SMKhQKGDbQj+umhhIZLsIIQRD+duXgKH49\nnMPGvRkM7xlGiL9o5RIEQWhLnD7Ds1qtjB07ls8//xyAAQMGNFZNbZpOozpn+KRdklixNbl6RYS/\nt46ecYEcSS2q8f6HUoqZNabjBSHD2cet6Zh94oOYPSYOlbLuAYMlG3Zg3LEH7xGD8Js0CmVWIqqM\nY0hBkUjhHaA8F3Rejj9AbpkaQ5UafzcbIV62ev08kjNtbN5rJdBXxayx+ssegmUss/H8WylUVtn5\n1x3RdI7zvKzjXY6de4p585OTaDVKnpofR9d419XS2E6kVPDaB2kUFFno3tmT+XfG4O97+YNKheZH\nkmR+Tyhh5Q+5nDzlCCOGD/Ljzhtj8XSreatiQRDaNp1GxewxHXn/+yN8vSWJf13Xy9UlCYIgCE2o\nXpedjUZj9UlhUlISZnP9d08Q6mfF1uRzVkQUGc01zo44w5ldNWo65pnP546Lr7UeqcpE+jOvo1Cr\niHr2URR2G+q9PyErlNgGXOUYbqlQgqdjWJXZpiClSItKIRMfVL+2jfJKmWWbzCiUcO8sX9x0l/f7\nZrVKLH43lbwCC9dNDWXkFa7b2WLrriLeXZKOXq9iwUNxdIr1cFktjUmSZNZsyuer1VlIEsyZHsa1\nU0NdOr9DaBySJLPnQAkr1+SQfsqEUgEjBvtx3dQw2ofpCQryaNVzUoT6OXnypJhHJZyjf6cgOkf6\nciiliD+TC+kVF+jqkgRBEIQm4vS+e/fddx+zZs3i6NGjTJ06lVtuuYX58+c3Zm1tntlqJyGxoMbv\nXWxBQ127atR2zITEQszW2gdQ5rz3JZbMbEJun4tbx2hUR3agKDdg73IFsloGWQKPYFBpkGVILNBi\nkxR0CLCg1zjftiHJMl//bMJYIXPlFVriIrRO37cmsizz/pcZHEssZ0h/X+ZMD7us412On38p5J0l\n6bi7q1j0aMdWG0gYy2y88HYKX6zMwttTzcJHOjJ7epgIJFoZSZLZtdfA/Kf/4pX30sjMMjHqCn/e\nfq4r8++MoX1Y695FRri4W2655ZzP33vvveqPFyxY0NTlCM2cQqFg7vh4lAoFX29JwmoTK6sEQRDa\nCqdXSgwePJjvv/+exMREtFotMTEx6HSu68Vvrc6e81Babqb4Itt8Shf5v7q2XTXMVjupWaUX3Tq0\nrlUW5sxsst/9Ak1IIOHzb0NRWoDq6K/I7t7YOw2EylxQ68HND4CCChVFlWp89HbaedevbWNngpXj\n6XbiI1WM6nv5y/y/W5/Htl3FxMW488Bt0ShddGK8bksBHy/LxNtTzTOPxBET2Tr7Zo8llvP6h2kU\nGaz07ubFg3dE4+st2jVaE7sks3ufgZVrcsnMdqyMGDXEn2unhBIeKoIIAWy2c//d37NnD/feey/g\nCIoF4XztgzwZ0zeczftP8fO+TK4aHOXqkgRBEIQm4HQoceTIEQoKChg9ejRvvPEGBw8e5P7776d/\n//6NWV+bUePsiNgA/L11NYYIAd56usX4cjSt5KK7dZwJODzdtXy/M7X62EoFSDW8H6xrlUXGwjeQ\nTWYiXnkClacH6s3foJDsWPtfCabTMy682oFCgcUOSQU6lAqZTsHmerVtZOTZWfubBS93BXMn6FBe\n5hyJPftL+Gp1NgF+Gv59fyw6ndMLhBrUmk15fLY8C19vNQsf7dgqh/1Jkszqtbks/z4HFHDDzHZc\nfWWIy0IgoeHZJZnf9hpY+WMup3JMKJUwZqgjjAgLEWGE8LfzZwCdHURc7nwgofWaPjyGPcfy+HHX\nSa7oFoqfl7gAJgiC0No5HUo899xzvPTSS+zbt4/Dhw/z1FNPsWjRIr788svGrK/NqHF2REI2EcGe\nNYYSRUYTR9MM9IwLZFy/9vh766tXSJwfcOi0SkyWv5dWXOwCVW2rLEp37MGwbhueA3oRcM2VKE8e\nQpmbij08Hsk/EKqKwT0ANI6TkuRCHVZJQWyAGfd6tG1UmWW+Wm9CkmDuBB1e7pcXIKSmV/LmxyfR\naZU88WCsy4Yrrl6by1ers/H31bDo0Y6Et8Il7SWlVt785CR/Hi0jwE/DQ3fFtOrhnW2N/XSbxsof\nc8jKMTvCiGEBjjAiWJw0CHUTQYTgDA+9hpkjO/DFhhN8sz2ZO6d2c3VJgiAIQiNzOpTQ6XRER0ez\nYsUKZs2aRVxcHEondmoQ6lbbnIeKKiuj+4ZzKLmIIqPpnO+dGXqpUirOGVB5fsBxdiBxNqXCEVD4\ne1+4yuJsksVK+lOvglJJ1POPobCaUO/bgKxSY+s7zhFIKDXgEQRAYYWK/HI1Xjo77X2cb9uQZZlV\nW80UGWXG9tcQH3l5238WGyy88HYKFqvE4//s4JJWCVmWWfmjY+VAoL8jkGiNV5MP/VXGmx+lYSi1\n0a+nNw/cFo231+Vv3yq4nt0us3NvMat+zCUr14xKBeOGBzBzciihIowQalFaWsru3burPzcajezZ\nswdZljEajS6sTGjuhvdsx/aD2ew5msfoPuF0bO/r6pIEQRCERuT0WUNVVRXr169n8+bN3HfffZSU\nlIg3FQ2kttkRJeVmJg6IYOqQaP7vg91Yahj8lJBYyMyRseg0qloDjvPJwG2Tu9AjNgAv94sPksz7\ndDmm5JME33QtHt07od77EwpTObZeY0E6HZR4hYFCidXuGG6pQKZzPds29h6zcTDJRnSYkomDLm+w\npdks8cLbqRQZrNx4XTiD+jT9GxpZlln2bTar1+YREqhl0WMdCQ5sXSdxdklm5ZocvvkxF6USbp4V\nztQJwaJdoxWw22V+2VPMNz/lkpPnCCPGj3CEESFBrev3WGgc3t7e5wy39PLy4t13363+WBAuRqlU\ncP24eF74aj/LNiWy4OYB4v8VQRCEVszpUOKhhx7iyy+/ZP78+Xh6evLf//6Xm2++uRFLazt8PHUX\nnR1xZs7DVxtP1BhIwLkDKmsLOM6nAD5Z+xcB3jr6xAcxe0wcqvNWv1jyCsl6/WNUfj6EP3o3iqIs\nlCf2InkHYo/pClWFoPMGnWOZfkqRFotdSYy/BQ+t820buUUS3+0w46aD6yfqUaku/c2HJMm89clJ\nUtIrGTssgBmTgi/5WJdKlmU+X5HFmk35hIXoWPRoRwL9Ly9oaW6KDRbe+PgkR46XExSg5ZG7Y4hv\npTuJtCV2u8yOPY6VETn5ZtQqBRNGBjJzckirC9WExrV06VJXlyC0YHHtfbiiWyi7j+byy5/ZjOoT\n7uqSBEEQhEbidCgxcOBABg4cCIAkSdx3332NVlRbo9Oo6BMfdE7LxRl94h37dB/PMFz0/r6euuoB\nlbUFHOc7M+yyyGiufuyz20AAMp9/G6mikugF/0bj6416/dcokE8PtywGhRK8QgEorlSSW6bBU2sn\nwtda9xM/zWqTWbrBhNUGcyfo8fe+vLagr7/PYff+Erp18uSuGyOavI9ZkmQ+/foU67YUEB6mY9Gj\n8S6bZdFYEo4YefPjkxjLbAzq68M/b4nC00O0a7RkNpvMjt3FrFqbS+7pMGLiqECuuUqEEcKlKS8v\nZ9WqVdUXMJYvX87XX39NVFQUCxYsIDAw0LUFCs3edaNjOZBUwLe/pNK/czCebq3r/1JBEATBwemz\niK5du55zcqdQKPDy8uL3339vlMLamjPzHBISC6t30+gZF8DoPuEUlFTVuvpBp1GhPr2yoLaAQ691\ntHcoqHn3jbPbQADK9h6kaNU63Ht0JmjuDJRJf6AszsYe0xPZ0w3MZY62DaUamwQnCnSATKdgC/VZ\nZfnDL2ZyiySG9NDQM+7yTmy3/1bEqp9yCQ3W8dh9HdCom3buiSTJfPBlBj//UkRkuJ6Fj3TE16f1\nvImy2WS+/j6bb9floVYruOP69lw5JkgMsGvBbDaZ7bsdf2/yCiyo1QomjQ7kmqtCCQpoXat7hKa1\nYMECwsMdV7fT0tJ4/fXXefPNN8nIyOD555/njTfecHGFQnPn66lj2tBovtmWwvc7U7lhQidXlyQI\ngoIqFtQAACAASURBVCA0AqfPAI8fP179sdVq5bfffuPEiRONUlRbpFIqmTsunpkjYyk2mti8L5ND\nyYVsP5CFr6cGrVqJ+SLtGznFlfzv50QmDozEx1NXY8DRJz6QGcNjSM8p45XlB2s8ztltILLdTvoT\nLwMQ9dyjKCyVqBN+RtbosfUYDuZi0LiB3jGrIa1Ii9mmJNLXgpeu5jpr8meSjd1HbIQFKpk2/PJO\ngP5KKufdzzPwcFfxxIOxeHs27ZV7uyTz7mfpbNtVTEykG8883LFVDXssLLbw2gdpHE+uIDRYxyN3\nxxAb3fTDQ4WGYbPJbPutiNU/5ZJX+HcYMXNyaKtrNRJcIzMzk9dffx2AjRs3MmnSJIYMGcKQIUNY\nu3ati6sTWorx/SP45c8ctiVkMbJ3OBHBYlcnQRCE1uaSzpg0Gg0jR45kyZIl3HnnnQ1dU5um06jY\nvC+TbQnZ1V8zlNfdCrHjYDbbE7LxP2s+xMyRsZSWm/Hx1FUPwfTy0OLvpaW4zHLBMc7MrwAoWPYd\nlUcTCbhuMl4DeqHe+Q0KqxnrgMlgPT3g1KsdKBSUVCnJMmpw10hE+TnftlFUKrFyiwmtGuZN0qNR\nX/rV9tx8My/9NxVJknn0nhjaN/GWm3a7zNufnuSXPQbiYtx5+qG4VtXO8MfBEt7+NJ3yCjvDBvpx\nz02RuLvVvH2s0LxZbRLbdhWz6qdcCoosaNQKrhobxNVXhogwQmhQ7u5/h5Z79+7l2muvrf5crK4S\nnKVWKZk7riNvrPyTZT8n8vjcPuL3RxAEoZVx+qxp1apV53yem5tLXl5egxfUltklif9tTmLHwey6\nb3yei82HCPZzP33cRBISCyg2mtFpaz6Z7BMfiE6jwlpcQubi91F6ehDxxP0oclJQnTyEFBCO1C4S\nTCXgHghqHfZz2jbMqJzslrDbZb7aYMJkgdnjdIT4X3qbRUWlnRfeTsFYbuOueRH06uZ9yce6FDab\nzOsfpbF7Xwmd4zx4an5cqzlht9okvlqVzZpN+WjUCu65MZLxIwPEG8IWyGqT2PprEavX5lWHEZPH\nBXHNlSH4+4kwQmh4drudoqIiKioqSEhIqG7XqKiooKqqysXVCS1Jjw4B9I4L5GByIX8cz2dglxBX\nlyQIgiA0IKdDif3795/zuaenJ2+++WaDF9SWrdiazLYDWQ1yrLPnQ6zYmnzOjAmTxQ44ZkxYrPbq\n9o4zbR9ZL7+P3VBKxNP/Qhvgi/qnr5AVCmz9JjoCCZUWPBwDyk4aNFRZlbT3seKjd75tY/0eCxl5\nEn07qRnQ5dJXFNjtMq99kEZmtokp44KYNDroko91KaxWiVfeT+OPg6V06+TJEw/G4qZvHYFEXoGZ\n1z5IIymtkvBQHY/cE0N0hGjXaGmsVoktvxaxem0uhcVWtBoFU8Y5VkaIMEJoTHfccQdXXXUVJpOJ\nf/7zn/j4+GAymZg7dy6zZs1ydXlCCzNnbBxH0opYsTWZXrGBF73AIgiCILQ8Tp8NvvjiiwCUlJSg\nUCjw8fFptKJaE7PVfk4LRW23S0gsaLDHPTMfwsdTd9HjeujV/OeGvgT5uVfXVnHoOPlLv0XfMYaQ\nW+egOvorSmMRtk6DkDUy2HAMt1QoMZqUZJZo0KslYvwvbAe5mOPpNrbttxLoo2DmaN1lXXX/bPkp\nEo4Y6dfTm5vntL/k41wKs0Xi5XdTOXDYSK+uXvz7/lh0uqYdrNlYdu838M6SDCqr7Iy6wp8750W0\nmrClrbBaJTbvdIQRRQYrWq2CqROCmTEppNXtBiM0TyNHjuTXX3/FbDbj6emYA6DX63n00UcZNmyY\ni6sTWppgP3cmDoxk7e501u45yTUjYl1dkiAIgtBAnA4lDhw4wGOPPUZFRQWyLOPr68srr7xCjx49\nGrO+FssuSazYmlzdMnH2rAeV0nHienZgUVpurnWHjfo6Mx+ituMaysxoNarqQEKWZdKffBlkmahn\nH0FpMqI6sgPZzRN7p75gLgG9D2g9kOQzbRsKOgWbnG7bMFZIfL3J0eYx70o9eu2lBxLrtxawdksB\nkeF6HrorBlV9tvy4TGazxIv/TeHPY2X07eHN4//sgFbT8gMJi1Xii5VZrNtSgFar4J+3RDFmmL9o\n12hBLFaJzb8U8u26vOowYtqEYGZcGYJfK9oJRmj+srP/bkU0Go3VH3fo0IHs7GzatWvnirKEFmzy\nFVH8diSXDb9nMKxHGMF+YvWeIAhCa+B0KPHaa6/x3nvvER8fD8CxY8d4/vnnWbZsWaMV15Kd3zJx\n9qyH2WPiLggsuncIwMdDS0lF3SsOzrRd+Hjo8HBTc6qg4oLbdI507Irh46nD31tHUQ3BxNmDLQGK\nVq+jfN8h/CaPwWf4QDRbl6Kw27D2GQ/mUlCowNPRx5lu0FBhURLmbcXPzbm2DUmW+d8mM+VVMtNH\naGkffOlX3g8eMfLJ/zLx9lLzxIOxTTrDocpk5/m3Ujh6opyBfXx45O4YNK0gkMjJM/Hq+2mkZlQR\nEa7n0btjiAh3c3VZgpMsVomfdzjCiOISKzqtkumTgpkxMaRVbUsrtBxjxowhJiaGoCBHW50s/70X\ntUKh4Msvv3RVaUILpdeqmTU6jg/XHGXF1mTun9nT1SUJgiAIDcDpUEKpVFYHEgBdu3ZFpRLLuWtS\nWytGQmIhdkk+Z3ZEkdFc63BLnVaJ1Sqds7WnVq/DbrGiVilOBxyO7T+1GhUgs+tILsczDPSJD6J3\nx0C27L9wVsWZwZYA9rJyMp99G6VeR+TT81FmHEWZnYQU2gHJPwCsFY5AQqmm3Kwgw6BBp5KIDXC+\nbWPrPitJmXa6RqsY3uvST5Iys6t45f1UVEoF/76/A8GBurrv1EAqKu0892Yyx5MruKK/Lw/dGYP6\nMnYNaS52/l7M+19kUGWSGDcigNv/EdFqWlFaO7NFYtOOQr5bl4eh1BFGzJgUzPRJIfh6izBCcJ3F\nixfzww8/UFFRweTJk5kyZQr+/v6uLkto4QZ2CWZbQhYJSYUcSS2ie4cAV5ckCIIgXKZ6hRKbNm1i\nyJAhAPzyyy8ilLiI2lomio0mDiYWOnWcAO+/Q4jySus5cymCAj0oKCgDHLtszBwZy9KNJ/jtSG71\n/c+szhjTL5xx/dtXBxfnD7YEyHr9E6wFRYQ/eje6EH/Ua75CVqqw9R7tCCQ07qD3QZLheIEOGQXx\nQWbUTp63pmXb2bjHgo+Hgtnj9ZfcDmAss/H8WylUVkn8645oOsc13X7l5RU2Fr6eTHJaJSMG+/HA\nbdGoVC07kDBbJJZ8fYpNOwrR65TMvzOaEYPFSUNLYDZLbNxRwPfr8zCU2tDrlFx9ZQjTJwbjI8II\noRmYPn0606dPJycnh++++47rr7+e8PBwpk+fzvjx49Hrm3brZqF1UCgUzB3XkYWf/8H/Niex6DY/\n1M72kAqCIAjNktOhxMKFC3n22Wd54oknUCgU9O7dm4ULFzZmbS1WbS0TPp5aDOV1z47w9dSy4Ob+\neLk7puO76+o+yTiRYajx638mFfHcHYOYOTK2xqGbVUlp5H36NbrIcMLumYfqz60oKo3YeoxEVlhA\nVpwebqnglEFDuVlFiKeVAA97nTUBVJpklm00IQPXT9Lj6XZpJ/JWq8Tid1PJK7Bw3dRQRl7RdCfP\nxjIbC19LIjWjijFD/bn3lqgmnWHRGDKzq3jtgzTST5mIjnDjkXtiCA8VJwnNndkssWG7I4woMTrC\niJmTQ5g2IQRvr0vfyUYQGktYWBj33nsv9957L9988w3PPfccCxcuZN++fa4uTWihIkO8GNUnnG0H\nsti87xSTBkW6uiRBEAThMjj9DjY6OppPP/20MWtpNXQaFX3ig86ZKXFGn46B7D6ai8lS+xyG0nIL\npeXm6lCiLrUPtHTsxBHs537BUCjHcMtXkG12Ip+Zj6qyGNXxPche/tijO4GlDDyCQK2j0qIgzaBB\no5KIC3SubUOWZVZuMWEok5k4SEts+KWtrpFlmfe/zOBYYjlDB/gyZ3rYJR3nUpSUWnnmtSTST5mY\nMDKQu+ZFoGzhgcTWXUV8tDQTs0Vi0uhAbpnTvlUM6mzNTGY7G7YV8v2GPEqNNtz0p8OIiSF4e4ow\nQmi+jEYja9as4dtvv8Vut3PXXXcxZcoUV5cltHBXD+/A3mN5rNmVxuBuIfh6Nl0rpyAIgtCwnH4n\nu3v3br788kvKysrOGVYlBl3W7ExrxPktEzOGx7D7aG4d9wYZeGvVoQt27Djb2bt31Geg5dkM67dh\n3LkXn9FD8J0wHPWmT1HIEpa+4x2BhEoH7oHIZ9o2ZAXxgWZq2d30HL8dtnE4xU5suJJxAy59Sfmy\n1Zls21VMXIw7998W3WShQLHBwoJXk8jKMXPV2CBun9u+Re9EYTLb+egrx8/S3U3JI/fEMHSAn6vL\nEmphMttZv9URRhjLHGHEdVNCmTohGC8RRgjN2K+//srq1as5cuQIEyZM4KWXXjpnNpUgXA5PNw3X\nnG5dXb09hdumdHV1SYIgCMIlqlf7xr333ktoaGhj1tMinR0OnGmLUCmV1bMezv5evqGyzlUSZ5y9\nY8fccX+/kbNLEu+v/pPdh3IoKf97u9FeHQPZWsdAy7PZK01kPP06Co2ayEUPIyfuR1mQiTWiK7KH\nG9jN1W0bWaVqjCYVQR42gjyda9vIKrCzZqcZDz1cP1F/yUHCnv0lfPBFGgF+Gv59fyw6bdNc0S8s\ntrDg5SRy8s1MnxjMTbPCW3QgkX7KMSA0K8dMXLQ7D98dQ2iwuLLUXFWZ7KzfWsAPG/Ixlttwd1Ny\n3dRQpo4XYYTQMtx+++1ER0fTt29fiouL+eyzz875/osvvuiiyoTWYmSvdmxPyGLXkVxG9gknKMjL\n1SUJgiAIl8Dpd7bh4eFMmzatMWtpceySdMHWnuevbNBpVOe0TLjp1CgVIMkXO+qFEhILmTkyFp1G\nhV2SWPT5PjLzy6u/fya8GNsvnNF92pGQVEhpuQV/7wsHWp4t590vsGTlEnLvjaxLLmV63gaqZBXb\nLRGMsZuR9L4ote5UWRWkFmlRK2U6BtY9DwPAbJFZusGEzQ43XaXHx/PSgoSU9Ere/PgkbnolTzwY\ni79v0wzwyy80s+DlJPIKLcycHML117RrsYGELMts3lnEJ8sysVhlpo4PZt617VrFNqatUVWVnXVb\nC/hhYx5l5Xbc3VTMnhbKlPHBeHqIMEJoOc5s+WkwGPDzO3dF1qlTF7Y3CkJ9KZUKrh8fz0vLDrDs\n50QG9Qx3dUmCIAjCJajzHW5mZiYA/fv3Z8WKFQwcOBC1+u+7RURENF51zdyKrcnnzI242MqGs1WZ\nbfUKJODcmRD/+znxnEDibLsO5+KhV1NabsHXU0fPuICLtn6Y0k+R894XaEKD+K33KGLSduLhYeU7\nczzjuvtRWmln09Eyrh0FiQU6JFlBfJAJrZPnRN/tMFNgkBnZR0PXmEs7kSoyWHjhrRQsVokX/tON\nmMimuaqfk2diwStJFBZbmTMjjFlTQ1tsIFFVZef9LzPY+bsBD3cVD90dxaA+vq4uS6hBZZWddVsc\nYUR5hR0PdxVzpocxZXwQHu4ijBBaHqVSyfz58zGbzfj7+/Phhx8SFRXFV199xUcffcQ111zj6hKF\nViA+wpfBXUPYcyyP9btPMjA+0NUlCYIgCPVU5zvdm266CYVCUT1H4sMPP6z+nkKhYMuWLY1XXTNm\nttpJSCyo8Xtnr2w4n4+nDn8vLcVlzg2KhL9nQpitdg5c5DEBTBY7JoujtcJQbmbbgSxUSgVzx8Vf\n0GKS8cwbyGYLYf++nz1Z6Yz0yOWkxZOofl3RqhV8urOM1CIY1FuJoUqFv7uNECfbNvYft/LHXzba\nByu5aohzgzrPZzZLvPh2KsUlVm6aFc7wwYHVW6A2plM5Jp5+JYniEivzrm3HNVe13Hal1PRKXn0/\njZx8M/GxHjx8VzTBgaJdo7mprLKzdnM+azblU15hx9NDxT9mhDF5XDAe7mLbZaHleuONN/j888+J\njY1ly5YtLFiwAEmS8PHx4ZtvvnF1eUIrct3oOA6nFrFkzRHa3dSf9kFNt124IAiCcPnqDCW2bt1a\n50G+//57ZsyY0SAFtRTO7nZRk85R/vx2pO5hl2f0jAtAp1GRU1RBaYW1XnUmJBZgt0scSimqbjEZ\nasqm3cYdeA3qg2L0MGaueQ9Jhr0e3Zne3o1DmWb+SDPh4abnpEGHSikTH2TBmcUCBSUSq7eZ0Wlg\n3iQ9alX9VxhIksxbn5wkJb2SscMCmD4xuN7HuBTpp6p45tUkSow2bpkTzrQJIU3yuA1NlmU2bCtk\nyfJT2GwyV18Zwtyr26FWt8zVHq1VeYWNlWty+PHnv8OIuVc7wgh3NxFGCC2fUqkkNjYWgLFjx/Li\niy/y+OOPM378eBdXJrQ2fl46br2qC//99jAf/nCUp27qj9bZidyCIAiCyzXImuBvv/22zYUS9d3t\n4vz5E3qtCqvNjt2JmZfj+rUHYOMfGfWus8hoZltCdvXnBkMF7ss+RVYoiHruUdyyE9BrKvilqh2j\nR0Zitsks3W0EYNjA3kiykvhAM3p13T0nNpvM0vUmzFa4fqKOQN9Lm1nwv++y2b2/hG6dPLnrxogm\naZ1Iy6jkmVeTMZbbuPOGCK4cE9Toj9kYKiptvPt5Brv3leDtqeaB26Po19PH1WUJZ6motPHTzwX8\ntLmA8gobnh4qrr+mHVeNDRJhhNCqnP9vd1hYmAgkhEbTJz6IyUNjWLsrjeVbk7lxYidXlyQIgiA4\nqUFCibO3CG0rdBoVfeKDzpkpcUZNu12cP3/iTJtFmL87JqsdQ1nNqy70WhX+3nrMVjuHkovqXef5\nQzV7HvwV35JCkvqPoE94ALp1y6lS6pA69cLbTcXKvUaKyu3ERIQTEhyEr5udMC+bU4/1028Wsgok\nBnZV07fTpQ2k3P5bEavX5hEWrOOx+zqgUTf+MMbktAoWvp5MRaWde2+OZPyIltmPmpRWwWvvp5FX\naKFrvCcP3RVNgN+ltc8IDa+i0saPm/L58ecCKqvs+HipuWFmO64aE4SbCCOENqClzuYRWo5bpnbj\nz8R8tidk0TXKj/6dm2alpSAIgnB5GiSUaKtvNM7sapGQWIihzISfV827XdQ2f8JQ7lg1UZfScjMl\n5c7PoTjj7EDCo7yUvns3U6X3YFe/MczZuxaF3Ypy4FVc4edDdomdLccqCQv0YsiAnigVMp2CzE61\nbRxJtbHzoJUQPwUzRl7a3IJjieW8+3kGHu4q/vNgLN5NsO3hiZQKFr2ehMkkcf+tUYweGtDoj9nQ\nZFnmx5/zWfpNNnZJ5ropocyeHobqElpnhIZXXmFjzaZ81m7Op7JKwttTzbxr2zFvVgcqyitdXZ4g\nNJqEhARGjRpV/XlRURGjRo1ClmUUCgXbt293WW1C66TTqLh7encWff4Hn68/TnSYF4E+bq4uSxAE\nQaiDGOl+GVRKJXPHxTNzZOw5QyTPV9v8ibOHU9bEbHEMqHTTqfH11NYrmGgf5EGlyVo9VHPwrrVo\nrRZ2DJ/KoFAr7nlJSMFREBAIkpXAiBievSOWPJMvRZVqOvibcdPUvQqmpExixWYTahXMu1KPTlP/\nk+HcfDOL30lFkmQeuzeG9mH6eh+jvo4llvPsG8lYrBL/ujOa4YP8G/0xG1pZuY3/Lknnj4Ol+Hqr\n+dcd0fTq5u3qsgQcr82Pm/JZu+V0GOGl5sbrwpg0OhA3vQp3NxUVNW+kIwitwoYNG1xdgtAGtQv0\nYO74eD5ff5yPfjzG43P71LgLmSAIgtB8iFDiMp2/q0VNaps/URd/bx0b92ZwKKWo3islCktNDOoW\nzI6EHMKyUul44iD5we1J69aXB70SkCUlth5DQbKCmz9aN08UkoqiYg3eOjvhPnW3bdglmWUbTVSa\nYOZoHWGB9V+GXlFp54W3UzCW27j7xgh6dm38k+pDf5Xxwlsp2OwSj9wdwxX9/Rr9MRva8eRyXvsg\njcJiKz26eDH/zmj8fC6tbUZoOMZyG2s25rFuSwFVJgkfbzU3Tw1j4uhA9DrRpiG0HeHh4a4uQWij\nhvcM40haMfuO57Pm15NcPaKDq0sSBEEQatEgoYSnZ9vbeun8wZX+3jr6xAcxe0zcBYm8TqPCXa+5\npFDCXa85Z1BlfZgsdk6kl+CmhqE7fgBg7/hreKCTAa+KCmxdhiCrAaUaPIKw2iGpQItCIdMp2Lm2\njZ/3WkjNlugZp+KK7vX/dbLbZV77II3MbBNTxwczcVTjD5hMOGLkpf+mIMnw+H0dGNDbt9EfsyFJ\nksz3G/JY9m02yPCPGWHMnBKKSinaNVzJWGbjh9NhhMks4eutZvb0MCaNCkKnE1fpBEEQmopCoeDm\nSZ1Iyzby028n6RLlR+eolnfxQRAEoa1w+iyyoKCAdevWUVpaes5gywcffJD33nuvUYprzs4fXFlk\nNLN53ynsksy8CedOfDZb7VRU1W+Vg7+Xjl4dA/kzqeZZFDqNkkFdQjiSVkzxRYZkAuQWV9Htz98I\nLMzheJf+yCF+9Kj4A9nDF3tUHMhW8AoFpYqUfC0Wu5IYfwse2rrbNpIzbWzea8XPS8GssfpLmi3y\n2fJTJBwx0q+nNzfNbvyran8cLOXl91JRKuDf93egb4+WtTNFqdHKW5+kk3DEiL+vhvl3RdO9k5er\ny2rTSo1WftiYz/qtjjDCz0fN3KvbMWFkoAgjBEEQXMRdr+Gu6d146asDfPTjURbeOhAvdzH8WRAE\noTlyOpS466676NSpk1iOSe2DK3ckZIEsM3d8PCqlErPVTmpWafVcB2cM7R7KDRM7UVpuZvuBrIvU\nIGGTZJ6/czAf/3iUA4mFNd5OX1nOwD0bMWv1/D50Eg/5JqJEpqLbcNSyFbReoPOmuFJFbpkGT62d\nCF9rnTWWV8os2+RYTTFvkh43Xf0DiXVbCli7pYDIcD0P3RXT6Ff6d+838PoHJ1Gq4IkHYpukTaQh\nHTlRxhsfnqS4xEqf7t48eHsUPt6iXcNVSoxWftiQx4ZthafDCA1zrzkdRmhFGCEIguBqceE+XD0i\nhtU7Uvls3XHun9mjzQ5nFwRBaM6cDiXc3d158cUXG7OWFqO2wZWSDNsSslEqFSgUiur2jvO35jxD\nr1XhoVdjKDOfs3uHSqnE012LTqvEZJFqfKy/ThYDcOvkrhw7+WuNtxu4ewM6cxW7hk+lX0AlXXUl\n/GkJJNLdHdkOSo9gZAlOFGhRINM52EJd2YAkyyzfbMJYITN5iJaosPr3yR88YuTTrzPx8VbzxIOx\nuDfylog7fy/mzY9PotUoefJfsXRrQasL7JLM6p9yWfFDDihg3rXtmDEpBKVo13CJEqOV7zfksWFr\nIWaLhL+vhhtmtmPcCBFGCIIgNDdXDori2EkDB5ML2bL/FOP6R7i6JEEQBOE8TocSvXr1IiUlhdjY\n2Masp0WoKywA2HU495xdNeSLdEMM6xl20d07vt+ZWutjGMotLN1wHDd9zS9jUF4mXY7+QbF/CGm9\nBvCyzz5MkpLKuL6465Qs221E4ZFO/17dMduURPlZ8NRd/PHO2Jlg5a+TduIjVYzqV/8r9ZlZVbzy\nfioqpYL/+2cHggMvbQtRZ23bVcQ7S9LR65U8NT+OznEtZwaKodTKmx+d5NBfZQT6a3j47pgWVX9r\nYii18v36PDZsL8BikQnw03DjdY4wQqsRYYQgCEJzpFQquH1KV55espeV25KJj/AlMqTlXJgQBEFo\nC5wOJXbu3Mnnn3+On58farW6Te8zXldYAFx0m0+lwhFQ+Hufuyoi2M/9nNvV1iJytt+O5tX8DVli\n2I4fUCDz68jpzPJPx0dlZavckaFdAkgtsLD1eCVxkT60M2pw10hE+dXdtpGRZ2ftbxa83BXMnaBD\nWc9lkMYyG8+/nUJllcT8O6Mb/QR78y+FvPdFBh7uKp5+KI64GI9GfbyG9OdRI29+fJISo40BvX24\n/9YovDzFhjlNrbjEEUZs3F6AxeoII2bOCmXc8AA0IowQBEFo9vy8dNw+pQtvfnOID344ytM3D0Cn\nFbshCYIgNBdOn+G8//77F3zNaDQ2aDEtgbNhwcXIwCNzetMh3OeiW4hC7S0izuj01wFCcjNIieuJ\nV8dgRrvv55TVg47DemKXZL7YZUSpVNGtS2dApnOwuc62DZNZ5qv1JiQJ5k7Q4eVevxMyq1Vi8bup\n5BVYmDUtlBGD/S/5+Tlj/dYCPvoqE29PNc88EkdMpHvdd2oG7HaZj79K48uVGaiUCm6d054p44NE\nH2wTKy6x8t26XDbtKMRilQn01zBzcihjh4kwQhAEoaXpGRvIhAERbPojk2WbE7n1qi6uLkkQBEE4\nzelQIjw8nOTkZAwGAwAWi4XnnnuO9evXN1pxzdHlhgX+XvoLAgmz1X5B+4aPpw5/b90lbSOqNVcx\naNc6rGoNe4Zfxb+9T6BUQEpILwb7all/uILMYhv9enbF28uTMC8z3vraV37Issw328wUGWXG9tcQ\nH1m/K/ayLPP+lxkcSyxn6ABfZk8Lq/fzqo8fN+WzZPkpfL3VPPNIR6LauzXq4zWUIoOF1z88ybHE\nckICtTx8TwwdW9DqjtagyGDhu3V5bNpRiNUmExSgZebkEMYMFWGEIAhCSzZzZCzHMwz8eiiHbtH+\nDOoa4uqSBEEQBOoRSjz33HPs2rWLwsJCIiMjyczM5NZbb23M2pqlusICfy8tlWbbRds7esb6VwcP\ndklixdbk6mGY/t46+sQHMXtMHDqNij7xQedsO+qs/r//jHtVOXuvmMjQ0AqiNOUckNrRv18khWV2\nfkgoJ9Dfly7xHbBZTcQF1txqcra9x2wcTLQRFapk4qD6b6n17bo8tu0qpmOMO/ffFt2oQxq/XZfL\n0lXZ+PloWPRYR9qH6RvtsRrS/kOlvPXJScrK7YwaEsjtc9vh4S7aNZpKkcHCt+vy+PmsMOLaRBhk\n4AAAIABJREFUKaGMHuqPRi3CCEEQhJZOo1Zy9/TuLPzsD77ceJyYdt4E+7aMixaCIAitmdPvtA8f\nPsz69evp3Lkzq1evZsmSJVRVVTVmbc3SmbCgJkO7h3Lv1d1rnTdx9tTnFVuT2bzvFEVGMzJQZDSz\ned8pVmxNBmDG8A4M7R5KgLcOZ1fu+xXl0v3P3yj1CSC93yCu9U6jXFLj3rMvaqWC7/+sQpIVDB/U\nB6VCQZ8ICVUdvwW5RRLf7TDjpoMbJulRqeoXKOzeb+Cr1dkE+Gn4v/tjG3WHgpVrcli6KptAfw3P\n/1/LCCRsNpkvVp7iuTdTqDJJ3HlDBM/+X1cRSDSRwmILHy7N4O7Hj7JuSwF+vhruvTmSd1/syoSR\ngSKQEARBaEVC/d25YUI8VWY7H605is1e94BvQRAEoXE5fdaj1TqujlutVmRZpnv37ixevLjRCmvO\nZo+JAyAhsRBDmQk/Lz29OwYgA+9/f/Si9wvw1uPv7ThJrm02xYETBdglmUPJhRQZzfh4aOjVIYCD\nKUW1FybLDN2xBqUssWvEVOYGnMRNaWenrjsDw73Ym1rF1WN7MHiQG4UmD9p5WwnwuMi2IKdZbTJL\nN5iw2mDuBD3+3vU7QUtJr+Stj9PR65Q88WAs/r71363DGbIs87/vclj1Uy7BgVqefaxjo+/q0RDy\nC8289uFJElMqCAvW8cg9MXSIchfzI5pAQZGFb9flsnlnETabTEiQY2XEqCsCUKvFz18QBKG1GtI9\nlKMni9lzNI/vdqZy3ag4V5ckCILQpjkdSsTExLBs2TL69/9/9u47sMr67P/4++yTM5Kc7IQQshNI\nGGEpKA5kOQAVDdbRotY6f9ZWH58+tnW0T9vHuqptrRO1roJQFcRVEHAAssJIIJMssvfJycmZ9/37\n45BAQhICJmTwff2lZ9znezLIuT/39b2u6dx6663ExcXR2to6mGsbtlRKJTfOS+4yynPt1iI2nWKr\nRUZySOfWjb56UzS2Otm8t6Lz/1va3OwrakCpAKmXDCHIX8eMmjyijxZSGptKQGo45xsOcMQdQPol\nE2hzSrz3fSu3hXtokgzo1BLxwa5TvtePv3FS3SAxe6KGSYmnd+W+ocnFH58vwuWW+NV98YPWaFKW\nZd76oIKPP68lMkzH7x5OIiTo9LeYnG3fZzXzt5Wl2Nq8zDnPwt0/jsHPT3QDH2y19U7WflrDV980\n4PHKRITpuP4qX+NVEUYIgiCMfgqFglsWpHCkwspnO8qYMC6ItLjBbb4tCIIg9K7fZ5lPPPEELS0t\n+Pv7s2HDBhoaGrjzzjsHc23Dnk6jIsxiOOVEjiCzjqkpoZ0VFtB3b4rewofeAgmAKdFGkleuxalW\ns/OiK3kksACvrKAlcRpjdCre+q6FNqdMuzIEWVKQEurkVFXp+ws8bD/oITJEyZI5p3eS73B6+eML\nRTQ2u/lJ5hhmZgSe1vP7S5ZlXn//KBs21jEmUsfvHkoiyDK8Awm3R+Kfqyv4ZGMdWo2Ce1bEMG9O\nsKiOGGS19U7Wbqjhq299YURkmI7rFkdw8flBp70lSRAEQRjZ/HRq7lyaxh/f3sNrnxziidtm4m8c\n3p8fBEEQRqtThhKHDh1iwoQJ7Nixo/O2kJAQQkJCKC4uJiIiYlAXOBL0VfWgUMADmZOJDjV1uV2n\nUTEpIZjNWZUnPaev8EGrVqBQKHC6u+6B9LyzGndlDVH3rWBxlJsIVTu7GMek1HAKalx8ndfO7GkT\naPeoiTC7CTL03dyyoUVi9SYHWjXcskiP5jSuIEuSzPOvlXKktJ15c4JZujCs3889HZIk8/I75Xy5\npZ6YMXqeeCiJwIDB2R4yUKprnTzzUjGFJXaiI/U8dHfciJkMMlLV1DlZs6Gazd814PVCZLiOzMUR\nzDlPhBGCIAjnsrhIf5ZdnMDqzYW8tuEQD1w/GaW4QCAIgnDWnTKU+Oijj5gwYQIvvvjiSfcpFApm\nzZo1KAsbSfqqeggy6wnt1tm5Y+rGgWM9IjoqI4LMOiYnBpNVUE+zreetFW6vzBO3zWDT3gq2Zvm2\nePg3N5CybRM2YwAHxk/mWvsmWrx6os+fiscr8/Y2K8mxYSTGx6NRSSScYtuG1yvzzucOHC5YPk9H\neNDp9ZF478NKduxpJi3FxM9uGTsoFQBeSebFN8v46tsG4mL8ePzBJPzNw7sx5LbdTfz9jVLs7RJz\nLwjijpvHoteJ7RqDpbrWydoN1Wze5gsjosJ1XL8kgjkzRRghCIIg+CyYOZZDJY1kH2nky53lLDov\nZqiXJAiCcM455VncI488AsDbb7896IsZaZxub2dPid7Gd57YR6JDx9SNDh2VEZOTQrhlQQqQ22MF\nBfhCjgCjlp2Hajpvm/3NOlSSl+1zrmR5424UWi+GmfPQB+opt+m5f/kMymwWrE4FSaFONKc4D/5s\nh4uyGompKWpmjD+9E/3N3zWwdkMNkWE6Hr43flAmF3i9Mi+8XsLXO5pIjDXw6C8TMZuGbyDhcku8\n8a+jfL65Hp1Wyf23j+PSC4KHelmjVlWtkzWfVLNlWwOSBGMidGQuieSCmRZUgziKVhAEQRh5lAoF\nt181gcdW7mTt1iJSYgKJi/Qf6mUJgiCcU055JnfLLbf0eaX7n//8Z6/3/fnPf2bPnj14PB7uvPNO\nJk6cyMMPP4zX6yU0NJSnnnoKrVbLunXreOutt1AqlWRmZnL99def2bs5SzoqHbLy62i0Ogny1zEl\nKYS508awv6CBRquDAJOWjKSQLn0koO+pGwcKG3Be6uXG+ckUVlgpr7Wd9JiM5BBa2ly0Oz0AxJQc\nJrb4MBVj4gmdFEGa9jD24HEoAi00tHr5w79LmDheQ9r4EEKMbkKNfW/byCv1sHmPm5AABcsu1Z1W\nlcOhfBsvvlWG0aDi1z9PwH8QggKPR+a5V4rZtruZlAQjv/1FIkbD8K02qKh28PQ/iikpb2dctJ6H\n7o4fEWNKR6KqGgcffFLN1u2NSBJER+rJXBzBbBFGCIIgCH0IMGq546oJPLNqHy+vy+GxFTPw0w3f\nix2CIAijzSn/xb3nnnsA2LhxIwqFgvPPPx9Jkti2bRt+fr3vhd+xYwcFBQWsWrWKpqYmrrnmGmbN\nmsWNN97I5ZdfzrPPPsuaNWu4+uqr+fvf/86aNWvQaDRcd911zJ8/n8DAwWmMOBC6Vzo0WJ1s2lPB\n3GljmJQYzL78epptTg4UNaBSFbJ8biIqpa9ioK/+E02tDlpsTsIsBh5dMZ33Nhb4jtXmJMisJyPZ\nF3JU1bcBoPR4uGDrOiSFkt2XXMWvA4twyUpaxqUTpFTw5rctaPUGkpMScThdZJfmkR4R3+v7srZJ\nvPelE5USbr5cj17b/xO56lonT/7tCJIk8/A9cYwZhBNvt1vi6ZeK2ZnVwoRkE7/5ecKwnlbx9Y5G\n/vFWGQ6nxIKLQ7jtR9HotANfOXKuq6xx8MH6ar7e3ogkw9goPZlLIpg1XYQRgiAIQv+kxQVx+fkx\nfLajjHe+zOOOxWlDvSRBEIRzxilDiY6eEa+//jqvvfZa5+0LFizg7rvv7vV5M2bMYNKkSQD4+/vT\n3t7O999/zxNPPAHApZdeysqVK4mLi2PixImYzWYApk6dyt69e5k7d+6Zv6tB1Felw7aD1ThcxysR\nGqzOzvDixnnJQN/9JyxmPQEmHeAbO3rLghQyL03s3CLSsQ0k1GLAT6ciZfdmAloaODj5AubH27Go\nXOz3SyY11J/vCto5XOVi/sXT0KjV7Ni9l+ameq48Pxqz4eTu0pIs896XTmztMksv0jI2rP8n+212\nL394vgirzcPdP45h0oSBL3t0uiSe/PsR9hywMnG8mUfujx+2/RicTonX3itn4zcN6HVKfnlnLHPO\nE6PGBlpFla8y4psdx8KIMXqWL4lk1rRAlCKMEARBEE7TNXPiyS1tZntODWlxQcxOjxzqJQmCIJwT\n+l2bVl1dTXFxMXFxcQCUlZVRXl7e6+NVKhUGgwGANWvWcNFFF/Htt9+i1fpOiIODg6mrq6O+vp6g\noOMnbEFBQdTV9T5eE8BiMaBWn3xCGhpq7u/bOWNV9W00tvZc6XBiIHGiA0UN3LnMD73W9+W+YPIY\n1n1z5KTHXTA5iuioQBwuD01WJxZ/HaFaNdE9HHNBvJGQ5zbS7mek5sLZ3GXMpsZrIGHWFFodEqt2\nWkmKiyEyLITyymqKy31NMR9duZMLJkXxs6snolIdv2q/fquNgnIvU1J0XDvP0u9tGx6vzJ/+dpCj\nVQ4yl4zhpuvj+vW80+FwePnv3x9kzwEr50218MdH0tAN00CiuKyNR5/MpbjMTnK8iSf+ezxjowxn\ndKyz8fM8nPT3/ZaW23lrdSkbv65FkiAh1siKG8Zx8ayQERdGiO/x6HauvV9BGOnUKiV3Lk3jiTd2\n8vYX+SREBRAedGZ/wwVBEIT+63co8cADD7BixQqcTidKpRKlUtnZBLMvGzduZM2aNaxcuZIFCxZ0\n3i7LPc+97O32EzU12U+6LTTUTF1d6ymf+0N53V6CzD1XOvSmvrmdopIGwiy+P2yLZ8Vgb3eRlV9P\nU6sDy7GtGZdNHcP/vfE9uWVNnb0qUmMs/Gh+MoZuexunbPqYGo+bbRcv5ccRpSgVYEuaRqBGxdp9\nbUhKHdMmT8DldrNjz8HO57XYXHy6rYSDhfU8umI6KqWS4iovaze1E2BUcM1FaurrT+5l0ZtX3y1n\n594mpk3yJ3NJ2IB/D9odXv74QhHZuTZmTAngwTvHYbWe/P0farIs89W3jbzybhkul8wVl4Xyk8wx\naDXeM/qanK2f5+GiP++3vLKdD9ZX8+3OJmQZYqP9yFwawXkZvsqIhob+/9wOB+J7PLqN5vcrwhZh\nNAsL9OOWhSm8su4QL32cwyO3TBuUpt2CIAjCcf0OJebNm8e8efNobm5GlmUsFsspn/PNN9/w0ksv\n8dprr2E2mzEYDDgcDvR6PTU1NYSFhREWFkZ9fX3nc2pra5kyZcqZvZuzQKdR9TppozcnbssA39aM\nG+cls+ziBFpsTkwGDR99U8x//+M7HC6p83ENViffZVezJ7+WCydFdfamsG7bTc2az6gJH0vUjCgS\ntAXkKiKJS4omr9rF4RqZ86dNQqvRsG3XPtodjpPWVF5r472NBSy7KJl3P3cgAzct1GPy6/+V5k83\n1fHppjrGRet58M64Ad+/b2/38vvnCsktbOOS2SHcsyJ6WH4waHd4eeXtcrZsb8Tgp+KBe2KYNf3U\nvx9C/5RXtLN6fTXf7fKFEXExfmQujmRmRsCIq4wQBEEQhr/zJ0RwqLiJbw9WsXZrETdcljTUSxIE\nQRjV+h1KVFRU8OSTT9LU1MTbb7/NBx98wIwZM4iNje3x8a2trfz5z3/mzTff7GxaOXv2bL744guW\nLl3Kl19+yZw5c5g8eTK/+c1vsFqtqFQq9u7d268KjKHUMVEjK7+eBuvJJ/zddYwF7Rgh6qdT0+70\nEGDSEWYx8N7G/D5DDodL6rz/R5fEU/Kbp5FRkHXplTwSUIxdUhE8azpuj8yb37ZgCIwgOjKcuvoG\nCkt632KzL68el3MsTa0yC87TkhDd/y0RWdlWXn+/nAB/NY/cP/ANJ21tHn73bCEFxXbmnGfh8Ycn\n0NQ4/K6El5TbefofxVRUO0mMM/DQXXGEh+pO/UThlEqPtvPB+iq27W5GliE+xo/MpZHMnBJwWlNh\nBEEQBOF03TQ/mcKKFr7cVc6EWAuTEkKGekmCIAijVr9Did/+9rfcdNNNvPHGGwDExsby29/+lrff\nfrvHx3/66ac0NTXxwAMPdN72f//3f/zmN79h1apVREVFcfXVV6PRaHjwwQe5/fbbUSgU3HvvvZ1N\nL4erjkqHxbNjeWzlTpptrh4fF+yvIyM5lOsuiee9jfnsya2hyebuvD/IrGVyYggHihr69bpZ+fVc\ndGQPjtxCctNmcHmKA6PSQ45lIomBJj7c00qLU8UlU9LxeDxcmKTgmx1qbO2eHo9ndwZwqFgiYYyS\n+TM0/X7/5RXtPP2PI6iUCn51XzxhIQN7Em61eXji6QKOlLVz6QVB3HvrONSq4XUSKssyX26t5/X3\njuL2yCxZEMbN10UNy0qOkab0aDur1lWxfXczAPHj/LhhaSTTJ4swQhAEQTg7dFoVdy1N43//uZvX\nNxzmidtmEmgSFx0EQRAGQ79DCbfbzWWXXcabb74J+KZr9GX58uUsX778pNs7Qo0TLVq0iEWLFvV3\nKUOuo+LB5ZFo6SWQUAA/v24SoRYDb32Wy/acmpMe09jqYnNWZb9ft72mnqp/vYzS34R0xcVcaMih\nQvInfvp4Kps8fHqwjQtmTkOn07Iz6yClegduj9TjsVQKPwzaGAx637aN/pbBt1jd/OH5IuztEr/4\nWSypiaZ+r78/mq1uHn+6gNKjDuZfFMxdP44ZdiX69nYv/3irjG93NmEyqvive8YxY8rwHWE7UpSU\n21m9rprte3xhRGKsgcwlkUyf7C/CCEEQBOGsiwk3k3lpIu9tLODV9Yd48IYpKMXfI0EQhAHX71AC\nwGq1dp4cFBQU4HT2v9njaOCVJFZ9VUhWfh2NVicWsxadVtXj1A1/o5aNe4+Sc6TxtJpi9uXCXV8i\nt9oY+7tfMtF0FMkFcvp0lEoVb21rYExkJLFjo6itb6TgSAm5PecRgBKjLhFQ8qP5egJM/bu673b7\nxnLW1LvIXBLBRecP7JjLxmY3jz1VwNEqB5fPDeWnN0YPu0CiqNS3XaO61klqopEH74ojJOjkEatC\n/xWX2Xnu1TK+3u7rLZMYZ+CGpZFMnSjCCEE4l+Xn53PPPfewYsUKbr75ZtxuN7/61a8oLS3FaDTy\nwgsvEBAQwLp163jrrbdQKpVkZmZy/fXXD/XShVHksmnRHCppYl9hPZ/tKOXKWbFDvSRBEIRRp9+h\nxL333ktmZiZ1dXUsXryYpqYmnnrqqcFc27Cz6qvCLr0fGlt7rpIAaGlz8fW+qgF77bDqMuL2f48m\nOZ7QKaEYDh+mwBBLzNhwtubZKW2EpYvS8Xq9bNu1D2+vgQRYjPEg+zFnspoJcf37EZBlmRffKuNw\nQRsXzrRww9KBnd1d3+ji0acKqKpxsmRBGCuWjxlWJ6SyLPPppjreXF2BxyOz7MpwblgahVo9fNY4\n0hwptbN6XRXfZ7UAkBzvq4wQYYQgCHa7nd///vfMmjWr87bVq1djsVh45plnWLVqFbt372bWrFn8\n/e9/Z82aNWg0Gq677jrmz5/f2ctKEH4ohULBrVek8tjKnXz4dTGpMRYSxgQM9bIEQRBGlX6HEnFx\ncVxzzTW43W5yc3O5+OKL2bNnT5cPDKOZ0+0lK7+ux/tUSpAkOPUw095p1QpcHt8RdBoFFrMep8tL\nS5uLQKOGud+tRyHLbJp8MVMPfYtV1hIxYyotdi8f7GplxpTJ+On17DlwCKutrffXUQWDHER0mJKr\nLuz/3sh/f1rDlm2NJMUZuO+2cQN60lhb7+TRpwqoqXOx7Mpwbro2alidlNraPPztjVK+39uCv1nN\nA3fEkpHuP9TLGrGKSu2s+riKXfuOhREJRu78cTxx0eph9X0XBGHoaLVaXn31VV599dXO2zZv3sz9\n998P0Lk9dPv27UycOLGzF9XUqVPZu3cvc+fOPfuLFkYts0HLzxan8dT7Wby8LofHb52BQd//XlyC\nIAhC3/odStxxxx2kpaURHh5OYqJv+oTH03MDxdGoxeaksZdtGH1VJfRXRyAB4HTLpMcHd44M3f7M\n2wRWlFKQPJkr0t1oFRIV0ZOI8tOxcnMzlqBQEmLHUt/YzKH8I72+hlKhw6iLRaeBWxbp+908cvue\nJt5ZW0lIkIb/uT8BnXbgmjlW1Tp57KkC6hpc3LA0kswlEcPqxDS/qI2nXyqmrsFFeqqJX9wRS5BF\nbNc4E0UldlatOx5GpCYaWb4kkslpZsLC/Kmrax3iFQqCMFyo1WrU6q4fUSoqKvj666956qmnCAkJ\n4bHHHqO+vp6goONbCYOCgqir6/kCgiD8EKnjLFw1O5b120r45xd53LkkbVh9XhEEQRjJ+h1KBAYG\n8qc//Wkw1zLkOhpYBph06DRdR1wGmHQE+esGrD/EqWTl17Ps4gRMXheWf72PW63BftksMvSllCuC\niUpL5OBRJ1nlHpYsnIQkSWzbvQ9Z7q1eQ3Gsj4SKqy/WIMkOnO6T32d3RSV2/vJqCXqdkkfuT8AS\nMHBXBiqqHDz2dAENTW5uXhbFsisjBuzYP5Qkyaz/spa311YgSZC5JILMJZGohlmPi5GgoLiNVR9X\nseeAFYDxSb4wYtIEs/hAJwhCv8myTFxcHPfddx8vvvgiL7/8MhMmTDjpMadisRhQqwd2jHWH0NDh\nPT3sXDCY34Pbr55IYaWVnYdrOW9iFAvOGzdorzWSid+DoSe+B0NPfA9OT79Difnz57Nu3ToyMjJQ\nqY7/MY+KihqUhZ1N3RtYBh0b5bl8biIqpa8qQKdRMSkh+LSmZfwQTa0OWmxOGv78D/R2G7tnL+TH\n0dV4ZAXG6TNweuHtbVamTpqA0eDH/pw8mlt6v9LspxmLWmkkOKCND7YU9vo+T9TQ5OKPLxThdsv8\n6r444mIMA/b+yiraeeypApqtHlYsH8PSheEDduwfymrz8MJrJew5YMUSoOaBn8Uxabz4h+V05Re1\nsWpdFXsP+sKICckmli+NZGKqSYQRgiCctpCQkM7JXxdeeCF//etfueSSS6ivr+98TG1tLVOmTOnz\nOE1N9kFZX2ioWVR8DbGz8T249fIUHl+5i5f/fYBwfx1RIcZBfb2RRvweDD3xPRh64nvQs76Cmn6H\nEnl5eaxfv75L8yiFQsGWLVt+0OKGg+4NLBusTjbuPorXK7FwZkxn5cS86WMHNJQI8teBLPfYMNNi\n1qM9Wk7r+x/RGhRK8kXRBKsrORKQzJhgC6t3taLyCyQlIZamFisHDxf0+joaVSB6TQQ6rZvCqsOA\n1OV9Atw4L7nLcxxOL398oYjGZjcrMscwM2PgmoYVl9l5/OlCrDYPd9wUzRWXhQ3YsX+oQ/k2nn25\nmIYmN5MnmHngjlgCB7A65FyQV+SrjMjK9oURaSkmli+JJF2EEYIg/AAXXXQR33zzDcuWLSMnJ4e4\nuDgmT57Mb37zG6xWKyqVir179/LII48M9VKFUSwkwI8Vl6fy4kfZvPRxDr/9yTQ0g1R5IwiCcK7o\ndyixf/9+du3ahVY7uvbT99XAcuu+SrZkVXZWFFw9J47g09zCER1qJCbMxLacmpPum5ocCtAlEOmQ\nkRRM1ePPgNeLfMt1LAospVH2Y8yMyZQ3utl82MEV889DkmW27dqHdKxkVakA6YTqVYVCg0Ebj0Ih\n4/IeoSOQOFHHVpGOrRySJPP8a6UcKW1n3pxgliwcuNCgqMTO488U0Gb3cvePY1hwSciAHfuHkCSZ\nDz+r4b0PK0GGG6+JZNmVEcNuJOlwlltoY9XHVezL8SXD6akdYYSoMhEE4fRkZ2fz5JNPUlFRgVqt\n5osvvuDpp5/mD3/4A2vWrMFgMPDkk0+i1+t58MEHuf3221EoFNx7772dTS8FYbBMTw3jkilRbNlX\nyeqvirhpQfKpnyQIgiD0qt+hRHp6Ok6nc9SFEn01sOw4uT+xoiAjObTHEEGnUeJ0S52hQKBJS0ZS\nCDfO9/2hMvhpyMqvp6nVgcWsJyM5hOVzEzuf3/2++bZiirfvJXD+HGZP8qKql3GnTENSqnjz20Ym\npqVgNhnJzi2koamly5rHhpmwOzw0tToI8EsC1Fw2HdZsbem+bOD4VpEwi297xnsfVrJjTzPpqSZ+\ndsvYAbu6nVfUxu+eLcTh8HLfbeOYe0HwgBz3h2q2unn+1RL25bQSbNHwyzvjmJBsGupljRiHC2ys\nWlfF/mNhxMTxZpYviSAtRZwYCIJwZtLT03n77bdPuv2FF1446bZFixaxaNGis7EsQeh0w2VJFBxt\nYdPeo0yItZBx7EKTIAiCcPr6HUrU1NQwd+5cEhISuvSUePfddwdlYWfL6TSwzMqv54nbZ3b+94kh\nwtVz4mlsacftkdColYRaDF2aSN44L7lzmkb3Rprd71O7nBy46AEUOi2xty5EVf4d3qgEAmPHsvFQ\nG62SiQuT4rG22tifk3fSOu0OD4+umM7nO1zsyIa0eCWXTtPyVVbP79Ni1hNg8o0H3fxdA2s31BAZ\nruPhe+LRqAdm0sahfBu/f64Ql1vigTtimXN+0KmfdBYcPNzKc68U09TiYdokf+6/PRZ/c79/Lc5p\nh/J9lREHDvvCiEnjzSxfGikCHUEQBGHU02pU3LU0jd+9tZuVnx7miQgzQf76oV6WIAjCiNTvs6+7\n7rprMNcxZHQaVa/VD901tTqw2V0nhwgqRY+NMq+eE4/N7uoMIXQaFWEWA063l9ome5dwouM+gPKn\n38BdVUvUvbdgqtmLrNbiSZqIrFRT7zEze0YyCoWCbbv345VO3o7R1Opg5YZyyqrCkCQXh0rz+ffX\nwUxJCmHTnoqTHp+RHIJOo+JQvo0X3yzDaFDx658nYDYNzMn5wcOt/OH5IjxeiYfuimPWdMuAHPeH\n8Eoya9ZXs3pdFQol/CRzDEsWhIntGv2QndfKqo+ryM61ATA5zczyJZGMTxJhhCAMB+0OL5U1TmLH\n+omJQYIwiMaEmvjRZUn884s8Xll/iId/lCE+RwiCIJyBfp91zpw5czDXMaQ6tlFk5dfTaHWg6NaX\nocOJFQUnBgxvfprLd9nVnY/r2O7x7YFKnC6pM6S47pJ41mw50uOUD49XpsXmRF9XQ/XL76CNCmfs\nrAgUFdk4Umag0PuhMEeQPt6feoeR3IJiausbe3w/Wo2OkgoLCgXYXIV4He1s3H2UudPGMG96dI/b\nSMoq7fzxhSJkWebhe+IYEzEwaf++bCt/+msRkgwP3xM/oA0zz1Rjk4vnXi0hO9dGaLDtbGBuAAAg\nAElEQVSWB++KIyVBdM8+lezcVlatOx5GZKT7k7kkgtREEUYIwlBqd3jJLWwjO7eV7DwbRSVteL3w\n0F1xXDBz6ENgQRjNLp4SRU5JI3vy6vhkWwlLLowb6iUJgiCMOKJOHVAplV2qHz7fWcaWHqZsTEkK\n7qxs6BgjujevtsfpGQAOV9cpF3llzZTX2jrvP/F2u8NNo9XJkk/fJNLlJvr+G9FWZNOo8scYE092\nhYvsQ81EjI3A1mZn78HDvb4frXIcSqUWu6scr9TWefv+ggb+947zulR5eCWZVz8+zFf/acftUBI6\nzkV2ZTVpqaYex4Sejt37W3jy70dQAP/z/+KZOjHgBx1vIOzLtvLcqyVYWz2clxHAfbeNw2QUvwa9\nkWWZ7Fwb//q4ikP5x8OI5UsjRZAjCEOkvd3L4UIb2bk2cvJtFBa30VE0p1RCYqyBiePNTEkXfV0E\nYbApFApWXJ5KSZWVj78rJnWcheSxQ38BRhAEYSQRZ2Mn6Kh+6K307sTiie5jRPujos7W4+0dQUVM\n8SEiCw9REZ1AonwUFKCaOAOHF978roXZ56cDCrbv3o/H6+35PajDUSkDcXtbcHqqutx3YkPL4AA9\nq74q5Jv9ldQX++FxaNAFOvDoHGzc7Zvh3n1M6OnYsaeZZ14qRqmCX9+fwKQJ/md8rIHg9cq8/1El\nazfUoFYpuP1H0Vw5L1SMqOyFLMscPNzKqnXVnWHEtEn+ZC6OJFmEEYJwVnUJIfJaKSyxdw0h4oyk\np5hITzWTmmDEz0+MJxSEs8mo1/CzJWk8+W4Wr6zP4fFbZ2LyE+PEBUEQ+kuEEt043V72F9T3eN/+\nggauv8QXBvQ2RrQvPW0J6aDyuLng6/VICiXKy2cSqWym0jyO4PAw3tthZUx0PEGWAAqKy2ho7Hl9\nKqURP81YJNlFm7PopPtP3H7SEarYa/3w2DVojG78Qh2dj+0+JvR0fLeziWdfKUarUfLrBxJIH+Ip\nDPWNLp55qZjcwjbCQ7U8dFcciXHixLonsixz4FAr//q4itxCX5XNtEn+ZC6JJDlefM0E4Wxob/dy\nqMBGTp6N7NxWikqPhxAqFSTFGUlPNZGWYiY10YifXoQQgjDUkqIDWXphLB9+U8ybn+Vy7zXp4sKH\nIAhCP4lQopu+RoQ2tjo4UtGC2aDp9TF9UdC12uJEk7O+JqClgfyMWdwU30qbrCF4+jSK69zsLldy\n5fwk7O0Odu/PQaFQAt0bXCoxahMABW3OI8h4TnqNjoaWTreXrPw6HE1anM06VFovxsg2Tvzb2X1M\naH9t2d7AX18rRa9X8ttfJA55v4Hd+1t4/rUSbG1eZk8P5J4V4zAaxAf47mRZZn+Or2dERxgxY0oA\nmYsjRIAjCIPM3u7lcD9CiPQUMykihBCEYevKWbEcLm1ib34dW7IquHRq9FAvSRAEYUQQoUQ3fY0I\nVQBP/Wsfwf46tBoFTnfPEYNOo8TpPnkqRm+BhKm1iYxdX2H3MzFxYQx6pZXGhAwUai3//K6BWTPO\nR6VSsWPPHtxuDwrgkowxbN1XgXzsoEZtHCqlnnZ3JR7JCkCgSYu1zdWloSX4gpfqKi/tdUYUKgnj\nGBuKbu0jTqyq6OB0e3scadph4zf1vPhmGQY/FY89mEjSEJ7Muj0S766t5OMvatGoFdx5y1gWXhIi\nrlp0I8syWdlWVq2rJr/oeBixfEkkCbGnF0gJgtA/HSFER2PKIyX2zko6lQqS442kdWzHSDSi14kQ\nQhBGAqVSwR2L03hs5U7e31RIUnQg0WGiGbQgCMKpiFCim75GhHZ8aOwpsOgwOz2CG+cn8dE3xWTl\n19NgdfT62GB/PQa9mtRP30HjcVM2fx4LLVbqNUGYExL5PNuOITiG0GALxWVHOVpVA0CQv557M6cg\nyzJb91WiVYWgVQfj8bbicFd0HvvRFdNpd3o6w4WGFgcBJh2tVhl7tREUYIpqQ6U5OS7pqKqA4009\ne5oa0tEM8/PNdbz8djlmk4rHH0wiftzQndDW1jt55qVi8o/YiQrX8dDdccTFiBPsE8myzN6DVlav\nqyL/iK+HyHkZAWQuiRzS750gjEZt9mMhRF4rObk2jpQeDyHUKgXJCUbSU82kpZhECCEII5zFrOO2\nK8bzwtoDvLQuh9/+ZPoZbYUVBEE4l4hQogfL5yaeNCnjVHQaJXMmR3WeqN84L5nFs2N5fOUummwn\nhxgWk45HV0zHsyeLgsIDNI4ZxxWzdXhxYZp+HrJKQ06dnoyMVBxOJzuzcjqfa9Cr0aiU3LwgmcKj\nDtraxiHJHtpcRXTUY2Qkh2A2aDHo1V0ChQA/HbWFBiSvAmNEG2q/rg0z9VoVF06K7KyqgJObenZM\nDQFfM8xP/lPL6+8fJcBfzRMPJTEu2q/fX7eBtmNPM397o5Q2u5eLzrdw1y0xounbCWRZZs8BK6vW\nVVFY7Asjzp8WSObiCBHcCMIAabN7yd/ZwLZdtX2GEOkpJlJECCEIo86UpBAumxbNpj1H+demAn6y\nKHWolyQIgjCsiVCiBx6vjN3hPq3nKBSw7OKELmM0250emnsIJABa2pzYbQ5qH30GFArmPHQVQa4j\nuOPTUfgHIpsimTsnhBaHim279uF0HR87Wl5rY+X6HBbNGIvkHYdCoaLNWYAku9BrVcyeGNEZKpwY\nKMgSlOdr8LTLjE/XkD4pnKz8eppaHQSadKSOs3Dj/CQMuuMdozv6T/QkK78ercPMu/+uwhKg4Yn/\nSmRs1NAEEm63xFurK9iwqQ6tVsF9t45j7oVBYrvGMbIss3u/rzKisMQXRsyaFkjmkghix4owQhB+\niDa7h0P5beTktZKda6O4rGsIkZJoJD3FTHqqiZQEEzrdDxu3LAjC8Jd5aQL55c1s3VfJhNggZqSG\nDfWSBEEQhi0RSvSgr2aXvXG4JOqa7ESHHZ800Vd/CotZj/vf62jPO0Lo9YuweEqR/cxIcamg86fC\nEUiLQ015RTUlRytPev6O7CoOFelxOE043DW4vU3H1uFFqVCgUiq7BAqyDPZaA552NRqzC5fOwbKL\nJ7Ds4oQ++0T09bWoKJZ5d3cVwRYNv3s4iahw/Wl9zQZKVY2Dp18q5khpO2Oj9Dx0dxwxY4auWmM4\nkWWZXftaWL2umqJSXxgxe3ogmUsih7SiRRBGMl8I0TGi8+QQIjXJxIyMIOLHakUIIQjnKI1axV1L\n03jizV28+VkucZFmQgLE311BEISeiFCiB32FCX1xeyRqm+ydJ/h99aeYHq6h5pFXUQX6E3dRJAp7\nNe7UDNDocOgjyC/T4Pa42LH3QI+v1WzV43aa8Eh22t1lXe7rGOd5YqDgaNLhsmpR6T0Yw+002+ic\nrtHXhI2evhayDI4GPY5GPaHBWn7/cBLhobpejzGYvt3ZyItvltHukLjswmDuuGmsOAHAF0bs3NfC\n6o+rOFLWjkIBF8wI5PrFIowQhNN1YgiRnddKcVl7Z5NhtdoXQnQ0pkyJN6LTKQkNNVNX1zq0CxcE\nYUhFBhu5aX4yb3yayyvrDvHfN2V0qagVBEEQfEQo0YO+woTeqJQK/v7hQZpaXV0aQXZso+jYJtEx\nCSNj3bs0t7YR88CP0Nmr8YbHIIVGIRvDya03olSq2L3/IO2Ok4MRpUKLQRuHLHtpcxbSfa5HxzjP\njkChqkLCUe+HQi1himpDoex5ukZ/vhayDO31epxNeowmBX/4VTKhwdp+f50GitMlsfL9o3y5tR69\nTsnP7xjHJbOCz/o6hhtJktmZ1cLq9VUUHwsjLpxp4frFEaJ6RBD6ydZ2LITIs5GT20pxedcQYnyS\nqXNEZ3KCEZ1WnGQIgtCzCydGklPcyM7DtXz8bTHXXpQw1EsSBEEYdkQo0YuewoRJCUG0O73sOFRz\n0uO9kkxjq6/vQ/dGkDfOS+6sXDAZNHz2xpc0r9lAY0gEk0JseBUqPCmTQWugxhNCs0NNZXUtRSXl\nPaxMgVGbiEKhps15BEk+ebqHxazrrNaIDQmicI8dFDKmMTaU6uONMPvbDbrja7E3r56KIwqcTTrM\n/gqeeXQCoUFnP5A4WuXg6X8cofSog9hoPx66O44xkUOzdWS4kCSZ7/c2s3pdNSVHfWHERedbuO6q\niCHr8yEII0WrzcOhAhs5uTZy8k4OISYkH6uEECGEIAinSaFQ8OOFqRyptLJhWynjYyyMjw0a6mUJ\ngiAMKyKU6EXHBI3uPRe8koTJoPFNs2h1YjHrsDvcOFzSScfo2EbRsZUjzGLgvS9z8X/pFQCMV04j\nQOOhJWYiWr0Jj18UhVU6lAqZ3LzcHtflpxmDWmXC6anH5a3v8TFtDjdrtxYxLyOGvTs8ICuITHTh\nUkmdlRonTtfoz9fihrlJNB3VUdTUQHSUnt//VxKBAZpTP3mAbdnWwMtvl+NwSiy8JIRbb4g+p08Q\nJElmx95mVq+rovSoA+WxMOL6xZFEn+NBjSD05sQQIjuvlZITQgjNsRAi/dh2jKR4EUIIgvDDGPRq\n7lqazp/e2cMrnxzid7fNxGw4+xd1BEEQhisRSpxCR5jQoXtY4XJ7eWzlrh6f27GNouP5TreX5tXr\nSaytoCo1jevGS7SqTeiTx7Mp10FokhmPpCApxMnRGDNHa1u6HE+t9EevicIrObC7Snpds8Ml8Z+d\nR/nPZ3aamyVWZI5h0WUhfTa07ItXkvnHm2Vs+raB2LF+PP5gIgH+ZzeQaHd4+evrJXz1XSN+eiUP\n3RXHBTMtZ3UNw4kkyWzf3czq9VWUVfjCiEtmBXHdVRHnfNWIIHTXauvoCdFKdp6N0qNdQ4iOKoi0\nVBPJ8Ua0GhFCCIIwsOKj/Ln2ong+2FLEyg2Huf+6SWJCmCAIwjEilOiD0+3t9US+I6xwur19Ttg4\nsW9DY0U9aV99gkujZeriOJQKL9rJM6hplfm+Qs/saA0Bei9R/p4Tto/U0WB1olJoMOoSAAm3dAQ4\nuTKjgyxDW7UBt01i7oVBLFkYhkKh6LOhZW+8Xpm/rixl6/ZGEmMNPPrLRMyms/tjU1bRznOv5FJS\nbidhnIEH744jMmxoGmsONa8ks313E6vXVVNeeSyMmH0sjIgQYYQgAFhtHg7l+aogckQIIQjCMLHw\nvBgOlTSyv6iBjbuPMn/G2KFekiAIwrAgQokeeCWJVV8V+rZoWJ1dGld275rcV1PM7n0b2l56Az9H\nGw2XXkBiiJeWoBj0IRG8v9HKtCkXolDIpIQ6UShApfBVZHi9EpuzKvHTxqNUaLC7SnF6bH2u39Gg\nx23TovZzs2xx6Bkn8R6PzF9eLea7Xc0kJxh59BeJGA2nV2XxQ8iyzKZvGnj1vXJcLpmr5oXy4+vH\noBkBJxB9BVpnwivJbNvZxOr11RytcqBUwtwLfGFE5BCNYhWE4aJLCJFro+Roe+d9nSFEqpn0FBNJ\nIoQQBGGIKBUKfnrVBB5buZMPthSSPDaQcRHmUz9REARhlBOhRA/+tamATXsqOv+/o3Gl3eHhloUp\nJ51k9jZh48S+DfacfBreXkurJZh5cwNwokI/aRrbCtvxj0hBp9MRE+DAoD0+SaPV7iKroB69OhKN\nKgCXtwmn5+QmmydytmhwNOpRarwEj3MQHHhmJ6xuj8QzLxXz/d4WJiSb+M3PE/DzO3uBRHu7l5fe\nLuPrHU0YDSoefyiV8YnDvzridAKt/h1P5tvvm/jgkyoqqpy+MOLCYF8YcY5WiwiCtdVDTn5rZ0+I\n0qPHG/5qNQomjvcFEGkihBAEYZgJMOm4/aoJPLd6Py+ty+GxFdPRa8XHcUEQzm3iX8FunG4v3x2s\n7vG+bdnV5JY2MjkxhHnTxxLkr0enUfXaFLODLMsU//rPKGSZwCsyMGll2lMm0yZr2VoKs2ZGU9/Y\nxKdf7mZKUgjXXRLPqk2F7Mmrp82hxayLRpJc2J3Ffa7dbVdhrzGgUEqYxrShVJ9ZhYTLLfHnvx9h\nzwErE8ebeeT+ePS6sxdIFJfZefofxVTWOElOMPLgnbGkjQ+hrq71rK3hTK36qrBL1Uz3SSz95fHK\nbNnewAfrqqmscaJSwbw5wSy7MoIIEUYI55gWq5tD+TZy8voOIdJTzSTFGUZENZUgCOeuifHBLJw5\nli92lvPuf/K5/coJQ70kQRCEISVCiW7qmttxuLy93t/Y6mJzViWbsyoJ7nYVvHtTzA4NH35B2859\nNCQmcfUkDTa9Bc24JNZuszEpfRZeSWLbrv00Wx1s3H2UHTnV2No9KFBh1vvmWbe5ipDx9Lour0tJ\nW6URAGOUHZVWwumiS6PN/nA6Jf7vb0Xsy2klI92f/74v/qx1npdlmS+21LPy/aO4PTJXLwrjpmvH\noD7DcOVsc7q9ZOXX9XjfiZNY+uL1yny9o5F/f3aYo5XtqFQw/yJfGBEeKsII4dzQEUJk5/maU5ZV\nnBBCaBVMGm8mPdVEWooIIXoz0FvIBEEYWMsuTiCvrJnvDlaTFhvE+WkRQ70kQRCEISNCie5k+dSP\nOaY/V8G9bXbK//d5FFot069JQAK0GTM5VOXCZYrHaPBjX3YuzdbjVQC2dl/4YNDGoVLqaHcdxSP1\nXiUge8FWaUSWlBjC7GgMvucH+XdttHkqDqeXPzxfRHaujemT/fmve+LPWtlzm93Li2+Wsm13M2aT\niodvj2X65ICz8toDpcXmpLGHhqdw8iSW7rxema07GlmzvpqqWidqtYIFF4ew7MpwwkJEGCGMbi1W\nNzn5NrKPbcco7xZCTJ5gJi1FhBD9MdBbyARBGBxqlZI7l6bx+Bu7+OcXecRH+Z9RQ3JBEITRQIQS\n3YRaDOi1Shyu3qdbdNfXVfDKv7yOu7qO6BvnER2ioC0qCdlo4dM9XqZNj6WxuYXs3MKTnqdVh6FV\nB+H2WnF4KjtvDzRpMfppqKhrA3wZiq3KiORSoQt0oAt0dT62e6PNvtjbvfzvXwo5XNDG+dMC+eWd\nsWjUZ+cDbEFxG8/8o5iaehfjk4z88s44QoJG3vzuAJOu35NYOng8Mlu3N7JmQzXVtU7UKgULLwnh\njlsSUCncZ2PZgnDWNVvd5OQd347RWwiRnmomMc5w1v4tGg0GaguZIAiDL9xi4McLU3h1/SFeXpfD\n/9w8DbVK/HsnCMK5R4QS3eg0KmZPjOSrExpdnkpjL1fB2wtLqH7lXbRRYYxN0yDpDKjHT+Y/eS6S\nUzOQjm3bkLpVZ6gUfhg0MUiymzZXUeft/gYtT9w2E1u7m1+/+r3vNWr98Ng1aIxu/EJ9H+wDTVqm\np4Z1abTZlza7h989W0j+ETsXzrTw85/GnpUtE7Is88l/6vjnBxV4JZnrrorghqWRqFQjY7tGd6cz\nicXj8fWMWPNJNTV1LtRqBYsuDeHaKyIIDdYSGqqnrk6EEsLo0Gx1czCvjm07a8nJs1FeeTyE0GmV\nTE4zk57i25KRECtCiDM1EFvIBEE4u2alRZBT3Mi27Gr+vfUImf387CYIgjCaiFCiBz+6LAmlQsHe\nvDoaW3suxz+RAvhiZxk3zk/uLI+VZZmyR59BdnuwLExHrQZXyhRq7Epc5gTMJiPWpmoam1u6HU2J\nUZeIQqHE5ihAlo+fmE5LCcFs0KLVqAiz+FF2xIuzRYdK68UY2YZCARaTjsdvm4HZ0L9KA6vNwxPP\nFHCktJ1LZgdx323jUCkHPxRotXn468pSdu1rIcBfzQN3xDIlzX/QX3ewnWoSi9sjsWVbI2s/qaam\n3hdGXD43lGuvCB+R1SGC0JPmFndnFURPIcSUNDNpIoQYcD9kC5kgCEPn5gXJFFW08PnOMsxGDZef\nN26olyQIgnBWiVCiBx3TNBbPjuXR17+npa3vK9aSDJuzKlGplJ3lsc1fbKVly3ak8YkkpWix+4ej\niojh39+6SZ0chtvl4PIpRmzN0V1OYJWMxePxw+GuxiMdDyzGhpm4cb7v2DqNirGBweTVtaJQSRjH\n2FAc+0w/LTW034FEs9XNE08XUnK0nXkXBXP3j2NQnoVAIrfQxrMvl1DX4CI91cQvfhZHUKBm0F/3\nbOhtEovbI7Hp63rWbKimrsGFRq3gistCueZyEUYII9+JIUR2ro2jVSeHEOdNCyE2WiNCiEF0JlvI\nBEEYenqtmgcyJ/Pn97L4YHMRbo/EkgvihnpZgiAIZ40IJfrQ7vRgPUUgcaKO8liNx03Z48+BWkXq\nonFIChWqSTP4usBBVPxUAHbuPcBFialdTmCLK1Ws3uTGoHehUNXh8ECgUceU5BBunJfUWYVRXtHO\n9m/aUSoVWMbZkdS+7R96rRJJlvFK0ikbmjU2u3n86QLKKx0sujSEO24aO+iBhCTJfPxFDe+srQQZ\nbrg6kuuuijgrlRlnW8ckFrdH4ostdazdUNMZRlw5L5RrLw8nyCLCCGFkampxk3MsgMjOa6Wi6vhJ\nsF6nJCPdv7MnRMI4A2q1gtBQ84gY6zuSnc4WMkEQhpdwi4Ff3TSVp97P4qNvivF4Ja6ZE49CMfo+\nIwmCIHQnQok+9HXVqScd5bHuN9/DWVZB6FXnEx6hxRE3AavSQG6rheRIE4cLjlBUXsM7XyhYcUUq\nOo0KhULPx1/b0Wng55mBmI0zexznVtfo5HfPFdJm93LebB359c2d9zlcEl/tqUCpUPTZ0KyhycWj\nfy6gssbJ4gVh3Lp8zKD/0Wuxunnh9VL2HrRiCdDwyztjSU81D+prDiW3W2LTtw2s3VBNfaMbrUbB\nVfN8lREijBBGmsbmYyFEno2cfoYQwtA41RYyQRCGr9BAP35101T+/H4Wn2wrxeORuf7SBBFMCIIw\n6olQog99XXXqicWsx6+5kdK/voEmxELCTDNOrQlFwgTW7/GSmJJEq62NrIO5AHyXXY2fXk3mJUm8\n85kDpxtuWqgjJNBX5XDi3l+vJPHefwr4dIMVh01FUJSHMmvPVx37amhWW+/k0acKqKlzce0V4dy8\nLKrzj91gzbXPyWvl2ZdLaGx2k5Huz/0/HUeg/+jYrtGd2y2x8RtfGNHQ5EarVbB4QRhXLwofNVtU\nhNGvSwiR20pF9ckhRHqqifQUM/EihBhWettCJgjCyBDkr+e/b5zK0//K4vOdZbi9EjfOSxLBhCAI\no5oIJU5h+dxEvF6JrfsqkeS+H5uRHEL1H19AdjiJvWkWGp0a18TpHKzyYIhMR6lUsn3PATxeb+dz\nsvLr0avHcrROYuYENVNTej5x/demAj75tAmXTYvG7EIy2nG4enxorw3Nqmt9gURdg4vlSyJYvjQS\nhUIxaHPtvZLMvzdU86+PqkABNy+L4prLw89K34qzzeWW2Ph1Pf/+tKYzjFiyIIyrLw/HEiDCCGF4\na2xyHesJYSM7t5XKmq4hxNSJvhAiLcVXCTFSJ+ScSzq2kAmCMPJYzDoePhZMbNpzFI9X4paFKShF\nMCEIwiglQolTUCmV3LIwFRQKNu/teUxosL+O1BgL8xT1lGz4ClN6POFJerwR45BCoigvCyHIEkj+\nkVKqa+u7PLe1Tc93B7yEWRRcfXHPTcicbi9ffd2Mq1WLSu/BGG6nr79LPTU0q6h28NhTBTQ0ubnp\n2iiuuyqi877BmGvf3OLmL6+WsP9QK8EWDQ/eFcf4JNMZHWs4c7ok/rO1ng8/q6Gx2Y1Oq2TpojCu\nXhhOoAgjhGGqscnVGUDk5Nm6hBB+ehFCCIIgDLUAo5aHf5TBM6v2sXVfJR6PxK1XjB+VF3YEQRBE\nKNFPvkaTii77dCcmWHC4JfJLm9ixv4Jxq/6CWaEgccE40GjxpEzBpQ/FYInB7nCwZ/+hLsdUKDQY\ndfGoVXDL5Xp0mp7/0GzeVkdjhQaFWsIU1dY5aaM33RualVe089jTBTS1eFiROYali8I77xuMufYH\nDll57pUSmq0epk/25//dHou/aXT9qDldEl9urefDT2toanGj1ym55vJwliwMG7VbU4SRq6GjEiLX\ntyWjqlsIMW2Sf+eIzvgYEUIIgiAMB2aDlv/6UQbPrtrPd9nVeCSZn141/gdVsQqCIAxHo+tMcRD1\ntE937dYidmRVATBp/3f419cgT0vBHK7HkzgR2Wgh2zoGGQVtzUdxezxdjmnUJgBqls7RERXS84l/\nUYmdN96rQqGUMY2xoVT3vYdkbJipS0OzknI7jz1diLXVw09vjObKeWFdHj+Qc+29XplV66pY80k1\nSiWsWD6GJQvCRtU+SKdT4outdXz0WQ1NLZ7OMGLpwjACRBghDBMNTa7OyRg5uTaqak8OIdJTzaSn\nmIgTIYQgCMKwZdRreOiGKTz3wX6+P1SDxytx55I01CoRTAiCMHqIUOI0dezTPbHCwNBmZfr3/8Gt\n1zPnymichkDk6CSqiabVqSbM5KHCbu1yHL1mDBqVP4HmdmZNNPb4Wg1NLv74QhFuj8zsOX4cqm45\n5fpsdhcer4xKCUWldh5/ugBbm5e7fxzDgktCTnr8QM21b2hy8ezLJRzKtxEWouXBu+JIju/5fY1E\nTqfE51t8YUSz1RdGLLsynCULwvE3i18jYWjVN7p8AUSe7aQQwuAnQghBEISRzE+n5peZk3lhzQH2\n5NXx4ofZ3H11Ohq1CCYEQRgdxNnUGTqxwuC87z5D63YScnkGaqMW98SZfH3EgzcsEK1GJiagnTcK\njveSUCvN6NVReCUnLfYjuDxBnVskOiZg6DRq/vh8EY3Nbm65LopZ55nZuFtJVkE9zbZeOlwCTTYX\n73yRxwWpY/nfvxzB3u7lvlvHcdmc4B4fPxBz7fcebOH5V0ux2jycPy2Q+26NwWgYHT9aDqeXzzfX\n89HnNbRYPfjpj4URC8NH3ZYUYeToDCFyfc0pq7uFENMn+5OeYiY91UxsjB8qsQdZEARhRNNr1fz8\n+sn8be0B9hXW89e1B7jv2oloxXQdQRBGAXFWdYb8dGoCTFp0Bfmk5O7BFR5C6qxwPGMSadQEUqdO\nIFipJM5ix97u6AwwFKiPbduANlcRsrONFpuT4AB95wSMhhYnrjozbc0qYhPUbFHHiJoAACAASURB\nVCsuYsMB31SMSYnB7C9soKWPYGLrrjo+X+dA8sLPfxrLxbOC+nwvZzrX3uORee/DSj78rAa1WsEd\nN0Vz+dzQUbFdw+H08tlXvjDC2uoLI66/KoLFC8IwizBCOMvqG12+fhDHtmTU1B3//Tf4qZgxJYC0\nZJMIIQRBEEYxnUbF/ddN4u8fZnOgqIHn1xzg/mWT0GlFMCEIwsgmzq760FG1cOKc9xPHZ7ZYHVy7\n5SMAJl+dhEerR0qZzJZ8HcExoZQdrWK8RU2A+fgWCYMuHqVSi91VjleyEezv2yJx4gSM9no9jmYV\naj83zcpmFK2+9TRYnXy9r4roMGOvoYTbrsZWYQRkHrgjlovP7zuQgDOba1/X4OLZl4vJLWwjIkzH\nQ3fHkTBu8MbPOVweapvs/VrbD9Hu8PLZV3V8/HktVpsHg5+SzCURLJ4fhskofl2Es6OuwdU5GaPX\nECLlWAgxVoQQgiAI5wqNWsV9107kpY9z2Jtfx3Or9/Hz6yfjpxOfUQRBGLnEv2A9ODF4aLT6KhQy\nkkNZPjexS3gwPmcnoXWVqCbFERIfiHt8BgdqFJgi03G6XOQX5LN8dkbnFolv9rnRqgJxe1twenwN\nMjOSfX0eOvpTOFs0OJr0KDVejFE9j/6sa2rvcd3uNjW2SiPIYIpqIyGhf/0gOvR3rv3OrGb+urIU\nW5uXC2dauPsnMRj8Bico6PheHChqoK6pvfN7cfWcOGx294CFFO3tXj79qo6Pv6ih1ebF4Kdi+ZII\nrhJhhHAWdIQQ2Xk2cnJbqak/HkIYDb4QIj3VRHqKmXEihBAEQTinqVVK7lqaxmufHGLn4VqeXbWP\nX2ROxqAXDbcFQRiZxNlWD04MHsBXobBx91G8XokDRQ0A6NrbOG/753i1Ws5bHIs7IAx7SAxHKmII\n1Gj4dmcWE2L9O0+YZ6fFsfeQAxk37a4igv2Pb5FoaPFt73DbVdhrDCiUEqYxbShVPU/acLqlk25z\n29TYqnyNJU1j2tAYPfxl9T6mpoQN2Am82yPx9ppK1n9Zi1aj4O6fxDD/ouBB3a7R2/fi2wNVOF3e\nLoHRmYzIsrd7+XSTL4ywtXkxGlTccHUkV80LHTV9MYThp/b/s3fn8VHW997/X7Pv2TPZgZCQBBJW\nQQVEBUGxKi7IUore9tjT02Ptco62td6eY3v7094ud0+1tfVUW49HjxVEq1itIIqKCigSwASzs2Xf\nl5nJ7Nfvj5lMVjYlTCCf5+PBg8w1M1c+VybLdb3n+/18Wz3sOeBk52ctlJY7hoUQF84Oj4SQEEII\nIcQItBo1372uEI1azc7SRh59aR93rZmF1STBhBDi3CNXXUMMXFVjqOLK1si0iQt3bcXodmFfXog2\nxoS/aC4fHtYRl5ZOU3MLExOCrFmSB4Dbo/DiVi8KKr59rQV7/NxBAUGs1YBNb+RolR4AS7oLjX54\n8HA8XocOZ70ZVKEREjpLaOnR9h7vGbuAb2rx8NhTh6g65CIjzcBP/nkyEzNNp7WP03Wi18LtDQD9\nIQXAuqV5p7xvV2+AN7c1s3lrMw5nAKtFwzdvSOOapXYsZpmbKc6s5lZPZBRESbmD5gEhhNUSCiFC\njSmtTMiUEEIIIcTJqdUqbr9mKjqtig/3N/DIi8Xc/c1ZxJj10S5NCCFOi4QSQwxcVWP4fV7irAbU\nNTVMLdlFIDGOKYsyCWZP5bDHgpJYhM/n56NP9xMIeFGrVaxZksum7T7auhSWXKCjaLIeGPzHwu9T\naD9qQgkqmO0udGb/CWs06NSR0RLeHh3OhnAgkeFAZw4Me/zxLuBH6pkxkk/2dPDks0dx9Qa4fEEC\n312fhck4+hfuJ3othiquaGXlZTknHQnidIXCiDfe6Q8j1t0YCiNGawqKGH+aWz2RppQlZQ5a2gaH\nEBfNjuWiuUlMytAxMdOEWkIIIYQQX4FareLW5QVoNGq2760LBRNrZxF3iku6CyHEWCChxBCx1v6m\nlEMlxBiZkZNAzLP/gVpRmHrDFIIWK77saXxRm4E1ycCuzw/g7A31fHj38zraOs0caYhnYqqa5RcP\nT64DAYVHfl9DT7fCpBw1bpMfz/EX1gBgTn4yO0ua8HTrcDWaQQ22DAda0/BAYiTFFS2RqShDe2YM\nHEHh9QV59qVa3t7eikGv5ge3T2TJwpGXFh0NJ3othurocdPl8By3J4bT5edv77TwxjvNOF2hMOJb\nN6XzjSuSJYwQX4uiKDS3eiMhRGn5CCHEnFgK820U5VsjIURyso2Wlp4oVi6EEOJ8oFapWL8sD51G\nzdbPjvHwi8X8ZO0sEmKM0S5NCCFOiYQSQ/Q1pRzYx6DP7LwklnZUcrjhMPppmSTlJeGbNpfddTqs\nSRNobG6louZI5PFqlZEjDbEY9bB+uRGNZvC7oYFgkJ89UkJ1pR+dxUen2onqJIFEYoyRby3Lo7NF\nyycVDlRqhYw8HxfOSmV/ZQvtPSfZAaERE9uL6wfdHjoFor7JzWN/OMSho71MyDBy9/eyycoY3eka\nQ53otRgq3hZaxWQop8vPG1ubeeOdFly9AWxWDetXpvONJcmYJIwQX8GphhCR6RgZMhJCCCHE6FKp\nQqNzdVo1b+48wsMv7uUn35xNUuzZPXcTQoivQkKJEaxZkguEpgR09LiJt4WaUt48L5XSy36ASq9j\n5nU5+JMzaDGn4lAXovIH2Lln/4C9qLAYcgE1yy9WkRAzvIfDg384SHWlH40+gCXNOeJKG0PNzkti\nx85OPtnhxGrR8qPvZjG9IBaDToNGrTqlC3i1CoIj9NDsmwLx6edd/P65o7g9QZZemsh3vpmFwXD6\nTSTPhL7X4kB1G62dveh1msh0lIFm5yUNmrrR4/DzxjvNvLmtGVdvkBirlltXpbN8cfJZmXoizh+K\notDU4g0FEOEgorXdF7nfZtVw8QVxFIWX6MxKN0oIIYQQ4qxTqVTcdOlktBo1r390iIf/JxRMnMrK\nakIIEU0SSoxAo1azbmkeKy/LoaXDBSoVyXEmGn/1O3zNbUxYPhVDcgzeqRdwoN2OLsbMZ/tK6XG6\nIvsw6SagVZtB1cqFhVnDPsfu4g6KP/eg0ihYMhyoTuGaPzPZgiUYw1P/fYy4WB2/uCt3ULPJoWHK\n8S7gRwokANq73Pzuz4f5aHcXRoOaf/nuJC69OOHkhY2ivtfin1aaqD7chtWs57UdNcMCo75j73H4\n2bw1FEb0uoPE2LTcuiqN5YuTJIwQp0RRFBpbvJSWhUZBSAghhBDiXKFSqbj+kmy0GhWvfFDD/w0H\nE2mJlmiXJoQQxyWhxHEEgkFe+aCa4ooW2rs9TPR2ctXTL2K0x5J5SRaBnCI81nRU/sm4ex2UVdZE\nnqvTxGPUpeAPuiia3Dts30frevnN00ciq2VodMdJCYaoKvfzxcf1xMdqeeKhmVhNwWHNKvvClC6H\nZ8QL+Bm5iSNO8wh41LibrXxU2UX2BBN3fS+bjNSxMxfRqNdGkv6Bx9h33N0OP5u3NPDWuy30uoPE\nxmhZsyKNqxYnYTRIGCGOb2AIUVLuoKSsh7aO/hAixqpl/gVxFBVYKcyXEEIIIcTYd838Seg0al56\nr4qHw80vM5Ot0S5LCCFGJKHEcWx4r6p/KoSiUPjmy6gCASYvz0EVn4Bv0lQO9ExEhcKCXBWtF2Tw\nyReNeH0azPpsFCWAL1DDzoMuKmrbI40kHY4ADz1ejdsdJGWyD6/21JpT9rYZcLeZ0OgU/u2uXCZk\nmPjdxuJIaDKwWaVBpznhBfzQaR6eLh2uZjMoKpYvTuLbazPR66IzXeNU9R1jd4+fja/X8da7Lbg9\nQeJitKy5Po3llydHbcqJGNsURaGx2RMJIErLHcNDiLlxkZ4QmWkSQgghhDj3XHnhBLRaNS9srYis\nyjEhxRbtsoQQYhgJJUbg8QUormiJ3J5UU0rWsUqMU1JInGbHN20eTapMnAEDySYnqqCXK+dmce38\nSTzzupeGNhVOTw3eQGg6R18jyUBA4eDn0NTqZe31aQQsPWzb4zpeGQAoCrjbjLjbjai1QWyZDiwW\n+PMbpYOChf7PEeSWqwoG7WNgSAGhaR7lRzs52ujA1WzG261HpVYwpzqxpVnHfCAB0NXt4/Utzfz9\nvVAYER+rZd2N6Vx5WZKEEWIQRVFoaA4t0VlaPkIIYRscQmSlG1GdSoMXIYQQYoxbMicTrUbNc38v\n49G/FPOva2aRnRYT7bKEEGKQUQ0lKioquOOOO7jttttYv349DQ0N/PSnPyUQCJCcnMyjjz6KXq9n\n8+bNPPfcc6jValavXs2qVatGs6yT6nJ4aA8vQ6nx+1j44RsoajWF1+cRzMzBFZdFWU8qXd3dPL/p\nQ1QoBBWIt0wEJQVU7XgDrYP2qSjwzrZueto0XHJhPKtXpBJUUggGg3ywr4HACI0eFAV6W414Ooyo\ndQGsmQ6SEg2YDFp2lTSMWPsH++pBpWLd0imDlvccyB9Q6Gj3033URtCrQWPwY0lzodEHI80uBzaN\nHEs6u328/nYTb29vDYcROtbdFA4j9BJGiOEhREmZg/bOwSHEgrlxFBWElujMlBBCCCHEeezSmelo\nNSr+9OaXPPZSMf+yeha5GbHRLksIISJGLZRwuVw88MADzJ8/P7LtiSeeYN26dVx99dX8+te/ZtOm\nTdxwww08+eSTbNq0CZ1Ox80338yyZcuIi4sbrdJOKtZqICHGQFu3h9l7tmPr6SDl0skY0xJw585k\nX/cEgijs2F2MoigogFYdA0oKgaCbbnfNsH262w242zRkTzBy5z9MRKVSoVGpUKvVIwYSahU4mo14\nOkOBhC3TgVqnMDsviV6Pn5bO4b0qINTEcvveOjRqVWR5z4EUReFv25o49qUBFBWGODemZHdk5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Lxbz96VF8gSDrlk6RcwIhxBknoURYIKeQQFoaFZ7JoNYwJSk0ouDNT7zUtgSZN03LnHxd5PF7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\nlBARERERERGRkPgPcKlrd0NHaCEAAAAASUVORK5CYII=\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": 337
+        },
+        "outputId": "8be18626-937b-4bf3-baa1-c7cdd091fba7"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.subplot(1,1,1)\n",
+        "plt.scatter(calibration_data[\"predictions\"],calibration_data[\"targets\"])"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fab03a46208>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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nHPa2noYvMLbbeaJeyuMblO05FRSHsKjeJXn/ovoabGiaXZRDfV9967hk5/ZX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\n6NCNNwziK+kAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 720x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jByCP8hDRZmM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s0tiX2gdRe-S",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "outputId": "30f3c899-da3f-4b37-ea1a-d9a85bacaffc"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fab0585c2e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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sNg5zEVGxEvpR2mQ0Yv2quaoGIQBYtXQG7r5xadogBChbR65Yh7lYj45o6hK6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\nLLwCGE4AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kMQD0Uq3RqTX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
+        "\n",
+        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "POTM8C_ER1Oc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "5779d20f-f3e7-4d95-aa47-33e2da37a56f"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "6cbd8ed2-f0ab-4821-829e-87b4476fa47c"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x,10))\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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tWyaPx6PVq1drZGREktTS0qL77rtP5eXlevXVVyVJo6Ojqq6uVmVlpZYuXarjx4/HbyYA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\n4tXPowAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WvgxW0bUSC-c",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8YGNjXPaSMPV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9YyARz6gSR7Q",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "35e64dcd-37cb-43fd-e12c-b2669c11c97c"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFKCAYAAADScRzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQg0AgGGEGgAAw/4P1DJKJgyt6msAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vO0e1p_aSgKA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To verify that clipping worked, let's train again and print the calibration data once more:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZgSP2HKfSoOH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "6b2a9074-3fba-4777-d319-e9590bd54582"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.79\n",
+            "  period 01 : 189.02\n",
+            "  period 02 : 166.68\n",
+            "  period 03 : 146.39\n",
+            "  period 04 : 129.43\n",
+            "  period 05 : 118.61\n",
+            "  period 06 : 112.17\n",
+            "  period 07 : 109.98\n",
+            "  period 08 : 108.61\n",
+            "  period 09 : 108.23\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "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>195.8</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>51.6</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>44.4</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>162.8</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>195.8</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>223.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>436.7</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         195.8    207.3\n",
+              "std           51.6    116.0\n",
+              "min           44.4     15.0\n",
+              "25%          162.8    119.4\n",
+              "50%          195.8    180.4\n",
+              "75%          223.9    265.0\n",
+              "max          436.7    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 108.23\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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94hneP4Htu8v4dH1esMsRERFpVQolRKRFudxeDhRX4XJ7g12KiEibdUV6MmEh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Uzcd3sKG1e7yEgfH0FMEB+RqMKRY/4+fwiPNwqA0SCxfEkyFaUOVixNJmWats3odDoKCgoAqK6u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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gySE-UgfSony",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "c13bcd65-0511-44ef-f3a1-e9581f7f3bdd"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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WzU01k/jVrpOyTS7mz7bhixsX4+n/fbC4B0jDG4gAFIXGqVwFOSYm+ZKMhxY3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\nXu4yAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UKEqrnl4HaDn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/validation.ipynb b/validation.ipynb
new file mode 100644
index 0000000..f055aa5
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1591 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "validation.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/avishekdas128/Assignment-5/blob/avishekdas128/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": "7b3c5c05-035b-4242-951a-23d159afc89e"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2625.7</td>\n",
+              "      <td>536.8</td>\n",
+              "      <td>1426.7</td>\n",
+              "      <td>498.8</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>2126.6</td>\n",
+              "      <td>415.4</td>\n",
+              "      <td>1145.8</td>\n",
+              "      <td>379.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</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>1453.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>788.8</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2120.5</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1165.0</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>3131.2</td>\n",
+              "      <td>648.0</td>\n",
+              "      <td>1724.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "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       2625.7           536.8   \n",
+              "std         2.1        2.0                12.6       2126.6           415.4   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2120.5           433.0   \n",
+              "75%        37.7     -118.0                37.0       3131.2           648.0   \n",
+              "max        42.0     -114.3                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1426.7       498.8            3.9               2.0  \n",
+              "std        1145.8       379.0            1.9               1.0  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         788.8       282.0            2.6               1.5  \n",
+              "50%        1165.0       408.0            3.5               1.9  \n",
+              "75%        1724.0       605.2            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              52.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "cf5b2f88-4f99-41bf-d831-77c34243f0ff"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.describe()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>178.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>262.9</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                206.0\n",
+              "std                 115.3\n",
+              "min                  15.0\n",
+              "25%                 118.9\n",
+              "50%                 178.4\n",
+              "75%                 262.9\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "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": "2561a386-9e5a-4c03-bd28-bb865c014628"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2686.8</td>\n",
+              "      <td>545.6</td>\n",
+              "      <td>1436.4</td>\n",
+              "      <td>506.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2302.5</td>\n",
+              "      <td>435.7</td>\n",
+              "      <td>1153.0</td>\n",
+              "      <td>397.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>20.0</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>1479.0</td>\n",
+              "      <td>295.8</td>\n",
+              "      <td>792.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.6</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>2151.5</td>\n",
+              "      <td>437.0</td>\n",
+              "      <td>1171.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3191.2</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1716.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>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>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.7       2686.8           545.6   \n",
+              "std         2.1        2.0                12.5       2302.5           435.7   \n",
+              "min        32.5     -124.2                 2.0         24.0             6.0   \n",
+              "25%        33.9     -121.8                18.0       1479.0           295.8   \n",
+              "50%        34.2     -118.5                29.0       2151.5           437.0   \n",
+              "75%        37.7     -118.0                37.0       3191.2           651.0   \n",
+              "max        42.0     -114.6                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1436.4       506.9            3.9               2.0  \n",
+              "std        1153.0       397.5            1.9               1.5  \n",
+              "min          20.0         2.0            0.5               0.1  \n",
+              "25%         792.0       280.0            2.6               1.6  \n",
+              "50%        1171.0       410.0            3.6               2.0  \n",
+              "75%        1716.0       605.2            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "e5b19edb-16d6-4b93-bcf5-3cc9614acd14"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>210.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>117.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>17.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>183.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>269.4</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              5000.0\n",
+              "mean                210.5\n",
+              "std                 117.7\n",
+              "min                  17.5\n",
+              "25%                 120.9\n",
+              "50%                 183.8\n",
+              "75%                 269.4\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z3TZV1pgfZ1n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Examine the Data\n",
+        "Okay, let's look at the data above. We have `9` input features that we can use.\n",
+        "\n",
+        "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense  will get you far.\n",
+        "\n",
+        "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4Xp9NhOCYSuz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gqeRmK57YWpy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's check our data against some baseline expectations:\n",
+        "\n",
+        "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n",
+        "\n",
+        "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n",
+        "\n",
+        "If you look closely, you may see some oddities:\n",
+        "\n",
+        "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n",
+        "\n",
+        "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n",
+        "\n",
+        "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n",
+        "\n",
+        "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fXliy7FYZZRm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Plot Latitude/Longitude vs. Median House Value"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aJIWKBdfsDjg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n",
+        "\n",
+        "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5_LD23bJ06TW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "outputId": "bb7866ee-52c0-49ea-f178-49d9fba2a0c6"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(13, 8))\n",
+        "\n",
+        "ax = plt.subplot(1, 2, 1)\n",
+        "ax.set_title(\"Validation Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(validation_examples[\"longitude\"],\n",
+        "            validation_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
+        "\n",
+        "ax = plt.subplot(1,2,2)\n",
+        "ax.set_title(\"Training Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(training_examples[\"longitude\"],\n",
+        "            training_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
+        "_ = plt.plot()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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fz4IFC3p1o7q6tosv5WV2rNbgVJ0XUCmPuxq2H04wa6zvmi5/d8rK8qXsV4GU/eq43st+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\nZe4kObZdCCH6gp2wAYXWLo5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JRDpHuePRBDl5OQRyTDBS7z91NIwflkwP6no8jB1t4LrQ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\nWfd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xLIVSkoHBJIV8gxUD8dcwJibm1kfXSvQW92OZhc21Yxp0lU/h20mCRI7QSDQSxzKc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\n",
+            "text/plain": [
+              "<Figure size 936x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "32_DbjnfXJlC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n",
+        "\n",
+        "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n",
+        "\n",
+        "**Go back up and look at the data from Task 1 again.**\n",
+        "\n",
+        "Do you see any other differences in the distributions of features or targets between the training and validation data?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pECTKgw5ZvFK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "49NC4_KIZxk_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75<sup>th</sup> percentile value for total_rooms per city block?\n",
+        "\n",
+        "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n",
+        "\n",
+        "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "025Ky0Dq9ig0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3:  Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n",
+        "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JFsd2eWHAMdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n",
+        "\n",
+        "By the way, there's an important lesson here.\n",
+        "\n",
+        "**Debugging in ML is often *data debugging* rather than code debugging.**\n",
+        "\n",
+        "If the data is wrong, even the most advanced ML code can't save things."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dER2_43pWj1T",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BnEVbYJvW2wu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n",
+        "\n",
+        "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xCdqLpQyAos2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 4: Train and Evaluate a Model\n",
+        "\n",
+        "**Spend 5 minutes or so trying different hyperparameter settings.  Try to get the best validation performance you can.**\n",
+        "\n",
+        "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n",
+        "\n",
+        "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rzcIPGxxgG0t",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "CvrKoBmNgRCO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wEW5_XYtgZ-H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D0o2wnnzf8BD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n",
+        "\n",
+        "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n",
+        "\n",
+        "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n",
+        "\n",
+        "See how much better you can do now that we can use multiple features.\n",
+        "\n",
+        "Check the data using some of the methods we've looked at before.  These might include:\n",
+        "\n",
+        "   * Comparing distributions of predictions and actual target values\n",
+        "\n",
+        "   * Creating a scatter plot of predictions vs. target values\n",
+        "\n",
+        "   * Creating two scatter plots of validation data using `latitude` and `longitude`:\n",
+        "      * One plot mapping color to actual target `median_house_value`\n",
+        "      * A second plot mapping color to predicted `median_house_value` for side-by-side comparison."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UXt0_4ZTEf4V",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # 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",
+        "  \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",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zFFRmvUGh8wd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "c62df875-cdc2-4019-817b-834a99851232"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 234.65\n",
+            "  period 01 : 233.28\n",
+            "  period 02 : 231.92\n",
+            "  period 03 : 230.57\n",
+            "  period 04 : 229.23\n",
+            "  period 05 : 227.89\n",
+            "  period 06 : 226.55\n",
+            "  period 07 : 225.22\n",
+            "  period 08 : 223.90\n",
+            "  period 09 : 222.60\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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2Gs/iYGNPdOBM4oJn42LnbPH1Sy7qJdmok+SiXpKN6Xp9oL+8vDwOHz7M6tWr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\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I-La4N9ObC1x",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Xyz6n1YHbGef",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(\n",
+        "      validation_examples, validation_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "i1imhjFzbWwt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "f5c5a8d7-1d89-43f3-f9db-bf1b3e664d53"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 216.12\n",
+            "  period 01 : 198.53\n",
+            "  period 02 : 184.48\n",
+            "  period 03 : 174.59\n",
+            "  period 04 : 168.47\n",
+            "  period 05 : 165.71\n",
+            "  period 06 : 165.17\n",
+            "  period 07 : 165.88\n",
+            "  period 08 : 166.78\n",
+            "  period 09 : 167.74\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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bD8c6bIvZTVJ2so07FBERqV5sGmCmTZvG/fffz+jRo9mw4drNDOfNm0fbtm3J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\nUcMAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "65sin-E5NmHN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 5: Evaluate on Test Data\n",
+        "\n",
+        "**In the cell below, load in the test data set and evaluate your model on it.**\n",
+        "\n",
+        "We've done a lot of iteration on our validation data.  Let's make sure we haven't overfit to the pecularities of that particular sample.\n",
+        "\n",
+        "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n",
+        "\n",
+        "How does your test performance compare to the validation performance?  What does this say about the generalization performance of your model?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "29671b85-1d9c-43d1-89f5-8323b643a051"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\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",
+        "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",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 162.74\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yTghc_5HkJDW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_xSYTarykO8U",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "218b6283-aa12-4a7a-fe4c-e52767b3d137"
+      },
+      "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": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 162.74\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "joVjuWYVaS5l",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file