From 306b769cbf69c31ab81775e3d845f8eeaeedf9e8 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 01:39:55 +0530 Subject: [PATCH 01/11] Completed intor to pandas --- intro_to_pandas.ipynb | 1703 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1703 insertions(+) create mode 100644 intro_to_pandas.ipynb diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb new file mode 100644 index 0000000..503cb32 --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,1703 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": "fc1cfb9d-f22d-4425-8cb9-023098868d5e" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "daQreKXIUslr" + }, + "cell_type": "markdown", + "source": [ + "The primary data structures in *pandas* are implemented as two classes:\n", + "\n", + " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", + " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", + "\n", + "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fjnAk1xcU0yc" + }, + "cell_type": "markdown", + "source": [ + "One way to create a `Series` is to construct a `Series` object. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DFZ42Uq7UFDj", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "c834fc5d-3f4d-4fb5-d05d-f46979eace45" + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "outputId": "223c8dfe-bbe3-460a-e6d0-9417145fe80e" + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785\n", + "2 Sacramento 485199" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "outputId": "5fcd8822-01c8-4b54-bb0c-226948832a10" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean -119.562108 35.625225 28.589353 2643.664412 \n", + "std 2.005166 2.137340 12.586937 2179.947071 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.790000 33.930000 18.000000 1462.000000 \n", + "50% -118.490000 34.250000 29.000000 2127.000000 \n", + "75% -118.000000 37.720000 37.000000 3151.250000 \n", + "max -114.310000 41.950000 52.000000 37937.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean 539.410824 1429.573941 501.221941 3.883578 \n", + "std 421.499452 1147.852959 384.520841 1.908157 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 297.000000 790.000000 282.000000 2.566375 \n", + "50% 434.000000 1167.000000 409.000000 3.544600 \n", + "75% 648.250000 1721.000000 605.250000 4.767000 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 17000.000000 \n", + "mean 207300.912353 \n", + "std 115983.764387 \n", + "min 14999.000000 \n", + "25% 119400.000000 \n", + "50% 180400.000000 \n", + "75% 265000.000000 \n", + "max 500001.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WrkBjfz5kEQu" + }, + "cell_type": "markdown", + "source": [ + "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "s3ND3bgOkB5k", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 226 + }, + "outputId": "1cf986b2-2893-48c3-a532-9f515c80e795" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -114.31 34.19 15.0 5612.0 1283.0 \n", + "1 -114.47 34.40 19.0 7650.0 1901.0 \n", + "2 -114.56 33.69 17.0 720.0 174.0 \n", + "3 -114.57 33.64 14.0 1501.0 337.0 \n", + "4 -114.57 33.57 20.0 1454.0 326.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 1015.0 472.0 1.4936 66900.0 \n", + "1 1129.0 463.0 1.8200 80100.0 \n", + "2 333.0 117.0 1.6509 85700.0 \n", + "3 515.0 226.0 3.1917 73400.0 \n", + "4 624.0 262.0 1.9250 65500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 397 + }, + "outputId": "b70e3fe1-f601-45b3-8252-8f3acbafdb85" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "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": 104 + }, + "outputId": "f5b9a476-2d0f-4291-919f-cf4756bd8e10" + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "118424b2-dd51-43ca-88ba-c1e45afd42b2" + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 129 + }, + "outputId": "75e8d2f3-3130-442e-a219-3f32d0a67f6f" + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "b6da5ac1-94bb-49bd-e26c-01cb9603c03d" + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "5d71b672-c4ac-4542-a514-890145b03dff" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "ed436b44-45a4-442d-cdc2-ba245258ea7a" + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "outputId": "e91334b4-5da5-4b4d-b5de-607f012ca054" + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
\n", + "
" + ], + "text/plain": [ + " City name Population Area square miles Population density\n", + "0 San Francisco 852469 46.87 18187.945381\n", + "1 San Jose 1015785 176.53 5754.177760\n", + "2 Sacramento 485199 97.92 4955.055147" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "outputId": "d1f0b7fd-5a12-4b02-9eac-d6ef585de945" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities['wide and saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densitywide and saint name
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" + ], + "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": 14 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "a533dd5e-e5f8-4395-9c6c-bd21e7c7c97d" + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "bd841b38-e17f-4f8c-a7d2-b2fed3430d1a" + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "outputId": "94437a19-f365-4029-b296-e4d59b1aa43a" + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densitywide and saint name
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0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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" + ], + "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 \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "outputId": "5939b9f9-fbab-4a1b-a237-6e3547468793" + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densitywide and saint name
1San Jose1015785176.535754.177760True
2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
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" + ], + "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 \n", + "1 True \n", + "2 False \n", + "0 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "outputId": "1f197136-0295-4d43-857b-e09f89f1b9a2" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([0, 2, 4, 2])" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densitywide and saint name
0San Francisco852469.046.8718187.945381False
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "4 NaN NaN NaN NaN \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "\n", + " wide and saint name \n", + "0 False \n", + "2 False \n", + "4 NaN \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2l82PhPbwz7g" + }, + "cell_type": "markdown", + "source": [ + "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n", + "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n", + "in which the index values are browser names).\n", + "\n", + "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n", + "sanitizing the input." + ] + } + ] +} \ No newline at end of file From cee82817cbcd9f4ad0f0cecdadd954341ee4f968 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 01:48:30 +0530 Subject: [PATCH 02/11] Completed first steps with tensor flow --- first_steps_with_tensor_flow.ipynb | 1746 ++++++++++++++++++++++++++++ 1 file changed, 1746 insertions(+) create mode 100644 first_steps_with_tensor_flow.ipynb diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..54a95d6 --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,1746 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "first_steps_with_tensor_flow.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ajVM7rkoYXeL", + "ci1ISxxrZ7v0" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 444 + }, + "outputId": "0d0021ab-27c7-430d-ddfe-6af7e35f19ff" + }, + "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": [ + "
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9512-119.336.632.0728.0136.0461.0149.03.0109.1
12471-121.637.930.01428.0287.0989.0287.03.7154.4
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8119-118.434.217.03667.01209.02636.01054.02.4175.5
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "14657 -122.2 38.3 18.0 1953.0 265.0 \n", + "3533 -117.9 33.7 13.0 9947.0 1675.0 \n", + "1251 -117.1 32.8 35.0 1267.0 212.0 \n", + "9512 -119.3 36.6 32.0 728.0 136.0 \n", + "12471 -121.6 37.9 30.0 1428.0 287.0 \n", + "... ... ... ... ... ... \n", + "8119 -118.4 34.2 17.0 3667.0 1209.0 \n", + "6708 -118.3 34.0 22.0 3313.0 1235.0 \n", + "5760 -118.2 34.0 42.0 2309.0 685.0 \n", + "14858 -122.2 37.8 39.0 2492.0 310.0 \n", + "5180 -118.1 33.9 34.0 916.0 162.0 \n", + "\n", + " population households median_income median_house_value \n", + "14657 658.0 270.0 8.0 393.0 \n", + "3533 4071.0 1582.0 5.4 316.6 \n", + "1251 710.0 204.0 2.5 169.6 \n", + "9512 461.0 149.0 3.0 109.1 \n", + "12471 989.0 287.0 3.7 154.4 \n", + "... ... ... ... ... \n", + "8119 2636.0 1054.0 2.4 175.5 \n", + "6708 2381.0 1063.0 0.7 168.8 \n", + "5760 2609.0 673.0 2.7 162.1 \n", + "14858 808.0 315.0 11.9 500.0 \n", + "5180 552.0 164.0 4.9 222.0 \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": 320 + }, + "outputId": "ec26984e-dcc9-445b-ae68-bf39d3d87a55" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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50%-118.534.229.02127.0434.01167.0409.03.5180.4
75%-118.037.737.03151.2648.21721.0605.24.8265.0
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" + ], + "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": 52 + }, + "outputId": "fc318c82-b987-4450-a419-b88b321e8da8" + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56367.025\n", + "Root Mean Squared Error (on training data): 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "4cf39988-a1dd-4e95-a23f-18915a74db31" + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "outputId": "6c44d701-ad6f-4d8c-f27e-147258833b82" + }, + "cell_type": "code", + "source": [ + "calibration_data = pd.DataFrame()\n", + "calibration_data[\"predictions\"] = pd.Series(predictions)\n", + "calibration_data[\"targets\"] = pd.Series(targets)\n", + "calibration_data.describe()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.1 207.3\n", + "std 0.1 116.0\n", + "min 0.0 15.0\n", + "25% 0.1 119.4\n", + "50% 0.1 180.4\n", + "75% 0.2 265.0\n", + "max 1.9 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "E2-bf8Hq36y8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n", + "\n", + "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n", + "\n", + "First, we'll get a uniform random sample of the data so we can make a readable scatter plot." + ] + }, + { + "metadata": { + "id": "SGRIi3mAU81H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "sample = california_housing_dataframe.sample(n=300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "N-JwuJBKU81J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red." + ] + }, + { + "metadata": { + "id": "7G12E76-339G", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "b7effcc5-59d9-4f08-d9cc-6dea572fce00" + }, + "cell_type": "code", + "source": [ + "# Get the min and max total_rooms values.\n", + "x_0 = sample[\"total_rooms\"].min()\n", + "x_1 = sample[\"total_rooms\"].max()\n", + "\n", + "# Retrieve the final weight and bias generated during training.\n", + "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n", + "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + "# Get the predicted median_house_values for the min and max total_rooms values.\n", + "y_0 = weight * x_0 + bias \n", + "y_1 = weight * x_1 + bias\n", + "\n", + "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n", + "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n", + "\n", + "# Label the graph axes.\n", + "plt.ylabel(\"median_house_value\")\n", + "plt.xlabel(\"total_rooms\")\n", + "\n", + "# Plot a scatter plot from our data sample.\n", + "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n", + "\n", + "# Display graph.\n", + "plt.show()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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tUNnO5/dj/eZGPPzSLvzot7vw8Eu7sH5zI3z+8JXx5G7KUpJxs47UglY721vu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\n+ceivdsdPL9UF3ppSA+CVKUitJtdroz9ewLksJV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" + ] + }, + "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": 983 + }, + "outputId": "2f9005f3-c15a-4ce9-970e-9549cbfb8775" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5\n", + ")" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 209.12\n", + " period 01 : 187.07\n", + " period 02 : 173.57\n", + " period 03 : 167.79\n", + " period 04 : 166.39\n", + " period 05 : 167.02\n", + " period 06 : 168.03\n", + " period 07 : 169.15\n", + " period 08 : 170.92\n", + " period 09 : 172.17\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 165.2 207.3\n", + "std 136.2 116.0\n", + "min 0.1 15.0\n", + "25% 91.4 119.4\n", + "50% 132.9 180.4\n", + "75% 197.0 265.0\n", + "max 2371.1 500.0" + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 172.17\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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WV/LQG79xflIUk67pfsbbF95pKJ+R5kz2ge/JPvA92Qee1XT8cNZCibOpuYQS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rv2V6DVKLjrthimTIxskvAjaJ/Isszeg2ZWrjVw8KhnfGdsjJ5pk6IYOTQEjaiE8Zr09HTmzJnD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\n0cpRdeAoJ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9yCpmOzu9m03ciqdQaKSxwoFHBl/yCmTY5i1IgulJZa2jpEQTMwdepUpk6d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dZW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\n90NRFCr+5VGchypJmJpJ2QP/xUu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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ajVM7rkoYXeL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "T3zmldDwYy5c", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=500,\n", + " batch_size=5\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "M8H0_D4vYa49", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality." + ] + }, + { + "metadata": { + "id": "QU5sLyYTqzqL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Is There a Standard Heuristic for Model Tuning?\n", + "\n", + "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n", + "\n", + "That said, here are a few rules of thumb that may help guide you:\n", + "\n", + " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n", + " * If the training has not converged, try running it for longer.\n", + " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n", + " * But sometimes the exact opposite may happen if the learning rate is too high.\n", + " * If the training error varies wildly, try decreasing the learning rate.\n", + " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n", + " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n", + "\n", + "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify." + ] + }, + { + "metadata": { + "id": "GpV-uF_cBCBU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Feature\n", + "\n", + "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n", + "\n", + "Don't take more than 5 minutes on this portion." + ] + }, + { + "metadata": { + "id": "YMyOxzb0ZlAH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 983 + }, + "outputId": "bd73a609-6356-4fb6-9cd8-0cedcf453d2a" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"population\"\n", + ")" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 222.79\n", + " period 01 : 209.51\n", + " period 02 : 198.42\n", + " period 03 : 190.28\n", + " period 04 : 183.81\n", + " period 05 : 179.72\n", + " period 06 : 176.98\n", + " period 07 : 175.98\n", + " period 08 : 175.97\n", + " period 09 : 176.15\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 131.5 207.3\n", + "std 105.6 116.0\n", + "min 0.3 15.0\n", + "25% 72.7 119.4\n", + "50% 107.4 180.4\n", + "75% 158.3 265.0\n", + "max 3282.7 500.0" + ], + "text/html": [ + "
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8bWH3IYUgfw2Th5lp37LpVf2ImumZFEnrqEC2/JLF8N7xtIoMvPRBQgghxGXG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\ny+aS+NQj14+pKovQ7ViFylGLc+S9yKn9GjjTLWo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8/PrH4eHhFBfXH+QDhIUFotW2ToAX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GNBoNa9eu5YknnuAHP/gBr7zyCpmZmdx2221otVq+//3v88gjjyBJEn/zN38Tb3p5pRkuU2Fu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\nna/70nt6ul5L7+npOiunpl9O38jOzmbmzJkADB8+nNr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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ci1ISxxrZ7v0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "SjdQQCduZ7BV", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=1000,\n", + " batch_size=5,\n", + " input_feature=\"population\"\n", + ")" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From d8e4e04b1177209bf0ac3a2c197cc8dedc855c67 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 01:57:00 +0530 Subject: [PATCH 03/11] Completed synthetic features and outliers --- synthetic_features_and_outliers.ipynb | 1104 +++++++++++++++++++++++++ 1 file changed, 1104 insertions(+) create mode 100644 synthetic_features_and_outliers.ipynb diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb new file mode 100644 index 0000000..267620a --- /dev/null +++ b/synthetic_features_and_outliers.ipynb @@ -0,0 +1,1104 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "synthetic_features_and_outliers.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "i5Ul3zf5QYvW", + "jByCP8hDRZmM", + "WvgxW0bUSC-c" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 444 + }, + "outputId": "b14449ea-ecc1-4ae3-f6cf-cfa2bf3f72c9" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn.metrics as metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
10845-120.837.520.01417.0263.0853.0263.03.3108.3
7377-118.334.152.01801.0313.0714.0293.04.7479.0
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13374-121.937.137.01150.0203.0511.0179.05.7398.5
4249-118.033.829.02641.0637.02413.0619.02.8165.1
..............................
770-117.132.711.02397.0523.01566.0514.03.9145.2
9179-119.036.115.02288.0401.01238.0429.03.077.4
15803-122.437.748.0409.086.0148.070.03.7335.0
16005-122.437.852.03225.0667.01494.0619.04.5500.0
4593-118.134.028.01516.0363.01011.0344.02.6160.3
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17000 rows × 9 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "10845 -120.8 37.5 20.0 1417.0 263.0 \n", + "7377 -118.3 34.1 52.0 1801.0 313.0 \n", + "15857 -122.4 37.8 52.0 1925.0 568.0 \n", + "13374 -121.9 37.1 37.0 1150.0 203.0 \n", + "4249 -118.0 33.8 29.0 2641.0 637.0 \n", + "... ... ... ... ... ... \n", + "770 -117.1 32.7 11.0 2397.0 523.0 \n", + "9179 -119.0 36.1 15.0 2288.0 401.0 \n", + "15803 -122.4 37.7 48.0 409.0 86.0 \n", + "16005 -122.4 37.8 52.0 3225.0 667.0 \n", + "4593 -118.1 34.0 28.0 1516.0 363.0 \n", + "\n", + " population households median_income median_house_value \n", + "10845 853.0 263.0 3.3 108.3 \n", + "7377 714.0 293.0 4.7 479.0 \n", + "15857 867.0 515.0 2.9 450.0 \n", + "13374 511.0 179.0 5.7 398.5 \n", + "4249 2413.0 619.0 2.8 165.1 \n", + "... ... ... ... ... \n", + "770 1566.0 514.0 3.9 145.2 \n", + "9179 1238.0 429.0 3.0 77.4 \n", + "15803 148.0 70.0 3.7 335.0 \n", + "16005 1494.0 619.0 4.5 500.0 \n", + "4593 1011.0 344.0 2.6 160.3 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "metadata": { + "id": "I6kNgrwCO_ms", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll set up our input function, and define the function for model training:" + ] + }, + { + "metadata": { + "id": "5RpTJER9XDub", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VgQPftrpHbG3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \n", + " Returns:\n", + " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", + " after training the model.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label].astype('float32')\n", + "\n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + " \n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Create a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", + " \n", + " return calibration_data" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FJ6xUNVRm-do", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Try a Synthetic Feature\n", + "\n", + "Both the `total_rooms` and `population` features count totals for a given city block.\n", + "\n", + "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n", + "\n", + "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n", + "\n", + "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n", + "the final RMSE should be.)" + ] + }, + { + "metadata": { + "id": "isONN2XK32Wo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**." + ] + }, + { + "metadata": { + "id": "5ihcVutnnu1D", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 983 + }, + "outputId": "c9de27a6-04bf-494a-c101-ce025be701f8" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE\n", + "#\n", + "california_housing_dataframe[\"rooms_per_person\"] = california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"]\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 237.51\n", + " period 01 : 237.49\n", + " period 02 : 237.46\n", + " period 03 : 237.44\n", + " period 04 : 237.41\n", + " period 05 : 237.39\n", + " period 06 : 237.36\n", + " period 07 : 237.34\n", + " period 08 : 237.31\n", + " period 09 : 237.29\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.3 207.3\n", + "std 0.1 116.0\n", + "min 0.1 15.0\n", + "25% 0.2 119.4\n", + "50% 0.3 180.4\n", + "75% 0.3 265.0\n", + "max 6.2 500.0" + ], + "text/html": [ + "
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njBkzRrt/eXk5jh8/jnnz5uk83+rVqyGXy9GlSxcsWLAA+fn5cHFx0W53cXFB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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i5Ul3zf5QYvW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Leaz2oYMQcBf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZjQrZ8mcHFiU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Identify Outliers\n", + "\n", + "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values. Ideally, these would lie on a perfectly correlated diagonal line.\n", + "\n", + "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n", + "\n", + "Do you see any oddities? Trace these back to the source data by looking at the distribution of values in `rooms_per_person`." + ] + }, + { + "metadata": { + "id": "P0BDOec4HbG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 394 + }, + "outputId": "69abd29e-aa37-4fbb-8ec2-848dd6c387d8" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])\n", + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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0Am6bYcXRv11V5P6xljQKWi3qa8qweuks9PSLn/d7Y0aNiIiIiIgmJieCNQAw6JULkBJZ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\nS9NdVlJ6oYVShG5JEL39SeLUzY8mdhzH94/MoUl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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jByCP8hDRZmM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "s0tiX2gdRe-S", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kMQD0Uq3RqTX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n", + "\n", + "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:" + ] + }, + { + "metadata": { + "id": "POTM8C_ER1Oc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9l0KYpBQu8ed", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Clip Outliers\n", + "\n", + "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n", + "\n", + "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n", + "\n", + " clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n", + "\n", + "The above `clipped_feature` will have no values less than `0`." + ] + }, + { + "metadata": { + "id": "rGxjRoYlHbHC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1313 + }, + "outputId": "54a6d238-79b5-4860-fc55-ddbc05f7c4c7" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()\n", + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")\n", + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.80\n", + " period 01 : 189.06\n", + " period 02 : 166.70\n", + " period 03 : 146.56\n", + " period 04 : 130.41\n", + " period 05 : 122.00\n", + " period 06 : 114.71\n", + " period 07 : 110.04\n", + " period 08 : 109.00\n", + " period 09 : 108.66\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 191.7 207.3\n", + "std 50.4 116.0\n", + "min 43.9 15.0\n", + "25% 159.5 119.4\n", + "50% 191.7 180.4\n", + "75% 219.1 265.0\n", + "max 426.8 500.0" + ], + "text/html": [ + "
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7IgEvpeSCYMnuixCSOpNBK/pZTMfncOLosrjBHeWPjf+OkNtHugPrmThWXz9/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dUXerLKDQ7+/bb03y6bMdPH2us6aL7nhC5cZ0jPFMFsTYZHSLDb+jzZYrwRjqc9LT5cBSKkCk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\nd9zdnZe+oKAAPz+/8sd+fn6cPn36cYVmmspycovVesXNzQ0vLy8A1qxZQ69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+ "text/plain": [ + "
" + ] + }, + "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": {} + }, + "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": 0, + "outputs": [] + }, + { + "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": {} + }, + "cell_type": "code", + "source": [ + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From b2840de0fb2219a65a9dbaf7a50866085da99c08 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 02:01:05 +0530 Subject: [PATCH 04/11] Completed validation --- validation.ipynb | 1497 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1497 insertions(+) create mode 100644 validation.ipynb diff --git a/validation.ipynb b/validation.ipynb new file mode 100644 index 0000000..268fdfa --- /dev/null +++ b/validation.ipynb @@ -0,0 +1,1497 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "validation.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "4Xp9NhOCYSuz", + "pECTKgw5ZvFK", + "dER2_43pWj1T", + "I-La4N9ObC1x", + "yTghc_5HkJDW" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 320 + }, + "outputId": "79ccab0d-91bc-4785-8e3e-0a4c8bb64f7d" + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 34.6 -118.5 27.5 2655.7 547.1 \n", + "std 1.6 1.2 12.1 2258.1 434.3 \n", + "min 32.5 -121.4 1.0 2.0 2.0 \n", + "25% 33.8 -118.9 17.0 1451.8 299.0 \n", + "50% 34.0 -118.2 28.0 2113.5 438.0 \n", + "75% 34.4 -117.8 36.0 3146.0 653.0 \n", + "max 41.8 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1476.0 505.4 3.8 1.9 \n", + "std 1174.3 391.7 1.9 1.3 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 815.0 283.0 2.5 1.4 \n", + "50% 1207.0 411.0 3.5 1.9 \n", + "75% 1777.0 606.0 4.6 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "JlkgPR-SHpCh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "outputId": "fb4dd62a-30a5-41b2-e522-a1a057ebd2e4" + }, + "cell_type": "code", + "source": [ + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "training_targets.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 12000.0\n", + "mean 198.0\n", + "std 111.9\n", + "min 15.0\n", + "25% 117.1\n", + "50% 170.5\n", + "75% 244.4\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "5l1aA2xOHpCj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "fLYXLWAiHpCk", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "outputId": "3bf36728-05a8-408b-e29f-7a1901f5ef0a" + }, + "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": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 5000.0 5000.0 5000.0 5000.0 5000.0 \n", + "mean 38.1 -122.2 31.3 2614.8 521.1 \n", + "std 0.9 0.5 13.4 1979.6 388.5 \n", + "min 36.1 -124.3 1.0 8.0 1.0 \n", + "25% 37.5 -122.4 20.0 1481.0 292.0 \n", + "50% 37.8 -122.1 31.0 2164.0 424.0 \n", + "75% 38.4 -121.9 42.0 3161.2 635.0 \n", + "max 42.0 -121.4 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 5000.0 5000.0 5000.0 5000.0 \n", + "mean 1318.1 491.2 4.1 2.1 \n", + "std 1073.7 366.5 2.0 0.6 \n", + "min 8.0 1.0 0.5 0.1 \n", + "25% 731.0 278.0 2.7 1.7 \n", + "50% 1074.0 403.0 3.7 2.1 \n", + "75% 1590.2 603.0 5.1 2.4 \n", + "max 28566.0 6082.0 15.0 18.3 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "oVPcIT3BHpCm", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "outputId": "1086c4cc-1cbd-417c-8b95-e0fe584d2eee" + }, + "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": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 229.5\n", + "std 122.5\n", + "min 15.0\n", + "25% 130.4\n", + "50% 213.0\n", + "75% 303.2\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "z3TZV1pgfZ1n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Examine the Data\n", + "Okay, let's look at the data above. We have `9` input features that we can use.\n", + "\n", + "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense will get you far.\n", + "\n", + "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data." + ] + }, + { + "metadata": { + "id": "4Xp9NhOCYSuz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "gqeRmK57YWpy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's check our data against some baseline expectations:\n", + "\n", + "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n", + "\n", + "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n", + "\n", + "If you look closely, you may see some oddities:\n", + "\n", + "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n", + "\n", + "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n", + "\n", + "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n", + "\n", + "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source." + ] + }, + { + "metadata": { + "id": "fXliy7FYZZRm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Plot Latitude/Longitude vs. Median House Value" + ] + }, + { + "metadata": { + "id": "aJIWKBdfsDjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n", + "\n", + "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`." + ] + }, + { + "metadata": { + "id": "5_LD23bJ06TW", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 498 + }, + "outputId": "86d10d9d-9406-4028-ca1e-71c14931646b" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(13, 8))\n", + "\n", + "ax = plt.subplot(1, 2, 1)\n", + "ax.set_title(\"Validation Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(validation_examples[\"longitude\"],\n", + " validation_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n", + "\n", + "ax = plt.subplot(1,2,2)\n", + "ax.set_title(\"Training Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(training_examples[\"longitude\"],\n", + " training_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n", + "_ = plt.plot()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M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\n84fNE9buX9z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+ "text/plain": [ + "
" + ] + }, + "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 75th 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", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \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", + " 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", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\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", + " 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", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\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", + " 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": 627 + }, + "outputId": "7cf53b0f-8ab2-4cb1-d906-45a8b598cf9d" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " learning_rate=0.000025,\n", + " steps=550,\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": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 208.90\n", + " period 01 : 193.66\n", + " period 02 : 181.82\n", + " period 03 : 171.22\n", + " period 04 : 166.26\n", + " period 05 : 163.43\n", + " period 06 : 161.76\n", + " period 07 : 161.02\n", + " period 08 : 160.88\n", + " period 09 : 160.93\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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33kKj0WBjY8Ps2bOxsbFh1qxZTJgwAZVKxdSpU3F0lOOC96JSqXii8RCyCrI4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\nSZ4+SIVbrovFLdfFoq7JiqPna3A4rxonC+twsrAOapUcsya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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "I-La4N9ObC1x", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Xyz6n1YHbGef", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i1imhjFzbWwt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " learning_rate=0.00003,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "65sin-E5NmHN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 5: Evaluate on Test Data\n", + "\n", + "**In the cell below, load in the test data set and evaluate your model on it.**\n", + "\n", + "We've done a lot of iteration on our validation data. Let's make sure we haven't overfit to the pecularities of that particular sample.\n", + "\n", + "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n", + "\n", + "How does your test performance compare to the validation performance? What does this say about the generalization performance of your model?" + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "a851002b-2ac2-4533-9aef-bca52f5002ad" + }, + "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", + "rms_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "print(\"Final RMSE (on test data): %0.2f\" % rms_error)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 161.09\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "yTghc_5HkJDW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "_xSYTarykO8U", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(\n", + " test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 8b807d9fe30447172fe27f25d81ca8570696711d Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 02:12:56 +0530 Subject: [PATCH 05/11] Completed feature sets --- feature_sets.ipynb | 1536 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1536 insertions(+) create mode 100644 feature_sets.ipynb diff --git a/feature_sets.ipynb b/feature_sets.ipynb new file mode 100644 index 0000000..2408b60 --- /dev/null +++ b/feature_sets.ipynb @@ -0,0 +1,1536 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_sets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "IGINhMIJ5Wyt", + "pZa8miwu6_tQ" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 1240 + }, + "outputId": "57f28ee0-840a-47fe-9174-8322d3ff368d" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2651.0 540.6 \n", + "std 2.1 2.0 12.5 2224.7 428.9 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1460.0 296.0 \n", + "50% 34.2 -118.5 29.0 2129.5 434.0 \n", + "75% 37.7 -118.0 37.0 3157.2 647.2 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1435.2 502.1 3.9 2.0 \n", + "std 1187.9 391.3 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 792.0 281.0 2.6 1.5 \n", + "50% 1168.0 409.0 3.5 1.9 \n", + "75% 1715.2 605.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "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": 383 + }, + "outputId": "5c3a0c17-1623-4adf-fdc8-e794bd218e4f" + }, + "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": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms \\\n", + "latitude 1.0 -0.9 0.0 -0.0 \n", + "longitude -0.9 1.0 -0.1 0.1 \n", + "housing_median_age 0.0 -0.1 1.0 -0.4 \n", + "total_rooms -0.0 0.1 -0.4 1.0 \n", + "total_bedrooms -0.1 0.1 -0.3 0.9 \n", + "population -0.1 0.1 -0.3 0.9 \n", + "households -0.1 0.1 -0.3 0.9 \n", + "median_income -0.1 -0.0 -0.1 0.2 \n", + "rooms_per_person 0.1 -0.1 -0.1 0.1 \n", + "target -0.1 -0.0 0.1 0.1 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "latitude -0.1 -0.1 -0.1 -0.1 \n", + "longitude 0.1 0.1 0.1 -0.0 \n", + "housing_median_age -0.3 -0.3 -0.3 -0.1 \n", + "total_rooms 0.9 0.9 0.9 0.2 \n", + "total_bedrooms 1.0 0.9 1.0 -0.0 \n", + "population 0.9 1.0 0.9 -0.0 \n", + "households 1.0 0.9 1.0 0.0 \n", + "median_income -0.0 -0.0 0.0 1.0 \n", + "rooms_per_person 0.0 -0.1 -0.0 0.2 \n", + "target 0.0 -0.0 0.1 0.7 \n", + "\n", + " rooms_per_person target \n", + "latitude 0.1 -0.1 \n", + "longitude -0.1 -0.0 \n", + "housing_median_age -0.1 0.1 \n", + "total_rooms 0.1 0.1 \n", + "total_bedrooms 0.0 0.0 \n", + "population -0.1 -0.0 \n", + "households -0.0 0.1 \n", + "median_income 0.2 0.7 \n", + "rooms_per_person 1.0 0.2 \n", + "target 0.2 1.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "RQpktkNpia2P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n", + "\n", + "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n", + "\n", + "Use this information to try removing features. You can also try developing additional synthetic features, such as ratios of two raw features.\n", + "\n", + "For convenience, we've included the training code from the previous exercise." + ] + }, + { + "metadata": { + "id": "bjR5jWpFr2xs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jsvKHzRciH9T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g3kjQV9WH3pb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "varLu7RNH3pf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters." + ] + }, + { + "metadata": { + "id": "DSgUxRIlH3pg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 644 + }, + "outputId": "1a421e39-55f1-415b-c16c-e798665cbbea" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "assert minimal_features, \"You must select at least one feature!\"\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "#\n", + "# Don't forget to adjust these parameters.\n", + "#\n", + "train_model(\n", + " learning_rate=0.001,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 229.53\n", + " period 01 : 221.87\n", + " period 02 : 214.32\n", + " period 03 : 206.87\n", + " period 04 : 199.56\n", + " period 05 : 192.38\n", + " period 06 : 185.36\n", + " period 07 : 178.51\n", + " period 08 : 171.86\n", + " period 09 : 165.43\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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iUhj50BBebvMsjla12HZ+F7PC53Ex57K5yxVCCCHuG2lgVK6WnSWTh/vy9KDm\naDUaVmyL5bM1kbjq6vJG+yl09mrPpdwEZoXPY9u5XRhLjbdfqBBCCFHNSQNTDWg0Gh5u5cnMp9rT\nqqEzx86mM2NJGBHRGTzWbAQv+D2JvYUtm85s49OIBSTmJZu7ZCGEEKJKyU685ah5xyobKz0dW9bG\nyWBF1Nl0DsYkE5+cS9dmTejRoCOZV7I4kR7L/oQwLLUWNHCoh0ajMXfZlUbN2dRkkot6STbqJdmY\npqKdePX3sQ5RCTQaDd3969DS25mlW6I5fCqVUxezGNe3KRNbPoa/Wyu+j13PutM/E5l6nPHNg3GV\ns/gKIYR4wMgEppzq0hXbWlvQqZUH9jYWRJ1JIzQ6mcupefRo3oxu9TuQWpDGifST7Es4iI3emvqG\nOtV+GlNdsqlpJBf1kmzUS7IxjRxG/YDSajQ80q4e7zzZniZ1anEwJpkZS8I4fb6Ap1uNZ2KLx9Br\ndKw5uZF5h78mrSDd3CULIYQQlUImMOVUx67Y3saCzr6eWFvqORqXxoHjSaRmFdK7RXO61A0kuSCV\n6PST7EsIw9bChvqGutVyGlMds6kJJBf1kmzUS7IxjUxgagCtVkNQh/r8Z1Ig3h4G9h1LZMaSMC5c\nKub/fCcwocUYtBodP8Ru4Isji0kryDB3yUIIIcRdkwlMOdW9K3awtaRLa0/0Wg1H49LYdyyR9Jwr\n9Gregs5125Gcn/rXkUoHsbOwpV412jemumfzoJJc1EuyUS/JxjQygalhdFotgzs35K2JgdR3t2fv\n0QRmLAnl4qUSnms9kfHNg9FoNHwfu54vjiwmvVCmMUIIIaoXmcCU8yB1xbXsrk5jdFoNUWeuTmMy\ncq7Qu0VLOtdtR2J+MtHpJ9l/+SAGS3vq2nupehrzIGXzIJFc1EuyUS/JxjQyganB9DotQ7o0ZMaE\ndtR3t2fPNdOYF1o/yeM+owAN38WEsCByKRmFmeYuWQghhLgtmcCU86B2xbXsrejS2hPtNdOYzNwr\nPNKyJQ/XCSAhL+nqNCbhIAYLdU5jHtRsqjvJRb0kG/WSbExT0QRGoyiKUlUrnjVrFocOHaKkpIT/\n+7//w9fXl9dff52SkhL0ej0ff/wxbm5u/PTTTyxfvhytVktwcDCjRo2qcLkpKTlVVTJuboYqXb4a\nXEjKYcnmaOKTc3F2sGJifx9aejuzLyGM9ad+ptB4hZYuPoz1GYGjVS1zl1umJmRTHUku6iXZqJdk\nYxo3N8MtH6uyBubAgQMsWbKEr7/+moyMDIYNG0aHDh3o3r07AwYM4LvvvuPSpUtMnjyZYcOGERIS\ngoWFBSNHjuTbb7/F0dHxlsvpUnBcAAAgAElEQVSWBubelRhL+XnfOTbvP4+xVKGbnxejezWhQMnh\nu+gQYjJOYaO3YdRDQ2jv0VYV05iakk11I7mol2SjXpKNaSpqYKrsKyRPT0/69OmDhYUFlpaWLFq0\niG+++YZmzZqh1Wq5ePEiJ0+epFatWqSlpTF48GD0ej0xMTFYWVnRsGHDWy5bvkK6d1qtBp8GTvg3\nceX0pWyizqRx4EQiTTxcGejTGQcrB6LTY4lIPkp87iUecmyMtf7Wo7z7oaZkU91ILuol2aiXZGMa\ns+zEq9PpsLW1BSAkJIRu3bpha2uLTqfDaDSyatUqBg8eTGpqKs7OzmWvc3Z2JiUlparKEuXUr23g\nrYntGNLZm6zcIj5dfYQV22Np59qON9tPpalTE6JSo3kv9FPCEiOowm8chRBCCJNV+dWod+7cSUhI\nCEuXLgXAaDTy6quv0rFjRzp16sSmTZuue74p/0A6Odmi1+uqpF6oeGT1oHpmuB+92jfg8x8O8/uR\ny5w4n8GLo/x5t88UdsbtYWXkBpaf+IHjWdE8G/AYjjbm2TemJmZTHUgu6iXZqJdkc2+qtIHZs2cP\nX375JYsXL8ZguBrU66+/ToMGDZg8eTIA7u7upKamlr0mOTkZf3//CpebkZFfZTXX5O8lHax0vDGu\nLZv+vLpvzFtf7ae7vxfBPVvzRqA330avIfxSJNFJpwhuOpSA2v73dd+YmpyNmkku6iXZqJdkY5qK\nmrwq+wopJyeHWbNmsWjRorIdcn/66ScsLCx46aWXyp7n5+dHVFQU2dnZ5OXlERERQbt27aqqLHEb\nep2WYd0aMWNCO+q62fH7kcu8tSSUpCR4qc2zjGo6lOLSYr458T2Lj60kpyjX3CULIYSogarsKKTV\nq1czb96863bGvXz5Mg4ODtjb2wPQuHFj3n77bbZt28aSJUvQaDSMGzeOIUOGVLhsOQrp/igxlpZN\nY0oVhR7+Xozq2YRcYxYro9cQl3UWOwtbRjcdRkBtvyqvR7JRJ8lFvSQb9ZJsTGOWw6irkjQw99f5\nxByWbD7BxZQ8XBysmTTAB58Gjvx+cR8/xm2luLSYNm6+jG42DIOlfZXVIdmok+SiXpKNekk2pjHL\nYdRVSQ6jvr8c7a3o6ueFAkTFpfHnsUSy84vp17I1Hbz8ic+5THT6SQ4khONi44ynXe0qqUOyUSfJ\nRb0kG/WSbEwj10IS90yv0zK8WyOmTwigjpsduw9f4q0lYaQl65jS9jlGNBnEFeMVlhz7lqXHviO3\nKM/cJQshhHiAyQSmHOmKK+Zob0XX1l6AQlRcOn8eSyQnv5ggXz8CPfy4kHOJE+mxhCYcwtXWBQ87\n90pbt2SjTpKLekk26iXZmEYmMKJSWei1DO/WmDefCKCOqx2//TWNSU/RMzXgeYY1GUiBsZCvo1bw\nzfFV5BbLNEYIIUTlkglMOdIVm87JcHUaoyjXTGMKiunf6u9pzMWr05jEQ7jZuN7zNEayUSfJRb0k\nG/WSbEwjExhRZSz0WkZ0v2YaE3F1GpORasHUts/zaOMBFBQX8FXUcpYd/4G84qo7CaEQQoiaQyYw\n5UhXfHeuncYcjUvjz6hEcgtK6O/rTztPP85nX53GhCUewt3Wjdq2bne8DslGnSQX9ZJs1EuyMY1M\nYMR98fc0ZvoT7fC6ZhqTlWrJtIAXGNIoiLzifL48uowVJ1aTX1xg7pKFEEJUUzKBKUe64nt3dRrj\nSWkpHD1zdRqTV1DCAN82BHi05nz2hb+mMRHUtnXD3cRpjGSjTpKLekk26iXZmEYmMOK+s9DrGNmj\nMW+Ob4eniy27/prG5KRb8UrAZAY36kducR4Lj37Dyug1FJTINEYIIYTpZAJTjnTFlcvJYEU3P0+M\npQpRZ9LYG5VIfoGRgb5taevRinNZf09jDuNpVxs3W9dbLkuyUSfJRb0kG/WSbEwjExhhVhZ6HaN6\nNOGN8QF4utjya8RF3loaSm66Nf9q9yIDG/YhuyiH+ZFL+C56rUxjhBBC3JZMYMqRrrjqOBusy6Yx\nR+P+msYUGhnkG4B/7VaczT7PifRYDiYewdO+Nm42Lte9XrJRJ8lFvSQb9ZJsTCMTGKEaN0xjDl2d\nxuRn2PBquxcZ4P0IWUXZfHFkMati1lFYUmjukoUQQqiQTGDKka74/iibxhgVjv61b0xBoZFBrQPw\nq92Cs1lXpzHhSUfwsvPA1cZFslEpyUW9JBv1kmxMU9EERhqYcuRDdf/otFpaNnSmVUNnTl/K4mhc\nGgejk/GtX4fBPl0BOJ4WQ2jiIXKKcvHzbEZRYamZqxblyTajXpKNekk2pqmogdEoiqLcx1oqRUpK\nTpUt283NUKXLFzdXVGxk496zbA+7AAr0DqjLiO6NSSy8zMroNSTkJeFm58KYh4bj4/yQucsV15Bt\nRr0kG/WSbEzj5ma45WPSwJQjHyrziruUxZLN0SSm5+PuaMOTA5vTsI49W8/uZMeF3ZQqpXT26sCw\nJgOx0Vubu1yBbDNqJtmol2RjGmlg7oB8qMyvqNjIxj1/TWOA3u2uTmOKrLOYu28Zl/MScbSqxVif\nkbR0aWbmaoVsM+ol2aiXZGMaaWDugHyo1OP0X9OYpPR83J1smDo2AGc7LdvP7WLb+V2UKqV09GzH\niCaDsbWwMXe5NZZsM+ol2aiXZGMaaWDugHyo1KWo2MiGPWf4JSweNNCrbV1GdG9EypVkvoteQ3zu\nZWpZOvCYz3B8XVuYu9waSbYZ9ZJs1EuyMY00MHdAPlTqdPpSFiu2x3IxORfXWtZM6u9D0/q12HFh\nN1vO7sSoGAms3ZZRTYdgZ2Fr7nJrFNlm1EuyUS/JxjTSwNwB+VCpVy1HW5ZsjGJb6AVKFYXu/l4E\n92xCRnEq30av5XxOPAZLe8Y0G46/Wytzl1tjyDajXpKNekk2pqmogZHzwJQjx+arl4PBGm93O3wb\nu3DmchZRZ9LZfzyRZl61GdqiK1Y6q79OfneYxLwkHnJshJXO0txlP/Bkm1EvyUa9JBvTyIns7oB8\nqNTr72ycDFZ09fNCo4FjZ9LZdyyR9Owr9Pf1p72nP/E5F4lOP8mBhHCcrZ3wtKuNRqMxd/kPLNlm\n1EuyUS/JxjRyLSTxwNHrtDzatREzJrSjQW0Df0YlMv3rUBIuaZga8AIjmgziirGIpce/Y/GxlWQX\nyahWCCEeJDKBKUe6YvW6WTa17K3o6ueJpV5L1Jk0DpxIIjE9nyBfPzrVacPFnISr05jL4dSycsDL\nzkOmMZVMthn1kmzUS7IxjUxgxANNp9UysJM3b09qT2MvB8Kik5m+OJRz50t5uc2zjGo6lGKlhOUn\nfmBR1DIyr2SZu2QhhBD3SCYw5UhXrF63y8Zga0kXX09srPREnUknLDqZyyn59GvpS+d67bice3Ua\nsz/hIAYLe+rae8k0phLINqNeko16STamkQmMqDG0Wg392tfn3Sfb07SeI4dOpjB9cSin4oqY7P80\nY5oNo1Qp5duYtSyIXEpGYaa5SxZCCHEX5Dww5cix+ep1p9mUKgq/RVwiZHccV4qNtG7swhP9moFl\nAati1hGdfhJrnRXDmwziYa/2Mo25S7LNqJdko16SjWkqOg+MTGDEA0ur0dA7oC4zn2pPC28njsal\nMWNJKMdiC3ih9ZM87jMKjUbDqth1fHFkMWkF6eYuWQghhImkgREPPFdHG6aN9mdifx8Alm2NYfaa\nSJratmJ6h2m0cvEhJuMU74XN5veL+yhVSs1csRBCiNuRBkbUCBqNhm5+Xsx8qgOtG7tw4lwGM5aG\nEXE8h2d9J/JE89HoNDrWnNzI3MNfkZKfZu6ShRBCVEAaGFGjODtY8/LI1jw9qDl6rYZvfznJJ98f\nwdu6OTM6TKO1a0tOZZ7hv2Gz2RW/R6YxQgihUtLAiBpHo9HwcCtPZj7dgTYPuRIbn8l/loQRGpnJ\n0y3HM6nlWCx1Fqw7tYnPIhaSlJds7pKFEEKUU6UNzKxZsxg9ejQjRozgl19+AWDFihW0bNmSvLy8\nsuf99NNPjBgxglGjRrF27dqqLEmIMo72Vkwe7stzQ1tiaaHjh12n+XBVBHX0DzGjwyu0cW/Nmazz\nfHDwc3Ze+F2mMUIIoSL6qlrwgQMHOHXqFKtXryYjI4Nhw4aRn59PWloa7u7uZc/Lz89n/vz5hISE\nYGFhwciRI+nTpw+Ojo5VVZoQZTQaDe2b18angROrdpwkLDqZ/yw9yNAu3kzqMJa27q1ZHbuBDac3\nE5F8lPHNg/G0q23usoUQosarsglMYGAgc+bMAcDBwYGCggJ69+7NlClTrjvfRmRkJL6+vhgMBqyt\nrWnbti0RERFVVZYQN+Vga8lzQ1sxebgvdtZ61v1+hvdWHMKdRszo8ArtavtzPjueD8M+Z9u5XRhL\njeYuWQgharQqa2B0Oh22trYAhISE0K1bNwyGG09Ik5qairOzc9ltZ2dnUlJSqqosISrUtqkbM5/u\nQOdWHpxPzOGdZQf5NSyZ8T5jeNZ3ArYWtmw6s42PD33BpdwEc5crhBA1VpV9hfS3nTt3EhISwtKl\nS016viknBnZyskWv191rabdU0Zn/hHndj2zcgH9P6kB4dBLz1x7hx71niYxL4+XRbfh84H9YfjiE\n388d4KPwuQxvHsSw5kHodVW+KamabDPqJdmol2Rzb6r0t+6ePXv48ssvWbx48U2nLwDu7u6kpqaW\n3U5OTsbf37/C5WZk5FdqndeS0zur1/3OpoGrLW9Pas/a3af5/chlps35g/4d6zOs81BaODTn+9j1\nrD2+mX3nIxjfPJh6hjr3rTY1kW1GvSQb9ZJsTGOWSwnk5OQwa9YsFi1aVOEOuX5+fkRFRZGdnU1e\nXh4RERG0a9euqsoS4o7YWuuZEOTDK2P8cXawYvP+87z9zUFsrngxvcNUHvYM5FJuArPC57EpbhvF\npSXmLlkIIWqEKruY4+rVq5k3bx4NGzYsu69Dhw6EhoZy5MgRfH198ff359VXX2Xbtm0sWbIEjUbD\nuHHjGDJkSIXLlos51kzmzqawqIR1v5/h10MX0Wigb2A9Hu3aiDPZcXwXE0LGlUw87WozvnkwDRzq\nma3O+83cuYhbk2zUS7IxTUUTGLkadTnyoVIvtWRzMj6TpVuiSc4owN3Jhkn9fWjgZcPGuK3subQf\nDRoeqd+dgQ37YKGzMHe5VU4tuYgbSTbqJdmYpqIGRvf222+/ff9KqRz5+UVVtmw7O6sqXb64e2rJ\nxqWWNV39vDAaFY6eSWNvVCKFhQrD23SkuUtjTmWe5VhaNIdToqhnqIuT9YN9TiO15CJuJNmol2Rj\nGjs7q1s+JpcSEOIuWFnoCO7VhDfGB+DpYsuvERd5a0kYxVnOvNlhKj3qdiYpP4XZhxaw7tQmiozy\ni0oIISqTTGDKka5YvdSYjbPBmm5+XiiKQlRcOvuOJZKTV8Iw/460cmvK6cwzHE+LISI5kjr2XrjY\nOJm75EqnxlzEVZKNekk2ppEJjBBVyEKvZUT3xsyY0I66bvb8EXmZGUtCyU8z8Eb7KfSu143UgnQ+\nP/wla05upLDkirlLFkKIak8mMOVIV6xeas/G0d6Krn6e6LQaos6ksf94EunZRQz1b49fbR/iss5z\nPC2GQ0mReNl54GrjfPuFVgNqz6Umk2zUS7IxjUxghLhP9DotQ7o05D8TA/H2MLDvWCIzFoeSkWTL\n64Ev07dBT9ILM5h75CtWxYRQUFJg7pKFEKJakglMOdIVq1d1ysbBzpIurT2xstARdSadAyeSSM4o\nZIhfIG09WnI26wIn0mMJTYjAzcaF2nbut1+oSlWnXGoayUa9JBvTyARGCDPQabUM6NiAd54MpHEd\nB8Kik5n+dShJlyx5td2LDGrYl9ziPBZFLWfpse/IKco1d8lCCFFtyInsypGTC6lXdc6mtFRh56GL\nrP89jqKSUto85Mq4vs0o0GSwKiaEs9kXsLOwZeRDQwis3QaNRmPukk1WnXN50Ek26iXZmMYs10IS\nQvyPVquhb2A93n2qPc3qOXL4VCrTF4dyOq6UKW2fZ+RDQyg2FrP8xA8sPPoNGYWZ5i5ZCCFUTSYw\n5UhXrF4PSjalisIfRy6zdvdpCq4Yad7AiQlBzdBaF7AqZh2xGaex1lkxtPEAutTpgFaj7r8zHpRc\nHkSSjXpJNqaRCYwQKqLVaOjRpg4zn+qAfxNXos9n8NaSMMKP5vFC66d43GcUGo2G1Sc3MOfwIpLz\nU8xdshBCqI5MYMqRrli9HsRsFEXhYEwy3+04SU5+Md4eBiYNaI6hlpE1sRuJTD2OhVbPwIZ96VWv\nKzqtztwl3+BBzOVBIdmol2RjGrmY4x2QQ9vU60HMRqPRUMfNnq6tvcjMLeLY2XT2RF5Gr7EkuE03\n6jp4Ept+mqOpxzmWFoO3Q30crG69QZvDg5jLg0KyUS/JxjRyGLUQKmdvY8Ezg1swJdgPR3tLft53\njneWHcShqAHTO06jg0cA8TmX+Ch8LpvObKe4tMTcJQshhFnJBKYc6YrVqyZkU9vJlq6tvbhSZCTq\nTBp7jyZQVKRhVNvONHHy5lTGGY6lRXM4OYp6Bi+crB3NXXKNyKW6kmzUS7IxjUxghKhGbKz0PN63\nKa+PC8DDxZZfD11kxuJQSrNcmd5hKt3rPkxSfjKzDy1k7ckf5eKQQogaSSYw5UhXrF41LRtnB2u6\n+XkCGo6dSWffsUTSs4oZ3qYjvu7NOJN1juNpMYQnHcHTtjZuti5mqbOm5VKdSDbqJdmYRiYwQlRT\nFnodw7s14q2/Lg65/3gib359gPQEW/7d7urFITOvZPFF5GJWnlhDfnG+uUsWQoj7Qg6jLkcObVOv\nmp6NsbSUHQcvsnHPGYpKSvFv4sr4fs3IJZVvo9dyMfcyDpYGRjd9FH933/tWV03PRc0kG/WSbEwj\nJ7IT4gGg02oJ6lCfd55qj099R46cTmX64gPExcErAZMZ0iiI/JICvj62kq+jVpJ1RX45CiEeXDKB\nKUe6YvWSbP5HURT+iLzMmt+uXo6gWT1HJvb3QbHK5buYEM5kncNWb8PwhwbT0SOgSi8OKbmol2Sj\nXpKNaWQCI8QDRqPR0N2/Du893ZE2D7kSG5/JW0vDOBxVwEv+zxLc9FGMipFvo9cwP3IJaQXp5i5Z\nCCEqlUxgypGuWL0km5tTFIXw2BS++yWW7PxiGtQ2MGmAD3YOJfwQu54T6bFY6iwZ2qg/3ep2qvSL\nQ0ou6iXZqJdkYxq5lMAdkEPb1EuyuTmNRkMdVzu6tPYiO6+IqLPp/BGZgIXGkscCulPbzpXY9NMc\nST1GbMYpGtVqgL2lfaWtX3JRL8lGvSQb08hh1ELUAPY2Fjw1qAVTg/1wMlixef953v4mHKeSxkzv\nOI027q05k3WeD8I+Z9u5XRhLjeYuWQgh7ppMYMqRrli9JBvTuDvZ0s3P83+XI4hK4MoVDWPadqOB\nYx1OZsQRlXqCo6knaOBQj1pWDve0PslFvSQb9ZJsTCMTGCFqGGtLPWP7NOX18QF4utjyW8QlZiwJ\nRZvtwYwO03jYM5BLuQl8HP4FG09vochYbO6ShRDijsgEphzpitVLsrlzVy9H4IVWA1Fn0tl/PIn0\n7GJGtnmY5q6NOZ159urFIVOOUtfeC2drpzteh+SiXpKNekk2ppEJjBA1mIVey6NdG/GfiYE09DRw\n4HgSb34dSmaigTfaT6FnvS6k5KfxWcRCVsduoKCk0NwlCyHEbclh1OXIoW3qJdncu9JShR3h8Wz4\n4+rlCFo3duGJfs3IUpL4NiaExLwknKwcGdNsGK1cm5u0TMlFvSQb9ZJsTCMnshNCAKDVaujXvj7v\nPt2B5g2cOBqXxvTFoZyL0/Fqu5fo7/0IWUXZLDz6DcuO/0BuUZ65SxZCiJuSCUw50hWrl2RTuRRF\nYe/RBH7YdZqCKyU0rVuLCf19KLXK5tvotVzIuYi9hR3BTR+lrXvrW16OQHJRL8lGvSQb08iJ7O6A\n7FilXpJN5dJoNDTwMNDZ14PUzEKO/XUCPEdrA2MDemFrYUN0eiyHkiO5mJtAE8eGWOutb1iO5KJe\nko16STamqWgnXpnAlCNdsXpJNlUrPCaZb3ecJDuviPru9kwa0BwbhyusignhVOYZbPTWDGsykIc9\n2183jZFc1EuyUS/JxjQygbkD0hWrl2RTtbxc7ejq50lOfjFRZ9LZE5mAHiseb9cTFxtHotNPcTgl\nitNZ52ji6I2thS0guaiZZKNeko1pquQw6nPnzt3tS4UQKmVnbcGTA5ozbYw/zg5WbA29wDvfhONm\nbMb0DlNp5dKckxmneS90Nrsu/EGpUmrukoUQNVSFDcykSZOuu71gwYKy/3/rrbduu/BZs2YxevRo\nRowYwS+//EJCQgLjx49n7NixvPzyyxQVXe0+f/rpJ0aMGMGoUaNYu3bt3bwPIUQlauntzMynOtA3\nsB7JmQV8tOowm3Yn8kTTcUxq8RhWOkvWnf6ZTw7N50LmJXOXK4SogSpsYEpKSq67feDAgbL/v92u\nMwcOHODUqVOsXr2axYsX8/777zN37lzGjh3LqlWraNCgASEhIeTn5zN//nyWLVvGypUrWb58OZmZ\nmffwloQQlcHKUseY3g/xxvgA6rjasfvIZd5aGoY+px7TO0yjXW1/zmfH89qOD/j5zC8Ul5bcfqFC\nCFFJKmxgyh82eW3TcqtDKv8WGBjInDlzAHBwcKCgoIDQ0FB69+4NQM+ePdm/fz+RkZH4+vpiMBiw\ntrambdu2RERE3NWbEUJUvsZetfjPpEAe7dKQ7Lwi5q47yndbzzHCeyTPtZ6Io5UDW8/t5IOwzzmd\nedbc5Qohagj9nTz5dk3LtXQ6Hba2V3fyCwkJoVu3buzduxdLS0sAXFxcSElJITU1FWdn57LXOTs7\nk5KSUuGynZxs0et1d1L6Halor2dhXpKN+Tw1rDWPdPJm3pojhEUnE30+g6eH+vJJ0HRWR21i++nf\n+SxiIY807sq41sOwtbQxd8kC2WbUTLK5NxU2MFlZWezfv7/sdnZ2NgcOHEBRFLKzs01awc6dOwkJ\nCWHp0qX07du37P5bfQVlylHdGRn5Jq37bsihbeol2ZifrU7Dv0b78+uhi6z7I47Pvo+g7WF3xvTs\nScuAlqyKCWFn3B4Oxh8huOmj+Lv7mrvkGk22GfWSbExTUZNXYQPj4OBw3Y67BoOB+fPnl/3/7ezZ\ns4cvv/ySxYsXYzAYsLW1pbCwEGtra5KSknB3d8fd3Z3U1NSy1yQnJ+Pv73/bZQshzEOr1dAnsB7+\nD7myYlsMETHJHItLZXjXRvwr4EV2xe9h27mdfH1sJX6uLQlu9iiOVrXMXbYQ4gFTZSeyy8nJYezY\nsSxbtgwXFxcAZsyYQbt27Rg6dCjvvfcezZo1Y/DgwQwePJh169ah0+kYPnw4ISEhFTZIciK7mkmy\nUR9FUTh2IZOvNx4jt6CYBh4GJvX3wcq+gFWx6zideRZrnTVDG/enS50OaDVy+bX7SbYZ9ZJsTFPR\nBKbCBiY3N5eQkBAmTpwIwA8//MD3339PgwYNeOutt3B1db3lglevXs28efNo2LBh2X0ffvgh06dP\n58qVK3h5efHBBx9gYWHBtm3bWLJkCRqNhnHjxjFkyJAK35A0MDWTZKNObm4G4s6nsfrX0+w/nohW\no6Ff+3oM6tyAiJQINsRtpqCkkEa1vHncZwQedrXNXXKNIduMekk2prnrBmbq1KnUqVOHadOmcfbs\nWUaPHs3nn3/OhQsXCA0N5bPPPquSgm9HGpiaSbJRp2tzOXY2jRXbYknNKsS1ljVPBDWjnpcFa07+\nyJGUKPQaHX29e9G3QU8stHd0DIG4C7LNqJdkY5qKGpgK57nx8fFMmzYNgO3btxMUFMTDDz/MmDFj\nrttvRQghAFo1dGHm0x0I6lCf9OwrzF4dyZpf4hndeDTP+k7A3tKeLWd38GHY58RlnjN3uUKIaqzC\nBubvw6ABwsLC6NixY9ntOzmkWghRc1hZ6Aju2YQZE9rRwMPA/uNJTP86lNxEZ95sP5VudTqRlJ/C\n7IgF/BC7gYKSAnOXLISohipsYIxGI2lpaVy4cIHDhw/TuXNnAPLy8igokF86Qohba+BhYPoTAYzp\n/RDFJaUs2RzNgnUx9HTvx9SA5/Gwq82eS/uZeeBTIlOOmbtcIUQ1U2ED88wzzzBgwAAGDx7MCy+8\nQK1atSgsLGTs2LE8+uij96tGIUQ1pdNq6RtYj5lPt8e3kQsnzmUwY0kYMdEa/tX2RQY27ENecR5f\nRa3g66gVZF7JMnfJQohq4raHURcXF3PlyhXs7e3L7tu7dy9dunSp8uJuRXbirZkkG3UyNRdFUQiL\nTub7nSfJzi+mnrs9E/v7YONQwKqYdcRlncNGb83QxgPo7NVeDrmuBLLNqJdkY5q7Pgrp8uXLFS7Y\ny8vr7qu6B9LA1EySjTrdaS65BcWs/e00e44moNFA74C6DO3iTURaBBtPb6HQWEjjWg0Z6zMCDzv3\nKqz8wSfbjHpJNqa56wbGx8eHhg0b4ubmBtx4MccVK1ZUYpmmkwamZpJs1Oluc4k5n8Hy7bEkpefj\n7GDF+L7NaFDPgrUnf+RIyjH0Gh39/jrkWi+HXN8V2WbUS7IxzV03MD/++CM//vgjeXl5DBw4kEGD\nBl134UVzkQamZpJs1OlecikuMbJp33m2HjiPsVQh0MedsY88xLmCU6yO3UhWUTaedrUZ6zOSRrUa\nVHLlDz7ZZtRLsjHNXTcwf0tISGDDhg1s2rSJOnXqMHToUPr06YO1tXWlFmoqaWBqJslGnSojl4sp\nuSzfGkPc5WxsrfQE92pCQAtHNp3Zzp5L+9GgoWudTgxpHISN3jy/d6oj2WbUS7IxzT03MNdau3Yt\nn3zyCUajkfDw8Hsu7m5IA1MzSTbqVFm5lCoKuw9fImR3HIVFRprWc2RCUDPydMmsillHUn4yjla1\nGN30UVq7tayEyh98spf791oAACAASURBVM2ol2RjmntuYLKzs/npp59Yv349RqORoUOHMmjQINzd\nzbODnTQwNZNko06VnUt6diHf7TjJ4VOp6HUaBj3sTd/2dfg1fjfbz/+GUTHSxs2XUU2HUsvKodLW\n+yCSbUa9JBvT3HUDs3fvXtatW8exY8fo27cvQ4cOpWnTplVS5J2QBqZmkmzUqapyORSbwrc7YsnK\nLcLL1Y6JQT7YORWyKiaEM1nnsdFbM6zxQDp5Bcoh17cg24x6STamuaejkLy9vfHz80OrvfEXxAcf\nfFA5Fd4haWBqJslGnaoyl/zCEtb9Hsdvhy8B0LNNHYZ1a0hE2iF+jNtCofEKTRwbMrbZCGrLIdc3\nkG1GvSQb09x1AxMWFgZARkYGTk5O1z128eJFhg8fXkkl3hlpYGomyUad7kcupy5msnxbLJdT83C0\nt+TxPs1o7G3JmtiNRKYeR6/VE9SgN30adJdDrq8h24x6STamuesGJjw8nCn/396dx1dZ33n/f50l\nC9kXkpAFQhbCvsgmO8gqyiaLIILtPXO3zu10OvVh565lxmrLtPeNv/a+Z6z+qlU7VSgFBWQRZScI\nyA4CAclGCCRkT8hC9uTcf2ipWsFzkOR8T/J+/iUxnnx5vK5LPlzX95zr6adpaGggLCyM1157jfj4\neNasWcPvf/97PvroozZZ8DfRANM5qY2Z2qtLU3MrHx7L5f2Pr9Dc4uC+Xl1ZNr03uXUZvJOxmcrG\namL8u7G0zwIS9JZrQOeMydTGOXc9wDz++OP84he/ICkpib179/L222/T2tpKcHAwzz33HFFRUW2y\n4G+iAaZzUhsztXeXgrKbvLUjnYxrN/D1trFwUhIjB4Sx7fKHHLp+DAsWJsSNZk7ig/h28rdc65wx\nl9o4504DzB13vlmtVpKSkgCYMmUK+fn5PPHEE7z88stuG15EpHOLDvfnfy69j+/O7IPVYmHNrgz+\nc/1FxofP4Omh/4NIvwgO5H3MymO/4XzpRXcvV0TayB0HGIvF8qVfR0dHM23atDZdkIjIN7FaLEwY\nHMMvv3c/I/pEkp1fxc//6wTnzjn4l6H/xMyeU6lurOHVc3/kzbQ1VDbob7oiHY1L7z386kAjIuJO\nwQE+/I95A/jhwkEEB3jz/sdX+MV/nSbZNoJnR/wzCUHxnC4+x8pjv+bj68dx8XM7RcRgd9wDM3Dg\nQMLDw2/9uqysjPDwcBwOBxaLhdTU1PZY49/QHpjOSW3MZEqX+sZmNn10mb0n83AA4wdFs2BSImfK\nT7E1+0PqWxroFZLI0j4LiPSLcPdy24UpbeRvqY1z7noTb35+/h1fODY29u5X9S1ogOmc1MZMpnW5\nfL2KP354ibySGoL8vFg6LYXknt68k7mF86UXsVvtzOw5lWk9JmKz2ty93DZlWhv5K7Vxzj19FpIJ\nNMB0TmpjJhO7NLe0suvENbYcyqGpuZVBSeE8Pq0X1xqzeDdjC1W33nK9kITgHu5ebpsxsY18Rm2c\nc9fvQhIR8UR2m5WHRsWz8u9H0jc+lHPZZTz35nFKr4SwYsTTjI0ZyfWbhfzm1Cu8m7GF+uZ6dy9Z\nRFykKzBfoanYXGpjJtO7OBwOPk4rZP2+LGrqmojvFsh/m9mHBu9i1qZvpLi2lFCfEJb0foQBXfu6\ne7n3lOltOjO1cc6drsDYXnjhhRfabyn3Rm1tY5u9tr+/T5u+vtw9tTGT6V0sFgs9ogIZOyiayppG\n0nLK+ehsAQG2IJ4YOQ27zcrF8nROFJ2h6GYxSSEJ+Nh83L3se8L0Np2Z2jjH3//256KuwHyFpmJz\nqY2ZPK1LWk4Zb+9Ip7Synq7BvjzxYG/CIppYe2kDOVVX6WLvwrykmYyJGenxT7n2tDadido4R1dg\nXKCp2FxqYyZP6xIZ6seEwTG0tDpIu1zOx2mF1N20snzkVML9g0gvz+STkjTSKzLpGdSDQO8Ady/5\nrnlam85EbZyjKzAu0FRsLrUxkyd3yS2s5o87LpFbWE1AFy8WT06mXy8/NmZu5UzJeawWK1N7TGRm\nzyl427zdvVyXeXKbjk5tnKO3UbtAB5W51MZMnt6lpbWVvSfz2HTwMo1NrfSND2X5jN6UtuayPmMz\n5fUVhPuGsbj3I/QP7+3u5brE09t0ZGrjHN1CcoEu65lLbczk6V2sFgtJscGM6hdFUUUdF3LKOfBJ\nPl27dGX58GlgaeXT8gyOF56m6GYxicE98bV7xiZfT2/TkamNc3QLyQWais2lNmbqSF0cDgen0ktY\nuyeDGzWNRIX58cT0FALD6/lz+iauVF2li92XuUkzGRtzv/GbfDtSm45GbZyjKzAu0FRsLrUxU0fq\nYrFYiOnqz4TBMTQ0tZCWU8bhtELqa20sHzGVyIAQ0iuy+KQkjUvlGcQHdSfI+/b/g3W3jtSmo1Eb\n5+gKjAs0FZtLbczUkbvkFFTx9o50couq8fe1s3BSEoP6BPBe1vucKj6L1WJlSvcJzEyYio+Bm3w7\nchtPpzbO0SZeF+igMpfamKmjd2ltdbD3dB7vfXSZ+sYWkuOCeWJGbyqt+axPf4+y+nLCfENZnDLP\nuE/y7ehtPJnaOEe3kFygy3rmUhszdfQuFouFpJhgxgyIpqyqngs55Xx09jpBXiF8Z8Q0rFbLrU/y\nLbhZRGJwPL52X3cvG+j4bTyZ2jhHt5BcoKnYXGpjps7W5WxWKWt2ZVBW9dkn+T4+LYWIbs38OX0j\nlytz8bX5MifpQcbHjnL7Jt/O1saTqI1z3HYFJiMjg8WLF2O1Whk0aBDZ2dn80z/9E++99x6nT59m\nwoQJWK1Wtm7dyooVK9iwYQMWi4X+/fvf8XV1BaZzUhszdbYu3cL8mDg4hlaHgws55Ry5UERlJSwb\nPoVugWGkV2RxtiSNi2Xp9AjqTrCP+zb5drY2nkRtnHOnKzBt9teD2tpaVq5cyejRo2997de//jXf\n//73WbNmDdHR0Xz44YfU1tbyyiuv8Mc//pHVq1fz1ltvcePGjbZalojIt+bjbWPRpGSe/+4IkmOD\nOZVewnNvHKeuIJZ/G/kMw6OGkFt9jRdPvsSmzPepb25w95JFOpw2G2C8vb15/fXXiYyMvPW13Nxc\nBg0aBMD48eM5fPgwZ8+eZeDAgQQGBuLr68vQoUM5ffp0Wy1LROSeiYsM4NllQ/nOg72xWS38eU8m\n/7kunQfCZ/GDwf+dMN9Q9l77iH8/9hvOl15093JFOpQ2G2Dsdju+vl/eyJaSksKBAwcAOHjwIKWl\npZSWlhIWFnbre8LCwigpKWmrZYmI3FNWi4WJQ2L55fdGMbp/N3ILq1n51knOnIZnBv+QB+MnU9VY\nzavn/sjr59+mol5XmEXuBXt7/rCf/OQnvPDCC2zatImRI0fydfuHndlTHBrqh91ua4slAnfeNCTu\npTZmUheIiIAVfxfO2cwSfrfxLHtO5XE6s5TvzxvLtOljef3U2s8+AK8ikyUD5/Bg8iSs1rbf5Ks2\n5lKbb6ddB5jo6Ghee+014LMrMMXFxURGRlJaWnrre4qLixkyZMgdX6eiorbN1qid4eZSGzOpy5fF\nhPjys+8M54OjV9l+5Ar/++0TDEoKZ+nUZWR1vcB7Wdv545l32Zd1hMf6zKdHYFybrUVtzKU2zrnT\nkNeu7/F76aWXSE1NBWDTpk1MnjyZwYMHc/78eaqqqrh58yanT59m+PDh7bksEZF7ystuY+64BH7x\n9/fTNz6Uc9ll/OzNE9y4GsWKEc8wsttQrlbn8eKJ37Ihcyv1zfXuXrKIx2mzz4FJS0tj1apV5Ofn\nY7fbiYqK4sc//jErV67E4XAwfPhwfvrTnwKwY8cO3nzzTSwWC8uWLWPOnDl3fG19DkznpDZmUpc7\nczgcHL1QxLp9mVTXNhEb4c8TM3rT4lfCuvRNlNSVEeITzKMpcxkcMeCe/my1MZfaOEePEnCBDipz\nqY2Z1MU5N+ub2JCazYFPrgMwYXA0cyfEc7joELty99PiaGFQ1/48mjKXUN+Qe/Iz1cZcauMcDTAu\n0EFlLrUxk7q4Jiuvkrd2XiK/5CaBfl4snpxMQk8r6zPeI/PGZbxt3sxOmM7EuLHYrN/uzQpqYy61\ncY4GGBfooDKX2phJXVzX3NLK7hPX2HIoh8bmVvrGh/L4tF7kNl3ivaz3udlUS/eAGB7rs4D4oO53\n/XPUxlxq4xwNMC7QQWUutTGTuty90ht1rNmdwbnsMuw2Cw+Nimfi8K68n7ODo4UnsWBhQtwYZifO\noMtdPCBSbcylNs7RAOMCHVTmUhszqcu343A4OJ1Rwto9mVRUNxAV2oXlM3pjD65gXfomimpLCPYO\nurXJ12KxOP3aamMutXGO2x7m2Fb0MMfOSW3MpC7fjsViIaarPxMGx9DY3EJaTjkfpxXSXO/Ld0dO\nx8/Hi0/LMzhZ/AlXq/NJDO6Jn1cXp15bbcylNs6508McdQXmKzQVm0ttzKQu91ZuYTVv7bjElcJq\n/HzsLHwgiT69vFifsZmMiiy8rV48nDidB+LGfeMmX7Uxl9o4R7eQXKCDylxqYyZ1ufdaWx3sP5PP\nxgPZ1De2kBQbxBPTe3O9NYNNWe9T03ST2IBoHuu9gITgHrd9HbUxl9o4R7eQXKDLeuZSGzOpy71n\nsVhIjAlizIBoyqsbuJBTzkdnC+jqE8kTw6fS0FrHxfJ0jhScoLqxhqSQnnhZvf7mddTGXGrjHN1C\ncoGmYnOpjZnUpe2dyy5lza4MSivrCQ/y5fHpKfiHV7EufROFtcUEeweyMGUu90UM/NImX7Uxl9o4\nR1dgXKCp2FxqYyZ1aXtRYX5MGBKDwwEXcso5eqGIumovnhgxjaAuvlwsz+BU0Sdcqb72pU2+amMu\ntXGOrsC4QFOxudTGTOrSvvJKanh7ZzpZeZX4eNuYPz6RgX19eDdzC5cqMvGyevFwwjQmdx9Pt6gQ\ntTGUzhvnaBOvC3RQmUttzKQu7a/V4eDQuQLe3Z/Fzfpm4qMCWT4jhTLrZTZmbqO6qYYY/248NWo5\noY4Idy9XvobOG+dogHGBDipzqY2Z1MV9qmobeWdfFh+nFWIBJg+NY8bobuzK38Xh68cBGBM9krnJ\nMwnw8nfvYuVLdN44RwOMC3RQmUttzKQu7vdpbgWrd6ZTWF5LcIA3S6emEBp1k42Xt3K1Mh9/Lz/m\nJs1kdPQIrBaru5cr6LxxljbxukAbq8ylNmZSF/eLCOnChMEx2G0WLuRUcPzTIm5U2PjhtDkE2Hy5\nVJHJJyVpXCrPoHtgHME+t/9DQdqHzhvnaBOvCzQVm0ttzKQuZikqr2X1rnQuXqnA227l4dHxjBoS\nwtac7ZwuPocFCxPjxjArcTpd7M49kkDuPZ03ztEtJBfooDKX2phJXczjcDg4drGId1OzqahuIDK0\nC8umpWALKeOd9M0U15US5B3IguRZDIsa4tIDIuXe0HnjHA0wLtBBZS61MZO6mMsvwJc3Np9j76k8\nHA4Y1juCRQ8kcKriGDtz99LU2kxKaDKLU+bRzT/S3cvtVHTeOEcDjAt0UJlLbcykLub6S5urRdWs\n2ZVBVn4lPl425oztydAB/mzK3kpa2SVsFhtTekxgZs8peNu83b3sTkHnjXM0wLhAB5W51MZM6mKu\nL7ZpdTg4fL6Ad/dnU1PXRHS4H8umpdDoX8C7GVuoaLhBmG8oi3rNYVBEfzevvOPTeeMcvQvJBdoZ\nbi61MZO6mOuLbSwWC/FRgYwfHENdYwsXLpdzOK0QS0MA3xk5HR9vG5+WZ3Ci6AxXq/JICI6/9UgC\nufd03jhH70JygaZic6mNmdTFXHdqk1NQxeqd6VwprMbX28a88Yn07+PFhswtZNzIxstq58GeU5jS\nYyJeVns7r7zj03njHN1CcoEOKnOpjZnUxVzf1Ka11cFHZ6+z8UA2N+ubiYsIYNn0XlR6XWFT1vtU\nNVYT6deVR1Pm0TcspR1X3vHpvHGObiG5QJf1zKU2ZlIXc31TG4vFQs/oIMYNiqamrom0nHIOnS/E\npyWE74yYjsXWwsWyDI4XnqboZjEJwfH42n3b8XfQcem8cY5uIblAU7G51MZM6mIuV9tk5VWyelc6\n14pr8POxs2BiIolJ8G7mZnKqruJj82ZWwnQmxo3FZrW14co7Pp03ztEtJBfooDKX2phJXcx1N21a\nWlvZdzqfzQcvU9fQQs9ugSybnkIBl9iS9SE3m2uJDYhmccojJIX0bJuFdwI6b5yjW0gu0GU9c6mN\nmdTFXHfTxmqxkBQTzLiB0VTdbCQtp5yDZwsIskSwfMRUmhz1XCxP50jBCcrrK0gMjsdHnx3jMp03\nztEtJBdoKjaX2phJXcx1L9qkX61g9a4MrpfeJKCLFwsnJRET38A7GZvJrynAz96FuUkzGRMzUk+6\ndoHOG+foFpILdFCZS23MpC7muldtmlta2XMyjy2HcmhoaiEpNojHp/Uip+k871/eSX1LA/FB3VnS\n+xF6BMbdg5V3fDpvnKMBxgU6qMylNmZSF3Pd6zblVfWs25fFyUvFWCwweWgck+/vyo5rOzhZ9AkW\nLEyIG82shBn6ELxvoPPGORpgXKCDylxqYyZ1MVdbtUnLKeNPuzIoqqgjyN+bxQ8kExpdzTuZmymq\nLSHQO4D5ybMYEXWfnnR9GzpvnKMBxgU6qMylNmZSF3O1ZZum5lZ2HL/K9o+v0NjcSu/uISyZmsil\nutN8eGUvTa1N9ApJZHHvR4j2j2qTNXgynTfO0QDjAh1U5lIbM6mLudqjTemNOv68N5MzmaXYrBam\nDo9j3LAQtuVu53zpRawWK1O6T+DBnlPwtd/+HSWdjc4b52iAcYEOKnOpjZnUxVzt2eZsVil/2p1B\naWU9oYE+LJ6cjE/XUjZkbqW8voJQnxAWpsxhcNf+uq2Ezhtn6XNgXKD35ptLbcykLuZqzzbdwvyY\nOCQGq9XChZwKjn9aTHWFN08Mn0ZgFy8+Lc/gZNEn5FbnkRDcAz8vv3ZZl6l03jhHnwPjAk3F5lIb\nM6mLudzVpqiilj/tziDtcjk2q4UH7+/ByCH+vHd5K+kVWditdmbEP8C0HpPwsnm1+/pMoPPGOXe6\nAtOmnzqUkZHB1KlTWbNmDQAnTpzgscceY/ny5Tz55JNUVlYC8MYbb7Bw4UIWLVrEgQMH2nJJIiLS\nxqJC/Xh60WD+8ZEBBAd4s/1ILi/9KZuxfvP4b/2W4m/vwvac3fzy+P/hYlm6u5crHqrNrsDU1tby\n5JNP0rNnT3r37s2yZcuYP38+v/71r0lMTOTVV1/FarUyc+ZM/vmf/5l169ZRU1PD0qVL2b59Ozbb\n7R8UpiswnZPamEldzGVCm4bGFrZ+nMOu49doaXUwKCmcBZN7cKz8IKnXDuPAwX0RA1nQazahviFu\nXWt7MqGNJ3DLFRhvb29ef/11IiMjb30tNDSUGzduAFBZWUloaCjHjh1j/PjxeHt7ExYWRmxsLFlZ\nWW21LBERaUc+3jYWTUrm5383kr7xoZzLLmPlH87iVTSQZ4b+gISgeM6UnOcXx37NnqsHaGltcfeS\nxUO02QBjt9vx9fX90tdWrFjBP/7jPzJjxgxOnTrFI488QmlpKWFhYbe+JywsjJKSkrZaloiIuEFM\nV39+vGQIT87pj38XO1sO5fDaujymhSzm8T6L8LLaeS9rO//rxH+QdSPH3csVD2Bvzx+2cuVKXn75\nZYYNG8aqVatYu3bt33yPM3e0QkP9sNtvf4vp27rTJStxL7Uxk7qYy7Q2syKDmHx/PGt3prPt0GX+\nc8M5Rg3oxnMzn2X3tV3svXyI/3v6d0zoeT/LB88n2DfI3UtuM6a18TTtOsCkp6czbNgwAMaMGcO2\nbdsYNWoUOTl/nbaLioq+dNvp61RU1LbZGnVf0lxqYyZ1MZfJbeaOiWdocjhrdqVzNK2Q05eKmT12\nEE/fN4gNmZv56MoxTuSdY07ig4yLvb/DPena5DYmcdu7kL6qa9eut/a3nD9/nvj4eEaNGkVqaiqN\njY0UFRVRXFxMcnJyey5LRETcoHtkAM8+PpS/f7gvvt42Nh64zB/eLWRWxDIW9ZqLw+FgfcZ7/H8n\nXya36pq7lyuGabN3IaWlpbFq1Sry8/Ox2+1ERUXx9NNP8+KLL+Ll5UVwcDC/+tWvCAoKYvXq1Wzb\ntg2LxcKPfvQjRo8efcfX1ruQOie1MZO6mMuT2tTWN7Hpo8vsP5OPwwEj+0by0Lhu7CvczYmiM1iw\nMCZmJHMSHyTA29/dy/3WPKmNO+lRAi7QQWUutTGTupjLE9vkFlbz9s50cgqq8PG2MW9cAj0SG9iQ\nvZXCm0X42bswO3EG42JHefRtJU9s4w4aYFygg8pcamMmdTGXp7ZpdTg4ePY6G1KzuVnfTGyEP49N\nTaLQcpHtOXuob6knNiCaR1PmkRyS4O7l3hVPbdPeNMC4QAeVudTGTOpiLk9vU13byMYD2Xx0tgCA\nUf2jeHBsFKmFezlWeAqAEVH3MS/5IUJ8gt25VJd5epv2ogHGBTqozKU2ZlIXc3WUNtn5lazZnUFu\nYTU+XjZmj+1Jckorm7K3cq06Hx+bNzN7TuWB7uOwW9v1zbV3raO0aWsaYFygg8pcamMmdTFXR2rT\n2urg0PkCNqRmU1PXRFRoFxZPSaKmy2W2Xt7BzaZaovwiWNRrLn3DU9y93G/Ukdq0JQ0wLtBBZS61\nMZO6mKsjtrlZ38SWgznsO51Pq+OzZyvNmxTLsfKDHMw/igMHg7v2Z36v2XTtEvbNL+gmHbFNW7jT\nAOMZ19pEREQAf18vlk5LYcLgGNbuyeBcdhkXr5QzfcQgnh4yjC0573O29AIXy9OZ1mMS0+IfwNvm\n5e5lSxvQFZiv0FRsLrUxk7qYq6O3cTgcnEwvYf2+TMqrGggJ8GbRpCRs4QVszt5OZWM1Yb6hLOg1\nm8Fd+2OxWNy95Fs6ept7RbeQXKCDylxqYyZ1MVdnadPQ1MIHR3L58NhVmlta6RUXzMLJ8aTVHmPf\ntYO0OlrpE9qLRSlz6eZ/50fVtJfO0ubb0gDjAh1U5lIbM6mLuTpbm5Ibdazbm8mZzFIsFpg0JJax\nIwL54OoHfFqegdVi5YHu43io51R87b5uXWtna3O3NMC4QAeVudTGTOpirs7aJi2njD/vyaSgrBZ/\nXzvzxicQFlfJe9nbKKuvINg7kHnJDzMi6j633VbqrG1cpQHGBTqozKU2ZlIXc3XmNs0trew9lceW\nQznUN7bQPTKARyf35IrjLLtz99PU2kxicE8eTZlH98CYdl9fZ27jCg0wLtBBZS61MZO6mEttoLKm\ngQ0Hsjl8vhCA+/tFMXV0GHsLd3O2JA0LFsbFjmJ24gz8vfzabV1q4xwNMC7QQWUutTGTuphLbf4q\n+3ola3dnkFPw2af5zhoTT3xyA5uyt1FUW4y/lx+zEx9kbMzIdnlIpNo4RwOMC3RQmUttzKQu5lKb\nL2t1ODh8roANB7Kprm0iMqQLi6YkUuFziQ9ydtPQ0kj3wFgeTZlLYnDPNl2L2jhHA4wLdFCZS23M\npC7mUpuvV1vfxJZDV9h7Ku/Wp/k+PKEbh0v3c6LoNAD3dxvG3KSHCPa5/R+g34baOEcDjAt0UJlL\nbcykLuZSmzvLL6lh7Z5MPs2twGa1MH1Ed/oPgC0575NXcx1fmw8PJUxjUtxYbFbbPf3ZauMcDTAu\n0EFlLrUxk7qYS22+mcPh4NTnn+ZbVtVAcIA3Cycm0hJ6hW2Xd1LbXEc3v0gWpcylT1ive/Zz1cY5\nGmBcoIPKXGpjJnUxl9o4r6GphQ+PfvZpvk3NrSTHBvPIA3F8UnOIw9eP48DBkIiBzE+eRXiX0G/9\n89TGORpgXKCDylxqYyZ1MZfauK70Rh3r92VxKqMECzBxSAwjh/nyfu52cqpy8bJ6MSP+Aab2mIjX\nt3hIpNo4RwOMC3RQmUttzKQu5lKbu3fhSjlrd2fc+jTfueMS8I8pZMvlD6lurCHcN4yFvWYzsGu/\nu/o0X7VxjgYYF+igMpfamEldzKU2305zSyv7Tuez5dBl6hpaiIsIYOGUHmQ2nSA17zCtjlb6hfVm\nYcocovwiXHpttXGOBhgX6KAyl9qYSV3MpTb3RuXNRjYeyObQuQIARvaNZNKoIHZd30F6RRY2i43J\n3cfzYM8p+Np9nHpNtXGOBhgX6KAyl9qYSV3MpTb31uXrVfxpdwY5BVV4e1l5aFQ8MYlVbL68nYqG\nG4T4BPNI0kMMixryjbeV1MY5GmBcoIPKXGpjJnUxl9rce60OB4fPF7AxNZuq2iYiQnxZ+EBPCu3n\n2XPtAM2tzSSHJPBoyjxiA6Jv+zpq4xwNMC7QQWUutTGTuphLbdpObX0zWw/nsPdUHi2tDgYkhvHg\nuK58VLqH86UXsWBhQtxoZiVMx+9rHhKpNs7RAOMCHVTmUhszqYu51KbtXS+9ydo9GVy88tmn+U4b\n3p1e/RrZmvM+xXWlBHj5MyfpQUZHj/jSQyLVxjkaYFygg8pcamMmdTGX2rQPh8PB6YxS1u/LpLSy\nnmB/b+ZPiqc2MIsduXtpbGmkR2Acj6bMIyG4B6A2ztIA4wIdVOZSGzOpi7nUpn01NrWw49hVth/N\npam5laTYIOZM6sbJqo84WfQJAKOihzMv6SESY6PVxgkaYFygE95camMmdTGX2rhHaWUd7+zL4mT6\nZ5/mO35wDEPus7A9dzvXbxbSxe7LogGzGBYyFLvV7u7lGk0DjAt0wptLbcykLuZSG/f69Eo5a/dk\nkl96Ez8fO3PGxeMVdY3tV3ZT11xHpF9XFiTPpn94n7v6NN/OQAOMC3TCm0ttzKQu5lIb92tuaWX/\nmXw2H8yhrqGZ2Ah/Hnkgjmv2M+zOOogDB33DUpifPIuYgG7uXq5xNMC4QCe8udTGTOpiLrUxR9XN\nRjZ9lM3BswU4gLGDYrj/Pl/2F+7mUkUmVouVcTGjeDhhGgHe/u5erjE0wLhAJ7y51MZM6mIutTFP\nTkEVa/dkkJ1fXFwxVAAADuhJREFUhd1mYeqwOJL6NvD+lQ8oriuli70LDydMY0LsaGxWm7uX63Ya\nYFygE95camMmdTGX2pjJ4XBwKb+KN7emUV7VQJCfF3PGx9MadoUduXuoa64nyi+C+cmzGNC1r7uX\n61YaYFygE95camMmdTGX2pgrIiKQ/Os32Hn8Kh8cvUpD02dPu547KYbM5hMcyj+KAwf9wnozv9cs\nov2j3L1kt9AA4wKd8OZSGzOpi7nUxlxfbFNR3cB7H13m8PnP9scMSe7KA6MDSS3ec2t/zPjYUTyU\nMI0Ar861P0YDjAt0wptLbcykLuZSG3N9XZvcwmr+vDeTjGs3sFktTB4WS3Lfej7I3UFxXSl+9i48\n1Mn2x7htgMnIyOCpp57iu9/9LsuWLeOHP/whFRUVANy4cYMhQ4awcuVK3njjDXbs2IHFYuEHP/gB\nEydOvOPraoDpnNTGTOpiLrUx1+3aOBwOTqWX8M7+LEor6wno4sXssT0g4go7c/d+vj8mkgW9ZtE/\nvI8bVt6+7jTAtNlHANbW1rJy5UpGjx5962svvfTSrX/+6U9/yqJFi7h27RoffPAB69ato6amhqVL\nlzJu3Dhsts4xXYqIiPyFxWJheJ9IBid3Zc+pa2w7fIU/78kmOtyPRyd9j+yW4xy+fpz//+wf6Bfe\nmwXJs+jWSffHWL/5W+6Ot7c3r7/+OpGRkX/z7y5fvkx1dTWDBg3i2LFjjB8/Hm9vb8LCwoiNjSUr\nK6utliUiImI8L7uVmffH87+eHM3EITEUltfy6sZMCs4l8fe9niQlNJmLZen88vj/5d2MLdxsqnX3\nkttdm12Bsdvt2O1f//Jvv/02y5YtA6C0tJSwsLBb/y4sLIySkhJ69+5929cODfXDbm+7KzR3umQl\n7qU2ZlIXc6mNuZxpExEBP+4ZzsKpVbyx5TxnM0u5eKWCB0dNZdqwiWxI30pq3mFOFn/CowNmMTVp\nPPZOsj+m3Z8i1djYyKlTp3jhhRe+9t87syWnoqLtJk3dMzaX2phJXcylNuZytY2/3cIP5w/kbFYZ\n6/dn8cHHV0g9ZefhMQuxJeayM3cffzi9ng8u7WdBr9n0C7/9RQBP4pY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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "IGINhMIJ5Wyt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "BAGoXFPZ5ZE3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RidI9YhKOiY2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Make Better Use of Latitude\n", + "\n", + "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n", + "\n", + "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco." + ] + }, + { + "metadata": { + "id": "hfGUKj2IR_F1", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 364 + }, + "outputId": "496176e2-6d21-4c48-e0f1-03c9b2b534d6" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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mNLdjaK1oHQ1xWNXaqPETFBZnlU21gE1EzIVrrUKfbqjldXe/eT6d68xGVIjL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\n7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+ "text/plain": [ + "
" + ] + }, + "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": 644 + }, + "outputId": "cbf49de2-dc71-41ee-ecee-4cd889451ed5" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#\n", + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)\n", + "\n", + "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)\n" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 227.02\n", + " period 01 : 216.83\n", + " period 02 : 206.73\n", + " period 03 : 196.70\n", + " period 04 : 186.80\n", + " period 05 : 177.05\n", + " period 06 : 167.45\n", + " period 07 : 158.04\n", + " period 08 : 148.86\n", + " period 09 : 139.95\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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sPRlXyxocvXGMOYGLCEu+YOhyhRBCiHIlDUwl9/dozPMDm6LTali7/wIbdyUx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\nUewo4+PaE1S0XCQ1LJkQv2Avj/zLjZQ2Q01LSB7QrNhcamM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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "pZa8miwu6_tQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "PzABdyjq7IZU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n", + "\n", + "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`." + ] + }, + { + "metadata": { + "id": "xdVF8siZ7Lup", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U4iAdY6t7Pkh", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 60679c7700c1ff64258dcd2867578ba6c0c981d6 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 02:29:33 +0530 Subject: [PATCH 06/11] Completed feature crosses --- feature_crosses.ipynb | 1570 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1570 insertions(+) create mode 100644 feature_crosses.ipynb diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb new file mode 100644 index 0000000..31abd43 --- /dev/null +++ b/feature_crosses.ipynb @@ -0,0 +1,1570 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_crosses.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ZTDHHM61NPTw", + "0i7vGo9PTaZl" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 1239 + }, + "outputId": "405cfb12-f55f-4a8c-8c05-892f3cddfefc" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.5 28.6 2633.6 537.5 \n", + "std 2.1 2.0 12.6 2154.4 413.2 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 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" + ] + }, + "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": 627 + }, + "outputId": "0da1ed02-3b42-4f29-f00c-68d41de0299c" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 244.62\n", + " period 01 : 171.39\n", + " period 02 : 122.76\n", + " period 03 : 240.70\n", + " period 04 : 189.01\n", + " period 05 : 161.67\n", + " period 06 : 191.86\n", + " period 07 : 144.22\n", + " period 08 : 154.35\n", + " period 09 : 151.61\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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TDEBm/JXoNTo2FH2LW3V3fcBCCNEDSAJzHoqikDrEgq3FSX7hyR5NyeYRONxO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+ "text/plain": [ + "
" + ] + }, + "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", + " # 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", + " # 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", + " # 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", + " # rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0FfUytOTNJhL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 627 + }, + "outputId": "430da216-4257-47d9-e6a7-5b59644b9d57" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 168.93\n", + " period 01 : 142.70\n", + " period 02 : 126.26\n", + " period 03 : 115.11\n", + " period 04 : 107.30\n", + " period 05 : 101.47\n", + " period 06 : 96.99\n", + " period 07 : 93.53\n", + " period 08 : 90.65\n", + " period 09 : 88.23\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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HFemBkwZGCCGEqEdSUpLZtWtHtdadPXsu3t6N77j83Xc/elCxHrg6OZWAEEIIIW7vo4/e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\n+xbiFpN9Rl2STXykwCRAJpW6JBs1eIZ8vFbXSkP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PXAqFQ3SOu2kZbZv9cxl/yB/9fFaa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\nEEKIlCMFRgghhBApRwqMEEIIIVKOFBghxILq7u5m3bp17Nu3L/oU3m9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZTDHHM61NPTw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "JQHnUhL_NRwA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model." + ] + }, + { + "metadata": { + "id": "Ro5civQ3Ngh_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RNgfYk6OO8Sy", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AFJ1qoZPlQcs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Feature Crosses\n", + "\n", + "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n", + "\n", + "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n", + "\n", + "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model." + ] + }, + { + "metadata": { + "id": "-Rk0c1oTYaVH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train the Model Using Feature Crosses\n", + "\n", + "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n", + "\n", + "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`." + ] + }, + { + "metadata": { + "id": "-eYiVEGeYhUi", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xZuZMp3EShkM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 627 + }, + "outputId": "ac63ba90-0609-4330-f236-dd3b09a56ac3" + }, + "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 : 162.92\n", + " period 01 : 134.87\n", + " period 02 : 117.96\n", + " period 03 : 106.81\n", + " period 04 : 99.06\n", + " period 05 : 93.34\n", + " period 06 : 88.95\n", + " period 07 : 85.46\n", + " period 08 : 82.69\n", + " period 09 : 80.43\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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kAnMb9GhdBx8PR9bFnCE9u4DBYf0xYWJJ7ErKjDJrxxMREal2VGBuA3uLmXu7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\nXelkMx2J0tI9kihomruGGZ+MJl53WLNYUupMjNKUem3o9bM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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "0i7vGo9PTaZl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "3tAWu8qSTe2v", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-_vvNYIyTtPC", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ymlHJ-vrhLZw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Try Out More Synthetic Features\n", + "\n", + "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?" + ] + } + ] +} \ No newline at end of file From baa173f944cd7ec6a09441e7276b6144d8c5cce4 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 02:49:14 +0530 Subject: [PATCH 07/11] Completed logistic regression --- logistic_regression.ipynb | 1583 +++++++++++++++++++++++++++++++++++++ 1 file changed, 1583 insertions(+) create mode 100644 logistic_regression.ipynb diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb new file mode 100644 index 0000000..ee560f6 --- /dev/null +++ b/logistic_regression.ipynb @@ -0,0 +1,1583 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "logistic_regression.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "dPpJUV862FYI", + "i2e3TlyL57Qs", + "wCugvl0JdWYL" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 1239 + }, + "outputId": "df8ddc00-9ebb-4f30-fd28-107b0e9c69b2" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2639.4 538.0 \n", + "std 2.1 2.0 12.6 2151.5 414.6 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1465.0 297.0 \n", + "50% 34.2 -118.5 29.0 2119.0 432.0 \n", + "75% 37.7 -118.0 37.0 3146.5 647.0 \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 1427.1 499.7 3.9 2.0 \n", + "std 1142.6 377.1 1.9 1.3 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 791.8 282.0 2.6 1.5 \n", + "50% 1166.0 407.0 3.5 1.9 \n", + "75% 1727.0 602.2 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "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": 627 + }, + "outputId": "19276198-6cd8-48e5-e194-9784828d56f6" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 0.45\n", + " period 01 : 0.45\n", + " period 02 : 0.45\n", + " period 03 : 0.44\n", + " period 04 : 0.44\n", + " period 05 : 0.44\n", + " period 06 : 0.44\n", + " period 07 : 0.44\n", + " period 08 : 0.44\n", + " period 09 : 0.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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CCPVIgVNNEvKS+OTQXC5kX6KDWxte6fQCbrYuaseqcf0a9qKxfUMOpRzlbEaM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\nUo6kFrLjWA7Do/yVjmT2VCoVXrYeeNl60N+nN3CJoa2ys38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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "JjBZ_q7aD9gh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Can We Calculate LogLoss for These Predictions?\n", + "\n", + "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n", + "\n", + "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability. For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n", + "\n", + "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily. Remember, `LogLoss` is defined as:\n", + "\n", + "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n", + "\n", + "\n", + "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n", + "\n", + "Given the predictions and the targets, can we calculate `LogLoss`?" + ] + }, + { + "metadata": { + "id": "dPpJUV862FYI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to display the solution." + ] + }, + { + "metadata": { + "id": "kXFQ5uig2RoP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "69555f29-3314-45d1-cdf5-136b6fbf4a2a" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "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", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \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", + " 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", + " 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", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\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", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \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": 627 + }, + "outputId": "093ff6b3-23b7-4035-9e4e-b01705b60236" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.60\n", + " period 01 : 0.59\n", + " period 02 : 0.57\n", + " period 03 : 0.56\n", + " period 04 : 0.55\n", + " period 05 : 0.54\n", + " period 06 : 0.54\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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bw68XNuopUiGEEKJ+STFzH2nn7UDEpM442Zqzdu8lvtoUR1V1zR310d6pHa0d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\nxcwgyUamXpKNOmgUDRN94ylqLiW7Ppeiq+WMdR9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i2e3TlyL57Qs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see the solution.\n", + "\n" + ] + }, + { + "metadata": { + "id": "5YxXd2hn6MuF", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) \n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "UPM_T1FXsTaL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i-Xo83_aR6s_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n", + "\n", + "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n", + "\n", + "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC." + ] + }, + { + "metadata": { + "id": "DKSQ87VVIYIA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "d797ced9-88b1-4ff6-a9fe-80756eec5763" + }, + "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": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "AUC on the validation set: 0.72\n", + "Accuracy on the validation set: 0.75\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "47xGS2uNIYIE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n", + "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n", + "obtain the true positive and false positive rates needed to plot a ROC curve." + ] + }, + { + "metadata": { + "id": "xaU7ttj8IYIF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "b9d1b578-28fa-40ef-ff2e-40d1a483a9a2" + }, + "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": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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CXXjJ7NepSAkL0Q0URWH5m/sor21tt390Shi3j+pDr1BvlZIJNZQ1V7AyI428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\nq6ixhBUZaRQ1lRDg5s/ipLkkByWoHUs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+ "text/plain": [ + "
" + ] + }, + "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": 662 + }, + "outputId": "284a35ab-7181-495f-adeb-3c62f2cc367d" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000003,\n", + " steps=20000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.50\n", + " period 01 : 0.48\n", + " period 02 : 0.48\n", + " period 03 : 0.47\n", + " period 04 : 0.47\n", + " period 05 : 0.47\n", + " period 06 : 0.47\n", + " period 07 : 0.47\n", + " period 08 : 0.47\n", + " period 09 : 0.47\n", + "Model training finished.\n", + "AUC on the validation set: 0.81\n", + "Accuracy on the validation set: 0.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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QlzQ4zRTafRL2VnbEpm6ntKZM7TgN9AtwpaePE0fO5ZKZW6p2HCGEEEI10uA0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\nFR6Pp8ex/Px8Hn/8cUKhENOmTWPWrFn9VbbEIa8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odrOd3PSJ5KZPBKAt1M7x+tJI707d\nUYrrSthZ/iE7yz8EwG1xRcPO+JRsRrlGaAFCkUFIAUdEhhSL0dwZYMYC8wmFQ5xqrIjO4SmuO8be\nqv3s7VyAMMlkJTs5K7rNxBj3aGymJPXyiMQ5BRwRGdKMBmN0xeW5o2cRDoc53Vwb7eE54u+5ACGA\n2WjGZXHitjhxWV24LE5cVmfka7djXc/bzTZdvi4ywBRwRES6MRgMDLN7GGb38NUR1wHQ0BqILEDo\nP0p5sJJAayONbY1UNFVzIlB2yfc0Gow4zY4zIcjqioQfixOntTMIWVydz7twWRwaFhP5khRwREQu\nwW11Mc07hWneKec819rRRqAtQKC1kYa2RgKtAQJtjZFba+fXzuf9LfWcaqzo1fe0m+2dPUBdoSdy\n321x4uwekjqft5osff2xRQY1BRwRkS/BarLgMaXhsaX16vyOUAeBtmA09ATaAp3BqLFbMApEA1JV\n02nChHtRh7XbEJkTt6X70FmkVyg6dGZ1YjNpPSBJbAo4IiIDyGQ0kZLkJiXJ3avzQ+EQwfamCwag\nhs6g1NgW6UEqayynvaH90nUYTKTY3KQnefB1riWU4fDic3hJt6VpiEwGPQUcEZE4ZjQYoz0zvREO\nh2npaCHQ1khDZw9RoC3YIxRFe43aAxzxH+Ww/4se72EymPDa06OBJ8PhJcMZud/bOkRiTQFHRCSB\nGAwGbGYbNrONYfb0i57r9bo5WV5DVVM1FcEqKhqrqAhWURmMfC0PVp7zGqfF0TP4dN6G2dMxa98v\niSP6aRQRGcKsJkv0MvnuwuEw9a0BKoOVkfDTLfgcqz/BF3UlPc43Goyk29LOCT8+h49kq0vrBsmA\nU8AREZFzGAyG6Fyhq9JyejzXHmqnuul0NPhEwk81lcGqyEannZuddrGZbN2Cz7BoAPI5hmE1WQfy\nY8kQooAjIiKXxWw0M9yZwXBnxjnPNbYFz+nxqQhWcTJQRknDiXPOT0tK7THHp6vnJzUpRYsjypei\ngCMiIn3GaXEwLiWLcSlZPY53hDqoafZTEazsEXwqg1XnrBQNYDVa8DqG9bi6q+u+zaxL3OXS+jXg\nrFmzhn379mEwGMjPz2fq1KnnnPPMM8+wd+9eNm3aBMCrr77KCy+8gNls5sEHH2TevHmcOnWKH/3o\nR3R0dOD1evn3f/93rFZ1a4qIDBYmowmvIx2vIx2Y3OO5pvbmaOipPGvY62Tg1DnvlWJ1n3Npe4bD\nR0pSshY8lKh+Czi7d++mpKSEgoICiouLyc/Pp6CgoMc5R44cYc+ePVgskR/I2tpaNmzYwCuvvEIw\nGGTdunXMmzePZ599lmXLlvH1r3+d//zP/2Tz5s0sW7asv0oXEZEBZDfbyEoeQ1bymB7HQ+EQ/pa6\nnkNejVVUNlWf9/J2iAyfOcz2yM3S9dXR7Zij23OOM+eY7VgUjhJKvwWcHTt2sGDBAgBycnKoq6sj\nEAjgcrmi5zz11FOsXLmS9evXR18zc+ZMXC4XLpeLJ598EoBdu3bx05/+FID58+fz4osvKuCIiCQ4\no8GIxxZZJXqyZ0KP51o72npc3l7ZVEVDa4BgWxPB9iANbQEqglW9WgW6i6UzHNktDpzdQ5DZjr0z\nCDl7BCQ79s6QZNEl8nGn31qkurqa3Nzc6GOPx0NVVVU04BQWFjJjxgxGjRoVPae0tJTm5mbuu+8+\n6uvreeCBB5g5cyZNTU3RIan09HSqqqou+r3T0hyYzf27CqfX27tVSGVgqV3il9omPg3mdhmFB5hw\nwefD4TBN7c0EWoM0tgZpbG08c78tGL1/5vlgdFHEimAl4XDvw5HVZMFldeK0OnBZHTgtDpxWR+dj\nZ49jLmu35ywOzKbz/1M8mNsmHgxY5Oz+g+L3+yksLGTjxo1UVPTceM7v97N+/XrKysq455572Lp1\n6wXf50Jqa4N9U/QFeL1uqqoa+vV7yOVTu8QvtU18GirtYsCKCysuYyoZNqAXc5RD4RAtHS00dvYI\nRXqGmmjq/Bpsb6KxLdjtWOSc0421lNaduqyeI6vJetawmoNh7hSsIRvuJDfJVjcp1mSSrW6Sk9wk\n6dL6Hi4UBPst4Ph8Pqqrq6OPKysr8Xq9AOzcuZOamhqWL19Oa2srx48fZ82aNUycOJFrr70Ws9lM\nZmYmTqeTmpoaHA4Hzc3N2Gw2Kioq8Pl8/VW2iIgIRoMRu9mO3WwHPJf12lA4RHN7cyQIdQtEwc5A\nFDwrNHUdq22po6yxPPIm1Rd+/ySTNRJ2um5JbpK7ApDV1fnYjdviGtJ7ivVbwJk9ezbr1q0jLy+P\noqIifD5fdHhq8eLFLF68GIgMSz3yyCPk5+dTUVHBqlWruPfee6mrqyMYDJKWlsasWbN44403WLJk\nCVu2bGHOnDn9VbaIiMiXYjQYI5OZLQ6wX95rQ+EQTe3NWN1w7FQ59a0N595aIl+/qCu5aE+RAQNO\niyMahFKSuoWgaDCK3Oxme8KtNt1vAWf69Onk5uaSl5eHwWBg9erVFBYW4na7Wbhw4Xlfk5GRwaJF\ni7jrrrsAePTRRzEajTzwwAP8+Mc/pqCggJEjR/L3f//3/VW2iIhIzBgNRpwWB163G0uz46LnhsIh\nAm2N0cBzdgDqutW2+M/0DF2A2Wju2St0nhDUdRssV5sZwpczi2qQ6O8x5aEybj3YqF3il9omPqld\n4ldft01rRxsNnYGn7jwhqCsYNbQ20B7uuOh72c32C4agFGty9LHT4hiQ1agHfA6OiIiIxAeryUK6\n3UO6/eLzicLhMMH2pvP2BJ19rOI8u813ZzQYcVuceB3D+Kfcu0lJGtirwhRwREREBIhssuq0RC5p\nH3Gevca66wh10NAW6BF66s4Timqb/bSFWgfoE5yhgCMiIiKXzWQ0kZqUQmpSSqxLOS9t1SoiIiIJ\nRwFHREREEo4CjoiIiCQcBRwRERFJOAo4IiIiknAUcERERCThKOCIiIhIwlHAERERkYSjgCMiIiIJ\nRwFHREREEo4CjoiIiCQcBRwRERFJOAo4IiIiknAM4XA4HOsiRERERPqSenBEREQk4SjgiIiISMJR\nwBEREZGEo4AjIiIiCUcBR0RERBKOAo6IiIgkHAWcy7BmzRqWLl1KXl4en3zySazLkW6efvppli5d\nyh133MGWLVtiXY5009zczIIFCygsLIx1KdLNq6++yre+9S1uv/12tm3bFutypFNjYyPf//73WbFi\nBXl5eWzfvj3WJQ1a5lgXMFjs3r2bkpISCgoKKC4uJj8/n4KCgliXJcDOnTs5fPgwBQUF1NbWcttt\nt3HrrbfGuizp9Nxzz5GSkhLrMqSb2tpaNmzYwCuvvEIwGGTdunXMmzcv1mUJ8Ic//IHs7Gwefvhh\nKioq+Pa3v83rr78e67IGJQWcXtqxYwcLFiwAICcnh7q6OgKBAC6XK8aVyQ033MDUqVMBSE5Opqmp\niY6ODkwmU4wrk+LiYo4cOaJ/POPMjh07mDlzJi6XC5fLxZNPPhnrkqRTWloan3/+OQD19fWkpaXF\nuKLBS0NUvVRdXd3jB83j8VBVVRXDiqSLyWTC4XAAsHnzZm666SaFmzixdu1aVq1aFesy5CylpaU0\nNzdz3333sWzZMnbs2BHrkqTTN7/5TcrKyli4cCF33303P/7xj2Nd0qClHpwrpB0u4s9bb73F5s2b\nefHFF2NdigB//OMfueaaaxgzZkysS5Hz8Pv9rF+/nrKyMu655x62bt2KwWCIdVlD3v/8z/8wcuRI\nfvWrX3Hw4EHyYPbpGgAABFlJREFU8/M1f+0KKeD0ks/no7q6Ovq4srISr9cbw4qku+3bt/Pzn/+c\nF154AbfbHetyBNi2bRsnTpxg27ZtlJeXY7VaGT58OLNmzYp1aUNeeno61157LWazmczMTJxOJzU1\nNaSnp8e6tCHvb3/7GzfeeCMAkyZNorKyUkPuV0hDVL00e/Zs3njjDQCKiorw+XyafxMnGhoaePrp\np/nFL35BampqrMuRTj/72c945ZVX+N3vfsedd97J/fffr3ATJ2688UZ27txJKBSitraWYDCouR5x\nIisri3379gFw8uRJnE6nws0VUg9OL02fPp3c3Fzy8vIwGAysXr061iVJp7/85S/U1tby0EMPRY+t\nXbuWkSNHxrAqkfiVkZHBokWLuOuuuwB49NFHMRr1/914sHTpUvLz87n77rtpb2/niSeeiHVJg5Yh\nrMkkIiIikmAU2UVERCThKOCIiIhIwlHAERERkYSjgCMiIiIJRwFHREREEo4CjojEXGlpKVOmTGHF\nihXRXZQffvhh6uvre/0eK1asoKOjo9fn/+M//iO7du26knJFZBBQwBGRuODxeNi0aRObNm3it7/9\nLT6fj+eee67Xr9+0aZMWRBORKC30JyJx6YYbbqCgoICDBw+ydu1a2tvbaWtr4/HHH+fqq69mxYoV\nTJo0iQMHDvDSSy9x9dVXU1RURGtrK4899hjl5eW0t7ezZMkSli1bRlNTEytXrqS2tpasrCxaWloA\nqKio4Ic//CEAzc3NLF26lH/4h3+I5UcXkT6ggCMicaejo4M333yT6667jn/+539mw4YNZGZmnrP5\noMPh4OWXX+7x2k2bNpGcnMwzzzxDc3Mz3/jGN5gzZw4ffPABNpuNgoICKisrueWWWwB47bXXGDdu\nHD/96U9paWnh97///YB/XhHpewo4IhIXampqWLFiBQChUIjrr7+eO+64g2effZaf/OQn0fMCgQCh\nUAiIbKFytn379nH77bcDYLPZmDJlCkVFRRw6dIjrrrsOiGyeO27cOADmzJnDb37zG1atWsXcuXNZ\nunRpv35OERkYCjgiEhe65uB019DQgMViOed4F4vFcs4xg8HQ43E4HMZ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "wCugvl0JdWYL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "id": "VHosS1g2aetf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One possible solution that works is to just train for longer, as long as we don't overfit. \n", + "\n", + "We can do this by increasing the number the steps, the batch size, or both.\n", + "\n", + "All metrics improve at the same time, so our loss metric is a good proxy\n", + "for both AUC and accuracy.\n", + "\n", + "Notice how it takes many, many more iterations just to squeeze a few more \n", + "units of AUC. This commonly happens. But often even this small gain is worth \n", + "the costs." + ] + }, + { + "metadata": { + "id": "dWgTEYMddaA-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000003,\n", + " steps=20000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From fd649b2a8d4c7de5eee72e3bc97d797ac5ac5e76 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 02:55:23 +0530 Subject: [PATCH 08/11] Completed sparsity and l1 regularization --- sparsity_and_l1_regularization.ipynb | 1141 ++++++++++++++++++++++++++ 1 file changed, 1141 insertions(+) create mode 100644 sparsity_and_l1_regularization.ipynb diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb new file mode 100644 index 0000000..ee7da00 --- /dev/null +++ b/sparsity_and_l1_regularization.ipynb @@ -0,0 +1,1141 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "sparsity_and_l1_regularization.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "yjUCX5LAkxAX" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 1239 + }, + "outputId": "b4031bba-dc53-4fa6-d5a3-3decf30d5b43" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2626.9 537.3 \n", + "std 2.1 2.0 12.6 2173.2 422.9 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1449.0 295.0 \n", + "50% 34.2 -118.5 29.0 2114.5 431.0 \n", + "75% 37.7 -118.0 37.0 3136.0 646.0 \n", + "max 42.0 -114.5 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1423.0 499.2 3.9 2.0 \n", + "std 1160.5 386.0 1.9 1.1 \n", + "min 6.0 1.0 0.5 0.1 \n", + "25% 785.8 280.0 2.6 1.5 \n", + "50% 1159.0 407.0 3.5 1.9 \n", + "75% 1705.0 603.0 4.7 2.3 \n", + "max 35682.0 6082.0 15.0 52.0 " + ], + "text/html": [ + "
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" + ] + }, + "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": 592 + }, + "outputId": "f3f46365-b3bf-4239-b3df-d4470af8c9d9" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " regularization_strength=0.8,\n", + " steps=300,\n", + " batch_size=70,\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", + "\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.35\n", + " period 01 : 0.31\n", + " period 02 : 0.29\n", + " period 03 : 0.28\n", + " period 04 : 0.27\n", + " period 05 : 0.26\n", + " period 06 : 0.26\n", + "Model training finished.\n", + "Model size: 527\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Sw24zBQcHk5+f3/Bzbm4uQUFBABQVFbFr1y4A3N3dGTZsGCkpKQBs2bKFf/7z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\nHOsCIMdpn1duyj1OLOalP62RrBw2B9W5VVTnVsWOTYdD+Ed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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "yjUCX5LAkxAX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "hgGhy-okmkWL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n", + "stronger regularization gives us smaller models, but can affect the classification loss." + ] + }, + { + "metadata": { + "id": "_rV8YQWZIjns", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " regularization_strength=0.1,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 8c8896b0a1a70aae26d8aae6cf2324c6ab51b021 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 02:57:20 +0530 Subject: [PATCH 09/11] Completed intro to neural nets --- intro_to_neural_nets.ipynb | 1179 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1179 insertions(+) create mode 100644 intro_to_neural_nets.ipynb diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb new file mode 100644 index 0000000..acbd66e --- /dev/null +++ b/intro_to_neural_nets.ipynb @@ -0,0 +1,1179 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_neural_nets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "O2q5RRCKqYaU", + "vvT2jDWjrKew" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 1239 + }, + "outputId": "79a65713-6c5c-4ff7-f68a-2eb03269db8a" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2653.3 541.2 \n", + "std 2.1 2.0 12.5 2180.2 421.0 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1463.0 297.0 \n", + "50% 34.2 -118.5 29.0 2134.0 435.0 \n", + "75% 37.7 -118.0 37.0 3154.2 651.0 \n", + "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1432.9 502.7 3.9 2.0 \n", + "std 1145.5 383.2 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 790.0 282.0 2.6 1.5 \n", + "50% 1170.0 410.0 3.6 1.9 \n", + "75% 1725.2 607.0 4.8 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "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": 662 + }, + "outputId": "dd03a78d-1133-4b7c-bc4c-10b3f4d3c326" + }, + "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 : 165.74\n", + " period 01 : 148.40\n", + " period 02 : 141.10\n", + " period 03 : 133.04\n", + " period 04 : 123.51\n", + " period 05 : 116.84\n", + " period 06 : 117.68\n", + " period 07 : 113.43\n", + " period 08 : 105.82\n", + " period 09 : 104.72\n", + "Model training finished.\n", + "Final RMSE (on training data): 104.72\n", + "Final RMSE (on validation data): 104.57\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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SUpLvuPyll55rqJIanDQwQgghRDOSmprC3r276/Ta+fMX4O7uccflb731QUOV1eCa5FQC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\n7tNORhXO2fQlFZhe0E5lXMrGmJSLcfWHbAZFxDMreToWs4W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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "O2q5RRCKqYaU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution" + ] + }, + { + "metadata": { + "id": "j2Yd5VfrqcC3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search." + ] + }, + { + "metadata": { + "id": "IjkpSqmxqnSM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=2000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "c6diezCSeH4Y", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Evaluate on Test Data\n", + "\n", + "**Confirm that your validation performance results hold up on test data.**\n", + "\n", + "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n", + "\n", + "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)." + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "a33ad28b-733b-4c00-a5c8-e0147903d486" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "# YOUR CODE HERE\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RMSE (on test data): 104.18\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "vvT2jDWjrKew", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "FyDh7Qy6rQb0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n", + "\n", + "Note that we don't have to randomize the test data, since we will use all records." + ] + }, + { + "metadata": { + "id": "vhb0CtdvrWZx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From f69fb5783c71440a97dc2476e54b9ab8f6e375a0 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 03:02:28 +0530 Subject: [PATCH 10/11] Completed improving neural net performance --- improving_neural_net_performance.ipynb | 1725 ++++++++++++++++++++++++ 1 file changed, 1725 insertions(+) create mode 100644 improving_neural_net_performance.ipynb diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb new file mode 100644 index 0000000..7b5ff30 --- /dev/null +++ b/improving_neural_net_performance.ipynb @@ -0,0 +1,1725 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "improving_neural_net_performance.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "jFfc3saSxg6t", + "FSPZIiYgyh93", + "GhFtWjQRzD2l", + "P8BLQ7T71JWd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 1239 + }, + "outputId": "a8d0fc64-1d4b-4605-91c5-18fbfed440fa" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.5 28.6 2657.2 541.6 \n", + "std 2.1 2.0 12.6 2181.0 423.6 \n", + "min 32.5 -124.3 1.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1465.0 297.0 \n", + "50% 34.2 -118.5 29.0 2133.5 433.0 \n", + "75% 37.7 -118.0 37.0 3167.0 652.0 \n", + "max 41.9 -114.3 52.0 30405.0 4957.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1434.1 503.5 3.9 2.0 \n", + "std 1140.8 386.6 1.9 1.2 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 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" + ] + }, + "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": 662 + }, + "outputId": "27420eec-e561-431c-cca7-c3cf916c79ac" + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 150.26\n", + " period 01 : 132.43\n", + " period 02 : 124.55\n", + " period 03 : 110.08\n", + " period 04 : 105.23\n", + " period 05 : 103.29\n", + " period 06 : 104.69\n", + " period 07 : 102.26\n", + " period 08 : 99.88\n", + " period 09 : 103.81\n", + "Model training finished.\n", + "Final RMSE (on training data): 103.81\n", + "Final RMSE (on validation data): 104.26\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IVFkqMFbW3t+D5g3dOHw6jguXUwEY/+sszOqzmzAMw5bxREREqiQVGCvLn4Vp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\n1+bAE7x9pdNqKMtNpbLYyqJiK4WZJrTa2QmHPr+PekcTDY5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+ "text/plain": [ + "
" + ] + }, + "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": 662 + }, + "outputId": "a97eed46-442d-494e-cb5e-366f74bcc5e6" + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " 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 : 161.11\n", + " period 01 : 114.94\n", + " period 02 : 103.21\n", + " period 03 : 86.64\n", + " period 04 : 77.78\n", + " period 05 : 75.50\n", + " period 06 : 73.95\n", + " period 07 : 73.08\n", + " period 08 : 72.64\n", + " period 09 : 72.08\n", + "Model training finished.\n", + "Final RMSE (on training data): 72.08\n", + "Final RMSE (on validation data): 72.96\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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7k5iY6KyyTGGxWOh3+ekxUmpRGmlFGWaXJCIi0qjZnLZgmw2brfriDx8+zJ49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\nQjI0VYIWkhFWZWgh735uezhUBU2tQZ2/PVxdOD5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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "jFfc3saSxg6t" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ax_IIQVRx4gr" + }, + "cell_type": "markdown", + "source": [ + "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n", + "\n", + "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "D-bJBXrJx-U_", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n", + " steps=2000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "MrwtdStNJ6ZQ" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Optimizer\n", + "\n", + "** Use the Adagrad and Adam optimizers and compare performance.**\n", + "\n", + "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n", + "\n", + "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "61GSlDvF7-7q", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 922 + }, + "outputId": "aeb9c602-f65a-47b1-ec40-284b6b883c3e" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "\n", + "_, 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()\n" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 91.75\n", + " period 01 : 73.02\n", + " period 02 : 72.31\n", + " period 03 : 70.14\n", + " period 04 : 70.90\n", + " period 05 : 69.67\n", + " period 06 : 70.03\n", + " period 07 : 70.23\n", + " period 08 : 68.77\n", + " period 09 : 68.47\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.47\n", + "Final RMSE (on validation data): 69.62\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 214.65\n", + " period 01 : 121.88\n", + " period 02 : 112.48\n", + " period 03 : 103.70\n", + " period 04 : 90.70\n", + " period 05 : 76.50\n", + " period 06 : 71.80\n", + " period 07 : 70.69\n", + " period 08 : 69.95\n", + " period 09 : 70.03\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.03\n", + "Final RMSE (on validation data): 71.06\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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dxOJ27doRFlbwTIEdO3bg5+eHu7s7p06dQqvVkp6eTlRUFG3atNFXrCfm7dWM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+ "text/plain": [ + "
" + ] + }, + "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": 735 + }, + "outputId": "aeb81b84-e79c-4700-8713-903b73b56b02" + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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APITDKx0OHDig8ePH68yZM5o4caIKCgpksVgkSc2aNVNOTo5yc3MVEBBge05AQECZcS8v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\nLpKkRo0aqaCgQFlZWXr55Zc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+ "text/plain": [ + "
" + ] + }, + "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": 662 + }, + "outputId": "1c12e122-d076-4916-f647-add402b82aa8" + }, + "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": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 87.30\n", + " period 01 : 75.06\n", + " period 02 : 73.08\n", + " period 03 : 71.70\n", + " period 04 : 71.24\n", + " period 05 : 69.91\n", + " period 06 : 71.97\n", + " period 07 : 68.87\n", + " period 08 : 68.70\n", + " period 09 : 68.57\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.57\n", + "Final RMSE (on validation data): 69.13\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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HzSNTVFREYmIiBQUFlJWVcffdd/PGG2/YP8/MzGTixInceeed9vcWLFjA0qVLadWqFQC/\n+c1vmDRpkqNCFBERkSbOYYXM4sWL6dChAw8++CAZGRlMnz6dFStW2D+//fbbmTBhwjnb3XLLLUyd\nOtVRYYmIiFxSVq/+huHDR573e/Pnz2PSpBuJiGhT7ecPP/wAzz33YkOHd9Ec1rUUGBhIfn4+AIWF\nhQQGBto/27BhA+3btyc8PNxRhxcREbnkHTuWztdff1Gn795334M1FjGASxYx4ODVr2fMmEFycjKF\nhYW8/vrr9O7dG4A777yTWbNmERUVVeX7CxYsYNOmTbi7u+Ph4cGjjz5K27Ztaz2GzVaumRFFRESq\n8fvf/57t27eTn5/Pb37zG1JTU3n77bd55JFHyMjIoLi4mHvvvZf4+HimTZvGY489xhdffMGJEyc4\nfPgwycnJzJo1i2HDhtG/f382bdrEtGnTiIuL47vvviMvL4/XXnuN0NBQHnroIdLT0+nTpw/Lly9n\n7dq1jXKODutaWrJkCREREbz11lvs3buXWbNmsWjRIvsP9+siBmDYsGEMGDCAfv368dlnnzFnzhxe\nf/31Wo/jyDUqQkP9HLqWk9SP8uK6lBvXpdw434crD/D93swq71mtBuXl9W9P6BcbxuQRnWv8/Lrr\nbsIwrHTo0Ink5CPMn/86R44co1evK7j66vGkpaXy2GMP06PHFZSW2sjLK6Ko6DRHj6bw7LMv8t13\nG3j33ffo1u1yTNMkK+sEpaU2wI0XXniZhQsXsHjxUiIiIjlxoohXXnmLb79dxz//+c8Gv95qWmvJ\nYYVMUlISgwcPBiA2NpbMzEzKy8tZs2YNAwYMqHabnj172v89YsQIXnjhBUeFd167j+TSza3Jr6kp\nIiICQNeu3QHw8/Nnz55dfPrpIgzDQmFhwTnf7dmzsgclLCyMkydPnvN5r1597J8XFBRw9OhhLrus\nFwADBw7Cam28nhKH/aVu164d27ZtY8yYMaSlpeHj44PVamXHjh3Ex8dXu82cOXNISEjgiiuuYPPm\nzURHRzsqvFqVV1Qw7/0f6du1FXdN6O6UGEREpHmZPKLzOa0njdlS5u7uDsBXX62gsLCQV175G4WF\nhdx++7Rzvnt2IVLdCJRff26aJhZL5XuGYWAYRkOHXyOHFTI33HADs2bNYurUqdhsNp588kkAsrKy\nCA4Otn8vKyuLBQsWMHv2bCZNmsQTTzyBm5sbhmEwZ84cR4VXK6vFQniIDzsOZlNmq8DdTdPtiIhI\n02OxWCgvL6/yXn5+PuHhEVgsFtasWUlZWdlFH6dNm0hWr/4GgM2bvzvnmI7ksELGx8eH+fPnn/P+\na6+9VuV1aGgos2fPBqBLly68//77jgrpgnRrF8jXP6RyKL2ALlGB599ARETExbRr14F9+/YSHh5B\ny5YtARg+fAQPP/wAu3fvZNy43xAWFsY//vHmRR0nLm4In332KTNnzqBPn774+wc0RPh14tCnlhqD\no5rkvt+XzsLFexgf14Frh3Z0yDGkfjRo0XUpN65LuXFNzSUvhYUFJCVtYfjwkWRlZXLffTP597//\n26DHaPTBvk1ZhVnBhxmv494ujD1HAkCFjIiISI28vX1YufJr/v3vdzHNCu6994FGO7YKmWoYGFgt\nVlqEZHAoqYDikjK8Pd2dHZaIiIhLcnNzY/bsuU45tkaxVsMwDGIDoym3loDnSfYm5zs7JBEREamG\nCpkaxAZVPvptDchm95FcJ0cjIiIi1VEhU4MzhYxby1x2H8lzcjQiIiJSHRUyNWjZIoA2/q2x+OVy\nPO8kuYUlzg5JREREfkWFTC16tuqKaZRj8c1jl7qXRESkmbr++msoLi7m3XffZufO7VU+Ky4u5vrr\nr6l1+zOT4X3++VLWrFnlsDiro0KmFj1bdwXAEpDDHnUviYhIMzdt2q306NHz/F88y7Fj6Xz99RcA\njB17DcOGVb8MkaPo8etadAuNxmJY8AjMZffBXEzTbNT1I0RERC7G7343hWefnUfr1q05fvwYjzzy\nIKGhYZw6dYqSkhLuv/8hunXrYf/+M888yfDhI+nduw//+79/orS01L6AJMCXXy7n448/wGq10L59\nJxIT/5cXX3yePXt28Y9/vElFRQUtW7bkuutu4NVX57NjxzZstnKuu24yCQnjuOee39OvX3+SkraQ\nn5/P88//H61bt76oc1QhUwsvd086+LfjYMVhCkuLSM0qom2Yr7PDEhGRJmjRgWVszdxR5T2rxaC8\nov4T7PcJu4xrO4+v8fOhQ+P59tu1XHfdZNatW8PQofF06hTN0KHD+eGH73nvvX/yzDN/OWe7L75Y\nTseOnfjDHx7km2++tLe4nDp1innzFuDn58fdd9/BwYMHuOmmaSxa9CG33XYHb731OgA//pjEoUMH\nWbjw75w6dYrp029k6NDhwJkljBaycOEC1q5dyeTJN9f7/EFdS+fVNSgaDLD45eoxbBERaVIqC5l1\nAKxfv4bBg4exZs03zJw5g4ULF1BQUFDtdkeOHKJHj14A9OnT1/6+v78/jzzyIPfc83uOHj1MQUH1\n86zt3bub3r0vB8DLy4v27TuSkpICQK9efQAICwvj5MmTF32OapE5j9igaJYd/vLn+WTyGHNllLND\nEhGRJujazuPPaT1x9FpLHTt2Iicni4yM45w4cYJ161YTEhLGY489zd69u3n55b9Wu51pgsVSOZSi\n4ucWo7KyMl588c+8/fa/CQ4O4U9/+n81HtcwDM5eydFmK7Pvz2q1nnWci1/uUS0y5xHlF4mXmyce\ngbnsS8nDVl7h7JBERETqbODAwbzxxqsMGTKMgoJ82rSJBGDNmlXYbLZqt4mKasfevXsASEraAkBx\ncRFWq5Xg4BAyMo6zd+8ebDYbFouF8vLyKtvHxnZn69Yfft6umLS0VCIjHdMQoELmPKwWKzGBnalw\nL6bMcpKDadU3w4mIiLiiYcPi+frrLxg+fCQJCeP44IP3uP/+u+nevQc5OTl89tmn52yTkDCOXbt2\ncN99M0lJOYphGAQEtKRfv/7cfvst/OMfb3LzzdN46aUXadeuA/v27eWll+bZt+/VqzddusRy9913\ncP/9d3Pnnffg5eXlkPMzzIZo13EiRzbJnWnyW5u6kQ/2L6b0cDeujh7KtVoN26may7L3zZFy47qU\nG9ekvNRdaKhfte+rRaYOfll3KYc9GvArIiLiMlTI1EGoVzDBnoG4tczl0LECikvKnB2SiIiIoEKm\nTgzDIDYoGtNSBt4F7E2u/nEzERERaVwqZOooNigGqOxe0nwyIiIirkGFTB3FBHbCwMCtZQ67te6S\niIiIS1AhU0e+7j609YvA8MnneH4huYUlzg5JRETkkqdC5gLEBsWAUYHFL5dd6l4SERFxOhUyFyA2\n8OzHsNW9JCIi4mwqZC5Ax5btcbe4496ycgHJJj6XoIiISJOnQuYCuFvc6NyyA6bnCQrLTpCaVeTs\nkERERC5pKmQukH2WX389hi0iIuJsKmQuUNef55OxBOgxbBEREWdTIXOBInxa4+fhi3vLHPal5GIr\nr3B2SCIiIpcsFTIXyDAMYgOjMd1OU+ZWyMG0AmeHJCIicslyc9SOi4qKSExMpKCggLKyMu6++27e\neOMNiouL8fb2BiAxMZEePXrYtykrK+Phhx8mPT0dq9XK3Llzadu2raNCrLfYoGi+z9iKNSCbXUfy\n6BIV6OyQRERELkkOK2QWL15Mhw4dePDBB8nIyGD69OmEhoYyd+5cYmJiqt1m2bJl+Pv7M2/ePNav\nX8+8efP461//6qgQ6+3sAb97juTC0I5OjkhEROTS5LCupcDAQPLzK1eJLiwsJDDw/K0WGzduZNSo\nUQDExcWRlJTkqPAuSssWAbT2aYXVP49Dx/MpLrE5OyQREZFLksMKmXHjxpGens6oUaOYOnUqiYmJ\nALz00ktMmTKFxx9/nJKSqusVZWdnExQUVBmYxYJhGJSWljoqxIvSNTAa01KO4ZPH3mQ9vSQiIuIM\nDutaWrJkCREREbz11lvs3buXWbNmMXPmTLp06UJUVBRPPPEE7733HjNmzKhxH3WZOTcw0Bs3N2tD\nhl5FaKhfte/3L+vJqtT1WAJyOJxxkjGD1L3UmGrKizifcuO6lBvXpLxcHIcVMklJSQwePBiA2NhY\nMjMzGTFiBFZrZdExYsQIPv/88yrbhIWFkZWVRWxsLGVlZZimiYeHR63HycsrdswJUHlxZWWdqP4z\nIxyLYcGtZQ4/7Mmo8XvS8GrLiziXcuO6lBvXpLzUXU0Fn8O6ltq1a8e2bdsASEtLw9vbmxkzZlBY\nWAjApk2biI6OrrLNoEGDWLFiBQCrVq2if//+jgrvonm6taCDfzsMrwKOF+aTW1hy/o1ERESkQTms\nkLnhhhtIS0tj6tSpPPjggzz11FNMnjyZW2+9lSlTpnD8+HGmTJkCwMyZMwEYO3YsFRUV3HTTTbz3\n3ns8+OCDjgqvQXQNigYDLH657NJyBSIiIo3OMJv4Es6ObJI7X5Pf4YKjvPDDK9gyI+nrPZLf/6a7\nw2KRX6gp1nUpN65LuXFNykvdNXrX0qUgyi8SLzdP3FrmsutITp0GJ4uIiEjDUSFzEawWKzGBncGj\nmJPlBaRmFTk7JBERkUuKCpmLFBtYOWDZ4p/Dbo2TERERaVQqZC6SfbmCgBx2H9HEeCIiIo1JhcxF\nCvUKJtgzELeAXPal5GIrr3B2SCIiIpcMFTIXyTAMYoOiMa1llHnkcTCtwNkhiYiIXDJUyDSA2KDK\n1bytATnsUveSiIhIo1Eh0wBiAjthYGD1z2aPBvyKiIg0GhUyDcDX3Ye2fm2w+BVwKCOX4hKbs0MS\nERG5JKiQaSCxQdFgVGD45rI3Wd1LIiIijUGFTAPpWuUxbHUviYiINAY3ZwfQXHQIaI+7xR1T88mI\niIg0GrXINBB3ixudW3bA8DrJ8RO55BaWODskERGRZk+FTAM6e5bfXepeEhERcTgVMg2o68/zyVj8\ns9mj7iURERGHUyHTgCJ8WuPn4YtbQC67juRgmqazQxIREWnWVMg0IMMwKlfDdj/NSTOPtKwiZ4ck\nIiLSrKmQaWC/jJPJ1jgZERERB1Mh08DOFDIWfz2GLSIi4mgqZBpYyxYBtPZphdU/j32p2djKK5wd\nkoiISLOlQsYBugZGg6UcW4tcDqYVODscERGRZkuFjAPYu5cCctil7iURERGHUSHjAJ1bdsRqWLEG\n5LBHA35FREQcRoWMA3i6taBDQBQW7wIOZWZTXGJzdkgiIiLNkgoZB4kNjAEDDL9c9iare0lERMQR\nVMg4yNnzyexW95KIiIhDqJBxkHb+kXhZPbWApIiIiAOpkHEQi2EhJqgzRotTZBZlk1tY4uyQRERE\nmh0VMg4UG3j2Y9hqlREREWloKmQcyD5Oxj+HPZpPRkREpMG5OWrHRUVFJCYmUlBQQFlZGXfffTeh\noaHMnj0bi8WCv78/8+bNw8vLy77NokWLmD9/PlFRUQDExcUxc+ZMR4XocKFewQR7BpITkMOuPdmY\npolhGM4OS0REpNlwWCGzePFiOnTowIMPPkhGRgbTp08nJCSEhx9+mJ49e/L888+zaNEipkyZUmW7\nsWPHkpiY6KiwGpVhGMQGRfNtyWZOGjmkZRURGebr7LBERESaDYd1LQUGBpKfnw9AYWEhgYGBvPba\na/Ts2ROAoKAg++fNWWxQDICeXhIREXEAhxUy48aNIz09nVGjRjF16lQSExPx9a1sjSguLmbJkiUk\nJCScs93mzZuZMWMG06dPZ/fu3Y4Kr9HEBHbCwMDin81ujZMRERFpUA7rWlqyZAkRERG89dZb7N27\nl1mzZrFo0SKKi4uZOXMmv/vd7+jUqVOVbXr16kVQUBDDhw9n69atJCYmsnTp0lqPExjojZub1VGn\nQWio38Vtjx8dA6M4aCazf0c2LQN9cHfTGOuLdbF5EcdRblyXcuOalJeL47BCJikpicGDBwMQGxtL\nZmYmpaWl3HXXXYwfP55rr732nG06depkL2769OlDbm4u5eXlWK01Fyp5ecWOOQEqL66srBMXvZ9O\n/h05mHeUshZZbNqWSpeowAaI7tLVUHmRhqfcuC7lxjUpL3VXU8HnsKaBdu3asW3bNgDS0tLw8fHh\nrbfe4sorr2TSpEnVbvPmm2+ybNkyAPbv309QUFCtRUxT0dW+XEEOu9S9JCIi0mAc1iJzww03MGvW\nLKZOnYrNZuPJJ5/koYceIjKIoBIwAAAgAElEQVQyko0bNwLQv39/7rnnHmbOnMnChQu55ppreOih\nh3j//fex2Ww888wzjgqvUXUIaI+7xZ2KgBz2HMmFoR2dHZKIiEizYJimaTo7iIvhyCa5hmzye+XH\nt9idu4+SH4ez4K7ReHs6rIZs9tQU67qUG9el3Lgm5aXuGr1rSao6M8uvxT+HfcnqXhIREWkIKmQa\nyS+FTLbmkxEREWkgKmQaSYRPa/w9/LAG5KqQERERaSAqZBqJYRh0CYzGcD9N5qlMcgtLnB2SiIhI\nk6dCphHFBnUGwBqg7iUREZGGoEKmEZ094HeP5pMRERG5aCpkGlHLFgG09g7D6p/HrqNZNPEn30VE\nRJxOhUwj6xoUA5ZyiixZpGUVOTscERGRJk2FTCOzdy8F5GicjIiIyEVSIdPIOrfsiNWwYvXPZrfG\nyYiIiFwUFTKNzNOtBR0CorD4FLIvPQNbeYWzQxIREWmyVMg4QWxgDBhg88rmYFqBs8MRERFpslTI\nOMGZcTKV88moe0lERKS+VMg4QTv/SLysnlgDcth9JMfZ4YiIiDRZKmScwGJY6BLUGaPFKY7kHqe4\nxObskERERJokFTJOcqZ7yfDPYV+yupdERETqQ4WMk8QGxgBg9dd8MiIiIvWlQsZJQr2DCfYM+nli\nPI2TERERqQ8VMk4UGxSNYbWRefo4uYUlzg5HRESkyVEh40RnP4atWX5FREQunAoZJ4oJ7ASAxT+H\n3RonIyIicsFUyDiRr7sPUX6RWP3y2ZWciWmazg5JRESkSVEh42SxQdFgmBS5ZZCWVeTscERERJoU\nFTJO1vXMOBl1L4mIiFwwFTJO1iGgPe4Wdyz+OVp3SURE5AKpkHEyd4sb0S07YvE+yb7jx7CVVzg7\nJBERkSZDhYwLOPMYdrl3FgfTCpwcjYiISNOhQsYFnClkLP7Z6l4SERG5ACpkXECET2v83P2wBuSw\nW8sViIiI1JkKGRdgGEblcgXupRzJT6e4xObskERERJoEFTIuoutZ3Uv7ktW9JCIiUhdujtpxUVER\niYmJFBQUUFZWxt13301oaChPPvkkAF26dOGpp56qsk1ZWRkPP/ww6enpWK1W5s6dS9u2bR0Vokvp\nEtQZ4OfHsHPpExPq5IhERERcn8NaZBYvXkyHDh149913mT9/Ps888wzPPPMMs2bN4v333+fkyZOs\nWbOmyjbLli3D39+f//znP9x5553MmzfPUeG5nJYtAmjt3QqLfy67jmY7OxwREZEmwWGFTGBgIPn5\n+QAUFhbSsmVL0tLS6NmzJwDx8fFs3LixyjYbN25k1KhRAMTFxZGUlOSo8FxS1+BoDEsFWaXp5BaW\nODscERERl+ewrqVx48axaNEiRo0aRWFhIQsXLmT27Nn2z4ODg8nKyqqyTXZ2NkFBQQBYLBYMw6C0\ntBQPD48ajxMY6I2bm9UxJwGEhvo5bN+/1r+sJ6tS1mMJyCYl5xRdOql7qSaNmRe5MMqN61JuXJPy\ncnEcVsgsWbKEiIgI3nrrLfbu3cvdd9+Nn98vyarLSs91+U5eXvFFxVmb0FA/srJOOGz/5xzPCMdi\nWLAG5LBpRzq9OgQ22rGbksbOi9SdcuO6lBvXpLzUXU0Fn8O6lpKSkhg8eDAAsbGxnD59mry8X57G\nycjIICwsrMo2YWFh9laasrIyTNOstTWmufF0a0FH/3ZYvAvZlXK8ToWciIjIpcxhhUy7du3Ytm0b\nAGlpafj4+NCpUye2bNkCwJdffsmQIUOqbDNo0CBWrFgBwKpVq+jfv7+jwnNZsUExYECx+3HSsoqc\nHY6IiIhLc1jX0g033MCsWbOYOnUqNpuNJ598ktDQUB5//HEqKiro1asXcXFxAMycOZOFCxcyduxY\nNmzYwE033YSHhwfPPfeco8JzWbFB0Sw7/AUW/xx2H8klMszX2SGJiIi4LMNs4v0XjuxbdEbfZYVZ\nwUNrn6S42CD6xEQemNy7UY/fFKhP2XUpN65LuXFNykvdNfoYGakfi2EhNqgzlhan2J+Rhq28wtkh\niYiIuCwVMi7ozGrY5T5ZHEwrcHI0IiIirkuFjAuKDYwBwOqfw+4jWndJRESkJipkXFCodzBBLQJ/\nXndJyxWIiIjURIWMi+oaHIPhZuNIYSrFJTZnhyMiIuKSVMi4qDPjZCz+2exLVveSiIhIdVTIuKgu\ngZ0Bfu5eynVyNCIiIq5JhYyL8nH3JsovEotfPruOZjo7HBEREZekQsaFdQ2KwTBMssrTyC0scXY4\nIiIiLkeFjAs7M05Gj2GLiIhUT4WMC+sQ0A53wx1LQDa7NU5GRETkHCpkXJi7xY3owA5YvIrYlZpG\nE18WS0REpMGpkHFxsUGVs/wWexwnLavIydGIiIi4lnoXMkeOHGnAMKQmv8wnk6PuJRERkV+ptZC5\n7bbbqrx+9dVX7f9+/PHHHRORVBHh0xpfd1+s/jnsVCEjIiJSRa2FjM1WdWr87777zv5vjddoHIZh\nVD6G7VHK/qxkbOUVzg5JRETEZdRayBiGUeX12cXLrz8Tx+n6c/dShU8WB9MKnByNiIiI67igMTIq\nXpyjS9AvyxVoPhkREZFfuNX2YUFBARs3brS/Liws5LvvvsM0TQoLCx0enFRq2SKAVt5hHC/PZteR\nLCYO7ejskERERFxCrYWMv79/lQG+fn5+vPLKK/Z/S+PpFhxDRnEmR08mU1zSF2/PWlMnIiJySaj1\nr+G7777bWHHIecQGRrMqZT2Gfzb7kvPoExPq7JBEREScrtYxMidPnuTtt9+2v37//feZMGECf/jD\nH8jOznZ0bHKW6MBOWLBgDdA4GRERkTNqLWQef/xxcnJyADh8+DAvvvgiiYmJxMXF8cwzzzRKgFKp\nhdWDjgHtMLwL2ZF8zNnhiIiIuIRaC5mUlBQefPBBAL744gsSEhKIi4vjxhtvVIuME3QNjsEwIKci\nldzCEmeHIyIi4nS1FjLe3t72f2/evJkBAwbYX+tR7MZXdbkCdS+JiIjUWsiUl5eTk5NDcnIyW7du\nZdCgQQAUFRVx6tSpRglQfhHlF4mnxRNLQDa7juY4OxwRERGnq/WppTvuuIOxY8dSUlLCPffcQ0BA\nACUlJdx8881Mnjy5sWKUn1kMC12CO7Mtaye7k1Mxze5qGRMRkUtarYXMsGHDWL9+PadPn8bX1xcA\nT09PHnroIQYPHtwoAUpVXYOi2Za1k2L346RlFREZ5uvskERERJym1kImPT3d/u+zZ/Lt2LEj6enp\nREREOC4yqVZsYAwAVv9sdh/JVSEjIiKXtFoLmREjRtChQwdCQysnX/v1opHvvPOOY6OTc4R6BxPY\nIpBc/1x2Hslh9JVRzg5JRETEaWotZJ5//nmWLFlCUVER48aNY/z48QQFBdVpxx999BGffvqp/fW2\nbdvo1auX/XVmZiYTJ07kzjvvtL+3YMECli5dSqtWrQD4zW9+w6RJky7ohC4F3YJj+Pb0JvbnHMFW\n3gs36wWt/SkiItJs1FrITJgwgQkTJnDs2DEWL17MlClTaNOmDRMmTGDUqFF4enrWuO2kSZPsRcjm\nzZtZvnw5TzzxhP3z22+/nQkTJpyz3S233MLUqVPrez6XhNigaL5N30SFTxYH0wroEhXo7JBERESc\nok7/Kx8eHs5dd93F8uXLGTNmDHPmzLmgwb6vvPIKd911l/31hg0baN++PeHh4RcesdAlsDOg+WRE\nRETqtIRyYWEhn376KYsWLaK8vJz/+Z//Yfz48XU6wPbt2wkPD7ePswF45513mDVrVrXfX7FiBd98\n8w0eHh48+uijtG3btk7HuZT4uHvT1jeS5Io0dh7NYCIdnR2SiIiIU9RayKxfv57//ve/7Ny5k9Gj\nR/Pcc88RExNzQQf4+OOPmThxov11RkYGxcXFREWdO0h12LBhDBgwgH79+vHZZ58xZ84cXn/99Vr3\nHxjojZub9YJiuhChoX4O2/fFuKJtD1L2pJJSdBRv33h8vNydHVKjctW8iHLjypQb16S8XBzDPPtR\npF+JjY2lffv29OrVC4vl3F6ouXPnnvcAY8aMYenSpXh4eADw4Ycfkp2dXaWrqTqnTp1i7NixrFq1\nqtbvZWWdOG8M9RUa6ufQ/V+M/XkHmb/1dWzH23HnFZPpExN6/o2aCVfOy6VOuXFdyo1rUl7qrqaC\nr9YWmTOPV+fl5REYWHVAaWpq6nkPmpGRgY+Pj72IAdixYwfx8fHVfn/OnDkkJCRwxRVXsHnzZqKj\no897jEtVh4B2uBnuVARks/tI3iVVyIiIiJxRayFjsVi4//77OX36NEFBQbz++uu0a9eOf/3rX7zx\nxhtce+21te48KyvrnMe1s7KyCA4OrvJ6wYIFzJ49m0mTJvHEE0/g5uaGYRjMmTPnIk6teXO3uBEd\n2JE95j52HE4FLqzLT0REpDmotWtpypQpzJ49m06dOvHNN9/wzjvvUFFRQUBAAI899ph9vhdnulS7\nlgBWJq/lvweWUXqoB89PnkyQf82Pwzcnrp6XS5ly47qUG9ekvNRdTV1LtT5+bbFY6NSpEwAjR44k\nLS2NW265hZdfftkliphLXWxQZSuMHsMWEZFLVa2FzK9XVg4PD2fUqFEODUjqLtynFT5uvlgDcth1\nNMfZ4YiIiDS6C5rb/teFjTiXYRh0C47GcC9l97Gj1NJLKCIi0izVOth369atDB8+3P46JyeH4cOH\nY5omhmGwevVqB4cn59M1KIbvM7ZyyuM4aVlFWg1bREQuKbUWMitWrGisOKSeugT9vFxBQDa7j+Sq\nkBERkUtKrYVMmzZtGisOqaeWLQII8wojozybnUezGX3luTMmi4iINFcXNEZGXFP3kBgMawU/5R7G\nVl7h7HBEREQajQqZZiA2sHIG5HKfTA6mFTg5GhERkcajQqYZiA7shAUL1gDNJyMiIpcWFTLNQAur\nB+3922F4F7Iz5ZizwxEREWk0KmSaie4hMRgGpBQdpbjE5uxwREREGoUKmWYiNqhynIzhn82+ZHUv\niYjIpUGFTDMR5RdJC4snloBsdh3JdXY4IiIijUKFTDNhMSzEBnXG0qKEHWkpzg5HRESkUaiQaUa6\nBleuhp1rppJbWOLkaERERBxPhUwz0vXncTJW/2w9hi0iIpcEFTLNSIhXMC09ArH457LraLazwxER\nEXE4FTLNTPeQaAw3G7syDmOaprPDERERcSgVMs1MbFDlOJkSj+OkZRU5ORoRERHHUiHTzHQJ7AyA\nJSCH3XoMW0REmjkVMs2Mj7s3bXzaYPHJZ+fRTGeHIyIi4lAqZJqhHiFdMCwmP+UfwlZe4exwRERE\nHEaFTDN0ZrmCCp8sDqYVODkaERERx1Eh0wx1CGiHm+GOJUDzyYiISPOmQqYZcre40TmgAxavInak\npDo7HBEREYdRIdNMdQ+pfAw79dRRiktsTo5GRETEMVTINFNn5pMx/LPZl6zuJRERaZ5UyDRT4T6t\n8LH6Yg3IYZfmkxERkWZKhUwzZRgG3UKiMdxL2XnssLPDERERcQgVMs1Y15+7l3LNNHILS5wcjYiI\nSMNzc9SOP/roIz799FP76507d9KjRw+Ki4vx9vYGIDExkR49eti/U1ZWxsMPP0x6ejpWq5W5c+fS\ntm1bR4XY7J2ZT+bMY9iDe4Y7OSIREZGG5bBCZtKkSUyaNAmAzZs3s3z5cg4cOMDcuXOJiYmpdptl\ny5bh7+/PvHnzWL9+PfPmzeOvf/2ro0Js9gJa+BPaIoxMv2x2Hs1SISMiIs1Oo3QtvfLKK9x1113n\n/d7GjRsZNWoUAHFxcSQlJTk6tGavR2gMhqWCHccPaNCviIg0Ow4vZLZv3054eDihoaEAvPTSS0yZ\nMoXHH3+ckpKq4zays7MJCgqqDMxiwTAMSktLHR1is3ame8nmncG893/ktSU7yTtx2slRiYiINAyH\ndS2d8fHHHzNx4kQAbrnlFrp06UJUVBRPPPEE7733HjNmzKhxW9M0z7v/wEBv3NysDRbvr4WG+jls\n341hYGAv3thppVWnYtwtPmzek8mOQ7lMTYhl3KAOWK1Nc7x3U89Lc6bcuC7lxjUpLxfH4YXMpk2b\nePTRRwHs3UYAI0aM4PPPP6/y3bCwMLKysoiNjaWsrAzTNPHw8Kh1/3l5xQ0f9M9CQ/3IyjrhsP03\nlu5BsWzP3oVH+KdcFtWVg9uCeHPJTlZsPMK00V3oHBng7BAvSHPJS3Ok3Lgu5cY1KS91V1PB59D/\nHc/IyMDHxwcPDw9M0+TWW2+lsLAQqCxwoqOjq3x/0KBBrFixAoBVq1bRv39/R4Z3yZje7QYmdh6H\nj7sPB05vh9g1tO67i7RTR3n2X1v4x+d7OFGsLjwREWl6HNoik5WVZR/zYhgGkydP5tZbb8XLy4tW\nrVpx7733AjBz5kwWLlzI2LFj2bBhAzfddBMeHh4899xzjgzvkuHp5slVUcOIjxzMj1k7WZWyjsOF\nybTomoL1dAAb0qL44c3jTB7ehcE9w7EYhrNDFhERqRPDrMtAFBfmyCa55tzkd7jgKCtT1vFj5k4q\nqMAsa4Etoy2Rlm7cOqoXUa1ct8+2OeelqVNuXJdy45qUl7qrqWvJ4WNkxDV1CGjHjIB25JbksTr1\nW75N20xJ5AGOVxzimdVJXBF0JVOGXIG3py4RERFxXfordYkL8gzk2s7jGdt+FN8d28IXh9dQGJrK\nj6Sy7ct1xLcdzG97XYnV4rgnw0REROpLhYwA4OnWguFtBzE0ciA/Zuxi0Z5vyPNNZ2XeYtZ9/Q1X\ntR/C6E4D8bDW/hSZiIhIY1IhI1VYDAuXt76My1tfxvb0Q/x72xcUuh9heepnfJ36DcPaDiA+ahAt\nWzStR7ZFRKR5UiEjNeoZ0ZHLwu9kw94jfLjjG0oDDvN1ympWpqylb6vejIgaTJRfpLPDFBGRS5gK\nGamVYRgM6tqBKzrdxiffHmDl4e+wtDrC9xlJfJ+RROeWHRjRdgiXhXTDYjTNWYJFRKTpUiEjddLC\nw8oN8V0YfFkk//pyLz8lH8Cj9VEOcJgD+YcJ8QpmeOQgBoZfgaebp7PDFRGRS4TmkamFnu+vnmma\nfLc7gw9WHuBERQ5+UWlUtEyl3LTh5eZJXPiVDIscRLBXoEOOr7y4LuXGdSk3rkl5qTvNIyMNxjAM\nBnZvTa9OwSxee5iVW/0wrR1p3z2XIvcDfJOyllWp6+kV2oMRbYfQMaCds0MWEZFmSoWM1Ju3pztT\nRscwuGc473yxj8PbPPBs0Zq+/W1kuO1ia+Z2tmZup71/FCPaDqZ36GWaj0ZERBqUupZqoSa/uqsw\nTdZuS+e/qw9SVGIjMsyH+CFe7DuVxM7sPZiYBLZoybDIOAZF9Mfb3avex1JeXJdy47qUG9ekvNRd\nTV1LKmRqoQvswhUWl/LxqoOs33EMgKG9whnevyWbszex8dgWSstL8bB6MDD8CoZHDibMO+SCj6G8\nuC7lxnUpN65Jeak7FTL1oAus/van5PPul/tIyyrC18ud64d34vKuAWw89j1rUjeQdzofA4MeIV0Z\n0XYI0S07YtRx1W3lxXUpN65LuXFNykvdqZCpB11gF8dWXsE3P6TyyfrDnC4tp1Mbf6aN7kKbUG9+\nzNrBypT1HClMBiDSN4IRbYdweateuFtqH7qlvLgu5cZ1KTeuSXmpOxUy9aALrGHkFpbw/soDbNmb\niWHAyL6RTBzSEa8WbhwqOMrKlHX8mLkDExN/Dz+GthnI4DYD8PPwrXZ/yovrUm5cl3LjmpSXulMh\nUw+6wBrWzkM5/Our/WTmnSLA14MbR0RzZdcwDMMg51Quq1O/ZUP695SUl+BucaNfq8uJbzuYCN/W\nVfajvLgu5cZ1KTeuSXmpOxUy9aALrOGV2cpZ/l0yyzYexVZeQdd2gUwdHUN4sA8AJbYSNh7bwqqU\n9eSU5ALQNSiG+LZD6BYUg2EYyosLU25cl3LjmpSXulMhUw+6wBwnM6+Y9776iR2HcrBaDK4eEMW4\nge1p4V45z0yFWcH27N2sTF7HwYLDALT2DiO+7WDG9RhGQd5pZ4YvNdA947qUG9ekvNSdCpl60AXm\nWKZpkrQ/m39/vZ+8E6cJCfDk5qti6B1d9ZHs5MJUVqas44fMbVSYFfh5+DAw/EoGRwxw2DIIUj+6\nZ1yXcuOalJe6UyFTD7rAGkdJqY2l3x7hy+9TKK8w6d05hJuviiakZdVJ8/JPF7A2dSMbjm3iRGkR\nBgaXhXRjaORAYgOj6/z4tjiO7hnXpdy4JuWl7lTI1IMusMaVlnWSd7/cz/6UfDzcLFwzqD1jrozC\nzWqp8r2AwBZ8sftb1qRuIPlEKgCtvEMZ0mYgA8L74uVW/1mD5eLonnFdyo1rUl7qToVMPegCa3ym\nabJx13E+XHmAwuIywoO9mToqhq7tg+zfOTsvRwqTWZO6gaSMbdjMcjysHlzZ+nKGtYk752kncTzd\nM65LuXFNykvdqZCpB11gzlNcUsaitYdYlZSGCQzo1orJIzrT0rdFtXk5UXqSDembWZf2HXmn8wGI\nbtmRoZFx9ArprsUqG4nuGdel3Lgm5aXuVMjUgy4w5zt8rJB3v9jHkeMn8Gph5bdDOnLD6Fhyc4uq\n/X55RTk7c/awNnUje/N+AiDAw58hbQYQF9GfgBbV3wjSMHTPuC7lxjUpL3WnQqYedIG5hooKkzU/\nr6xdfNpGx4gARveL5PKY0HPGz5zteFEma9M2sunYFkrKT2M1rPQJu4yhbeLoGNBOg4MdQPeM61Ju\nXJPyUncqZOpBF5hrKSwq5cNVB9iw8zgAAb4exPduw9DeEbT0bVHjdiW2EjYfT2JN2kaOF2UAlWs7\nDY0cSL9WffCwejRK/JcC3TOuS7lxTcpL3amQqQddYK7ptAn//Xo/3+48xqnT5VgtBn27hDKybySd\n2wTU2NJimiY/5R9iTeoGtmfvosKswMvNi4HhVzCkzUDCvEOq3U7qTveM61JuXJPyUncqZOpBF5hr\nOpOXklIbG3dlsPKHVNKyK8fMRIX5MqJvJP27tbLPElydvJJ81qdv4tu0TZwoO4mBQdfgGIa1iaNb\ncBcsRs1dVlIz3TOuS7lxTcpL3amQqQddYK7p13kxTZN9yfl8k5TK1v3ZVJgmPp5uDO4ZTnyfNoQF\nete4r7IKGz9m7mBt2gYOFRwFIMQziCGRAxkY3g8f95q3lXPpnnFdyo1rUl7qToVMPegCc0215SW3\nsITVP6az9sc0CovLMIDLOgUz4vJIenQMwlLLAN+UE2msTd3A9xlbKauw/bwCdx+GRsbR1q+Ng86m\nedE947qUG9ekvNSdCpl60AXmmuqSlzJbBVv2ZbIyKZWDaYUAhLX0YsTlbRjUMxwfT/caty0qK2bj\nse9Zl7qR7J9X4O4Y0I6hbeLoE3YZbha3hjuZZkb3jOtSblyT8lJ3jV7IfPTRR3z66af21zt37uQ/\n//kPs2fPxmKx4O/vz7x58/Dy+mU6+UWLFjF//nyioqIAiIuLY+bMmbUeR4XMpedC83LkeCErf0hj\n054MymwVeLhZGNC9NSMub0NUq5rnlakwK9ids481aRvYk7MfExM/D18GRfRncER/Aj1bNsTpNCu6\nZ1xPwelCvkpezZBOV9DKEuHscORXdM/UnVNbZDZv3szy5cv56aef+NOf/kTPnj15/vnniYyMZMqU\nKfbvLVq0iJ9++onExMQ671uFzKWnvnk5eaqMddvTWZWURnZBCQAxkQGM6Hv+OWkyi7NZl7aRjce2\ncMp2CothoWdId4ZFDiS6ZSfNSfMz3TOuwzRNvs/Yykf7l1BsO4WbxY3fX3YL3YNjnR2anEX3TN3V\nVMg0Shv5K6+8wgsvvICXlxe+vr4ABAUFkZ+f3xiHFwHA18udq/u3Y0y/KLYfzGFlUio7D+eyP7WA\nAF8Phvduw7Aa5qQJ8w7huuhruKbjGL7P2Mra1I38mLWDH7N20NqnFcPaxHFl6z54unk64cxEqios\nPcH7exexLXsXHlYProoaxpq0Dbyx/Z/8vud0FTPSrDi8RWb79u38+9//5rnnnrO/V1xczOTJk5k/\nfz6dOnWyv79o0SLee+89WrZsic1mIzExkW7dutW6f5utHDc3raMj9ZOWdZLPvz3M198nU1xiw2ox\niOsZwbhBHejWIajWOWn2ZR/iiwOr+S51K+UV5Xi5eTKswwDGdB5GG38tWCnOsSF5C2/98D4nSovo\nHhbDzH7TCPMNYUfGXp5f9yoVpskfB/0Pl0f0cHaoIg3C4YXM448/zrhx4+jfvz9QWcTMnDmTCRMm\ncO2111b57sGDB0lJSWH48OFs3bqVxx9/nKVLl9a6f3UtXXockZeSUhvf7crgm6RU0rIq56RpG+bL\nyDrMSVNw+gQb0jexLu07CkorBxZ3CezMsMg4egR3vaQWrNQ94zwnSk/ywf5P2Jq5HQ+LOxM6j2Vo\nm4H2OZFCQ/1Yty+J17a/jWlWcMdlt9AjpKuToxbdM3XntDEyY8aMYenSpXh4eGCz2bj99tsZN24c\nkyZNOu+2gwYNYu3atVitNf8hUCFz6XFkXkzTZH9KPt/8kErSz3PSeLeonJNmxOW1z0lTXlHOtuxd\nrE3dwE/5hwAIbNHy5wUrr8TPw9chMbsS3TPOsTVzB+/vW8TJsiI6BbRnatfJ58xUfSY3+3IPsHD7\nPzDNCm6/bBqXhdTe6i2OpXum7pwyRiYjIwMfHx88PCrXsnnzzTe58sorayxi3nzzTcLDwxk/fjz7\n9+8nKCio1iJGpKEZhkGXqEC6RAWSW1jCmh/TWfNjGl9+n8JX36f8PCdNG3p0DD5nThqrxcrlYT25\nPKwn6SePVy5YefwHPj20gs8Pf8XlrXoxLDKO9v5RTjo7aW5OlhXx4b5P+CFzG+4WN66LvobhkYNq\nnZm6S1BnZva8jYXb/8HfdryrYkaaPIe2yOzcuZO//vWv/O1vfwNg8ODBREZG4u5eOYdH//79ueee\ne5g5cyYLFy7k+PHjPM+YqRAAACAASURBVPTQQ5imic1mY9asWfTs2bPWY6hF5tLT2Hkps1Xww75M\nVialcSCtAKickyb+8jYMPs+cNKdsp9h0LIm1aRvIKM4CIMovkqGRcfQN64WHteZtmyLdM41nW9Yu\n/rPvv5woPUkH/3ZM6zqJVj5hNX7/17nZn3eAV7f9gwqzgjtUzDiN7pm604R49aALzDU5My9Hj5/g\nm6RUNu0+e06aVoy4PLLWOWlM02Rf3gHWpG5gR/ZuTEx83L2JC7+SbsExhHqFENDCv8mv8aR7xvGK\nyor5aP+nfJ+RhJvFjWs6jmFE2yHnvXaqy83+vIO8uu3vKmacSPdM3amQqQddYK7JFfJy8lQZ67cf\nY2VSqn1OmujIAEbWYU6anFN5rE//jg3pmzlZVmR/393iTqhXMKHeIYR5hfzyb+8QAjz8m8RcNa6Q\nm+ZsR/Zu/rP3vxSUnqCdX1tu6TaZ1j6t6rRtTbnZn3eQhdv+TrlZwe09ptIztHtDhy210D1Tdypk\n6kEXmGtypbz8//buPD6q+t7/+GvW7AnZQxJ2gZAEsrApiOC+tS4gq1D99dYudrXoo1xqqz7aR3ux\n114fIrVa7L0WUcKiFbXgxiIVDEgW1rBv2ROSkITZZ87vj5mEhGzDkGTOwOf5zyw5Z/INn3OOb7/n\nO9+vy6Ww76RnTpqT7uUMosKMTM9OZnp2CtERHeekaWF32tlXe4jy5gpqzOepNtdSY6rF4rR22LYl\n5CSExhEfEkd8aKw77Kgs5KipNtcSk93MhmMf8nXlN+g1Ou4fdhe3D77Fq2/EtQxgz04fiLnZ0uk2\nxzw9MxJm+p+cM96TIOMDOcDUSa11qaozsaWgjH/vr8Bsdc9JM350PLflpjIyNcqrsKEoCk32ZmpM\nl4JNtbmWWs+j1WnrsI9RayDeE3DcQSe29XmkMaJfQ45aaxPIDp4/wjsl62mwXmBwRAqLxswlOdy7\neYqq6k38Y/MRDp+pJyU+jKdmZxET2fmkjW3DzH9kLiRLwky/kHPGexJkfCAHmDqpvS5Wm5NdhyrZ\nsreUUs+cNKnx4dw+PoUb05MIMvr2TTxFUWi0NVPTJuC0PprPY+ss5OiM7p4cT+9Ny2N8SByRxvBe\nDzlqr00gMTssvHfsI3ZW7Ean0XHv0Du4a8gMr3phHE4Xn+w+y8avTmN3uEiJC6Os9iKxkcE8Mz+7\ny2kEjtWf5C/Fb0qY6UdyznhPgowP5ABTp0CpS+ucNAVlFBypaTcnza25KSR2MyeNL7+r0dZEtckd\namrMtZ7n7rBjc9k77BOkM5IQEkdcu4Djvn0VYfAt5ARKbdSupO4Ybx9eR721gdTwZBaNmUNqhHcL\nPp4ou8Bbm0sorblIZJiRBXeMZGJaAlv3VfD2phKiwo08PS+HlLiwTvc/Vn+Sv+z7Ow6Xg+9lLiQr\nXmYA7ktyznhPgowP5ABTp0CsS32Tle1FZWwrKqfxorvnZOxw95w0GcNiuh0cfLUUReGCrbFNL07b\noHMeeychJ1gXdCnYtPTmeHpywg1hXYacQKyNmlgcFt4//jH/Ls9Hq9Fyz5DbuHvobei1PU/5ZbY6\n2LD9BFsLylCAW7KSmX3riNbpAeLjI3jnX4d494tjhIcYWDw3myFJnf+H4XjDKVYUv4nD5eA/MheS\nLWGmz8g54z0JMj6QA0ydArkuDqeLvUdq+KKglOOl7jlpNBqIjQwmMTqEhJhQEgd4HqNDiB8Q0qch\nx6W4LvXktLlNVePpzbG7HB32CdYFkxAa6xl03L43Z1hyErW1zX3W3mvZkbrjvF2yjjpLPclhSSxK\nn8PgiFSv9i04WsPqz45S32RlYGwoj92TxqhBA9pt03LefFlczlubSggO0vGL2VmMTB3Q6WdKmOkf\ngXw9628SZHwgB5g6XSt1OVPZxI595ZRWN1PVYOZCc8cxLq0hJyaUhOgQEqNbHvsn5FywNrb23rTt\nzakxn8fRSciJC41hfHw2k5JySepmcjZxicVh5YMTm/iybCdajZa7Bs/gnmF3YPCiF6au0cLqz45S\neKwWvU7D/TcN5b4bh2DQdzwu2p43+YeqWPnRIXQ6DT+dNY6MoTGdfn67MJPxKNkJY6/ujxUdXCvX\ns/4gQcYHcoCp07VaF4vNQXW9mep6M1X1JqrqzFTXm6iqN3PhYvchJzE6hIRo92NiTChxUcF9HnIa\nrBcu+3ZVDccvnMJsd3/Fd0jEICYNzGVCQjbhxs7HY1zvjtWf5O3Da6m11JEUlsh3xsxhSOSgHvdz\nuRS2FpaxYfsJLDYnowYN4LF7RjMwtut/58vPm6J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CUlNTGTt2LAA33HADzz33\nHEajEUVReOKJJ9Dr5RIoRKCT1a+FEEIIEbDk1pIQQgghApYEGSGEEEIELAkyQgghhAhYEmSEEEII\nEbAkyAghhBAiYEmQEUIIIUTAkiAjhBBCiIAlQUYIIYQQAev/A0sUO6r0Y1nOAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "GhFtWjQRzD2l" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "OMoIsUMmzK9b" + }, + "cell_type": "markdown", + "source": [ + "These are only a few ways in which we could think about the data. Other transformations may work even better!\n", + "\n", + "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n", + "\n", + "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n", + "\n", + "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XDEYkPquzYCH", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "b7atJTbzU9Ca" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Use only Latitude and Longitude Features\n", + "\n", + "**Train a NN model that uses only latitude and longitude as features.**\n", + "\n", + "Real estate people are fond of saying that location is the only important feature in housing price.\n", + "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n", + "\n", + "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n", + "\n", + "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5McjahpamOc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "P8BLQ7T71JWd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1hwaFCE71OPZ" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "djKtt4mz1ZEc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Dw2Mr9JZ1cRi" + }, + "cell_type": "markdown", + "source": [ + "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances." + ] + } + ] +} \ No newline at end of file From 87f154b8b4874d3293188ca45d23943f218f3325 Mon Sep 17 00:00:00 2001 From: Hritik Vijay Date: Thu, 31 Jan 2019 03:31:16 +0530 Subject: [PATCH 11/11] Completed handwritten digits --- ...classification_of_handwritten_digits.ipynb | 2750 +++++++++++++++++ 1 file changed, 2750 insertions(+) create mode 100644 multi_class_classification_of_handwritten_digits.ipynb diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb new file mode 100644 index 0000000..3d33813 --- /dev/null +++ b/multi_class_classification_of_handwritten_digits.ipynb @@ -0,0 +1,2750 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "multi-class_classification_of_handwritten_digits.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "266KQvZoMxMv", + "6sfw3LH0Oycm" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "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": 236 + }, + "outputId": "503fff36-07b5-4e4c-87c2-db9283158457" + }, + "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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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 ... 775 776 777 \\\n", + "5594 4 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n", + "4214 3 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n", + "5183 8 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n", + "8482 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n", + "8944 7 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n", + "\n", + " 778 779 780 781 782 783 784 \n", + "5594 0 0 0 0 0 0 0 \n", + "4214 0 0 0 0 0 0 0 \n", + "5183 0 0 0 0 0 0 0 \n", + "8482 0 0 0 0 0 0 0 \n", + "8944 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": 424 + }, + "outputId": "a414e958-1edf-455a-d399-696c3ecd874e" + }, + "cell_type": "code", + "source": [ + "mnist_dataframe.loc[:, 72:72]" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "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": 978 + }, + "outputId": "82dd8b00-8547-4388-c530-3416cfe9d38a" + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=35,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.67\n", + " period 01 : 3.84\n", + " period 02 : 3.74\n", + " period 03 : 3.58\n", + " period 04 : 3.29\n", + " period 05 : 3.37\n", + " period 06 : 3.29\n", + " period 07 : 3.33\n", + " period 08 : 3.23\n", + " period 09 : 3.29\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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79oLHd+7cyY033siiRYt47bXXLI8/++yzLFq0iMWLF5OW1vc2N4kL1+Dt4czu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\nCSHKZCQ3zU5ump1rLw9cp9/bP8ipxo7g2HxVvZfykx7KT3q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5yVGOiMgkaWVqi+eREBGpFoOEiIgk4IiEiIgkESwYJEREJAFHJEREJAmDhIiIVCspKQkn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\nynL478GDBzFmzBh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C\n", + "text/plain": [ + "
" + ] + }, + "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": { + "base_uri": "https://localhost:8080/", + "height": 978 + }, + "outputId": "75f0999b-a447-4bbf-ad5b-13a44a6a1bfb" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n", + "#\n", + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \n", + "\n", + " periods = 10\n", + " 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\n", + "\n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.78\n", + " period 01 : 4.01\n", + " period 02 : 2.68\n", + " period 03 : 2.65\n", + " period 04 : 2.21\n", + " period 05 : 1.98\n", + " period 06 : 2.31\n", + " period 07 : 1.57\n", + " period 08 : 1.80\n", + " period 09 : 1.69\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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MlClT8PLyuu5Rt/iDUqFgYkwEBiB++3mjxysUCsZHxAKw8vyvJq5OCCGEJZjt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\nkA70V0DsAef8Gd5ERHTL/HxlmGxQY7JBPez7AxYbmtp6hh2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" + ] + }, + "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": 350 + }, + "outputId": "f3772059-0c30-4b8b-f90b-03076442d33f" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 1173 + }, + "outputId": "1c2138cc-5019-437a-c884-e7076ae25894" + }, + "cell_type": "code", + "source": [ + "print(classifier.get_variable_names())\n", + "\n", + "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n", + "\n", + "print(\"weights0 shape:\", weights0.shape)\n", + "\n", + "num_nodes = weights0.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights0.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n", + "weights0 shape: (784, 100)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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YDAaDwWAwGEYRl5Q19Q+CZn/4d7vVa0kkhWBb5IY9FSovjKy3yp6G7KC127Hq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1Ry5fU4h+/K9Wn7f21x4qmBNU4T87/tgJS3CHOAj+YN23Ww8DrUemg7jFoPobFST54wCY4V8S24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\nDrIlD5G25N3V2Dvky23uV+Y81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72UmYO5iF0Mw+J0uYOGHRiL7GLbUKVLQfDzxJUWvLZ5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\ndbjfGVdp6KyDg+j73fVViMnVE1NHH+5j10X0g6RZeq0f7cc8w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dtPOuKm2jYV19yNugyz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UyM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L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\n1aSIyCzVE2Bdo4jWpCaUQDfp1iGZp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N1d53GdmyF/s85hFEl8WdAYh52xyJbJrO+PTdZ2\nmKs3zffK2/4IK7hXjx9X9Thn02Wy314S1trtA2QBfLEJOs6HH77RK7ttwxrT3HXQcXee1NdyiGwZ\neey0vlWr6rHlbSAHc8/GlTr/U+sBtOloHz5voENfyxi6ftP0sI8IbIkbmKbtSWMoz0U3aciTK7Te\nOiYJ46/hCPLHuLkd6kjfHBuD6zR1camq9+STr3vl+Dh89sAw5uiZ0/V7/IXQdneQ1WO7M3ZyqI1j\n4nF8Da9dUvXYHvzCq1VeOTtXnzvnk/KloZy2QOci4tciDedK8mVr3Xh2XqlX5hwkPWe0fpnnL84R\n88jDW1W9JLJ97DwES87cTTrfldD1q35Ga6//RHqcntM5X1E/2dCHnXw2gZnQvBdmoM9e2lal6k3d\nALvK0AXkVMibV6rqNe2q8cpZZHnO5yeibXNlo0Scqu3Yt7T2aPvTlTdC855Ae5rhpg5VL3QZf3M+\njF7HGvnQdthwBv3oMzFOXh22yM2dhbwfRSnQ1nPfERGpKEJev2iyWt73xAFVb/MXYfGZmod8O4k5\nes8WE4vj86Vj3Tn3x9OqXnYKcivMvxNrC89d1xsfzVdN+/X6XrIQ+Tt43zPi5JIJ+zAuOJdHdJxu\nm7ZDmOfSqd82765R9cbpu9IXoW3qnkd/y1ii8xRwHoTM5chH09+k96gxtN4N91Cei/I0Va+b8kZx\nDq/4NL337L2EPBopszDOOT+YiEjyVJ2XLtJkrUVbhS6/dw5GfwHWncM0fkVEZt6AfWTXFZwXr30i\nIjnLcH1r9sDOWGft0XlTErKxL81dinuI2qf1XNvWiXlk/iMYi3mbp6h6HY/iHMtuwLzJ+cxERMru\nRw4pzosY76xvYbIH59woCU7OkFC7ntsjCeeIyczQuXMaab1vPnnIK+fMWajqcd6VYcr12N+vreJD\n9ejfjS/Bbv5crd5/7DyFe7WvfAn3n/U7MJclZOr8PaP92B+2vIH7mSmP6Hwpw5QLKVCA8TwypPPG\ncR4rH90vxibq2+/MxTTuG9CPWnbo3IScn+960PQGrnX26hL1Wt0fsOZzbiOes0RELv8O153zzLBV\nuohI3TbkiZz9uZVeue2U3lvEL8K+I9SCeZ7vxwIleg70+bB+xqVg7vZP0fXO/Qo5cX786qteeXqB\nnqM/9NnbvXLrHhxDcK7OO7VqLa4Z59mNT9Fz76iTp8fFImcMwzAMwzAMwzAMwzAmEXs4YxiGYRiG\nYRiGYRiGMYlcU9bUXYXQxqKtM9RrQz3vHh430qNlLv58hL4ODtZSWYeg1r2CMKjkMoQdxTlWtxxm\nFkOSibuWLvXK553QtmWrZuOzp5JUa7a2Czz344Neef5HYbU21KnDiGNI1sMWwK4Fsy8FIZi1e2Hj\nlpygw5vyb9TWgZEmnuxK0xfkq9c4jJDbii2oRURGSVpS+xRCOTt6dNht4WyEgqWUIxSt46S2L0sg\nOcDVlyBjG6hHmFpcspbEJOYjRIzlLGybKKLlag2vQtohjr1fVAz+DlF4b/YK3Y7Nu9DnOHxPWVmK\nSLidJDI6qvN946PwVFci0U92mLkUUndqm7Yp5H43pxgHuHG+trvedwbtsWoawt+fO3hQ1fvw+vVe\nuaYNx9RFVn8Bx9rQR/aB1Y9qOZWql4t6KWS/vet3e9+tuoiIZKfiu8qdUNWmLozTWRSSzn1ARGSs\n7/pZMIuItJO8is9LRKR9HyRKHNKb6FgzxiRg2s6djnM59LYOsV73IMJEebz0OZaQ99+5wSs3X4Ak\nISUJ13B8UMshO45CgpI8FdIj16aw8wT+ZmlkUrHuFyefOeaVp62HrI7nJxGRVpIisgWia/8cl4L5\nq2SmRJT+Kwg5ZummiJaoDtZjHurs0+tlWy9eW0SyGVfi2kfrS3I5r4t6bgyTDLD8wblemUNpXavq\n+CBeY6vTOEf60ELWogkkI0nL1P3y1KsIFS/MxNzfe05LgQpovRsL45jGnD6WWKTlNpEmjuQObP0t\nItJ8HP27hCyQWZIkItJXjRD2ZFIu5K2bpuqtL4YspOcc5seBGr1+5qwma+5GrIVsY91XpyVYLD/s\nOwfp7+pHVqp6+3+yxyvPuwufzdI5EZHuS2jvQQobHxvXfXPGGozT4HSMg+FevQecP1/vsyJJ/iZc\n9Ann+Kpfw9pfeRf2gM42QIZpzxrHY8LZy7JEqes05kmWooloGRHLBTuvYBy88rqWwBekYw7Nrcf8\nnFmoZZ3+MvSj09tJGl6Uo+oF5yDUntvDtadPpTmFz4PlbCIiHYe15DDS8NzEUnsRkfY9WBdZFlI6\nTcsO3n4W+5P1D6/yyq4MvPsY2o6vb0qllhkzI320HpMdeeisli9WrIdEqfci2pvXAhGRiQmIqAab\nsDbkb9Typ/pXIdkpuAHz0OVfH1P1eK7MKEEfaX6zRtXLqNC2zJEkMAXXsv3AVfVa7kbIcAfJrnio\nskHVS8jEte2lfUrjVb23aSYpdcEafPbyBXquWbgWi39CLq4RS4pcqXwuzSlNb0D25gtoaV+cH33i\nwm/e8so560tVPZaW8R5vYswV0oFR2v+NO3uClp24H6lcJREnSClL2px25Hsw3ivue1vfa7AksDQW\nbcA23SIiPj/2aZdIXpS3Vd8T1+7d9a7HyhLflp01+rMzcB9TcDPGZbhJ78Wy5mPt/0LcbV452ZGK\nXt0JuVfZrXgeMtSu55eRkF43/sTYsLOHPkB9/+Y/r2+RM4ZhGIZhGIZhGIZhGJOIPZwxDMMwDMMw\nDMMwDMOYRK4pa/KTJOHSY0fUawU3IUxIKESPw+dFRHxpCI8cHUX4T3y8znDMri6JJHnprtISjsK7\nEMp+7DcIYwxTaO/CjbPVe4YpO39sEr6n9jmd7T1QgTCmLnKPiXWkVcPkasShvn01Oku3LwufseRL\n67xyqFZnox8f0mGxkYZlBz2OiwRnjOZwr4JbtEtWLDnEzPgMzuXCo/tUvfj0d3dJCbc4DkMUQpu+\nEOHCeZvw/gEKfxQR6TkLmV3bZZzH9PvnqnqNLyEzfEwh2tvvSCmaahBynDYb/dEN/+dQYnY3CDfq\n44shqcEUnYT+fXP+ZfTV4kU6g3raQoTlhah9C7J1mC47CYyO4fonOGHEpW24FuzC9OHbN6t6KZSd\nP6UF3zVQi+8ZdJxf2Hkpax3OIz5Vy+iatiOEcJjG3/CYHiub7lzulePoM7pPareJKVPQxzpPIRyz\nz3GgcmULkSapBH3QF9TykRgKtQ1Q+PaIIxNIpzDM8/sRkrvuIS1jiKLQ0n6SQsQ6ksUAZa8v2Foh\n7wY7DohoRwMO40xfrGWT1eRQUrgW4cd9V/RcyXIZniuyVmp9IEtkqh+HA072Wl2P3b4ijb8MbRhy\nJDvx5KoWS7LMgSHdhv30d/clfEZKiQ6dzqbzz54KB7IL2/6o6gVnkoyBXFwGKaS/65iWfqVMx/hN\nprmRZbAiIpkp+OzEAkiZakg2IiIy73a4MviLcB5Rzk9ALAUOsyTzRh3SP9w5KNeTkqWlXjk6Vh8k\nO/T1kTQle43uZ+zM03MKexVX6jLUjnPJ3wJXv7cPvanqJZDzT3IprmHTaczDoUF9XWaux56I3YbY\nKUJEpHIFvrdxB8L1Kz+6QNXzUxvzeUQ7eiB2jTvzE8h02kN6XVSSmwi7bjVTiL/rupI3F3ORlpjo\n4+vqhOQkk+barFW6rRteQH/v7MVnxDgueYO0Fw2fwzwZJJloYbqWK81ZjHl3sAFjInANqU1hDsav\nu4azbDx9Lvrylae0/CCRZAYtr6NPsHOTiEhPq+NsGmE6D6F/swuMiEj6crRjEktTkrS0cwXNvUM0\nd2Qv1+3IchJ2j4lJ1Pt8nr87ScabtwnjKLlCSx/YLa36LexhCmZq2WR2EmSAmUsglXclgTlrsEdi\nKVSCc414Pe44iGPNclx0xoav371Gwx/hvBPlOJ2xm2AMtdtgh94fXn0OLj0sGR52HNaS/Fijdj+N\nlBGzinRKAk6NcOUI9koV6zDeMpxrxE57vEaOjuhjDXdgj8FzRctuLZNKX4S2Z7c03ieL6P1fEt17\n85orIlK3/aJcT1hKHu/sUQfpnmfBX2I/MmdA3zMd/zXuzV/69+1eefmyWapewa24z2zbh1QnHYe0\n3K2fUlUU3ob3/OYHz3vlT3z1AfWeqy9hvj7/66Ne+ff79D3roqkYz2fqcAwfSFmn6qXmoU3ayK2J\nz0FE5MrvIe+Oi0Obxgb0/OJK9l0scsYwDMMwDMMwDMMwDGMSsYczhmEYhmEYhmEYhmEYk4g9nDEM\nwzAMwzAMwzAMw5hErplzJp5yE6TN01Z91U9A78/6+ak3agvJTrJQzl4CTeiZH+5W9djqqv5FaBcz\nHT1g3R+gSRwagc5t0QOLvXLV81pXO+v++V75ygt4f8GaUlWPNaesu45L1jka2IrPF4/3FN6ktWes\nEW0jO9LMhdoCcHRgWK4n7fvpu1fo65mU8+7Wda4dGFt5jg5AD5g6Q2uT2TqSNZWuJprtr/vps5ML\noOsbdizMo+OQzyhrCr53yM1NQI8cfRmUA8dxrmNN60gIbdDxjtY7Bheg76dQ/p7MhTq/Rvth/b5I\nUrwAWlq/Y61c9zLGS8ktGH/+Up2/Ir4K+tGGy2QnuVT3iY6LyJ2QnYLvSnK+t+sIclgMhtAGCUkY\nLwnZOg8AH1PveWh7A+Vagz/UTRpjynWw+V6dV6WPcgBlLMG4Sl+oNd7M+W2wZSzfoucr1/Iy0rA9\na9in+zfnpWC77PFRredtehU5lfKWQeucWq7H4rhjMf8n3NxYTDtZVLIddW+VzjkTmIZcCDwfZjjz\nC+t0u46ivwRm6mPNoNwd/dQGiVe07bePcikkT0FfcnMMseVqpBmluSJzlda4s9U3525Z+vAyVS9E\nc23/Jcx/viyd82iwBTr3oUJcvzgnb9BAY++7vnbo6cNemfP6iIiU5+C7hkibz1bh7nmwhfe0B+ep\nevXPwrqyrwDnNDGqJ95kynGUuRTXb2xI9/ORvuu7Lg7W4ZplLNNrMufqadsNfXnKTJ0DhG1O2ymn\nV+mSOare2f845JW7fonygnt0cjLOBdN1GXlS0tKRY2LOHUv0Zz+KfIDpRbi2CU5f6q3C51U8gj1R\nwys6h0E+WZ2z7ffllhZVz78P/SyDch4ld+p8GLwPijScZyYwXbdNHVmfJidhPkhbpO12hw+gn/W2\nIqfC/n9/TdVLo3xkPQOYu+eXlqp6nMeMc8vsv4AcCOPONRnYhzVpSg72G62vagvhgmJc5wQav25u\niO6zaLeuk2g3XldERNoPYG/IOeDa92oL3eRE/fmRJrEA+8PUmVnqNV6vgpV4zZem5/gksrhmG/po\nJ/9JcBZdQ8rRxPtLEZG+q/hets/mnCRJhTqPIX9ebhmO9eppvTfMysI4DdE4TynXfTiOrIbZop3P\nQUSk5xyOaaQLe4xonz6ncKvO/RhJePxNuHsPyq8Ron0f5/wR0Tm8Lr+Jeam+Q+d2CySi7dc/iD2h\nm8eFc7yU034hie7vml67rN6TvhT7es5FxrbmIiIZlEuGz8PNu8l5Pfk+w5el98ajlG9osBnrPuei\nERGZcsdMuZ7wuaTP1XPlmUOYF2JoreI1XUSk4kbkQcs5g7koqVjfQ4yFKScXWXjzPauIyOVm7H1O\n/Ah7rL/40t1eOXRJ7xXD9Hygn+bkr/7sM6pe3bN4JjBnGvIibt93VNXbNA+5TfUaoufy1ArM+c1n\ncNxzHtDrdrjz2nkRLXLGMAzDMAzDMAzDMAxjErGHM4ZhGIZhGIZhGIZhGJPINWVNw70IBXJtGad/\nGiE6oxSKNj40quql5CNM6OwvECZafM8MVa/xZYTqZ5FdpRu+zeGPCzfjs+tfw/unrC1X7+HPDmTh\n/WwvKyIy1IyQv4zPrPbKR7+7Q9Vb8EX4QUaRT+jokA5TuvALhJT7SxH+ODqoQ/la9yNsukgroyJC\nyd0Ig+u5pOUJHccgO+NwtuAMHTapwvYonJStUEV0GKCvAGF7cY5Vci+F7fmLEW7YcgDXgiUgIlqq\nEiJrzIGruh3zSSI3UE/20U6Y/BhZZncegf1g0Z3TVb1esrpl+zw37D5jgZY5RRK2dXTDxLPoe6tf\nRIhe9lx9PCPduJ7lNEY6DukQQrYGzV4J2UHjnhpVb8q9sKyv+t1xrxzuwXUdbNOhhuPnEd5buRTW\nuf21WmrDUsnYAUxTbI0oItLZgvYNvYI+VUR2lyIizSQ3KVuD1wYbtK1q24U2uZ6wNKzNsVwMVCIc\nksfbaJ+We8RnIFR3iOQXTTt1eG5cKuplkV1nxlwtPWrei/clklUph9Pmb9FzKocpN+6Cna044ajJ\ndE79ZKEccCR3HM7dtB3zdYKz7nDo+RitNQVb9fG17tdh+ZGELYQ5jF1EJH0x5qioGFyjtrd0W/d2\no91Kb6RJf1yP7aR89Je6HZDDcLi2iLZSvbwdMsfaNvTnldO0hK+rCq8Vb4XMxe+E6scHcI5XX8X8\n4nPCt5NJ6sZSArbfFBEZ7ePwbbwWm6RlV38WGh9h8rZiHmBLZhGRqrM1XnnxTZBvndt1XtVb+Tfr\nvfJwB2SJjdSHRURqqB22fvlGr9yyR/eL8g/hu6p+DbkS95d9P3lLvadiQalXjk7ANXzrx2+qejMW\n4XwT0jAuY1P02jxOMrS3dmNeHxvX7ZGUQvuAFhxf2nwdCu9KbiLJ+BCsgV378oq7sT6N0Hza8Iae\nJw9eQlttWI7rvyRBryGt3VhrUkhWEROvpSMzN2Fv23cZc96yCuxLHt+zR73nk/fc5JXPV6FPdPTp\n/dWMLdjL8RrRsLdG1av8AM6j8zhC6117+vERtGlfNdZqnyNHdq2MI03qdEiAWIYlIhKiPVzfBZIx\nL9dSRF4rJmgebScbbBGR1EpIakdo3hxs1nsVXgv5OrEUzJXPppRhXGXR3iljmV5zh7vRDnyP49rr\nxvpwTuEuHJ9rGx89B/t1vgfj/ify59L0SMLXPHROX8urh7Cvn7IJ611SgZZAsuwunuRsyQl6DkkK\n4vyjSPaePFXLa1h22n8Fn8dyNFdelD6L5DXUd+Y89Iiq19UBC29/LstJ9TVvpTneT7Ke5iNa6pa/\nErLCZNoftTl7GZY6Xw+GaB3rr9My/2A+jit3A+6/XQv4eNp7BqdhbI8O6ucD/fVok91PwOJ6bmWZ\nqrfmw6u88mW6x+H5zN0/jIziu3jt6rmg74FLH8A60U0y3uUdFapeYhH6av0e7BfiYvVjlHhKdTLn\nY3hO0uHMQ678zcUiZwzDMAzDMAzDMAzDMCYRezhjGIZhGIZhGIZhGIYxiVxT1hTjw8vNu3TYL2cB\nz9+M8M/z/3Vc1fMHkaGenZc4NFBEJJckSo3bkBU72skuX3ALQo2aXkd46py/WeeVu6qczOirEV7Y\nTSGTbvhtmCQCF36JkLWSG7XW6PITJFcqQXjcwFUdAjZM2aLLN5KEo1HXK9x6HbRMBIf/D9TrEPPc\ndaVemcMD2w878oRShO1x+HDabO3i1by7xiuzA48bqpqYjxAxdsZKysP/OcRMRKSbsn6zC1C8Iy1o\npzDA4jsRYtzwms62zu/jsNXxER0e3UfhkPlb0NfbDup+1n0a51ioI6LfNz461tadNeq12BRIfTKn\nIbw1OlaHyDa2kvsHZZoPVGpXj4vH8PmZPQhnLrlVy71YJsZhp10Uij1zi84szxKHcwcQTl4xr1TV\nq7gN72t6HVnh3fDJvNk4j8Gr+OxGZ74q2op5o+MA2i1toZ4D3LaPNC07cFzF9+hr03miya0uIloe\nIyLiL8NY7DkNl6I4R57gL8bcxHJGlsCIiPRVo3+PkUSVXbK6T2s3pCQav0XkUufKfIQkeByS7jLc\nhfDUMDl1jQ7oY23dh7GdOgPhsnVPn1X1EouvX/h2gBzb2OFORM+17PzFUl0RkbHt6NN12zEvJcRr\n2R47EhZuwBriOhMMNWHtmvsxOEMtCWJddN0msldgXazfCYdD192E+8uFg1hzl350harXdRj9N+9m\nyMxcOWkizfcsSR1s0uHa/SyZfVAiTsdhhBnHOnLJ6ZVor8xFkE8sn6nlvuEOXPf+5vcON1+yGU4P\nXWcwlhLzdFj/yV8e9MrzP73cK7cdwpzlP6vDslPJuWX3f0Iuk5qkx2ImudmxRMKVpzXvQBsvnwkp\n3HBYj0WWvlSfgGyh4apet6csRLi+LJaIEpOMEHJXsjNBkoaWg9jPxDuuZTffu8Yr8/zncySVeU14\nX8ZSXEuW1IvoeW6wB8fU3Y++8rFbblDvaakj50KSTE3J0+tTL0kR25ogn3Dbml220sgFpdOR7bKj\nSWw7zq+tRUsz+JiuB+zyyXIgEZGxN3CMLGvtcRwE+Z5kmCXxjjyy4xjGPcsvUiq1g2DrPvTp0EW0\nKd/HJDkupBd+BulpPt2rDDh7/gGSi8STo2hbi5awxCbrvvonRntciT76bemDkGnU/UGviz18XW59\n14/+bxNLTn6xqXo+zSuGxL7rGGR2LXvrVD1ek1LICTbUpK8fy5VEb48U7LSbfwM25SxnY9czEZFa\numahZnzvSOhxVS9IrmKNL2FtLXTSIvCcwsftOrZx/50Yw2vuGuHO15GmjcZHIE/vo/JJPn6GXAfL\nbtVpSmLpvj0qCn344mPHVL0AufOuvhMSoMBU7VrGUqQkH9o0QHL4i0+dVO/JLUX7JNOeLcq5L+I+\nwnL74GUtvQ9U4Ls4HQKPXxHt5tlC98NuagC1134XLHLGMAzDMAzDMAzDMAxjErGHM4ZhGIZhGIZh\nGIZhGJOIPZwxDMMwDMMwDMMwDMOYRK6Zc6b696e9cnyC1j6y3XBUDJ7xZDqabNZpdbwD3XRwrs5V\n0k4WrMX3IReDa6XNVsblj0DAzJbWSY5Gb5Qsk8cqKf+KY8EcOg9dacps6NVcbZiPNGa5q6Arbdx1\nTtWb8oEFXnl8DMdQt03bcU77i0X4Q1+WiMBW0BNOTo1+simMD+K8/EXaTpW1kpz/JNzer+qxrr2v\nDrksXF0nWzP2XUU9zjvgvseX9u5aS7b8FdG23Wzznpiv9ZOsPRzphd6T8+a4x8F9acixtCu6Q+su\nI0k/5TNq6ND5JooD0KX3kl1xBlmPi4jMvnWOVx6h3C1nXjmt692A8dd1BPrgC/u1BemU2dAHJ+ZC\ne918Du0Z5eSv4PnAdxbzweVTWns8MxvXnG218508ACN+zEusP2ULdRGR08+e8MrFs6EZv/iqHrOV\nt+o8MJEmcwW+e3xUj8UEOmd/CfSuPU6+l8Qs1Bsn+2J33mvZVeOVOT8E52IQ0Rr/EbI59qXTOHfy\naUTHol0TSAM9NqznymHKH8P5fcadY2Br0byNyD/mjvmSuzHGOGdI7o1Okqdr6NDfLz3noH9OcGw4\nU6l/c64VlQNBRAruQC6Pfpono2L1eOH1j/MKDHdoa9uYADT+tU8if0zJB5B/INqx/FV/k/493Krn\n9CTKEZMZQB/jPEEiIvHUx4basU673+tLS3rX8nC3zhOVmHp9tfXH92Psz54zRb2WtZpyBFFqANf6\neoxyIlX+xUKvfOrn76iYf6E8AAAgAElEQVR67ZRP6lIz5tS19+u8PQM01734jW1euSgT+Rd6BwbU\ne3qewd83/h1sug/8SNs1D1C+rzeefNsr3/UPt6l6bL+bQ7kAa16oUvW6aF8R9GNOCpZqO9u0Oddh\nU/P/0kt7B9fqe2QM8+vMD8Aqfsyx3B5swNrKY5Zz5omIBMimt/c85oBkZ96NpbHIdvBllCOL7Y5F\nRKbOxr6Zj8fNucj9bfrtGNvDPXo+6KOcVLxfK9yo+3nXUfTFhFy0YZqTl6zw9uubF5HzeA05uYP4\neuasKfXKY841jE2kvUAp5hx3neV8PFd3I/fXNMdmepjmsJRKHF98GvLUjDjXqfQh7LF43kvI0nvZ\nnhNY07lNg/P0WEklG+L+Buxpes/pfDtMI1nFJxbqvinR1+/3eH8Rrl9ysb5/4HtEpveiPg++tnyO\n5Q/OVfVqfn/GK4/QHqP7lM6pxLlMuygnZO85zF2c20ZEJJnGfSJZfbc61tcDtWgP3l+1OPcPg++R\nT694o96zJJO9dyPlU3XvlYecdTfSpFXgeuSu15bWPJ9Nexhzal9tt6rHeTF9Obg2gULdL3po/ub8\nraEr+h4ncyFyFvHes+pJ5LhNcHKJjYcx7jmPnpuvLz4Ffa75NVz32KB+9tBBOUb5njNnbamq10i5\n/WLI3nugWed7vboN+XiLv3GfuFjkjGEYhmEYhmEYhmEYxiRiD2cMwzAMwzAMwzAMwzAmkaiJCcfP\nyzAMwzAMwzAMwzAMw/j/DIucMQzDMAzDMAzDMAzDmETs4YxhGIZhGIZhGIZhGMYkYg9nDMMwDMMw\nDMMwDMMwJhF7OGMYhmEYhmEYhmEYhjGJ2MMZwzAMwzAMwzAMwzCMScQezhiGYRiGYRiGYRiGYUwi\n9nDGMAzDMAzDMAzDMAxjErGHM4ZhGIZhGIZhGIZhGJOIPZwxDMMwDMMwDMMwDMOYROzhjGEYhmEY\nhmEYhmEYxiRiD2cMwzAMwzAMwzAMwzAmEXs4YxiGYRiGYRiGYRiGMYnYwxnDMAzDMAzDMAzDMIxJ\nxB7OGIZhGIZhGIZhGIZhTCL2cMYwDMMwDMMwDMMwDGMSsYczhmEYhmEYhmEYhmEYk4g9nDEMwzAM\nwzAMwzAMw5hE7OGMYRiGYRiGYRiGYRjGJBJ7rRerjz7ulaNiY/SLExNecbg77JUTspNVtbikOHxG\nTBS9EqXqtR686pWzlxa9x3tEGl6/5JVTp2V65aTcgFcO1XWr98TE49hHQkNeOTAlXdXrPtuKen3D\n+Oy8gKoXH0xAOQXl/oYeVS+Wzr33UieOx6evZcb8fK+cX3KnRJq93/pnrzzUP6xeS6Bz6e/o98rB\nqRmq3vjwmFfua+j1yoGiVFUvY0mBVx6geiO9Q6re4FW8ljo3xyt3HW7CZ0/X7TM+Mu6VkwpTcNx1\n+rqnz8v1yq1v1+E9xSmqXs+pNq+cvb7EK0fH6GeWMQkYJoMtfV551LmWw904x8Uf/aJEkhPP/tgr\nX3rronotvzTbK7fVo5+NjI6qetNvnInXaBz4MhJVvfEhtPVgM853sK5X1WvuwjibuXWWV+493+6V\nR3v0NcpaW+yVo6Ixtk8/d0LVm7Ks7F3rhao69OetQ7u17a6V9yKL2rfzCPrY+KC+RvGZuBZLP/13\n7/l5/12qjz3hlX1p+rpHReE8T/30gFee85kVqt7pn+C1xCSfV47x6ek8e0OpVx7uGsT3OHNqfGrC\nu5Zb92HsdJ5rU+8J5GIsBWn81r52QdUrv3e2Vw5dRt+M9cerenx8MfRa/6UuVY/nof4+vGfKnTNV\nPV8Q17Z4+n0SSWpOP+WVd/5wh3rt/u992St//yP/wyt/5Yn/UPWu7H/eK5etuMsr/487HlT1/vrf\nPuyVM8vmeeXOupOq3t6fvIVj+MG/eeXxcYy/np4j6j2vfu05r7z//HmvPK+0VNWbO2cqPo/mhpwN\nul7BnA1e+dRv0c8XfuyvVb3DP/6BV176WYyxr919j6r3yFfu9soVKx6RSHPk0e955YEaPbdlrcM8\nlVwU9MrNu6+oekNtA+/62f3d+v9JfoyrtIVYn7qONqt60YkYwwk5fq/ccabFK/sz9R6L92JDtBdz\n55eU6dgvBancfrhB1UvIwefzHmbMmSvDrVgbWo82euWMWTmqnr8E12/amo9IJDnxzI+8cn+t3gfw\n3F54xzSv3H7IOd9sXOcYuv695/VakzojC589inHAY0JEJKkAc+NQB/rBWBjHE27t1+8pwnv482KT\n9TzZdRz9JWsl9skTYxOq3mBzyCuPhDAH8F5GRGSA9mF87hO01xIRiYrDnmjZX31FIs3hX2Msdl9s\nV69NuRd7i+pnznjlQKHeeybmod8m0WvNr11W9TJWYI/K1yZKL4uq3/I9Du+Juk+3qvdER+M6Beeg\nv0Q7azPvh3k9jk/XY1aoXXnNrN9+SVXLW1+KY+3Eupjs3OPwXFG+9EMSSX77l3/plWcsmKJeG6X7\nqcQC3E+1Hm9S9YIlaV6Zx0uU0zhXqzEO5j+4yCsPdQyqeruf3u+VU5OSvHJuEG1bepfeO1z+w2m8\nJw/9KHV2lqq3/6mDXrkoA/dL7r1T1RH0v2g6j4V3LVD1Dv0B63N4ZMQrb/zEOlWP54DrsUfle42s\nxQXqtbrnqrxyyT0Yl8O9YV3v9xiniSW4hjmri1W9xu24NkMtmBNzb5yq6tVsO+eVeT8cqsGesu5l\nvfecoL4+6y+XeeUjP3xb1Zv9IfQfXjPDHXoND7fj+MJ0rMNOn0vMxzwUTc8efBlJql5cAHv3snl6\n3ydikTOGYRiGYRiGYRiGYRiTyjUjZ+KS8WRnkH4lERFJ5AgZehoYuqx/bfAX4wllLD2ZH2jWnxco\nwxPT1gP4xTZ1un5amUxPs/lJVOdp/LIUHaefOSXSLyP8iwI/RRYRGRvE08q02fj1Z3xE/zLCESHR\ncXgyNuicU3wafi3LmJ/nlfvq9S88YYpYkRKJOHya6XOy1WvhZnz3OFV0f2HpqcYTytI7ZuCFaP1E\nu+kVPNHnXwHHBkZUvcEB/HLQ/3aNVy65qdIrt+6sESahEE/c+VeEwfqQqnexCr9mlGzF5/EvQyIi\nAxQJMkhtmuhESk2M47pwFEf/FR2hFWrT7R9J4gI43ynL9a8S/EtdoBy/lHSf1L/qVO/QT5b/RME8\n/XS87yKiFTLX4Ne5lnMtql5xJSK+ouMx5sb60Nbpy/PVe868gF/8C8vxC3LQ71f1+JdAHmPJ0/Qv\nQSN9OiLLe48TncbRPGnzMLbr3tC/QPWF3v2X8EgRn4I59c+ezFNU1vQP4VeVrlP61/WizfhVIToe\nfbrnjG7vlp34lT9lJn4pH3J+tR2mXwuyl6LvZy4ppO/R15PbJ6UcvxTldRepeu0H8Sv1CEWWldyn\nf63iKJ0R+pWy9ME5qt75nx/yyrmL0G+vvFCl6mXSr/fF0yWibPvfL3vl7FT96+0DKxH98eLxw175\n6KM/UvXK70KUyYMrNnnlJ/e/oeod/enPvPIP/+43XvnWRYtUvcR4zA9RUYh2GB9Hn+pr0mvzjpMY\niw+uXu2V85brX7dKN+KXu5ERzA2/+dxPVb36DkTaPvzAFjoG/avaaD/mhy/fcqtXvnPlMlUvY5r+\n9SzSJNOeY7BBz91dR/GLbgLtM9Ln56p6A41YexKyMIdxpKyIqEWYI3adLYhMUEQGR3b64qhNnUgN\njnLgvQ6vBSIiyaU43yv/dcorc5SQiEj724hiLrgd6+dF+kVZRKRgBTYr/jT63qn6e/kXx0iTUol5\nzY3GGxvCL+/8K2rylDRVj9t3mCJKeR1z6TyE/pF/c7l6bbAJfYn3orw/5KgokT+P+vkTSfl6LyK0\nF+FIyd7Lur9x9MRQK9aZhDwddcVR3JlLMZ/yuiIi0vSqXicjzRBdmzL6RV5EZJzWGm5HjjYSEek9\njfEyRmNkOKzbkSPAOGrF/WW7bR/GQawf44/7d0Kmfg9HJHN/dKNaE3PRDqFqzKkDTj/gSHS+B8ta\novdsKrr4HPqCe079tbRnXSoRpSwfa+7lU3XqtcUfwpe9/tOdXjnJ51P1/Cm0F6FooM6jOsImPxd7\njtBlXL8rh3Rk4+xizG1dfbh+fK/T+paOuC67Dfc3HB149Q0dgbX09oVemffa7+w5peqlJ6OtR8bQ\nL6+8riPgB4bQd1bfjevFkd4iImdO4Dgi3IQiIhKmNa39iI4y5Pu4E/++1yvnLdNrSNIU3KcP0T3m\nmV8eUvVmfxJnEEWKBTdSqvw+RGDX0V4v/0bMvX5HtZOzFusTR5TGxui9bAdFjg42or3ztup5naNq\nBkitkbFMj8XeC9hn8V7q8q+OqXqDYbS3Rc4YhmEYhmEYhmEYhmH8/wx7OGMYhmEYhmEYhmEYhjGJ\n2MMZwzAMwzAMwzAMwzCMSeSaOWe6yaEj6OR+6b0CXWPGXORT4fwQIiJR9PgnJgG6zQRHC8n5F5QW\nvEXnE0nIgq5sYgxaa9b2+p0s7rGJrBclreIV7QSSTRpq1iuPjOnM9Qy7NaVTXhkRkSFyIGk9AP1q\n2hztZsA5bGShRJys1ZQHwtW4099FM6DfbnF0mEHS2V59Ec4ePjp/EZHAdFzfmv3Qf/odbWk2XSs+\nBtbwDw5pp5+UVHxGH7VdXFB/dg4dw5WXkeW77BadfIJdZmpeQr1Sp14LZfvP2QwXITdfgKtljCSs\nkx5wct3w3/FZGFcjA/r6sb63gPKW9DnjoD2ENsgh7XnBokJVj50FTjwPt6XpqytwDD06J0zZEly/\ncdKC94V1Xooxcid5rwz+IiIBynjun0o6VyeDehfNV1er4Cwy42atbx+o164tkablbYwrdhwTEQld\nxDH2nqMcOQv0vMK5rLrPaBclJjgf/bubXGGK7p6h6nWdwrXupLKP2jdQrh0IRig7f/vhenznTJ3T\ninMfDFJOr1EnBxW73nA+m1GnD7Nmu5G+t+JenZsmMUvndIgk8ykXytK/1U5ENw9Db9zRAQelVHLH\nERH52gN/75V/+9YfvPLPP/F5VS+NcjF98sv3euU9Tx5Q9Z7YvdsrT7sJ+XwGGjCWG880qvf8y2Nf\n8soN26F/Ty4Nqnpd9XBeeOU7r3plX6zePoQG0dbRsdCMt9TtUvUWf/FTXpnzdfzyn36n6q0K/k+5\nnnQcgNY8dY7e37Tsx3rd9Drmf3+Zvjacx6WP8jlw7hgRnb+paWe1Vy7YWqHqTYxir8F7lehY7G94\nXyEi0kF5nZQ7hJMP48Jj0LxXfnC+V2ZnShGRjJU41k5yBqm4Z7aqN9SOMZt/M87DzZE1PvLe+6f3\nSyvNp0FnX8V7Qnbbcd0deU/UT06fGSv0eseOM3lbkPctxtnzJtAarPbDlBeEr52IzvfS8EfkhuP+\nICIyFqb5j/LAuPn0Mldgz8cueezGJ6LzKfKxdjgOXgEn11ukYWfB7tM6tx07hqVVYpy2vaOPMZdy\nTPRWYf0svm2aqsf7fs4l0045ZkT0WIqjXHFdJyi/pZPbLo32lPUvoh3LnNxpnIep7xzWDH+Fvs49\nlCuP+0LfBZ1jiNdFPl/OfyEikjZP58yKJLwfLPbpschzQALlR5uzWu+1/eT+ys6obh7RtEXYE53a\nhhwvFbS/FBHprCJH1nxc27patOH4uB5jl/4La+u8G/Scx3B+oa5O7BvdPDqLH1zilVt21Hjl6iY9\nTy7dMNcrH3oRc/WiLXNVvdmLdC6USFN2P1whJyb0Osb35km0Rx2o0fckBbcgVxnv2WMP6DF29QXc\ndxXfjX1LnZNDMI5chTl3F6emCc7We8+65/DZmcsxl097cJ6qxznCeij/E7s0iug1veMI9lIDV/V6\nwjmpei9h/CUW6fxhI9X6XsbFImcMwzAMwzAMwzAMwzAmEXs4YxiGYRiGYRiGYRiGMYlcU9bEYY4s\n8xERiSWJ0iBZCLshrPw+P9vJOZoQtpjtpzAhN2xwqBPhoByemr0SllW9F9rVexJy3l0Kxbbc/8+x\nItQ+TOH4bmhg4Q0IGxwfx/mFrjihhiTp4OPjcDiRPw9JjTTcjj1ntd1ugKwo23YjRLjEkT5weGX+\nFoT1s/xCRNtOl61BvbBjMx5NfSFEIaix1A/yVmh7titvIby88haEwLUe0+H6HI6aswDhwpe26VC5\nKVvRjnEUos9htCJaLhNHMpq+dn1OqYW6P0US/t7mFt3P2KqP5U+u7Su3TccBSEJ82Tr8fc4DsHFu\nfBmh04NDut/WdWBcJFMoZ/UByNkKyvUx8Hhmy+SMNC3xyVqDtt/9GCz7coP6Go+SPXPaYoS6dp/X\nYzZ/FUKee05gDFzafl7Vi4m+vs+rs5Yj3Lz2d2fUa0Mk40spxnlOOGG3LNPsv4QwzDzH0jWOrDw5\n9DezRdtd8/wYT9LBC09AqhbnSFj8ZPEam4zviY7V83V/LcJ9yz+CfhXu0Pa6RbcjvHlsmMJHj2kb\nyUySTSWz1JLCY0VE4mh9KvjqXRJJstejL4VCZ9Vrz//9o1755q/BJrplh7b43DQHYe6tl/Z75Yxk\nbQc5/07ITzoP41pMz9cW9f/+3S965cLlK7zyk1/4vldeuELP6WPDmCv+7RdPeeVvzfqsqrf7MUim\nFizCnHn6uLbXPXoR0qi8jZj7T/1ov6r3+e3f9Mpf/9rHvfIX/lN/b28v+l8weB30vkT3US2lYMve\niVGyja/Q8rQxkmYGKOTbtTa++jz65zBJ9Wqf0vbUhbfj+vZUYZ4Kk+zPlSvlbCj1yokk++69rOfA\nfpq/657FWpi2QM/RbOscG8DYbnNkH231OMfcCozLzGVaDjTYqKXpkcSXASmFK0tJX4IxwhK2mGS9\nvrfvx1rIYfJsTywi4suExLCPpVHjei/LskBeczNmYy/i8+nx23IaMobyv0BfH+rWErZwG+bNtr04\n394mLceNOkSSHzq8xBw9v4xTqP5wD9Zj16a6+4QeH5GGzyVjsb42LAeLJklfbkGJqsbyvNTZkD+N\n9GvZHsvOeklSxNIHEb1H9edhvWsgmWP3Rb3/ZZv2AEmUOk9qCUsGyfpLH8ZaMNyr91h8v8ISZlfK\n76dxwFJilgmJ6DaONC1XcHwly0vVayzL7CcJuyt737cHkqKpOZBG5WzSciWea8ub8F29jtwrUIDz\nz1mHeoNP4hol5+u+ntKPtm4n+Uprj5avpHZhnJeuw3qXeVEfQy/dOyXkYQ4pGdVSWn8xjjU1CX20\nz/k8d78eaVjKdOGn2vp66scwN0XPIutrV1JK+8D4bEiSXOkp38P7knnsaBl9Cu314knixBb17n16\nMd3DHv8tziMr1bnXoP1cgNrg0HfeVPXmfXyZV85ZjfcMtum9bBdJgXlOiaO1VEQkY56+Fi4WOWMY\nhmEYhmEYhmEYhjGJ2MMZwzAMwzAMwzAMwzCMSeSaehoOgXOlNyP0Gmc896Ulqnosc+qtRnhW2kwd\n0hMTgzC9QAHCbEeGdEhsx3GEdieQI8cwhX+mTtPhYqFahP5nzUX4Weuxi6qen2Qpwxw+ubZU1Rsd\nxLmHKLt/TLy+RomVFGJMUqsJJwyWJSvXA850PlivpTjp5ARTcDsybLvytNEQQkPZ3afpgg53zatE\nux59B6Hc6x9Zrep1n0HIdiKFknE/Y4cZEZFAArnUnMT36uBjHfY9QCHamaU6VK72NWTTL7kBbhNV\nvzuu6lXeiYztPeRgVna7lgnwOUWakT6Ef0ZFOeHWNOaSChGSyQ4DIo7DFTn+xDlj9uzTkBMULYAE\nJiVWP8sdPozwx6kbcf04a3+4VYf8cUjiKLn3PPrDF1S9/POQTKy5AaGUPU7oYlMnzmlwL/qoP6hD\nP99rjJWtmaL+7j55/dpQRKT3EubAcFiH9E69H+HNqi85rmA8NgvvhAyibb8O6+fw8IJscn6J19Ij\nDoM+8Dg5FWyAdNDnuB/x2MmZj+/pOa/do9gxivswh/qKiIz2oy+MkZNTsuNe4cvAXDHchXm4/JH5\nql7Lnhq5Xkxd9oBX7u4+rF67+9uf8MqfvflvvPKX/uZBVe/iLoRL//wfH/fK33j+KVXvtvkIpb1j\n6VKvvHCpdrl4+7mDXvmVJ+ES9cj3HvLKN8z5qHrPk7/5hld+bM/zXjkcrlP1Dl16zCvf9104PGUs\nfkfVSyQXjgPfgUPTXd//vqqXswjyDpY5thysVvWC0yHVuB6yJv7unI2l6rWuUxh/LF1oc9wmxqjf\nstQge5WWXGQuwBhpfF3LwRiep0ZJopo6A3salqOJiLSSsyI7Y8QkavlOTjEkWY01OD9fg54rE/Ox\nb2F3juT8NFUveBLSGT5udggTEUl1QtQjSSxJN115Fss/82+C5LPzhN5XcKh9F8koUx3nOeXkRLKU\nfsetI7kI14nnvNaDNV45Ol73I95vsktNYraed1t31ci74TpF8h6I3f1c15tBaqtQFdbW+Azt6pS+\nyJEaRRh/Ceb17tN6DeZ2ZYlcQo6+Nom56Lcsl3Adry68A1nS7K3Y23FfEhHx5+OYmt4kx7ZiyCIS\nnPbpPq771p/IJkmNiEh/A46vahvkzVnpWoaUtRry7vZz2POyXF9EuwGyE1hUjN4rhtu0TC6SJMZh\nvHU5MjieiwrSsaYH5+gxVtqE9mX5jutQN05yr4EruJauK1bzLsiJee+eSZL/JMfdl8ff2HGcx+k6\nvS7yetd1DHO660w7bTnmnm5KA8ESGhGRzsPYE6QkYg+ds1FLuq48r6XUkabjBObAZMelrfENjINR\nkuBlr9EpKC4+jnuIgk3kbOesSbz37K3DejLYpNeQAMkF/eRy2rIXa597v1P9NCTDeUUYH74svd6x\nC19wNtYCdw3n+6wD38b+JrtAX6P8G9He9duwT57q7lH36/7kYpEzhmEYhmEYhmEYhmEYk4g9nDEM\nwzAMwzAMwzAMw5hE7OGMYRiGYRiGYRiGYRjGJHLNnDOZpDONcixmJ/Kh+WPbSVcbOESasjTS8Lo6\n0KEB0hoGkDfD/bzoeBxH05s1XjmbLKs4f4GIzjMz2Em2zY7GNCUXGrPoWLLvi9N63vER5NoIkz4x\nLvW9c3ykz4VGme0LRURik/RxRJpwBzSoOZu1fnGU8jvweTa/rvX//nJo/qoP4LU0x/qV9XvcL+Kd\nvCbBWegLqWTn3fgGtJsps7RtKesVc5cj54KbO4gtmnM24HxdbXigHFrB/lroyUODus9xX+VcBAO1\n2r6SrRwjTf3bNV7ZzbHDdoQD9P+4FK0bH+7AdWlpRe6T4dM6h8HIGPpnVgtyxtRUO5blNCd0UX6g\nuHR8b5pj582WiqHLGB/33LJW1eM8Vklk2zwxphOwnH8LOtX6TpzTojidS2aQrNzHyao5PvjeObKu\nB11HoedNDOjv5nwR/VfQH0edfsXzVtse6FYD03VuB85vk1iAa3j6qWOqHucrCI9gPmij/F6XW7SG\nfMVNsMW+sh+67mhnnVj597CTbngTGuBwi85F5Cf72by10Ox2ndca/hCdE1vWtrxdq+q5VoyRpLPz\nba/88LrPqddmFUN7/Vf34tyn33G3qvebX7/klevbsSb956f05z35NvK9tJyCjjt3rs7BsugTsKG+\nvB95a3ooX8rRpsvqPTu+Ckvrp36J9//Dkz9S9b7wLx/2yt955O+9smst+nc/+KRX7qP59Bv33afq\n/dXPP++V3/g6jvXmb/61qndx26teuXCqRJyMpWRt7KxPvJZzji9338LjKoX6XNMOfa3jUjEn5m9B\n/3at54/+EH1r1iOLvHLnMcy9u17VeY7ml5Z65ZpWrE9HqvUavnIa8lPNWoOyOxYvvYU1uKABGnz/\nfdqCNCYB28e2vZiH+tv15/HaWlQpEaW/Bp+dMlPnGhyntmp8DefExy0iEjcL82k25fjgfZOInp/b\nD8F+252f2TKV5+oh+rxwo879199LOQ6XYi/bsqNG1UugnHIplehvru037+va9yC/TfI0PS+m0j6s\ntwo5OaITdG4IN3dJpGH7dtfSeoxy06lxGaPXGm6fvvNYJxIK9B61fEGpVz7+EubUBbfpnBAdZEfO\n1umZyzBvtB6oV++pbcM1nDUPk1bDC+dVvfoO5PeppjG7LujkITlEVs692G/6z+v5qvU46qXm4TMy\nV+hr2U97rkiTVoH9OucSERG5+hbmorhYjL+uY3p9b6A93BSak8/v0tcvlvYZQT/y/oyEdB4/Hs8t\ndL/oL8U14hwzIiInt2Of0tGH19w90KrpuAfppHpnrup8UhXU34KUG+jsvguq3vTF6C+hdnze3l/v\n1Z83RbdppAlOxzzqjrFLvzrqlUsfQL4mN49h5SPYH4Yo12zPaZ2TkPOztO7DGsI5s0T0np3nNs5v\n6d73L/oy9lxdtbjW8U7+yWjKFdu4HfeSY0P6Pj2F5s4Eyq8U7xzr5SdPeeWRUcxJF395RNVz8/i6\nWOSMYRiGYRiGYRiGYRjGJGIPZwzDMAzDMAzDMAzDMCaRa8bVcCjR+KgOGWI7QmW3WKTD8novInyP\nbbBdi122T2UpROtb2m4qlaQuCckIT4pNQphR6HKnek+Ajo8lSW6I8ugoQhc51C0u2bHhJblO+lyy\n/e7X4a0JmQi3Y4vZxNyAqtdfj9DzPO2QFxEGKYQ2YY4OwQq3IdSWw6xyb9CykNo/wha7ZC7slcPN\nuh0zKCT17g8iLDsuTkuUYstwDS4897JXZjvEkV4dosjHd+XZQ17ZDSvzl0DuEG7ldtTyMZbMtV1A\n+yz6wGJVj0PeYyic0j8lqOq1HdWyn0hStBbt0bxXSzh4THBoX+dBfTydvZAOslVfsExbwYUohLej\nCWGwQ6Na/pQXxPm3dSC8vHIJLMbrtulw1PIH53rlCRqLHAouIhJFEjsOVQ036RDU0XGM4dIshGPW\ntWur5im1mB84tLt+u7a1TXPsUyMNh9OmzdOSry6aU4vvgY11vGMROEBz09QPI3z0/I+1tXHmaozT\nd3bBrnPR1rmqXv8MDfsAACAASURBVOgc5uj0DEgX/FMxb5aVzlTv6aBwaw7pXfPR1areUAj9J0zS\nsqkfWqDqdZzE59W/VuWVi8jqVERb3frSMe6vnD+n6gnbzWvF3PvG50O7/e9vfEa9dvINXOc9hxDe\n6t/2nKr36b+D1MdfgD7xqXv/l6r30jpIWJJpzP7zD/X3Ji+CXiRvHuy3Q50Yf2/+07+q95TdgrDs\nBLIFPfitX6h6C7/8sFf+3C8r8J4EvVi1Xt7nldPJnvi+v75Z1fvqff/klb/+5N965WPffULVK/+Y\n7iORpvkNhNoPh/X+JrUccwSHJvPaIiKSQtLYuACkS1PvXanq1b6CdmSL57AjASpaWeqV2yjMu+oY\njnVmoQ5rP15T45WDSRgTK0jGJCJSko358eoRhN5nF2upS24B/uZw9Qs/PaTqVXx6iVeOJ9nWQLO2\nQW0kO9tIw9Ild55s2o5rxnJaV2p75RmyMl4MKb8rV0qegraeICtfnodERHouYO1huRHP9+MzdMh8\nJ8lde06S5e8q3db1JJdLKsBczftfEd0evA6wDExEpOcsJDX8GSkVek8wMa7lxJGGpUIxPi2RYPtw\nXy721AN1WlaeRHbcqXPR14e79T7ywJuQMiWR7XHHfi1R4u/KWYeUB0eegyy4PxxW73l6LyQon6P0\nDL94/XVV74Z587zyEEmJL9Q3qHozpuF7K6dDohPlWKKPNZHsI4R+y+u0iMhA7/Wz0h68inF/9LKW\nVLL0kuWwQx36eGauwpzFe8Kpjm063y8m0xzMUm4RkT/8fqdX3roAsrXvPg1Z8ZLycvWeU2SZnZWC\nMfalD2pp8kdIFvydT3/aK0/L17bznHbg+CnMNbx3FRHpIyl7Ka3NNb/Zo+p1tet+H2l6SYbElvQi\n2t788mMYR9yHRUQqHsAe89lfoe/f9ZHNqh5bypfejvUkNla3d6i9Bn/0YMxNUBqC0o3r9Hm0Ye/T\nR/Ne1mI9p05QO0y7/xavfOHZV1S90pV4rbfqUa885DzLSJuNe4i89bhvu/yoTieQvVZbdbtY5Ixh\nGIZhGIZhGIZhGMYkYg9nDMMwDMMwDMMwDMMwJpFryprYRaj7nM6yzOGuHA7ZTWGSLoONCHtjyZSI\nyEgvu/zg/9nrdOgPS5aiSebCGZzZ+UNEJDEAp6SoaEgHMkuXqnrN5xCWnUqZx6OjdbjsSAihWKMU\nDp1aXKTqsUyKpUwcYiuipVbXA85w33VSZxxPngLpAkvNfBk6G3xynpZi/YngXC0DaX0TkhsO5+u7\nckLVS1+ANolPQwguu7Gwm5KISFIeQgwHSd7CIY4iui8wLLETEUmbi9D7vEUIdeu9oOuxbC+pBMeQ\nkKNdAKZ+YM67fm8kYJVGco5uCz4OloINd+mQ27K5CLGLoxDwi69oSUj+DIRfp5K8qO+gdiD51Le/\n7ZV/9Q//4JUvv47M6FM3Vqj3nH/8uFcOpOO4q0/rDPfFU3AMyWUYzxxO7pJE0gwOzRcROX8Rn5/R\nhutXvFTPL9wXrwctpzH/ZK/S381z4PnHEAI561N6ngqW4n2XnoS7S9G9M1Q9fy76beZ2hHi2HtKh\n0+yqxm5LRSTZbD+s35O1CnPdIgo5btmhJQyhEGSTgVTU6zyjXRoG6rE2pM3BnDLYocPweS7rIBnh\ntIe1BIYlIZEmOhr97PH/fFm9dvNCuCg99O/f98qnX/ilqpe7ECHW27/2W6/8tU8+rOttxJh95btw\nL/rlPz+l6sXHPeuVq+oRnv+9J77ilT/+r1rWdOfRLV75QgPa9zvf1Y5RMTEYE8PDkPgc/8lvVT2W\ncBTdgbBs18Xw3hUrvHJmJo4h659uVPWe+fwXvPJ9P7hDIg2HROc6Ica81rBbDDsLiuiQ6FAN7U1i\ndb/lNeS1ryOknt1dREQ2LUe/eG4n9iP33oqQ7a//5DH1nmWVkLT95JlnvPLosJblfPuzcORiaUF7\nrw6Tn7ca88gorSexKVp62vg6nC3SaK4YdKSnY+PXzwEvSHPFmLOn5L1JUiHas/uMvuYsYeN10W1r\nllD1knSp+7Te87LTYALJtFlGz5J3Ee06wuOo/6puG5YWd76DMZuxUofqJ2Th89m10XWq6jiIz2DX\nkv56/b0sfa1YLhGHj2vcSTeQtgh7Rb7XiJ2vpVzD5NLJ7ShRWmY3pwRjPVBBezsnJQPLTOp3QqZT\nkol+se2IdmApycGe8nPf+55X/tZf/ZWqN05r7iySKfY6TqEDnVg/g3RPku5I8zpJ7pu/Ca4/Tbv1\nesxOSZGGJVkrVmk5MkvleT4IOO5h9cewT8urxLV0XTRTyZmtaQfaJjhb34/cthGdNZlk2n+75ENe\nOSpWO5G988+Y1zJImvbymwdVvV98AesTu1wm+vQ8ebUG91zrPwTZ98Hfa5noMI3tSy+e9crLb9HO\njFW7HAl3hOF0CGUPawn8EPXHsSEc74nH9TjY9ZNdXvnBr9zplceHdGqElHL0aZ8P7Z2YqOezqCiM\n9egY3If4Z5fi/859+lAXjrV0I7TtfV06lUG8H/cXdXvewvE4c3RMDD4/SPeOWbP1vlsEY7u/HWMi\nz0kVou5H9RZfRCxyxjAMwzAMwzAMwzAMY1KxhzOGYRiGYRiGYRiGYRiTiD2cMQzDMAzDMAzDMAzD\nmESuKUAcJBtitu0TEQlOJ6s6srodbNZ640Gyu0sqg6YzUKG1hoFS6AFDtbBf7T2vrdFSZ+B7fYt1\nXhTvs3KL1d/hAWj+RsgeMRR7RtXLmArt2MQENI5JSWWqnt+Pc+zvhz4xOlofT99V5F8ZJu12wLHj\ndLXNkablEHIQBEvS1GtNZCeauQQWcO17dA6QnM24Bqzt7tin7QdT50Hzydpr1x6x4WVctwSywmZ7\narcvvfW7/V55819u9MrVz+p2zCMLvowFOKe02VqnO9gCLXKAcu+0vFmj6h0+DI1jWRHZYY5qrTnn\nzSjTUs33TQzZr444uWQGGvC90aSfHevTuXcuvQWtZU42zjenLEvVa6pCPpDLLRg7w46V9hPfhO0v\n54kKVmJs91/VVnxT75rllbtP47PnLND5eliTP9BAc0iJ1oUvjIcNYjL1ndFBfawzE9B/q8+hz7o5\nZvqqu+R6MvU2zDFdZ3T+pxDZCuYsgrVo9WM6XxPn8GCLU3e8NL6EMTbv47BX7nLygrE9ec66Uq/c\ndxWae1fLPUbX9/BrOL51H9e+1a2/O+qVay7ie2/YpOfUpEJou9/4KfTKS2+Yp+q1V+EzAll4T4tj\n15tY8O45siLBE59HrqWHP7JVvTbjDmjZv/3QI175g/9yv6p36WmcY+k0tHXKDL0WbF3xca/8/Is/\n8Mo582apeg37kMvpO99CTpI3vw8r0cOtO9R7hvvR3zgH3J4f7FL1/vHvf+qV/+0HyEez4DMfUfWu\nHnnDK7/8LdhQnm/Q+Yp8cZjLZjQh/4qaRESkoVOv/ZEmLh1jPy6g9epjlEuul8ZBSo0+prhkvC8x\nG/lFOk/qnEqcC4zPf/08nZshivLRfeSz0Orz2P7oxo3qPXzV5s/G5z2yfr2q1xHCOhFPuSem5up1\nkXO9nXka/aotpHN3zJ2D3BacJ4Vz9Ihom/ZI03seuv3gLD1H8brRcwrzRpaTx5D3Ipy/LnOuzhHQ\nvB95u4Y6kRska6XONRhDedoaX8Oam7QJn9f2jt5f+TKwB0qkHHLDvXqtz52J/Cu+TOw3U6bq/Hyt\nB7DGZS1D/oaLlMtCRCQ9C+vpcAdyNETF6DwcWav1OUYazjPTdUKPHb5XaKfzcvPn5N2A/th5AjlY\n3NyAPQM4z1Qf9j6dR5pUPc5nxPcxJ6qx1tyxepl6T10D+tlnPobxG+3Tx5q9HNdzsIXus5zcjj4/\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3rpFZux21cprf03WOUpZQzQ96Fgrk6HvEz0LhJkg1OxOcmpUv/AJr/vJFqOMS6dgJT6Lv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\nVHKk8unfe/H+k9ifHt+i3R7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wDMMwDGMCuWiRDK6pwbprEW1RxlZhyZO1rj0QIEvcIeip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SXVnin6ZlM/525gItg2N5TCrtu1u36LmnfHqpF2/cvN+LC9LT5eMYHCLJ\nvyNvLs3GZ/3iiSe8+IKleE6dOFHfQ7alD9GYTU3TksCUclzb1CKM8+hoLZFtOoznRX7+0sJkLVtT\n1u1BvW+P+PgKAGGh5wSOMWOulvpFJVD/pnml84Cefwa60L/Tb8X3CPV7tJQrfRokRbWvH/PigtX6\n2YrHKZe0CFShr7t7wEANXuP1KSFRl8Hg+zXlC9jT9Pn1dw8jQ7g/PM4TnbISbLMdS89gbUccmbFT\nosHFMmcMwzAMwzAMwzAMwzDGEPtyxjAMwzAMwzAMwzAMYww5p6ypeTMkHJ92pDe7fwZ3pLh0uMeM\nOLlaRUuR1lM4b5kX1+7YqNrtfwEpbDnjq7zYX9+t2s19AGlWh59DWu0zHyGl+tu33qTes2E/UvBv\n/lfIL1gGISLyFMmcVjUjVTg+V1fp/vt7kFN94fI78Pfi9d+Ly4NMoeQCnHt303HVrvM4UhSTl+kK\n9OEgNg33J31GrnqNpT59zUjb5XRKEZFgK15LLEQa1/AUnXIXT/KbUAfSuzgVTUSny/VWI/2spw6p\n9oN+XQE8rgypkizRiU3X7kXsStG6CamWEz8zR7WLikHadyX1P66wLSLSvAnjwFeIFLhqpxJ+2kx9\nbcPJUD9JtRz5SgQ5umSTQ1OoS6cmJ5O8jd1duo+2qnbjL4CM6/Y8VFc/ekxXjf/87Vd58f0/gszx\nwWuv9eL8HJ1G3ULp2+ymwQ5KItpVoo1cjKZ+VmuNejvwWtNHOD6uKi8iMtCDvsTuIa5MqsA5jnCT\nMwP9vp/Gm4iIrxjjil3Ujv9tj2p3ybdRRb6vFX3drZhfs6XKi+vakao6q7RUtZs9H6nU3dVI8S+i\n+9Pc+KZ6T0Ii0nizciCL8/uPiQbzCI/TgW4tI+noxbUIbUGl//KVU1S7xAJUzPflYywe+bN2aZj+\n+UUyWsy//3z8w1nwbiEJ5OknMadUvbdetbvmJ3B8+tM9P/LiZUu1cwSP9bf+HXJd1zltzU+wDh1/\n7WUv3voO5ILTSrR7W0s31tbUE5C55Dsp5Meex3nklqNfNjmy0zY/+uLq2bO9eNJts1S7VpJ/JpXi\ns1wnB15zRoNQC+bHKZ/TcuyeGqRpRyVgLjny1Guqna8Q/ZG7givvGyb5b4D2NJ2OhKNgFdK5Q+RY\n4ctFX7/p4U+o97AL4fAgPsd1YYpOwPhjWXnhrOWqXWws5seWauzT8hfo++jLR5p21XPYY7FEXURk\n3HXh39P8g36SGiVN1P224QPMI3krIOmrW6vlT0zlU5D0pc/VUpSnf/OGF7MLU1K83n9UnUY6/Lg8\nyCp4HzbQrWXdiTlY77r2ou91O85S7OI38yrMFRNvWKbaddVUefEg9Y94x/2Opa+9J7FHi3Bkeb78\nFBlNGsh5ypes5QlJU7Bv8ZMcr/mjatUuk5ysSkjOUnqd1u+wrDCBXDpPv/uuajduJdbCjpP4rPRJ\nkEU0bNH7B95DszQlY4buS43vQ3bFLmDVf9duTQUknW8jp8vUqdrlrb8F95Hlaa7kwn3mCSfsBNW8\nUe8V8+gc2bkqnp6RRPRzx6Ip2Jc0tGl5cyrJ7edNwNhed0BLcn00Th+4/Xa8Z8ZkOgY9JtLp2aKm\nCmNx6mrdj9jVtf0E5HYs/RHRrrW8nmc6kqEQXZcuKrOQUKzHXtchvV8PN0njsQ4nl+g5lZ81+Ly4\nr4uItO6EZLh6HWRdqVO0zK6Hngu5//D4ENF9uo+c5DLoeZZlTCIiOcuwR+0kCXdCtu6bMQm4vqEe\njN+2ffq5n+Wmbfswx/fQ3klE9+G889CHh0Ja+lV85Qw5F5Y5YxiGYRiGYRiGYRiGMYbYlzOGYRiG\nYRiGYRiGYRhjiH05YxiGYRiGYRiGYRiGMYacU4B4YBPqB6z56T3qtcN/ft2L9x6A3m7Bsmmq3Xvf\n/ZsXT74A+iu/YyXYQ9ra155/y4v/47Evq3axsdCfsu7+J499zYsf/cYz6j1sZ9i6HbpN1mmKiNz8\njTVenJgHrV3lC9oK9JZPwx42owh1TE69/bZqx3rR6g83eXFSidbW93doXXG4ad1GdT5WT1avsfa8\nn/THfQ3a5quA6k/0k+bPre3RthtavATScrINpYi2fI6n+iJ8DDFJWrvOlmfpU3Pp/7UmM6MUGvd6\nH2pRVD69X7UrvBz9caAdWu5gs1+1S54MDX5yMfoFW6aJaKu1cJNWBg31mRcPqddSp5IlOFlB56zQ\nNtZsX55aBu1nywdag8lWqAlUY2fFivNVO7Yz/+V3vuTFicXo3+61jCWLz7b96Cv5C/S8UbcJ9yqS\ndNL9wWbVju2j08iWr4VqDYmIxGVBH8z62JIbtI64i/WjukRRWMgmK2iuBSAicub5s1tS+zJ03Z7T\n1I9ZZ9+2S1v/DVMRjMZO6OezU7UOncfw3qoqL07aj3s/bqG2W+/ugLa7+TDqmrBFqIhIItXkSKC6\nCt2ntIacaziUn49xmbtE10k5+Sjq77Ble4pTE6HfGZvhZN0PMc9f8YNPqtcef/C/vZjtP2NP6roU\nJzphcb36JtSLcC3Gee7mujyXPHSpajcwgPsbqMXcvfxG2KRve1nX5ZmUhzoIeRfA7rL02tmq3Xdv\n+akX33slzjdqn6758Jkf3ezFLXTc7jzO833jOtQtib9pum6XqesRhJviGzH2q9/StQqSJ+DeNa6D\n/r3UOcboBLITPQ5NeRvtM1wSaf1367PkFF7kxf0XY1+QkbfAiztad6n3pGRhvQsEUGfl9JN7VTue\n1+OyMacEJp9S7YaGsAb7MrHODgzoMTsyjPklKhrz8PhbtZb+9OPYP437t+slnOQuR791axTxtW3c\ngHvo1vqKp9pVI0M4J65dJyJy1RWoS/Tu2u1evHS5rhOVSutQD81zPKf3Vun6CFzLbjgX/f7DF3Vt\nxksvxXjmuhTBZm2tzHbFnQexZqaMz1DtfFTDK20O5oN+51qe+ivm3byHrpRwk07HFevYfaeV41wy\n56BOh2udznXqUidhz+bLders0P61bS9qY7BNuYjI4CBqQ+VUoN5SKITryXXPRERO095s4icwDuIT\nilS7jHnYZ6QU4bWIZdonOSoedREnXY81uM+vLah5nW3bg3PiMSqia4aMJtnnabviujdwrwqvQh25\nUKd+9omhOiYZdK/T6/XzSEYS7ml8Et6zbEDvqcpKcW35WafmDObqxCY9Fgtm4z3j2Do6Rc/VLZuw\n/rGtdNYCfa+5GFnGTIyx5i16j8o1K1vJltytMZk8+eNtxcPBMD3HDIV0TTTen3DfSsl1rK8XoV3H\nQVoXd9WrdmU3YB8zMIDvBNKm6/mnaX2VF3OdGb62k27UzyetR/H9Re552EcmJOn7ExODuaJ13zoc\nQ4Wu63T6Cey7J1AdvcQivfeMoXWn7RD6iFsTJ6VY1xxyscwZwzAMwzAMwzAMwzCMMcS+nDEMwzAM\nwzAMwzAMwxhDzilruuIHt3px00GdIlvxL5d7cV410ofinRT8shuRtrTzYciNlnxDy6SeWfMvXnwn\npTp1HmlR7Vp3wtL1RAPS+MvWITX3s7+8Q73nvmu/78WP/whyrK9cfotq9w1KWe46hdTF7CU6Ra/p\nwyo5G8/+ba36912TkObNlqExjhSokWwEZ1wlYYctHNmSWUQkkiwTA7VI40wpy9TtKCWa5Sx9TTr1\nN4NSY9n+LN2xEoyOQ+rXyAiOqfMY3e9InYLZsAOp55mzIOfg9FMRkVAQ6XEsPUos0XKOQCNSJRMn\n4f6wjbGISPdJpCbXr4eELz5Hp8u6Ka7hhI81b9UE9Rpb2rEFJtuvioh0n0YqbdcJ2PFF+WJUu/gc\npFWzVSlbqIuIxMQiPT9tElIFR0Zg6TxS7ljKUt8JtuNv+1t0uiOnciaQPbtrF915CCmTPWQF6qbV\n8r1ponRU19LZtbwMN/VrMU+VOBafQ8O4NnxPshbrNEx1Den+9FXrcTDpEqQPZx9A/95/6LRqF6T0\n/UQal/1tSDk+8IiWiuaSDKb5gyovTpmqrRI5nTn7fKSWFizT537mIxxT8iSkuHMfExGJz8eYYylA\nxkydItq0kawYtergn+bS717nxQ/f/mP12lf/AnlfbyP6Y8PbWjoy4WpIJGo3wmoy3bFI5ZTZ5SSX\naN6kpYi+cRibaTPRrmMnrZGluh/lX4JU5N8/8LgXX3XhYtXuG4/d68Wcyrz0gQtVu2N/2OHFUZGY\nh6Iv03uCzU9v9eKsFJxfy+82qXbll6OP5BVI2Kl8EutJ0dVl6rW4dBwzy3SGB7V0tfoNsr6lFOsU\nkpWIiPoZjC2yS+frBT8YhNw3gq7hoWee8+Lsxc5+pBFytXiSjeacr2WtA37MnUXzYZ8dFaXXsZER\nrNstJyBJ8p/pVO0yaJ1kuQRbyIuIlH9WSyLDCUusu47qvWJfHdbMjAXoQL2V+jz4vvG8u+N9LXW7\n8O4VXrwqNM+Ls5z7wfcgjux2ee1yJdAtezBO0yZi/rvoPK2tzZyH86h7/bgXT7ldj8XOKsw3LHt2\nLd75OHrp/vLniIiklOl5PdywlCl1st57BhqwriXQnmbSJxeodt0kVYmKp/1Dkr4/wXY8r7DMxC0v\nEGjGfqnuICTHWWSpe+gZ/Vw09ROQO/ScxPuHB7UUPUj7uRM7IF1jqb2ISM1rsOouvhr/3/CBthrO\noWeUlh3YSw34tcwnMnr0ZE1NZH8catPXMioR46qD5OxZ8/Wa1LIdch6WPLnrO4/T7JnYD08M6j0Q\n2yEnkFwwK4TP7dit5eC9J7Df903A/iPGkaAOdGDvGBWPR2mW8Yjo/VriOKx3sela6tz0XpUXZy5G\nH4t15FSdh7S0P9ykTsZYd6V+Q0H0p+5T6N9xKT6nHeaZuMyPH9sslR0IYf5xn6WKb4B0d4isztle\n3i15EE9zCj9XtvbqZw1fAT6rYAHmlBMvrVfteG/b8B7m1+LLP94Se6Ab/W+oz5l7Rwbd5grLnDEM\nwzAMwzAMwzAMwxhD7MsZwzAMwzAMwzAMwzCMMeScsqakJKTFd0RVqdeq1n6EeCulsw3qVJ2rH/53\nL44h15XavTpl6JPXrvLiBEoz+vH3HlXtPn8FJE8PPfELLz619g0v7ncqgF+7cKEXr/3mz7z4wZ/d\nqdp94zP4e5fNQTrp9NXaSYblMT1dSHe8/f41qt3aX6Hy82UP4Lh9aVo2Ex2lU8fCDbtjuA4x7MI0\nQDKkzoONql0fyScypyD1cnCiTiOMikIab0wM0nMjI7WUa3gYKYHNR+AEwOls0Qn6PTGUylj3DlJ6\nB3u01CWdqrz3VqJCdlyuTr3rPooUSq62PjyoK9wPdEMalTSBJBeJWg7E6YvhpvEdpNFFJevrEkGZ\nqgWXkKuWMw6C5FIx+SrIEpNLD6p2UTROOY3fX6urjedVlHtxUhJcwEYoTbz68HPqPSkFpWg3jHRS\ndlAQEUmZiPTHpEyk7PYHdcqoj1wKOIWXXWBERPIvxXXhtNX8K7R7WdXTJHX45nUSbthdqfrVI+q1\nsk9jzuk+jWNs3aqdGUpvhPyyhmSfrhNDygRcw6YPIYOZPUef85N/x1w8MR9jJxjCuCq/eZF6z4Af\n47fsriVenJSk3Ww6ZsNFhPtP1Wu7Vbu0JKQcs2PKiSc+Uu1SKpBayv2565iWNDTvptTVGyWsHPvT\nZi+++nwtAXr8fsiDli9HiruvRFf076xB/8xbgnW2Za+WP73yp3e9+Hg9zqmzV7sZfP0WuOB86884\nhgfXYE1q6tLj98B/Y92+7SvXePG7j32o2pXfsdqLD/0BsmK+FyIiJxqxZuSQXOnwrzeodmmJuNeb\njiJtf8GkSapd9uzxMpoUXI7PSy3VrmARNKm2HcW9cmWQg+ROVnQF1sVex10kjqTFLG9pbdDXJj0H\nadWBeqytxVcgrdt1J2ysxR6EJTod+/VcmUIp5e11kGO4ewJ2muI1bdLN56l2/mbcb5YsZk3T97Fp\nN+5x5sXLJZz0VEI6mDReu5ikTkH/ZHl86nQtHUwuhXThzLOQn0zI0e1Y9p21ELIDvuYiWvrHqf/s\nqOM6sByqxRw/ifbQueV6r8jStKlfwJ55aEjLywcDuKehLuqzRVqazPe+iCQ1Na8eVe14XzEa8LVx\nZSH+Y1gLx12PcdCwSbs1jVsBqRnvYVqPaUkRy1N4Tap7U/+9Lcexx+zw4/qWvoV+MXemXks7D+DY\nCy7CNWvcWKXaJZIMNZlkbB2HHTdKchhq3gI5Nv9tEZGBHuxRJ94KHW+EUxrAX+NI+sJI+mxIxNxy\nB1zKgZ9HmjdreS472bHMbmRY763ZpbNlH0n4Juk1KWcB1pBAE/pRdAL27rkr9TrT14S5m/cYT/7w\nJdXu/KmQ3fromZD3riLawZL7XrRTToBlmNx/2WVPRGRkcPSeM0REfFmQQbYf1/eH95Tslln3nh47\nLGvLpzIMgwH9rMZrCJdEadygP5fX2TRya2LJ5rb//EC9JzMT9yR/NY7BdY/Mq8C6dmYz9sIlV2q5\n0om/Yi/Lc2rrfn2sXQex1sSk4bNKr9US1Z46ctbVS42IWOaMYRiGYRiGYRiGYRjGmGJfzhiGYRiG\nYRiGYRiGYYwh9uWMYRiGYRiGYRiGYRjGGHLOmjMn1kJjt+Glbeq1jCToBuNioJ27/hc/Ve2e/NJX\nvbgoE3q1YKvWzD/yDLTs80l7/rX7blbtQh3QVm7+wR+8uPgy6GXzJmnrxrx01JHII3vJn37tT6rd\nV++7yYsnXn6xF/f3aw3stodxrN/8zh+9+KFbb1DtSrOhh3v2+y978fyJE1W7KXfOk1GFJNGu3eQI\nWSQOkf540LH98lMNjKxJ0OJ1nD6p2qWWQovdUQctfKxTJyUmHvrwqAR0Q7Zg6zrWqt9DlnKxZFGZ\nWq5tS0++hBoqhefhfrPWVUQkYzbqa7BNqGtTmz4LWlrWKzZ9qO0M2Za8VJfe+KcpuYXqjLyi9eDR\nVC/nNNVMmXjbLN0uEfegowEWqS1ba1Q7rqvD2uGBzqBq58vD/fV345j6yb7c/dt1negvQ1QrKPM8\nbanYRTUCulLQD9jmW0SkdQv+PtfAcI+1gywVU6ajvwwPDKl2rBEdDeKzcPwla7SddDvZgrNt+aBT\n56L6FdSq4ZpAeZfoeYXr53BVhH179Zj9wjdu8eIQ2YnyWAw06Roa3VTjJWcJ6k1UHXhTteNaAmyJ\nzjU4RETiLoF2v/0Q7lWgRa8TJTeg/ldDC8YfW1mKiMz8yjIZLXq7UR/pYI3u35d/BmvPjhd3efHV\nP75btevrRY2JmrcxFje+u0c+jn//yV1efOjFfeq15kaqy0Drzox7UBMn6SltDZw6A+0e+y+s9Qlx\njnVnDfTkR0+i7sHFa8pVuwWrsC50HcaY3X9Gz6cF6Zj77//DZ714y399oNpVvwlL5uw7LpJww/VU\nql7doV7LWoj5iHXyXMdERCSZ6pzEp+J6Rifqa1j9d8yV5bdc4cVsJSoicuTF572Yaw0E2zEPdx3T\n607aNGjw2dI0t2K+anfwz9iDcN2C/Iv1vFGyButGqIfqIEQ5dqn92C+kl2ONbDum630N+nWdgXAS\npPWp17H6Tp+J65JEdWX6HZvfxg+rvDhtLs6D3yPi1CHahfpPHSf0PmXa51GfK3k816jD+pu7olS9\nZym9lkY1cZqd2gsFs/G3W06gbldMip5Pg62YowZoPRZdHkd6q6nmyiuwmJ50l96TVj6DsVg8RcIO\n7/W4zoqISNG1mGdatmHe5P2WiEh3A67Vocdxbabfoc/l9FM4l0iyq3/0/fdVu1mlpfjcbtQbKqH5\nNaVczwdcE4htsE8d0evEimWYz7geUrBB12oZojqQxVdh7at8XtvV5yzHPrdjH2r5+cbpGkP+U6jR\nJBdLWGFraHdf1bEXazrXQ3LrRLFN8sSrYQ8fFaX3fdEJVEeIxmVktO47CQnYm/QOo/4T10V0a/r1\nt2J+SC7D+L10ta6713gc+7XGN/DMsehuXZuLz5evC88HIiLxZPUdTc9E7vHFZiTIaNKwGfvLaKeu\nJj8j8nV391+p0zBG2KLefe7vp5o+QmPRtXyPpDlh70vYIxVm4TsFrmUnIpJFzxR8DG7NmY5G/L3u\nI5iH3HVCqNRPyhR8bvpUXRcsIRd1U+PScK+q39L7L76WE2bL/8AyZwzDMAzDMAzDMAzDMMYQ+3LG\nMAzDMAzDMAzDMAxjDDmnrImtvhYs0Sn4M26/zYt/cceXvXj/jZ9U7b74h3u8uL8bKbJ1bx1X7e75\n3LVevOEtpIMXXaTtrF761ye9+MpvXunF7/z4LS9+7ldvqPe0UmrupyhV9csP3araFZ0HS9gzG2Bx\nOfkibambFP+eF3/7PlyHUJuWUpz/H1/04jk9SGl6+d9eVO3Oy/+SjCZsp8cp0CIinSSlSK1AKlru\n0hLVrnUX7DW7m3Hvek53qHbBFqTTsrVvKFmnkoU68bkZZI0WEw8ZhJsa2d+ONLO4TEqx1hlwkpSs\n06//QeM7Ot06YzFsjdP5ujiSpEhKL695Ham/KWXaMo+lVuGG03l9xdpKNXMuzqPySfSzmpe0VXM0\nycLisnCNop170328jdrhnFqOaZvHYD1ScCfcgbw8TklMIptIEW0R2HUUKYRNTvp23spSL+48iM/t\n2FGv2oUGIL3hFGNORxUR6SNrW+6X/W3awjtzkZZXhRtO6+QxJSKSPgMp9dz3WTYkIlJNNqdBkv00\nOfbheWQfyBKlw3/WKdZH/o4+M+kCyEMPbkRfX7lAXxfucyytkmGdN9+8GZ9VQJaK0T6d0ttxFPc4\nMgYprGWfmavanXkB6czjb8baMNSvZZhDIf3vcLLg67CnHvyJttdMJkngwWpIgFY2aYvsnirMmyyv\nufROLcl9+89ItU8rwzXPL21Q7Uo/gUlrWcxVXrzvF+u8ePp9Ot36ua/D5n4SWahPKS5U7V7/T6yt\nKT7MG4f/slO1m//gpTjWaUhj3/2wluHM/RdIrU4/DnnW0To9HiIpbXo0hL8+krn2O+nWiblYC3mN\nrDum9y1sHxtsx3lmlOn1k9O+O+sPy8fBNswDnFZNa1zOYj0fcMr/8DBSpQMB3efK74D1cvW67V4c\nGau3gccf2erFeatgM9u4QVusj78att/NezFXsP2siLYKDjdx2eiP7Tv0mEi5DutBXzPWKn+lXkNi\nSHIdT9LLpAJ93P2d+Bt8Tpnz9XiJTSQpkw+S1K5KjInU8XnqPVk3Yp7r6cT8nnO+7kcxMZCBpJVC\njhYZ6UgRIzGfpk2DTKrJsS4uuBB/I7Eu14zOAAAgAElEQVQI+4raN4/pdqNspT3IEuc5+pzrXseY\niyKZxcCAYylM88W4JfgbDev1ulh8Ley4WRb36WE992bOxZy4cD/OP4H2X7xXEhGJz8Ocknch1ru0\nmXrf3fAejimXJEmBmi7VLmU+jqF2Le5JdJJjw0yy48AZSLBYui8i4o8Zvd/je2vwuf5TWq6ZOgN9\nMI72yS0fVat22cswt1Vv2OzFLA8UEUkpwH4kFMJa6vNNUO1iYzGPx2dibCck4f1D/fo5gyX12YvG\neXFPpT4nXrdZkuSv1vcwQHbtsSTnTizWssmEPF12wfv/wmT175GhkbO2CxcjQzgvX4GWxcUk4vjT\naL8dcqT3vB/jNS19qvaMDo3D+2JJmtlzWl9rll9OnIXx0t+M/bsvX8ua2ndinh8O4h7HZunntBEa\nm3ys7nUeolIfPpLr815ORKT7BOYEfo7Omleg2g3/X+6jZc4YhmEYhmEYhmEYhmGMIfbljGEYhmEY\nhmEYhmEYxhhyTlnT+GWQDZ0IvKxei4zEW+/89e1e3OA42Bz59UYvTq4gt6ZmLScINCK9kN0cHr//\nL6pdTx9SfWtJGlXfgdQidoUSEbnhHqRbFy9GBfC22l2q3bdugLPU955/2Iv/zx33qnZ3/xb/joxE\nilTTPu2g0d+P1NLGjbguy29arNq99LXve/Gtv/mNhJvEIqSmNX6gUzw5dYudsNyK1unTkfrVfgBp\n3nGZOkVsOISUOHZ+cdM1uTJ3L/0NXx6OJzZVOxBwGmGoG8eaOklXzI+n9LaUMqqsP0n3CyrYLj1V\nSKNz07w79iBdOvs8pDm27dTp0TnLdDpuOOGUuLq3TqjXTh1Cvyu5Dim7Az3aJYMr5rN0ZMBJSeR7\nyqmGGSU6tTR5Ev5dvxbH1HESaX1ZM3X6dqAK/SBtFvpU8kRdtb9zP/oYu1e079Kp63EkOevci/fk\nXazTW5PGIYW05wzmivat+h52RWHMTlogYYfdmiJjtbMAd8gB6t81L2gZRC5JDUI0jxZeWabasXNE\nw7sY99d862rVjiVzGSStqiBZhb9aO6H4Sc7YfhLytJzZOnVz3OU4phC5hjS8r+ehNJI2Nq3HXBko\n1fMGu2nt/cVHXlzgSL9UyvA4GTXS0nUq8slHUfn/3l98xotPPa7XhvE3Q4bUtQ99rvKYdmKbWgjJ\nxPE/bvHiCCc9/dRj+NzmZtybhFhINk49sVe9h9fZpV+H5CU2XrvfTerD8TWRk90zf3tHtZvVv9SL\n2WFiZrG+N8kF6GO1DUhdv/0n2pmRU/9Hg9hUzHPRSVpmV/0mrhWnM4+7SjtUdR5HCnzbVsiyep3U\n9jRaP1my6Ep3B2gNLr8D+5YdP4UEreJuPTENRkDKxHI+X6aWUgR7Mf/7T2M8u1LnqHjMS0kl6CNx\njmz35NObvLjkejjJDJYOqHb175I73EIJK+w6VXCZlt6cfpLGHO1z0ubo8+0jh5y4DMikWnZqCRBL\ngfNmQ2hX+Y6We7EsgOfgorkrvLi7Q7vtdHYflLMR7ziztNZjvLCrZEKBlj7kLsYaEWhGX+R0fBGR\nPpLF9jUh5vnYbTcqkGyvfq2W4/E+oZuk0FmLtdSW3ZtYWtdVr8diFt0fdrDMXqDXrqRS9P36bZDf\nBE/gnrr7m96TmHsjST4dl6UlF10k+6k+gLlyyqUVql3dOlyL6Cjasw1q2W58NSRFvD/odfbdg716\nbIYTljINtOsSD0PkWBSVh77qHk8nPVukkgTG70hHYmi+bid3qsagXjNYVtJ5HH1neDy5Jvm0RIyl\nR2eex7h092vxNOb8x7DnzVikZY6D/oGzxlHO5/L8ylIrfn4TEenY3yijyTA5ZoU6tWNRHD2TBRog\npYuM089MCdnYF7GklJ8d3c9Ko7IarlywkNw8Qz1nf07lPbOISG8dxgS7hrpyoj46j9IbsC/jciAi\nIi1JkOhnTicXsCZ9rBmzeE7AZ/H1EtH97GxY5oxhGIZhGIZhGIZhGMYYYl/OGIZhGIZhGIZhGIZh\njCH25YxhGIZhGIZhGIZhGMYYcs6aM/sf+5sXs95WRKSlFlbTGfkQEkddqLWVqdfCIvD0VtStYR23\niCgbvOW3/ZsX7/ztr1Qz1unlnAfdV/Yu1J/JStb626d+/boXPzAfeu29f9qm2n3uqzd6cU8LtJ7l\nhVpDWPk6bCjZ/sxfqXWRL/32Z3I2Ovx+9e9vPfPzs7YLFx2HofNLmaLrs8SRvVwEfVUX5egr+8hy\nMEi2xPFOv2AN4PAg9ISuXjM2DfpK1vZ1kS7UZYRqciSQDWrLjlrVLqUc2kU+j6F+rTUMttA5kUY5\nIU/3n3TSEHI9Gq4/8z+O3bHj/mdp2Qq945Bf63S5JgvXwclZqms9jLsa9RLaD0K3GqzTWkiuJeOj\na9G4XteTiorDtY0hm+7EdPSpvhr9t/NWw7ozlt7D9nMiIkkToPf2V6E+Qny+rvHhI5tBtnFX9s4i\n0kt2hmzbmbFQ68yTJ+jaN+GGaz6FOnWtn/E3nV3v2j+g73cv1X/Jv5wsPnP0tfFXowaGrwjXqW23\nrrOTS/No5xHUF2GdeESE1t/mk00o1zFw58B9/wd1KSbfMvOs7xHRtQTicjCnDHTrukkpFZi/Mkk7\nHJOi9buuXjicbPsp1rFj9fpafuIH13vx7778mBff+OnVqt0rP33Di3NToSlv69HjZfGFs7z44GZY\nqV7yH1eodif+iPppc/5lkRf/5iGs4UumTFHvGVeBvs+1Sg7/RdeSiSarYa4Nce1ly1W7jx5e78Vc\nE6GkSNevqPo7LLj5LnENDRGR5156z4sX3PWAhJsA2c+mTdF1diKisBjGUd2P9oNaMz8UQP/MvaDU\ni335bm0PrDUxVOeifb/+e1EJWF/aK494cf6yUvk46tdjr8KW3cFGbYecdyFqUUy5c5kXH/7V+6pd\n8SdQP6a3DvNmf7uuE5i3CnM5Seul6ukDql3aHF2XI5xwXZDal3W9pmHaL3AdNddy21eC8Vf/Dmqn\nuXUz8i7F+badRi2K/BW6vlnzdtQn6aO9UkQk9swDXfpvx2VizhuiOgzxmXp/pfZRNCe7dSnaD2Fe\nSiHb7/Y9er7i2m65K0rxOTF6vxYRPbq/4/rGYbzEJOt6h1yvMO8i3APXbtdP/85ejL0Zr1UiIoce\n2eHFk67Hmtv0jt5H+iuxzk68ZqoX91F9zERnHWs/iPWTd5GRcfp6ci22ybSHDjn9ovxO1DY689wh\nL85bMl61G+ihvQTZAXfu0/NL/mWjZ4meuQDPSe5+v+sw7SvIZjltprZW7jmBe8i2xm6f6KB5OIlt\ntkf0Hv84rYtps7EOVT2DOYprGomIBGpRq6SfaoCllDk1F8dj7knIwZ63w7nmbLMdQXWIQu26nkv3\nQdQvS58HC3R3bzzoH726QSJ6TnXrfnLtVK7n49pO8zrUuIGei5z7k0TXkPfseSt1/276qArtqB5P\nCtl5u7UzuUYp19+MdWqnZc1Dvx0K4tp2O/NLXDbdxwicH+8VRHQ9VJ6j43P0dyP/N0t0y5wxDMMw\nDMMwDMMwDMMYQ+zLGcMwDMMwDMMwDMMwjDEkYmRk5Ny5NYZhGIZhGIZhGIZhGMaoYZkzhmEYhmEY\nhmEYhmEYY4h9OWMYhmEYhmEYhmEYhjGG2JczhmEYhmEYhmEYhmEYY4h9OWMYhmEYhmEYhmEYhjGG\n2JczhmEYhmEYhmEYhmEYY4h9OWMYhmEYhmEYhmEYhjGG2JczhmEYhmEYhmEYhmEYY4h9OWMYhmEY\nhmEYhmEYhjGG2JczhmEYhmEYhmEYhmEYY4h9OWMYhmEYhmEYhmEYhjGG2JczhmEYhmEYhmEYhmEY\nY4h9OWMYhmEYhmEYhmEYhjGG2JczhmEYhmEYhmEYhmEYY4h9OWMYhmEYhmEYhmEYhjGG2JczhmEY\nhmEYhmEYhmEYY4h9OWMYhmEYhmEYhmEYhjGG2JczhmEYhmEYhmEYhmEYY4h9OWMYhmEYhmEYhmEY\nhjGGRJ/rxZ2P/tyL06Zlq9fa9zR4cWxavBdHREaodgmFKV48MjTixX0NPapdRPTZvydKHp+u/h2X\nluDFbftwDH31+HvRSbHqPYUXTaIPQthb16XadR1txXsuxnv6mv2qXeuOOi9OmZKF9x9qVu1SyvGa\nv7LDi1PL9bUUumaTFnxKws3pvU95cdeRFvVa2rRcL07My/Di3oY21S4yJsqLQ11BL46Ki1Ltkooy\nvbhlT7UX+/KSVbvE/DQvHuzr9+LhwWEcQ62+P4njUvGewIAXB1t7VbuEXHxWdAK6eMu2GtUuKh6v\n+aifBpy+mbNwnBdHRKBvDYX6VLueM51eXLb0Dgknp3c/6cXdJ/S9Sa1Af2p6v8qLM+YXqHYxNC4a\n3jnlxS1NHardjE/N8+LKFw55ccGqCapdoAb3J2tBkRcHW3A/9rywW70nIRbHUH7NdC9u21qn2tXU\nYizNvXW+F9e/eVK18xXjvgXrME6jk2NUOx6LvdU47tj0eNWucTeO49Kf/lTCzf6Xf+PFSSVp6rVG\nundtDbgnRfPGqXbDoSEvDlR1f+xnFV5T5sX+KvTNuPQE1W4oNOjFh14/6MVz6LqffOmgek/WlBwv\n5vnWvZ6V757w4opbZ3tx23Z9vwe6MQe0N+H+TPvUXNWu9uWjXlx4zRQv3v/4TtVuwvKJXjzz2i9K\nOGlt/cCLq9/eq17jtTBzdr4XR8Xqay6CeS7Yjvmm+1S7ahWTEufFydRfeqr0mI2IwvqZMaXYi+s3\nHsbxzNHzAdPwfqUXRyfqscPn0bSJ5vQiPacHatAXJ6xZ/LGf1dve6MWRdNzcD0X0XmLc5Bs+9u/9\nbzn01h8p1v27/MJyL26idaOI9gUiIkdew/sqrpzmxcOhYdWO9ztddI/Tp+aodjEptHfBdkmOv3fM\ni6dcXKHe00Prgb8JnxMcGFDt8ivyvDguy+fF0Yl6v9R9HH8vMhbre+8ZvR5H0b0L9oe8ON4Xp9vF\nYZ097+vflHBy5vBzXhyTrD93eADzpL8a81/3sVbVruhSzJNNm894caBaz60Fl0324lgal40bqlS7\njFm4zoE6/A1f0cfvMULt2Ev0VuI6jwzofpQ6E2v9UBDjJT4nUbVr317vxSU3TT/re0REhgfpGtEe\nlfdXIrqPzLj6Hgk3xzY86sVD/foY+ZijfZibIiL1M0N8Nq5BoB7XfahPn8uAX//7H+QsLlL/bqNn\nnLa9mLNylmA9jsvQ8/pgAOMgUId73N+s96ixmRh/kdGY59zrnliMOT+C2rljMW0a5hHek7ftrFft\nBv04viVf/YaEkw3f/rYXZy7V17K3EuMvmsZpwDmP1Bno30l07rWvHlPtEsfjtcx5hV4c6tJ78qZ1\nWNd89B5ep335Seo9/Z14vuHnSt53uf/OpL12bKreA/F+Pdga8OLek3oN7+3HHihrMq5D6lT9vMjH\nNOuGL0m48fuPe/HAQKd67eTzH3rxjNtv8+Lbll+l2v1tw8te/PCnvuzFX3/il6rd+m/j32XXYp4q\nmLlEf+4bb3vx9Ovv9OKOjm1eHBen19IN3/+rF6/4Nt7zxr//RrWrOB/zf+nqFV7cWa/7XGo+nn9+\n97mHvfiT39N7E77frduwz81dUaLa8bw2/crPi4tlzhiGYRiGYRiGYRiGYYwh58ycyTkP3xA3vHtK\nvTbuGvx6wxkovlz9a1rnEcomicA3v26GDX8LmTUP30K6vzAkZONbzoyZ+IVihH6Bat2lf5Vt2IBv\nT/nXas6ccI+Pv8Hu79DfxuatHO/FnZSJkrO8VLXzV+IXsoJV+MXN/WXAzT4JN4Fa/IoQm6a/6e88\njPvTR7+68TfsIiJ5S/GtIf8ixRkwIiK1a/HLdt4KXKeRIf0LUPsh/CqRMQ2/zIZ68M1yrPMLf/Nm\n+tWWM13q9C9cI8P4yXGYvp3kb9hFRPrbAmd9j/sLXKAJGRkD3fhWPS5T/1rF3/SHG+6P0Un6+Kqe\nQ3bL0DCuc+xp/Sv8CP0Sm3cR7k3bU/rb8RrKTuBfdiOd7Da+ngce3eHFiXE4vskLdLYN/yoblYBf\nwfIvnaja+Z/GL017n97lxdkpKardAP3K4SvFaxlz8lW7hrWYv/IoA6hli86mCg3pX0fCTfJEZKd1\n7G9Sr/GvQemzkNHWdUhnu3H2X0Mb7vHcTy5QzYb60Pf5l7vBnn7VLrEU2YlpPvyi130Sf3vimmnq\nPSGeE6NwQN1H9K/S6Vm4J1UvIIujrUfP63Nvw7En0i+4nIUlIuLvw/3mebTi2pmqXdu2Whkteltw\nP4ovna1eGwhiLuJfpfta9HWJoV+iB4MY27ym/b9/BIM2SPOVO+flLEa2TNMuZCvlLcWv/c3bT6v3\n5C1GdkhMMtbMhHy9hrftwa+v+bT2uZkukdFYx6rfxZgtWqXvTTStu6EerDNuBiRnuI2bLGGHswum\nLNcf0LEbv5TnLsSvwJwtKCJSdhGuIf+i2XywUbXLmYk9TSTtM0L0S6qISBu9L28J7mnJDByDm9nE\nfYTfw2uaiMix9/FLIK8Tvji9nky4ANeij/pZ0gSdxRxqwbFnVyBjNj5X/xI9mvCewM2YDtCv9VGU\ncZF/sV5rzryI9bPkesxznZl63h2ha6b2GM4v6ryv7KX+EqLsQN6/iIikl2OdbYnDWEx02nEGciPt\na909S0QM1up2yjDvOa73BPF52MPkr6S1Wm/PpeZ1/StyuEksRFZ000dV6rXsxXgOqf07jiNlapZq\nN0jjon0nzpmza0VEhikbKaEAfXWwT89nnOnE+68uWrcHnEyXBMrC4OeEjoN6ref9MB9P1gK9R22n\njJ0oygLnzA8RnTHNe7uBTr3Wj1tTLqNFzgWlXhxPmXkiOkOG57z02bmqXfsu3LcWyjrIcrLAO2l+\nTiJ1RfUbup8O0H4uppOuGY3ltm1OFu8g+kH+MmQ7JBTodXGgC9eWlQWHHtmh2qWXYM+XNgPnO+xk\nseWW475xpho/b4mIdOyjvhT+hFKp3ozsmLQKnY0y7VM3e/Gx11/w4pXT9P5waAj7tOnjMH6DQb0u\ncia9n+brza89qtqV34EM6k3fQ0Y77yNbnT1lbRueNTof+pUX17Rp5UFZP9aDd771iBdzJpOIyA3/\neb8XT8rDPm3A2U+nkpomdxGycho2HVXt8peWybmwzBnDMAzDMAzDMAzDMIwxxL6cMQzDMAzDMAzD\nMAzDGEPsyxnDMAzDMAzDMAzDMIwx5Jw1Z9iVJ8bRODZ+CL0ra24He3WtEnZh8hVAw9nXqPVhQ/34\nG1wtO22K4xJ1CHq7lPHQ8g2TBpidY0S0Joy1uU10DiLaKShE70kv13UAuk5DixyiY23brSujs/NC\n7ZvQQrq1adxaHuGGNYvxTh0XrjbPutrkiZmqXete1HDImIHrMRTSmtsCcsZq2ojrm+jUY+FaEv46\ncgmgyvrD/VprmTYd+keup5G7VFfBDvWwmxS6eFSsru3Dn8V1eVw9eKgT9TWSqc91HdOadO7DBdpg\n55+GtetxmVrPG0E1DCLJwYBruoiInNpEdaOoAE3p+VqD33MCuvRucjBjzbOIyKGX9+MY6P8Lr4H+\n1K2N0d2Ha8lzRdcRXcuBayIkxWPuyZina8mw1redtMOtR7RzWuGyUi/mcR7ruC0UxDk1P8IM68vT\n3foidE8CVL8iY4HWW8fSHMauD80fVat2QzSec6hSfM8p7RJw+vUjXpw3h+oIfYj/H39Ca48DQcyP\nxeRgk3O+Hou1L0FnW0J6984nd6l2lS+hHk32XJyvWzdj4tVTvZgdis78/YhqlzFJz1/hhOtOjWTp\n+a/rJMZLxlTqq/o0JCEV17O3DTp7V7/MtQpYh569SE8wDe+jnkxcJvq0vw7HE+O4SIR6ofGeeOlq\nL67bs1m1m3zl1ficgxu92K2xlV0Mh4XKjW95cc26/aodz9dBckJktxURkWGnNlu4qad6TREn9BrM\nLn/dh3AN+3qDql1sDK6hP4jXJlyi9eS8XmXMRb8YGXTceMgVc4TmA66ZMtCljyGeaiGwKwrXsBIR\nKaO6OpFUk+TA2kPycVQeRE2uilW6XkVcFvpZsIHuY46uOePWmwsnqnbhiB5k6bRW8JrZ5tQkzKAa\nh/XvYRyxK4+Irg2lag2eV6zadZEbVC85mEWSm6DPqevUsgP7K16Tuo/rWlU8P/RTzZ9u0e2y6NjZ\nPWagR+/PuZ/zuhjp7JXyVpTKaFL19AEvzqWaKSIifnLBTJyAPu26jLEjIdfSSXRcEbkeWYjqeInT\nTXntYadQrink1hsKVGEfwzXROg/o/YivEPefXZ3ic/UcyOM+mWo+9Z7RdQKj4nG/on24LgWX61pa\nrfSMMu7cJS/+P9OxD/VEQu16jiq+Aes276fdPSrvjzKp7l6j47Q67iLsWTuoLk+KsyZx7UF2K2X3\nJ7cmUWIJ7nXjh3Bv6wvpsZNM+9KUaagzkjdPP39yDdWqp+Hulz5X7/86qZZR3oWo/xRwap5mLdJ1\nicJNxkxcs47DulZSqHOTF0++/Bovrt+h789lsy7x4ide+7EXP/fA71S7m3/+FS9+4QHUhbnsW1eo\ndlXP4bp980k4176593Uvvveyu/XfXrrUixf96/VefMdK3W5fJZ5T//z+s158z2rtnHxzNPrFpT/4\nghf/6Z7vq3a3/uwmL37v+6948YSZep0YGTn3/sYyZwzDMAzDMAzDMAzDMMYQ+3LGMAzDMAzDMAzD\nMAxjDDmnrInT49g2TEQkk6zNOGXUTfNrfB8pQykklXHT8jLJQi4xD+362hy5A6Xqs2V2wQqk3EZG\n6vTtyg+3enEe2wVG6u+mxl2JPL+oGEojDugUPbYL95Pta/oMR/5EKal8fq6kK3by6KXgi4hkL0CK\na/shbWXGFo6crtnt2DBz+iFbnrIETUSn62aTvWvHAf25mZRuyHKgIUrhi4zS9ye5kKzTR8jGLlKn\ngkb7kEpcTXIHH6UriogkFSNNlNOjk0u1ZWh0vO5P/4CtuEVECi4Ic54okUJ9hKU8IiJZi5FGyVbx\nlc8cUO2mrK7w4tPrj3vxhFX6uFlOxqn6PZu1DHDqNTO8OC5DS63+QWCrtjTOLyMrQZor2GJaRKSF\n+mnRcqQ5n3r/hGpXSNKRojVTvLjXsbwdos869SJSJHNma8nQUP/oWmm30vVor9ZjLH8u5gieUxvf\nPqXaBcjib8I1SBfuq9bnnEBWoJwyW7B6kmrXcwrH0Ufyvsxkkkv06XHe3I12jc/v9OL0JC1pmHgx\n+harDiqumq7adeyGtIctiQcdq9K0aZivdv11mxcX5DhzaITjBRtGeJ4ccaQUvK6x3XjRirmqXVdt\nlRcn5UPKUuNITNgqPo7sSavJltwlYzbmSZbMunKYAEmKQj17vLhs2R26XQDHOn4+vDtDId1/6/a/\n58UjQ7gurvVzTDzOYzCg06aZ/va+j30tHGRR/3bXBrWP4XvcqPtV5kKyyN6Esd1zUl+baJInsPXr\noRf3qXZ545Aezx/L19CVCEeTPJflF66kdICsnDdvgNSsvEDPgWwVnJ+Nebljl17DgwP43KQsjHuW\nLIuItO7F2K64SMIKS4DiHIlqYhGuRRsdw0C3liewJneAxmzjB3q9KyAL7qQFuIcnH9/9sceUTWsz\nW4y7+9/O/ZC9HDgDKcXkfC3jTSNZ0plK3I+MFj3v8rwZnQQZrCuJZttXlnl37dbSrz6SrZVo19yw\nMEgSoHjnGFku00NrXNEleh1jSRFLtBLy9LVJLsNawZbbPEZFRHpOwnK3rwZrUizJ+Xbu1Pa48+ZM\nkbPhWksnFCaftR1bpYuIJFCfieHnMUf+2k72ytG0V2eZrYiWRo0mxTdUqH93kJ07z2s+x546bxX2\nejXvYK+X4NNW8VxeoPsIzjHZeZbqoeeYPNpHslwsfYa28+b+lkjPM5n5uh81kRQxk63so/QaEaTn\nhOIbsV9zn5W76Dz6mj9eJuo/o2Xp4aZlOyRKrdv1PFDxpcVe3N8Pidyzmzapdt+/9VYvZlnredfM\nV+18Pkicr//Z5704Olr3i+Jr8Tcmv4p9cn8/5KX/+coP1HsSEkq9eHgY4+Vn/32/atewlvfX6Jy3\nLFum2kVGYt+39UePefHhGi3pun3VQ17851cheap+Ue/ZPrH0Xi/+4IR+rhGxzBnDMAzDMAzDMAzD\nMIwxxb6cMQzDMAzDMAzDMAzDGEPOKWvqrUOaIDvliDgpkJvhEpI5V6fIcg4by2Fyzy9VzTqPIj0p\nhtIw+5ocCRCljGaUISWqr4NcKRJ1ClzBalQs76lCSlj5jVeqdrU7PvLirBlImYyK01WVuYo7p9l3\nHtYV2WPTIYfhc08r19eSJUT5utB3WGjeivvjpm4G23BcvjzIIPxO2m0uORLE+pAuHOrWDjHJWZCN\nxccjJXegdIM+KJId8D2OScE1c1M8g524Tv2UfhyXrtOoE9KR2sj3wHX+Yien7uPof4F4PSwio/H3\nOV3YTTfsqUEabI6+xf80nKLOKcYi2kmtvx0plEmlOv29eRPuVcVNs72Y3bxERLKXQQZ34klIAsun\n6Grj/ZS+zhIO/2n0HTd9l2UW3BcjnFTQWfei0vqZlyD1mPPZxard8b9CjtF7BteIndJERHzF6LPF\n5CjGkh4RkZQpoysxZLep9DlaBhnxMVIcx+hH8udjkmDJU3SSPmd23Ai1YA6re0unUE69F9eUU47Z\nYY4lEe6xRpE8NNNJEd7/GuQTLJNy5UCD5M6VmgKZoute0deAuWLBXed5ccPbJ1U714UrnAwFcS0H\n+7X0pvhyjKv2w5AnNOzQEsPceViTGrfiOmfM0n2iZStSZjk9OG2Ovs7tO5A2HhmL+avmZcg681dr\nV7Z2cu4ougzys2MfPqraZU3HnD4wgLEd7NOSxdypSFnuqId0MDlHO3h11sARh38ect0Oi1afXSIQ\nLuJprQlU6vWOx07SZEgB3HGw66+dzvAAACAASURBVCVIWspmlnpxlLOGBJsxL/M4LV2knWn4OJLL\nMRf1HMc8VfPmcfWe7SfR9+eU4hh8cXoflDgR60FFIVLD00o/XupwbF+VF89YqaUK8TTnh1oQs+OK\niEjaKDqnsYy3dbvujyx9YDeptBl6cWbJNq9X+SyBF713jE/FXqLwci0L9lejHcvwu2h/6Loh5azA\n2lq2juRi6Xr+89M+4C/r13vxeeXaSev8KMgnssndpW2blikEarHHZ/lTYpF2sEnIP7sMJ1yMuwZj\n3d17slyN5c+8fxMRiYjFZDJIsh92nxQRKbke14ZlQz2VWi4y2Iv74BuP/cOf/gqHmE9fc7F6T1M1\nnkMCL5IjaVBLSseX4u+lVqAvDThut+wwyv00faqe/1kK3FuPPhIVq69Rf8foSUV91GdCTrmD7MXY\nU1Y9g7UhY7aW7XXsh6So5Cr06YQcPQ7aqZ1vHD43vUJfl+543PvoOEjLWNrW16T300f3Qs544YO4\nv2de1JJjHpt5i9Cn/M1a/llP8qzBLtzfmHQ9P/PemPt8pLM3jksfvb2NiMj4C6E9LVmp17tgEPsR\nfyvi9h79nF58Le5dUQWes3/zsy+odltehWtnPJUSKc7KUu0W/ttdXpxJ0vn2Y1Q2ZbwujbDnj3/z\n4tn3QWaVPkX3kTaSbu3/0zNe3NCh54NDLzyFdtV4lnr45Z+odnFx5BAWg3H+zsm3VbtHnvi2nAvL\nnDEMwzAMwzAMwzAMwxhD7MsZwzAMwzAMwzAMwzCMMcS+nDEMwzAMwzAMwzAMwxhDzllzxn8Smqvo\nJF2rJPZjdKCskRfR1rSss+0+0abasT64eQv+Btc6EdG1Cvo68Tcio/G3U9JmqvcEAtD+Z5RDOzs4\nqLWGrPPrroV+ueuotqNjPXlMMvTjbJEpItJ1BHVMuHZC3Tu65kNkDH1Hdr6EHV8hNJluzRl/NfS9\nbDecu1TXCWDtax/VfnG1oD5fqRcHAtADxqZqO+ohsk5k62auXRLv2DPHpuD+dB3DPUkdr3Wr7I3J\nmlZXyzxMNRwG+3A8wWZdwyY6MZZeQ59h+20RbWMabmLTcO4xqWe39hbRFp1ZC3UBo3i6V3ue2OHF\ncz61QLXra0V9hK4A4vVb9qp2OYdxvstvRt0SfyuukVv7IzIJfZ37QNEsbVuXnAzNanAF7odrb5pN\nluyRpEGPPYcut2MP6nOkVGht68FXURukYtXH/on/NWyBGWzU808i1X7gcRqTqrXJRzag5sTkBVQP\nxNF5R9C0EkXzd1ScrnfQXYnx3E3jiusvuLWDtuyELSDXnNnx/vuq3U1LUTsoNhr353STtlCecyGs\ntTe8CWvu8/L0XN60B3VJeslSnG3dRbR+O9wEaXy4FqlN+zG381yWNlXXu2rahXY874a69T3kOgPH\n30L9mNCgroOWkkBz4xHUtphw2ywvbnjvtHpPP9VB4ToAPC5F9LVke+KkdF3Dxt+FfjlCZUe6G6r0\nsRZiXhrqw3jOmavrr1S/RZb3t18m4SZxPOqVnNlWpV6beAFqArVsJcvU+bqmXkotrYVktXr0vWOq\n3fSr0T9PvgX73fypeu0aonWIa9MkTcCxRpzpUu9Z81nURWjdiL3TC1u2qnaVf0cthDsvQl2Bxr16\nTm3uwt9fMAn1uU5s0nWdhqhOVNlCzENu/Z5gQNctCCe9NWSfHNS10xreQz0urhPVsUfXhOC9bfZ5\n2Pe07HTspKneC+9lq186otrVtmFfmhSPtXruZ5d48b5Htqn3FMxAv0rJx34te6ne//qpPuFPpn7O\ni8/srFLtuM4M76/W7d+v2s3vxRguXYp7yPscEZFgE+2J9FIdFvxkkc228SIiqeWYO+vXco013S6B\nbJkLL8P4dfd9UbGYK/vbsQ71HNb7/El3zcXfoNo8X7jvBi8ecer1ZVKdGraFzqmYo9pVrv1AzkZk\njF6beW3ooXXaremSUoZ9DNck8dfoc+dnOrnkrIfwv8ZPtX241qOISIBq4vhKsIa4daLY9rxpPeal\n9AV6nuQahTwWfUm6TlT8NIyD1uOoGTM8iOvq9rerf3yPF6emYt4eXqNraQWots/ICP5eUo6uGzcy\nhHUx0If7NuU6XSeK61n20njg9UdExMf1n0ZhLO791dNeHO/Yh6dMxnP6lz77sBfnpOpnn3EzrvLi\nTy/H+rR86lTVLonqorX7MefMffAm1a6/H3P2t5/9Ly/+4S0PevGBal3/9PK5GL9VX/+5F0c6tR0X\n3LYI50Hj9OVb/1W1W5SFmnqJdNynXvhItZv9mc/isyIxDl7cskW1W3XvuR8wLHPGMAzDMAzDMAzD\nMAxjDLEvZwzDMAzDMAzDMAzDMMaQc8qa2D6b05lFRAZ6karKVmRuan0yWdOyzS+n64mI9DUi7S2H\nbNc4/UxEJD4NxzEyglTVgQA+t6N5t3oPS3cyKpBSHejREqyOg0gHZxtittsT0ZamoS5cB7aBFtEp\nenz9on3a8ra/Vctowg2nALop65xGnVwMWUX7IZ3666NUW84KG3LSOttaNnpxTBzS72Ljtc1ZVCLk\nACNlkKpFk2QsSP1F5H/2Be//h3WfY2tytop0YTdfTsNMLNA2kvFZkAPV099Ln6nTFzl1ejTpOtyi\n/s1pmSwnEMeuuGEL0v5YysRWciIi0STVu+yzSL376G+bVLu+EGwBH//Vq148swSp4c89tlm959OX\n4O9FJ2LeSJ18VLWr34MUQE71nXTjctWu9kOM9bhM9ClOdxcRaT+A9OXYeFyvxEI9r7npmeGmYzfG\nVSalnouIDAWR1lrzFlJh08u1JKZ4BFaALJc8vv2UanfBjWfPW45P0HK3xESktndO3O7FvY1I827f\n26De86mff9KLu6vQLvkRPQeeaMT5svRmXKa21+06hD69bBXsqBNLtB08z6ksGzr0ik7Xn3entlwP\nJzkLkK7edUrPkzyXZy2gtaZRW01mzca9r1sHuUj3MS33bTuGNSk4gP5RXOJYqdK1YHlf/Vr87Yk3\n6BzorpoqORuN67X8SUi+yVasEVFaDtN9CsfOafeDfm0P252Kdry2Blq0dWVs2sfLN8MBp/izjElE\nS4rSpmH8+Y9rW97ihZjrArW4x+UX6ZT1XpIiZeagT48M6lT5rOXY+yTk4NpkzUJfGnYkbfUk38lY\nCHnM9cOLVLunPkL69Ws7IR1cOFmfO9v+xmWRfL1fy5NYzth7Cnss3wQ9Zmt3OP0pjPhP4x4mT9Zz\nSkJuottcRESCbXpfwet7J0kC3XsT6sB1YTlGSrmWxs5KggSD16Rtv8P1z8/Vx8rSnQiSEaaP19LB\nzInoV4OD6G/5K7UksK8Jr9W/iTngtgevVe1Ylj8yjP1CRKT+3ZZttkcDlmo0vKXlcymfgrQ1d2Wp\nF0c5duRxjgz+Hww7zxqD/ZjDuI/E5en+Ekk21PxZLDk+tVb37fQM7B27T5Jc+Pi7ql0y2cunTy7F\nsQ12q3a8124/iLVmqG9AteO9J/fhtp163S66Utu+h5OEYuyd6pw1JIvKC/TSvJu1bJxq100lJNo7\nqX9nTVLteH+XMQ378L6+KtVuz883eHFyBs2n5+FzWSooItKSDPl+sAR75s7DzaodlwmoXY/9h895\nVp7yadSqeP7BR714grMuRiWgv/HfzrugVLUb6NHvCzdLHnrIiz/67g/Va7M+BUvrJz7AHqT23eOq\nXdXOV7z4+7+/z4u5JIiItnr/60OQU33wvcdUu/wyfFZ/K8bvdVfh2h79/dPqPRNy8Z7MCRhv02+7\nWbWr2fXeWT/3m08/rNrFxmKO/uKXfubFM6dqKV0gUOXFB36N6/DTH2gb8cLyS+VcWOaMYRiGYRiG\nYRiGYRjGGGJfzhiGYRiGYRiGYRiGYYwh55Q1JRUjPbVlu+PCRGl0nOY3MqSlFCybCdQgZc91ccmY\ngdQ0di+KiNKHGBFBTkkxkMpERyPNr36zdpWp3wS3JnbaOL1Rp08ODCH9ccOjcCNZWq5TlMeXaTnC\nPwhUaylF4aVIF+ZU1ZgkLWtyikeHHZadpU7WKbjDA0jdZRkR33sRkZRcpNfWbITTQLBBV/XnFN/h\nQZLfOBKbQapqz6nJ6bPRDzKnuU5d6Etdw0h/dCvSs7sWV9PPX6XTz3qpgjw72LATlIh2moql1Nmu\no1pelHe+Ti0OJ5F0fO1ndBomO7dMWoW01WFHcpZWAtla93FIC5or9XnMugvp8Ft+C5na9Fk6xZrH\n+rNvfejFHVR1/dMXX6jekzIV/SPUjvTEASfFM2Ui0hCTUzH+Wk5ryWIjpZdPvhnpz321Oj246BKk\nxXJqZcM6LQXqH9DpwuEmbTZSLQ+8vE+9NutGVIpnV7pUJ22+gSQSnBZ68XduUO2SknDdenvhDpSW\npp0jTm57HP/guUjJ/vR87SeXmuQSzMNlpVoydfg00oIX/z/svWd0XMeV/VuIDaCBRqORcyMQIJhz\nzkmico6WLNmyrXH22GNb8x97LKexxx6N83gcni3LsmTlnCiJQWIQcw4gQSLn1EjdaKT34b989z41\nJtdbo+bDl/P7VGRXX9xQdapur7PPvn6+0/7gxQOi32L6zM3rzm5Zgb+jhiRU5IRStlTO7f4LJJGR\nRk4fmtaduJd2ynzuWpxHfBLui+0e1fgG3HzSZmJMVP9VyrPYkapsJuJhS7V0u+K037zlcL4aHcVY\nCTTWiu9klWOec2wdpnlpjHSOyJgP2UzHPuki4a3EOA3RmsP3xBhj4lxI/Q8FMI4GLQlq7rIqczlh\nt5eeA1KelpCPFPgkcoHpPCr7dbZj7Rkn96JSy91smNxuxsPol7FMzpfsmZgHKSmVTjs6GnuGuhN/\nFd/JWIj9CD+71l65LvqScU195MKXmiTlIJ19iJ1vbof8af1c6Zw2QjLMGN4DWnKgyuVSNhVJ0mZj\nvzDUJGN+Ksmc2OXPlSmvt2MnYgzvX0YH5ZrErnljtH+xXaJyVsGRJNSBeVBYinPNXusX32G3UZYH\n5k2Tsg+O47wXts+V9yxFt+B8Ri05TPsO7I2zVkGiZ7sGDVtSsEjD52FLXcZpPLFDjrdSyn17TiAm\nDtZi7Pdb+/I4D54jy2hsedq+H29z2rx7zSOHteYeKcUcpXeIApYEVkgZG78PhIcQA92pco/V14Z1\nIobkqk0HZOz1UJmAOC+uz3amGbfmZiRhlzZbiu0px1rI74GJOTJO8r6H55u9l2V5c3wi9rXNO4+L\nfodqa532vCjsz31hrHe735X7sMrT0pHvb3QNyHedstmYL26SdLE83RhjzvwR0qoVN2PNZTcqY4zJ\n2Uh7B5L0du1rFv1sl7JIc+zZ3zjtef90p/jsta8/7LRPkqNxZZ68Z6uuwB4zJgbP8dyz74l+Oav8\nTnsxyWt//tprot9Xs2522oFePIfiWSi98qlNm8R3Sq/B/qG/GvO89v03Rb9PfwbypaIsHG9JcK3o\n9/H19zntR771Waf9xOPyeGfP4b5c+/17nHZUlJSGNlfje/4Z0p3KGM2cURRFURRFURRFURRFmVT0\nxxlFURRFURRFURRFUZRJRH+cURRFURRFURRFURRFmUQuWXOm7zy0r+lzpaaMtYuDTdB0pk6XWkMm\nbbXfadu2ZGxP3U/a88rV94t+nZ3Q73XXQo/J9S+CzdK2NED66jSqOZPrzxL9ThyH/dvL77zjtO/c\nuFr062qEzjQuBtrcnEVSPz5COuDAKdT1SJslbVDZpvpywHaObKttjDEppdBrurzQyMbES13jQHet\n02b70yOvHxP9ppKWlq0t6147I/rls/UjWTj2HIT1n8uqc5FaiJoLXOfItkOufwG2zFkr8R2Xx77P\nuBe9VD/Gtnvj+hX8dz1l0h68fQ+06znSsfJDw3rjghV+8RlbrrImOypG/vaaXIZn3bodGu9ZH5eW\nq3y9xeXQVwfbpOU718NYQXWZimdBM/78izvEdzZHo6ZCznqMgeTsXNGv+yz0uN1HtzntgRpZb2eQ\nbF/5vNPmyeO1bMHczt0EXXfqNBmvWmtl/Z1I03cK2tcYy640muw6y25DoZTWLdKWsuiW6TjeObKe\nrK4T/UaKMBbSs2BB3tHxrujH9cNG+vFMXRmIG3Y9B65xkF6Gc81eK62g2YqX69bMXTND9OMaJelk\nu5lAscYYY6ZMR8we6cO52mMzxZqbkSSHamh07Je68ZateFZFV8EOM9ghzy+1CuOOrW6jrTHBNQ2K\norG+sIWzMcb4qPbGyDC0/+4U1FpqPPS6+M7E+B6nPUBzJy5VWljzuNz7S9gBz7lnoegX60ZdFLYG\n5ntijDEVN1zltEXdjCHLcrsBWvsMWQ4iIsS6EcsTC+Ta0HcMcYBr4AWtmlShMM55jGrONO2Wc7H8\nRsxZXq+86XNEv/aa3U57vADje3SU7Gf9Ml63Ve9CP7qHSz8v9y15T2I95v3SR771sOi3fiViRVI8\nnmlKpZxT4QDOj+9lvDV+xqy6TJHERbULO3fLOhyN7dhzxFMss+sixtP+qI1qn7T3yZjX2IXYduXd\nsHDtPSb3sjze89ZjrUmZivvPscsYY/LmLHfaBZXYs4yPy35JSVgzY2IQX7ze+aJfXx/2ZS4XYkNc\nnNwrmYlX8JkbNRFGBuXfHekNmcsJ15zjZ2CMMaV3odaRbwauZTQkLeV7D6PmTNZav9NuPiPrRPFe\ngGv/dO6UdTWLluCzLc9hjmUWYB7keGVtxs8/8ojT/tM3vuG0ey7IfUvxlagNyLUeU9Lk3jM+FeN7\nZADPJGeG3N9wDZaxYdyXUct2ebj78tUOKr4NMW7UqrsSTXvRnHUYwyN9clylcF1E3iu55H3xFGIt\nrN9yyGkP1cn6Qp5E3L8h2q+efBm1aexaMn/dhWe9ZQf2r5+4Rdb0S6Fjz6P6omxJb4wxe59G3a55\nUzB2UqbJRa3zA8QvdwnGVWKe3AMFm+T7baTh+pu/eOBb4rPyHMy/2x+63mknZcvaQbUv7nXa/FvB\nlNvkmvT2t/7itBd8bInTfihXHm9sCGPal4d7w78bJCfKdWfnY3iOXH/ti3/8sei3Yc5bTvumT6Ju\nTWKiX/R76PN3Oe3HHsNe6jvP/Fz06+9FvdqzT2K/lGTtMXgN8MvtsDFGM2cURVEURVEURVEURVEm\nFf1xRlEURVEURVEURVEUZRK5pKwp3EOSgXFpGReXghTItGkk07Esk0//ep/TZrvs1CopKfLmIiUu\nqxgp0ecP/UX04/RHtvcbqMH5NdRJm9F4kh6dOoSUxijLZq6iCJaUD9wM6y62TTTGmNzZ6DdG1oR2\nCj6nFyayHefeJtGPZT0F0kkvInhKkU5rp45HRdM9oGbXcWnfluJHumGwBWl1VUukTWb2cqTkhilt\ni+Vfxsg02SQ/0tQ4TXmgVo45hq07ey1La7ZED5HcofOoTJdNyMTz6if77NxN5aIf20+O0vgLWymZ\nOculZWwkYatJTtE2xpgUP+4FW5THe2SaX9v2WvoO7nnnXpnOy/KT9EUY64f+sl/2i0X4yM1Dimbb\nacy/BaXynmSSTSZLsNIrpL08p4ZmzseYSrBsUFmSs/fVQ+ZirP4YUvXZbpYtN40xZtrNsy96jEgQ\naEHabeH0fPFZ48vVTpvlErHW3BlqwX3LXUo240ekLXhiMu517f6XnHbZ4jtEv0Ef0vLr3sA5JKVg\nHExY0gT/jUg1Z7tmlkUZY0wWxYOWtxF7M5ZKCShLOzsPIvYEDstYHjUXa83YMM7Ja8nTouMvubR9\nSJBKa1vM5m9CPAwF8JwG6+U447h7vglp99mpUnZw5YPrnTbLLW3JZ7wb60vrLjzD9gnM7aylReI7\n3cdxb99+FinABenS9nX/OciuVlTBnrL2uZOiX9E1sH7uJ/lhpiX3nZjA/YuLQxwaC8v47PXL8400\nHCvPHqoVn7Ft+VgQMaZgjrwWD1nk7v4jJElF6+RCPj6Ca+Y9QzAoreKTsvH8L7yKlGgvyfkaTpwQ\n30nIgryl5xDGUopfypDY4rqlG2vrL778ZdGvhVLAp5fhPoQs6WBbLdbMjFyysz0k9ze8/zI3mIgi\n5kSWXBvCHZibQ+dp/lnSbv/tyCkPtULiEGXJmhZVYG4HqZ9nqpwv2Ush2xhswd/NmIN4318rZS69\n7ZAhNbdiLhbO3SD6hcO453FxWCPHxmQc4nnV349ju91yvxZHe4T23RiLSfkyBT9trpTRRBqWBNr7\n8tatkI56qrDPsO2V3eUYg7wvcLukhe2Fdqx3RV6sn+lL5Hpc/fopp735I5BjdO3B+O4dlHPixb/+\nzGnvfO2A0974CWnLy/sbsR/JOif6hbpw/JqnIcW50CH3vON0/3gNyfDK9SRtuiypEElO/B7vejnz\n5L3kcRYOYN/cd7JT9Cu8Cc/DR2OO55sxxvRfwJgepLkd7A+KfiwLXrwae5bhdtzXgZDcx6+bA5n2\n1Hxcx+LKCtGv+Ha8s/acwpga7pLnMPcKHK95F9a4QmuN6COZaO9BrM3RCXL/Z0twI82F97BP+/jP\n7xOfJSfjmsfHsUf95IaPiH4/f/VHTjtQjz3IZzZ/QfT7xAbEt698DHKjnz//DdGvn94Fp6y422mf\n2/OE0/YtlWNu27ffdtr/8cJ3nPaDG6U9+C/f+L3Tfusbv3baA+fl++dzL0Pi9sAXbnLa57e8Jfod\neOOo07775z902j+//zOi36Ip8j3TRjNnFEVRFEVRFEVRFEVRJhH9cUZRFEVRFEVRFEVRFGUSuWTu\nd2olUghZ2mGMrJ7d/gHSluK9UkqRdwVSd9j1Idgu09S6o5AKFCJHJZYxGSNdQmp2IY0/OwMpjbOu\nmyW+U7vlrNOesh6p17akYbgdf7c4E2nyQvpjjGk5YsmS/oZVpdtHjjHsMmOnjIZ7ZRpcpGmjVDq7\n6nkqpWWzu0+05fQzSG4tQw1oxyTK43UfkzKEv5G3SabwjVAVeZYy8b3m/zfGGF/xNKdd9y5SvlOn\nSolc9jw4i9V9gGsv8cpzCI5T1XP6u7YEIZYkfOkzkObdvO2U6CekBvKUPjQttUibnDpjuvjs2KOQ\nG1XehBRKO+03mpy0AucxHouurhT9bOnb3ygokheVUknp3DT2n9+GtOy2gKyeP/ueBU47ayauo/PM\nadEvYyrOqec8Un2PPyGlS1yBPyEO8q45V0t50vkXIcEoIulJ2pwc0e/QX5GKXLH8oybSZE7H30ub\nIe/nOEmHes9hHlVskOmPLClt2IK4Of1meb6t57c47dyZqIRff/w50a/rAGRELGWK80BqxhX3jTFm\n34/h+LTwK0hNjU2W8rS29zD/epsxr7LipNvQB49sc9oz74LzSPxauZ50kKNGJjmxcWq4McZEx0t5\nVSTprYZ0hGODMVJimVKCNWngnEyRTSrCGlBEVkR2Sn9fNUlHFkJSw85kxhiTMRMyBt9srDtxSZC8\nREXJmH5q63an/bO/QD78w89+VvS7+6NXOu3qnZiLPq90VAiRIxU7EvK6b4wxPc1ISY8jhyfbEcyd\nK+NwpOltwvE9SZZckiRPURw3T8s0/GR6xlXzIeGMTZRuh3E0L1xeyGnbDsq4x+vfub1IL28jR5LF\ny2T8P/065lgqXUf7LikTS1+G8ZNQj2c3aDmc+DIhhThfB/fEaYtlHGIHDHbEnLN5puhn7zkiScce\nxIPxYWu9I9eR2JSLxzJ2V+Lpl5cl5UoZyyET5X1uRplcaxIT0W98FHMsORn7l56hreI73ixIq4Za\nII9rPiH7pZViDzM2hvnWdvKA6BemeOgh18zsbCnNiC1A3OhxP+20+d4ZY8xQgxwjkcaViOeTf608\nx8592G/zeaROk+tn9x7EJu9t+IylLcYYs/ROuJ3FJuHvelLlfnPBZ1c47QDFdXaptJ1+GvdBGrZ8\n8zyn7auUEs2JCYzVkSCOUfOo3N+4S/F8vGV4jmOt0oGKXXSKyAmq+1CL6GdL8SNJ/jKs6SmlUlIZ\noLIBY/QOV/mgdPzjUhDsYljbKK93ygz8rbhUrMEJOW7RL60O/+6jd7A91ZD+Lp4ipX4uks7ntOD+\nZ6yQktajv4cjUWYxvUfFynU2jmRI6ZUYlxNWHPKQ03EClS4YrJPrYLq1Z400M+/F/usfNv+L+GzN\nDMSpB38HCd8jz/8f0W/7d7HHnH03jvfQQ/eKflGxiDMnfopn0rqjVvQbG8J6fGb0Maddte7jTvvg\nn38ivvPLN37ntGvfRRxdWiHjy7aH0W/69fjt4GfffVz0+85T38I5/AFxfdoDV4l+lVehbEBfH/bn\nL+/dK/pNUAmYleZ/opkziqIoiqIoiqIoiqIok4j+OKMoiqIoiqIoiqIoijKJ6I8ziqIoiqIoiqIo\niqIok8glxcAdHzQ6bVe61GO60qGJS6A2WxwbY0yQ7HKHWvCZrQ8e7oFGNmsWNNUN2/aJfok50Guz\nPj8hD/+fkCUtrb3Z0PxxbYKS26RWuPFN6L9nVkFDeGirtK6cNgNWiQl0PsnFXtEvkayaR0kzFx0n\n9bzuQml3F2niSG8dnyprOLDlYN851J5Iny2tEyfIqi86Dr/p2da5rduhc89aCu11nFWbIYY0+d4S\n6HGD3ait4stfYCQ4h4z5sE1L8hSKXiMDOKdKGgt2bR836a0NlQviZ2WMMW6qJTMSgs47d7XUqg62\nXr4aCbM+gntx+DE5J7jeUusbqMOUtc4v+nXUdTnt4pWoj9B7XNYJCndBl+yZRhbZTV2i38FjqOU0\nfy5qxOT6oDe+cf1y8Z3BRsSD4W7oMYuXbRL9ejsO4jukMz/dJOs9NXXhnK5ftMhpv/3k+6JfMdX1\nqH4V9WdiouXv04s/Kc830nBNFrYbNsYYF+mMS/NQ34GtfI0xpucCdOTxVJept3eP6Me1gwIdiGEu\nn6yvkUFWxx27yE6V4lJykYxR4y+dcdrhfmjmuSaYMcb0kGVsL9Wl2PVb+XwqZvmdNseUnoNSM59M\ndqn8WWejvJex9Fyr1puIFXl+zwAAIABJREFUwmN43LIYz1+JehvhIGod5GyQlvK8ZrKl80i3rAnA\nNW24TltSrqz3MtQFTX9yJuLhYDfmi1XOxpRUIob++qtfxbGGZUwPnMCxl35uldMeH5U11mLicc/5\nHnXtk3OW9whsEW3TcxJxKSfvot3+13DtiLwiacU+FuLnirZtYR5DtTnY2r3hjbOin68Kxz/0KLTn\nvmS5V3n/NPYgm9Yg5s+chr1K2LJvL12KsRWk+561TNZ1CpxB3Yy89ahdEuqWxxugemR5VKOi5kCt\n6OeKxfZx1lpYrIdaZB0Ou35JJBml2nWZy2VdD64tk5iB2hNsT2yMtDnnehHJhXI/J+tBsY213Fd0\ndaIeAe+bGg9sw/lkydoYzYdRp619B2LwkGUN7C3GHq3ijo30d+RcTPYjTqZkytjD9PdjDR6l+o7d\n9c2iX3K5rCESaXKuwHhsf1/ayyeX4loy5+MZc90WY4zpK8PzanwJ82j+dXNFv7o3UNtixTc+5bRH\nR/tFv5gYxOiRAey5OD7OKpJjrux2xP/cynVOu7N5p+gXn4w4Ek11N7LXl4h+nbvxDnb0GPZ2w6Ny\nT5A2Bfsbnm+8pzBG1nEpnWMiSuc+jJkmq96VJx3rFe9n6l+SdRtTpmCcxSZjnUiy7NDd9K6VRmtI\nqFPO7WvLMUfivbjnKXvx/YRsORd5Pd7wjc1Ou+uo3IvM/hTq+DVTvLffbUNtiIfBZrRdGXIt8dAc\n45gUtvYEdg23SJOci7H06zd+JD579mt/dtr7fvpzp821ZY0xpupa1KZp21brtP/5vx8V/dyJuAfb\nzqDu3dMPPSP6bf406hoeeQL1tSrXIGbtfvuwPNenUWemgN5J9tXUiH7zF2Pt+syDsL5eP1v+PvDx\nDZ932neuRJWYqD++IfplLsP+q3juDU77W3fcIfr1By9da1YzZxRFURRFURRFURRFUSYR/XFGURRF\nURRFURRFURRlErmkrCn/Csg2+i/ItPEBsqGOJTvl7BV+0c9TjBSpiQmks0VZVs1soTbYDXtEWw4T\nOIXU3LJlSNeMIUu8rn2N4jvZa5EqyJbg7ftk+iSnAYf7kUo2tVmmB+eux9+98ORxp51opZoPB5C2\nFJOAe2TLZpIvs6wppRhpocayBY+n+5uUjfMInO8Q/fj85XfkNXsrkL6dloW07NhYeY2cCsyptZxW\nHArJ55iQAPlFShpkNBMT0pKOz89d6nfaw0NSltO+G89/uJ1sYOddXNI1RDI92+p72EoPjyQs9Zh1\nxzzxGaeqjvG5WvaXqR6kbwabkcJr2wGzjSynOs97YInol/EMUlJPn0Iaq59s6KMtW8H0udAnpPqQ\nAjw8LFNGzz+OMXGiBsf2umUK6qHzsEGNi0G657QCaXvYO4jn66/E87VTWvvO0RiZYSIOy1F6jkh7\nSJYYCotvmbFuYkkW4puJawmcbxf9+s8hZo9TfE2ZIi1i2XKd518GWdLbMcszA8/4/KNIJ9155ozo\nt6gc6a6lM5Duacf/Pssa+m+crpYxeuQUznXFzZCx2Wn3l9P6NYGsNhMy5PgZaMM4DrZdXO4VIolS\nPqUEJ3qlzKfutf1Oe5wlcdb94nT/hAQ8N3ch1vC22nfFd/y3YoCnnsLYyZwp5ZodR5Gy3V+Lv8vj\n1RhjvFMxJjJnTnXao4NHRb+egxj3KeUYiyyVNsYYX6VcdyNNGcmBbIlWHKXUh3uxF7Al0yxlYrvP\nVMtKljVlPRSL3jwsU7GLKHZeOAM5mJ9kK6EOuc6w7Sqnw7ftlNKCOA/i/EAj5ke4R6ZXt+zHuttN\nVsEzr5ABcZD3gGSJPtIvxzqfU6TJXu132sPWdbB9eQftCSfG5PnwGjVKkosBa46xbD25CO2BjlrR\njy1SO/fi77pJavTaj2UqfGU+JIb3f+97TvsHn/mM6JfUCVl61zlIVTOmSllBoBFxc3wce4dA4Ih1\nriRXJ8vz/KssyXajtLmPNF17MdY9VRniMy4DEOzCvqXHkpmE2jCvMlcgHtp7kBQflRsYpRg9Kq+x\nq+YY2iShZbvd3mNSEh6XjDnWfOodnE+5lFYNDWHfcuFJxEf/bdKGvq4d8qwZU/1O25Y6t53GebDs\nhUsaGGNM1urLF1OLboQ8pP09GXt8tKdmaWib1S86HmMwSHvypV9eK/rFJaBURSiAPVtCulyP02cs\nc9oDAyRnX4F7FOOSr8H9dZj3iUm4X+Nh+T7SThLw3kbEwtIbpol+nXvwPe8srO8jfTJOjtBeu/E1\nrLkcT4y5vPHUGGM+dzVssR/fvV18tulzWLv/8PBTTvvTd8hx2/gypIMF18K6+sfp/yD6RdHUdCVi\njJRmZ4t+3/zCL532Z2+91mmHw7jvC5fK+55KUuJUei9N//5boh9LQh/YCBlcSUW+6LdyGsY3l1Hx\nzpLnuusPkKj2kYU8S5aNMSbXK2WzNpo5oyiKoiiKoiiKoiiKMonojzOKoiiKoiiKoiiKoiiTyCVl\nTc1vIbUqb5NMc+QU2e4DqNIdOCelI3lzIIVoO40U7XiPlFKk5COlaXwc6amjQZmqn7cR6ZssteI0\nUzuFvHULUghL7kL6VZRf/jYVFYPU484DSLPMXuMX/c78GanI0z+F1Ho7Pa71vVqnzZW4A8flNXE1\n79zbTcTpJZcGrgxvjJR5jQTIvWKdrPDfcxLn7C5ASqFdOdzjRepzKIR7mJYmZSYDA5DEBLuRRpia\ngxS4wd4L4jsjw+jHqX7xCTKF3J2JsdRTg2PEe6RTFacpsxFDzyEpN8lehdRGdg6y0/rTqmR6WyRp\nfhtjOG26dBbJvwZzs5fGlu0kk5CLVLx+Skn3zZRSikGqeM+V5wctCVDqLJxHXhCp/ztO4dlaBjGm\n/aeID8u+Dqlb9aPSzSDOg3TcNfeucNqcJmiMMSPkWpBKKaOJvbLCvS8G59pBKcB+Sw7D7gjmBhN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dpv1X3w/2x9B/PZlvv2nYNEIH2TtNaMBNkUi2Iti2GOe2wVP9wuJYEseR1qhOwgLkZKYxt2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WII+kTLZUaNo9sB/z9wcNbGBAyg56W3EN+8nOeLgb9MdXN20Sx8zMAFU85TpQCmu2SLlN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\n4F7HFcp0cE5f9jUjrda2fh7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iw4lbdl7B52XH4Rjy5DG07qlxYldSFI7Huo8xeQlO3Pze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33GBql3SU+DnCPc54j7roRlSQ3+PF/YBe54D3mZbYKNMaaA+g+1d+JvzcmUfR+q6Z7lrISWe6hJ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\nvF/VPY88xzrujlOyh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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kL8MEhNgrx9N", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the # weights will probably just show some fuzzy blobs or possibly a few parts of digits. You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n", + "\n", + "It can be interesting to stop training at different numbers of iterations and see the effect.\n", + "\n", + "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n", + "\n", + "What differences do you see visually for the different levels of convergence?" + ] + }, + { + "metadata": { + "id": "VB4YQ4GdqQv-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 6328 + }, + "outputId": "0cd5db82-d155-44ea-a6a8-e2f6ba89cc94" + }, + "cell_type": "code", + "source": [ + "#Steps 10 \n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=10,\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)\n", + "\n", + "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()\n", + "\n", + "#Steps 100\n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=100,\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)\n", + "\n", + "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()\n", + "\n", + "#Steps 1000\n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "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": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 29.14\n", + " period 01 : 26.86\n", + " period 02 : 27.16\n", + " period 03 : 23.31\n", + " period 04 : 23.00\n", + " period 05 : 19.02\n", + " period 06 : 19.26\n", + " period 07 : 20.09\n", + " period 08 : 19.56\n", + " period 09 : 14.95\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.57\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ftanN42fy0Gt5YlosY+JCOVlYxQufJLM/s/icxymKQnz4KKZ2vYmKukre3DmPI2XHHJBY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\nirqssu6cx6sUBX9vVzq1NxDooyfIx40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MGTIEEyZMwOnTp/Hcc8+hf//+GDx4MI4dOyZHSSIi0hNZRiRJSUn49NNPUVpa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\n+9oAAAAASUVORK5CYII=\n"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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "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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slDE2b/kukjdvKbW31jZ6rIBN8ZXZ+8mxwEZK74Mj6Bdja04tbE3tUD9MTdHn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\nFg+aB+tdExvuKxAfjP3ApaoXvs/9d5SXrzrWK31bab+5ir3ZUKe2eXcCV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QYAACAASURB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\n8vVtFNmnVE1cM3mKM7NxMdrK4O0NzM2qHC87l8rTWi+BzCnhC87Pz+fRJC/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ClgX2Xpi7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\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ugHTH0hhSj8wcKnurMw3rmJER9mi/o9+RvJibOBeZMbDZtvGjlsQmjth3qzbLFvZ0D6Sn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\nj74Zg5dQzXzkWYwT51bopZCfS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AzDFCH84wzDMAzDMAzDMAzDMEwRovPrl0bTBYWvL6BP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M2QGL44x4amGbrvRoKlUPe45ixajNeX4+7tO5XUfJePn5oyQvOhT3/cnF6OuRmY19k72GtbKD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\n0uLWVfHsouKZntt5KWh2EffwfNwaskvDXolSWaE6ZBZ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kX/u38rgduqCnFid/wr6vTAv8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GUVjaO847OgHa7oCh1ZbAJrOotWgSYseD90aXLPkP5D29E5mZmweNsxGlaJg9Zzn4JWM2Cd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\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Ki1oYi/4UQLZXVpovFUVRGi/G37swC8ey8WQqbXEbNkbEwyX5mIEzyn6vrKFSvyEdcU0E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\n/XR9bmgMKfqeGWjpsOA4fea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wluSF/MJaolGf+iKu5N6b5L3etEPETRcsUdRNdjauucW9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XKbrxBCFJIUp38HPAvm1+I3Z7itrFxbtKaXq9xei+vEfKC8rFS0IpYeDdrPxQEvKM/UGS36cpt7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Q8uKDP+Ma/KSxYEBpomQ7SC8DdmA8F2rA0umSXfg76hPlR8MG+/ozltQXKA3r6m17UE8rjx9I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\ncCKosOP2rae87+9g6Ze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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 14.89\n", + " period 01 : 13.48\n", + " period 02 : 9.28\n", + " period 03 : 8.11\n", + " period 04 : 6.84\n", + " period 05 : 7.06\n", + " period 06 : 6.23\n", + " period 07 : 5.68\n", + " period 08 : 4.77\n", + " period 09 : 5.62\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.84\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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y8MMPW/NrB4yYCH/UKoVdyXkta4/HBsqENCGE6C8s1tNOTU3lr3/9K7m5uWg0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\nGrsA8HB3IXVqGOlJEczSh+A+hhtth1sLrvUJIzV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T5ShJRER6IsuIJCEhAR9//DHKysoQEhKCTZs2oX///rh48SIWLlyIzz77TI6y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\nwiAhIiJJGCRERCQJg4SIiCT5P0r3z1UsyCUbAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "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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+bgu3IM7Qse8QwNf4qOmofUDrAFVl8bpOjnWQbm0jdaeyT/M+8RqFmFhG6s90\n1uEe8nZnsXP6zsI9+CZjPNam83ugtX+c+2E8mlvxmjOORGa29CDWk5YSXueISlVTuWP3WN6PTPI4\nXpkUGStRI8BvOF9DSo5jPEbOR52Z6ixeS4bW7Qo4gzaKe5pLnWauQs2B8gqMW4tfuSx2/3shz5y+\nCrUYvEagb12C+Na7oQg10a5Vu47OWbqH7mrtZH7Zu1GLbfIrU7S9/11eH8HMjOzXTuMebH14fLnh\nuanqesLDDfPKuw+PlUWp6JPjB7GfGL+A1wChtdNiBiDmnFrK66CNen68tnNI7cxgQ+0gz0LEwfzf\nsA6FzkWsCLubr6WVp/FuQPehXpm8bqWnk5O23Qfj/cm4p6xNx5jOXo6xHu7Lpc3pulO0FfEhnYwr\npZSa9orpa1r+F96jsDZX5B9mx8oO5Wq70gH3NP+jucyvoQ796+yKtrW25nO7uRl1TGqasD+vr+Xx\ntPwkYkBpDebs5uN4f7g5IYGdk7VjC/xWQ/J8+cGtzK+jA3HN3Bzr4hNLX2R+dI7FDMO+7sOF7zC/\nhf9CzZTOBszzk1euML+znyN+Pb3K9LLaXd1491v4xbOGo4j5HlsQf7as3Me8Fn91v7ajHhim7Zp0\nvked/Qmk02ldsJS9vA6Q+2CM91ffwbV94xHXLS35t4fqFMS6fgn4pnALqUWjlFLnP9qo7fY61Glc\nt4LHym7SLqe+xPjua5iLVAZ86QrE4benJTG/2a/PUteCZM4IBAKBQCAQCAQCgUAgEPQi5OOMQCAQ\nCAQCgUAgEAgEAkEv4pq0pvJLSDGjUqxKKRXzOKTlzM2RAtlaz9PPYgaA7tBWhdS70gO5zO+GaaDe\nFG9HSvHQO4cxP3tCmyrYgvRop75IhczY9Ds7x30QUtzdI5AqXZlzgfk598OxlS+s1Xby6MHMz4lI\nYS57YqW2gz15Gra5GegHB95E6n/c3TyN7q/XIPF266f8b5kCrVVIo76ax+UwnVPQbqUkDdhnKE9t\nd4tBCpudMyTBTvx0lPlFxqO/3W9Au1cczmd+dVlI8wyeAUnI8hO4h+qLZewcz0FIH/MMRB9QeTql\nlGok0tI5JUijq8jglJihfZHq1kGkgTO38pS6uHsxBmn6cHMJl8xj8rYm1im8uAq0nOgZPAXfkUir\nnt4AP3tra+Y34DakAFYcQn/QdGullKKqvz4JSMNsbuLp4H3vBn2p4jT6oMkP89LFk9/r6S++wd89\nh76hctlKKRUxAZQzK0c8R1ASp5HQlFF7f6QKd3TwOOQcjnHe3YFYVryTp4y6hbqr6wlrd6Sst13l\nFJZQIjPe0Yi0Vko3VEqpnBUzYFT+AAAgAElEQVRI3Q2Zg3Myc7isJ/33rLuRyp25j8+D6MmQuj2x\nEinR0x6BpPqed3msnDsDMpA0Lbs2lVMpzAgVorMFz0EpU0oplZKbq+04IqMY6s0pEtvfgQQ8pWT6\nRHGanq0XTw83JShlxcyCPwf9u65uWKvch/gxv64WxJGaC6Duentw+WPHCFBMGtJBCbR04tRGy1b8\n+3wG6IudhzHW8yv5nDAj65MiKfhhffi99puCdP/aFMTkE6u4nOuAZMRx5wjMt+wVXA5ddSHAUPnQ\nK79yCiSN49cDfW4CTZZKvSqlVEsD9ioefuiD/A18bWD3fxixJHpSP+a3c/Uhbd+4YIy27Q1U6PZq\n/F0bK9yTmRXGGaW2KKVU7SX0iZ0/rtdaziXRXYiUOh23TmE85nU0gkJlfQ2KjfdgtF/2OowFtzg+\nF9vrOCXSlBj8NKinFhb8XmvPY145+4OqfOGTP5mftSv2M3Rf21DG5dUDp2BNCjSDbWkYOznb9uN6\nC0BVoDT6jGX7FQWlS/++Eynzzy1/kvkNvwdy83SdPvk934eNfBbxuaUCMXjIHC4P21SLWEFpig3Z\nnFLePYjT100NSyes8TXFtewY3UdbWeKVpdtA5eomZRMsbOAXv4C/QxQQWmVNNeJeQAtfZ1P/wJpH\nCfHma0Fjcwjn8dqc/F0nW4yrA3vOMD8b8hyUbr/t3e3Mz5KskwlTsH/zdOR7uz8/wnldZGAMHRTF\n/OjcNjUcAxHzM3/kNONdp0DJmjo5Udsvz/6A+TmQNnvyI5R78ApNZH4fLHhd24mR2KOmf8P/LqX7\n3jECcyetAO8ZN08azs756vP1f3vO23PuZ37Pr3xP21m/YM4OvG8e86sqPq1txxCMF3dHTsNxCQM1\ntIW8W9y36Anm9+m976vribgnEAPrKziFtoW84/iOCtF24JEM5vfWnejXu2ZP0rY/obYrpdTl5XhX\nX/Y7aETPf8SpR47+WKMOfHMA19uDNbfVQAmPvBlr8NtLvtO23Y+/Mb9nngK1zicOdMh//TKC+dWW\nYN47euGdKe2nPczPjVCwvPyxdygxUJirjoPe1+fl25QRkjkjEAgEAoFAIBAIBAKBQNCLkI8zAoFA\nIBAIBAKBQCAQCAS9iGvSmk6sRooYVWlQSqlYogSzbxlSdmMjQ5hfxD1IozQzwzVy1vH0M49BSJEt\nIpW9j3y5k/kt+AzpTn2mIf29OgV0nbbyZnZO7hqeLv1f/PrbXvbvVpIuTKucz3vrduZXm4p02YHB\nSJc9m8OVkGia1RM/PKjtsmOcHmJMUzY1qNISTZVWSqlLu5GmnXAX0j9piqhSSjXkIs21IQvp9bET\neYX7gxuR3jy0Hcfsg3j6Nk2Pa7uI9GuqMNOYzivc0xRrmr4dYbjX8HbQqVwHghZhY6iEf3wFKrZH\nDwEd6/y+c8zP9yD6y4ek8lF1A6WU8hzGqWCmRMhI3J9LOFczqM9BO4WHQ32oqZKrazj2QUplXj3S\nELvaefvZuKGdsteijcLn8PTg/O2gK9C+KT8OilPfSVzNwJaMP5r+V32Kp5B7EuUrSh/raOBzxdIS\nVCaXYNy3rS3vi1YzUHya8pGqGXo7V5JpreFtZmp4JGBsxhnG7dFvEUdpavLwu3jare8EULuKd+BZ\nxj0xnvldXAY1mk5Co3EkqcNKcUqZlzPmFU23XvDkDHYOTbvtJspuaac4TSysD/o49wBS6MfEc7rb\nHwegqHHqENJHy+vrmd/EYaB9drXhmYpS+fiJmBitrhdoan32uVx2bOi9SL+uPQ+6Sep6ntIaeRNS\nbv0mYm5TioVSSjUX4vn9p2KNbMzhCoLOkaCsOPeHTRW8KHVTKaWiiaJVH1+kVB88z9VnbiUqHNYe\nmGP93MKYn/tA9HXBZsSX2jo+p8LG4TnqM0C1MlLdhizi8cbUqL2MdTx/G6f6uRH6swVRx3MM5jSG\nbqp0Fo323L7qAPO75QHQb87/hvVl9PN8zlK6Ee1Teq+utZzuRfuEUjut3fhYokoUlPbRVsspKx2E\nhlR7HuPHSOFriUPf+Y7DWMhayddPqsBoauRvx7yi+xKllBr4FGiP3d14Rp+xIcwvaBj64NxnK7RN\nVXmUUqo2E3TGov3YE9ga9lQ2vqD55x3BHtM1CnOsrIjvbQqrce+UznfEQHNJfAF0JapEExTF1enc\nPCGLVd4AylO7YeyUEyWtglOgOrt78P1aTw+nEJka9aWIc+HT+7Nj27+FguKgkBBtu/bjlNeM3VDc\ndCMU+M5GvmdwIkpy3W2Yv0V/8hhA42USoYkVkrILF45yOkdeBcbIso1QgfEP5gqgn7/0qLZLtmPN\nTBjPx9yJ3RjfuYfwfhE+idOVJt6brG2XvogbZUc55f/8Mrx3hXw8R5kSjUUYw7/uO8SOzZ85Qdsx\nd+F96tZcPme9BoBSS/cfW5Z8yPymTcQ621yK9SX4Nj523nwMNPr3Nvxb2x8PImUavLky2ej+uEaf\nyVirwqr4nO3pwfioKsT70c+PvMb84vqBytNvMdaBxd8MZX5LZoK+NCQM8TRl92Xm9/Syl9T1RMkB\nxLarJ/i7amoh9tGNLYipwURpVCmlHnwefRycCHr8qfd+YH4R90K11+FPrJlVp/i7VQFR053x3iJt\nl6Wgbd55cSk7J/3XX7U9bTj20DWNfD9C91iFh7BnDkgaxPzWvQYK1qznoV7X3cJjY9hI7JWrI0F3\ny/uNU6L73jNEXQuSOSMQCAQCgUAgEAgEAoFA0IuQjzMCgUAgEAgEAoFAIBAIBL0I+TgjEAgEAoFA\nIBAIBAKBQNCLuGbNmRtuA2+1aE82O1a2L1fbg4eC/xgyk3MmK86AF+tAJH+NqMsGn8+F1MbIP32a\n+TVXwq/mIvjQ/sngBqad47KCTpHgmPolo16D1VouR/fyD5DFvm8i4Yh/e4z5vbNhg7Y/fnyxtpd/\ntoX5TU1O1nbhLnBTKy6VMr9gH86dNTVonY7wBC5FbOWMehPFf6J+hfuwAOZ3Zgt45C724LjXXOB1\ndlwdwN/MuYy+P5HFJdmiSL2DsQugO310GfpuwpLJ7Jy/3ofUGpWJLqnhso+0JkQiqb9g5cjrJsWM\nxLhNJVJwE2Zy2T4zC9TsKSVcc2ONmSPfg18Y9jWX0/tf0UOk7E99zOsZONmh3ksVkcQdcPs/y7K7\nkro1TXlcurJ8P3jKtH7Fnte5RH1wKHjdUbOma9vBgdeioBjyDGT1msrAz+4w8MJTloP7GToC13OO\n5PV2crehxlFLAbij0fePZX5tjYgbPkngf1/9ldcCcSOy0IrTuk0Cx0DENvMxFuxY4M2QhGwqwrNY\nOvA4lbrqrLb9BvqTc+qYX1c3xszRHTjHw8mJ+Xmko3bEiAWQD7y6OU3bvkP5WPdMwL+3/Qd1EUZO\n5/Ulyk+CO+zqiNhQWFjO/Hxc0S5UPvsykbxUSqkCcl7qhxinSfP4nKX1NUyN0NtQSyuU6tkqpQr/\nQBxxHgAeds3RBuZHa/Z4j8R4tHHiNU16iOw05WF73MBrTJxaijUqJAZ9Q+sTTbbjy70rqctQfgT1\nJiZP5W1pQ+rgWBNJ8MtkHCrF1xIql+1J5CSV4rVBinPRn359PJlfzjrUvgnh2wqTgNZ08RvHY1ZL\nBeqg2ZKaLunL+TNTmd8eMhamLp7A/Bqy8cyJj2C9KzTUurEibW3nC6lVN1LPp7OJx0r7ANQHOf0T\nqaM2ntddaqtCjQBrV/SVsY4X3aeFzkNNrrJj+cyvmkh4t5aAx+8cZBjDZO0yNYJuQk0z6+l8bahI\nhxTyld8xloa+MI35pa7Bfq6gGOMx3JrH507STp6x6I+wKUnM79T7uF5XM+oOVh5BvYYnP/2UnfPh\n449re+ozN2r76q+83kRTMdaFwCTULPhjyU/c7+1vtV1B6naNfekm5ld/FbE/YTRiRZdBpjrja4wr\n79f4NUwBulbROjBKKRXph5hzKR9jMMKC12zwciHz4Fe8N4SF8Vi5esd+bc+filoyQTP7Mb/u1WiD\ngi2I6xYkjtL7VkqpWxfivWHWXahlZFzD/yLSyzc9jFhh78fXZrsDWIMjp2HdsTJIaZ9bgf0SrSkX\nfTdvI/Pjuep6wTcKEswv/cJj/kt3QP7504WodRM5l9ckVAin6iqJ/+cM9Tx9yX7BbSDqx1xdz+fL\nwwuxL03sA3vjuk+0fe+CN9k5vx/H3Nnw/Gptbz3D5dBjf8U756krqBuUW873NlQePOVpXDthVCzz\ne/aje7XtGYa4W1/B14j0n/EeNPyJeGVqVF9EXA+J57WSwoYjRuzciHe1u798hfmlb0C9pUsr1mh7\n6V9/Mb/bm1EfdjGpU8P2EkqpmDsRY6/s3Krtb7/EO8lnWz9m53z/EPp43Di0U0ctl5PPz8H7+OAE\nvPcWH7vA/AI9sL40XMF6Pvipu5nfmS9Q5yh0Lvox6u5xzK/4JN49/P6mXKlkzggEAoFAIBAIBAKB\nQCAQ9CLk44xAIBAIBAKBQCAQCAQCQS/imrSmmrOQpz6fm8uOTSTUj4CxyDle/vj3zO/GeaO1ve8L\nyAqeyeY0qTvKkU7/7LJl2p5HqEFKKVVBpOGCbkHKUFMp0owCiOSoUkodWooUwuZcpP7Pf/k15jd5\nzBht776AlKZHF89kft8MfUbb23/DtW8nVCillIojsn+WDpBbTHh+OvPb+AJSUkcp06MxE23jSChe\nSillSWRCPUf10badjyPzG/skUjSXvbhW2+MHcyliKst8IQ99RSV6lVKqjqSznVgLeb+48Ujd7DFQ\nBvr2RcpZO0lNi5rA07fzDiIFkqbHtVVxiXWHIKRvD7TH320u4PK9wbMgrVd5DpK9rWVckq27+/ql\nbweMRcqtz4ggdizze6RbNhNZ9tZyfn+NV0H/cgjGs1/YzNP3Bt/x96mSSU9xqpAVSdXNO4J0RUv7\ng7jvwWPYOZWEBld+ACnK1QZ5u4skffkMSWl1c+TjcvRYyN2Vl+P5bP/kdMjwaYhDqT+ChuM9mqdt\nuoT6qeuJhnzcY9GfnOrnHIW0yYCJiGH5W9KYX10TKBd2hJLUf3w480uIBj0omMRNxzAeAxwDMBa6\n2pHK7RYKP+e+nDJAqUxDhmL+2XhyWUpPQp3ZuGaftoM8OYXlFKE9UpoelZRUSqmjGUgvHx5B4rwZ\nc/u/7teUsCB0h7QVnObS1on2s8wlsdVA7TEnUsaVp0FXsrTnKdFm5ngwb0LHO/MDp9r2Hcb7/r9o\nyAKdL+hWLjNaTKjKlPoQOoRziC6vQHzxiUYKuaVB+ppSIUpL8Hd9A3lfnz+PNG1KabVxt2N+uQZK\nh6lBqdn2QXx9svEElcnGDbaVBae6NLVhHRp4L6RR22q4PLU3idmU4mSU+fUjc7jqLMYFpbe59OOy\npZTmFD0OXMy6SxXMzyUW51HJ7Wqyz1OK9wOl0RjvlV7DlVAL6tP4323MJ7TZSGVSlJ3AWDLS5v0H\nIOYf+gn7tIzl+5nf1Wys6WP/BSq1sQ/r0xBrPQjtu6uL7yt8E9HXhzdib7PhKGgAh9J+ZecUbIJU\n7M5Pd2s70p9Tcoq24nmLLUClmPn+g8wvfcUObQc6YQ+Us+Y883NPwPVz14NGEjaH7wHC7/lnirQp\n4OSEOdaYy2nWlDoY4I41ySjtbuuPvUGoDeYplZpXSqmHX5itbQtbxGhjf/sQSmg+oR9akLgXl8AH\nNN3n16aAHmJhz+XWhyZgP0cpdx7hPFZOegly8ClfHNF20Pi+zM+d7Is84rGHqTjOacGltbxtTYna\nSpQ+uPoL31O+svQxbWdu3aztnGOcrkT3es8uf1nbNW80Mb81hzGf938CSte6n95hfp99sV7bF6qx\nd3j9jke0HezNy0rsfRPlKYKIRPSicZyW4hyBPcaC1+7Q9umlfG2Omw1qGd0z16bytd7OC31ob4+x\nd+XobubXUc9pOaaGJVnjGjK51DldJyfegm8AZz/8mfmtO4T+eeRfoLE9fu8s5kffQzxiETdrMgqZ\n38HXv9b2yJcWattzxR5t7319DT1FJUZibgYQSfQcw9jsE4y1a83HGJu3LprE/PqNxvX6jAN9P3Pz\nZua3cS/i/H2k9EXB5XTmx9ZTXsFDKSWZMwKBQCAQCAQCgUAgEAgEvQr5OCMQCAQCgUAgEAgEAoFA\n0Iu4Jq3p2Dmk00cHcPWe31aDovTUhGHanjyXE3MaMpHeHBGMa1DKi1JKuQ5B+nvkbqQCxYfztPYP\nv0M66PRzudoe8xwqnje3dtBTWAq9y0CksP3y5mvMb+3+Q9ourUBq7hOvfcn83po/X9vTFoHu4+DP\nU6OzVqMa88of/tR29DaeWjr2oWR1PdFN1BJairhqSJcHUpjdYpHe1XCVp7NRqs+cJ6Zqe/fS/cyP\nEpEGBCFNjaZ/K6WUsz3+ro0fqBAuUeir6gtc1cqJKPW0FOM5XKJ5mrc7GXMtREUi/wwfczR9zzsc\n17Dx4mmw7USlwZ2oNLRU8lRLH5d/ViP7X5G//aK2zQ0qEiFzQUMI6gCtrMbQfoXnkSoYd0+CtkOj\n+NyuPAxKUXc7xk7MYzz3Lms1VKPciXqMbwziQfpvf7Bzugn1ob0D8zR4UB/m9xRRs9i09jNtf/z5\nOubXn8Qlb1+kPOed48oiVs5QdaJUJrdwrl52+B1UmZ/+EVdcMQWoYpjfRB7b3KIxtnLWIkZ01nM6\nwYRXb9F2yidcuYuiLhMxLOsoKCwTJo1gfrUFSI+nqbbOhD5R8AdPyaSUQzdCXTJSJGzJXFr07lxt\nU9UzpZQafhvGYzZRtjuYmsr8nvxgobapkknpX/x6boNxT4qLIvzPKD2Qq+2ApBB2rCENsYemslNK\nkhEFG/CMIfM5pai7E+1cshv9NHAuV+FoKkR8biRqSIFTkYrb3cVpos7RiLWjZ4HW2WVYP2Puwt+i\nqk6WBoqPM4nDzoTa0lzF42QEUV9pJ1SC2gtlzM/Wk8dhU8NnXIi2zcw4L66O3D89ZGnDt0xeRF2l\nhdBce7p5W3e2oE0dQ0BxcycqTEop1VyKdc2XqOd0EOqSuSX/TW3n26AYRkViza2r5VTR9C2gOIy4\nAxSsJgON19odY5gqQbnG8vT/yuNYTxxCoJ7iEsP9rF1s1fXCyc2gFUaGcMmLYjNQJcc9DkqCuRUf\nt7mfI2ZZWGPOOvvz5/CdgD2Md3/QaWvyeYw6tRX0DqrwMYhQNHu6OAWaKgANjgc1rbmE79foOnt5\nO+gw6kueWh/9IChd5ecQN4zUNEqbjLkHlIPyTE4Ldgv9ZwVGU8AlDm3tEMj3Ubbe2B92kXlUm8Hp\nc3W5oAynF4ESaJvDlY3ayL6jk1DRjcpLweS9Yfs59OmiaaA72BuodBYkPmRlkf3WRL4IdbdhH+RF\nqPe1GZXMb/97UObJLME4NdKawuZD9YjGq5K/OG2I0sJMDTqv9pzmKpg3uuAZvZMQo8ZNGsn8el5b\noe22NuxfRzzHKUVDW/Ge+SjZs1SfLGZ+b695VtuNjdgvTBuJPWp5BVd73XAMtKTH56EERUs5X8da\niaJf8DDQz347xlWDgiIxZ0NvRz8ZKWfNJYjDl46g1EX/27ny675X+fVNjbinUcajtiCDHSvZjX3k\nkQPo48n3JDM/9/NYFzf/AOpRqIFCFliJf3/1LsplJPTl4/umN2Zo+4eH3tb2I99C4cqo0LnpbdDT\nQttRfiPmoZuZX2NFrranB+K+fYbymFd6FOvJkllLtP3KT48zvxuO4d4/enm5th97Zjbzo++wfwfJ\nnBEIBAKBQCAQCAQCgUAg6EXIxxmBQCAQCAQCgUAgEAgEgl6EfJwRCAQCgUAgEAgEAoFAIOhFXLPm\nTDuRBR2waCg75rwGNUPa28H566jjtUWoxOIX70Lq6qEnb2N+P36J2hSppB5NaQ2Xfnv+MdQtMCPc\n61fu/kTbbg5czpXKoa0nfMLFC29hfneMQC2GwBng/VK5PaWUKiSyhxt/hMzZ+MRBzK/ffaijEO82\nTdsNJbweBq0Jcz3gmQgu9pHVXOYtzAf949wXfNTmvDrml5mKex4UCB76qKk3MD9F5A33/4E6H5F+\nXKI4cBaXv/4vrJ3BTw8dN54dc3RE/YTL277Tto2zE/OjNUUoDzb1COdP2tuAB0ulU8/uvsj8hpFj\nB1ZDJm3qv6cwv4qGa3MI/xdkn0ZNDT93LssbOB6yjIfe2abtkUt4+zEJVkJMTr+Uy/xumI4aE75D\nce2ODl6HyIZwwR38MCYqc1AHwHd0CDuHyqpaOoELfuU450Y/NhfzvK0SdSkevZvL0LuSOkm0BoKN\nFZ+zTTngFfsngf9dcpzL9fYZwmXKTY3MX1BLprGVc2S93BH37AIwpoOm92N+jcXoB2cig21tw2U4\nyw9AYnLsy+AROzhwLq1ZEMaCvQ/+7vrnUd8rNoi3y/jFydqmc8xrBK8dZEZkR2kNJDs/PmepfG/I\nqLC/tZVSqmQXOM9+Y0O07dbPh/m1GuqcmBJULtUxyJUdy/wLMWbQeMT/biJRrpRS5tZYertJfRIL\nQ02TLlKrK+ouzOeWBs6tp7KyVmReXVyO2hFeIXx8+CaHaLtoF/jU9gG8byqI5H1xDeYRjZ9KKVVB\n6tG4x5B5eYnXvvIZAg5+0XGcE34LH+e0js71QPneXG0bZXkDZ2L9v/Az2jD+YV4jgcpdUxl6WltK\nKaVcgzGOaX0bY0x1DQ3RdvFhSH76jsDad2XlCUURmwCZUFpD5NIpvo5FEVnmjG2ok3LDQ/yZinZg\nLHgPx7wv3pvN/Oi8r09FrQyr4fzZ2+t5nDMlEmdjX2rjxqXYD32PGoL+nWijHR/vYn4z371d2/l/\nol2cozyYH52b9eVEWtmOrzW0fwfdP1zbIz1v1fYfS5axc6a+hf1wXTbmS2h4HPMrPYp1st8EzBfH\nEB6HKi7Ar+ooxqi1J2+jqjOoY9JM5lu9QYa9bSTkwt0nJipTo/Qo4oBzH/4sdEzTfdqp9bwuzsj7\nkrRd/CPi1IAxPK6sW4X+n3kTapfs3s+vt+8SavosGJusbXMrxIqNP3GZ4xkLUacuJhFztq2Cr0e0\nTuKmb3E/Le28JhCtgzNtKuZp2ja+b+mbhDoXJadQyyRsWn/mZ+XM6++YEl8/jDop02fw2qPRt2H/\n0dKCfc6G575hfkczsH7GpKNOyIm1J5lfRATfZ/wXKw7wGnz/eRL1Rd698w1tz5kzUds3P81l6HuW\nQI478s5kbVta8nXxt+e+0Lb/GLzPvPL2YuZH3x9bKvBeFTl7IvPr7sYc62zBWvLnkneZX2E11oyb\nlOlx/qPftZ1RzPcZd3/5lrY9UzD2nUN5LaOhpGbMkMcwbt9Y+Dnze+Ol57TduHmHtsMD+ftiTQZi\n4uCQEG2bmWENylrBa7nO/nCBtpc9/oO27/uK14g58Cnq59LaqHdOncb8/EZiXZt+JFfbHn7828iE\nVzG3o/7CutNn1HDml7XhL3UtSOaMQCAQCAQCgUAgEAgEAkEvQj7OCAQCgUAgEAgEAoFAIBD0Iq5J\na7r7vTnaPvvVEXYscQnklJuI7LSRxlB5BimVT75+l7a3fcdTeubPQsr2jYNAD3r/99+Zn91fSC26\na8wYbUeSlF0Xey7B+edppCvGBoPy8tWPG5nfK189rG0nf0j0dnZyio/XGFyj7QQoHJ6JPNWuNh3t\nUtsD6pfvcJ5qWJGSqa4nuoh8cVQov0dzG6SFHf0JlJ3wMH/mV9uEtMwyIgHn4O3I/JhM3q1I43I0\npL1Z2mLoUZlRG0ek3jc28napykfaWtQkyJnnnuX9WLAZqZE2rqBJxY7hVCoqSV1/GWnZYx4YzfyK\nt0GK0soS903TbZVSasQiLlFsSlAKjIUTT03NJ5KaITeEaPvkB/uZn09fyNbR1FxfV55GnL4Tqd3W\npP0qDnE6nusgUBfKjuEYlWuvKOZp+wExGFcbNx3U9rhYLjXp4YQU0p9/R9qvuUHy9tEoyAxSaerU\nL/YyP3d/POPV30ELcB3A6TBWTtcv7VcpLj9spDXF3RSvbZoKW5/D29A9BjTF1kjMy/b2KuYXOg+y\nzOVnQVVQXIVZFe3H+KlPwzwYSe4n40gWOycqGNQ6OwfESmPqb8FJUAuI+rbyGGyIL2mIj1f24291\n93BJYv8gpIxSWdjTnxxkfg6EchPC1an/Z3Q24O+21/I+DAgjdJ5jiJMlOeXML24hKE92/oihJQaJ\n8bBbkBJcVwiqgoUtX7pbSiGbfOIPrHcJNw8i53D6xe5PIXGZOCvhH/38pyA116MKqdc2BqnrfBJ3\n60tBkWhu41TnxitcuvS/aKtuYf+283X8Wz9TwffGcG3b+zr9o1+/WUivp3KnSinlTsYxlUe2dePX\ns7AABdTFBQOyvZ3TttvbsWcIHA3KcNa6/dqmVHGllKo+hdRz+xDQHCdOGcb86jIQHyoJBbetlrd7\n+NwE8i/MP3tCZ1aKUxGrz4Ie01HP+9s4Vk2JPsOStX3x+3Xs2M1vgiq0701ITQ+ICmV+7Y2EshOH\ntj257DjzC/AEzanvfQiil7/ifpRWf3kpqKUOdqCZJd3N9wqNRYi7VD6byrMrpZRLlJf6Oxilue19\nMHd8J2NdtLTnc9s3AvSTPS9/ivt7cQ7zqy3g8d/UoJLWnYbSCFVVmHPBniHaNkpfn1qOfqB90NXE\nqUIzbyaUG0IprWnkbT1rOPavV8sQv4fcQKhmhxSDSwTGyA8/gqZRWsNj3n1zQbfpIWtcfBin8QaN\nxFi1J1TgqiwuuU3nnIMd9my5W9OZX05ZGZ7jLmVS0GccOO9eduzCGlCeBs0HjWjkXE6VjC/DPrDm\nPKgs457lFKCd76Nt6do1NC2C+dnYYD4PDkVbfvH9b9r2/ZW/i760FiUyfnniPW3HhHJq96z/4H2x\nqwtjZ9uyfczv0Z8wrwpTdsI+zOmpWYQSfSEf++nJo3npiGkfvKGuJ1JIWZEFnz/FjtVWgV5Wvi9X\n23aG98DI6THadvHA+iOw578AACAASURBVBlhKG/x46Ogd7761SPaDoqdyfyKsiCL7Tcec2T1M6u1\nfd83r7JzSi+jhMfUeyDFvuKJr5nfXZ/er217e9Cxruz+g/lFTMI9Jb+KMXxxxWrudwe+S5Sfw9q8\nctm/mR8tvZBw37PKCMmcEQgEAoFAIBAIBAKBQCDoRcjHGYFAIBAIBAKBQCAQCASCXsQ1801XLFmr\nbUozUEqp+kKk6+z9CmlctgaVFJq67+8Oasu0p3md6RNLQamhVdKfmc7VWS6SdK/E+1GdvfAPpIQd\nTeOpfG++dJ+29/yB1Md599/M/Ja/CnWSG8KR8hxxC6ch0RQuC6JGQtWjlFKq8CDS0GPvR0XnDc8v\nZ34DIkLwDy6wYxJc3IZ02hvu4pWlqapEzpegoDUZ1E5ufAg3lr0RleK37edV1O8hCiCUkeDox5VC\nqi6hHwMTkGZaWwaFCqOKlXcoUvzzUjZp26dfAvPrIHSHtkqkLLv292Z+v76NtLXbXoByl6VBfaGx\nAddImg+lgnJCW1BKqYZM0E/CDSJW/ysGJoKSdeEYH9/d6WjoMYvQlvEGiiGlYVE1G4+EQOZHVS9o\nW4bOG8j86khq7ZVjSE8dNAd0GPNTfE64D0Fao+NOpN/auXAVCVtC9Xh0DFKsjeo4y/6NGDVx2GBt\nG9MnA6dAOaExD1SC9hqe0m9g0ZgcDv6Io30N6iKFm0Hjqya0A/++vszPKRSUonJCNXOJ5HOsqRjp\n4GYW6PuMHzgFKD0H43jsI2O1vY/E9SDPf76212AoVLS1lTE/2l80blad4yoAzuFYG7w8cE5BKVcN\nKc7Hv0fcgRToy3vSmF/MnQbulgnhGof+qL3In9eJpLXbB4AGEnBTJPOrOFlAzsGzO4VwJbbs39FX\nVJmseOcV5kfntoMt5tW3X4IWbFzDh5A0b+8bYDcU8jan1264ghhXm8Kf3TUaY4RSaR1K+VriT+ai\n4xVQbToaOP3A3PL6/nbkGAgKUMEWruRH1dKoco1jJKfn0vHt6AqFp+q8S8zPNhh04pIiUGxaKjiV\nwjUI/VCZhnjQUABq9fnTnO47kqT1txJVmJoMTn2wsQFlM34B9gFFWzhlxfkhjDNKU6Q0QqWUaiRj\noToH/ejszccZVTczNc59skrbFdUGhcmXN2h76G1YkC9t4SpWEWRepHyL/eHkN+5kfk3VUAPMXYdr\nJLwwl/ld/m6T+jvEPAD1j6KTnNJQl4ox1t2BfU/QDK40VJsOeo0joZZ2dnA1uJZyjAPfeKzbV9Zz\nNZvmEqg71hDq+u5XVzC/EU8mq+uJiFmI5X98tp0doypjlIY/fDbfy9J929Gf8T7hVsLjjxmhdFMl\n0/g0TikqrMKYdiQx1coJe+bZz3DFV0oR9CN08VaDChNVzpxoD/phUUoR89u9HuUkRiSAKkLvTSml\nOtMxZmIXYqzTvY5SSgWYc0qfKfHBps+0vW0Jp94k/RuUkPISqPx4DeRtnvkTnrff4hu1/eXi95jf\nsyvxt/KOgyr0/sqVzG/Ox6DlUJWjt1c9o+3XF37Gznn8ZlCyJsWBwkYpo0opdZyoD8c/i3u9QGhB\nSilVX4/SFwd+xHo+MIHvCYLjUS4jdDj6yScphPk9MB4lRX46yPdypgClIZecOcOOOZHyFEUlWF8G\n+/IXnovffaXt4p14N0gayd8hvJPwzM2FiN8H17/F/M5eBd374R8/1PboIuxDS1O5ErGtJ6iNW79E\naYRqA31x+8vox1PZuNenP+HUPKWwDyrLwN/qN4+rOpVexjvxoTTsS+fN4dS8vPP8/dEIyZwRCAQC\ngUAgEAgEAoFAIOhFyMcZgUAgEAgEAoFAIBAIBIJexDVpTYNDQrR9LjeXHyRVzq2IAknfaK4GVJGP\n9LszOaD5NC3lKhe0uvp9c0A3CruNpy7GVSH908kb1bPdnwT1yP0oTxl1I3SW2UOgLFKyL4f53ZiM\n9GDv0Ui3Sv3lHPMzI4oxTy17TduZ63cxv8FPg2JS8CfSpic9NoH51V7mSh6mRuQwULSMShyU7hAW\n8vfKE0opVbobbRVxO9QmbP7g1yvahJRr5wFI3Tz94W7mF3svScVuRSqnvVsA8epi5+Ts2wo/QhlI\n+YyrNNDU84v7kVY2zkDfuWkO+sfCGlOh/DBPS7S1Rjp4+makq3u4cPUKz5H8+qaESz9QBuzP2rBj\nyf+epO2i3aA7dDbzVNrKw0jLdh2EOVF3kdMYHPoiXdohCKmcp7/jim1UYSEw4O9VJDpquPJC6R6M\nozFDkeJ49gJP1Y+3BkWg6SpSc80s+PfkPh6gkYTMQWp08V98bne2IO278C+kLnoZVIOYkswUZXL4\nT0I1+OrzJeyY11CMn9OfH9Y2VUBTSqm8X0Er9EpCvDWqh9kSNR1KX7y0maf1j30oWdvbP0UMS54L\nRZG2Sk7/yv4N88AtLETbHh5JzK/OKkXblacw/nxH8fRqSrOjanhWF22ZnxdRxGskabCh/fjc6+m+\nfvy03D9BK3Ty5CoFxQeRfhs6C2no+RtSmZ+NByhtzoSORsepUkq5D8H4LCVj2nccb79D30M2JJso\ncswahpR5J38er7qa8bdKjyFuuEbzuVyXhTXcYyjiM6XQKMXVRKiKVWsJTyOuz0Q6dN0FrH32oZyy\nqCz4eDY1Kk5iPFo5c5U2StnxSAxQ/wSqrKMU2tCoPNXhj7ji4YU5cmnvz8yvpwvj9uJ67DsOkvTo\nAHdOrXr2+S+0PSIa9NcBQTxuUIq5zUmsue7DeQxsyEefdLWCfhicPIr55e3HmPN1wzy1MNKCs0lM\n5Znd/zP6zATtx6uSj0efOLK+fAhFx5AYHiu6u7FOhiRjr1Sbz9cQBz+si66DQG2szuUUtu527FsG\nPDaDHEHftpbxe20txhyJfx7p9D8/whVIho3DM7WQeRU6hcfdDl+M35SPoXTiNphTZJsLQAuY+BJK\nDWR8e4r5ObqHqOuJsr2Im4lxnMoVOhf7TapmZ+PB1eJyN2OOFBDaz4AkrtJZcA50grKNaKehCxOZ\nX00K1IIyzpJ3F0IxDJjYl51TeR7zxZyUPDCqTBbtwh7kLHkvmjB9OPPL2ITr0RICC/59K/NT5PoN\nRN2x7iJ/t+ii68tkZVKc+WC9tvde4nMi+K8QbXvGI562VPA9kP/NUFv66v73te3twilFe18FFckz\nBHvAzX98wfw6OtAWMx4D9Wjjqyhp8OxbC9k5H78CSh9VaIu9g/vtOA/qjZUVYvK3u7l6T+pa0CuT\n7sSeKmzELOZHFa2oEmLRTk47/XjzJ+p64vGf3tV2fQ3vx5OfgBaZ/BLe08tzOKXIvQ9iZfqlXG13\nlpYyv0OrEZsGku8N8z6Yzfz69WAfc+RN9HEToWD1McyxvkMhR9beibG55JdPmV/xRVDDvP/AOPvX\noo+Z3wdrsK6V7kEc6mzlezZKUV348u3arjjM1W6pcuvfQTJnBAKBQCAQCAQCgUAgEAh6EfJxRiAQ\nCAQCgUAgEAgEAoGgFyEfZwQCgUAgEAgEAoFAIBAIehHXrDkz6EnwWFOf4xJvJUQei8pJr9r8F/Ob\nd2Oytid6DNJ26HwuqWVtD65X2jfgtVlYcF6phQ1uuaMD3M/ubvC8mgu4pGLJoVxtt3Z0aDt4FJdx\ns7DFtX//D+qbTF3Ma8Q4kjocRSdQ3yZ0Bq+Pc+4jSAJSGbchrryOgpEvbGpkHAcXvvnAZXaM1puw\nJfKhXsM4L5vybC+vPa/tiBs5n5fyrWndB+/+Psyv9GCutl36g3/dUQcOYe15zk/Mzgc/dfh8cBDr\n6jl/OyAaEnVh2eBYU76yUkq59kdthWPfgD9f28SvN3oOuMjpa8GtjLl9EPO7+CtqBMSYmM97cR2u\nnfggHy+568EL9R2HMZ2zltcWySpB+/UjNYWM1Tlo7ZM2IjU95J5hzK82jdR/IlLIDdkY60aJz/MX\n0Qdxwagt0mXQsK4twXiztsS8bDPUuRhwAzjKhz/cq20ak5RSKvUkeLu0ZlS/eC4B6D2M18wyNSqO\ng+9u5MybkXum0pvnlnO5+jYSw/q6I5ZQWW2llPKfhPoJdkTeNu72wcyvtQpS8WE+RK6ZzBc7Bx6z\naL0hc3PU6zjx9fvMr+9scKyt3cF/72zh9ZDoWKKS9z0dvPbV/m/2a3v4bahbRWsjKaWUtTOvy2RK\nhN6CmghWTrxWSS3hG5fvy9W2ewKXdq89j7owrUT21ljHxSUK9WhqylEfomzlaebXNxh1Q+jYzyhG\nzYI4R96HUYvRNy4u8douzuS10xpIzRmXSMTW6tNcDt09FrG28gT2C44RvEYKlcwOugN1eVoNNUMq\nDxOpSRPHU6WUailGvZjOeoPULamXRuWyza04T9zaATWH6nKwXrlE8bo9XV2YY4WXd2i71SDzu2E9\n6qf5ECnes1ewhs+Yy6Wb48MQ85f+hf3XyGi+Nvslh2ib1jTwDItjfpU5qBPlHYt9Wl05lxv3IjXc\nqDR8xp505hc1kdcQMSXqSP2isIl8n3bxG9QZiFo0RNvdhpiSux7rpPcorEk27jw+m5tj/njHoSZa\n+vd7mV/so5C6bWlGTHZyRk202DsWsHOam3O1XVWEeB8bbKg3dhbX8/JCXYeT729gfnakTl7UA1jj\nSsleWCmlwmeN1HbWWtReMMrNUkn16wEPImmd8vt5dqzrR0gRu8XzmjkUfSZhL0DXeGXYW/hFYo2j\ne/6eTj4u3AcjZvtdRd0kt4E4vzaD1+uzcUUtsVBvrGNj7hzJ/ApJbcAZT6F2x/YveW3G4f0xziqJ\nVHwzkRBWiteqsnRA3xseXXklB6vrhRxS68yIKlK/Z+0KSF8veHIG86tPx3y+8SZef4fCwh71Pywd\nYDflc+nwnVvXajsmGfFw2r8xRwt+T2PnuNpj3ie9gppPD40fz/ze34jaJW/NXaLthL68DhF995vs\nN1rbHR38Xi3tMBa94rDOWjnxGpiZ6xDjhz4Qr0yNy6sQS+hYUkqpi/mIPzYf7dO2G6kxo5RSQTMQ\n811iMQ8ytvD3z1e/fkTblaQOmqUV38/Rd+nEFxdpO3Mj3tMP7uB7opjbsG8ZNQx1qy7+tIb59ZmO\nez1Paus+dsctzG/7O9u0Pf0drMFnP+L7pQOpqC84owvvajmZhcxv/2W0BRfj/v8gmTMCgUAgEAgE\nAoFAIBAIBL0I+TgjEAgEAoFAIBAIBAKBQNCLuCatiaLHkB8XPBspmgWfILXvpiFDmF/MYqQGXfxq\nk7YP/GcP85v2/jO4KSdQRxwcIpifoyNS09rbkbaU8i3ky2iam1JK2TkhHTV4LM538OPSoitfQArc\n+IlIBaXSn0opVUekrztIOjRNyVNKKb+kEG1bn8A95R/l9JqSE0gVu/F9TrMwBUIjIF0XODWSHWsi\n6ZFm5FOdMb0+YytSsCJvRhpYyh8pzM/BBnSCvAqMCyPNJJmkeX756iptWxEKy71Pc6m5EeMhH1u4\nBdLLAxYlML+slUiLDZgIaodHTAjzqyISb0PvQQpldQpPz6w+DTpQlD/oA7u+4+nM01/5u+Q00yCW\nUKhoKq5SSuVdwf3RsV9M0imVUmr8/cnatnJEuuLez/lzOBA6iytJSTQ3/N32SqTq/7kNKYXTnoEk\nZ+D4/uycyCZQCdJ/4GmI7B7IOKpvAbWq7BynUgRNQAqpfRbmZeyCf55H7bW43uFP97NjVJLZ/+F/\nvMT/M84RWmFcIqcd9BA6T1kdUpjHzeMp0e01kMS9cgz00kFzeYorTd/vagEVyqNfOPPr7MTfWvYJ\nJGcfeI3QJwzp0f0j0O5mZhgXRtnvK2uPajs7FXFu2KIRzM+TSEbTVHEzg5zy2EfH4RiRDj/5PZd5\nb9mKuLzgW4Ps6P8I2pZF27P+0c/aCWPYmDJP52l3J6igHoM5/YnS0ZxssY65jQxhfrXnMfbrmjEv\nR0wGhS1gfBQ7p7EE7UwpEm59OA2lLgDXLt6JdHxjPMhcekbbLlGQIHUM4RLZTfkYb/lEFr6jq4v5\ntbRzqpGpQSksqavPsWN+AZgjuWtBGw2axdumNgOx0mMg+q6jsY35tdcihd2aUB96uvi4uCEcf/ep\nT5E2P3gQ4v8LP3P57VtHIj4MIRSn3HIuoxvuD4oSpZC1thYwP1tC50n9EancPmO5fHvtZayT1WdA\nW/B05vsqRwPl0JTo6cCY2fbiV+wYpX9a7sZ6FzKLU+odCSXXNQjtV3yM02taSjH23Yegr6uqOMUk\nbTloG3Stbig4ru3+D3GKMI1zlEp8MusK8xtE5Gb9b0YMdunD427JccyrdkIjDJ/CqRkWFqDl9Z2N\ncWS9+yzzu7QUVIfhT/xLmRon10G628ogMWsfSuIHiflWjpy6uv9n0CwGjQVd0rgHKdiFtvFKwN7Y\nOGcpra3PFOybHf1AWfQayKl0dXUXcL0JuF72dk71C78Jaz+lgd/6Ft/z5v6K2BN9C9653GP4OtHd\nCfp4TRrmpe94PmerzxHp6nHKpIj0wz1RKrpSShWRveiLaz7S9uG3fmJ+7sGYi7REQvBM3odVF/Ac\nwSMmarujo4r5hd+GtZDuU/L3YO9p6cLH0bAIvHOe+OI9bT/8GN9HFJ8G/TCexO2RT41lflRqef9n\noCS98tJ3zO+djx/V9ppnluJ6EzkN3TWWl4gwNTobETetDPTwmfdgvIeMmaTttja+Ly89A2qPWz/s\na41ruqUd9kHeiSgpkLXqMPMLJf2fswfUP0vyHjP7vdvZOSnL0IZ2/thHRU/jfp/f85S2TxH68AVC\ncVJKqbeXwi9nHfp+2BIu+53z1OfazksFVWv0Mzz27r2Hy5QbIZkzAoFAIBAIBAKBQCAQCAS9CPk4\nIxAIBAKBQCAQCAQCgUDQi7gmran8ONLQb17EU7XOfIU08n6jkfLnEMRTmJc+8oG27/3qBW17nD7B\n/ApP79d29AKk/zQ2coUAJyekKzo4IGUvlNCsGg1qTa59kTJ/5RekI7VFeDA/PzekdjsTlQxjdX+/\nIaBMmJkhBXPny98zv3B3pC8HTkdKefYGns4UfDNPNzc1zqcg9b6zgaeVOfXHc9r7Ix3Z3pdX56ep\n8rt+RlV/D0dH5mdjhTS1Pp64dmktr0y+5ss/te3njlTGpFikjdsa1Gyu/oZUuT43Y8yVH+UqNWFk\nLBRtxbObW/N0WTvyjFdWIYXZmA4+9mGM/fJDqJw+LpmrJhmpC6YEVUbpiuFKIGP+jfTCrG+RHhw7\nhtNmKJXJzBLfZW2tOA2QqhTs/BwphL6ufG5TtaqavZhXZQdytV3cwtOyu1uRqkqV00YkDWB+DYT6\nMPBeqKAd//oQ82vMJXQBkkrr6MtTP6/8Aqqk5wikTyY9yeNayb4cdT2RdBfoPK1lXBHj+KdQqbv1\njZnaXv2vX5nfrUtAFR1CYtiFVZwm5hdK0klLQCcrO8tjb3Mb0q+T+mH+1V7CPKjJ4pRN28UO2qaq\nbGX7c5lf8VVco48X4kHhJh7XQ+ZgzlIVq/LjvML95S9BwXOyI8oYA3lav2uMt7pe6GhAe4XcEWs4\nirZIWY652FjDaaIhUzA3u9uQ9lx/hadlm1kiZvlMwHrXlMfjaUkV0sYHx4Lu4DsK57i6cvULd3fM\n+6spUDCwcbNjfi3FGKcnzkDZYtQkTmH2HoU+aCkE1YOqUSmlVBdJ86YxwCWQx5fAAdevD5VSqi4d\ntC4XB77WdDbjvqgaYMYqTnWJmAulIzoPKOVOKaVcQ0O03dWFuWjrz9fP0BBQgO64idBDyVqaYki3\nHpuMfjh2FHuLhClcTbA+G2PLfwTGbXMVp/HmEaqZG1EZ62w0KFpFYx0qPoY12DuOUy4aiNKN4mH+\nf0btJfShn2F96nsv2sXWCevB1S3HmJ8HoVQWHgQ1z8bTgflVHEMsyrsAKlj/qTwG0BIA1Sewbt/w\n/B3adnHhClltbeibzG8+0zZV4lJKqZjHofaS9TOJ4+M475TS5Tqb0W9tbSXML38blKryzuOZxr16\nF/OrLfxn+qYpQPeXwxJj2DG6b+uoBaW3uoY/Szuh9li7gQJak8HXkOAbQf/t7EQcbSrm9DQLO+yX\nPKOxR7ciSjJNTbxEAX0fyNkJ6r1nCH/X+OY9qLLdeSuoIg1XORXdwgbXMyd21tJTzK+aUOuGPpdM\n/Dg97cgF7KGHPqhMiqTXXoGt+F54w5PPavvd+c9pe9aMMcyvPgexwncMaKdLH1vB/B754UVt29oi\n3pSkcjpM2V70D6UOjnj2JW03NfE9n9lKUPjiFz+m7TuG8v3+M3OxR4ubSWItD/0q/WfElLmfv6vt\nKfV8Lfnl6WXaDvFCbPUfz2nopz7CPjGSM95NAxK/mvP5nCiuAk1nxy9ow6n3c3rfis9QwuTOh7Bf\n/e34ceY39V20b/YuvGvE3svpfZ8telnb930BpbvvHkGbzfOdzs7xm0CoyatAN0wpX8787v8G4zHh\nw9+1TZUulVLq5Hf45kHjlaXzfubn7YL4kPwyqHC2tlwJ9tUfHlPXgmTOCAQCgUAgEAgEAoFAIBD0\nIuTjjEAgEAgEAoFAIBAIBAJBL0I+zggEAoFAIBAIBAKBQCAQ9CKuWXMmcAw4u20tFezYyDjwuTKX\ng4tVeIrLMt71CfhhDZXgYF7afJH5JT4B7mHpWRyz9eKc7IIr4O/5jgQn8epqnBNwC6/h4uiIOgph\nc8FZ3fiv35nfDaPAHba0Bx/fIYBLQVZkgr9WfhA1SGZ8+Cbz2/XiW9o+tAvcTyo1rJRS3R1cQtTU\nGBQHaTijHGZXK7j1Z1aBx5r0NK/FET8Ncm51qRgL+09wKe0AUj+mgUijvvMTl8x74HbImY0dAb5m\nVgbGj/VRW3ZOSQ34qB2bwZ2tJfw/pZRqK0ONgICpeHaj9OLWt7Zqe+RMyHG3HOTcelp/wJrUETLK\nzLWUkxoiXLH8f4ZTX7QrlfZWSqliwquNuhd86nNfH2V+tGbAVcKZt7O2Zn6Xf8FYDff11fbqQ7ze\nSzrhZDoSmd/6EvBUfRIC2TmdpF6Huy+4/vVpvKZJ7OPg915ZiTnvbMfrYbRXQXb0huemaru7u4P5\nNRNp+GIiwx5xP5efzr2IduEC7aZBXRrmTnNhAzvm442aVxUncR83zue87OLtqONz5So4wNllvHaE\npzviFq2H4eDI29CSSJc6krpTHfXoK69BvI5EKakrVJmOvxs6lUsNW5P6MXmncY6LA6/nkL8e87mo\nEvUXAjw5V79/DOJXcS7q2VRl8fXJk0ikmhpOYZiL5Yfz2LGWEsQAn0Dce3cbj/FZm1HXg9bOsQ/i\nMsTHvkZ9Lyo1bZSbDfDG3zpzEfUhAsla2NxsrI+A32ZonaiaIl7PxtEe91dex+u5UdB6Zm3liMnO\n0bxGlrULYoVnPGLAie+4HHplHsZB1GhlepiZ/fMhUpMr7gHU6sn+mdcJyN+AcRv1MGpj2Tq7Mb/y\nFNRY8ojFMxtr9F3dAb/R/SEf6h2GNhw1mcesWhJTgkltGp9EvtabmWG9KtyL57A0rIuWzlgP7P0w\nHptLeP2BKiLL6zME883DECv+D3vvGVhV9YT9LtJ7770CCQkEQugl9N5BAREQG4KKgoqAvWNBUbGj\nKCIiTem9d0Iv6Y303nu7X+67n5l9lQ/v/3DzZX6fBvbsk332XmvWOufMM0NbQxuadFIfrtdUfl/q\nyzAGjUyxvoRM5vURihKxh8k8gzkSNpPX7LEj7eEtShDXaI0ZpZRyDEd9G+9eaJltaopnTVsuK6VU\n5jGsrZ5jUDPKxJqvzSW3sS74T0dtltJb+cwv/ghqQ3UiNSHPredzjF57xzDUjMo6xeuS+Qx8EKsh\nGDwF98mpqwc7RmsKWnjg80BVMq/PMundyZpddhdrUtl1fm9oK/ucvdgL2ATxuWjtg/XT0hL7mOQD\nqKfhM5iPuexT2DsFDMFzPLv9EvMbE4WxZdsR4yptL2+57UtqhtG6N7W6uppBj6KGEa2dY9uZr58D\nTfQ10gxHfjb20zte38mO9SP1yWidsZCpscwv5zz2ej69+2r2NDP+UbWyEGucpSXG7d0tPD67eCIO\n23bEup1ycaNm2wXy+oQ9nlis2QlH4Ec/2yilVEk+1snf3kMtvFXfLWJ+pdXYE+QkHNRsJ3/eHpzG\n9TVf/6nZ/qf8md+FZLz3CcrwpKRiTxk1ko8Xv0hccw/SBrvoMq/rdDYe8SfjbcToHw59y/xMTXFP\nA4YN1OyknfuZ39jJ+DxQmYZ9QSj5fPLmi9+wcz7ehFoy1qH4O85RPL5knUQdHCvScnvIKL7pKCT1\nFNPuIL5cP32X+YWF4nmVJKKeUWU8j6mNZfguwvcl9f9BMmcEQRAEQRAEQRAEQRDaEflyRhAEQRAE\nQRAEQRAEoR25r6zp4Bub4KhLo+4+h8hASNqqdzSXMexaiTawMeMgjYmaydMBiy4iXdOetLG2dOOy\nJu/OaBtsYoJjFk8jlSj/Om8pm3AdrdEcI5HSNOOTBcwv8wBS6vKPZ2i2kQn/Doumind9GvKcjLi/\nmZ+1OdKFfZ2RXnjjV94Gr6oe6U3BPecoQ0PbKhac4KnttD1fx35Iwyy8wOVpiWeRShfYGc/YxY6n\n4dsGIjXUJAuv/eT06dyPpPLvO4GUz34dkYJr7srbm0aN7Yq/S9Kola5tafU9yJ9qspDimX2Kt8zL\nJTKpxlKM4cDhoczvry+Qrjm0D9JRLx3jqcndo4mcjnfd+5+5dxLXHr0slh2rSEVaez55vs4ePE3X\nxBbjMWIiepq21HPJRRa5T1SW9Po03nvx81c3aHZlHe7f2PmQxNEWzkop5T0Kz7ciEdftPYbfcxsb\npE+GkK6ed7/iadneY3FeUxPSTLN06cEu0Ui1byhCuvvdb3m6cdhInmpqaJqrkdLrOYK3ST2zETI0\nnwpcY1Iel7GNXzYar7cNr2dsxOOUpTdSNGlrUX3L97sHkdbvTmJR1ALINGp1bb+DY0hL8K+2arZD\nRxfmd3ULYnFgrEAqcAAAIABJREFUR8xZmyAu+7Dxx1g1PY4xfPbibeY3bBIkJo0pkNWFjePtV0uu\n4Fgg71r7P0OlADnJPGW++3zcs1PfouVleA/eDjMjIVWzO3lB5nL9FI8pHT0xbs8lEslLGJeP7b+M\ndPoJsZAIFJyG7Kq5h06uWYfWsz7jELuafufXYOaKWB0VEKDZVUlcVmBN2kCXl2G85G+9xvwCuiMN\n3YKs7xHjeZ/lpop69SChqiZjG1N2jMoG6vLxXlz78XaY1aT1a3kC0rf17cidI3BeayueA02jVkop\nK7JncCJtrM2dsBYW69rL55biOfR5DPOypZE/7+psxFsqZaKt4ZVSysoPz7E6A++P7iOUUso2EHO4\n7AbmQQdjHofKbxO5ZW9lUPrPxfuN38HH7bC3sV7F/4G9mV0Yb1efvgcp+MGjMQ+cgzszPyt3jNXk\nH7BXvLqTj+/xMU9odksL4vj5D7Cfdovk0i/fUdjbVGUh3idu4q9NY3zQdEgOPPqEML8OxK8uB3ug\niIFc8l8Vj3tRnIVxFDZvHPM78c6vmj3ps57K0LTUYR3b+/5ediwyDNKeykQiaSCt0pVSKuk77Ktr\nGzCmo18axfzMzLBGtY3BmKYyXqWUcvbC+8y9c0KzvQf994Li3hefQ25+gbbO0QP5voLuxWx8sfbR\nduBKKWVN5mJtGeQmlXe4DDz/Ko5ZExmqqQOXLDrFeKkHRd5x7Bs76CSjgWMhUbIjMq6T7/7B/PLL\nsYdr+hN7vW6BAcwv7FnsMSsrUdKi63wuv9vyHuRVY4Igm/nrs92aPXYa36xfuHxIs/u8Mkyzu+va\n2jvYIh68vXmpZhsbc8n2xNXLNHv1HPg52/LYX0/i9eurn9Jsn+hY5jdLJ3U0NPTZBQ4fyo4dev17\nzY5+As804Rjfb//yx1uaTcs9rJj2CvN7ZQ1iZe4efMb8Yf8h5rd0yUzNXvLUJ5rdMwRx78s9H7Nz\njr+7XbND+8PPzJ6vzVQeaUzKmahWLld16Y81vFcD5mmtbp29m4w918hJiLfu03kcyr3CP3vokcwZ\nQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH7itr6rsQaWAH1hxkxyoTkCLb46WJml1XxVPw\n7c4hfdsx3E2z845xiYn3KMgTaLVxc3M35ldShFRBU3OkA8avQ7Xs7stms3PquyDFvTIbds4p3mko\neDxSuHIvo7KykRmXdMX/iPRJv9FIfza24Lcz8gWkMRkZIRWtLD2Z+TkF8fRZQ2Nqj9RGa38udSm9\ngudF09nMXLikqOtEpN3e2QOpwYS3eL3wBJJa6j8Vqff9m7l0xmsE0vxbyTELV6QEttTxFM+ae0h5\nrM3HGKkv4d2acsjYqiHprZGzeRqs3TGkFTaVw8/Sk6cbPrwU7/HuNoyZ2Ed5OiTt6mRoAkZifqT9\nySvS376F9xsRidTL2wkZzG/MZHQJa6qEZMDUVtetwxqpfRf/ROpdoBufi69+j6r0JTcwjoxJFXcq\nIVJKqVaSDugdjfjS1MRTzdvaMCZSNkAao5cVdCCStqI4pPs7RPIK/FUpeP3Aqcitb6jifzftNxIT\neGa3QXAi8qpWnbzI3QFz02cQUrmrD3F5x6Z30WWOpm8PjeCV9ac9jhLw61es0GxLXVpnAUkl7jcZ\nqdxV6ZA0OHfn6dC3f8Q1dH4KVe1zjvMufOFDEQNuHEbcGDyMpwjnHSUp0URGOv5p3lmFSr9o0unZ\nLbwSfvcBXPZjSIyIbCP6yb7s2DnSXYl2dyhM4d2k3OyRrm7tiJgX48jbvM1a/rpmf/zcc5p9OoGn\nEdOxQ2O3Y1fMg2ryPJXi3eYqUpAmX1jGuzVdvQppxcQReL85qbw7WNpF/Jt2gIuZx7UsJlY4du8v\ndK3KLeEyKb8g3lXB0DhHQ2ZXoZMJ0O5mtem4HyY6+VMzkUKUXIS0wCGKx5+yW7g3AWNxP2y8eScr\nmxcQ3zIPIfaaWJO4OZaPEddynEP3ZRV3C5lf4AT83bhPINXVSyn8B2JuUtlWdQGXqHaaj/W0lHTQ\nYKnhSimPIXyuGxL3CEjlXcL4fTn42rp/PaejB5fK93wZ6+I9IvGkkiSllLqyFnvPga9Bfh7Vge8P\nL6/erNk+I5FOH7mQSDJ1UrKrnyGNv9fyKZodPIXvmyxJ99L6EnQgrCvlc5ZK36JfwbVSSZ1SShUG\nYG67EmlR8o4jzC9salf1IDl3ENcRFcYloIosk6Y2iB0XvjjJ3CxIzLGzRQxsbuCS3IsfoxNMxDys\nd3YhXJLb3IzxTjuOFddg/777+8PsnN5kDHZ7EV0Wt77yJ/PrHoFx0VKDZ+Ld5b9lR4k/Q7pqo9uj\nunfFfoF2f9Xvoff8iOfaeQgv6/C/sn37Cc1+/vsn2TEzM8S5tB2QGvV+mXeFdXHBv4vyca1/v85L\nRkSZYx+1Zv5KzV6yfhXzs7fCOLh3LkOzZ7466V/fg1JKhZHPSDY22FM9tIZLAmtq0OnLwQGxlXZk\nUkqp7HjsicK8seZM+GgJ86Odiy599JVmm9lfYH4px/B3I8YrgxPWE/Pv/Vkvs2NLf0Ynq+3LUbLk\nWjovl0Gl+COGYI59c4TLlerqEKc8luLz1KdP8X3VhY9x3rsvztfsK+cgSY125V1NP1qMa6WfQ0wt\neCmOskJ8lgwaifn711dcXkklpfRz5awnefflfv3xfhsbse7f/Hwb8wte0F3dD8mcEQRBEARBEARB\nEARBaEfkyxlBEARBEARBEARBEIR2RL6cEQRBEARBEARBEARBaEfuW3PGyg3arJhY3uaS1uVI24WW\nZ7RVrFJKRU5G27njnx/V7Jp6Xkfh/DnUI5jx3lTNTt93nvk1kvoi9uHQMbqPgObyzm872TkN+dDm\nek+Epqz8GtfMdzCBprguB3pTpx687WFKPtpGGh+G3jhqKRcAtrXhXhx7Gy3j4nNymN9jH6NGjoMD\nbzFuCGxD0bquKoXr+j1HQl9YmQzdeOUdXiMhYBaeP233WU1ajiqllE0A9JoX1qM+hKMN13k3lkPD\nW3GL/C1SQ8R7PG+vbGSO4Urbm178+wrzayWtbrv1hk5UX3OhsgTPmOqV7XWt07P3QuNJdYcFpN26\nUkqZWJLpZOB6JTmkJkdLK69VYkra3NM5EV7J9eVXv8Xz6LEQms7cgynMr/gexkj/Bf1xgHeWU2V3\nMX9ce0JLWxaPWgfhT4xl57S1QQNdXQm9aEUyr/lgaodaVb6kdlG+rhW8uTPqp9BrSNvE60mRIaHK\n0+/h/3V1X1wG8po2hoa2rdW3m/SKQttyWo+Gtp1XSqmpT47UbFrnwtich/NPSI2S/deg6c8r4/Ng\n1RuPaba5MzTalUl4Jpnb77BzLH2wNqRsQoy+EHeX+Q2dhnFG65DQFs9K8fa9DYWI17aBTswvhLSW\nvleM6+vSl8cKE5sH127y9m+IN/bWvDZXUGc8Q8fuuNb93x9lfsNnQV9NY+GPP+1ifv17Q8t+MxP3\nzMeJ35c8UjeIDitae4LGBqWUMiG1QejYCe7La4R4eGL9MHdDfZwAxwDmR9u+1uUhthaeucf8PIZg\nrfaZgvjskMnrZlBt/YMg9xDinrkLn2N0rbGPwH1LPshr/QT0DtBs2p66obiO+dF6dI31GLeNjbzm\nFY2D/iOxF0jejL0JC2ZKKRt/tLSm9cJcAnmNteoKXLsPqQNTdoXXCSyLw7/9ZqAFcJVu/Sw8l0Uu\nSbc4EDK3Inb4LJ/6n37/N9w7hb0njV1K8bjZ7QXMt5o8vmcxMUEss/TA+D774T/Mr6EJ+7krn2zV\n7MIK/nphQ1BD0C4Ec4fGftqWWymlBryOtt9pR/Zp9skdvJaWK6lV1edprM3n151mfn0XoZ5b2mHU\n7qA12pTi+zVae86WjCmllKrK5M/e0AyZjfdi6cHrqez5DDViRi9Ea+P4n3lcGTgVnzUcO2MvcOfL\nU8wvbGaUZtM92/XP+T20tkKtu8wC7Gl6TEGtiOig/66nVJGKc2Z8/DA7lrYZdQPvnEcNm6Evj2B+\ndO6kF+L1Opnytb6K7N0do7DuNLbxOERrkxmabgEBmv33Kv4ZrN8EPKvrGRma7bjHmfml5GNtdR3k\np9l/nOLPcI56VbOfWjtPs+uqs5hfZR3ef/+V0zU7+wzq91zbd5OdM3Pth+RfiGvpF3jdG59o1Me5\ndx01vFw7RTG/GxtQO2z8h9iTmZjwcb54JGLjsjce1eyDa3ldo9JqfPaZrAxPQQL29dMm8jouaxag\njhetP9OwjLdEHzAE9yB0GuoGxv24hvlFPDpLs42MsHbZ2fF72OslfJbJ2IIaPrT2y9fLlrFz6J53\ndxzm8kO6Wnke/ljfb+/Ga097mre+9u6FmmHNzdirFFzle4JP57+r2Ut+RFw3Mue1yVJ/QQzwevP/\n+yQlc0YQBEEQBEEQBEEQBKEdkS9nBEEQBEEQBEEQBEEQ2pH7yprMzNA6tySep5W59UH6v4UbUjSb\ndC0CXcO7aLb7QaQpZzRyycXAoWjVV3AW6dtJF7jkYvibMzT7wBtoT+dqh9TUy6mp7JwdZ5H62vMU\nZE3zx/M2rfv/wHucsQLtwTO38lT9iW/g2M63kOrWw4i3JM69hBSpwJ4Bmk3TY5VSKnE9Uvm835ui\nDI25E9J9W/14a8abG5HuVU+uKyCEt/QzJu3EQ8ZDZlJyiUu0bDsjTdEmAWnFQeN5a1vbAKTN1pM2\nZ4VJSN1M+5aPuR7jkLZ6cx/ubXhEIPNzG+Sv2VZuSB0sucXTt4MmImW75DLeh5FO1kSlTK6BaLdo\n5sRT4bPieJqtIQmZhflh7cFbWuefQ1pdfTHupZkrvz5Pkrpfk432cUl3+XUHuOP1i0jqeiVpX66U\nUtEvIe2vLAV+Vp6Yi1kneRtAKjcJGfSQZjtzxYW68i3SJ+nzpGnY+uvLPwLJU2Mtjy9dX8Bc37US\nKZhUaqOUUpFjuHzT0DSWIs3WtQ+XUBVdwHspv4HUy7QCnoaZ/AfG8dA5SNdP2MulR1tI3AslcqB5\nsbH89Y4kava5RNimJlgeZj4xmp1z5xBiYiuR2Y1/diTzy/gH0jVvXzxkSy+e0ltPpEx2YfArj+ft\ngJ1i8D7MkhFvaft2pZS6sBcprV0NHFIdHHHt7kMC2LGmaqx/xiSNdewivtZk78FaSMe0vyufCCO6\nYt6bmeI9UmmtUkqNHw35mBGJ1e49IbPK/juenWPqZKHZbc1I39bLn+jr0fbb5/7kkos+U9BCMukM\nUvUDwn2YH5XA5OzEeHOM4WuOVzBvR21wiBLHMeq/23Zn7MM1ho7ozI4lHsI97TQSa5yFK5fYVCZA\nrnT3G9w3a908sCCysTofjH33wQGanbsvmZ7CZE2mVFrVkMvfxzbEB7cBkAx0MOXrXeB0jLnEX5D+\n79iZjwsqMacSTTNHvu5UJnLpliFx6Y4xc/fbS+xYcSXWq8ufntDs8Ed4C9OCW5A15BOpskcIX2ct\nSAvuytuQYnfsxKUtprZYU2ztMV7yc/Dcb/zE507XBbiXNRmQSc3+/BnmV5WHvXH+cax3Y99fxPyK\n0xD/gkYg9iRu3cP8rPpgHJiaEmlpLZe/u0fxcW9ojm6CbG/8Mt6alrZD7kD2ZoPm9Wd+VKZpZITY\n5tSNxxELIn/btALtgB9axaUFP67CPiGdrMFdBkKKaWbN9w/OfRDraJmA9F089oY/gVjpNRpttY+s\n5m2YO4Vh72NG1mNankEppbL/RoyiJSf0e1S9BM+Q1BKJSWQnvie3C8W+eenG9Zp9Z+d65nfhNPb1\nLvmYYzsv/sL8qivxfheMRivtN598hPnNfBNSoaJ47JPpWBn//jx2zlNDUZ7i+WfwedO5B1+fWlux\nl6My9NJMvg9rasFnrvp6fM7IvnyG+U3oiTGRsAevQce/UkoNeWyQepDQttjPLJ/Ijj0eibmUsRPP\n6uGPZjC/eBKLE34/oNnGVnyf9t1TKzR77hdPabZeUjpgJTZx7rEBmm19E98PDHxtFj1FXX0c72PN\n3s2anXXtAPM7+B0k5xNX4tmvnM8lWO98g7V528e7NXvys3xvvHzj+5r99RPvaPbT3z7H/MpSM9T9\nkMwZQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH7itr2rXiJ82O6MfT6OJ/gRTHMRhSFv+J\nEcwv6wTSNz17I43fqcSF+bn0Qjrg10s3aHa1rquT99dIQconHSpo1WZ9d5Nf1iJ1ytQe6Y7ntvI0\n2NEPo8L9wS9QIdtJ12nI/gJkICWkcnbupTjmd3E7/h27OFazuzpYML/U4zxN2dCUXkN6c/Jl3u3G\nntyrLg+hQnbRKS51qc1H9w3a8cnYkg+hHNJNx9kZ8paaezydMpOkineej64SNCXTMYF38Lm5H2l0\nnfsgFbQmlXf5oNfXQGQ+LXXNzM/IGN9NUplF8fls5mdHunXYBiGFvK2Fd6jwvU/Hiv+VsttIq7Xx\n5Gm6eUQOE/IwZDkFJzKYn20IOrwk7YUsJfaFof/5d1vJe/TWfZXbWINnah8EWUDBBcgKzV2t2TnV\nqXg2iYeQaugW46f+C+dASCPvHuNp2bUktd5/ElKvz2/gXd4sf0enKm/S6cZ3aDDzc+sRoh4kNkH4\n23lH0nTHMLbuXEBM6D85hvkd3Yr3cmkbYkz/x3ia99vDkH5dk4lnVXaPp6ynkpRtN9INJMwHMTnj\nJJeKduyFVP7UuAzNPv8rv+8Rg/BMqpPxd5OP8048zraYfx6DAjS7+CqXZngMwDETklKedZjLXwN0\n8iBDYmqHv1t6jUsl25pIl62OWBdpRyallLLvjPWPyppszvC1odtcPPvEP9GBrEcwl1LcuYrnE+qP\nTiWmZK3xntCJnXPpB8jeouehKxRNx1dKKStvxPGCwxizUQPDmV/u6QzNtiPrSlMZX8Np1zxzT8SH\n+oJq5mflb68eJK1NSDc3MtV1UvgbaeXe/QM0Wy9XCo1FlzBLd7yXpmouq6QdHx1ICnxTJZeBu/VG\nHMw/jbXU2h9jxKkXT6/P2IR10dIX86i5OpH50a6NZnYYF1a+dswv4090znQiEkM7nazJmHSFsbiF\nGEI7dSmlVOgC3jXKkOQdx3iMXMI7i/SxRizPPId9o75j5Z3jkJyEEclKyU0uHQycBjkU7SiXdZ3v\nF3q/jC4u219CavyIVZDrRJFuiUop1oGrugD377cl65hbOInJgZMgo9uz4mvm130a7vm9/JOa7dqX\nr7MWFogVjY2QkVSm8hiQk4L1yOkJvs4YgkA3SMjq8vn4CY/Bc6zNxjrm1oe/lyuk25JbR8Tl40d5\nN8+A0xjHfi6Iw3s/3c/8aFfDKaRr3mdfopzCnEFcYrJuxQ+aPSwSe7GeY3j3mfJ43g31/0ClS0op\nZeaCeGNBZK3mjrougfPw+um/Q6bX1MT3vH278fIChmTGZy9pduoBLs868w3GYHg/PJvMK7xr4/B5\nuJ++vTCfzcx4d8K4r77V7O2X0N2stpbvqZI2QDoU+TS6NW1Z+plmvzv8SXbOieuQszWUYd02131u\nS/xrr2Z3n/+sZm9YyOUrUz/G6xfewrpyY+d15tfnaUjUzcjn1KMf8nvp2Y3vBw1NUi72XLTjsFK8\nHMW1zdh7lqTxeGFjhfU/fO4kza4o5CVC5ozD9wWFV7FexTw/kPll7MFn9bAZ0zS7zzjE8tPvbWbn\nPLkGHa8OrPxIs11deWmE6ashyaorxB7kw99496faXMQlczIXqYxVKaUqCvE+QjzwuWjD898xP3os\npNejSo9kzgiCIAiCIAiCIAiCILQj8uWMIAiCIAiCIAiCIAhCOyJfzgiCIAiCIAiCIAiCILQj9605\nM2w52qK21HPtIq0z4OkG7Wd9GdeLWrijXks50SX7jOE1bEquQ4f4yONjNXvvnyeZn89UaCbPvI/W\naKOX4lorknitki8+36LZr61dqNm09oRSSjkQffXEaOjkvnt+A/Oj7eimP4F2wm49QpnfAFu0tTz8\nJTTP/Wf0Yn5Bg3jdC0NDS6GU19SwY72fgn6Y1jXxn9GF+dUV4zwTa+jt7MO4Dp22m6xJRy2Y2nRe\nF4a2U72wDlph2tq4VVfDJWoydLVJ+0kL07G89sG9I6g/0eVJ6DOTfrnG/PynYCzVFeD9mTnzmkX3\nLkEXm3cQOtHuMbyGQ0k60V3yrm7/M8nnUVOiqYLXKfDsh/mXuBl1KezdeC0B2s7VrxdaNJbe5q2a\n60ib7XspmJdDXudt9Wry8Xq3vkXL7MCJuK/x22+yc0JG4p7ZkVpVzQ28RkNGMnSveW9twGv35bU2\nLH3wHk+v560JKY2lqHvh2gU1e/R1Ls5/BB3xhE+HKENTTFp/24Ty+OPcDTUchpD6ECVXeLv6QSNR\nT8A2BH60hoZSSrnGoMYXbWHuNYLHm+BSzPWis7g+YwssD/8cPMvOmR2LOgCDSS2F86sPM7+aFOj2\nuyxGy8H6al7PoZXUaqHvo/Iuj+UJZ1Crpus4tPx1j+Htmm8e4e0sDUljMXTodD1SSqnETYgx9H24\nDfRnfg2kVlLC36jxEeHH6yhUkfostraoM1BUwuNp72low2nhQlvPopZK7kFel6fzUNQDorUc6nL4\nGk66JCv/mdCI6+tShMzE88jcBm15Uy3XrZdexbOna2mXcbxeXWU8f/aGxmskxnBDaS075t0Pzyvz\nNK9jQKE1IvxInYDmGv6eazLxvJJuYz3xd+G19/LPo9Zb4FTMyxuboO+PfrwPO6eoHPcwYlgArnsv\nrznTVIZ1o+wy4mvAI5HMzyYQdQXq8hEf20itHKWUyj2GOiQupIUw3R8opVRb64OrxeY1Anuu5PUX\n2DG7Llgzrf1Qv8grpifzo3Xu6knNgZjlDzO/cx+gFkW3xagZcyuO1wyszcP6OfpttGZNWIe6CR2f\n4teQ+hv2Fe59ELdDH+Vtv2tzSO2w65hHdG+klFIFpCW4kel//wZbkYD6ZdXJiDWF+bwuD92XPQjc\nO2FNPraJr+NT30Ub3fRN2E/ox2MiqZURNgv3I2crX5OMSEAbMy9Wszes/Zv5vfQ66kBc3o66NT2C\nsX6mFxayc5YtwpixIDWo/vhmL/ObuxRtu0uv4LpzSZ0bpZTqYoX4Qutc5B3jNeCcY1A7yHsSPls1\n6up9VSU/uLb2VRXYk+vbTncjY9CctDLf+e1W5ueTive1ajP2X8bGvMZO+OPYS5iYYP6WxGcwv5OX\nUY8rahGe59DFeG1ar1Qp/tnE3Al/96uFPzG/ea+gTXdtLWL6/O94/afGRqxjxuaIyRW1fM0pIPU6\nXfshBnTpyz9XHnjte81+6MsvlaH5gNRa2b3qT3Zs8DOoAxQcHaDZJQm8hlKnZ/r962uf+eI4+3fE\naKz5zt1Qg+XIB7zOzrRPUTf2n1dWa/aQVfj87UFqwymlVOZO7AFbWhErLt/l8Tr3Q8wJWivPoyff\nU/74HeLD+9tRsyjrNK+zWJOBtX7PFcSNd75/nvkdXXtE3Q/JnBEEQRAEQRAEQRAEQWhH5MsZQRAE\nQRAEQRAEQRCEduS+siYza6SLXVzH04y8fSFnsSDtcvWphhs/3qnZVuaQ+UwnKUxKKWVkjvTrEpJy\nO+u1qcwvi6RLz1yF1MCsnZA42XZ2ZucMDofsxdgMf8e7L081N7XB9dWRdoaPvf0Q84sj6bO0NfCt\nfduZ36h35ml2YxNkTee3XWZ+RZVIg42ctEgZmptncW8iOwWyY7TFXVhvpHmXJ/E0tbTDkBNEL0Gb\ns5Z6nr7t3B3pjJZE4mRiyVOdc/bg9dyicE5jCSQDevkFbRFoStLJTe3MmV/nuZB9FMdBEhKok2rd\n/A2p4jTVNeyhbszPuSeuz/xPXENzJZfiZBb9e3tEQ9BtOtKbrby4XMncDvPPxg9t4iyceRvrIx8c\n0OyAGsgUW5q5HMZvCuQONCW9rY3PbdrKmKYA2vjiGnroWoaW3kAqdt5RpLAm3chgflFjIZFwJDKk\n7H28BfOt60TCFhag2e5D+TivJ61P048grbH3K+OZX0sDvxeGxrk30o/zj/K29nZEotRG0jCpdEkp\npa5vRPyIIXKeDkYdmJ+pKc4zMkVq7bUfePp/t8cg/XOIREtTB9I6d6aXDTuHpv4aGSH1N6Avv+81\naUjxbKhDCnhLI7/P1ffgZ0vaBjv24OuEo8K/aeylLUeVUqr3bC4dNSRmLkh9LSZt7JVSqtNspNNX\nkuvrYMyfjT25t+EkzVs/t0tJO1/rYNwXUyfe1rPmHuQOqYeQOu3iBYkKjWNKKXXnb8QyE2Osi+6B\nXKpK5Q6t5LlROYhSStUXYY75TkYMaWngkujiM7hnfi6Q8uWf4m1VXXXXa2gSN177z2MRC9E615S0\nnc48yOOPpTliYFUS0qObqvjaUE9asga4Y47pW912moe1i44ZN0c8+8oULk0IHQoZA5UyeRB5jP59\nJO3FPspL187b0hVzvY7IPqnMUSmlWoh0iz5jKg1SSqlqMjZ9DKzgtrJCvPEcU8mOOfjhWO45yH1t\nvHR7m124F3SPat+ZP+tOD2NNKrmGPWpxFZcBpm9DOv2+q1c1+6kV2EcWxfH22x2fQLzK2k+ux4W3\nfTWxxDPw7Imxkrb3NPMLmos9jJUj1s/qfC6RpS3fHSMRW+118hcrLz7XDU0rKZsQ7MFjPl3XTMhe\nj16vUkq5HkfsLL2K57NwOd+/2xM5dS3Z54f5cBlD7nGsz1S+OGwyZIUWbnyP1cEYv3fTZzV74Tjm\nt/VrtH+e8BDaR3fO92Z+dD/tEI64cfV7LqWIO4sxE90HUlsrH76emDnydcOQfDj/K81eves3duyt\nx2Zqti+Rcq76iLexphIvKgfKvcmlbn49cD/fe3iBZj/3w2Lmt+SXTzX718VvaHZeOfYbMxeNZef4\ndpmg2WVlkHM/s2Yu83PzHabZd3bg/erHRGA/XGvBMYwpT0dH5uc2CJ9Hf38HnyUX/8BbOvvp2rIb\nGns3jJ/Nt53FAAAgAElEQVTe03gcWLdio2an5mNv8taKBczv0FuQAPVdAInTkNdGM7+yeJRU2L5q\nh2bPWcslQCffhlSMliP54klIvJ56h9eSqMrCuhPQHXJxl1Q+J6gM19IbcY5KYZVS6pHxQzW7PAfr\n7N+/HmV+Q3sj9r761nzNrkzmMm0fZ76v1yOZM4IgCIIgCIIgCIIgCO2IfDkjCIIgCIIgCIIgCILQ\njtxX1nTuo/2a7d2FpxifOIIqxMN9kSZUruskM2kiJDC0C4CZHU+vswtEqpJdEGyadqkUr7ps7oD0\nciplKrvBu8/0eghp+/WkS4a1LuXvxpdIYev2HLoYtbVwOYeLLVKfQmejerVPJe9A0tyMdPD+o5GC\nSjvHKKWUuQvvDmRouvZHinn8BV6pOnI4pD5l13D9bS28w0Ig6c7SWI4U7Qpd+msb6VBCOznd/DWO\n+UU/i/tbTFJQG0vx2s11XDLlNQHp27R7RdYu3pXCqQdS5c8fQOp6ZCqXXNBq6a52GAvGFlyCRTt5\n2Hsi1c0p2pP5+RRVqAdF+U2MaSphUEopU3uk+uaehTTAzoOP785ReP85CejC1Hf5MOaXTqRb5kQu\nkn04nvnR7k+RL6BbWsFVyOjKb/JuBr4T0a2pJgdp6AMHBjC/phrM+6pMzCMznZzD0RoppFYkDbHk\nEk/frstG+jKNITmnbjG/Zl28MTT2IUjpLT7HU9sPfA7paDO5xtHP8OfTYwEkFyZWGKvJP11lfj6T\nEIvp3+0yk6fFNhDJhWM4UuBzj0IyRmU4SimVuQvP2K0XUkbzLnOZD00fDmyDLKBFN7cvb8V8HvgM\n0rz16fS1ZMz4k242zTp5pb77jiFhHeky+Vy88SskZ27eJH0+g3dX8h6HWNZA4qmpbl2sJam5DhFI\na6/L4lIK63CM/QB3aEeo9NDMgb9215lYk2hXKJo+r5RS+cfQrcjMEWtV4r67zC+QSNpKLmD+eY3j\n3SaqSiF7CRiDeJD/D5+LDlUPdi4GTyMy1w5cdpb+J67Fjuwt9B0EHaIhraCdPfTSHssGjOOmcqz/\nZrpORkWXEBOcovDaXuOw/jbXcSlUdRrkc1QW1VjB9xmmpHtk13noFkRjrVJKld7E2mDlg3FFx4hS\nStlFICaUXcE5XuN4J86mar4nNCQlGVirarO5rClrB/avQXMQe9L+vML8Ip+BTMXYDFviuM9PMb+I\nudGa7RiBZzN2Tizzc47CviD1TazbJ36FNGPEczymX/8c8vLASZAVVGXx9bOCSOdq0jBG3QbxLm82\nzpDoZB66qNk+w3hnrvht+LuZBfhbHbsFMD/3GP5MDQ1du/PS+HsuIHsaC0/EXlMb3kGKdrWpy8X8\ns9Tt87e/gVILwx4ZoNmjVo5hfvmnMjTbLwRxiv7dYt0+g0o9zewhSTux5Rzzmzx/uGZf/BtrX/dB\nvPMolXTt+mCPZo9/iV9rwWnSUTQJY85dV2airYn/25CM6YHYY2rK7/mLrz6i2TYB+ByY8vt15nct\nHbIf3wn43FKXz9e7M++s0ew3tm6CX9095mdhgc+tUd0wPoYRyezZjVwilnIQnyfisxGPh4/hUmnX\nmaQbI5GQmlpxWdP6ha9q9uAZkPnbNfNOfYWnce2dvHB9FdkZzM8jdJB6kMyPnaPZb77+ODtWVYe9\nyje73tLsb5/7hfnNfRXlSGz9sH6+MPFt5rfsGUgOh8zGZ8Jrn+5kfl1JjKbzMqYS6+LaFVxKN3c2\nOjl5k65+xmP582ltxV7x66fRkcvdnsuaqAwt70dIYyfMimV+QSMgf3rnYUjSEnN4rHjjhXnqfkjm\njCAIgiAIgiAIgiAIQjsiX84IgiAIgiAIgiAIgiC0I/LljCAIgiAIgiAIgiAIQjty35oz/r3Q2ivt\nAm/7OvIhaDXjj6H+QO8n+zG/fNI6jLbLLjzD22Y6En314W+PaXZkcADzCyDtkE9/Cr/wYdAn6vW3\nVaQuSkMe2n2euH2H+dFaMvbbbmu2Q1d35mdiitvW1AS998W1XKOcVwaN9sOfotVY+o6LzK8qntdt\nMTR3SZ2Z3nN6s2PF56GpDHoEumxah0IpXrvg5LoTmt01lmtkrx3DfYtqxrNy8+Vtw2gdn7yL0FqG\nTI3Q7LJbvHaQEWmD3nEINND1BTXMzykCzyviDsYw1SsrpVSPbqi9Ye6MWgqm1lzLfPpb6LKHvgSt\n8NFPDjO/AU8OVA8Kc9Ker06nrfcg9VroPbJ05drKO3+g/g6th5G8nmvwbUjLXqqhLo3LZX5Ue52x\nB7U2nHtAL2vtw1uB0lpTtfnQhd/ewGsSFVag1kb0GLSma9Vppm0s8HppZ9Ga26cLb0lpEwq9qK0J\n3vvl/VzzHBbE288amoTvLml2cwtvJz30MWiJrb2hd9W3Io77Afr1fFLTZeJK3hY8YzPmYnYR6gPZ\nWvIaV/Tf5wvRZptqbN11NYHSC1EXwH5vwr/+v1JKjVyM2gotjXgfmVt5vZIBT2I9KSQtlePiErjf\nONR9uL4WNRxSC3iscLLBXA/uOUcZkmZSo8PIhP++0W0e6pvRGKevExL3HZ4hbYVcm8fndkk61oa8\nZLzHThO6ML8bOzCOHaxQ+6Sc1NXycOBzsboeNUlcAjAn6ot5PFWkLkoNqZ3jEcDrEOWS9sDhc1F/\noEhXl8FncBCOEZ29X7Q/87N05/HL0BRfxHXZBPG2plZ+qJlQX4R7GDCK196ojMe8svHHa7ToaiBl\n7sc4jngUY1hfz66IrMcdSB2cVlI7oly3LtL2uOUJ0MLnnOd7rLA53TW7IhHX3aSrTeMcg9jZUIJ9\ngL5mUfFp1JeiLcHzDqcyvyZSh7Aj3x7+z9A6M7cP8v1cK6nb1fob5kfIPF5zK20j2mzT+kL9Vz7M\n/KpLSD03V4wDG2c+btN2Yx/4yNpV5HoQN2prU9g52SWY543bUUtm8Bu8Ra2pLd6jc3fUtqHPUyml\n7nyL+mV+01DDxsSE1wIJeRzz1C0N12BLakAqpVQNia8uvFSGQWgkdZhinuf7qCtfoxYk3UdePHOS\n+fmQuphpmaiBNGwWr7MzZhH2cIVnEH/Kr/N5dTspQ7MdryAW9ZiL2iN2oXxf28EU68GBNXgGo3R1\n465swn7JhdQ7tNfV+2olNRxpC+qUzTeZn1sv1BgKj8Z9qM7ktc7Kb/H12ZD0W4l1lo51pZS69Df2\nmJ064vNZx3ndmV8fb7TWjlv9s2YHPdqV+QUOxzNsbkYMWDZhIfP79ugBzb55C3Ep/zSuZ/67fJ63\nNuOe9/VE6+e2Nh7Tl43DeU8vmabZpZfzmN+ctS9qdsEt7MG/fOt35rfy52c1O3AG7ou+Rtbd3/Ea\n4z7+WBmaZbOmaLa5sxU7Nm8MxvFfK7dp9pQZQ5ifkSk+h+SdQc26YA8P5ufaG/vtzD+xXz2flMT8\nKr7EGuzmgdhkbIT5tvyHZ9g5tPbPkgmv41p788/AvRZi77nkp6Warf+c3lCMtdB/Oj73rlu6gfm9\nEov1fcbEWM2m9aOUUqrLTN76W49kzgiCIAiCIAiCIAiCILQj8uWMIAiCIAiCIAiCIAhCO9KhrU3X\nG1IQBEEQBEEQBEEQBEH4/w3JnBEEQRAEQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCEdkS+nBEEQRAE\nQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCE\ndkS+nBEEQRAEQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH5MsZQRAE\nQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAE\nQWhH5MsZQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCEdkS+nBEE\nQRAEQRAEQRAEQWhHTO53MOnsr5pdnV7GjmVeydTskKEdNfv2wTvML2pKlGY3VzdqdkNxLfOz8LDV\n7It/x2l2qKcn/7vFxZpdXlOj2VZmZprdd0Yvdk75zQLNNjIzhm1uzPyuXIjX7H7jojX73N4rzC9m\nUKT6Nxwi3P71/5VSqrWpRbONzfltr8mq0Oyukxf952v833Jn3/eabe5sxY7tXXdYsx/+aIZmV6QU\nM7/0vQma7RTkrNmm9hbMzzbIUbMv/3ZRsyNGdWF+rQ24H/X51Zrt0scHr+XnzM7JOoBrKEso0uyc\n0lLmN/K1sZq947Udmj32xVH8Wr0xtjL+wZizC3NlfhW3CzXb1AHv1yXam/kZmeK7Ti+/ycqQpF37\nQ7MdA0LYsbrqPM1uKMW8qs2vYn7H/zir2X1iu+H1Ivm4baxs0GyXyED8ndIS5mdkgveb+PNVze5G\n7nPatgvsnLbmVs0uy0JMiVoygPldW3tGs30H4hoaSuuY375deE8dvbw0u+dcHgOMzDDn6sh9qUri\n78lzeLBm+3WeoQxNbe09zY7ftp0d27/7nGaHeWNs9VjQh/mZWJlqtrNXjGbf/XMr89uy7ahmTx87\nSLO9RvLxc+U7/N3h7yD+FCZh/t7dfJ2d02kaYuCh745p9tA5/Dl6xeD6yrMxf79dvpH5+btizo14\nZqhmb/1sD/N78dcvNbvo3mnNTv6NX5+5NdaD/iveUIbk+hZcQ/zpJHasQ4cOml1cWanZg6fxZ5h8\nHOf1ewXv18o6iPk1NSG2HXkbz3fwypHMz8TETrOrcnI0u64QsdWzZ3d2jrGxpWb/tewLzZ74Ph/3\nyesxh4uLsVa1trYyP98IjFnnGNh0ziullGNgJ82++8N+zQ6c3ZX5Zf2D9bj3s68qQ3Pstdc0u6SK\nx0qfQHfNdo8N0OzyO0XMz6UnYs7RNUc0u0uPYOYXMAXzJfWPa5pt5WfP/OxCnDS7nuyRWuubNds2\nyImd08EYY87IBHuaI6sPMr/OXRFH7yXmanZzSwvzG7gU47GpGmtBzb0K5kdjceLFFM22tuB7Aq8w\nrLMxT7ykDMl3CxZo9pD5A9mxDka4L1e3YA/nH8z3lDQe5uxP1myHrnxdNHPAfKFriGM498s/maHZ\njcW4R37TwjS7SrefPrcFsbajH+aO2yB/5leZiH3Z7Su451268/Hm1t9Ps2/+ir2Ntbk58+u0APvc\ngrPY05s56PZ1gdjXBUTOVIbmyq9rNNshwp0du/0H9hY2ZGwFPcz34Y0V9ZpddBLvpaqKf9botriv\nZhdfQay0cLNhfgk7b2l22HTEJnNHjIOKJL5PrsvBuGgowt/1HMmfD13Dm2rwuYiOWaWUaq5t0mxj\nC+xh6DqjlFLxW29odnU97oO5qSnzC5+Ie9Z5yAJlSM6897Zml5XzeOrXD7HnyHbsN1xsbZnfnaws\nzfZyQpwbNW8w88s/kaHZ4c9hba3O5jGKfrZKO4X5cuwWnu2r6xezc659jeuLeWmIZp/58BDzK6vG\n2jryZex5j39+lPl1jcW837MNe5Zh/fl67NIbn31Wv7pesx8dzN873VdM++ILZWiqqhI1+5O5fN19\nes1czc4/naHZnaaOY35NTeWa3daG9aX4biLzu/jHJc3uGI6YFTyzH/MrT8e+Of9gKvzm4/sFSxsf\ndk6HDtgDFiVhfvhE8r3T6bc/1+xS8p1CxJgI5mfljT1W2haMH338b6n79znrNzqK+dUWYy/xb581\nJHNGEARBEARBEARBEAShHblv5kwl+VW5hXy7q5RS/tH4Rt/UBt/G1zVyv5Lz+GY6OQu/1uSXlzO/\nIf3xrVLMWNjn91zlfgvwC3DqrruaHTwxHH/zcg47p5ZkEzh1wbfyjSX8G/XWtjbNtnDHt+jRvcOY\nX0UK7kt8drZmD/Xhvxrf3o1v1woq8A3uyAWxzK/0CjIflGETLpRS/L3UF9awY5NeRpbJnjd3aXav\ncfxb3VryXH198WufW0wo8zvw5jbNHvrKCM2uK+J/l9JIfoFrrsG3jq3NzczPY2CAZps54heUTp17\nML/KVDyfmgb88ld4OpP5/XNxn2aPW4RrzdydwPzs/B002zkKv7rFrTvD/BysrTXb613DPsgrG/DL\nWkE5/0V09udPafbh95GRED2ef1M7/DF8A19+A9lk9UW6edCMb7otLPAr3q2Nx5hfAZnDno74Za2S\nzAm3AfyXv9wD+GXS3hPjKOdICvMLGIlxZWqHZ60fv8+vf139G0fe/IX9OzQW2X0pJ3ENtrpfeR0K\n8GuI6vyvL/0/8ecL72n22LcnsGOPRuNX+HPf4hcWG0+eQVaRlq/Z6UnIPGhrbWN+9mQ83rmZptnu\ngwOYX0YRvsHfuuwTzZ6yeqFmx7zkwM5J3Yhf/wdOQnYM/aVPKaW+WPCOZo+f2F+zh0byXz1DZ+GX\nyfwjuFb9r/pxn36n2c59MTY9BvNx5td3iHpQBI9FrLDy4ZkP1WnIdIlwQpZiTQZf7/ouw/Xln0rX\nbHPnAuaXdRS/EjmS55nx1y3m13EO1sW4n5Hp0nFwR/Vf/LToQ80eTX6ZbKioZH42nTD+rIMwDui8\nVEopS3dcn70PfimN/57/4ljXA3M49LGemn2ArD9K8Wff+z/ew/8C/QXS05lno9BsioZyrE/1OfwX\n4WpXPNdQX8zfykz+vNNI5pldJxfYwfzv5h7G8y5Ix7yMeQHPt76Ux0AaE2mWcERf/uztOuLvVmSR\n657VjflVkTFcdAq/WPrP4nPWwh1ra0+SMdvSwNft4nPZ6kHRmWQYFp/NYseaSCzqOQeZlJXJPFuy\nkuzn/CYj6Lc28YyvI2uQZdy1N+5tG98eKueeuCaaIXH68+Oa7ePCY/qAR/FLsR3JjKLZLEop5TsB\n10ezeIuu5TK/vGSMg3sk29zeimdOm29GHHGLRQytSOAZIUnH8It3wBeGz5yhY5PGUKWU6jQBWdf0\n3pQn8iy2m7tuaraHA+JUiy7D7+THyHBzssHe2NaVZ3E0NGH8FF/AGL5H1t8BS/k6c+Eo7hPNeL30\n0znmR1+7hmS6jH1pNPOzcsc10febdSyV+ZmZ4KNc5/7YO7kPCGB+ecfT1IPiXh4yzKMf6smOJey6\nrdl9u2HCpKTzz2rDyL6gI4lLlrqspsR9+OyXT+aIuZMl8yu4iJjQh2Soum3C/EvfdJOd888lZHOc\nexLP87lvn2B+beTz4kcL1mn2yt+eZ36ZO6Emeezj2ZrdUMGzwOvysPdcsQZ7+iPf8H33hLcmqgfJ\nOw+/qNnv7tjAju1e/r5m073FvpVfM7/EXMQjmtE+8u1HmN+g57BnOPUV4uNfj55kfhN6YjzZhmCt\noXPCvDvPYDn57u+a7eaBuFFx93fmZ0ayy6Jm4LMkXUuVUip02HTNtnwO47HgHI/R1iQb1jcaYy4/\nkX9eNHfgY1WPZM4IgiAIgiAIgiAIgiC0I/LljCAIgiAIgiAIgiAIQjsiX84IgiAIgiAIgiAIgiC0\nIx3aqHBOx8ZF6NzhoNOqBg2FrnHvxhOaPXkx74hjbEmqkpMuMBXxXC/a2gh9eUstNMv2kbxzDq2G\nXp0NzXhyHuq2xM7ltV/oW2ypw2vnEq2/Ukq590K1Z9rdhdbnUEqpjGxoTiOHotZNSz3XWlsQDX7J\nBWgr9d2azt9AV4plmzYpQ1NUBK30Pyv/YseGPz9Msy1ccL3nPuY6R5/OqLXiOw7dNozNuG5u/XOo\nMm5qjM4R+urysaOgITy6HxrPCU/geqw8uAY4Ywu0mx2fxvk3v+J63oinUaHg0penNLvXkkHML+84\nnn9RAp5x12d4ZxVTK7zHmjx0WSi5msf87MOgmw7tM1cZkupq1GRJP3aYHdu+Af9+6gv83YytvHNa\nIOlu0FgJnbOtB+86VXAN47GDMb6/LT7DNf2OPTw0+95JaJljXoZuet3TXItqa4l7OeMN1OVJ3MBr\nS/mMQM0Hr2g8z4YGXU2OA6jCbkM6ShSSWglKKRWxCNXkq4rw3O+uj2N+9UQLPumzz5Shqa9H7NCH\n3uZm1IHIPAnNbcjwKcyvshL3qrkeMbVEV3fg3tkMzT5xG5rvJ197mPm5dIZ2+PT7qBnl291Xs0Mn\ncS38+kXQHnfxQdwMeyKG+Tl54t9NTZg7mce5/taV1GkwJp33zMy4jriAdFn4ay06OT3y2lTmZ+2J\nyvru7uOVIUm7js5pKVt47Rd7H9Q6sPLFNezZdIL5zVyFZ0prfFzee4359ZuJsU873N3af5v50S4s\n4bNQL4zWjggax9fF5mbUKmluRE00vYaa1vcqPIl5ZeHG9wRJd3HMhlyPuzevr+E6AF0ZzOwRDxy9\nw5nfnZ/+1uy+S1cpQ7PzRWjraacppZS6dQl1qUa8gu4OxXG8RkLaBcQS2iVl8PNDmd/5b7AO+fmj\n7l1LNa/RZGKHsU+fd2U5nlUnXY0YWquAPit9fS7nbljDy5Ow/7J05/Uccnahk1jok1hnM7bxMRc4\nA3WiKtMxzq79wWNqYFfEkZ6PG7ZbU3YqujHmHuZ1yxpL8TwKCjHHHG34+3Ui3QrLbqFuRshcXrOt\n8DzGd8Y12PoOSLYO2EeFPo74d+it3Zodu2w4OyeBrENupNOZkSnvKOpAOoMUXUIdlDpdLaQzcXhW\ngwfiffhP5V0zk7+7rNkuA/CcnCI9mF99McaSfxe+fhiCG1u/0uyMCxnsWMRs1IGozcWe//w/vItq\n34noPFWTjrU0LYnXPKL18WxIfQjv0bx+YlsL1ufGKowlus9vKOF1Qyw9MLZoh9YLG84zv47dAvB6\n5DOJa19f5nfke9ThGL4Q9W1Kr/G9J+2a6t4X8TV1A+9i6NgTMSBi3NPKkOTeQ7y+8iVf32lsPErW\n8DfX8/ospqTLYvU9PMNT608zv4ho7A89h6DD4a4PeHdH2t1r+3k8g5WPo26SK+lsppRSc6Zhrdl2\nAp18aDcwpZRK+RO1aqxd8Ny9x/BxtPFNdFkcPRrr+T+7+T2idPVH/afBq/jeq74M8d6vk+E7iiad\nQ5dmWjNLKaU8OqJu4JVPUNfRzJnXn4tcMEuzE3bs1Gx9nLqTjL2GJ6kTFfYQX+OStyOeVdVhzo39\n4DnNLi/kezFnT9QZi/v4J83u9AzvBHWbfEYMJh0jW3VdJs9/jzHYJRa1v5rr+Of+Q3tR82/JL5/i\n/Pe/Y36lpNvXjLVrlR7JnBEEQRAEQRAEQRAEQWhH5MsZQRAEQRAEQRAEQRCEduT+rbRr0WK3Sz/e\nlrHmHtKgO3oiVS7/EG/VZtcFUo8G0rKXtgFUSilTO6SGlsUhZe/WAZ5Km5wPWcDoIUgZTb8OuUPQ\ngSR2zj+XkbpJW3LtvcLTIv0ykWIV6YdUN1MTfpuCOyKN38IVKaxlN/KZ3+2zaMPm7wp5loUnf+8D\n+nVVD5L07UiVn/TBLHasJh9pvLmkbWu/5cOYX+5hpHnfXoe0LSo/UYqPhaA+SDe08eeteH9bjXTk\noRERmp2yH22srXVtjmnKWf7pDM0+efcu8zNbj+cVPBgphrvf2c38hj+D1POACZACJPzAUyibatFG\n3KUXUo6biUxPKaV2f42WsUsNLGtqacHcoVIjpZSaMgfPyto+WLP3nuLtpCeY4jwb0vr01i+XmV9C\nDlL3m0g720U/vMb8sk6f1WyatlqeijTiyY/w9P78S5BG0Va8trqWxDR1eN2TaMdMx5dSSg19E+O5\n8DbkWInpXIJ1YREkSk98s0yzT979jfmt2LRaPUgK0jG2cnYnsmO+U5AqSdPKs2/yVsTmjpCCVKVD\nKtTBhKfAD3sH77NPDWJiY30Z8/vqSaRezn/noX997cSte9k5we6QZlSSNNN/3uXtkMe8gHHRUIwx\nHH+Mt6u3D4X0xTUQafj3Lh1lfi2kPe7iHyBLWffU58yvW0CAZk/81LCyJio36Tibp9/m7MF99uiL\n2DjFlksfOhhB5mlL2sMOfZbPl6ILGMchM5BSrJfaNtYgRp1fj3np74Z1J+FX3Tgisttrx7DOjlzO\n06hLb2FdMzYjUlUTHod8nfEMHbtCfnF+P5dqjZ0AWWzqRsgSXWK41NnIjI9nQ+NiD9mZYzcu47C5\nSeIHkeQW6KSDDqS9ecR0jNvGci53CBuM95x0GmspbamrlFKOtUiPt7cjewuSAk1bpSulVORkjMFq\n0rLd3JlLjk98DPlrcBjkE2mH+H4p8jHsqy59AllFyOjOzK/gPCRd1n5Y3yMm8JbbZrq9niHpYIxn\nc/sqlzVR+Urv5yFpLrnOJSHOUVhTUs9j/5p3jO9lfcZgD9xYhrjWVMrlDlYBWMuK4iB/GrQEspS6\nomp2jgsZf05dYX+/bCPzmzINLe+bKrD/aNWl1hsbYW7S61atXEpbUgmZQdkBxGQvsr/X49/lPw/9\nX1NEZDoxSwayY03ViG3lVxCLRi3jJRSqM7BeJSX8t+zMygtyeSsfxIAaIpnSU03WwqskVtrpyj2E\nj8bNoX+n/9P8PdGyCS0NiAHXNlxiflSC10zWvqQbGcwvcgjaUzdVY1xU1tQyv7LjiD0R45RBobK/\n6Oe5hHb765C2zB6Ie/Hjy7ytMZV6d/FFjOoxnMcU5+6Ys0WXsF/tPYj70fhA72VSPK616xOz2Tk/\n//y6ZlemlGh2VSpv8W7njXneVE7mIpGzKaXU7BWQ71OpzLAMfq10bU28h/eUuYuXJ6jJxNz0e9fw\nsiZagoK2kFdKKVtvjB+XgXg+Qf0nM78OHRB/aJwqzOf3cPzrZG9G9kQX15xgfsGDsJf685eDmj2g\nDJ/9KhL4/qGDET7X9Hp1sWZXVnL5U0E51sx+QbGaXV/P33v0Q1jTr/wFGeqgpfyz8iwyNgtSIF2z\nD3ZifgFdw9T9kMwZQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH7itrCiUSAvsw3jXp1iZI\ngsImIT0r/yjvgHRsN9L0hk5EperkszwFtZWks4UPQ/ps2SnecWBoJCQwtJr6hIF47eoyfs7sMUgn\nvWnrU40AACAASURBVHkH0p0gD57KXFT572mNNO1JKaVy0/Eeh5Lq7Ddu8PcUQl7fzAGplXo5jF6m\nYmhsQ5FOdemTA+xY98WoXE273dTr0m4tSEeH+htIuaMdnpRSytkWqZzltyGZsgvhKV1UCpFagBT9\nukaksNroZE2eeXg+VN7g7cRf238aun7s/hzv96EPpjO/w+/v1+zhryJF1nNkEPOjHRxcYyBp01fz\nnj56knpQ7Fv1g2bfzuKSHXqfJlqjujrtlqUUH2e1pNNZ4HBeXT7nL6Qehvvi/d75/h/m5zEC98nT\nHZKGslt4nk7duQwpqn+AZltY4LX/2r2O+a3a9IZmT7FEmDq58Szzu/MtUhzp9Zib8irzQ0YjVd/I\nCPdr6c+8e0hjY4l6kFQkIvUy8plp7FhNDWLTzhXbNXv0CyOZX3UW0lptAzBn7dw7Mb/mZjzjzUtR\nrX7Ccp7P/NRaSPAqSBrvmR2I3TM+eYxfawYkEl5jkXK6f/l65lf2PtKZ+4TycUZxCUD8bmvDvCo4\nlsH8Qh9H5476KtzLFzd8xPwufsglfYbEygup8KnreZcxdxI7Er7BWNV3M6gmXSmybyF9dvg7zzA/\nKi2rq0Ac8pvOOxtl/Pnv3QwCZmNtbijjUpvyu7h/ViT1P4/IW5VSqrUJz6OqCmnyPl15Jy3a7SRj\nM1KHx6/isrK4dUj1rSJySF9PPn7NnblkwNB0fAbdHAov8u5ufp28NJveN9phQSmlLEicMT8FWbRL\nf951ZfcWdF+LIpI7b2++r3IfimM0jT7UA+tsxi0e/y2JtNrUBuOKdj5RSqnwwdhXndmLtGwnXfei\nEiIxCRmL1OtK0vlLKaW8R2HeV2Vij6TvWpm8FWMhuKcyKKU3IXNp1XW/s7XHfWkmsj+nSHfml7ge\ne9nez0FyYWLJ79+NLzGfuz7bV7P3vc07xDiQfaSbI+Relp7YG2Xv4VIyhy4YB6fWoFNmci6X0WVe\nxTgNGoj7bxPK90APPQx5zY630EVHLwt2JtI+Sz/YPrrORXknuMTL0JibYR4VXtDtb2gXxnKsfZlf\nHWd+Q1dgD9fTGOtE3skM5mfXCXuVZiKZKj7H/25GGuZB34UYF31dEJeSD3B5rgnZf5k7wY/uNZXi\nkjvvgQGaHdDDn/llkq5gVUlYm4e8PIL5UUlWawvitaUZH8OuvXhXOkNSROSCibrPdzM/naPZzQ2I\np9dfzWB+c9ZgL9JIYm1VOpfDHPwY+74RS7E/MjLmXWGp9DZoIqRWFhaIzylH/mbnBA8bq9kFKegE\na0vGoVJKffT0t5r90udPaLb+c4ExiSO7P0RpheiOwczvejLmWC35HMRkiUqp0htclmlomuvw2Sr+\nOp/3AVMh3bUPwTwqyj7B/Fx9YjX7/AXsTeZ9+Szza6jHnub3ZZs1e/F63im1IAXrJy23Ym0XiL9z\niMeDrk6Q9WYXY7yUXOExlZZuSD2JsVCdyuX/3RZgD+wYhnl0h3R7Uop363INRxze9O4O5je8GjHq\n39ZFyZwRBEEQBEEQBEEQBEFoR+TLGUEQBEEQBEEQBEEQhHZEvpwRBEEQBEEQBEEQBEFoR+5bc8bO\nAxrU7Z9yXa2dJfRcnUl7PrdBfsxvKGmlXZ8HDWHERN5GLG47tPt/b4TmVq8jnjAZusHDey9qdqAb\n9O/+nbiukmpMfQtIvQZdTRMzF7wnpx7Q5tqc5nr0mEdRH6H4IuoF0LaaSikVOhf6vPzjqFNDW4sp\npVRaCmq49FGGx6ET9My2J/l7KbqM679AWp72GR3F/YgO/WwCdLbl3/FWfRU1qPczePp/v5tZY2M1\nm7aeMzJFnRRaV0YpnT44EVrFCD8+5i6uh060b/8IcoSPpRpS78DYAlphSzeuwTfphWPZ+9D+2H1Q\nAPOjdR/8Xjdsizs6tnqF8Pbl1zMyNDvrGOpF2FryVqpJd1ATYeAitOS0dud6dce9eB8+pL3z8XVc\n00lbXHqOhn62Oh31BxK23GDn0PpNtPXu9FG81WRrK569Q2fM7ZFLhjM/B1/U+Phu4RrNfvyrx5nf\nlpc3aXbgeIzL22sPM7+gOWjXTi7PYLQQPW9TE9dR12Tj3oxYhPZ8e9bwOlFeRK8+iNTzaGzMZ36W\nltDjDnkYtaVu/szbdeaU4joGz4afD7kBRTeT2TmFFYijt79DvB7Xowfzi5yJf5/4Cdpcow5cGx63\n+mfNbmpGzYoIUttBKaXsHNA2+O6WbZptNYXHACtXHosNiZUt9Op2XXg83b52n2YPCMPcCZnFW4se\nffsvzR72JtqX3zt3gvn5D8F5m1/8QrPtdS1cByyJ1ey6n7G+WJCaF/qaRN5dMY4iZmEr0KED3xYU\npGPeW8YjNupbJN/bgbaWielYV9xLeQ2vfq/SGkqIyZUZvKbJ9q9Qp2HFgPnK0FTdg6a8/BpvTW4X\niTUzdRvi4ZBVvH1vyi9YM3PycP1OTV7Mb9KjaJF+fhdqnOSWcV17BzP8XmbXEfPPlrThtPK1Y+fY\neKKGSvEt7DMqk3l8Ua2oheDvivfn6MRfzyEMezba8jd1P6+vYR5H9kukHXWBrsZHp0e6qweFJan5\nR+t4KKWUxzDEv5YGxJTcg7wexq17mMP+paixs3n1ZuY3JAp71ptfn8f/LxrC/JK33NRsu3A8w2pS\nl8c2iF+rEamNYU/W+rdWL2R+5zajjXr8cTyP6NkxzC9zC9rvDhqPggY16bxFtucorNu0VTOt56iU\nUnW5vNaSofGeTFrNb+Z7htR9mC9T3kbL3qJLvEZMbT7agleRse83nse9kz+f1mwfUq/Q1ITHvSEr\nMdeL4hDPKm6jVlfnSRHsHLsgPG/axnkQ2W8ppVTJFeyna3Nx3Y1FfD/tFYi9T2MJarXoW7EbmWL8\n3PoN9yugfyDzqy/k9TgNyVVSi3PsbP5+8y9gzmWeQh2TLj4+zO/8atQG6f0y5lVrA29PTfcsZrao\nl2Ztx+u4ZJ1Fnah6F9zb4J6YEx2MeI7CrV/+1Ozwuagj2dDAa728tnGJZu97E7Vkhizm8eDAp9i/\n0b1bm+6z7eQ38LeKyXij7byVUurYFny+6TL2aWVo6OfFwc/y92JqivliYoIaWjuX/8z8Rq3A2B+3\nCPWRPl/AawNOewT73NGPYMx8Pm8J83v8K9QieuOPVzS7sgj7Uv9ufA+48rmvNPuVJ7HHaq7mnytH\nvYUajFUZWI9dp/PX27Hsbc329cA9amjir1edgThflYI9L11zlVKqOEu3PuuQzBlBEARBEARBEARB\nEIR2RL6cEQRBEARBEARBEARBaEfuK2uy8ETKaKiu7TRt0Ra3FW0ZrUlLTqV4m+Qx82I1+8TvvCXu\nrsuXNXvphAn/er5SStVmIQWQtqSk6cEufXiqHG2jSNPhhi2MZX605aNrf6Q0OfXiKcoOwXj9pmqk\nkHvr0qVMrXEvnHviNXTZbMq1gbfdNDR1hUiB9CctFpVSast7aB02+bnRml12jUskfEehPdhkD6Sz\n1RTw9MoBpOUgTe9K3X6H+dk4I3WX3us68nrHtp9n50wah5Zyf6+FpGGsTkphR1L+k25kaLZLLz4u\nrImsjbZ1Lsnj11p2A/fCrjNS02qyeev15gbeQtSQhM/Fe8w5wNOyF76ClPnS20i9DPOMZn6NFZBx\n5e4j6YAz+Jigbag3k1bI01/gLZipRKfsKv7uh+u3aPaby3kL5h4DINOgErG2Vj4pCq/iWAVpye4/\nnV/rmfe3ajaNSS9NeZv5LXthpmZf+WSXZgc/xOWVqRuRUu33nmGlaUopZWyBkNvczFOMaVv6s2sg\nJcnTSR9iiVywMhNjk8YvpZQKnYH3ln4cY2bAKv6+/noJbdobSpD6W0Rawrrc4nG42wxIFT54FW26\nV65+kvlR2Wd6IZ7jyj++YH55t9FemV7D6dVHmF9lLZ5dzFhIL0tS7jK/6zcwvrkw6n/n52chnxv1\n6CB2LMwbktr4bMhVGz7bx/zqSSps3CeQDIfqxuP5DyDHm/LhVM0+9QGX49GW27Q1pI0NpFVxq39k\n5zh0hxzGd3BvcoT/ZnPgU6Sa9xoB2d+ZjeeYX0QU5JZ07BSSFtNKKXU+FeeZESlBdglP357x0gT1\nIInfcl2zuz3Zmx3L+huSkTrS1rSuiM9Zv6mQwVgRiXAdkSoopdSJg5AaDJuEv5VxIYP5NZYiRtsG\nIYW8PAFSCrdefL9gb484b9e/GznCpYPNzbgmlxhIwkuu83R92gq27BbGcHMLlxaU3sJ8zrsKv6Bx\nnZmfhROX1xqSchKXWlp5C9v93yB2DJ+LfUmjTlb+8IeIh8c/xFgf0qMr87MnEv2ACMyd9N953KVz\nm7aBjvsNctLOsVxqU0zauzq6QGZ2dxd/7bDwAM228sY+zIrsyZRSyo5cK72GfdvOMD+zG/GaHURK\nA3iF8P2+0smcDE36Vuy56nUyge4RiCuNVXh2tqFcd3yMyK5jRmEe3NuTyPyo3DtwLMZqZTKPP1lk\nf+I3AX7eAzAuUnfwPaqNH2SkTWW4VksXLpUPmABZTc4JyOA6GPE5a9cJz7GKyFsqdG3t20hpgIhH\nyH6Yv5yyC3kAWu3/l4dWQpbTpmsn/cGL2GOM6IZnM+yNqcwv+xjGe9om7MWu3OSt5xd8NEuzcw7T\n/TDfG1NpKFVSJxyDDMfYwpieojyHQ4ZbVY4xYGTM18Vdb/yj2T1jIW+rSOTPJmYokcpHYV6ZO3Pp\n9bqF2Ect+nqBZp/79Bjz69mdxw5DY26Je7b3rQ3s2KDHsD5Zkv2qsy2PP4nrsd4NfPMlzX7iI74W\nGJtj/f/7LdzP0RP4rq0mj3/W+j9Uk32PY1d3duyTn5fhfPJZjbYKV0qp9D8x5qyDMH9PrT/N/CZ+\nMFuzzc3xHDt04OOHSrVTD2GfFhLCy61YBzqo+yGZM4IgCIIgCIIgCIIgCO2IfDkjCIIgCIIgCIIg\nCILQjtxX1lSbgcru+q5JIdORfl26Aal9nqS6uFJKXU5F95jmKqQHe+taobw+F7IDEzvIE+wqeTrT\ntUS8XvdOqMztT9Kjb/4ex84Jn4o0ur6TkALcUs9lKI5+SCPO24/0uJAnuDyktRXpio7hSKWqSuHV\nl8sTCv/1mIU7T2drbeIpgIYmbRtSRstqeFr29OVIHU/ZgvSuTnO5VKitGWmTSfHobhASwqVCVWkk\nvZ7IaKxseTrb6Th0wJg20F+zbUkKbp+YcHZOwiZ0xnhlzROaXaqTYJk5QE4WRCq50y4KSik16i10\nuqHdgWpzeEq6XUekltL04bTfeFcBU9LxydCYOeD+6btYWVggzd2rJyRip9/7jfldSsGYnjYbUihL\nez5nG0m3nPGzYzX76mY+r6pJt6s7WeicsG7nG5rt5M67SGRdO4rrpp02+vFU+LpyzJd7R3HdDeV1\nzM+fdCPI2Y1zXn7lEeaXex5j9k425AeZ3/AU1Gkfc1mOoXHsgniR+vtldsyuCyRzgRF4pkPfmML8\nEn5AuuWN/ZizEz7knT1yLkK60HMZKuHf+nwv83OzQxp9wKj+mh06HrGhro53xqjOQxr+ksWQBVzb\ncoX5DVqO7lrL5kACtHfF58wvtC9ieefJqKwfGDua+ZmbY6xe+QnV+H0G8xidXbpVPSj690EKs1cv\n3tWOdksb8Tzee30hl39aXUXMoqm0+7/kcqVuIRjfVJbY51ne3czGJUCz6VqTdRXzzXWwPz1FNZK5\nVHADsZH+HaWU6tqVrLMjIcnZs42n/XYqw3kjSEc0a3+evkvnfd097DECdF0Wze15NyhD4+yJtSZ7\nN5c+5OYiLviH47qy/9F1LHKDhJbub+h7VEqp2FEYn5ZemG+uXrxTXuhcpHMbGWE9cfKBjLC6Kp6d\nU1WFGFB8F3I+2wDeESiVrFfe4yBTrk78764Rxpa4hkGvPcSOZR5EfHHqhm5NZbe5BJJKPQzNxdPY\nR0xcOZ4dCyRSMLpu24e7ML+6YsxNKo/vMiSM+dVmY1+w4zfMq0demcz8/Gywf7Vywz43+hGshUZm\nPBW+8zjsf+vqsFYF1vC4m38KHXGO/I3OTWPsY5mfQxjiZOE5vN7EBcOYn10wro/KSS9vvMj8Ajvy\nuWloLJ0wj5x9uaTKIQzrIpXoX9jCuw4OfSZWs5P/wpzIKCpifiakO4+ROZ6DfRjvplJKpGaldzCm\nC0/g73Z8uic75+payMYcHBED9LG36DQklf7Tsc89/PEh5teVdPEquotruJHJpaIxpIOna1/sAZuq\nuISv5BbeUwBX0P7PlMThtWksVEqpZxdP1+yzpPvWoTf5Ok274NAYtWAh38/d/hXS+d6Llmt20tlf\nmZ+5I/bNm95CKYRrpLPUpTi+rx3SH3sgDwfErskj+jO/YHfs5Sy9EF86DubXmpcBSTOV1OQe5h0w\nR0bhc+rFNSc0OzCGd9y6exZrlaEl20opde5DlLqY9cUqdizjJGSff3wKGdLIPvzzogWRXF76+Ev8\nvyf/7OtA5KFDZuP+hsRy6f3FDyGD773iec2uD8KYq6/PZee0teFeF1/Cnv/U8evMLyUPst4nFkzU\nbCtdiZbCq3heAQMgffvk0ReZ3/x3H9Zsj/7ks+1ovj6V5vO9sh7JnBEEQRAEQRAEQRAEQWhH5MsZ\nQRAEQRAEQRAEQRCEdkS+nBEEQRAEQRAEQRAEQWhH7ltzprkausGuM7mmjNYT8XWFhvfKVa7dHhOD\n887thcaKtjFWSqmgLtAX7voVet7+nXktCtqyKy0LWjFfUsPmfBJvu+Z5FfpbS1/ovfd8x9u0jnsa\nNQKy9uM1iq/kMD+3GGg67+2C/jtwKtef1ldAv9xYBn1/fSGv+9JQWKseJEEzUCOhRdfu+eJPaGva\neQBaVR/7gt+bilpco58LnreRCf9+r4K01/QchVoFbkQHq5RSftOg5y66AF117T3UGNLX5jlwHVrB\nzo9Ag09bSirF27NZ+9hrtu/QYOZnbo725sl/4f226eoruffCeSV3oN+urOHPzbvng2uJXnobNSrM\nXa3YsYJbVzXbxAo1Ahp0LSmf/xHayB2voh6Nma7Vab8XYzU7ex/mwYk7vMX44iXQEXucgTb3yEfQ\npQ5fzk5RrQ2oXUS15A3V5cyvvhhz5FQ85phnEr/HTlGoddBhD3olnvmb13NpJG1gJ704RrNtfXnt\nqz9e/EazF2/opQwNbdXdc/mj7Njh17/V7KFvoQU5rUGglFK3UzI0O7oP5lH6gRPMb/tmtGAcQOJo\n35VzmF9hIuYVrb1kbQ2t84FVa9k54z56VbMrU3fgul/n9RdSNqEuQg2pFzBoxQjm14HUAaitRY2h\n6kJeT8rFD3UbytJRK6NEF/P1c9iQtJG2skmbj7NjtBbbwS9QP8DXhde5CIhFTMk9naHZY5eOYn4t\ndYjXab+jLkzwPF7rpvwe9NBOPTAnErbhnKiFXKHeQOZY0VnE4Jx8Xoep1xP9NPvEO39q9rRneD2g\nkgtYJ3duO6HZc16cxPyMLRGfQxZgf2BkxPcEFWm8xbOh8R6DPUeJbo3vORLP585GEl+N+Hpn1xnx\nw9IT61BVAm/LG/Aw1uDk9Xi9yBfGMr+cM6irRltaN1UgntG2uUopdfsK5ku3gYgHtAaEUkp5joRO\nvvgy3m92ke55j8d9KbuB+Vd8l9fbceuDNb3kBp6VsQXfVpbexGt4ByiDEtUJ72nXB3vYMXsrrJN0\n/9JnaDfmV0varHYg/XZN7XnNAVp/J7ZLF802u09tpPQdeNa0Fgit76KUUpc/W4e/S2oudtDtr86d\nRS2VMfNjNTvtEI9/roGIN24DUPfAzpvXjknZgv3fsZO41uHD+F62sYzXoTI0FqR2U0MRvzfVVtgb\nGP8/7L1neFXVEzW+Se+9J6SSBEiBQKihhN47UhRRQFEEFRQVuyJiL6AodlFRuvQmJfQSWkgIJSEJ\n6b33+v/0njVzfsj7PH8vL19mfRq8c25O2Xv2Pte1ZpH9zcCnBrC80gT0ZKF988IC+N7TzBX7nfIk\n7FeDJvP6mH8A/cOcwtAH58IWvMekvLOLHdNrHOpZ1S3UADrGlFLq7FXMJVoPfZx4DyqnLvi7Lt2x\nX3U4xPs42ZE+Sjlkz2bmzPeKZbd5XTIk4g7hvsQO5T3gtm/AOmlMauiASXyPdXk/xrdrBakv2Xw/\n12kW+nekxv+hxUamfL5UpqC29Q7B+82Wk+gN9OeHy9kxlj54R3SMQE8Uex9/lmdri15B/2unDJSR\nfkVeMcRy+3ohy/OdhprS9CfWbYfOvBcS78RpePR7Hf2vfn+W35uRZH9C37m7PTef5f341Eta3LMv\nrsvY2ozllZP+ZF7D0DfJxISPW4rsa/u1uDoD79i2QXwv30rWSe+RePZDde87ZevQ5+/r77Zq8ao9\n37O892fCEvyl7thPL/3tE5aXvB7f4Uis08vruCX69x9u0uLP9nFLeaWEOSMQCAQCgUAgEAgEAoFA\n8EAhP84IBAKBQCAQCAQCgUAgEDxA3FPWZEuociXnOe33+rUMLe45ERS2KFtOWzJzBOUzop2/Fp9K\n4naQlI4W5AEqkPtgf5Z3fiPobZRi/DexjZ0xbCA7xtKLWHfq6IUUVHpkR+wf6X9XSqnco6CQm7tC\nepN3kl8ThXM30En3f7iPfdYp8P7JYZRSyoJQG3e+s5N9Nv4dWIeVE+vJPjN7sbxjv5/SYhNjUPiO\nnua2ZLM/BCUu8+9kHGPDx0XeLdDZusyBVOHYWdzbDuacghtMxkU8kWP1J3a9Sim17XXILHwI9c5a\nZ43m2QP05oDJOIe6Mk7zNjXFdxiZwJKt6wJOgy08x20vDQn37qCa30o8xT5zDQOVff+bv2tx18lR\nLG/NU5CmPLUGspmieH7eu98DPTyRWDb+z/2Lwd/d8Qdoq1MXQjbUUMYpyrSOdOgPy7mbh9ezvFOb\nYVdJLQt3bohjedM9QG+9lgn5j58btwef+QGs+RrKQdFu146PS1OTe5bE/4yIxaCFbl36Ofss0APX\nWXQT8qfq9DKeR+5HehKeXQed5eLzPy7W4nbtcF1rnnyP5Y0gtsdtREpRUAObWv2zb22FRWd9PuRK\nBec4vd5rOKiqx77CGOll04HltbRgnNRVY4ykE3qvUkoZzYbsIGgS6LJeEbEs7/kfOMXVkAh5FHT6\nA2/9xT5rKsd9sbMEfZbKmJRSyohIP+qJ/DD/UBrL850EEnOv1xZqcUMDl3tVtqJuVpHxQi1Xuxlz\nK1DbIFDocy+grg17ZybLyzoCunqPZ4mFt17+2RXPo7CInMPWyyzPwgxz7uohSCWpDEUppfq/zu00\nDQ4iYWlnymnpJUT24+wNS2qf0SEsTxnhOxrIPqEmg8s0i85jnlr7Q2pracktdh3D8HdtnCHZ2fXa\nD1r8T0ICO2bRbMjGtmwCdXpkVy59S1iP5xgxDWtDdDivlWa2mOuBEzFmGhu4JXHxJcimLNywD7J0\n4zbi13+CVW3EeGVQ+EzEGtTyF5dsuw9FPaxIxrlnXOI2xP7d8Ax6h4dqce7RdJbnPRjPI+ARyBdb\nG7nMzNkf+4pSByrNw3yxcOWSbTditdxC7HZb6vk1jXse60fpFdQAG12bAHpOBXG4joI2fk2uMdh7\n2p7Dfs2lN9+TZm/7972tIdBYijW5sYTvt2vyYGHefgzmX9oWLrO2IXbpeeWYf0Y6KaIPlSISy/vS\n2/zeWJF5mr4R6xCVc7jH8vnbVIn6X1aIdw2bIG5rn5CRocWTlo7BudnxdZY+/4s/QiKsf95WfjhX\nT7Lm6mu0x0B/db9A7by9h/P1PSwBe7PwcZg7v6/m7yMO1pgX1Ob8zgb+rPfewTtUz0H4PpsAfp+/\n+Awy3GGRkVq8eAJqpmMUt27/5mPYdE/sCdlVn9d7s7z4L2AR7dQTkjNTW/4My4ncLuUo9kfuHlzC\n1lCKcR8yB/K4phpuS+7W+/6+L974DXLsvuO4PG3Hx3u0uEc45uK5lV+zvNFLUKfq8rA/tA/h+7KE\n7/DevnM33mue5ip6FUws6wtOo35bknXn5nq+zwgYDenR1tcgNZr5+RMsr+9ZjM0OZE+5ZOxTLO/J\n6ZAg/74YkqfaBm5Xv+C7l7U4Kw7XZ+3LpYjLfntR3QvCnBEIBAKBQCAQCAQCgUAgeICQH2cEAoFA\nIBAIBAKBQCAQCB4g7snhP7gd0hFHG05VjZkCulfZxXu4KjiB9utA6GPjCA1MKaWaa0HlDO4FCrit\nH6ep9Z0D54i6AtClbAiFvCCPdyR3Iq5Tlt64jtgRnLJ1ZMsZLQ7xhOOFvTN3A6IOGpQ6W3w2m+VZ\nB4DGVFsAamZLayvP01EeDY0y0hHbzd6efUYdQJpqcZ9qdFStXsNB1d27FZ3O+4fx3uFUGkWvX+86\nMHz2Y1p8aTUoYqOWg8qe8tsZdkwzuW/dZkGG9PtSLi14fBUkO7lxcA9z78spqOl78f2X4iDhmPHF\nyyyvvh7j2zEUHeTTNp5neQlX4JoRNUMZFMmrIQkpqKhgn3VogmvNwFfggkOfu1JKzfvkES1urACF\nsi63iuUVku/v3xnPt7SK5135HF3O536K767NR15rC6fVmjqA8mliAkqiS1deD4Y4x2rx1s9ApdS7\nt+38BhTMPqGgpMcs4tJGKjlw8EV9SdvFJWIzv1iq7ieqCyAF0Dv4uBEJp7UXHAOO/3iC5dF5EEjk\nW+kXOV3/9CFIDse9Auq0uakpy4uYMU+Lq6pAbd/zBhy9Rr7Lu8mn7IVLhU0g6lfqQe7Wl/IX5s4j\nn0EuU1HEafIvP/wxzjUaFNZRK+axvB3LvtPiDgGQPVp5XWJ5eUchD3KfPUYZEtQJqv+Sweyz+K/x\nrCJHgW6dfjSV5dmS9ao9cf9LOsYdcYz2gNpdHoTaWnKGrzVe40AxvnIAjhdFlaDWZ+7g93zGkte1\n+K9PIHXLPs7pwRcOQEZjcxy0bHOdBJDKXbvMwjOsvMVlot5DcK75p/CcjHTSIiOjf3dsMAQKy1lK\nFQAAIABJREFUiUuWVXu+LtoQl46bf2AeNZRzyQWV8Bib4fxtgjhl3TEM89TEErKuktzTLK8sGQ4e\nVdaQZng6YC3t5OPDjok/gzn70MwhWmwbyM/BPgt1nToqFZ3hsla7UFDPbd2wv2lr5bXcyAxrelUq\n1iAbb34v88q4LNOQqC8ie8AQfr0V18lexBfn1HMolxiWXkZN9hoN+XDu3hSWZ05cPgpPgQqvl4o0\nEPmXx0BIoXIPw/0n8QSfi/suoX716wSp1qR3uftdE9nLdp6Jmlxbm8Hy2tqQR+dRQzXfG1N3Kgq9\nlP/MTcz7/vpkA6AqH3UqYBLfU9YRWVPBYUiP7HXj24bsNzuVYf/qTyRoSillYoH1z94J8r6k3/g+\n0iESNaDsMiRkZsTFa8HTH7Fj0lNR56eNgry76DSf51N6QyJTR1wMs/7m9d8hEnWj/2uQVTTo9mL0\nOVJ3IFudzKcylTz/QGVQnCWOiReXcXnusGmQRxafRL159Dmuc2yqInJp8n7n3Je3OGhOwzioz4Pr\noAOp23oEdkTdvJWM+UulbUop1YG8+zmF4fuOL/+W5VkSeW7cb3gnGv82v6YtJ/GeEUD2a1eItE0p\npTKLUDdGXYOs6eLt2yyPyoKX//2/Lj//FR6DUPNtPLnka7Axar5bL8ir9C0dqKPljQNYn8JtuNT2\nRg5kvPNW4KUpfT2Xs1uRuV14BfX6YhrG2YixvM3EyT8w56hbq4UFl4X1eRV/9/sF2IcufonLu/f8\nEafFw0grgAvk3VEppe78g+dtTKR51WmlLI9KIF0Hqf+BMGcEAoFAIBAIBAKBQCAQCB4g5McZgUAg\nEAgEAoFAIBAIBIIHCPlxRiAQCAQCgUAgEAgEAoHgAeKePWfGzoGeXm8PRlwBlWt/aOaPruP9EdLO\nQP84fRr00LUZ3NK6tBIayg5Ek158gWvrQ8ZO1eKEs79qcdTT0JtdWHOSHqIsPNDboq0J/RqqUrgW\nupjo82kcl5jI8l6finM4/iv+Vkc/rgW/dQwazJB+sJbzcOCWWnorRkPDzBFaab1daW4++gH4+EAP\n2aZzHE8/C43nnM/RX6TqDrcM9YyEFq+lBVrQ6z9z+3CTUdB5eo9Dr5DCS9DsOkRwi8+pE9FvhDoE\nTlk6luUVXcC5evTz1+Lsfbwfxo5deHb9SC+TzDNxLK+pivQsIvrUwOnRLO/kGT5ODAnaOyKRWEYr\npVT1m9u1eNAiiBePbuD9VCa8MU6LWxrQNylgcjeW1y8Lf8suGLrudia8J8TnX8Om8O3xuH+5e/EM\nY956hR1jbA7rxNpa6FQ3vLSJ5U16DefqTPpd2bS3Y3kxLuFa7DEYWtmCk7z/is9I1BRq21xPbP6U\nUqrwFuxmbbvz/jaGwIblsHl/fNUs9tmNNehh9NPFLVo8+xmuYaZtAlyjcc21hbye1ebiOZYTHfrc\nr19geYfeeF+LM4tRD+oaMe4z/uZ9SM6egs52OulltPyNH1je94d+0eK1T63QYr01dwOxk+5G5lVz\nM68vwcHQC3ecE6vFaX/z/lRtul5HhkTyV+i1ZGzLrdjDpkJTbeWJXmXmp/h4dOwGLTe1IR69YjbL\nS9sB3TTtE1Ks6znQJQQ9wfxdse5QXbxTlCc7xjcQTQdsicWlXbCuF9JJ9OvoR+yt95GeREopNYqc\ne9E19NQwc7BkeVe/xB6h6wvokdXa2sTyfljwgRYv/fNPZWiUp0MD7juhE/vs8uc4x7B56G+mtzXN\n2Ih5UF2G9a7THN7PzsEV46KyDOuEiTWfB/RetbVgr1JRi5o1evoAdsy5vZibqWehwTe/yPsAhM1E\nfw3auyTwoR4sL21TvBZ7dsf/vyuK53sxnwHE7rUJdaNc12No0JIh6n6h5Cx6Fuj/V6NLb+zHzMke\nqDaPz51SYnXrTvYLnZ8ewfIyD6E+07nU2sx7CGYewPMwMsVJ1aajljnpejiOJX22wmMxFukYUEop\n9yA8+4YG9F6oLea9ZGzdUSfLM9Czwt7Pl+Xln8c8nfzeJC3e994elhcbG6XuJwImkt52l3gPy/pc\nrNG07llX8744SfHoEdT3EexDC0/z/ZIR6Q119Qrqq1MHXvcOfAdbetqnrUsU+hK9M4M3F9xyBusQ\n3bO98Rm35XXvjDlXmoX+GkYD+CCmvRprC1Gv6ktqWV7GXuxtO88j+1LdMmhizdcrQ2LmO1O0WH9+\n7YyxabmejTkbeY1bgl9PztDiIHf0e/l0xw6WN2/oUC3+ettuLfaO432IegXjWVmT/jsWKRhjeft5\nT5eY/rDctnDD+5K+V6hDuKsW25Xi2RxYyd91Onqhn+IZ0penT0gIy3vsddy/G5swJsb14O8ZheW8\n56Sh0c4Iz2rHq+vZZzNXvaHFFWXoRffPJv6u0SMY77sj31+sxUZG/CeHsfMxQDM2wi79XArv9zXQ\nDutn+8HoGWZG+t4d2nuOHZNLep118UO/0bxr/DcK52A8h4U/Ys+xei5/d4khPS2vncZzpH0fleK9\nZBz7ot6uf2cLy5v9wb0bkwpzRiAQCAQCgUAgEAgEAoHgAUJ+nBEIBAKBQCAQCAQCgUAgeIC4p6zp\n5n5QHsMmRrLPKC32+kFYZUX3C2N51YfqtdjaD3IeStFWSqmAHvh+aqcW0IXbWd2+CJoVtatsKAWN\nLubV4eyYtjbIhjI2g4Z8MzeX5VE7upp6nHdmGreFMzbCb1oRUaBvVWVzupmHC2h0Rua43uYWLmOq\nzbi/NLVLf13Q4pjFseyzPe+DvupF5FUeA7nttMVlUH+NzUAR9u7Kx0VtLSiCSavitDjsuRiWV1kJ\nSlwdsRl3jQIdtyaf35dGMi7KEmBtqJcwOBDb0vJbsKdrLKlneVNngRpZnw/qbEt9M8ujdoQZWzHW\nbRdwm8KBg+8f9XfQW6DAdU3jtrwFcRlabO0Bam59E5cJXPwWFN7IWaDdr37ya5a3cO0TWnxnC+ZL\nh5n9WN5b/pjPDcWYfy59QCff/+o77Jih772oxSX5oABH+fuzvF0fYlyOXITndHvrNZZHjUBLL4Oq\n2mEKN/w0MgJ9tiQd30Elj0op5R7aS91PUFnh2md+YZ8t/O55LY62nKvFL43ndtIRvqBKTu0B2u6x\nr4+yvNjnIEttdac0ej5fvHpgzhlfJLVtEaSiNnbB7BifMaB4fvkExk+4H68bZdm41zmE+tvUzOfY\nT4e/1+LqUi4BYiB6RgsL3IffNrzD0pb9tPDfv+M/orkZdTJsDpdsGBujNiathYTPdxSnMP/1JazI\nJ06P1eJLu3eyPG9i+2vji/mWW8ptGSsLQWvfEQ9ZypyFE7S4PKmQHTM8CvWK1rz3F6xhedT2laLL\n+C7s37fWY/y59cc40FPpK+sgR8g5jnpqotsTzPnqmbv+XUOh0xOQFlSml/5rXlE8aPjth3Ob33aj\nUIEKj2PcZqznEte6YZA8lV/FWurWj8tMQvs/rsUVFUT+ZAlZRc4eTvme/CHkZJVZkB45BYSyvGvf\n7dViu85YJ2oLi1heYymez7VvMU69xvAxnLEH9ZtKRdz68hqQ9M1ZLfb94CFlUBC5hE0gl4un74Yt\nsWMA9op6y3ZrIpU9/gEki91mcbmXuQvWiut/wV69tqGB5VH5w8BXINsLHgP5dXV1MjuG7lMy9uC8\n9bJb46kYB1QO7+QTzvJKszB2Wkhezgk+LtNPYL/W0QbzdMSro1jeudXHtbinMjwOfx+nxXp77w4e\nkIAWlEMaNugxvsZnr8d4PPgTvi8qmHtGO/WCzKScyAX9g51Z3uD22LMufXG1FifdwTynMmCllHpp\nBqyNbYgkvDqDS46dQyB5svfCO4SLHx/DFNXVeB+j719KKRUyE7W4lbRuKLmYw/JKkrEGhPAt+X/G\nqiU/a/Gij7g899RaSEm6xaCGHj5wnuXZWWL9dIyGdDDmFpedtpF9wKKHIIGPT+CtC+ie4w9Syxat\nwv7q4jdcknPhNObEw89izvZ+ZTTLqy3Bs+/qhPPOI/txpbi8ZlA45mlkX16fbdrj2e84j/vy6mou\niTPVfb+hsf7drVr88JvcqrsgFXMsfRPeDUbPG8zyyshevJLIn+5s5XWvmrz70fs04RkuKU3YfEmL\n9/6F5/gW2SOYH+YSuY5z0eLBxAT39taW/Syv+AzWzIJMPNPYXvzd1sQOEuSxS8ZosbExl6jueQ2W\n6+lXIakcPorvo0oT8Q7rE6T+B8KcEQgEAoFAIBAIBAKBQCB4gJAfZwQCgUAgEAgEAoFAIBAIHiDu\nKWsK6OmvxZRGppRShzbA6SY6FLS8O1e5Q0ArOS77IOQYzpEeLI+6q5g5gp7UHMZpnW4hkGPk1aM7\ns0MgqLTl6bw7e2MZaLpVxMGk3zQuYTizGVSyNXsgq9j01Qcsz4jQr5tJZ+b2Oup68WncCxNr0FEb\ndZR+V52EyNCoJjTy81/xTtWTP4TTSklShhbf2sPpZ5GPoWN4yi+47+2MOS3RPdZfi8OfB9XN2Ji7\nUhQlgbrbMRYUw8RdoIQ166ib5i6QhJgSiplLN2+WV08kboe/PazFY18Zw/LaiDyBOqYUn+Jj2F7n\nXvJ/UHKFuwocOwKXhuh5+uz/htJUUNmt3G3ZZ+HzJ2pxznlQyGeu5BTyjW+ArtjXB5TbpeveZXkF\n10DZvn0d9yLt7Q0sL6MIVGxXO1DDu/aBy1HHaVz6kPQXXFfMnTDPnftwp7PY3nimn70C+c/ST/mN\n3fUFKIoPLcB8rszNYHl7PyV5n4By286I/z5dXgAKpo1NB2VolFajnlEZk1JKtWuHc6mrg+PYCysf\nZ3k+XUCVv30UMpg+s/uwvKztmGOeI8CbbGzk8hbr9nDjcSf1OvNv1IAdB39kx8xegjFnYgyZgN4N\nbs1L67T4jT9e0uKco9dZ3sYXv9HiGZ/jvrS2cililwUPa/HL4+do8ZKP5rC8O1sgp/J6YaIyJCxJ\nrbjy+W72mc9IjJn2EzAP/npnK89zBoW+pYHIEzq5sry8IxgHjV1RxyN08rH8o8h7+VdIujJ34D5X\n5XCZ6LRnUQ+Tt8MdYu5kTimmEuSjy1ED3Fy5rDP0ScgeKzIgGU7eeIXldZmFtaQmC+fkGs0lPvve\nRK2Y9Q2XVBoCraT+U5msUkoFTgD13phIkkuu8bWBuuk490DNsnTnVOdq4moYTOShtWUFLC9pz3da\nbE9kFh5hmNvNdVyumkec06hkp10gl5O5Eze7O9sxLhw6cbeJ8grUqLCHIX0zNuNyIEtv1Hy7e8iG\nvAcEqPuFq9cgQegTwB2ygiZDYk8dlSqvcxmXI9mL1ufg2qm8Ximlyi5jjDzzySda/Ovrr+u+Dy4z\n1G2nuRl7T1fXYeyYxkrU8V7LpmtxwVU+d4rOY/xRmZWzJ3epoTKatCPYO3h25o5tIaMgF2mpx7gq\n1LnLZZf+u+zPEHCxxZ6m43AuYTm+GXuacH/UvcqbXFI0bCnaGdQV4DnqZe/nNmLPGtwB+w7991l4\nYg5//D7kE9TN7vafV9kx5mRtcO6Ce11bwB3Ccs9gr+jek7jT3uKy7eZaPBMqHbcL4RKsEiK9dIjA\n+KPuY0opZeHB65IhYUr2AZ+98NO/5lWTlhHUBUsppRpJywcHsha26t4/ne0wXvKyMJ9nffEky2tp\nwZqZvgX3fAWR8o/owveoo/pDzmhmjz3q7U38XYfW+5Y6vNNV1XEXsS7DIWX6/XvsF/5YGcfyXr0J\ntybqQkTdf5RS6q89OK73c68qQ8OKOGne2czfA6mrpt9oyLIubbzA8uj5W52HbCg3g+89qQthRG98\nn3dUX5ZXfBLv9K++j3cAutZcSuRyX4t9xC2TyM5sg3itzD+MvROth7988TfLCyftBNoZo657DeGy\nyaipcDGk50d/h1BKqav7IDGNvMsWVZgzAoFAIBAIBAKBQCAQCAQPEPLjjEAgEAgEAoFAIBAIBALB\nA8Q9ZU1nD4JSGeDGqa+DJkBCcGYvOilH9+dd472rQLGj7kr2oVwqYmEPan1lJqi+xcVxLK8kAXRp\nGx8ck3McFCGfgZymVnYbtCUrZ9AOq9N4B/Ueo7tq8ZowUHEPH7/E8mhHcX9yX6zy7Vge7aZ/+DfI\niXr24Y5WN3eAlhzKG9AbBL4uuNc1OmeBG2vRhd93MihdQYO5O0szcfMIngPaX/rmBJbXSCh4LS2g\nrF398jDL6/rCSC1Ojf9Di2szQf31n8rHkr09aMu3j+zQYlMr7rhz+KODWkwdi7K3cynFP/EY3w+/\nCFcTE3suwSo4nqHFdoGg8pvacNp47Ihodb/g0Rmdvj+d/Qr7bPHPb2jxsT/hyDT0qUEsbxTpXl54\nHrTlhtIbLI+6XVHnhNhl3AWNSiZq8kDbpe5j1r727Bgb/7u7EWxes5f9e/byaVr89p9LtDjtL04j\nrm+E9K30Kmjn3jHdWJ6DNcZI3mlcb9zWsyxv6GwyAe/SQf2/4rmfV2rxza3cmacNzHu1cfMhLX5v\n288sb+Pid7S42xjUul8/5NKZKVPxvH3CMd+oo5pSSpUlgkpt5gAa761kUEln66RBh345hr/zCByL\nrHx4Dcz/B053M/pBbvPBEi5Pm/YpHAnOrIQjn6Mvp6DWknE2JCJCi9e+uZ7l+bqCEs3FXv8dTtFw\n+1AXuOPfta2oh5klJVo8ZAT3OKm7gzlC50RDKae+2hP6OpVmnLjOa9mjMzFfsohLjftAfy2mFHml\nlErbizzqoMSFD0q5D8B32ASh/h1bf5rlhbThGinNPr2QU5nvrEZ9pnT1UW68jvv7cOmzoZEfh31B\nXQ6XTzt1xV2oToOko+wqvxZTK9DyLbxBo9ZLjwqOZGgxdXWy8uP10a03nNMsHDEu2tqw/jp19mLH\nmJlhrBddx1y+vYuvubSuF1VinfW6w/dBDraQPli64ZoS15zheX4YC5XJkBZ4j+J7h5zjuM9h3ATo\nP2P4s3Dys3DmksocIqO3cMfYOn6US4V6l0B+aB+Be1lzh8sAk2/iuW1e+5EWP//BNyzvk/mQaV+I\nRw0NHAb5imsUH0dUhpO5BW4kZ5O4+wx1aesdgu8r1Dm4lBFpWvSzkNGVXOVSbCtvjL9z32KP2qEn\np+p38bu/0nsnG4y5mkx+34cTt8a2VtQLc0dLlnfgI0iXB8zBNVelcrkS3b/fuIXnM+AJnfvTrlta\nXEf2Ga5EXhQwje/l6bmnE8mT50gukbYk8qI7ezEek07z5z3iLUhPC8shaaM1RCmlcm/jnYm+Z9UV\n8bpm68elqIbE1EmxWtxOJ22sSsG47fws7vOlz3iNCiMOemuW/KrFekc0lwGQmBz+Gi0obi/iczE2\nFrLMgjTUqIf7YXyk5PE50bELnil1oaPtNpRSyisM15GSBblSzyUDWd7FVZhXk4fj787vPYPlHf8J\necOI1KoimcswA93d1f8r+M/g72C2Hrjv6buxHoQP5/OASv+oY1hQfz4Pqm5gj0SleseWcxn95XSs\nIal7sH/4YOPLWmxpxt/HjEzBPaEOiQX5XKLZaRyu0dIV64SF7vv6Pg6pVeo2vLM3FNawPLuOuI7j\nf0MKp39ukaO5G5QewpwRCAQCgUAgEAgEAoFAIHiAkB9nBAKBQCAQCAQCgUAgEAgeIOTHGYFAIBAI\nBAKBQCAQCASCB4h79pwJJ/bUXqO4VoxqP/tNQf+ZiiSupXXpDau6wqMZWlx5jevo3Iegx8uZn05p\n8aCXueVgLdEBU4tL0hpD3fwljh1TlAdNdTbpA0B1rkoppSAjU6F9uG6aolsEPqM2fbdPp7E8vyjo\n89yKoO010fUqud/wjMFzdO3Rnn3W2kTsRK9Dlxe3ievLqb3a4MXQALfUcG29OelZsXr+91pM+18o\npVRlDnSIdfnoIxE4DX1lylN5P4f4bZ9pcb/XYalbcpvbvQ1aivNb9ggsLyN8uVXrvA/xHVTfn7j5\nMsu7mIbnOrYf+ipkpXLrNjvfu/dTMQR2LVulxU9+9ij77OBbv2pxl3A0SqF2mkopZWqLZ0j7V1Tc\nKmF55cRS0tcbfQqKL+ewvGmLlmkx1eA7dUNPBJdgrkXNORePv5tELBDfmsryTn+DXkixy1ADmiu4\nvfqUF6DJdg6GFZ/eLtrPF/0rck5laPH4ZWNZXk1WubqfoFbduYl8fPd4EVrlwTdI360c3tuD9vDo\nlIu5M+vZcSxv989HtJj20HD05bXNg/QUKToHXXtTM/pc2LTnY/uRL2Et+slj72vxy79xW3aqcV9h\ni7p3/QqvlYeOwIqR1uhnh8/k3xcMPX14FM675DWurQ8P5z0TDAn3MPQzMrXmtTwhAX0urEnNzE/m\nunZ7O2ib6T269jfvqdT1UfSxyie22gMj+bwyI3PbczBqQGU69NWOXXgPFzqX/IdhTOhtoO9swMJ4\nPgXXN3Rib5a3/230AaMWmZPfHM/y9n64T4v7T8d3tJGapJRSufm8V4Sh4UOsQGm/KqWUSia91IJH\noSeJ7xRu82tmh/Uu/1iGFrczasfyfKfCmrskHtaizlG8w4+lE+kx1Io+C/nXiI0rd5VVFaTPjG0H\np7vGSnHb8sRM9Npo3NLM8qh235PYrwdN4WMueQN6ZVDLbXoflFLKszffcxgSdI0rT+K25FUZqOUt\npN9SaysfZ/l5qDf+pG/Qjp0nWN72kye1OCKR74cprHzRd8t9kL8WV97CeHYdzve1qb+jhvrPRC8t\nh5t8ztbloN6nXs7Q4qQ03oOk93D0T7yzFeNjV9w5ljec9LYorUYNdYzg/REcOruq+wnr9rhn5q68\n99T+r/7R4nHLsN6XXOLrZ9+HsDe7vOEi/vuiASzv7DHU2KjOeI6VKbwXBe3D1XcZapipKe0Txf//\ntqVrhhZbk/1gQ0ktyztKelCW1aBnxbChPVheAdmrNFagHhjr1p0mYkFNbX7zD/J1NqcQY336ar7n\n+q+gdtIOwdzq+6vv0Q/vzYmop4HjeD39/iX0n3Qi9uoThnJr5QLyLtktEGs9r7pKFaRgr+TXF++Y\nJ3agnjY08/rnGY1aduCtdVo87B2+FynNw55l70bsVzcv4nXjhw+WavGuHaghwxpaWN7tAtQv+m6a\nfIn3COwWfP/2NkopNek1jHU7D95rql079FjLTsA6ZmLMewyFBWLtof2+fPvEsryPf0fPmNnDcF1d\nnuJ7C/UdwsdWTsc57EGPpujhvIeLGelBdvEfzPlZX69keSn/YGxmbEKtXLj2CZZ36XM848xi1PLB\nE/m6WHEDn01eMUmLG3WW6Nk7SK/PMep/IMwZgUAgEAgEAoFAIBAIBIIHCPlxRiAQCAQCgUAgEAgE\nAoHgAeKesiZLL2Jvl8Xt7U7tBKWL2pY6unAr1ZTdkJx4dYXEqU5nlxf/KyxtwwaA9pZ7iFO6LDxx\nTtTK2MQSdKv889nsmGZC+RvyGCiOrc06WtkBUKTi9kF+MWw4t0E9EQc6bydvby1uH+bN8rZtjdPi\ngZ1Ba44/msjy7K24BaShUXYRlO3UI7fYZ+6+sDzzfwiWYjGju7O8WvK8zhDJSeR4TiU7/SMkaZ19\n8LxN7bg9dc5O3Gvfh3BvMnbg3lIbO6X4+GlrAyX/xkZu502t8U6cxbjycuI0b9u9oE22NYLq7O7G\n82xyQZ819wBFz8SeU0sbS+rV/cLQt2FlXF/GpTe9n4GlXz2hz7p0CmF5+RcgT6hKBN1z02kumxkc\njnHgaYF5deswt3lMrMA4LsvGPM8k1ECPpTp6IoHXcMgvtn+8h302bSUoty9O/UCLY8O5tZ9ZPGwx\nvcIxtyvzOc2b/gzt2B4ykqZK/swcQu8vfdvCHt/v6supv1XE0jZ0OuaVowe3BZ/5POaiG5G3lN7m\nc3v4RJhIG5ngBiR9w+91ejbqQ8x8WD1OGA2Kdf55Lh206gXa+6geOL8bv+1neRcvYcx4OuK+R8SE\nsrzJE+Gx+8bU57XYMYzT+gtOZmhx4QXIfGJm8nFWGs8p74bEzle/1eIAnd3zsEWwFbdrD8nKrXV8\njnWeN1qLN7zwlRZHhnH/9roiUN6DHwO1u6mukuXV5JIx0RG07Nr8S1rsGcFNxa28UGvt3EEpplRu\npZTKK8O4vEMkdfUF3EKyvgnSkUEP4W9l/X2D5U3+8BEtTvkN8ln3gZxC7R/mo+4nmIyqjWuF3P2J\nFegZyDltQvjaUE7qKJVyhfjyfVDJRTIeiQY7/2g6ywt+BHsIY2PU3t8+XKvF/q68RhVXQery5MRn\ntTht+1mW5xELWj/dc/SczqUU7cj5UTmG3ua9y5O97vqZfq1vLL9/66JjBOZfwfEM9pkRuY7UG5Br\nDp3I50ElkdfSfd/tfC51++vXFVr87rs/afHi8Vy2R+HdBVJVv+6QwxQWHmR5zZXYz2x5628tDnRz\nY3mUTv/VVtDxf3rjZZbnTKTFcV9C3urlyK2UHaNw//r3wXyryeb1xdiC78UMjdZ67MX1FtmDHo7R\n4vi12F+amvDXFyo/9PbA/D311TGW1yXAX4tLilA3Pez5uPXvh5rYWAPJkxGR56bv4nX9zBHsRfsO\nQx228eey4Kgo7M0sfVArdv0Vx/L8yFw3J9frZm/P8sLHQQqXtQ/7APc+XFJomsntoA2JY6dw7ZO6\nj2KfLf8Ja3pzDcb6pY0XWd70J0Zq8TdfbNbivWt+YXnGRtjPNBJZ0qIxXB/S3g/7lG+/xnxZ+hHs\n7ssS+DzPu4C2BoPfxL67rY3Ln3IOQOI7YjzW5l7BXDZuTN5Nb5F3iSGN/N0ptxRj7AKpXXNe4/Iz\ncyc+PwyN0gS8P7n69WKfJf78pxYHDcJ1lpzn+y0nX+zTW+pxPy989BvLmzgF9dHKDe/2v770J8u7\nQdpgRM7HOZ09hXeakQuGsGN+/wh1dN4K2JZnJx5geR++j/3OgDDsp6Ot+XOMXor6cOtFXMcPKzay\nPF8X1J5uRVg/g+dwad6Vm5Acxqj/hTBnBAKBQCAQCAQCgUAgEAgeIOTHGYFAIBAIBAKGHjmcAAAg\nAElEQVSBQCAQCASCB4h7ypqKb4HuaWZqyj7rMwpU9uYqdCG2as/pdq62cMhpLAP19fxhLkVJIu4B\nuYRGPWwaJ/xs+/WQFncnXbqpM4Y7oXQqpVRYNOiaJVdAv6q6xbuzU3rwnSJce2Qaz3OzAw2RUpkd\na7lz0ZgY0IWpO05rPncL6DSQU/wNDbdBoIvb5HC6atZF0H1NSOfrwlTuplXfCCpicA/cd9euASwv\nlLhp+U+AE8CVz4+wvP5vL9Hia5vW47uJq5OREaeZ7nsD7k+WpAO4j86Bytcc53QhFdRDfUfxvXHo\n2E6lPPYdXVjeEAdIvOIOQM7XpwvvNO899t8dvv4r/iQ0ukmvc1ceCyfQATctB5XP2jyO5c1a/YoW\nn94M5ysfZy6v6fUU5lzuP5AVBofz68s4DLqwifXdHchqa1PZvyllft7Ut7V44ejRLK/4ImiM0R3g\nqHAlncsARr40Qov3vPq5FmeVcAeqIHfQW317YD5k7LjO8mx9UL+8nlEGh7Ex5AQuvbhsg9K5C05C\nlpXw+yqWR91UjC1Rwqm0Uymlzh1EjQ3PIK4rj3VleaZ/Q7LURBwhsm6Bjuo7NJods2Y+xs+w/pgf\ngTO4HNJ3AuZITS5qT/4BLld9aQJu9lPzQSVe+Si/9kcfGq7FefGoXV69uROb57D752jQ/xlQcZvr\neM0vT0bdPP8zJDshPfn5VBXi+se/DVmEtQOvp9fX7dVi6gxl5cVlM+WJcHrwiUT9ouvO1qWfsWMC\nPSFpcIuFU0uU7hnGBqE+ZO0FZT6NuMUopVTsXMgrC4hLiHNfPs4tLVGvL10Fpbh9Ll9nIx/jY87Q\naCNSJro3UYrPK5tASEEqrnMHqep6SHY6TsAasu7Tv1negk9n428RKeXV9ZzW71MOmdTRzw5r8UOP\nwd0n+QiXiXUfBnp8/kXM5doMLh2vIs5dA/uhBrTo9i1UquUzFvKLurwqlnf6a8ibe86FVMi1F1+P\nz30eh3N9TBkU25bDIax7CJcEBszA83AkDoSViXxvY+WPmh/jAXlIgE4+ln8ae9SPvsf+pbGCy7Zo\nHW/XDnuOmhrMndJkLr0/eR3P1MMBEhi6D1WKS3zofuanPf+wvOeJq52jNfZK0ZO5RDZxF6TJPefh\nGV774xLLo45Cnbh6wCC4fAX3ZvQw/hwzt2JMh0/BuD33J3eeoi5mJSUY+32e4W5NTVV4XuHE4fHG\nL/weho56SIsbG7GfSFgFGYNjD+62NnoJ9iNULnPyNy5/cifPOGwQav6osVxy501c9M58gj10XSN3\nraxJh9Sduoba+HE5lW0Al2XeL5g5cOnN5uXbtXjoFMg7Rr73OMtL+nq3Fj/+EO5lla6tBpVrUYlh\nUFcujbUgzoMRidgj5O2B06qZG28rkX0Ya/ONPZDoh4zk+30rIkerz8f6mVbAXeNqzmO8ffDLi1q8\n9f0dLO/DTdifG5ng3WffW9tYnp0l7m37LwzruKUUl03V1WWyz9wH+msxdSp0DOPubklrsf41kt8H\nfMbzd928vcTdchz2SONmcXff+X0hWdz1+gYt7tEd//3cb1zG25O8N1i4oAZmEPc6pZRaMB4SPGci\n7Tzx3k8s79g1HBdGnH+r6vjegbq8NVVjnl798jDLm/7pXHUvCHNGIBAIBAKBQCAQCAQCgeABQn6c\nEQgEAoFAIBAIBAKBQCB4gJAfZwQCgUAgEAgEAoFAIBAIHiDu2XPGiNiVWejs7aie2cwVmr1mnX75\nL2LbGukHPWByVhbLsye62AGDoCutusV7R+y/CI322PHQ3x77BxpZv+pqdszJPTiG9pWh9tZKKVVU\niZ4I1GpS36vkuwOw4vr81QVaXJ/NNdk1ddAaBo6DXnGQToO/Yy1sFbtMXaQMjXbG//4bnJMD7KTr\n86ArNtNdc9AE6NoLjmZoce4Jbq9s7oL7lrYZz4T2GlFKqbo69A6xD0WPhMJrsEa7tOECO8Y/GL2E\njMwxdJvruMVd3nnoJDu3hzZ1hE7Pm3AS/UZMTHC9th24Ltea9Hewioc2PHgOt5lr09mxGhI2pKdS\nZSrvzZCVhHOiVpn+gbz30raXvtTiYUvQw+D3tzezvIs/Qrt5k1j/Xd/MdfKTesJifuCb07S4HbFt\ntrXl1tcm0dD3D4+C1aT+3u3+M06LH/tophYnruW6Utpnpfez0JY7/MTHTuRi1Ipb3+Mzr0G8x0fJ\nKX6NhkZRMnSrNZncEt1uCPS4jWWoHTGvDmd5JUl4JofWHtXi6Bhezzp5w5Y38BHM3+cmvsfy3vsY\nNcya9Nw5uu6EFlfd5HWY9pkJnwdt/p+LP2R5LraoL8GD0b/CZzLXby/sjv4n9Xmo3307dmR5NkEY\n322tGDM71h9lec/99La6X7D1xLyqIz1ClFKqXSj6s3Qj9qn6Z513FD1ZfEbivpz/cAPLc+oEK133\nCGKRXcWt4oMmY+xnXoCduX9PWItaediwY27+gvrsTCy8Cy/xmp5/IkOLfcfguTlGcp25tSfqpNti\n9O5obW1geQU30etrzIvQe1/5hfeQsPHk329otDbBvtcuhPcZS96Mfk0NV7FX6fPcQJZX/C3pJUHW\nuEcWjGV51eT51xejT51fd94jgfaa6jsPNWtw9CwtXjhjBjum5h/UisgeGEtmznzPVpOBcyjPQl8/\nl958P9J2BT0TDnyBPhxDno5leflxpG8gqd+0t41SSkUvuJtRqGHQuydqnrGu71llKmpWLbn2impu\nAX/6INbPQbHoydJ7Ke97QHuaGJth3Wmw4d9nao21uq4O68n1H3AvbYL5HqNfJ9S5VlLXfjnCe/WN\n8YTVcJdI9FToEcvX2dZGjO3bpAdGwSbeu2MQeaYFcRlanFXMeysNeqy/up/oFo3rr7jF/7YiLRpL\n49GLjlrWKqVULrGlDx6B77vyI98zhI6DXa6pKdaTPi+8zvKamnCvCm5gz2Dlhzqn3y+0n4rxWJiM\n+x7ZK4Tl3biI+p+9A/XWgVibK6VUOen7aUt6jbj35n2dMo6jT0rnGXh/iv/xDMsL7IJeGf58yPxn\n0P6buz/awz6bsgx9Eml/wrLMWyyvwzzMvwufo6eVqx/vi1iTiWdTSt7bavR9ttJQ56I6Y76EzkMd\nL0/na6kr6WdzeR3Wqs/e/4PlPdyvn7obaM8opZTq/wb6jdXVZGjxlNfGs7zdb+/U4hxiqz3nfV7v\naV27H3DvjjX+8Lub2Gehsdijxm3Hej3jk2ksr+uiR7W4sRHXUlWcxvJsO6IOrnlyhRaPnsLvbXUe\n5lI86SMaMxv9i6Ij+H7h+HqszWc/xv6Q9oZTio+lk+sxX7r25v1x3nr7Wy1OO4pegF3IPkIppXLP\nYDwFPYa+q0c+PcTygvJhWa4rZUopYc4IBAKBQCAQCAQCgUAgEDxQyI8zAoFAIBAIBAKBQCAQCAQP\nEPeUNdkRmrKZowX77NShK1rc0AQp04AB3KZ1ysPw3TuxB9TAUEK5V0opF2JP/dcWWE5ZmXM75QWj\nQINuKgddevyzsF2j9EyllAohcoF2RKqVfZxTrPpGgM61+zTobNd0Eqx3Z0JmYWJDbG2tuJVtYQEo\n75dWgwYV4cttX4cM76HuJ4pP4fyTUziFr6UVnNHGZsiDTIz473Ye1ThnX0LdNNfJ3VY8vlqLX1k9\nX4vtCrjUrDIP59HOGHTwf74F/Wzcq2PYMVd/xDNpbcJ515ZwWrFbB1hgDnTBuMq5msPyrC0wppua\nQU078sMxljdsEcbwxPdg83voXW6FR6mIS//kUpT/iihCWza24JIzavvqTGQkXZ6eyfJ2z1ysxT7r\nQUmfvYJTEikVNNwe87nsUh7La2nAPbOwgNTDOxrnkHfjODvmxHeEqkrm/M86+vZHK2GtXHAKY8Ut\nkku1fn4RNuwdPEAJ9vXn9OB27TBPg5+ERa+egt9x4d2pqoZCFaGkdpg4lH3W0IDx6RyN68w5ksLy\nAkeBkjs5HHnW1jqp0AhIIcyO476P7Mpr9IjB87T4r08geaLSzpNJ3HI8jMgF27XD+OvoxZ+P22B/\nLfbrhTmRfnIfy/MbCNr8qfd/1+JiQllWSqkGIgnpPGuSFl86z+2FjY35emVIVBfCItXWndPLjUzw\n2aY3tmpxj87cht7IHHM49xBoupa2OvlwOii3dTWQa+plgI6OkBja9QGVNi0etq8X18ezY7rNxDxY\nu+ArLab0eaWU6kVoztbWkAs0OV1heWl/oqZ0WwTJZ0n+KZZ38gfI5TpFwT6zqIrLgjP34ftcZw9W\nhkbROUgS6nSS5J4vYI4Zm+J+XPqMU5NrG7AHaSyHhabeWrSBWHWb2pE9jU4JS63P176J2rb1x0/u\nmqOUUnZEFnxjJ2TBBRWc4j9iMaSs5m6QkTeU1rI8r1Gg/5udI2tkFbfv9SQS2sZK3IcLm7ikNHoa\nsUTn6o7/jORESFl8nLhUyKMv9iw+47AHPPo6p+pPfgZSodps1JuWei7Rr7oDaZQRke66R0ayPDs7\n1NfSUox9m0DIHWz9ufShmshGK8uwV8rQ2fL+sxp7Y08inwgfxG3nD3wJqfzop7HO2LS3Z3lsLBHl\neYiujtPrvR+gFr1J6/j4cfbGOLt4GRKgQTO4XK6GPJ/cOIyL8hq+P8w/jM9aGrDnDR7ErdPvXILM\nxCMM9aw6HffWc3QHdkzpZUiOM4k0zLM7lw4Gh0PO6DUc33H+6xMszy8S64tzJPY03v34mHPohHNP\n//2qFtvparlrXy6jNCSmzcI4sydyXKWUaq5F7Sg6g/eRkOkjWd6OZd9ocdQoXKNjGP++b5as0+If\nNkDCTOV8SimVvRuyqTuZWJuvL/tNizt38mfH5GZCShY5HTKrxExuK03fndoPhiyYvhMqpdTVL7Zo\ncVYRxsSwd6awvCby/jV2Avah5TeKWN7ar7Cv+Gbs08rQMDPDWOr5ZF/2mb0P1mvXw7C4b6rm0uWP\nFyzR4lmvYJ+WtYtLpk2tcK8yCvG+7De8N8trasI+vZ783mBG1lL/CC7/ciBjsK4A63veIf7ef/jM\nZZzrm3gmpjb8t4fsS6i9QYMhSbu24S+WF7mQtM8gUuchr/B3wjrdO7EewpwRCAQCgUAgEAgEAoFA\nIHiAkB9nBAKBQCAQCAQCgUAgEAgeIO4pa0q5AfqZnjLauz/cGBpLQNlt0bk11dWDqhVOqPD2obz7\ndtYVUIynjY3V4tzUfJZnaoJTTr4GemJIDf5uWSmnwmceADW082BQtPWSqYJCUKe6BYK+lZrPz4HK\nuAqvgMbo1IG3XO4WBnqr2X7QjQMGcYp7I6E83xcQ2dCo10azj8xs4OCRtApynr2XLrG88Iu4B8n/\nQOJwp4hT7maPB7UxZz/o+vEJnM4W6AbKWcepoC+euYm8Lpu5k87xZNDobNNB13xoIb+mStLt37Eb\nqKCtZznlMTMV46eQUMCnffwQy7v0Oaim9s626t8wc+VD//rZf4UTcUaxcudzMXEHaKyU8rdqzjKW\nN+fVqVpckQwK4T+fHmR5EdEYnxu3Q2amlztMmBarxbd2QeLVYQyeh1MA73jeIQCuAtVloBu/OJ53\nrm8hdYM6w9VU87kybTFcUY7+DOmO7yQu8dn5OhypYmaAouzQkdNlT6yE/HDyF8OUodFKpGAlaUns\ns11fwGVn+seQmq3/cifLW9DfX4tTf8Y89Z3KaZJvfQ1p2IeLv9fi8Su5jK2Dp6cW2xIXkbZ0yNjM\nTblks//r+I7CVHS4d+zO5WTUia4noZMm3OHySkr47DQLrkRF33CJoT2hb7e1gVasd0goL0jUYhsb\nXm//K6qJFM7Og9PEm2pA36YSr8s3brO8R1e/oMU/LYRkZfgMLqs7vxPP1y0Vfyuwz2SWl5P+txZb\nO0LOkbgRlN0SnWzI0g21f8IsONOY2nNJGJV6ZByH44xTJH/WHkOxZlZWEtmzTjYT1hPPIzMR6/5E\n3bjMOcyldIZGczWelbmbFfusmMgTnIgLRFMLX0Pa+6B+1GbiPqXFpbK84JHEjYfUAHpvleK0/NlP\nobaZ2GKvUhqfy45pJnsfvx4YI6Hu3J1r/xeYi+PfQb1N/Ia72dBx0t4Ve5p6nbSd+i9e3oJx2u8Z\n7mhVoaPlGxLRY7HHcu3BJYaVaZAK3dmMvcPYmfz8Nn+Nmh9D3OGKkvi+z8EHNcZ3IqTdpRl8b3Pz\nNKRv7cdi/cs8R6Tc57lEInIeZInrnl6jxV9/8SLLu072XrllkB9HmnKpcyipPYXH8Heboz1ZXlUK\napmpA56vs7s1y2vRyUUMDepA06ybY9Tpc+AUyB0S9yayvOBo7BdrGzG3gztwSZEjcUQyJm6PmQm7\nWR6V0N7cjM9oi4db2/gaTqUuQ55GTb25+SrL84+FDIZKxvTvJH7jsDcuvID96vEVW1le+64Y+x2e\nII5Hq0+yPCPT+/f/46kL08pF37LPfF2xbs/99BEtTviMO4XGPg/56smv4rR4ZL9HWd5TH+HfVLLy\n5XLuqDR7NL4vOADj49p5SMVb6vl4MyLjzdwJe97B4dzlxzWWuA/vxFiMeXkIyzMeh3dW21S89xob\n8/00lYqv+x016YUvnmB5A8PC1P8rbHx/O/v3qBlwhez3bKwW03c9pZSaTt7JDnwLOZCfK5cOdpyA\nehtxE3ukrk7cCXfXtq+1+JnHsHbR81v4Yzd2jKUl9kEm3qhzrQP5847MxWcfLfkBx5txedqydc9q\ncXU16nDYjOksryQPLlYObhgzDQ1covp/gzBnBAKBQCAQCAQCgUAgEAgeIOTHGYFAIBAIBAKBQCAQ\nCASCBwj5cUYgEAgEAoFAIBAIBAKB4AHinj1nAttDm9nWwj0fG/LRL8J1ALRdJWe5XbGxFbSw1DZL\nr2vvMJT0piA/GbmT3hNKcdvl9j3xd2+dhObNrxO36TZKQw+S+nz0ZbAL4z1iTHOg9zxw8qIWD+/L\ntWy2un45/wd71/P+CFSfR/VrFi5c3354I+wWu81SBoelF7TnDRW8Z0faH8SutA80j4tmRrC8M2uh\nXe00EM+qVyi3WrNwhlb5x+dhVzeoG/++gIehpT3xGWyUl69ZpMXF5/lYojrqcynQjFJbWqWUciS9\nEE7+ins7YP4AlteyHdrDm7nQ8Ree49bpNlbQhubnQRvd41Gui8zeC9s+72eUQeHsD3vcjLg49tmg\nN2Hv3VQP/e01XS+Bxsp6Lbb0gY11aADX6idewL2dMgh2lXqbQtsAWFw2VcFKL2U39LIb/+D9bDyI\n/WousR6/kZ3N8uY3oAuJdzdoxjf/eprlvfMKem8MXYBxcPDD/Swv1Btjp74QtSv7Nu8XMOB13vvG\n0Mi5gT4uHoN4T6XxS0dp8dVVGLdLfn6V5W14AXruIfPQP2H7B7tY3rjFI7Q4iNiMF13JYHkHE1AD\nljyHnlEOxA44h4xtpZSqzEcfgxrSNyPvLJ87VIPvPw36W6dU3vsg7SC+P3QKasXA57iFspMP6kZN\nDcaptU6rzxpiGBjUorEggfccoDbCMU/BHtzxFO8x0dSEPko9O8Nf2NiK9/aZ/PECLU76Gr2HanN+\nZXku3bHmmZvjWdcQq+cZn3HtetpW9ApKS8Jz8w/l62d5Fnpb+A5Hv5jyG4Usr/g05nBRKWy7Pbz5\neuk3BZr5ituoAZaWvA4FjLr7Omso2IXg+20DeB+v+hLUCBML3dgiSE3HGkX7fQ14uA/Ls3RHr7L8\nw+i9FPRwD/53y2EHnPU37OFry9H/ImgGX0tpXW5ngoFvbMbXxQnLsU4c+wi9g/S9xOyssD/JK8Wz\nd3H1ZXl+IaiptEdYY0U9y7PW2TcbEtknM7S4Mon3trH0xd916oF6YxfIx9WYWszTvAQ8T2rhrJRS\nZmTfRveh1/7g/fnKa/Gs3PrhntG52HMOt4ptJOvnkjewCWxr4/vuqIdhmW1KLHvPfHOc5XWbibxT\n67Bmdu/Er70qB3XIifTzsu/Ie0NUppSo+wljS7yKhI7lPTVq7uAcaY8mfc1vqsA9zCS9EIOGcP92\nC1fsURvJMdTqXCmlSuIxFnJT0H/Irzt6jZga8zlGceYX3He9jW7yd+e12DWaWL7rrK7pWp1+BOud\nZ5A7y3Pqgpp/7gu8h9CapJRSRWR9bs9dwP8zck9kaPHSFY+zzxxDsYczMsK7X/xt3ovNPhnX1fdp\nzMvGej63rUkvLGtXHDNXZ81dm439cMe5dG+DNVz/PuaShfFWT/oOWfrasbzyq+ghQvsiOTj1ZHll\nN9Cv6M557Js2rT/E8qZOw17n9YWLyXfzPQHtc3Y/kBGH97Gn1r7MPjM3p+MOtSk1L4HlRc5FX6Eh\nRnihv7aD914qu4z9cNcAfy0+m8V73TRWoYdUcTz2GdFB6N10ez9/1/AdgudQnIg11yOqK8sb+Cb6\nh5Uvw7rfZTCvQ9uWwRJ96Hz0k/rhi7Usb/ICjEFHd9S11A383YXCe+H//jdhzggEAoFAIBAIBAKB\nQCAQPEDIjzMCgUAgEAgEAoFAIBAIBA8Q95Q1nUyA/aCLLbcQ7hwGSv62NZAQjJvNaeiV10BHMzLD\nb0HndnEqaEcirSguBrW3VUfrpBTpdsSCztMVtGQjHZ03cARojan7QBWuud7A8pyIrfRDT0ASUJfH\nLUjzT4OifrsA1LZBg7j8qSiey3L+D5p1duOjnxl61zxDoeY27qfPUE7V6jAX11xfAgpfZVopy7uR\ng2sJbQIf8uS3nE4b1AGU+HEzY7U4+yy3zjW3wXPsPAy2x+d+BtWeUgWV4vaI8x+FzahLR87PLEvP\n0OK+j4A+XHSSSwuone+MT2Dj2q4dHz/lCXjGUXNAlbu67gLL6zSJ080NiVVz39Lih54bwz5LIfRZ\nKhe7U1zM8rZ+BIs3atsXO5nLsyKcQDstSQed2SuaW1Im/AXpH7WA7PESaH3zO3LpYA2hjO77E2Nn\nSJcuLK/7YlBam6oxTxd3fpzlFd/EfHbvjPk36SNun1xfi2fY2gQZQE0Ot7K9sxcWwC6zBylDo6oe\nlH8XHy59eGX841rcyQf3+vz8T1gevddWnqjLMz99mOUdehcyJ0r//OWLv1ne5KGQrjXWQcaQ9huo\nqu0ndWTHFJ7C3PEbD7lSSwOXoT7+DOZf2jrc25Qsbgc87GXU25z9oG+v28apqguXwrbQwg309L5v\nzGd5O17+VIsfXsNtp/8r8o6CIusYyenlo97DM8jYA2nPjavpLK+dEWrb8SuQRvVvamV5mQdxL6jF\n8UjdmmFmBqpz8uZNWhw9tbsW3/wljh3T4/nncR2XQCP2juRr+PVtoPMaEcteE50E6/wNnGtML4yJ\nwBlRLK8mn6zvRPZWU8XtOG+vgw14zKvdlaFhH4LadP5Lvo6FT8c5H3kPMk3/YC+W1yESa1fxSUgG\nyhO55MvaGxKbthZcc8E5fs2efXDf7MNRe3sOB028spJTyOsq8Lcyt5P9TQPf33SchhrbZ8Hd66tS\nSl36A+M2YgLWk5LTXHrqPoRYF2ejrtPrU0qpuuIadb/gHgG5kqk9l7nEbYGsd+QC2Num/87v34lk\n2KJOfHKYFrfp5mLgIEhe82/FaXFjM695Q1/D+mduBXlQQEey/zDie5uKRKxPVCLWXM33ii69sS5Q\nKVTsq1w2k0tq1MCnIOemdsdKKWXtCylP/K+4X2F2/F7ahfB13NCgdtLZ//A5Qd8BQmdhXhpb6F5f\nSF5nsr/Rj0e6r6xIxtxp1a1dVMoUPARS/pTDkEJ3m89l/XX5qNFV21C/di3nkmP6rlFfhlYL+TpZ\nsGMIxk/Xp7GW1hXxOXX51/PqbgjszKWiVj52d80zBCxtIY88+MNR9tmYFyHZPvcDJNveTlxO6hqN\n94eKFOxfD67iEqBuMZCiHD+Md8meHfm+73wqxlJ4K/ZeVeT95sIf/N4NfRv7hYo72Kd0mjGR5eUn\n412F3te6Ov6uc+wPXC+VKT7xDrdgrsrAunhrLWqwqSOfiz/uxp6o78tvKEPDjLQcyTjArdjNiSTQ\nvw/ssqNe5O0Abu3BfmLT75DQDouMZHkuvTE+zR0xfipv8/dPivYjseY2FOEdZOv6wyyv7Q+MmZG9\n8G5g7sxlbPSdZMTbeLcyMub3vQept1c3Ysw98fVc/nfbUEc2LvlQiyP78D20qS2vxXoIc0YgEAgE\nAoFAIBAIBAKB4AFCfpwRCAQCgUAgEAgEAoFAIHiAuKesyZlImSrquMsP7UI/Nghd7Y11zjnWpAO6\nJaHg22fx7tvOvUFnc7fy1+JqQvVSSilzJ8u7xpSeWV9QzY5pLMW5B8QSCYxONlNFOtJThwG9G5BT\nFKi0rZtAU2uuaOR5EaC8W+WC7qjvwJ//DyiowbyJv0HQYS4oXRXpeeyzhD9BC/NwB8XQd2pnlvfw\nC6CtbV9zQItHTIlheVRqVkBcjwKGcbrhnb2QBN2+kKHFJqT7vaudrjt6DaicDuG4t83NXHZGqfd0\nLFDXKqWUmjwa13RrLc7HbzqXfiWlgaZY8TvOodeLsSyPOl+F9FMGRUwoaLV2gZwKemwdqIfta0Gd\nGzi3P8sLjw/UYrf+cAWo11FkbyRAgjH+Azi8tLVxtyb6rHf+GafFUc34vvM/nVEUZdV4HhMWgIqd\ne5B37a8hLhLXN+G+/g9Vvz8ki5WucPyxc9U5NFjBzaDw6jUtvrOfuxD1eW22up8Y8CykUnk3ubvb\nyr+/0eLUPfu0uMOYUSwvZTc+M7EENfLgOztZHq0y3V+aqsUW33K6ZodHIa9qIc/u9E3Qt6vf4VKA\nZ1Y9rsWJq05ocd83uE3ZzZ1btfh2NmpPl36c4llEOvCbu4J26uPM3UWoY4VbB1C+jc05NZlKIA0N\nI3Msm05BfJzdWg+nAwsP1JsOflwOc+IE7ue4WRgTpRd4ffYi7h0ehHb/7szlLG9qXzzD3q+9oMVp\nZ3D/vUfzcz37PqRfnmNQn5N+/5PlXTwL2UdkR9QQC09rljdsKij+dP3Uj8v2LmR67AwAACAASURB\nVJBIXEpHrQnI4XXXuQ+XURoauUQ+ETqar3dZuzD2ez2J6zK14XMnYwMkac59sIexC+LjNvcQ6lun\nuaCDt7Tw2tvUCFlh8FBIbbNv7NFicwfudGnlAGq43xTsdarSy3ieO8YjPW/PEUEsz4+4dd3ai2ff\na2ksyyu5grGacTZDiy1MudzNuz93pTMkbIhjIHX+UEqpMcStzpTIefSuKxP7Qsq0/QdQ8B/7aCbL\nu/jFD/gOb9zL/q9xh5jmOsgn6PP1Hok5ZmHP1/DUjYla3EAcdqKXcIdJY1PidPMpaPvUnUgppWJn\nYV+W9CfkNe2jueMW3QIHReAzIzP+alAQR+YpVyYYBGc3QcYR1o2PR7e+OK+z32Gvo2+1YOuDmlNB\n9orBoVySlUrktdVEZmzpxb/P1RVjK/sErr/TeMjXj60+wo6hMh1zE9zD3mN4ywMjU+ydqjIwT69m\ncun9pIcgc2xHXG/StiezPJ+OeCdpyEd7AufufN1J3AA5RsdYZVA4REJaO3kWHyTFF9EWoedcrFW/\nvLuJ5VUuh7PRgEdQd2OmcAckr95w3HGMwN+9uv4iy4sdCBncmQ/x3U6uGCt6SSB1R6KSuF3LvmR5\nvZ/EHHMiDrFlGVyWF9kZa+bmQxi/oy25rGXTOsiVZi+dpMXb13Dn0cdjY9X9BN2Lt5/A92ltzbgf\npQWQQd76kd/35ha8K8x5HXtP+wAPlvfSpHe1+MVXIN09toU7zUZ1xns7dYZ1IM/+oWD+HKmbbMqf\n2G/ZevFzuL0BDlKntmEfOeSpWJaXcjlDi/u/AOl32XW+7lzdhvoycxWub/1zb7G8yGj+TqyHMGcE\nAoFAIBAIBAKBQCAQCB4g5McZgUAgEAgEAoFAIBAIBIIHCPlxRiAQCAQCgUAgEAgEAoHgAeKePWco\n+sd2Zf++Qywb3WOgCTW25HpjVQl9WBOJS6t5Xxhq31VKtMMtOtvphkJoSWnPC3Nn9J9pKKplxzj3\nhIa6rQWdGFobdT00iAD3rxWwm6X9PpRSqono6fxH47Pr2xNZnrsJvs+U6MT1FoDNOgs/Q+P85+ht\nETy6E/uM9mbwnwEtrZmNPcu7sxF9OsyIlpbaPirFr43aAKYd5L09QifjbzWcQR+J7sT6lY4JpZRK\nWg09binRu6dsS2J53V9ED4eCo9AKhzzKrZGvE315finR52++xvI8HNDPqPvz6OPS2szHj//M+2el\nHf3Ko1p8e+8B9pmtBZk7CbgvRrpxdvIsxuesqdAyr39/G8ubv+ZxLTYxgQ67upo/Q1Nb9F+gfZRq\nc9EvZvT7T7FjairT7npMlxe4tjXnBHTyN3NhZzjnqwUsr+AS6pC5Hc710icbWV7nZ0n/HWJjmqjT\neHfMwViy7cjniiFgSuyHr/zIdbXW5vjbLl2gi62uvsnyqO437zjG98jl01jee498oMXDG3APc/K4\nxbpXLmxcqZ63RxDX/lPEr0afmYhHMGfzU7glsTG53ogY1Mr2I7l1+pdPrNLioRGYR311tdcmEDri\nG/HQdgdN5ZamQxZxO2hDwmsQNOQZ+3lPpbw0WLP6uaB3jsdwfi+nT8f8q7yNXmdVut5u6z/HOK4n\nvSgWPsKtK48cQy+B04/CVnzB97Da/Pvlr9kxY1bA9vvnZ7/Ff380Vv0bqKV6/lFuD25OrM0rbmCM\n0fVSKaV8p2BetW1BDahO4/aZutZsBocjmWNWHrwPScIOaNSDSb8S2mdFKaVqKrHXME7APKrL4/ub\n0GnoG5X0PfYWPmP5+Da1wd+ivdSMzdBHrVm35pYVoj7k7sWcsCc9IJRSqrUJz8Gb9BjS2yvbdUKP\nDtqvL24lt7X38XRVd4NHT27f69zF8655hkDxKfS10/fJo3b1tXm4l7cuprE8nwz0B3p0BWro3+9s\nZ3mRfuj/FDplrBYX3IxnedaeGEvlqai7dF9bk8v75PmOwPNwDvPXYtoDTCmlsvZhjaB9Vbx0lsQO\nHfHse4ZhnGfqepVQtDZgfDiG8bHzf7N9/a/o/wTWZ31fJyOyj455bqAW1+l6SyZsxp6h51PoB3Jp\nLa/RTrboF+QViXcDl27eLM+jN/pcnFy5V4utvfF8OwTwYyxIX0PzzEr8dxdu39vWiuJmRt4NZqyY\nyvLonD3wAXrNWZrx5xHSB+tpzm7sp/P2815+Hn53n7OGQOVNrGPb13FbY9ofqEsk7uvYobzJpnss\n+lPt/xz73GmfzmN56bthT91Cxm07XR9Rj0H4Pp9R6Lnm6NZDi/U91lqbsL9qqcOaGzaU15f9X6Ie\nNpI1rlc03zfeTkMNePlH7F9bdO+fBeXor7pm+Xotfm4F74P4PxbyBkbv1xdqcVnhOfZZ8SX0DqJj\nOvSpHizvxCd4/rZn0E+Q9gNVSqlPt6/QYlNT1OGJnXn9sXXBmDE2xrs+tS13cuKNPmtr8ZntUvSv\nO/0Br+tBw7EGu9VgPlt58j1BQPDdbd5r0nlfXG9vzDF6/7r25eOiPIXvw/UQ5oxAIBAIBAKBQCAQ\nCAQCwQOE/DgjEAgEAoFAIBAIBAKBQPAAcU9+VI9RkDIZmfDfcW4ngDJknQZJSEMBp2EaW+JP2EfC\n/rjXCC6TOv8LqIfmxIrRjdjZKcXt2nJOZuAcykHH332a26r2SAUlKqAHaG7NVdyWtygLtLz+XWDr\n6dDFneWVX87X4qO/gt4fFsBtCquLQLssSccxjfHcaq3HlO7qfiIlD3TacCdu6dfaCgofpQHr6bT+\nM0HDz12F+1Sfz6mlJy6AdmtlDnrqmOe5zVnOTkhkqEzq4hZYsvm5c2rb7QLQxod1hkQpUGez19YG\n2ndNIc4v59RllldSBPlNh96QKlh5czpb2U7InKiU7q+l3AYwpi/kGF7PTFCGRMkd2LOln+VyAv9A\n0MZP74W8YfRL3IJ54vP4d/I3oNuNGMrvX9ZuSIWs2mPsxG/jdnnjP5ijxdOeBV3RoyPohdlX4tgx\n1aRWUDqgXtpH5XYz3puixfTZKsWtT59c8zTOYTC3b93+Gp7V1I9hDz75ZU6hvv4rrtH3w4eUoWFl\nD8q/f0wg+8wjBrR5Sps0N+fz4OZZWCuGj0Cdyj3B5XhjumGut2uH59NxMJeQXfsDY6b3K+O02IhI\nKeryOA0/iNgUUipx2bVClnfjJOZ518mwtfz52e9Z3iMLIROg0tgK3feZOeB5xS4dqv4NCesgNQj4\nfOa/5v3/wZ1tuM9UtqWUUn1eGaLF/7wL++POLVy+Yh8KCi+lz3Zf3J/lJS8FJXj2Z5AhmZhx29fd\nB7F+Brpjvbq5CWMlaiS3Nz28fIsWU1v7Wzv5ODI1xji4+O1pLdZLk7vZY40IGof74KCTSMT/hHPt\nNARj0a4Dt5928OHj1NAwcwA9uiSBy5U69gcF/ubPmB8mxpyW7TsMewsLV8i6TnzL5X2ZlyCfjJyF\n9b65nsu2rV1Rywtv415TW+yqWyXsGJsgzMWOT0P2UXrrDssrOIl/Z16BHKjfa2NYXmUyZFLFpVgj\nQ3pxaZ6pHeaicSruC5VmKaVU9n7UAM8nlEFBpbHNOgm8Cakjqdsxpi10kpDcMtzb9uR5jFs2luVd\n+h7PI/cKYnNHS5ZXX0LkRpGwwjYyhY1u3Q4uVW2uxnp3Yy3GTlIaf4bRfSCtiH0ZFuC0Viul1I1v\nsAfuuLCXFlNZv1JKuQ/01+KqdMgKs3beYHmlWbhHIVw9YBDQZ3X5By5DcvNGXbBwxxwrSspneYHd\n/bW4LBGf1TbwfX7gQMzZ5hrc9+x9XLaddhVz1jcQ0rC6IjzfnBxuYV6bhnrdawb2Vfo5UXIBUpeS\n21jrw57ke7HCMziHrgOx1t84zc+1Ngfrs00QZPiWHnydaGdy//5//Emy9y+p4vsFf1dIPags27kP\nl0A2lEFGGdUda2Z5RgbLO34QNdnTEfUvWFejjq06qsW9Z1MJFfYHqVf5HOs2DfW56jbG/eefcvnT\nG19gv7l8MWTBHTy4VXPPmXim1dmop/aBfF1csgxW0n+uxd5BLzutpHIYrg43CJqbiUyHq8SUaw/I\ngwrPYg25coi3lpj62TItTjsCOV5JfA7L2/k5PusVhfXecxh/jneOQsbWYTjWKwsLjJ/Ni19mx4z9\n4Dktri7E+p6UlcXy+vfE/vDCx1u1+OjfXNI1d83rWvzHcx9q8bzvPmN5VI585zTeTy6d4pLS2Ll8\nr6eHMGcEAoFAIBAIBAKBQCAQCB4g5McZgUAgEAgEAoFAIBAIBIIHiHvKmqw8QYk7+zt3FjEmVHbq\n2NOqs1jwHOSvxUd+gGuQnvpFu83nk67VF66nsDy3HEhbgqMgXUi+AKnQ8K5cMnWN0Jiaz4FSZ23O\nJQ30zG9ngnZoU8hdJAKJBKbmOuiFlHKplFKeUaBPGm2/rsXtRwWzvEodTdnQCPMBFU3pHDCiRhDX\npIp6LS67yimjt+MhpQmKxn136sqf45zZ0VpccA7HUGcfpZQqrkAn+67TIL9I2Qnq17EkTq9fuBy0\nv7gfQP3NK9vN8qhMypl0ie8QxDmAHZ3Qbbw6A2OuoYS7fV3PBlXV/EtQk8fMH8Ly7udztPOCZG7A\nG9whoLWVOLz8jnDje3+zvBFT4GBAaac+EXw82gWBRmxnh/Gx62fegX/N/E+1uF9HUBJrsvFsE45z\nKp+9Fe65XxSuSS99aCEyp29f+E2LH35qNMvr4u+vxSm/gQ4d8hh37+naA1TQXa/9qsVhPfm1u0d5\nqfuJxkbIdFrquERr3WJ06KeuPUMiuONC/xfgRERdXPKOZbC80zdBne9pBheS0zu5HK+S/K2cV9Zp\n8ehlkMFt+GQnO4a6Rcz+8nEtPvr1UZY3lrhP7H8b43FAT+5sVk1kG79ugkvDzP6cQx8wBfXFyspf\niwtS+PqUVXL/5qJzD8y/Ozr6v0MmrmP0Cjis5Z65yvKyiayT0tDd+nJp7JSlkFYcfA+OIeM/mM/y\n5n8yS4tbm4kjYRPGWOXNf3cH2P8jnlvfAVz+lHYcY3boEkgpzB24nGPra6AEuxLHHks3G5Zna4nj\nKpNxTjb+XMJ8azMowT2eMDx/uzoDz6osPpd9VlYN6YIjcXdxi/VjeZSmbeaM2uZqx6WxVKpNnVqq\nUvg4tXDEd1BXJioBtfLlTopNVZBm7HwN1PuwLpwa7k8c+ipTsKepSOOSLlN7rNUu9rgOpy58rc/e\nRfc+OG+XrlyqcHUXxj739Pjv8BqJPZbe3quEOIu4BENWcf0ir6fdJ0Ju+c9qrHHjl3Np8p1ijNUQ\n4jx6Yzun9GeRvLEv4/9/XvgNUqNeT/L1icpBvcdBUtduL///p66kPuSfyNBih87chcfKB/ue0kQ8\nX/pslVJq+0fYO4V6Ye0LGsedRXwncqcaQ+MIue9Dnuf7KupOU5pYoP4N1u0xL0yI3LSraxTLS92L\nvbhnGK7ZyJTf69AYIll0Rw049Rskbd2G8XWs5g5kKw3E6azoGHeFNLbGNRVWYr8UbsR1JO79/Mm/\nML4zL3IpDnV8MicuOuYO3PH05Ko4LQ6JeUwZEo+sxB6jKD6bfVaZBPlXcT5qWZA/f1dbOfsrLbYj\n68RY1YflzfgYf2v769hXeA7kcvYm8k5DnYJ2Ld+lxVE9ueSYvgu8+dnPWjypN3eWqsnCs44h+1/q\nuqSUUvve/UOL27vACe/RT7jcmj5DR2u8S9K5oZRSVfW4pshJC5WhkX0eEiLnCL4fMTLCvPIdArmW\n10B+D/Nv4DuqrmONaz+RS5UfGo45RtfFukIumfYbBAnQrd3Yi547gHYPA2fFsGMaGlArGsrwTjf+\nMe7kWVeNsVpRi7zpH/G2Btnn0MLE0Qb1oKGBSxvPfYA9dP+3X9Ti1P18r+gRHq3uBWHOCAQCgUAg\nEAgEAoFAIBA8QMiPMwKBQCAQCAQCgUAgEAgEDxDy44xAIBAIBAKBQCAQCAQCwQPEPXvOlBMdbN95\nXM+VvhX9QMxdoXHMz+a6dsdc9LaI6AQ9oN7SLzUd+mBqfedkw/XqId3wHVTj3Xsm7AKriF2vUkq5\nEA1g2HBoZ6kVt1K8103HCPydhMu8700jsYftPwBa+IR4bm/nmIxron1q9HZ2DUW8x4mh4eCFngZ6\nLd/+TeihMnI6dH1H98WzvL49YOPn0h063doC/n30udr44u8mfM9tyaLm4nlZuOIZd3sO48x1jws7\nhtr5dukBXXbHQn7/gp+Elm/7a9u0uL6Y24OXXoIW+3Q8xvOkpdxadNg46F3P/AM77gAjrp90jOCW\n64ZETQnGUs5ePh5DZ0OjHf4kbKe9x4SwvN/eQK+R8Q/HarFXF67nPbb8Ry3u+yp6wYyazq3frh2G\ndrua6GBT46DBD/Xh/XEKyzDHbIPQZ8rBn/dyaNcOpWnum9B+VtzkPRp8Y/y1uCYDGuDM/QksryEf\nYyRyIPT0teQYpZQKXWR4+2yKkqt4jn+tP8A+e+F7WDPueGu7Fl+4zXskBLVCp72J9PmYqLN+DT2D\neVpViO/Q98PIJD0S5qyep8WWlv5a3N75EDtm3Ero1S99Cv12zKN8LJ1ciT4ptN/QxQReKyevmKzF\nHU6gR4WpE9fMf/P0GhzzOKy0jYx5TU3J5T1EDAl7Yl3vM7KFfUb7FpiY4D6XJ/BeCRZupLcI6Rly\n6NN/WB69Z526o4dI/uXLLM+G9CGpIba3Jecw3m5mchvLMe/ANr2GWHwWnuD9DEYvwH2muvC21laW\n98iXsK5M/HKHFnd9cQbLc+uM/nBBEwZp8fWf97I8Cy9uA/v/sfdWgVGezfv/HeLu7oGEQCDBgktw\n10IpUiiFlio1qtQppX3rbpQaRUoLFHd3d0Lc3d3zP3rva+b5tRx83+Wfk/kcTbuzy+7z3LabueYy\nNdRm1tyBW6K72+J65uehP0vWxgssL3Ye9jErJ4xVWx9Dnx1iPU9tfh3C3FjesY8O6njIS8QqeTDG\nlbGHTwPpf9Kf9N3I3Mv3ibIEro3/L4mbeM8UqrvvTmxljWeH4mKMme73YE1N3cjX3qGvjvnHf9cU\nJG+4pmN3w/6bejZNx93mo9tNz0C+/tHr6e2M67frLd7LbtQ82GKbW2N/MtoGxxK7a9r3behQ9Naj\ndsxKKeUWjX4+J79GP72yap7n34weabQPYO8evFdaVSb6mNBeFtQ6WimlRs5CT6/zW7GmlK0/z/K6\njkG/It8AZXJqG/C+6BqjlFI1eRh35VfwnSRoRAeWR21/932LeeTlzHs0BYRj/XYMw7ysL+HnyEYy\nr9KJHXyP0ejJdesQ7yPR0IQ+UcOnYRxUGc4tvqTXRu0G9KZJ/vkyy7N04jbK/8XDjX+mC2txXu/3\nKM5ptMeMUkoFeLiru0XCj1gbIx/hluD0rBfhh/m3/dW/Wd6ChTjD+AwK0XHK73xNof2WJr89RccV\nqbw/aF0e5k/RafQWyS3FHtk+nfeIiVyE7w/f9XkD8QtrWJ71Xqw9wx4aouPC47y/UH1jo46p7bfx\nzPL3F7t13I30Usw19LAZOrWPupscW4f+fbN68zO/mRne88kV6MdTRPomKaVU1HCM/R0nMDYXT+K9\naU5/ij60DjZYp+j3CaWU8vYg/Utd0Ddr/Cv4rnboI35GjbiF/Y72YTKuLzb2WHvHvoseMRln+Vks\neQ96OF5NxxnJ6/3fWF7007hmu15eoWMLc3OW9/EDr+n4zU2blBGpnBEEQRAEQRAEQRAEQWhD5McZ\nQRAEQRAEQRAEQRCENsSstdXgPygIgiAIgiAIgiAIgiD8/4ZUzgiCIAiCIAiCIAiCILQh8uOMIAiC\nIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiCILQh8uOMIAiCIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiC\nILQh8uOMIAiCIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiCILQh8uOMIAiCIAiCIAiCIAhCGyI/zgiC\nIAiCIAiCIAiCILQh8uOMIAiCIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiCILQh8uOMIAiCIAiCIAiC\nIAhCGyI/zgiCIAiCIAiCIAiCILQh8uOMIAiCIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiCILQh8uOM\nIAiCIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiCILQh8uOMIAiCIAiCIAiCIAhCG2Jxpwcvb/hcxwkn\nkthjsQv76djK2UbHu1fsZHkD5w3QcXV6mY7Nrc1Z3smdF3Xc0Nys43ZmZizvvg/nkv/CY2c/2Ktj\nNx9n9pyQmV3x71pa6jjp5wssz7mrl44dgl10XHAyg+Xl387Xcaf7uunY1tOe5RVdzNGxpYOVjh3D\n3Fjeha9O6HjyRx8pU5N6dZ2OHXw92WM3vziq44iHe+q4sbqB5ZmR+3D26+M6Lq6qYnk9+kXq+OKp\neB33m9iD5blF++o4cyvy/MZ00HFrC/8c1VkYP47Brjo+9tkhlldeU4P307Ojji0crVhedTJeL7+8\nXMfR07uzvEt/YJwE+uH6OXRwZXlmlhjT0VMeU6bkzJfv4T1M7sQea23CharKpHOMT2/nMH8dN9VX\n6vj6V6dZXswzQ3RcnYfXW/XaOpb30DuzdFxyOVfHWzZhTD3x3aPsOdc/x2OW5P2F3R/D8o5+eEDH\nsff30bGdryPLu/gF5k5NfT1er1swy/PqH6Tj418c1nHfRQNY3vqVW3T8yh9/KFNzbPmbOjaOHxuy\nflzafEnHbg4OLC94dISO6/Ix/6zd7ViefSDWsMKT6Tp2j/VnefUltTpuR8Zw6ZU8HTeW1LHnOEa6\n67jiRpGOi8srWF6/F4fpuDIDY6ngaDrLi7+N/24k6//op0eyvKoMzFO6RjdU8PfnFIb35+MzQZmS\n7xYu1HH/ST3ZY831eO+tLa06dov2YXkpa6/qOHRmFx03VvF1184H433r21t13CWMj29zO+xr9qG4\nLu7dsM62NDSz59z86byO6ZjKO5jK8qydsL+npGFPG/joYJbXQtah4nPZOnbp6s3y9nyLuT35tUk6\nTv7lMsuLz8ZrPLJ6tTI1l9Z//q+POXXAHt2K26h+f28zy7Mh54lhI3rp2CculOXVl2BPsiHz1MqR\nnxkaKpFXeh3zz7tfmI4LL/HzSEtdE2Jyj+lYVEqp2kzMTaco7GNWLjYsryoN83TvDuwNo8b3ZXkF\nN/D+0gsLdUznr1JKWZpjTTH1fdzy7LM6Dh8dyR5L3Z+o4wpyJoid24flVSRg/WqqxPzz6BfI8izt\ncX6wcrHV8a0v+f5JhotKycdZ0Z2s4/6RvooSPCVKx8dX7tOxtxc/K1p74N+18cHr3dp/i+W52mNc\nVdZhbWylg1kp1Xki1h7HEOxHxeTsqpRSx7ZjrXjil1+Uqbm59wcdJ+zmn6XXEwN1TMd03oFkluc/\nDmtY6u9YXwMmdWR5ez/fr+O+43Eu9ezJ98W0P2/oOGwOzicWFk463vX6evYcZzvMbc8A3Du3nn4s\nzzMqXMctLRhzFVm5LM+ajDMzc/wtPW3DNZZXWoC5Teebgwc/O3S4H5/Xy2usMiUHli3Tcci0zuyx\nvz/ZpeNBcfjO5DeiPctL23Bdxy11uNeuvfh8sfPDvkjPuclkX1VKKf/R+D5RmVisY89+OA82lNey\n5zSUY7401zTq+Njf51hehC/ek+8A7Mf7N5xgeU1kPewWEqLjyIX87HDh65N47XE44x9de4rl0TNR\nSNf7lKn5ZsECHfef2Is91lyLvSbzIvahfVeusLynls/T8Zdv/q7jCT3498DIB3ENsndivY5+aDbL\nq67GY6ufXKXjAdEYZ26GMVJXUK3jTvfM/MfXUkqphF+O6TgpKUvHQ56IY3kVySU6zj+dqWPXSP6d\nujQee6E7OfuU3ihkeWcS8T5e3rBBGZHKGUEQBEEQBEEQBEEQhDbkjpUzNen4Ndbdkf/Fmv5ambYO\nv+JaWvCXvP03fgmNmoWKhLLr+Sxv4JRYHdcX4a8cTVWNLO8A+evh8Nen6rjnM4N03M6cv4cTK1FV\nQ6tyHGz4X4zStuNX697z8VeiwHH8l/eLp/DLftKnu3Vsa8UrM+oa8d4nLBml48bKepZHKz3uBknr\n8Guyawd39pjvaPxybWGNX/13vrWd5Y19bbyOI8fi18rsw/yvrLR6pN9k/CqaZ6g+urIH42L4sjF4\ngPxlx/geJr07k/wXfld0sLVledbkr5l2waiiyjW8BwdX/HWpzxz8daauiN+P/k/gL8Qtjfjr8Onv\njrO82gb8BSR6ijIpuw6exXs4cIY95u+Gv9BM/88DOk7bcZ7lnf4Fv8APewnj0asH/6tO/hnc08u7\nMLcf/2IBy0tahWq3W+Sv3FNnDtVxayuvBCirxq/Z4bH461HuIT6Ouk/HL+wHfzii485hQSzPg/x1\nqqURc7s0sYjl0b+Ijnprho5/eeoHlkff390guwB/vWnJ47+k97wPa2DMBPylzlh1ceVz/GXG0Q1/\nGXOK8GB5jZXkL0Dkr1BVqaUs78gmjKexZJ1yDMe1Pbae/3W4fzSqDCuqMV9SCgpYXuAe/HWA/hUq\nL4vfnzGvjtMx/QthwSk+ZwsuYJxFdcFfwI9+dZjljX17hrpbjHkW1yjJ8Jc683Z47+EPYgybteMV\noFFLsN5k7UXloKuhyuTgB/gr+sRXUQGUZ6g8ciL36vYWrK1Zp5EX/VBv9pxqUmlG9/Pg6fyvngnr\n8Fexhib85awmr5Ll5ZA5nEwqBqZM5q83bC4+++VvT/3jc5RSatAIXsFoahzb45oZCnSVuQ2pRPJG\n3qKP5rK8pNWocKskFScXV/IK4rHPY4+7uQrrcmYRnwe9SYVpbS6q4k6s3KNj31Av9py6Qsy/EFKF\nVXaDX0+33ljnD6/BGuLh5MTy+i3BfjeMrJs2Hrwyz9UfFVoB/fGX48rbJSzvdnKmulv4dcTamLgn\nnj3W7UGsDynrsY85t+d/6Ty6Gn857T4EFSylV/n1c++Bv8xuf+1vHU98wOU6ywAAIABJREFUZzLL\ny/gb58NJ5Fqm/oH90tWwphdfQaXK4GWY55XZfD0tuYA8p3Cs9wV/lbM8T3JP/cPxb9mTakOllGok\n97fgNP5qXJ/PK6LpunY3yCHnyIIKXn154tPDOqaV9PllZSwv8n7sIS4xuHf2vvwzj3wc1ZyWpCrQ\nyp7n0f3qxhdYp9y6YP51G9OVPef6XlTbOJNq090/HGR5XYOgRIhYgHWuiNwDpZTKuo3vJN0XYP22\nC+LqgKCpWGNpBSKtSFVKqQvPJ+j4yV9MWzmTRvb+ji68QsLHGe/XnpzJLWz5d6Yz127reNqLmAfW\nrvyMX0cqEWkVatBE/l3twPeHdVxFKsjaX8c5wvg9kFZsxnRABaSfK690pvtV8ibE4x/n1b55+1J0\nnFOItbHsFj//9XkuTsf730GlEa3OVEqpphr+ndjU0O/ipRfy2GP9XntOxy0NP+r4lad5FS2t5n/k\neZzFjOdIm7U4qzh1xnfTvNu8+siv0wgdW5DKMKfOWANzDN9F+77ygI5Tj+O7pHs0/77jQs6y4+b3\n1/HG539leblkvSkia9SD4eNZnq0zxuqPP27T8Ss/PcnyTj57W90JqZwRBEEQBEEQBEEQBEFoQ+TH\nGUEQBEEQBEEQBEEQhDZEfpwRBEEQBEEQBEEQBEFoQ+7Yc8YuBNrAsIHcTSVrF/RSF+PRNX3kgiEs\nz4K4SFjYIrZ05jq/EqJtsw+GXvbCJa7LirsXLlHp26GtrEpBHwXvIdzJIpZ0ez/6MbSfTnZcx+jq\ngX+36BS0n/k1XMvWbyJ6qRzbgl4gg+f0Z3kV8dCT7/oCvQO6d+7A8mhn/buBkz/pu3KLawi9B4fo\nuOA8PqeTHdeX014rtTnQIzv7c+3rrdPoMdEhEm4H6QZt/eAFuCe0O/qpb6D/jh3XjT3n5HvQYVqT\n3kbeIbzXRvA06MaLL0OjbWtnzfI8B6F/SekN6GVvHeLa9cEvQO/YzgK/Z/ZewN0rqNONqZn5BHSN\nGXt5t3HaO6e2HDrWbVuOsTxX6vpDTBuuHeLuCD2mQgPduQ/GauZ2Phc7PRmHf/cj9HXauhE9Yh40\nuBRcSIH+1on0Cmo/k2u3f3lro44f+fpBHefs565xtD9VDdEhOxnGpSVxJElcg674Rj3vM98vVneT\nbtNwbd27hBgeRV+YzL03dXztS96tP/pxrIFlt3G/t3zM+1xE+sN9ouez0ARf+4zrefuPgj48dw/W\ncu+heH+T3uZ9FZrr0XvE3BZzsVc0X//NzDBfii5jTR04LZrlpW2GbryV9nU6e5PljX0Y/QIsbLFG\njVs+k+XRe+z59DBlSmoL0ZfIuz/vgZR+BNevPAlrXkMpd5NqrEC/F2t3zAN6/ZVSKiwQ/SLKE9Cv\nKPMq7+MRGYS9y8UB60HYfKyh+cfT2HMGvgxtfF0R1vSbv15keX69AnQcHop5Sp2zlFLKxgFzbOKj\ncGFKXc/78iTEo4/QwMXoFee+N4XlNZbx3mymhjphGfvnlN/CfrD/M7i7FJTzzzz1YfQfaijDPQ6e\nwp2DbhBnLLdA9C7wNYwfB+JCyBwhyNq9+zB3DVn8+QM6ziP3uOQa75nS9Slcay/SA+JaBu/rNJD0\nH3LqiD4AtXm8D4kz6b3hEIR+Hd79+PkmewXvrWBKWoh7T59n+dqTvRd7RSPplZT0Gx/fvSdgTaY9\nWc6sOsnyzMzR72TUUtz3sgR+tvEZij4V5SnE8Y6MZ2MPKkpdKXob2Ljyc5hnX5ypnH3RL3DuZ0tY\nXvYpOExWkN4WXrF8vNWXYy2jjov2Yby/RlAe79Njaro+BddE62/OssecYzDOCs/jPNdxKO8vknYA\n551r+9H7pWUXX3869cR1u3AS+8uIR4ayPEsnnBd9R+E5e76B29yE53nfliB/vFdf4jzqkcudoOh3\nA3s3rK+Z8fzMRnv9OPjinJu83uDWZOiP9F/u+eBx9t+7X/vxH/NMwbg30CMm1fD+nMl3nNs7cc3D\nKnlPwhlvoo8o7Z3j2S+A5aUcxBnYKwxjs4I4Miml1NgX0OurnQXO59mkF97p0zfYc+iZ8FY6zizj\nXhnH8vKPk34+pFemQ4DhOxF5jV5DsX8e+IOvLxP98V7D2mO8BEyIYHk3VmMv6cCN50yC30C8x30b\n+XvMfgaOXJ2HwVFqzQvctYy6UnWYgT5oDY28X86peHynmH0/etO4ePHzYeLev3Qc4on7nXIUa3zX\n2bzPUWU5vsd5dsP8/Wrxlyxv6izM+8Q1OGs7G74Dh3mjH6CDMx6LnsN7cZqb4zwXNA3X6NynR1ne\n/JV3dtqSyhlBEARBEARBEARBEIQ2RH6cEQRBEARBEARBEARBaEPuKGuKPwbbtdosbm9n6YySv6Ez\nUGZfmcDLytxjUZ5VSSxc7fy4NXf8QZQgDSVyieBJXNqSvA4lvS0NKGml5cbcjFQpRw+UF454g/67\nzSwvYxvK2/Jvo6y5yGDt1z0Spb69+0NCc23LFZYX1j1ExyMfjMN7Ndig0jL0u0FBGkooHQ2204Vn\nUHJ3/cS/W3s1VqMk18IeZX9B47uwvEsvYcy4dkNJvtUtXjpt64UybXMiB6JWxuVXuI1kt0WQER35\nHPI0NwtuD55/Ik3HTbUoZ7YP5VaJNzaibNLLH6/ReXgnlnfiQ/xbvp6wVQ1/qBfLa2ni48mUZO+H\n3CH8Xi4BurkOn8O7CNdv0jRub+cQilLl0nhc23HvzGF5qdtgrbxpEyRK984dwfJqClAu3XEuSsPz\nv8JczNzMJWJUahMxH8+xc+fStIGRkAX89NRvOq6srWV5U8ZDHudNSr6dDJbxVsQyM+lHlLVPe2ca\ny6vOIrIFXolsEpyJ3XVLC/8se96E3V+f2ahXde3KrXN3Ld+h4yGLcI8nLOb3x8YD68qZDw7pOGYB\nt1SuSMKa7dQJ1+387ygvbyVlu0op1XUU1r1T23A9B7byOls7P8htqCwg6Vdu8+4zIkzHubuIzaiv\nL8tzi4RktTwdJe5UJqSUUvXF/NqaEiqhtQ/ha0rwEJTPJu7G2PcJ5bIA7yEhOr61BnbMjo68lDZ0\nLsp7S65BIhExhq9RmWR9cPTGHlcWjznq0ZuXhjcQq3UHH7JP13LZmzmRI7uE4vo7h/D1rnUQSpYz\n9xBL2Sj+2V0ziZ082cOp1bNSSu37CWsPF5Cahqp03Edbbwf2mHMcxrf3GezXXbtxyU4SucftR0Fm\nUR7PpS7NLZDqNVaglL+pipf1F5/Dfhz1GOZSFVmXwgwS4VMfYW4PXgZZgM9AbjW8dRlKw8e9Bpms\n9Rf8ftOpfvh3PNZ/fE+Wl7YHe72LN+a5A7F1V0qpzGJ+JjQlYfdhD7n08WH2WFohxn4skRMY7aSv\nbMT6FfcqZCqx9/O1jEo5C4ikobWZr42Xyev1XoSzsZkl/hZq58vPv3StriN7uEt7PmdtnbA+NDbi\nutrbcxmdhQMsittZ4Xy16/W/WV6PsVhfrNxwNvSIDmN5xvdrarL3YiyF3s8lDXauOEdSe3nPWIPU\n5Vecg+yt8f2k2wN8v0v/E7Kaez98GP9/zxmWV0Pkq0XknNx/DOQT9aV8n8nKxdzM/xXrSz9ik6yU\nUk7kHHD7V5wvO47k63o5kSulbIBUzbgfhy/Ae7JyxFpWW8vlryPeuFfdLdL+gC1yreG6xCzGXKon\n8s/8w2ksz4rIz12JxXH5Lb7mRc3B53Xwx7Xc8+YWlldzCPKqUU/hfORA9u17J01nz6nKwrrpHkEk\ncB9sZ3kdZqHVB/3sjuH87Dn8Uchmmqqx3vfpxe916VXs7+UF+M7ZspFLu72ifNTdZMtLsJCOGxvL\nHouciv3lr6Xv6fipnz9neWZmWHOozGfkQr7fte8PKVNrK/bIjCtbWd7FnfhuPegRnHl/W449bWL3\nFew5O158Tcej3lmqYx8Xvv63Hztaxy0tOMM4n+aSLvcYnEUdHXHvP7r/EZaXSizWx/fEntnryYEs\nz86Zn3eMSOWMIAiCIAiCIAiCIAhCGyI/zgiCIAiCIAiCIAiCILQhd5Q1lVahhNAxj7sUhESi3Ovm\nHpRdxdzLOybv/QJOB+6OKI0M6RrI8gYuhaNGeWq2jp1CuEgpagFkCBVF+He9S9Ahvyaby5Dq64lj\njy3KNVP372d5N86hnD7QA6VyxVXcpeDcTpRPxvRFKXOPubwE7ORPpCyKNKAf/eYElmdufndlTX1f\nRNkWdWRSSqkr21EuZmYG2UGcweGk9AZKtWgJeNqmyyyvP3Gyur4Jrz38xVEs7/jHKMXOLUX557jF\nw/Fej3IpVCuRDUV0Iq4DBuMDG+LCYUnKex0DeDmgU0fc4/QtcCzyGszdvoI7o+Q/5zZKD71Tebl2\nCXES8H1ikjIlfsQBgkrClOIl881ExvXHej6+5yxGKbt7NEr0ti9bw/L6zEIZMJ2zAcO6s7zqIrg7\nXP0BJcH3fvKWjlNP8PLEfsRJrbkO7zXnGC/d3HUJUo8BROJEP6tSSlk4oHx52xqMqXnv8U7o9WUo\nsy0gbhhOp7jE0MKJO3qZmqKLWNuaqnnn+u6kxNwhEB3/i4mLhlJKdY/jUkKddzab/w8ynyuIHIy6\nzyilVB/iVmJmiS2hz0K4z7U0cglLdTqu4ZD74bSRtIM7f/lGo3Qz4QykN8dv8bwpxGnrxO1/l1d6\n78aa4tkHZe31xTUsz9aXzxFTEjAOa37mVv45aElz17lYC5tq+L3e8h9I0yyII0dsIC91PvohnEE6\n9sGea+3B9wzvHrjO5/bCnaRPe0gZK5JK2HMS90OSQ+d5n8UDWN6VnyAldo3Cftzc0MTyqES2gbio\n0VJ1pZSKWYQS99yDcGiy8eKfqe+Afx7npoJKSehcUUqpEytRwj7wlSk63k9c5JTiZ6QOxIHHwSB3\nC3FFafeZLZD0ta/l4yL6GazR7dphLWr2xHWKvYfLi6jzi4UFxn3uhessL4vIi3IO4PV6PMHv99H/\nYH0wN0d5ulMEL9evuA7ZkA2Rqeec4GvqxCf43m9KqDzSyYtLbyY9QSRFxKmluY5fc193yLCS12Lf\nKcvlZ97AQdiDXbpgHlDZkFL/r6zhvySkYH7YHuHrk0MY3kM5cVdqaeb7nX8MHKlurcZ96vIQl3/W\nEBlc4ESyf9bzdZzKvqlkyCGQj1+n0Lvr1mRN5n7eYX5GDRiLM1xrE5EHVnIpq1NnnOdce+F6uIVE\nsbx0hbPG7tcgmR7xBnck3Pon3FVGe0HC7kEcKKmLqVJKdRmDf8vOH1K/2gL+HYK68jlHYywVn+F7\neMRi/LvUqdI1gt8PO1e8Rlk65t/pVVyyGDUU+4v7dC6z+F8JIY6b1w0OkztXwmm152B+Pyjl1yG3\ntw3A9ass5G56/sRRLmMb9ruo/tzZqONUnMObmnAPktat07GFvRV7DpUfVpfgvN/1Se7Ge/ULfL+L\nXoLHmgxrOnWjrcnB56AyQqWUcuyANaAmDd9h7Ttw57Rdm47ruOe8Z5SpCXDH+lV5m3/H2ffaZzo+\nkwApotkzb7C8vgtwPfIPYj73ee45lnfu8090HDYHLUxubeAtQiKjsfbSc+60+ZCqVVRwh7Aej2L9\nLy3C95MF33zA8m78ARlXVSK+i1IXUqWUsvGApNQuHJJHa4Pj61f7IB0tKcG9om0LlFJqxsevqjsh\nlTOCIAiCIAiCIAiCIAhtiPw4IwiCIAiCIAiCIAiC0IbIjzOCIAiCIAiCIAiCIAhtyB17zgxfFKfj\ngkNp7DFL0pvB1xsataLTWSwvpgt08pWF0PwFTeTWf/b20AoWntmj48CYcSzv+vpfdNzlvvk6tgiC\nHqw1hutAsxOg73cNR9+RwDjeI6Y6HTpdS0foEOc9vYjlFd+Ahu7H//yp4+BjXAdKtXtRxPIx52AS\ny6O2hx4T45SpaajGdT+8gduD9R4IXX8z6YtQdIFrX+vyobFrKIPWNzcxn+VZpEB/HdYX/X3S/7jB\n8oJIL6GwLug/VEQ0t6lZeew5XpXoV9JUhfdqacF/Y2yqgV1bxk70r4h6lNvUmhOteM/n0cso7xLX\nO4ZMhRbSj/QYsHLguvGSi7w3iCmh/REqknnviAsp6B/gfgK9Ssb34VbfeSfRw4f2Fxq5bCzLi/8W\nDZIWfvWijiuLE1metQuuZ+/n43Scext9MlwiuQ007V2SugZaYdtA3i8gvww9TbJL8Hm3nD7N8tK+\nTdPxjj3f6DhjE+9hc+0W5iy1VExez3Wq1tZEfzxamZxccg/M2/Fx6xwGbXE+sWqlvZGUUqo6DbpY\nakXpEMb7BGSdg42mA7EWnbRyMctrbUXfgbIsrE0hXWbquLKSW6KrTrDyNDfHPHDrzHuJrXriBx2P\nmwkLxJAO3EbQbzQsil1OQHvtaLBEN7fBlnX6y2M6rqjhPWeGLxmu7hZ0bXTowG2D3aKovSv2oewD\nvI8O7fFC+3EdfH8vy+vQEdfTvSd6X9m487Us/ivM2fFvTNSxGVkb27Xj233WUawbtG/Gia+Psjy6\nVtDeCVVkHCqlVPoljO1hb9yv49tr+GdyIj057IOwXtF+NkoplXsoRd1NMjahX5BDez536pswJwov\nQ1vfoUcoy7Pzx32k1uk5JXyNnrICFqRhp/A5Q+/jfXVS/sb+TF/byhl9e4xrKu3/lPgnbHkTL/He\nHVPnYk6Ykf44uYf5dY4a2VnHtsRCuZ05X6/SC9AbZdRjMDvfvOEQy0v5DmeE8L7zlClJTkGfNxuD\n9r/yK+wVrqGYp34juR16ah7eX4QLetkZ76FHHs539Jyy+uBBlte/I3pShXrhXnmQOW/scVR8Dq8X\nMA5nYTNznnfl2991nJ6K80bBm6tZ3uBX79Fx1hH0BWxnye8hPfM5Egv0pF95L0Fqpe31+Bhlauh6\nVnCM9xq88jn6ptD+Drd+vsDyQiehn8qe73BPJnjzc1qHBTiLW23GGmBhwXtehXljnlbmogdI9k6c\ngwImdGTPoT2CTnyH/annNN6Ls/fSp/HalTireMXy9aWxGufukAn4vhK/6jDLa9cO64NzEPaMLiMN\n/V0MFtymZOeb2/DvdudzrG8fvCfHUJxzjn5zhOW52OMeeNbhrBj7whSWV5aGnj3eg0LwgKH/JP0f\nORfQO23kypU6rqpKYM/IOIX3VJuHHjH5Cbz/ils4zmVHV+7Tsa+n4UzQC2edmlSs1esO8H12Yi+c\n1yPvxxhtbuB9omLbt1d3E5/euFftR/FDcFMT5kHus9j/Y2dxu3ra99V3FH2/hh5ND8HG/I9nP9Rx\ndCzvHRQxE+/jykf4zm1PeruVZSaz59A+f5s+3anj1ILPWN5H21bhfdfgNZycurG85yfM1vHLP2M9\nnPIc/40i/SL6bJrbYr1ytOG99468CfvxMe+/r4xI5YwgCIIgCIIgCIIgCEIbIj/OCIIgCIIgCIIg\nCIIgtCF3lDW1khI9r2Eh7DErJ5TouBHbOo9uvKy9Ig2lr46FKD1PNcgJoh4M17FfHCRPObd5SXTg\nWJQBX98ACyx7Yj1bdCKTP2cqXu/KryiztyMl1Upxy8FL36C8OHgStwylkid/Il2K7deZ5QWMQWnW\n4f+g7K33/L4sb8eXeKzrxEeVqck9hPJmY+mvSxeU3ZZehozo6hFuETv6dVh8Upu8no9xe7naApRh\nViSiDNC1B7exLr+BcVGThdJB39EogTNamT08/x0dzxs6VMeDFw1iedR+vfwKrPnyjqaxPK9+GKvp\n+3C/bQxW1bS09Pp3sGQrrOT2ftYWxIZYmRZaJujVm1t9z30OFpCOISip/HXpWpb38DdLdWxubkNi\nXs7b5Sl8/vRDKM0NHc7t1a99/5eOQ6ajfJZaV9ZWc1tVc2KBGPkI7mH894dZ3tOk3LG1BaW4Yx7h\ncpWDq1CCuvtHlNOzEnKlVG0DpG6tzXi94ipucdlQhrnOR5VpiHwA5c22blyyk7wOY8urO9bUg98c\nZnmjlkIGQ+3NGxvLWB4tqQwYiLJTCwu+7jU2Yp5Sm9lL61D+2c4gHaRyIzMid7Bx43KbWa9CzlGV\nAdmouT1fh6pzML7dyGevJXJKpbictsf9KPO+vu4Syyu+CLlDSFdlUs7+feFfH4vui3vgTixX6wu5\n7IraZ5/4GOM2KY9LOZ3scD09qiCZMlqzRj6OFaepFmM9ZRXea0J2DnsOtWSn1s+ezUEsz55YwtYR\ny3LXrnxN794Je0nBdUgWQ++NYXn5p9N0XH4V63OlweqbjoO7QcAkSBK2f7CTPeZMyuvp2cLMMA92\nroJ8IioA9+e+jx9nea2tKE2PWgIL2+pcLg1z74HPXJmKxypJST3dV5VS6sY5SBFr6iE5drTlVq27\n/8BaTqVqHf24xPB2DsbJlCcgYbm8ns+xEa9CDtvSgs8351luSewczmWZpiTuaexJVPqllFKN1ZgH\nm9+Cven4Xvzzxs6AnICucwUnubTHgqxZLUQe8vQj01nep9+i7L4zGRPx5Lp2DDHYxJP70UpeO+NP\nfg47cRkSmJhgnAMChoaxvN+e/k7HjUSiN3oW39XKLkLSVZmC8RZOpD9KKbX3fbQaMPXZRim+rjgY\nzuXUZrsqGe/R2Y+Pq8OrMb5798FZvOQSl5tXJuE1nDpiHytJ4vK+Ke8/qWMzM8g+K0pwD5J/4mOE\nfmcKDcZcziXyY6WUSt6zXMfD3lqi4+yrXGLjFIr3l7Ydspymam7XXFOB7zxH/gOLdaN8OCsV9zt6\nqjIpQx/Dea74At9rarOwv7cQmU6HUH+W5zsS43jHZ/ju18lsJMtzDcXaXZmP7zeV6Xw9zd65Xscu\nxLI84/ZGHVvaW7Pn+MX21LG9Pb6XlkYcZ3nW1ri2rU14r0YpYj0Z205RkEZGJfDvyh1m4KBCz271\nxIpbKaUcvPnZ1tR498X+f3vTNvZY+0lxOh79FO5JYBRf8+P3wKL+0Nc433QfzNtgUDk123fumc/y\nVs55RcdF5HuX9xWcW574aiF7zvJncH7dQaSnJ1M2sbzyIpxV6O8SYXNrWd6ATpBNUnv0vV/sZ3ln\nk7Afz+yP78fH43lrgGW/c/txI1I5IwiCIAiCIAiCIAiC0IbIjzOCIAiCIAiCIAiCIAhtiFlr67+3\n777y55c6pqVZSinl0gUlYrQU9MLv51geLan0d4PkosXwzzqQbvDnz6P8Z/BU3gW6hUgSXKNQRp2z\nG6VEqYncaYh2eB+z4lkdpxzipcxO7fH+srbCXaOdtTnL8x+HUretK7bruKqOl5/NfmeGjusKUZ5v\n7Jh/fBXK5eZ/+60yNbnZ6B6dvYc77jiSz0zLjxO+5ffRfQDKc//+EWVc0x7jnfvTduG6dZqP8sC1\nb/3F8iprUTIW7IlSv2JSsvbdxo3sOS8vWKDjQTMhDUvay51QAqKJq4kPJDrHNnCnn8nL0QG+oQLl\n4Dm7eGlpQgJKRukYjpjHS3+LSClnjzlPKVPy+lTUoE6axEuT3XuifDbvIEo8L1zi12XYHJTT02s2\n5DXuoFGei8d+fwMl2hG+XGbQcSTK/D5ciTLGFWue03HuAd5B3dwOc7GdJeYVdXFSSikH0tH/2gaU\n019OS2N5tLt/bEc4BGTlF7G84FC8913HMLYXrZjF8qjbQmg0f8wUHFv+po7LK7hkx9keEhbPIShZ\n94zmnevTd+L9U7nMjVtpLC92LLrNUycxK0dexhu+gBaq4/Pf/gbzxb0fLz+ma8DgbpCxdXwojuUV\nXMRYur4dJaNmBreSuFdRFpt7HKX853bysvGB8wbomO5JDsHcbac2F+tI1DjuTvW/kpWMtazBUHK8\n7yuUz9paQf466NEhLI9+fCpRqi/mpbT/9pzc89wVscN0lETXEWkpLbMvOMOfY+eJuZOUgMeiBhgc\nSIjUzTECe0SpQS5AHYA6zYNjVHMzH+c3v92tY2dSal4Rz+dseQFK4cf+g5vB/8qt/XBpaGnkLhL0\nbNHO4t/XKSt7SDDo56QyCKWUOvzOLh33eQTrcOo6Lu++ngGnmj4DIH1pR1zKzA3nkaIrkMJFLsSe\ne/BDXm498CH8u5VpkN+d3nGR500n6wE5qvz+1XaWN3kM5mJhKu5dWTW/373n4PUiBvBy9f8VKr0s\nvsZL5t064VzhNwJ7w963+blv+AuQiZbFQ25t48klmmd/xnro6QSpX+RC7opo44w5krQWTkM23phv\nwaO4OCh1B6TiJTch9Qsaz+fivh8gEaAuhjPv57KP4iu4FlnFkMG1D+EyFyrxdQzHnttQXs/yfOLg\nIhTY4R5lasrKsMfXVvO2BNeIjL7DTKxztTlcVm5D5E/Wrrh3Jz87zPLo94Goe7FHWrtxGWAJGU/+\ng7HHNdZBnltn+F607ws4VVK54M1M/pnciOx69Ns419ZW8bzqbPxbdL/z7s3HRcYeOIxae+Bz+PXm\nktLkLRiPvRY8q0xJymXI6JvreCuIzZ9h/ZvyOJx3DvzI3Zq6dyNnHbLf1Rlkwe69cW3NiESYOtcp\nxZ3P8o7gbOzaFfuOlTO/70212O+c/UN0XF9fyPKoyx0dO/TsoRSXoMVfxXtobOZ7SY8hGGPbNkOi\nN+dF7lRl54u1xy+Qy4lMwfnVH+m4OIF/ZpcgrBGRc+BSdOrdNSxv8BtP6Pj7xZAkDYzj4/HUUex/\nfQdhbn/3G5dTUefjYzfg/Lvm4Ac6rkjmcl96H2yIY1uG4Tuwdyy+27Y24RzQcSKXq340D/LDcPJd\naMhLfO21scPYjP8Vcrfui7nrc00Nxo+r6/8rFpXKGUEQBEEQBEEQBEEQhDZEfpwRBEEQBEEQBEEQ\nBEFoQ+THGUEQBEEQBEEQBEEQhDbkjlbaVw/CMq7PXK6JKjmP/hoBRBc7/HXuz1Z4FbqqY2uhHR35\nJLfELTwFrWWvWPSyqErlGkK/kbBabiJWiVevobfFxDcnsedkbMHnuLlug45t/ZxYXlUm/q3mWmgm\nvQ024lRneu8Hc3RcW8Lfa+l1aFbLLyMOvZ/r7qie7m5ANZS1mVwUsl6vAAAgAElEQVQP6dkXdm7Z\n+6DF8xsfzvKs3aHhnbxghI5PruV9XHoMh07+8Ofov+DlxK91JNHj+oZC3//Mx7CAHD6I91bJK8P1\nXfv1Dh3fa9BbpxOrVo989KKIu38gy6O2abRvkt9Y3uPDxg/6YEvSryNrO+/pYhfIP6MpWfTebB2n\n/3GDPVZBrN1tfaGtjCkLYXnePTGv4ndhThxZ/ivLo7bT0dSuM5Zb/9HeBw9NhI44aTX045GP855R\n1Lb7wNubdWy0eLcneu/Yx9DbYOci3h9hwZv36vjQV9DjJ+RwK0dzokse1RM683bWfAk0a+K9J0xN\nWi76CQx+Io49lkHuK9VsVxfy3h6U5jrolul9U0opr97Q0jaWoTdKfjzvzbB1GXo7UT0+tXu+9Qfv\nVxIViLHgEI4+THUVvG9I0XGs6/2WoO+KjTO3S80gvWU6TMHeEH+Iz7GmGnxGSweM+2PfHWV5gx+L\nU3eLa9+f1XHP5/i/M+ktaMCzdqN31e5P9rC8mE7Yx4oLsK5FL+T7rLkV7kEt6SUT6MT7Bt3agOsX\nOT1axx+8tFrHIV5e7DmuOcQu2gY2xM3VfBxZOuPfKjgAzXzIbO5RXkL2u6Ym7DNmZnyONZG9NXjw\nYB1XRHGryavf8L3F1GSSflh1jdyaNrQJ88qOnBOSNvIeMcFjsFfYkD0yZ08Sy+vzKPayyhT0CrEw\nrD9xM/rp2CEE+n5HX+yXqVt5P7iox9F/regi+u3R/mhKKeVM1nJ7f8y/wYaxRHt3UNvbRz/ivcls\nXPD6F19Az7HcMn4Oql6NuWnqnjO3T+I6j3hjGnvs2Ar02rt8DH2sug3uzPJo7zPnjugXY+XAe1HQ\nPllhM3DOOfQR7+1DLZjbz0I/GhsbrMfNzby3VOfp2N8zLqI/h7HX4+1s3N9HX75Px+Y2fBz9+e0W\n5C1Bjxgng615fSneB+1nM3B8T5ZXkUDW9Q7K5KTtRd9FuwB+juq8KFbH9m6w+U3/cwvLu0H6ukx9\nC306rCz4ten1BM6BeUexnqVf5v1eupNebLkncF5yCMW4T1x/lT1n8BxY554g33eMPdbGv4ueHJc+\nQq8W2iNMKaUCp2OsFp0mvTQNds1h47G+lGfjHN/QwPdjD4ONvCnZsBJ29a6kF6BSSg0ZiDNX2g7s\n6cY9acehMzoOJY+NeI6f8de/CTvkALLOBQf5sLyzn2HtoXscXVudgvg1MXPBGajwJvak/H3car39\ng+g5efv78zq28+NW1y6kf1nVWaxDxj3HORJzc8os2JIb++js+grrzZO/mL7nTLd5j+j4t8d5XyLa\nV9UjCWO/51J+f5qa0C+uXx/Sk/DeCSyvOh15VaQP2sRevI/XoGXo/5I0Hu+J9rl7/slP2XO+3gyr\n6sKzOL92f5a/18SfTuq4pBA9ns7ufY3lLf76UR23tuIMs3Lexyxv7mx8F6LfCTOv8jOgdyRfY41I\n5YwgCIIgCIIgCIIgCEIbIj/OCIIgCIIgCIIgCIIgtCF3lDX1vR/lsrRkVyml7IJQFluRAgurBg9u\nLbrtB5RgzXwVpYaPzVrB8mI7oFZyUCfIL7ZfuMDy+iSjxD+iD0rDnWxRglqeyEv5XLvD9oraa9kb\nyicbSTm37zi8HzML/htWASlvcwxGeVxjFS8HbyQ2q7bBuF4NFfwaeffjchFTU3YLUgq33ryEz5yU\nVVclo6wscGwnlld8HTKR0gu4B54GuZKtD0r6uvRDybeFvRXLo1bJVaRsb2BnlHE+8JTBQo68dvYO\nlG76x3VheUXE4tWK2ApaOvL3EDgJcrw9/0HJmRexMVZKKZfOsOS8/ivGY0YRH2exjrxc2pTUFaG8\nOSGDW8VHE4vO+Mso0+0xgcvnim6iBNzCHKXcfp14KWgjsdGkjvend3NbYztrlMNTSQ2Nb73M5TCj\nlkASN/gFxAfe3c3yOt0DaQaVBz7z0hyWt/kDyNumv4YSz5GGMuL84+k6/uMvyO16FpawPKMtnqnp\nGBOiY+OaStcZB7K+1uZXsbxDu1FCG0ZKf3vH8jlrbQe7SI/eWHOMUtEepMw/+Xwa3msc5u/3X22m\nT1ETfkLpfuoa2Hi6RHmzvN6vPK7jlhaMi4sfr2Z5HgOxBjY0YL2KGhPF8m7uhPQruDPsvYur+DU6\nTCRuod/dp0yJJZk7JTe45Ozin1gfInphfxowiZfpHtqM8u1cYonbsYRLha7/iTnnSMqyQybxe91+\nFFnLvoGdq7MdxlhDE7c3pbalFmRtfOcLbovpR8rGqcyx4Otylkf/rZwzsIT2CPdkeeU1WMvi18Ge\nOS+By+2sDVJHU+Pojf3EuoTvyfb+2Neu/Ib5lm5Y850uIy/6IYyz+MJLLM85rVTHFbdgT1pZyWUr\n7btjfy44iTXLMxQSC7/h3DLU1x8ybhs7SO5a6rlV67VPscdFLYGcrOQCH8MRD+LcR8u3K1L5Z9+x\nEuNk3NIxOt644m+W10o3ERMT0TtMx8U309hjA1+BnfvNLw7r2K2bL8tb+yKk7iGeGKtGOQz9t3LI\n+SMiip8Xrn79J/6tWNzPkH6Ys7W16ew5h9/GtRz4Cu7nMcO1fOKtuTo+swZrSNQAbq385CuzdNxU\nA/lE/rE0luc7HGvUiIVxOraw5Z/9zE+Q6ESNUyaHSlS9o7qxx+rqcIYoTYNUNPqZMSzPZR8kz1Tu\n0G0Bl1bT7wdpl7BODV42nuXd+BySGOfOkJxUpWK9NsqxC4/j9br2Iq0BDFMg5yquZ3o+9rtBBqmz\nJTk3D3rzdR2f/vg9lmdhgz04ex/kmp0eiWV5VMJnamYsxXyrL6xmj5WStg62djg3llfwvKFdcJav\nIPtEQxlfn+ne4ET2HSrDVEqpiJGROn5m6Wc6fssT58jc/VyuRKWmYfMxFhOq+VqdtRNjMWIRJCpZ\nOxJYnl8PrKc9yHeTkBn8e0tFEtb1kst5Oqb7pVJKjXmEtwQxNYdfx3fz8cvvYY+lbsS+5haG78jv\nzH6T5XkR2frCLyGTqqnhct/+rz2v47+eheW2vwdv9VFXifHz/toXdGxtg7X83XcfY8/58fnfdezr\niu+b5/ZzKeLUlZCUVuXg3+lq+N5fEo+57US+v3YLCWF5x8g61Lsr1uWQ4XEs79S7aCcxauVKZUQq\nZwRBEARBEARBEARBENoQ+XFGEARBEARBEARBEAShDbmjrMmMdAS3crZhjzWQLvJNVXgZKolQSqkY\nUgZdeAbliY2GEuvfdkCeQJ1Aqut4OduLX36p48eLUEY8sBdKxIzdsm//jDKjilp0p48JdmF5GdvQ\nRTy3FGXI3SfzMkuPgegYf2TlPrzeNJ7XSpxfSknJWlMlv0aNtKSaN7M2CUkHSSnoXF5en/ILyuZp\naWn8l6dYnkMEStupS4d/Ff8sdaSc8fQhlI8ZO5MHEoeqgc+jTK/TbjgalF8vZM9xCMT9SkjDWLLZ\nxh18/Iaj/Piblet1/EjAvSzvxnp89p7DMH5Kr+SxvPpCjPXgAaE6tjjNS0Sz4//dVed/hcrxeo2K\nZo/5xUH6UZ2J7uc1WRUsj87nsDiUJDYZ3Fkcw3Fvvl6B6/fgYu6CduMAHAxaWjDW712JzuqVpJxf\nKaXMrVGOevmLEzpuHxHA8uyJQ0rZVZQaunbjEqyBA3EtEn/F/ayu5+OyoBwSjKe+WqTj818cZ3mX\nP8N/j3l/ojI1ty6hhNbCjpdEO5LS6WZSnltwmJfADx2LUuWbJzG3MxL5+HONx3p24IfDOu4zgq9T\n6UTK5B+Isn7njohnDR9Mn6LKb2NumpPP0c7gIpGXSBy01qL02lgOfmY95Bj+bih9DZ/DpXlDSOl5\nwTlI+GYaym+3Ld+m7hZesZBT5R9KY4/1moV749Qe86jeUJY9aCScHuzJPpSy9SbL6/Eg5CzWrijf\nrs7hkiIqT528DOO2tQX19Ke/PsaeY+mC8vIX3oVLXnMLdwx5/Vm49FQlYj5bOHOXHytXnBGciRT0\n1C98L+k5CZ+9gUh/jTKS1IICdTexJecEn2Gh7LHtH0FmOWQKZBFhrdyq5vZhzLGAHOxDgcPas7yt\nqyE1oxLs/mN6sDwquQgeCeeX6mq4hjQazg8pl+H2QmUV9N4rpZQ3kesWXMTc6fzwKJaXtgdymaCR\neH/mtnzMjX0OrhTU9S7U4MDScSSX4JmSgNF47fpyg7TxHUiCus/FvLywio/HIcMwHl1jsL988tLP\nLK9DPvahsQ/jzOIQyJ3nqLw9IBIHusSj2EvpWUsppXzDcM3KkuEadCWdr/0tf+CeUonsnq0nWZ4L\nkXqMe36sjo/8yR3QOpCWBA7OkEd7DuBS+15zuDTI1KQchEzMsxeXidWVEFkHkcglrT/B8uxJ64DC\nM7iG7j39WR51I+v1ENzR8k5zyUXABMg+i07jvOkyFGtFZSKXRXeYB3nLkRVkDVk2luXlnYD0qN+D\nmOd2nq4srzIb++ypte/quPtT3PUs7zauRb9lkBJXVPCzsbXr3ZM1FRxJ03FTBT9Ths7DPk6dUen6\nrxSXGCXmkvYJB1NZ3oChOMM4d8K5iX6HU0qpe2Yu1fGqlyGhcWiP63z2Lz4nupDvnwnEhcndl39f\ntCPz/uKXuP4j33mB5bW0YL027jMUl0jsmRnE6bGokjvsOoZyFz5TM3Q5XI6+eGARe2z2R/fr2MEB\nkrH3t/3F8grz8L04/yzuydZfDrK8+9/FtQntiHlans33msQfMY59RuAa/vQC9r7Hv1/KnvPyyJk6\nvvQDzjf1+VxK5+KCObvnjZd1bPzO2m8qvjs7ulC5/S6W99iPkM/d+hvSqi0vfsnyJqyYq+6EVM4I\ngiAIgiAIgiAIgiC0IfLjjCAIgiAIgiAIgiAIQhsiP84IgiAIgiAIgiAIgiC0IXfsOZP413UdBw7i\nWjlrD2havfuhx8flT46wvAPXrul4Tm948L2z5AGWl3YTms5dl2DXtWDkMJZHrda2nYcecMQE2JVZ\n2nMtvEc0dMQWN6DhtPN2YHkBo6An9yJ2142GfgGl5/65t4i5Db+c9BpFDgrR8eXvucax4zRun2pq\nAqKg5XMK4la3lyvO6bg8D9ped0fet+fmQdwfT9KjIqeU9xTxJhZqwcSWsseTA1ielR30m46O+Pw9\nhkNj3VDKr/uPy9bp+L5HMZZq87jW/OoW9LaICkJ/oFde/JrlzR40SMc/rYal60s/Ps7yEldB73jj\nwC0dd+zL+w/UZPAeL6Zkw697dbzobW4NnH8Wem1zC2iK21lzfbEbsWmtycV7/f0XbmO96QD6I7zz\n0EM6PreLW2n3Hgfdr10A7rulNcZO7p7z7Dnhi9DDwNkdeUZbQUplJvSnxp5We07j3rT3xtjuN68f\ny7v1OXShtQUYL8aeVpfz0G+IG3WahsiuWEdt/bkNfXU6PmdTLfSutzK5Hfno6eizEEV6vHgYtPXp\nf8B2eviiOB0b16n29uj/cno7rmdSMizbHWx4z7Grv0MD7u2CudzBjc/z1E3oX3HsFubOwMhIljfq\nVWjyU9djz7iwiq+V1MbaMwg9XWx9+Fpe08A176bELQb2jR49+DWvzIBNeTLpgXTpJu9nMHYJ+nzY\neqIHQkEFX0OiyL26+Dn6IVUZxu2gpdgniy/l6LixCuNozDuL2XMKE7DPfr8WNq3GPgBrP0f/ntG9\n0J/j9KVbLM+K3JuYjBAdD1g0kOWVXMD78+iLXlOesfxati/iFqKmZtsfOKs89OUD7DFq1Wrri3XK\npT23YXbrijWn9AZ65Fi727G8sffgGtCxenrtGZZnZob5bL8FPdui78O6WX6T92L74y/o+Hu1R6+b\nDrH8zFZ6EWtbQR56ZaTuT2R5PpH4TLd/xjUKmcFt7XP2YUz7xOEMWG/Q6pecwTqiuFvx/0zJDYwl\nxdtdsTWr7Co+e5d7eM+t0os4zzXVohfi7FFxLK8jsZp2dUUPlqIi3kehPAF9g24l/6LjBrJ3uUTx\nvjy0X9j1K+hHMnZEH5ZXl4d+Ce69MF+6JPL+TO4+WJOb63A/hkzjr1dP+h169sbrpay7xvLoehrR\nX5kcaodsZsb78VSmYKxWxOPa2gXwM6p9ED7zwS9whhkSxHuFWDri+0FrM/pr0b5dSinVoQ/6azRW\nrkJM+iyWF/J+IJc+xnw5l4T5cfXhb1newx/itel6m76dX/dG8pjX0BAd5yXwfjvmNlivGhowFi59\nfJTluYfjTO69yLQNLg+ewbl7wvRB7DFqbU7PX9QqXCmlvv4T5/BuoaS3yO4DLG/RNJzOanJwDzy6\n8/X5t0/QP2XZx6t1PGcweuj1aB/GnvPcqh91/PlS2DO7xfqxPI9InP8dQzDG0s/uYHntrLAv2npi\n7c8/wftJ0XFpY8O/w1JKb6L3lY/Pv6b9n1k+Y5aOZy+dzB47+R6+K9zKxnV6Ye3PLG/dSxt1PO/T\nhToeaej3YueKaxo0BfNv3wr+nWTs2zN0XJ2P/W/6Y+TcuIP3EvONw5ofNX+qjp8Y8yDL84xDj6t7\nPnpNx3QvVkqp7Ov7dZx1DvNq6DPc2vzZcehtGu6Hz2f8ruHkxPspGpHKGUEQBEEQBEEQBEEQhDZE\nfpwRBEEQBEEQBEEQBEFoQ8xaW1tb/+3BNY+hpGvYS9xu8egHKDOjL2GUuYxbMPQfX/vhxSvZf/ft\n2FHHnQJQ6uxIbCeVUiqvDGXjQR6wUAsfjTJ5Oz8uF6BlgzVZkA5ETJzC8uI3bdKxG7FUvPYLl2YE\nxkIqc2IXZABGe1gq6wmagM9nlFNZO+H9enqOVKamsBC2Ztc/47IzpwhIAxLPwcauz0O8dvX8j5AX\nDH0N1y33BC9tL7mAUrKIh2FRZrT19AvCaxQWoizY3BzlfF8v/pQ9Z/JcjKV8Ysvu4M3LW52jcN0b\nqCTNMNRtyTi5uJ7fY8qQF3FPavJRQmnlyEsPG2tQ+hvWbfa/vt7/hQu/fqJjoyxl0zpcv34RsH90\n8ebzgN4DaiVobyj7LbmEEut6UkbdWMfL1SMfQWn3T8+u0fHSNSjhtbLitn8XfvlYxzZemAe5x9JY\nHrVQXr98s47p2qCUUmcSUZLfjpQhTp3B152zB1Bya2eN+zbmraks77OHvtLxir//VqYm/eYfOt75\nAbfgm/6fOcjbyiVklOybuD/UwrzD0AiWV3oeeYH3QAp19Bu+Bkx89wHyepgvTQ0Y61c+52XUPZdC\nVnjlkz06rqytZXn1TZAJuNpDvuNqsIPcsw9W2r3CUGbc7RleHr3/7Z06pnuSpa09y8vcC0vqHnOe\nUqYkLwcyn5xDKeyx2gzsL85dIV0wSi9LklCeTyVKMXN6sTw7MkeSf4IMycKJl4N3eQhW4tS6szAB\nZfJGC+bKBNjoBpL9qSaPl+qfWg2b3s79McaSzvLPTu/1sBewZm58bRPLu3cF3msuuX7HD/AxT2WK\nEz74QJma3KytOi65yqXK1Eae7s+pf3FrWt8RkBEd/hhnorJqXr7dozNK4H/Yhvny+uePsrwj36Fc\nuqYe96tbR8yJpPQc9pzhz2MeZPyFcd9UxaV9p8laOf0F2K0XneWySc9+sJLNIxa2YbOiWd7ml3Ff\nB82ErNzWcL7Z/yX2p4dXrVKm5KfFkOrR8aIUt2KP7BKiYyqlU0qpm+sx7tw8Ic8NXxDL8iwt/9nC\nNm0Pl14GjsB1amrCvH97DvbwHQe5FGrKKNzDe4dBAncjkUsfRj2NeVV8AXKx1haWpvyGYbxQK2o7\nX34moOetU+9jXAb34XbWlJgZT/7rY/9X/liyRMdRY7h8zis2XMdN9biexjlbfp20LAjBfTQ3yLu9\n+uKz1RXh9dyDe7I8BwfM2fJynB/atcPam3+Tnxs9I3HvD70FSRudy0op5e+Oc/e85ct1vPyRR1je\n9YwMHT+6Eta7dQbJp2c0rlHmfoxnKqlRSqnqFHw/67/0VWVK3pkB6cmMJ8axxyqTIU1rbcJgdQx3\nZ3mNpJ1E/EFYMPt687xvt+HsFEnOhKOG8zk749EXdfz78jd17DcB12vcIC73ra3CmHh9Eaykba34\nntt1DKT4Pn3xelW5RSzPJRDyLAsLjMuSzKssj7YauL4V+3atQaJdSKTPz/3+uzI1CScxbnd+s589\n1qc7vmfXluCsZ22Qp/V/CWPrjWnY7xe+y78XZWxA6xSPwfhenbiTf6/87QjOrI+NHq3jTo9Dpmln\nz2W8CX+S9Wwivk/UFBSzPCtn/MbQRL7DFZH1VSmlXLviNwH6XaqloYnlVZEzoA1pbZKyI57lNTU3\n63jihx8qI1I5IwiCIAiCIAiCIAiC0IbIjzOCIAiCIAiCIAiCIAhtyB3dmka/NV3H5Sm8xIdKCPo9\nFafj7J0JLK8yCeVsm7ajZPezt3hp5Nc/QLowZhlK4m58y90MOo9DyaO5Fd5+NXF0sXLmziLp21FO\nlFqI0kcfgwNVVQLeq+9QlIXml5ezvJy9KHE0b4fftwbM4Q4x7lEoD6Zl9pk7+DVy7YQSas+5ppc1\nVWbhM4fN5qXJ1FmAuhsUnealzgOehUykuhDP6TBqEsurHYQyXAeHzjpOO8slInVekD8lr8M9piXH\n1JlFKaUq4lGO5jcU966ugJd4pu9D+baDM+QOCWn8M1E5Xq/RKHuruM7dMHIPo/SeugWZWfBu3gHj\nOqq7Be12T8vrlFJqce95Ot78xhYdj7uPdwOnDkDNNYjXrtzM8ibdF6djzz64HxZ2XLZXloDrNGoM\nygubm+n94KXgDiGQU/n1QCm8mTm/lse+Qhlj/04opdx7iUsf5j0Ledz271GCmUYcxZRSauTj6Kie\ntglzMX0rLy196gfu1GVqsrahVHfqu9PYY7vfgExg9JuYV/VlXBITMB7X4+pnkBuVETcWpZRqIpKn\njL9QJtp/PpcsmptjjpQQ6YO1G8o9jXLV3uYo13QMQqmusxUvP6bj5+BnkH2ETOBuTe6niHwnH24E\niS9uZHnjiMsRdX2orC1T/39RfA3l9OW3+FoR/iBcdRqJrMTMkv8dxIM4o+z9DOOWlnwrpVQTkRKG\nL4bkycLSmeXZ2pJ5aoFradYRe2S7dnz+xqfDGYNKf1ubufxzwCOQll36CfKzCqOEjbj0JK2GBGvk\nLO7WlLkV+3FWEsZsmBd3sOlqkJWYmpo8lIc7RXiwx9KIW43fOMgbgqd2ZnnlidgLu42F62DZ5XyW\n5zMc+1XaKjw2fcLzLG/DX+/peM+3mC/e5PkedYHsOa1knpcU4qwS8zB35olwwPW0toGrSVUan9sH\nvoDkxoc4sVkaznaxcfi8NcRpzuhueQfl/P/M0CdwLjHK8eI3QP6VcBPyEDMLPhc9A7FmJd3O1HFn\nS1eWl38FJfhBvUfoOHQMl142NGBMtzahdH3OiCE6dnXg0q9csr7WlmNeZRZxiUT+YcjMOpB5VXCJ\nl8zTvZm6XlJnG6WUconE2dOnA+bf6d18n6WS/ZgZyuRQOaytH5ep//H8rzqe/Dr2xfi9XPrg44v7\nmHIKZzZvH34GMbfFZ8k9kqbjlpl87c2rhftLaC986KYmSBZd2vO5SM8+tCVDi2EOePTF+j93Ej5T\n93H8fO5/Gu/dJRASyjo3vtfvfwv7JHVM7biYr6HOhnXOlEQHQy5mnPOWTpDPlV+F3DD1eibLo9LY\nqAE4T984cZvlzRwAV0gqNypNK2F5Wzd+oeMiItf/8Dk4DW34ibfY+P0nSKa8yLX8ejd3EHrBFevD\n+e2YL93i+B7h6Ae5ZdJfx3Ts2ZePnWt/4yxaR/bSwY/HsbzafD6HTY1XZ8i1JjzJ18qabOyZXR/G\nnEg/eojlVVVhr5jzNMa3ozf/zLsvoR2CImd72hJEKaV+2PedjhsasH/ueRMS85hR3PE1YAwk2POG\nPq3jL39+ieV9vvRnHXsQCfPoSQZbOjKmSy5jLOVd5/LKnkSKf/AdjJnpnyxneYffuLNUWypnBEEQ\nBEEQBEEQBEEQ2hD5cUYQBEEQBEEQBEEQBKENuaOsKfvQDR0f38G7kg+fh9KdgpMoGQ0ylP2e/viw\njkd166bjhhJeEv38Owt0bO8CSVHkwmaW5+6H0m5zUlpfUQGpUUUGLym+nYPyVhtSApexjZdFdl6C\nEtnckygTpWWCRnw7QWLiYHC9ObQcJVddJqAE2GdAEMu7tB3vvcdcZXJoybq1mx17rC4f0oD2YyE1\nqE7nUq7zXxzXsYcvyvnMxvN/i0rKLvz8tY5jl3DHlNwUlHsFjEf5Yksj7vfAGD6WaGm4vR/pxt+N\nO7UoUubt2QdldFaHuNyt/XSUrdXXQDIVNLwvy8u7iHI7J9JdvrGad1HP2onSSz/eAP5/xiUKJccV\nCVxK0VyHUtBRD2EMF5/jUkTq0ETHareQEJZ3fg/G47YVmPf9I7kUZf6ncBeiDlLV1Uk6PvPud+w5\njaRsNX03Sh9DxnNJmIcjL23+L/c+OJr9Ny3LHtgPcyxoSieWV3ge1yJ4Mj6HUwh3+Ehec07HXs9y\nhzpTEDwdssx0Iq9SSqk+s+F+tekluDoNmMwdfPZthHvO6PsH6zjtQBLL841B6fTWvyATG+vF1wBb\n4gjkE4WxX1uLkuMuxDFDKaU2v/i9jkOJHCUxj5dbdy3FOk+d9o79fJzljX8RUtaCU/h3Txzi5fWN\nFcT1gijhdv/Iy2rveX2yulu4dsLnbW3i5dtFZJyln0nTcUg/LqEtLUJpd0w0ZDP2AXyvKbmK65l7\nHK/XaREvV2+ww/qVk4yy7NIb+Hc8Y7lLDd2Dz3wHeVzkMD7PU45iXHV/AGO05Aq/15aO2FvziANQ\n0gEuh+n1JOQYQZZY41PXX2N5ObshsQvmW4FJqCuAPMHW4PhnTeZIO0vIa79czN2Gyokr02OvzNKx\nxwBevk2lNN/+8LKORw9dyPJee+pLHb/8AuSqBYchF05O425NAW6QPgx+bT7eWz6XujQQJ5Tmesyx\nXRuOsbxmsn9G98K8943jY7giBWOuuQ77tmcvf5aniAzc1HWNgPAAACAASURBVFCnqdxbfDxOXTlT\nx/QsRvdBpZTyiMG8sNoLKUp9bQHLc4nEvL+9FfLhmlQuqWyqxR7nOwZSFNeekJK53+DjbdJISOJT\n4vGZ5i+bzvJsiNS0Iht5Xt35/pl3Hmdb+0CsKQ6B/IyasQV7kB2Rp1IHOaWUGv+K4aBnYkY+h722\nLJ6fb7p1wjWk66uXK/8sfmOxjjqlQiZ28wCfB0mpeI3x72CMZOzhEmdPIj1NOrFOxzYeOG/aefKx\nVHgdsrOAMZg7Dtf4dxJbH+y5haRtglHW6jMYUqGqInzP+vnl9Sxv0swh5DmYp8VXuOTCPcZX3S18\nI3CWohJzpZSa8RpcMStJ+4h+S4awvMoUPGbhgP3E7hx3A/IMwjnce0gI4vABLC8/Cfuae3c/HS8I\nxtjJOJ3GnjN9It7Tmr/gdLviRb5WXzqKudMpEu/BNy6M5dWW4TM5R0JWRiWKSikVGo3vhVQGlrWV\nS7py8iB17DRcmZzls+C09Ojbc9hjTdWQW9XVYR4d2nCS5c0fFKfj7H3JOqYuiEoplU4c9aj0j/5/\npZQKXLlBx7ey8e/2743zdFAcbyvy90v47vHp18/p2CO8K8sbEQ0poWdHrPEx9/Evca2t2ON8wiEt\ncwrfy/JufYU2Hb1m4Zx2feNvLO/YLazR//RNQypnBEEQBEEQBEEQBEEQ2hD5cUYQBEEQBEEQBEEQ\nBKENkR9nBEEQBEEQBEEQBEEQ2pA79pyx9oDuetBErnGndnRBY2HZm7D6BMvrNB6asLKr0F2amfPf\nheqLYUFXlATtuYNBg596ZI+Ok/ZDi5dTAl0f1UwrpVRPoscNnwu9e10FtyksTYIOu5nYDvdcwq1A\nr38DTVljJfqOZO9KZHlBHaDvdGoPjWTpda6NDvHjfS9MjQvR+eXs530prD2hn20g94D2D1CKW+N5\n9oee3js0juWVluLaWHti/NTVce1r6Q2MBWui4W2qRE8J72Fc425hDx1m8i+warVyt2V51y/gM04c\n21PHuQmnWF4IsT28Se5pdgm342tqhtYwpj96mZw/dp3luRF7zN7KtNC+B+XXuCb7QgI0nbT/QK+H\nuRVc5iZoHNtZ4PU6TuMazPCGf/68TuHckvLaZ9CZunaElvbS+gs6HvnmLPac4+/+hddeCKvXXR/s\nYnmjn4YKsyYX9n2034dSSlWTxy5ewHpg4837EF07hM8+7CW89vZX/2R5HXy4TbmpofPIrQfXf5/6\nFeNz1BMQE6f8eYPltZD17fgfGLd21tYsLzwEuup5r6N3gbklt6infZ5yr+E9WLtiXlk589eeuOI+\nHWcSrf6Yudy+/ejn6AUzbAhspsNm9GB5f7+CHju030G4L79GdF2y9cR8iwrg/VSqMknPLN4u538m\n5Vf00PCbwF+8sRzrV/R87Jnlt7iG+s8NsEmePArz9Ofnfmd5dDxGDMS/RW3ElVLqxiFYqYZOwZp3\n/MBhHcf68j4XdgH479iR6OtQcDyd5bk7w17y7I8YHzHTurG8krPohXIpFXr6GS9NYnnbl8PCe9zL\n6DVknLPVqXfXHj31EPYJ2ktMKaWsvfBeWkgfiJ5hvJ9AzAO4x9+9gntHdexKKfXJdnzm3uG4j6OH\n8J4L3sS6mv67zl2x7oXUNLLnuHTH+eHWr/h3XLvxtYz2lqLrv5ehp15NPcZwfiLGrZUr32c9eqIn\nx/YV+Hd75UexvD6j+JpgSjz7oycHnXtKKbVhKWxaRz08TMdlhv4fJWcwbq+nYOzPmjiU5V36EHtF\nrxfR2+f48h9Ynkck7sdXb63V8cNPTdNxOzMz9hwn0ouiL+0zksbngL0v5mJtIfod5ZzkZ5GK6zgj\neA7B62Xu4P1XfIZhPB/+HBbqfaN536mMv9BfI+CFacrUVGXic+ad4fbK/qSHipUL+gZ69PRjebvf\nQx/DkaRfnPNJvp7FLUPfuksfoV9E6DQ+bouJ9bJrV8wlC1t8bTJaRrc2478Td2DfDhnE142qDOxP\n1HL7l++3s7yBpM+fX3uMq+G9+dqbcQafsSIevaA6LOjO8ja9hPPXoz9NUKaE9oMz9jGsK8R+1Y70\n3/rxBb7f0V6DzvZYg60t+FfVCnLu6xONswjtd6gU/35maY+zgxsZOxdO896jF/dh71rwIK4Rfd9K\nKeVkR3qeFuD97FvOz7JlpC/ZgFE49wRO5HOsuR69qvKOpum4tKSC5XWZyM/rpubJ/2Bty9rG+8V5\n9Mc5y9kZ54xePU6zvLo6zOEeS9FvqLYqi+V9s3e1jhP/xnd7l868N83hb9Ezccx8smeS6Zf01wFF\n6TEO+465NcZPUxO/nq7+2HNpb7jdL7/O8mIeQT/GOrL2lhp679HfH86uPatj4/dK2n/zn5DKGUEQ\nBEEQBEEQBEEQhDZEfpwRBEEQBEEQBEEQBEFoQ+4oa2oiVsEeBntEWppMrUBrympYXlQvlNV5dcdj\n1YW8FKipDqW61i4o8yu7zSUcKcSWs+NElCH6JaCUzy7AiT2n8BRKqczNUZ5ffpvLmgL6w4rLvAvK\nJ5P3cqusqIchWqHlUsb3mr0fcpOcfSi3q8zkNtX+I9qru0nGVpTtOXfi5WJVxHKwvgjWqsfP8zLZ\neZ8QS7UW1JI1NBSzPGtrvL7PEJRtpezfx/KCh8GKncrYLO0hlwvoMo49h1qZ2T+EEs/8C7xUd1AM\nl6H9l9inBrH/rslHmVnEPIxTq7Xc0jV0LkrUaRnrYH8uE6A24qbm+Nco6+v3IJcrzV4M+8DPH4IV\nq/MabpncSORZFqQU7/s/eRkmLbOdORzXrNqWLxcLV67U8c/Llum4mfw7G577nj0nOhLlve0s8dtw\n145cwubgizL+4nOYv7U5XM7hNxyvN+09SHeKLnIb8cieyKsn9s4hXlwmdeQmyrcHK9NTeBrlnjVp\nfB2g8rni8yi1j1zYk+U57UcZplt33398jlJKFRxBqXNaFu53RhFf94b2RfmnB5EspvwOuZKxDL+1\nFSXbtATVwYvLi7pPwGu7ReO9Zu7lNuJUjnc7F+Xk5oZ/98C3h3Uc6A4pSkE5v5YxXbiVsSnJzMM6\nH2zPS+HLb+GxymSsL41l3Jq2ox/Kqs3IPJjxDC81nzbxWR2vG71Cx9++vY7l0fL308ewfg0aDxt2\no5zDMQLXL3k97rV3P37tOs4eoePIRuwX+WeTWZ5bb3ymYS7YZ499e5TlxcTgvJB/BCXkPkN56X9m\nET9LmJpez6E8uswgO/Mb2EXH+9+CZMzPlVvnUnnQyBiMdVuDBCjSH+cnfzLW57w4heVRq9JCMn/t\nO+Df3XXxEnvO/YNhG19XgGvm3ZWvG8nbUPYdMg6S0p5juAQr8xT+Xc8wyG1sPLnsrOA0l5/o95BX\nzf7ba3DQP+aZgrwDGIN5mXxdGzQBYz9/P8aZ/6QI/hoH8ViXDiE6TtjIzyzRz8BOuqEB/5b/wBCW\nZ++P82fgPsyxRY9hv/zwUW7LS88Vp79Ha4CYSVwSZmENaZp7BNbdYsUl9Q7jsEesW7FZx5Mf4N67\nm1fCYnzwGFyvkht8rfAdFKLuJt49sH5ZG+YOtbmvTMP6U3CKj7+OAZhj5lZYU0/c5lbEnYogLXEi\n1uLmVlzuu/NPWMw/O+kLHaed2qHjdpZ83yk4lKbj3s9DFld4nss52rXDvrboeZxbDv92nOVRKcSN\nLLzGwk/vZ3keqcijNszKsH8OnGZqwT3IzMAa2mV8F/YYlTzZBmF+zB9zL8urI3LdaiL9Mq4pVu44\na5/6APtiwCRuKU/PmGkbcGbxIxb33XsZbOiT8Tk++RJy6x4GSeuEJyGdq82FtbKnQV55+iBk0G7R\n+N5SY5Amp27C++swi+wlRjmyL/9+a2q82uP7RI4Vl4ld2XRZx/95/WcdD+vC7/fqGZAoPb5kho6D\nh/VleWZmmHPxp7CGDYzlvze0J/LuwmOY9zsuoIXCnAVj2XP++hnr97M/v63jujo+F70GYn/6ahmk\nsObteO1KDytcF7dwjIVCwzqUVwaJ5pi3IblzcuLX6NDr/1F3QipnBEEQBEEQBEEQBEEQ2hD5cUYQ\nBEEQBEEQBEEQBKENuaOsyYJ0t0788SJ7zJl0qvYfC/cBF19e5l1RDMmJfwi6Npen8y7dqaQbvA/p\nVt/ayJ2XgvpD/jB79qs6nj4QUpbx93EHBBviOlWcAFlUjUFe1NCA0v+GKpSc1Wbx7s7FpFKw/SiU\nutr2qGR5Hl1R+pSxB6VtPgODWZ6xI7+paamHXILKmJRSyj4YZZ0pF1HOfN8bvCN/RQrkS8d+Qunl\ngDn9WJ5Pt+7kOSi1LDjLS8lcIlFqaueNUl1LG5Ts3d7FS/cdieNV0lpcTwtzXo4a/cwYHWcewLgt\nuMhlH969IcGozca9K63mJZThdpgHDeWQxOQf5S4AkY/evZLRMW+j/DN1y3n2WPoWzLHHvlyg45o8\nPh7tfFAeScuWX/vyMZZ3dhVxYeqBcsJv3t/A8j5cskTH1cTho4TMnQYi1VFKKe+4EB0XnsrQsecA\nLqVY9cR3Op7/4Wz8O1l8zqasgRzDKw7zaveaIyxv2ATcm/oS3MPLaWksb9ykAepucuUYJIaDFnL5\nndkBLCwlZL5dOsvLsgfNxpxrroN0sN5Q+rty0yYdf/D+kzou38hdy9JSICPafgTd5SePxbU4c4o7\nRt2zEKXhecTdJ4+UdSulVPu5cJWgrlBGp42AkSgtdtuMe5qXyOUm/SZDqnFlF+Q7/R/gUr+zH6Ck\ndcIHo5QpCQ1HyS1151BKKedISA3MbSDRvP7TOZbXsRPG6q69cNya053LXCqKMQ5o6fScuWNYno0X\n9rj2VZAjn9yGst+/z55lz1nx0iIdpxTgOjtmcveepM2YSy5R+Hx1BXy8efTCPT10CpKAcS9weSqV\nMlF3hMZKXg7uN/Luyn1T1qBEu7GigT1GJclUyuTYibs6ff8yzjFjYzEnqGRMKaUeGzpXxxmbsQZU\np/O9/8ZhrOX9FmN9KCB7jdFdqTYH4yJ0Npw8CuMvs7yqBOzHK9cu1/FLv73M8lKO4bNTWQCVuSul\nlO+QEB1PJnFtER8Xhcexzqt/Vhz/n3Eiksrg6bxsvL4Y78PM4t//DnnqIs6eQ8fCfSt4LJeFOTri\nbFtVhTXZvRtfyxrKsL9MeAiSwAgiZUzP4LJ+P3KGprI331jutvP/sfdegVVVXfvvTO+9hwSSkIQU\neguEFnoVRHoVFREEe8Wurw0sqNiVqkhTmvQakN4JEEghCSGNNNJ7+V+c881njPW9evF3c3Iuxu9q\n6B4r7L3WXHPOtfd4xnPkPYy3Pq9ComTtwqVARWSvY0akLdRlRCmlOrRGSb9P7N/Lzyoz77Nz2g4y\nPxockBITMY4LyzHWBy7iblohY7DvP/Mx9ioTZ3Apl5UTZD/7D+PfnT+JO9JOex3PK2sXQbYd5IUx\nl5zD95TUsVN9g3ndZyiXxDQRZ559q+J1PGA0fw/liZDPlVZBsmhjx9sTpOzHntA+CPMDlRMppVT+\nHawnHR5QJiU4Cvvp8iTe7sDCHmuhazSk5A1VfE6hblwu4ZBUFhr27p5dcC9lbsN8mrOfy3CaiPPo\nX1ewh/HJwPNIt6Hc/cjPHNKjmdYQt9vZcAfbzZ+htUcv4sAX/TQXxNuT9gdHv4nXsVEiS59jylJx\n/k7v4s/evR8g8xKf8kxCyq5tOj50mq8hY6fjnpvwOWSaDQ18XhlZiefsvLOYX62tPVneobcgF2wi\n931NEZc0F5P7PmIM5uGHiFNvnUE6PnkBZE53E3EOPcINstaDuHeofH/8UP4s8OV8uPJNmow55fd9\nf7E8fzJ/11Vizb20gbd48Ajj58KIVM4IgiAIgiAIgiAIgiC0IPLljCAIgiAIgiAIgiAIQgsiX84I\ngiAIgiAIgiAIgiC0IP/Yc4biP5zrv12z0YeFWip6TeaiYlcvWILdvg4dqGcotwgs6wiNHdWEnvn1\nDMuzt4bu74u50Mzb+MLmsZ5Y5SqllDvRwtcSLVvbify91tdDH3b3JHTSDQY9uk9skI5ravDZ76Vm\nsLzcvdBuRzwJHWJtGe/7kn8iU91P/IfDuvReAtc6U7u60F7QxW56byvLG0S02P0fg72yrSe310z5\nHXadSRfTdNxk0BFHO+Iax38Mq/KYx9A7gupFlVLqDLGYdHPAv+szgPfwqamETtejG/pDXD7E+2ZY\nXIDGs+102Im2sgljeXVl0DKWpmCcNhj6qRj1s/cLY48iN9IH4ttFq3Q86eGhLC93P67HlWTEdw02\nxDnEvjGsB3o8zXhwCMs7fhK9Qdr6QKc7+gX0w8jZwzXAO5bDYq9HO4zLmlyuWR378CAdJ3yNHjid\nn+H37LUCYisbjzDEYJGdeR73WMQ4CHWDvbh2u7mB97gyNe3CoetvrOXjx3tgkI59iPY1sJRrad2j\n0QeoMgfXzorYFyul1DtPwm6zkYzNoY9zrf7vX8IatB+xZJ604BUdf/vSS+yYnMMYP+YGC1IKtUqs\nLsR7dW3NNfhFSdAolxK71LaDuT7Ysyvm8h6u0Bu7hPiyvC5PcvtJU0L7V+Sf4H2n6LkoIH2dfCL5\n+2tuxDh7bOkMHRdf5fPz7z99gmPI2Dy48zTLe2AuNNC//ITraWeDMeFG+yEopez88N+jFqMvTMoq\nrnGnfWbqST8bp1B3lrfvC/T58XRG77C9n+5leR3aYU6hPc+am/gaUUJsydvwVnYmwaMn1gZHYqmr\nlFIVpLfVhQ3oS9HZg/fGmv/ZbB3TXiPGHidlyVg30kl/n06DR7M8aptKjwmZApvjzJRcdoyVE/ZE\nhRfQm8Fo307nhznPoLfRpc/2sLx6sq55dMc5MrPgn2n7W9t1PP6jCTq2sObbSrfOfOybkoJ43H+l\n13h/qpJsrJPn0zBfBRnm/FGPYa1pIH2Pdr7O+yJ2Hob1zovsK3Lj01ieGbFJdghCX4mb2dk6HjqO\n9+rLIz2OAidG6rjgBt+zdJyKvkZmZrjuOfsSWF5OBs4FtYStuMX3Dql5mG9Cq2HjXnSNz0M2Vlbq\nfkLHsLF3ROgonI9QMkekkL6DSinV5QXsQYLHYB2rL+f7svglmKemvQQb+uKbfB9Ox0L3OOwZft90\nWMdPf/84O6YqH70x7MjeOON3fh3du/jpeO53b+q4tIDn2ZF+jPakX2TCst0sz38k9lK1hXjGCRwd\nwfI8UrndvClJuAAr5NjJvP+iy9/010hbw69h8Ez0f6kkz5jBgwexvIYGzM9d5uN5JPXwHyzP1hvX\nYDAZY7QPk9Fu3IX0xKHzuGMQXyNmzcIzQ+Yf6Htjacn3HrmkD1+IL8Zo5CLeC+n0Etja58ejp1W/\nKdx++t4FMv/zFnUm4eLBazpe8P0i9tqncz7VscvGYzpu52/oIRiB8R01A72b5g+dzvIeHYTr6tUa\nfdrqDXbk3t6YR4NisWamN6Lvz9H1J9kxobew7ni0R3xm7VqWFxaFPfnMufjbezfxXjIVNZiX3Drg\nOi6dsYXlbXsBPdyayJ4tfAZ/ftr/Nt5Hd/W/kcoZQRAEQRAEQRAEQRCEFkS+nBEEQRAEQRAEQRAE\nQWhB/lHWRC0aqf2ZUkrlXkSJZu/FU3R88eufWR61zqUl3/X1XNrTbuwkHSfvQZlQzIwYlnfvCsot\n3TqitOiP5SjNbR/IS4+9+0D2sv9HlCRO7s6tA+3s8F7Dx6GcPucKL5eytcVxBUmwGjOWZdsHw9Iu\nbSNsTI1yhsBxvPTQ1Fz+CdKwjg/zAipbD5T95R6DxWlMJJcTWNihrLWYlNXlpxewvBAiQ6g/h8/Z\nsQ//jDmHIHfpswgWiHs+QQm8k60tO2bk+w/r+MZPkEIZ7UjdSAlbeTokOpGduZSirhhlao01KOnN\n3p3M8lw6YOxXERlYWTWXzxWexz0RyJVR/5rLn+Pz2ljzEuPfN2BMT3lkuI5LL99lecdvwKZ15vu4\n3859e4LljZ8cp+PN6yFTGzeCW8t1CYY8oakJ5XtHvj6i45FvjWHH2B7CePOLw/UouJDN8uhc4RmO\nMvTi67zc+tgNlJP2asTY6/ciL4NNX4eyb3qfero4szyf/kHqfmJmgRLa0kRehl9E7LMLylDSO+Ld\ncSyvsR5l2tRi12dQMMs7QyzRW7fCGK6vqGd5/aKjdFxSDhv0Th1RtvtXYiI7JuAuxtbg2Sgrdo3i\nkgErK5Qzp2yIR14ALyF3aoeS1o7PYpwd+/AAy+veCterkFj0WhikVc7BPup+4RWL9YXaWyulVOoh\nzB1UHtj5uX4sr/ga5tCSGxgHNh72LM/GHdKtj15boeP/rHyW5dUT2dqE4fi3qJVq1gFub/rbcpQE\nz/0I0iore24ZaumA/3Yg599ofT3saZTtfvXKGh3PnD6c5VVnYmxbOuJvmxukQL79gtT9xD0S1q/3\nUrhVq1s7jJ+oAVi7Mg9xmWaEP5GZEDlLxu/8fgmdCRl37R7Ixgqu3WR51Jq9sQZ2u01NONdUFqWU\nUqFVmEfvXMQ9kZiVxfLKyXo13hky64vp6SxvxoeTccxt7NOM0i8rYv2aRGyDPXq1YnlphyF3CDex\nlXb4fMit757++znFyhJb3eCJXCN3bhUkgrX1mBvDw/k+siYXcyNdn8IfGsny7mVDFkDHhK8rzp9n\nd36O3P1gj3vnLNbzwJ5cgpqyC/esrSfmCioLUkqpTjOwz3PaAqkMtaRVSqmO0Rg7O5dCKhPoyWUo\nzoZrb2o8iFz13HIuJ+jWF/v32xtwbm/d5fubtFd+1XHsOHz+NnF87nUkUkp7L3zO0jS+BylKxN7W\nkpzffpGQWSmuiGESqlsbr6q/o3IfxhKdR28dSWF5mYWQIfXqA2lV1FP8M61+BnL2qADMa/59uNdy\nzl6M9XC+nfvX+BFraCq9VkqpvL8wx7h1hOTFs08Ay7Owxd42ezfORclVfq3DJmOtuX0OcqCAWC4X\nvLocz5I23rhfaMuEshtc6nV+H6RWVArcrTefD+orMVcEPoQxUZrFpc6epK1GyBC877o6/uzk1Qbz\nVeeReICI/+wQy7tXiRYAJr6ESimlwtpgbqqr5i0P5r03Tcf+UfgsTU1cilhShOdic3PcO6O6dmV5\nJ5Mg33ryhVd1/OszX7K8kU+hRUPipvV4f/fw73buxB+6Asa00zFtZ2Jxiu8zzKzw385Eqv3M6q9Y\n3p7FsA6394V0rbLyFsvLIvs+XyJnrCm7wPIGvMrbThiRyhlBEARBEARBEARBEIQWRL6cEQRBEARB\nEARBEARBaEH+Udbk0BqynNM/c+lDhxEol2tsRJmVRw9erukbiTrW3MTjOi5O5qW0Lj266JiWFlXn\nVbA834Eow8zaBknD6Kko0y04z0uUawrx/rpEwHXqyrJjLI86CrkF4z20m8rLsi99uknH3v0gcapI\n51ItH1KWfWcbypcdQniJqLE03tRQKRN9H0opFTYX5bQOgbjeZw/wLurhtSixtvdHSVfwQF5K9sPn\nm3U87xk4ONw+xh0NIqd11vHK11Cm9sRXc3RMXXqUUir/GkrF2z0GKVTFXe5ekbUH0gIrFzhUpCfy\nMu/Yp/E3qnJQap+WxsdPFyJromXK4YPasbzkwyjR6zJNmZT2i1Cumbr6EnvNizij5J2Bexgdw0op\nNcgT95i5Jcqyo8Z2YHm7fkYZZWtS3lydy+/FDcdxPy96BjKp8Ej8O9WF/BjqzlJThPvSJcyD5WVu\nxrU+ch2lgaNHx7K8BW/iRB/4CXKq6nz+7wZNw2esLUaJY+jj3VheRSaRyIUqk2NhiymXSlaUUirA\nP0jHDucwpje9tJ7ltSFuIz1fitPx+hc3srzukbg33bqjlHjnysMsr4SUyc77EPKWT8ai/H/vz0fY\nMT1iIIWqycfxRmcMB3/I0GyJ057vIC4xpPcflVIEtuXl0fnHUTJM59HytGKWV1sMCYc3Vx38a1I3\n/X25eiiRdfoRCWT6Bu6mYu0OySYdE769olhedQlKn59egHss/xSXcNC/4UlkV1Qq9P7DndkxxaRU\nvKYA94tzFJc0NBIXl8o7+Ew3d3FnkZB+uGHMiQOGnS93iarOIusdWXP3f3mQ5Y0xSCJNTU0JPovR\npS3pO8h0aOl90Ag+52f/maT+GzZO3DmNmhVOXAoXtdNLuOsKdcVpRfYPPy1arWMXey59ayZvPXw0\nxk+PUC59yD2CNdh/MPZBDwTz/cg94jLm2RX7uZQVvCybirht/XGNS65wCUJw3H2YSP9fqFudvR93\nSbF2xjW4W4J53Xo7v2YR/TBPUqcS7/7cBfLcGowJq0P42+bWGSzvzlnMUXFvw81n9ncTdVxfX8aO\nKS2FDMAlHPdf4e1zLK/oCpf1/g/2ZO+mlFJN9ZBtUPmZs2HsUHlWx7ZBOg6ayuUw965zKZ2pcXDD\nPrr/GxPYa4WJkBJS95wuXbn0PngypIPXv8LzimMQn6+DO2LPcOs8pFDFl/k+ssvCR3R8+bvVOg7s\nB/lw8nf8+lh7Yk23tSdjxCDZ9OyDOdrWC/dO7558zF1duFLHJUSiH9jIJaWDR0DeZ032FRYW3E3V\n3OBMZEoipuD8F13h5zL7Avbe9gEYq1U5/NnHNRJ77TaTMJdV3uH3C22LYWwnQbELxJzQfiraIlxZ\nBYmwcaz7lxLnK+L8W2eQ8VIJqiVZf0uu8/mvkjik1fXHep78K3/+PH0Oe95WxJGv6/guLM/Gnd/D\npuZuPsaZ1Q7+vJhyHXPbuCWQKNXW8s+c/hv2Ow5BOKb/S9yhKuBX5MW/h3uR7hWVUuraejzz9FmM\nfcE50gbD0Y63wTj+BfasVAKZeOcOy/v2/Ud1/MdL3+l43Ed87+kfhv9+eeLHOl447QGWdysX127Q\nBDht/bCUu/8tInO273z+N5SSyhlBEARBEARBEARBEIQWRb6cEQRBEARBEARBEARBaEHkyxlBEARB\nEARBEARBEIQW5B97zuQfztDxwMW870pJMrRzWUehjsgAwwAAIABJREFUG/Puya3RinOhU6Zyx3KD\n/fG1TGgrvWKgx3QJCGJ5RcmwV7NtBT1h8UVocUOm8B4auQdgdRXwICzPLv7Ie5pQzXzQYPTKKbjJ\n+wXkl0Ln3KEb/p61C++jk/wLdHLpBThfAwcFsbxrG5HXYawyOdSSzptYmxupI/rKATO5SZutF7Sr\np76H1WHX9txi/aFe0NjtWIveFqMn92d5uXuhI576NDSEljbQU3Z5Po4dk74JuuzCk9ANFhVwu7c2\nMdDt1pdCJ9pxXEeWl7YWfXVoD4jh705keanrYD9IexMYLfjM7qOe9zzpj9S6D7dMLjoLa1Z7B3yO\n86e4nWvXHrCEtXKALrkmn2swLczxne3oV0fpeNO7W1neYGK1TPvRpB7DtS2tqmLHdB6IPibOpCdO\n2i+8x9FtYiE5cmiMjts8wK9h0XXSY8cBYzR5M9eZ+5HeCZvWobdFa4NlaEUNrPle7D1bmRo7MmfZ\n+fIeCQ6kZ0L+aWi0u3fjNvSh03Fv3vweutqYLjzPoxfm4uSt6KUwaTG35i4iFvBlKTjvJZeg06Xn\nVimlvPuiR0AJ6UdArcKV4v2RAsahX4drq0iWV5mFe8ytLcb3pXXnWV5folnO3ofeUnaGfhOHN6Dn\nQPTIecqUhE+Htv70j7wXWwiZJ2nfG78x/J6tyMKc1VSPOSX7GB+3PrGYy2ivL6OFa3kqdOJ5B9Bb\nJGQW3mtpErfupPaSdOryMtj8NhBL5wrSD8jdiZ9z2kNp6gRcpwvbeI+sqBiss0VnMfbaRwSxvGOf\noPfV1OUPKlNjYYPtT/ya4+y1XiOh899FejRFB3I71aiH0bMqcxN68DgZ+vbQvUa3+eibRfvcKaWU\nnQ209reIBXWH1rjfjH0j7iagR1p4BObH2hJub+rWCZp52n/m1CE+945+YYSOb2/FGhL6CLdBvfYm\negnk38LY6rqI7x2Kr/F+BKbE3hf91vINVtqX1pP5gfQwsA/m/VnunMVx1EaW9pRQSqn9V3CeHG2x\nzgYZeuqMWvK+jsvL0Rexqgpr1b18Pq85uOI+v3MA90tjTSPLcw6GXfH2jfE6tiW9ipRSKsgb+7LG\nRtJ/xpJv+cvTMEd5OON+No4dY08mU1OQiHsnbx+3po18Kk7Hvh1xvxXd4vvyba/9oeMhCwfp2NaD\nr11//ecdHfd69Tkd+0fzvUpJ0VkdO5I+mH69sYc59yef20YtxAY+5zD2QV49+bxx5Qesd872mDdp\nbzillJq+GPNeAdkTHPzPHpbXZz76S5lbYP+WujWe5d3IxnzLd+T/HhtXfI6NS7az1x5agDml/BbW\nKo8u/PPWlWLclSUX6ZiuVUoppXpiPLaNnaLjlGO8P19TLcZ+1jX0JwmZiB49ddW8n035JfQM8Y9D\nP6r0zXyeDJmMv1GajvOaepr31+w0GfPmxhd+1nFjE7+n4sbg77l1wFx9dDnv90fnsrbdZihTM/gd\n9ETb88Yq9tqA57Gu3zmNNfPUprMsr7Qa80qPOsyP/oYepWGP4dyEkul23vDXWd6DMXgGWPnUjzje\nF+cpIJb3a2odjl5CMdXohegVxvtMJm/FvTTrmyU6XjrjCZYX2w7717eWzdfxhV9536lJfbH+RY7G\nM8T0k9xiPSkhQ8c91f9GKmcEQRAEQRAEQRAEQRBaEPlyRhAEQRAEQRAEQRAEoQX5R1mTz1DYnVbl\n8dKvxmqUOlcRy9AKQ3m5pT3KLctI6XVVOpc1RS+AlVR1BcpMnZ07sbyMFJQDhjyA8qH9b8OG6/rn\ne9kx3bqgHKnkGuRPHWfw8qbza1GalXMG78FYepxBJEpXluHfonInpZQqKodN3Pj3UJ6Yvo6XY9oZ\nbMNMTdItlNO6RHux1zK3o+zWtYOPjo+s5DZv/aZArtR1Cqy5C47yUq0yUs4WE4YStjpS4q+UUn7D\nYeX58zuwAJ6xcMzfHpN4FbKxXpNQAujn0ZblpWzE+fWKxmdK2cNt4ajFuL0PylYz9/DyRStiyVlX\niPdklHCEDuAle6ak61MY67dW8lLaR96Cxa69P0q2M97j5eT2ASgBNzPDmDu97zLL6zsQlrvbPvhT\nx8Mm83L18hTcz54xkNBkJaMsNPcet5fv7QPbyOwDKPvdfvIMy3uwH8ZbCZFSXPzsEMs7fhPXdP4X\nsEo8v5zLFAIGQ+o4kchIqjL5vGZufX+/r6Y2iLuW72evTXxvvI6p/WlDGbenPvgu7Oo7jcP86NiG\nW+JSr1t3X7xm48otB6OmQsaXHo/5LHwB7jELg327RxDOZ3XeKR379zRYk0dn6NjWGfdY/HurWZ49\nmQMvEhlMeMcglketcwPHQMa18w1eRt13BJdgmJLqu5Dw9Xg4hr1mR2xRqX3q9W/5+I6Yi7mHyiGN\nysiUnyB/8CJSsspMvtaETUS5cc5ZHFNTCJtztygfdkzxOchhqGTq1mo+H3R4CuOyoQryg/B5/FqX\npqIM/eABlPpOf+shlleVh3XRygVj8ewuPsZGvjZK3U+ydkP2EjeH204fXwu5Wr++uMf2HOTXMfED\nSA0GjsA1tfXmUorA9pDL1BSiRN/Lx43lURlaPbFupVbpBz7k+5sQP5R2U/voCoN0/M4hyEVaD8da\nZZRqWZNrEv8XrsnUQSEsb+BsnLNj6yDbirHn1ulbV2Fv1n40LxX/t+x+F+vTgHkD2GsDXh+m41+f\nh9yhS1d+z7qRfc8Xr6zW8cjSSpa3YDYkK+kJ2B9WZvDzfHrpUh07RXjoOGL0dB0XnstixyRdgfzf\nNRJ7NCsXbsn+68+7dNyWlPQn5eSwvLGPD9FxwVG8VzMrvr6FPop58vSnkE+4F3MZycUDkFt24qpv\nk3B4xVEd1zY0sNfcTsP6/Ojva3Q8/l0udYx9EPdf0gbs4VoP4OOW7ueyk3Av+bTty/Js7DEuXKPw\nnkoycD6Hvz6SHVN4GfIWKrVtqK5neUPfX6zjuxnY01QX8jFn542/ETQBMg3fgfwzFZzCe7p+FnLI\nYW/wOdQ53EPdL25vgnS6TwSXWFN7+PObsT65JGSzvD6vYXAVnME90lDO90Cladhj5txBS4xWcdwW\n26cz3kdjI8Z0cRKXQFLsyB7Vygrv23gvmpmZ0//Q4R+nTrG8k0kYv8M6YX4Pm8jbbyT8hjnAJw1z\nilF6n3b3/slElVLK0RHnrG2H1uy1WjIvFP6F58oHPuBrvKfnUB1np27Tce5xLvkKGAAZbtENrE8T\nY2NZXkRnyMLj47EmVddhXGQbZEP3LuBZ37YVrml5Gn8maT8FMq5LP/+k45fX/cDyFo+DhOxBK6zH\nxmccKiNNXfgC/p1uXP6aeIyPfSNSOSMIgiAIgiAIgiAIgtCCyJczgiAIgiAIgiAIgiAILcg/uzUR\nyUrgg7xM7fQ2lCGOeRflnkVXc1le4SmUPkU+io7d2acusLxzSzbpOHo+yk6TDv3K8moLUFZVkAhJ\nTmi3IB2v+3wNPUR5OUPO0doM3cEba3kn/ITb+Lz9I7mbCCW2A15z7YTSx+CAaJZ3YDkpV8xHKbyV\nO5cVRE+I+tt/yxR06Bmu49Kr+ew1zz6kpJnIt4Y+NZjl3dkC+Qgt79txinfpXrgU3anXvYvu+Z53\nnVme/WVIlFq5Q+6w4bvdOh7Vvwc7hjopNNXh2lXnlrO8Dgt76zj+Y0hHeszi5cyHvsL1iYpG2Vze\nbe5qQmVt7Ybi2vv05KWlNfe41MCUmBEHJSs3Pn4sHSAJKbmB6xvdrx3Lo+W8tWUoxZv51assL+Fb\n3IvtScl7yBBewls/ALKmhgaU4w54wx/HHOTuM85tca2pU0u/KH4PtHscXgKVREZ48pujLC8uGvdc\nYy1Kj7s/xUuUi2/i3i4g0kZHF3uW13r839/3puDKJjhrhXhzpzNzS0iZPPoQ1zuDUYafF2R8tcRh\nzcmLj0dq6RMyA2PG1pa78SRtR9mpJ3HqcXTCuQiZxd9EfhKkL6n7UbZ771Iey/MfgVLO7UTKNGBS\nb5Zn4wGnB9cElO1a2HMXEuq8kX8Ga0uABy/Xtg/gjiympLkJ88G9K/zz2g3B56UuVp2fH8HyGuow\nVxz+AKX1w9+bxvJs3HBemohjSqvuXGKYcxGyEurcFDZwqo6vb13NjomYN1DHzc24d1L3cvnnzXVw\nM3DvivWzwZaX6p/dCClT/864L2/+wuVKbsSlrZlIDOMMspRyImdU3OzKJNy+gfkn3JPPAx1jMHee\n+Qtz2MzFvHw7fQtkXgd2QfLUxovLh0PaYx7N2gPZQdSiXiwv9Wfsi8LnwcMh7yR3gqQUlGAslf6I\n9xA6is9lt/IwVu9uwjGdh3MpgJUd7jE3R5SDXzC4W4YNwr5i+Atw80xdx/cEj30+82/f+78lIgTn\nNfvPZPaanT/e+4gZWE9Kk7jL4o2jmL9mTYIUyj6QzyFUot+G7B0t7Pg22iEIElK6Nzn/1Tc6zssu\nYsdQ55aQjrge+Se5/GLmo5Cp7NgQr+MXv+NyMerGVU/K7MNn8zYBltbEKYhcayrtUEopd0f+36aG\nfv7pn3GXRHNz7FvGtcN9VZrCr6NnN6xdVBJo34rvPamskMpIb277nf+7VliPmxvJ9RmJvXHuZT7W\n6Xk7vxItGHo/zee2igq4oGVuxXNM5OODWN7eN3/Tcfsh2CO5d/Rlea7ENTWuF+4JSxs7lmdmwSV4\npqSwEHNKxFg+p1TmQD7eb1GcjmtLeOuCykJImawccb9VZ3GpG73nLh+EnKrsGt+7h8yBRN/BNQjv\n9RTmyYRELrUZ+iSuwd43V+uYSsiVUqr4ZoaOD5M2EC8/Y3BQIs8PFjYYU3SMKqVUcE+8P+rWZOfF\nJbIeZ7m7qql5ejie55du59Kecx+v1nH/d+GotPnZl1newDfIftEcN5lHZ+7OdeAdPGsMfWeyjh1s\nuLS6Ng/37FMrP9fx44OwHj/7ONdb+g/FXqw8DXsiozytvBzjh7aqyEvjLRSo/Le4DPN63zg+LhLP\nQ57Vizh6UXdIpZQa2TZO/RNSOSMIgiAIgiAIgiAIgtCCyJczgiAIgiAIgiAIgiAILYh8OSMIgiAI\ngiAIgiAIgtCC/GPPGbsA2LgZ9XEhPui1UnUX+iunIG4N6RYBLWTGfmiWK29x+yn3CGhJj392WMfB\n7bnN4+WrsN/tYoHvlqhF4HuvPcaOaaiENn7/XmgNJyzkPTSojXMjsb67uIdbX3u1w2cqPgPdevwm\nrsmmVn9ZO6Frtm/DtcxmFvf3OzI/YrtXmsp1umbE7tUtDL1CUlZxy1CnMFzXlHPQvz/y/HiWt+KN\nDToe2hlavIZ6gz1iB5xDB2IB/MuSrTretP8vdswj86GFtCPWrxXpfCxRrXBYFzQrqMritslUY+0S\nhfGXksQ1neZEmEw15Ek/8Ovt2hn3RGC4MimFFzHOQmfwfhOlxNqxqYH04rnDe/EUJqDnALXvDZ3G\ne0fQr2wLynDOkv/8k6VlnkMfl44PQ1t55ifY0NrbcH2nC7FyvJ6C4/tM6snyLnxKLJ2nQzc89J0J\nLG/vW7CVvvkRbEYnf8J1vztW7NCxtwvuPycvJ5aX/gvu9QDDv2UKWrXCODt5OZG91tsW76UmD/Oc\npaHvilM3Yql8B/rWUx9tZHmuPsQ6ndzntj5cY+0ajXvxxkr0vGjVj/dFoND7zzcMxweN78zyLn2O\nuZz2mTE3WLpW56EnV+AD6G9WcYdr5KklqVMw5qQOnbiW+S+yhrTrN+e/f4j/SzzIv1V0htshXvwa\nY79Nf8y7eWdusLzr+9CrJNALVpl3DvH+LNSO9e4BXLds2xSW1/lp9JZxCICGOuE3WEO2m/AAO6bs\nHsZf7T1ouu2IrblSSvkRC+WsHVjHMu9wS09HW/Q1Sr0Na18zgz94m3aYHBO3o5+LlRvvj1CehPEX\nEadMTkou6Y/HlxoVPgDvkWrN849msDwH0gNp8jSs98e+jWd5ZcTWuvPz6Gty92wSy3MMRz+e2jJc\nE2rX3DGO9+cKGYl+FlknsW7bGnoVPPAG9jfWTvS1ZpZXkYvz3ncK+rQd+IWfpJR16P814bnROr6S\nkMryLlzCZ1y0htsf/1vKStDrrMM8vobQ3lDZpM+PtaFnWytPrEmeMej1VV/J7Xvr7tXouIrsA4zW\ntg8tnavj69/t03FWLvphVNTUsGOKyvH3/Heid86NNL4XGbEYe9b5A+bruPAqt5Gl+5ncRDLOm/i1\nvkzm51TSk+jemgqW12cx3yubmph+6FFSdpv38UpYhzUpuDfmooQjfP20+RPrpK0VYqcQd5ZXT65j\n/HLYh/edy/vUpWzG3NT+CdwHBSnoh+HdgfeZrK7AuY4ajtfo/l8ppeJPodfG+Pno17TpxRUsb/xH\n6KlRU4T5wNKez9EH3t+pY0c7zKNRvcJY3oVjWHfCevHePv+WO4V4tij+7TR7beS7WHvqy3H+HQ29\n4f58F3vMPuOxp6T9Eo10HQk75sYa/pxx8GPcfxakbyONe4/pxo7ZuBR7xdkfTdHxoU8OsLz2sehL\nRvuaXj56neUVknv7AWJx72DohVRBLJ4LSK+pmlxur9720S7qfkLXcWtrbuMdMgPPdMXF2Ov0f4n3\nKPX0xH8XNOG8pa27wvIe+uw9Hd+5in6jITG8yZxfHO7780vRB+f7A2t1vPXlZewYOu/dLUU/JPrd\nhVJK2bpiX2Xrg3WxhtiGK6XUjSz0Q+rQpo2OI8J4v8Nu/riuAT3xrHbqw7Usr8fL/7wWSuWMIAiC\nIAiCIAiCIAhCCyJfzgiCIAiCIAiCIAiCILQg/yhrsnJC6ZzRWq+uHuXl94hcIunsLZZnaQGJCbUk\nbj+clwO6tEP51K0LGTo2IxIVpZTqQcofnSNwzLblkEH4uXFplSORVgyMRdl98p+8/Iza+d3IRrl6\nbDtuSexEypiotWirRp6Xthrlj43Eiq+ukNvH1ZL/DmirTE5dGcoIk/by8vqei1DKWXyTSGfm8FK/\niixYkVlfRMmd0Tp3RG8cR+2u7T25fMTcGtc1j5Tr+5NrV2qQxFB5Whmxwyy9zqVaNfkoA7QmpfIV\nBimdlwvKz9zbw7rOaQ8vew4llqFWTnhPVA6ilFL5x0hp8RhlUv78FeW3Fr9xO+nRs2CJ69gaZaKV\n7tzau+ouPr8zKX1tqOKypqs3IFu7TWysQ3vwUsP207vq+PyKUzqua0BpaWhXfkxtCSkNr4VU0iiH\npKXiXldQnkj/HaWUCmuP8kIH8tnTNl5geePeHafjQ8Re3bUjv4a0dL+fMj30vvLOaMNey9yD+eL4\nIcSTP+IWgUfeR/ln7FOwiM07m8XyHENRzn1+F/7ekBFDWV4BOa71MJRB27hjjBilVRkbIJ1Jz8G1\ncuvELT4vpOHe9u0Jecjdkzksz4McR8djVTaXIibFo+S/w3iU2Np58/ml98L7cfX+H+h86hDsyl9M\nxRrnFoXy2ex9XIbU70XYdf76CuRoY+P4mKBW2n4jYQ1ZeIZf6zWLPtRx//GQd2RfxXn26ctt7TO3\nQhZQXwYJh1cXLhFrqsc8Tu28qeRRKaUmL4UVJpU6G8dO1g5YddP158DS/SwvMipI3U/at4Y8sNDw\nWbatg9zj8WWzdFyewdeQhgqct+2fQFbZO4bvbyjHP0TZfL/XuSx495vrddz2Dt5TM1lL6RqklFJX\nl2/TsVME9iYW1nzv1ERsy2vLsEbe2c6t01uNxBzgHIK/186PjwtXf4x9OleUVvFy8Ace5SXvpiSg\nT5COzQ17RWq17EdsVcsM0m7nKGyDLxI5d+Rofg3tfLEHriRrV2QYv2dzTqN0v4nILEa+P0/HFcVc\n+kVlKtbWmDcim3k7gVtbsP41lOG10rt8/HZ9AXuCgG6Ydy98z6XYdM/boW0Q3o8Tl81U5OCcefMl\n0yQ4k/2/tQvffw14E+tf4vewt402SHaoBHT3auyXHHfwv3eL7C3siYTzxkYuuei8ADJcamlO7/kc\ng317Rj72S0F+OFF+w/jGvhuZH6kUxygpvXsSe0rv3hhnRZf4+hlHpO52vjgPB788yPL6z4xV94se\nPSG3dAzh+7ndb2HOo1L37jO5FDGYDK48cs4DhoWyvJ2f4XmPzjdD+nZleVQ+2EDGei/yXp0M73X4\nGFz3ciJH9XDiewz/Qbimnt3QEuLiz1zS1f8x7EVoexBbN/5MnZ+cr2PvUMgSL97iMnTn05iT/XnX\nD5Mwrn8vHVdXZ7LXLG0xV+5+E9bzM7/5guUtnf6wjl/45Xsdm8/+e3kalbg5BXKpUEkq7tmYxc/o\nuLkZ6yL9TkIppfyicU1cM3GuN/51nOU99/EcHe9ZDgnWzTVcsr5wMaTjof2x17mbye8xB3fsK+zs\nEHt38Wd5NVVEDsuHoFJKKmcEQRAEQRAEQRAEQRBaFPlyRhAEQRAEQRAEQRAEoQX5R1lT6jGUXl4/\nxOUwJZUoi60lMoaOIzuwvLR4/I3QYZD91JXwbvXmlsRhpzfKxRKO8n83xBclnzU56ChPy+G6zYlh\nx5z+GV2lE65CdhUzrBPLK7wCiU7XYMgx7Frx8rP0nXhPIWNJKV9rXuJuRsqKC+6iPC64I69FO3EE\nkoPujyiTQ2VD+aVc6lJ4HqVbLpEopbuy7BjL6/AUyiE7TIc0w9KOl6xbOaIs08Iaw+v3xb+zvEEz\nUc4eNAVSNd8idOUuS+FuMTXEISExCeWe/efwLvvxqyBNGfkqXAb847jsLPNPOPOYW+Jz5JVwh5gO\n3rj+BafgnmDry8dFSRF3RzIlI8bi/DcYXCTObDuvYwfSaT3Az4vlRU5AV/us3ZBZxP/Mr3VUa4xP\n2jHfNYrXM5/6EvIq6nzSrwPuiYsnuKNCvzYoGe1EOp5v/3Any6Pytgwio7tTxMfE1QN4bVQcSmST\nb/JyTOqQ1toTJdQll7jTRrcIXj5ram7vgJTSKIvLuoJ7cfKHcIq68yeXHbTrg/do64rPFTiEl07n\nHcnQMe1Qf/Az7jow6Fk4CDTWojQ0fTPeq7Utv88vpmBOiQ6Aw0lpYgHLo049NbmYr9uMi2B5O5dD\n0jJtABwSqIxJKaW6P4aS20urzur43joudysn5czP/jJJmRJrZ0g4ju0zyOdegmtNdQE+b+uxkSyP\nOgF0b4vrZuNuz/Kyd+Hz5+dBWhrSh1/rYTGQSeUdxLUJ6oN1rK6Uy2m3HcE5a+ePktsody4DoGX3\n5aVY96mrg1JKlSTj2hedguzKbzh/r9dvZOjYhsilI9q1ZnnpKbgfeinT0/lx7BMsbPhW6PK3ODcH\nP4LjR78F/VmepQPWu/JqnF83gzTs6ma4cPV7bZSOb+/h44dKsP2H4z5vJrJoo7vj/vVY72IsiDOW\nOXfJqkyFJKuGSPN8B3BZzl9fx+vYyhLnhe75lFLK0QXOFnlHIIWd+CLX9DYbHIJMSVNtw9++5hKO\n9e/sMqxVHaZz6QOVQ/Um0suihFyWZ0bOrbsP5t0aw142shf+fj05z1eXb9Fx6KP8PTQTyX/GPkiP\nHAK4o4sDWceoFNvMcK33vr1d/Tfax3IbSepAReX6rhF872CU85maorO4128m8bWbOv1YOhBHJm++\n/0o7gLly0puQCxac5o5XvYkjl0s49gK19/j8mPjTOR379cK4aCSy29v5fL2jrlvdH8I1NjrzdH8J\nzqNNTbgG4Ulcckel/Om/QXbl2pE7znh1xhxblJihY0+DFMfej/+3KaGOi+fP8ec2KimKnY7ZvLaI\nSyDpHNppGp4z6P2hlFI9iSzJZ0CQjrct4ftIKt8P8MD43nMYe4dxnnzNvXcT13TbFswbMWFcRqfI\nLWfryZ3xKPSZISMVcrTslYdZ3qQ34N5TlQuZ4sj2XBZadYc/w5maHs8t1HFjI78n1r76gY67hxAH\npe+4rGnhT2/p+MXRkAC98NU8lvfR7Gk6njEZcvt1m/gedURntCNpG4O/99tTL+j4sZ9+YseseuIJ\nHY9fgtihLX9OryTn88lVcEu7deY3lucejr1UczPG84Y3+bNtryg8Z3Z4CvNVydV8lhc5bpr6J6Ry\nRhAEQRAEQRAEQRAEoQWRL2cEQRAEQRAEQRAEQRBaEPlyRhAEQRAEQRAEQRAEoQX5x54z3eZCG7jn\ns33sNWqL3fuV4TpurOf6W6o3Lj7PNbwUz+6tdJx2BvrlDv24Vr+BWJE5tkVfinadYSVXY+irEugL\n/Wx2PnpW3DzO7U2DwqC79x0EPZ3RCrR5Lz7ToZ/jdezjyrVsof2hGbewha6Z2gYqpVQf887qfkLt\ne+0Oc+1r5jnoe3P3Q9Ma5MU1xzVF0Js3EzvV4ivcSpvi1RPX9MG3xrHXrv0Iy0ras6jncwN07BTs\nzo5J/hY60VbueK08lfchGf78MB1bED156tqzLM+S9MepyMLfcHPkWmaqSQydgR4smXuusTyqNzY1\nyWfQK6nnY73Za31JLxjnEHLODDrdy8uhZQ+bgD4/Xndbsby7RCPrFIx7zNaZj9vuj2J+uL3kTxzT\nDtre8fP7sGOukj41obPQ88n+gMFWMAPaa2oF6p/P+7SkJaO3xeET6OtQ39jI8oaTXgJ1xdDRGvu+\n5O69pe4nrYgl5HZDXwB3og/f9ibscduH8J4QwRO66Hj3m3/oeOjiESzPLRo9Cda9AIveGZ9MZXmV\nRN988FvooP1I359eT/PreG8pxrpvLHqFXN3L74kHZqMXSmUGejnl7TfYQxJt+NlP4nVMbTeVUqqx\nGnOFrx/GWXRv3j/MjLdgMClHP8BaOOKxgew1c9KXojIT84ZLCLcYb27A+bMjmvecXbzHThjpsXPj\n9c06drrIrVTNLmFtdSHWoGaW5PcXw0kJJL2XxrwPvfsvz61jeYNGoZeToyOu08hneK8vutY7k7k7\nez9fZ3sMx7Vy64DeCfs/5VbaI17l49nUpKxFr7fA0bwXR/tHMM/TefTsDydYXkAw3r+XM9bWMkPv\niIAw2oMG62fpTZ7n6oDeBcXELteG9DSg/WcFp6ESAAAgAElEQVSUUmr8y+hzRO2yL6w5w/JoP4d2\nbdB3Y/P3e1ne9BexVtNeGRVZfF8VvxK9bqItMEc1N/J159Qa9O8J7TlLmRK3Drivsnbze8fKFT2R\nskivstZJfL9QmYJeTu49sQek/UiU4n3RJvwHPU2ufMP7XVWXY02ie72q25hns/by91qRjrnRZ2CQ\nji9u5D2JYhei55GFJT7f7R3cBjqQ9Ndw78znHsr1Y+gJ13wVc/II0qtPKaVKrpLebHzKMwmOoZgv\nYrtxy9mcA5g/SnJwnq5d42vImHfRx6W2lFgol3E7cvoM4d8HPTLL03l/vCbSJ8U1EvuEKlfM3dFG\ny3HS16k8FePK2s2O5ZWUoK9JORl/xTm832H74ehzUpOPni6e7XlvvKKbOBfxa2AV3CEqhOVlbsZn\nbP2GMimWpG+XsR9ZdA+8X2qV/ufKeJZXS+yQM77CvDR4HO86lp2K547WpH/d9M+ms7y6Csx59cQC\n3SsR87Fxr+BAehkNd0TfoJNXeR+dLvXYYxYn4P1EjIlmeXbk7wWOxXtd9TLvaXJwGfqsRIZhPrVw\n4M+fbSbwv29qLCywH7m2+Rf2Gt2nUUt6f4ONteVW/n3B/2DnwXsezZqB7w4yL+G5o18kf+7v99YC\nHS8YjLlp3sO457PTt7Jjpn/1vo6T96EvTNAo3pP2+/nLdGzrhXXW2EfI2Rk9O2/uxbWLJD0XlVLK\nI5b0YMzDPG9r6G30+CCss2tP8H2FUlI5IwiCIAiCIAiCIAiC0KLIlzOCIAiCIAiCIAiCIAgtiFmz\n0aNMEARBEARBEARBEARB+P8MqZwRBEEQBEEQBEEQBEFoQeTLGUEQBEEQBEEQBEEQhBZEvpwRBEEQ\nBEEQBEEQBEFoQeTLGUEQBEEQBEEQBEEQhBZEvpwRBEEQBEEQBEEQBEFoQeTLGUEQBEEQBEEQBEEQ\nhBZEvpwRBEEQBEEQBEEQBEFoQeTLGUEQBEEQBEEQBEEQhBZEvpwRBEEQBEEQBEEQBEFoQeTLGUEQ\nBEEQBEEQBEEQhBZEvpwRBEEQBEEQBEEQBEFoQeTLGUEQBEEQBEEQBEEQhBZEvpwRBEEQBEEQBEEQ\nBEFoQeTLGUEQBEEQBEEQBEEQhBZEvpwRBEEQBEEQBEEQBEFoQeTLGUEQBEEQBEEQBEEQhBZEvpwR\nBEEQBEEQBEEQBEFoQSz/6cXvHnlExzO/eoW9dvrDNTq+lJGh477dolle10XzdLzr1Q91fOX2bZY3\nZf5IHbtF+ej4wpd/sbxuz/TT8Rfzf9Tx5ImDdbx9+zF2zLMrXtDx9S8P6DirqIjlDXt3io6trT10\nvPeNH1iepYWFju+WlOjY19WV5Xm6OOu4qKxcxz2e7sfyvlz4s46X7tqlTM3Jpe/ruDC/hL0W3C9E\nx1bOtjq2dLBieWfXnNZxRK9Q5Dlaszy3aFy7v748omNHOzuW1/uV4Tq+9uVRHdt62uu4KPseO6bn\nS4N0nH8W4+favussL3JghI5LLt/VccDYcJZXcOKOjj17B+i4rrSG5TXXN+m4Ih3nz8KO3z5eMfgb\nbaKnKFOSdvk3HZcl83FblVmqY5dobx1nHU1jeT5dW+nYIdBFx54RESyvLDcDfzu3TMf2fs4srzS5\nUMdtBvbRsbk5xlFZUSL/IM0ICy9k69iGXHellDIzN9NxY22DjgN78XvnyvJNOm6ortdx5KJeLO/S\n55gTur80WscFl5NZXmCvOB07OoYqU7NyHubDUe8+wF6ztcP42f4q5pwh5F5RSqn6yjod23vjOjo7\nd2F5i4ZP1vHch0bo+NtNO1neS6/M1HHQwDgdF6Zfxr9ZUUcPUc7B7jp2csac39RUy/LWPfOJjud8\n+7GO7946wvI82nTV8aG3v9dx96f6sjwv/4E6rq7O1PHqpz5Vf8cza9f+7Wv/NxQUHNbx2U/2stfc\nfHA9AsfhvrKw4XNFXjzuTe/Y1jqm11Yppcwtsdbcu5qnY6+egSzPxgnr1TWyxoXP667j7H0p7Ji6\nomodO0d56rg6u5zl1RZU6dghCJ8v89IdluffzlfHrcdG4t8xzKf5p3Ccdyw+R+nNApaXeSJDx6OX\nLlWm5vOZGPfTlkxmr03u/5yO2wXgvnzy8fEsz6OLn47tvb103NzM74OMrVd1HDUDfyPh+40sz6sP\nxkJIT6wh6ecxz+38ej875uGvntJx1rHzOt69nu+D+nbBfeoUjvu3Vf9OLK80E/cVnVNvbedz+bAP\n39FxbW2+jhN+/o3lJV5L1/GjP/6oTMmfL76oY58wb/aaZw+sd2dXYf9SWFbG8mKHYe5x64D9S+Wd\nUpbn3BbnrCwVa/DJbedZXtyjWKP++HK3jnuFhek4KSeHHTNwDo7JP4K9TSvDnqUyE/uPTaswDmLb\ntWN5AbFtdJwSj/vexZ6vs83NWJDDZ2P9OP0t33e7OTjoeOhHHylTc/XP73R8ec9V9lrHgZhLXCJx\njR19/ViemRn9rRmfK+XX4yzPKRxzZe5fGTr2HxjM8i5vw/rXbTLm0eq7FTr2G8CPIadT1RRW6rj4\nSh7Ly0/A9S+txjw86M2xLO/UEqwvnR7tqWMbN76fLiZrg70/9mmOvvyeOPUxni8e+PTv18z/G0pL\ncd3OL93AXotZjGfJ62v/wPsLdmN55jZY7+y8MOb8woewvPr6Yh3fvXlWxyXX81me30A83/zx5lYd\nT/xggo63vLGFHTPl00d1fOM7rPWtHuD3omtr3GPFqVjPT6w8wfKie+K+7zjzMR2nn97K8k78ekrH\nd0sx9zyzil+n8vIrOvbxGaNMTeq5X3V8e9sN9lo+eV+xCwfoOPvPJJZXVYI9Q+AIfH5zawuWl7cP\n5625Ec9ZTpEeLK+5ETeWjTvGvp2/k453L9vHjvFzw9hqbMLfbtud37P0pnUic3xVDt8HNdU16jjp\ndKqOvZz5cxHFrR32VTaeDuw1a1c8J4X3efh/HSuVM4IgCIIgCIIgCIIgCC3IP1bOPPQxvl2sKOO/\nMJ9Pwzdet/Lwre3I6f1ZnoUFvuWKexN/r1tONstrFYFfs1fOf1bHUz9/huU1N+OXnEdeeEjHwbE4\nvuOMR9gxdXX4hT/0MfxK0ieA/yLd2Ihfux4dgEqe7/avZnn3shN07Nk6RseXv+a/0HZ5Cu/jz1eW\n6PjXV/mvZS+sfFLdTwIfwi8P3kVV7DWXEPz6UJGL81R4mv8qSr+F9B+MioKUFRf434vAN/WhHfHN\ncnNDM8sruJyhY9+4IB2bWZLvC5v4MSc/xi/CnebgV4ToIZEsr+AcfpXw7o1fZp3b+LO8ujJcb9cQ\n/Dpalsl/1bp3Dd/G+w3GN/GXfz7D8qycUEXUhheQ/WssrHGr1hbya6jwpTCrlqmq5b/e2rfCN7xn\nfsEviWM+7MDy8o7gl87KXHx73OWFB1leSSL9lQKVLld/2KzjxsoGRfGIxa+Z/gOidHz4P9tYXusg\n/ILpPSBIx0m/72Z5PgPxWsFf+MXX3j6E5fnFZOB9p+GXSVopo5RSCd/jF5/YF99QpiaZ/GI6yHAv\nVlRe0/GUL97Tsbk5r2KbNwgVN48OQjVZwu31LC/EB+fQk1Qo2G7lf6/1AFSn2NjgmL3L8Mvs4McG\nsGPorxz19fg1Zd2zX7K82ctf1bGlpaOOXQOiWF5F+U0dd3sSVVglSbyaws0b49HSEr+aTPt0Ossr\nuJSp7heN9fhF1M2T/2oSOgvrS3URfmHNi+dVK7k3sWZeO4m1dcwH/BeUGz9hzqPVfbdWXWJ5Nr74\nVcYlGhUcV74+qeOYV3jVR309fv3P+wv3fNtJsSyvugT3eckNxE3NfH6mvzrdI3llifwatp2KNfPi\nZxhjUXN7sLy0Y7fU/WTGZ9N0bG3tw17bchrVWzY2WCOdnPi4bWjAWLh9CXOYT2R3ljf/Xfxi+gmp\ndDTO0ZU78Pd82+OalKXil2JLc/6b2ivjX9bx5ztRheu3l1cgtBqNX35zdmM8PrXkRZYXE448+v4s\nDP+u15pvdbzkO8ybPx34nuVZ7+RVGKbE1gpzma03/2Xy+E+omKAVI9ZWfP6rIWtcwkXMG53m8PGY\ndxTX49ZV5A1eMJDlld7EPmrIUPwNWpUTaM6rVS/9ek7HIV2xb6ot5mtE1kmsXSN6d9NxU7Vhne2M\nvQ6tbj67mVf5dOiHvROthPUxVIGfScF4GapMT+kVzBft+/Nzcy8BrwUMQnVP4XU+p5aQfVpBOj5L\nYEwblpd+GMf1eW2ijg++wyu+4l7GJ7V3IVUSGagmoFW9SillTvZp5mQv6xDI14nwSPyiTivWD7zD\nqykGvYnnkJvfYL/ZdhavdqtIR5U5rTKg11QppTz9eaWKKTn49godD3hjHHuttABz0dVLqDroG9mb\n5dFqNcfWGIPm5rxCv6oKa8NXr/+i43A/Xk01bzrm3ZGLsB7T/cvoF0awY059vEPHYaMx3zuTCkql\nlDrxIa5Vp7lY0+jeTSml8jOgDEg+gGe/pvpGlhe3IE7H19Zd1DF9flVKqTu7sV/w4Y+6JqE6D/Oh\nK6n8UEqpS3sxB5aSvZlLJ0PVopONjtN34H5pO54/GFH1gV0g9nMl1/megVYh04qqnf9BFXh0IK8m\nvnYHz7AB7tib1OZVsryAcag6vPgDqpdsDeuEDflvDye819oGPgd0egJV+5XZGM9H1/KKqp5DO6p/\nQipnBEEQBEEQBEEQBEEQWhD5ckYQBEEQBEEQBEEQBKEFkS9nBEEQBEEQBEEQBEEQWpB/7DlTeBn9\nEdr0GcReo1pkquctv8GdZJ5aBu3h7FH4G7SPhFJKmZvjrQx8FH1rEpb9wfIiSIfo0xvRpXvbD9Cu\nT3mV6x3vXYVjjw9xQ0jcuYrlbViJbs8Lp6CvQ0Up70S9/h28p4Fx0OBl3rnL8npYQmf64KfQId6+\nuIPlpfwEHbDPG6OVqSlOQH8Dj05ck0l7+Fja4hoYnYgCx0KXZ2UDLWibSVxDWFuCzvMhE9AXZveb\nvM/OkAfw9/Z9sEfHVOcXt3gYO6boC2jXi85l6bggmesTwyaih4pjK7iLnF7C+5W06Yu+JKW3MdbN\nrfh3lm1GQeecvgXa8ArSZV8ppdy78J42poTqU9s8yPseNBH97LVviavWGH5tqAa60wico8Pv8l5J\nA95AL6d7qRk6vnP0LMuz9UEPkcPv4F7qsxg66eoC7rh1eSX+hl936KZD2nO9qGcv6HtzD6CPTvRc\n7mZAXRnq7uF6JG/Zw7JyrhJ3hCro+PvM57rS8Dl91P3k/W24Dy6u+Ia9ln0zV8cZ26HT7f7yBJb3\nn1XoyZV3NEPHj7/Ou/qf/wK9H4K6oF/Qx1t4t3raF+z1B+Fg89EO9A7a/Cx36zuVhDnx8z3bddza\nk2uUX38ITjKf7f5dxznneL+m4L64ruYemHturuCf6fcf8dlf3wit+cVPVrO8+GtwcIsePk+ZElt7\n3OdZWQf4i6vRg+voBejsZ33E3dvsSQ+CrCMY37VV3G3iRirpbTECvb7uVXLd9M1j0KFPegXnMvMc\nelSUZHL3tv1fHdTxqFdwzx57n6+59H6uK8H76fV8HMv77RWM7S7BGGM+vfm9fWsDrj11pfC9yh1N\n2sQEqfuJoyPm0cR1v7PXAonbFO2l0P913rfnvWn/0fG00eg94h3BndM2bYfblHMb6PO/W8CdIF9Y\nAyeccx9jXvbsgTE394cl7JinR0zVccK36P0yZDHvqZe2Fi4fXZ6breOXiNZfKaXSzmBPc7sQ/Q7G\nLODdRo6vgYa+rQ/t2cPXz5Wr0Reg68xnlSlJL8DaHxHdjb0WS1xrLqzDut21N+9poszQLy0oBD05\nzv50kqU5EcfJPk9hH1qZzd2fPLvjWt3Zgnn85Gr8vW4P8vHhaIu+I9TB5vRvfJ6M7II9C3UqMbr3\n0J5UFdVwSysz7FlcSe8TRRwSt6w5yPLiok3cRM+AhSPW4Tvneb8wDy8X8l9Y7+28HVme/VCcD69y\n7B8urT3H8mJfxH16/Ru4IfV/jY/v7IPoa+IahT2DYyucs+LrWewYazdcx+xtWCM7PvcQy7t9BPdO\nQP8gHXefxvscWVnhs1sQ5z7qhKqUUl0nYezTnhw3f+G9yRycuVuXKckqRl+smjK+7ysk+/XUXOxz\nJnTmfTduF6DnR3MD9rXnln3F8m5lYD9XW49nmIc+4OeZ9sMrJw5r9FmnuoCvpcM+wF4nJ+kQ/lYt\nd7rNIHOP2Qrsa9v05z1iwoehr5GNG+5nGxv+vGBmhuvraAtnvFcfeoHlzZ1p+mdEStUdzGdld/hn\nDiM9fSqScb1pzzullLJxxzhzdEGcsuUay7tKnAFnTsM6du0v/sxduQlr181s9KsdNBq9fmjvMKWU\nateI8+vXG8/9TWRcKcX7edpZo7eROVkXlOI99W6cQ9+kziN4z84jn2HudLBB753IgFYs78Qe9BXq\n/F/MfaVyRhAEQRAEQRAEQRAEoQWRL2cEQRAEQRAEQRAEQRBaELPmZoMfJuHs9yjFrczipZsh01GO\nVpaCMq4lH//C8p5+GGXA0bNQ3nXi/Z9YHrW9CvX11bGxtOhsKsqJZr2Mv32aWAP7GWwAGxohCaHl\n4L3m9WV5tzbAIjublOiNfn8qy7OwQJlWYyP+XlFiBss7shoynEhiw3YwIYHlPb8Klr1ubtyC0xRc\n24XSaStnG/aaRyQsAne/uUnHQV5eLC+TlDd3ewh2sS7hXMZAS4TTf0Upms8gLqVwCvrvln7UgrVV\nN27pWlaIEuGUlSgJaz2BW2nf/h0lgbQU7XT8FZY38mnIpmhZsK0zf2/JK1GCGjgeJdHcSlopcyuU\nJUaPekKZEnov5iTz8v+OM1HSSu3B/bv2ZHmVZSjTLbqM0tLaYl7qbEbKm0PGQGKY+DOXhd3OwPvw\nJ1Z1VBYWMZ/fYwWXUDJPrRKNZdmVuZhvqnNQrliVw0sXi1JQWhryAMaBrRcvs7Rz99Dxpc8hRYl8\nhJfCU1leaM9ZytSsePxxHZfX1LDXenaA1C/sEYx9B4e2LC/zLEpt6X10+oujLK//6yh/3fDirzo2\n2vc6k3L9AdPx71rYoNyz4PgddszJq7gXH3xqxH89Rimlkjdhrisqx7XrNYdbaGbuQBmrnSfm19oi\nPjb9hqCs34mU/5en8zLqkFisNVZWXLbxb7m+G/Np/kl+XlwiMB8GjUKJ+g8LeFn27CVYU+iYO/4D\ntx2uJGPE1QFjOmYunxuT12Nu826P9dO3P+bdooRcdsyhTZBZDBwP+8cmgz0slSr7jYK0qtpQRtxU\nj3JhK2LfW2qw0m4meZFPQEqQc4qvi5d24TPN+vZbZWo+nQ77daNt5tTPYMG6/23ItaYuX8byaNn8\n8Pa4JhuOfs7y0tbjs1nYYJ3IS+fnpusTuC+cvHHf19Uhr6a4lB3j6g951udz3tTx0Bgunen45CQd\nJ20iFqTTZ7C8MZ3jdDxn8GAd38jiEo5RPTF3unSEVKvWIBPoMmeRji0sbJUpuXEQ1uF/bTjNXusQ\nhbHvTOzlK1KKWV5KIkrr0/Oxpk9fzCVsFta4bg1VkFLs/Ho/yyuugGUv3b9GkT3gDVKar5RSs96A\ndLXkGuTxt85nsLzCMqyLbck+2bUN37M4tsV/15dgDnEw7Ls2LIUktVMb7AWjZ3RleTfWX9bx6KVL\nlakpKIAUIHH5MfZaNVmvfLtCGmDfittT0/mn8ATmZccw/pmTTuAZIu517AGNcoeMjZClBk/F8051\nIa5vwSk+/6cmQEYaHYe9Yt09vtY3N+GxqzWRqad8zyVYAQ9hT5O7P1X9HZZOmG8tiGQn+AG+TqSs\nxx4hZiGXKv9bamsxRx188xP2Wo8X0dKiIhv3X2D7MSyPypeco7CWth3M85qacP8VpENS9OWLvFXF\n2O54nnLvBOnlxl+wB3ztNy6dvvEb7gkz0gqgsYavi64d8Pc8ozBXV+Tz/bl7AGQvyVsht3drz+2n\na4ognSslVtKV+RUsz7MT7vvOU55WpubU5x/o2K0Lb4NxbB2RZvbDuPUmsiGllNrxIdaXcW+iRUj8\nZ4dYXs+ZkCXRe6S2kK8hru1xrpvq8Dy/fTlkiTHhYeyYmto6HdPn/twSLtXqFoE9jYUt5nivfm1Y\nHn0uqrmLa+Le0Zfl0Wcr9w54LWMjl3QFTWmvY/82DyojUjkjCIIgCIIgCIIgCILQgsiXM4IgCIIg\nCIIgCIIgCC3IP7o1ecagDDNsCi+tz7uIkj8rF5SqLn7nEZbnHo1ypMNvozTZWDaZ8Q3KuEZ8CJnP\npmdfY3lv/75ex7evwFVi2NujdJy6mncob/cYpBUfzUJJ5p4FPO/puSgtNb+F761yTySyvNffQln7\n5z+9iPf24ncsLzIQLhXRrRE/+c2jLG/fW2t0PHW56WVNblEon0tecYG9Zu+Hkn9a2l1CHG2UUiq6\nP0o0vbsgTloVz/Jsyd+jpcS+0TEsr74epWWVxSgN9e4IaYfRJcvCDu+Plgsf+YZ3ru85BuXcnt1Q\nBtvH4ECVuhljmJYzD5jBS0EtHHjJ+/9w7wIvX2y3sOd/zTMFIRMhkch4byt7zTMUrkc5F1DieeOX\nXSwvbBokSubW+Lye3XkX8ZtrIRnzjs3QMXUiUEqpYGscFzAa160sDTKIe8m8fLtNnyE6PvQ23Iqi\npnRmeRd/weeIeQIOSo7B7iwvfBIcSVK3ozTa6ORQcBmSrmDixHL+uxMsr/tCLsMyNR1jcJ5WreeO\nUpPeg9PA9a8wptvO5mX4VXcga0jYgnLzYe89zPLMzDDep34KiVZDLb+3P34M12F2LGQMXz26WMel\nBneg2c/AEc8xAI4SlrZ8jDi74zpYmGNO/Xzxapb31i9wcXFxw/1blMMdwvKOQhb3zfu/4e/t2cb/\n3kPTdPzhDu6O92+pSMfcFfYoX8fKiCPEtlcxr4+bO4TlFZ7HfVFzF+c2q4i7HQ4dgvvewgGl6/Hf\nxrO8wc/guhVegJMFLcv2NLjJPdz3GR3X12P9zdx1g+V5xaG8l95XRrmSFyltPvwF7sWuQ7mbgXcv\nrIXFRKbs3sGH5XkeM60czUiAB6SORre4p8e8rOP5wyB9SD21nuV9/Q7kgg/2gjSMOt8opZRzO/xb\nzmGILU5as7znZ8CJqXtb7Ln698Q57LyA3+f7XoeEikpT9p2+yPLOX0/B3wgK0nHPQH59HuyLOXDy\nsnd1nJ/JZZOpazD3tB6AY3LOc4ehLc9DPjHpyy+VKbm8De+hfSSXTlPpCJVbNjdyJX+HIXAiSv4F\nJenHfuTymsYmyF76z8Hn7d4ulOWVluF+9myN9cqayDUTN3GJGH1/Jw/iMw2Y2IvlnfsTe1aXQMiC\nLQ17lPpylPRXpGG+2rWZyybHzYbchLq0WLvwebzj3Pu3t1FKqdwTcJurMMh96Xm/ewlzW7Rh33Ll\nB4w7zza4x6xc+Wfp9BD2GqeXYp2NGNee5QVNxn8XX8dezyEA5z1gZDg7JuE87jHaMKImh0tTrL0x\nFnLj4aJnZdhjpRC5avQTuAY1BtmHcxvMnXmnMKdSNyqllIpcwF13TUlFBfbrwz54nb328gOwo3lr\nPZxrMy7wddvMGuuVU1tcw8pK7jS4ZPbHOn76SzxPTR07kOXRZ1gqRaGtM5qbuZzNdyDmkVvE4c64\n1l/8BntHGzLG3FpzZ7PbxzBvth0bp+PCZC5zKb2KPTmVJXZ8fBrLMzoLmhoH4ljnGsHbW4x9D/u+\nm99hb+bcjre3aCD3LN3rUIcrpZQq+h4yJ9qO5PgNvgeZNjJOxyfOw4nzoZchdyu9wf92wdkMHdc1\nQJIW4c/3QT/ugkvzBLKGf/4kH5tPj8Z3DLYB2JtsX82lWpNfgIzr3FeYb28apKx0rPs/K7ImQRAE\nQRAEQRAEQRCE/18hX84IgiAIgiAIgiAIgiC0IPLljCAIgiAIgiAIgiAIQgvyjz1n/lwGvaKjDbdg\npta5sa/NRJ4jt7NKOwd75iH/QV+BhBW/srzZX3+o47x02JwNWjyc5d06s1H9NxwdYetlbsUtky8T\n69yYMLy/i2lcx3j5JHRu4z6crOPmZm6h1pVowW9vv6njNX/xHhJlZdB8Ozig10TKru0sj+rh7gdm\nFtC/h8/l1sG3f4d+r89z0GuW3OA20U2N0BCamcFuLHgq16tTMnfg3Jibc6vkrOPQ6YUMghWvpSV6\nGlTk57Bj8oltoVs3aEZ7mnG7N8c20C5WErvXIzt5/4oS0kdjaGf0bTHaMFcRG/n6ctg6Rj3N9a0X\nPoV2cfjH3Prv30KtA13s7dlrJTkYt1XZeK/efbm93YkP0aPJwxfn6N4lQ++cGdBk1xBbVN84rul3\n9cG1LytCX6Z2g2br+OIKbiHsEorx1vlx9CEqvcn1orbW6MWw+i3MIRMfG8byDi1brmMrS0xnxYbx\nG0zsKusrcA3NzHhvCDtXrrE1NZW30S9mWj/e32bDG9ASU+v6eb0DWJ6lE+biWjJ3NDSUsby6Svxb\nzh7QQZfXcT2vvxs0xj888ZaO75D38PIXj7Nj6sh9UHwV1q+5xzNYnnMr9KPpMBM2wX3d5rO8sjL0\nfyovx1hy8Y5keQfO4B6bNhr33717J1le/f2cU0kzAXtXPvdknIeOvHO/v7d2L/gLc1n7ZzCmG6vr\nWd6Vc+jF0Lk3en098MFUlpd/Dr0OKlJhK17ZDmPAv0N/dkwz+Rxubuh1ZjaG969wdMTYKb4Lu+JW\nw/la31CJPhf5paX/9f8rpdTdk7AutnHHunBhE1+3A7rxPjCm5vBVjLkhb09grz1O+m+EP4leDzkH\nUlge/ZyvrlioYyenKJa3Yxv0693643yWpvIeQ19tgxX20Y+xb+nyJKy9f5j3MjuGWtS/vuEnHQ+p\n43Pq13Pf0XHaXdyzx1J4X4q7V2D7vetV7Mv4TKlU+8noDXXw7RU69nDivYIiBkeo+0VoR/TYMVoh\n+w4I0nF1PtaxwHH8/aStwbijays9R0hhq40AACAASURBVEopNaI39k5/fLVbx99v2sTyPnrySR1f\nJ5bOR69j7Zs1YAA75tR36E3gZId7ouLWPZY34s3ROj7+CfZQtYb5Lu559KDyjsF95BDkwvLMiT34\nnoPo2dItjY+dzvN7q/tJNdljdV7A/y1bZ/SzOL3kTx1n7UxmeVXEcjtsGtbWyqJclnd02WEdj3gP\n+/ykn3iPITML/Hadug/9VKImYa9YfbecHTPsxWHkNfQ4Kb3Gzyfdk7ceil4mOccTWJ5dIOzCLe2w\nJ3IJ8mB5hdfQZ8aV9JhsbuT3REEC5i/PQXw9+Lc4OGA9SFi/kr02Zx76cBQm4bkg/+htltfxSVyP\nezk4F2m/n2Z5UyZifLv5weZ8zRH+fPjYWNzrV349r+OBC7F3MPa2LDiDflB3SA+4CDu+LvZ+Ffei\nqyt6w9F+mkoplbgP9z1d+1za8v6JbWdifsk7iWfT0mI+JsKn8D2wqck5hWtCx6lSSpWRcdxqNK63\nrSff3zz0FnrTxH+OecrNgecNfhhj8Oaf2Ds52vHnxeRkzKMx7dDn6fvX1+l4yuTB7JieizAHOLhj\nDrx9gPdEe8yajKUu6N30rCefK50icM8VXcCcMvGZ0SyvNAn75tY9sD65Gj67I+nt89+QyhlBEARB\nEARBEARBEIQWRL6cEQRBEARBEARBEARBaEH+UdaURcraqTWWUkolZKI8K+g8ypusnC6zvDZdUc52\ndf0qHe86xEuL2s1EqZZ3G5Q6Jf7OrSurMlH++OVWlDi+9jxKyXLvFLJj+r0GC6wD76C82Nycfzc1\n+m281+XzYPv97IpXWN7MV8bruCIDZacXvv6e5UU+OlTHVAq09w9u32ssAzY1JUQy0ljDy19vpUE6\nFFiPEkBqQ6mUUtXZKN/MPoFrXF/GS9bDx+Fcd3wE5eD5GcdZHpUeJROr21aDUbLm4sdtCm1GodTN\nzg4SmzpD+fZVYuPqFIx/h9qMKqWUrTOxv+sCmVTWlpsszykKZbU1RbAhdm5twfIiH+GSMVNSmQc7\nZR+DJW4+KZV0DEapHB2bSinVZQEswisycb+4hPESWVcPSBzyUlDq6+DO5TXZFzGO7f1RfpuVjOvZ\nVM/nDToWLYi1uaUDt5QN7A5JVlsyJsqS+L3deQAkAq5RkCTVlXA7zgvrzuE1UgL+4JKnWV55Mbn2\n3B3QJEQ/jXL2rEOJ7LW+XiiptydWfU31jSzPsTXKLR9cAjvk2louA0z8ATI+96gMHX/1Pbdi/GTr\n2zquysc4K7oMuVtNIbffzjmEstuer87RcdKBr1leTQZkOjYnUC576tQRlrf6MErNx/bEvBEVxu/Z\nPrMxhm/twPnb89YWlvfWxmXqflF8B/dVYSIvib6ViWsw/FFIG9PW8nUxYiEkfRte+BnHLOCluR49\nYRfbUEHnWoOEIwYyGu8ekN3e2QvpjnMwf6+pv+CesPVBya1HN25RW5Ebr+PLqzCm7hns1Ye9Cnnq\nhOdR6ttYy8evSyjmm8tfYF0I6M5lmLX5/O+bmrE9UIr+63Mr2GsLV2IcD4qA7GDpwkdZ3kuLYHNK\nbeRzr/M1PvZBzKmPLvhAx8dSLrC80lKc32HvztBxxmnIaKh8QymljiSg7H30CnyOjo/MZHkHL2MM\nUnvigie5HHLlzp06Pp6OPVZJ2h2WF9QVUjAqKfKK5nK3mko+Z5uSunuY570H8LmiNAWSBGpVffFr\nfm2olCm2B9aTQycusbzsbKxd9yogWVn/yX9YnmdvlNAHHsA86eOCedvVIE0OmwSJcPktzMH7tnK5\n5tiOKLun7/tUMpf4KCIloC0J2o3kcrtmct2G98X+xXdwCMvb/M5WHT//K5cAmoKQKfi3T3zE2wME\ndsb5jJqJe9HGYPdtcQCyk/JcSFP++oZbwA94DnbSSSsgJ/M0yMCppCOQSCxz997Sse9wfp6oTDr/\nCNa7hkY+BzYQq/PEr7Ff9Y7jYzhhC+5ZKgFN3snXz06PYT2hey4rwznyaH//pKLH3/tSxzGLH2av\n2dpif337EvaHOTl8bjD/CXsT/xGYR5oNkkVnch/8vAD33/zvnmR5RTdwDaiMdct8yKTcHR3ZMVQe\nH+QNidjmVzazvDlfv6bj4mKsY46OXIodPRKW7G5Ecnb9e/4M3LgZ8sr2c2A9vv3lT1gelXL2fKKT\nMjVthuG8G/flGUUY+3kb0LYj+sGOLK+YyH6sLPCcZJRfPjgW+9fVH+B8Zpzk854FeVanLQ9GdcV8\nQOX+Sillbo3r2NiIvQR99lRKKa+eeK6xsMUc4tbeh+U11eEeLjiPfZ7xmYTu006ehaStZwRfF82t\n+POjEamcEQRBEARBEARBEARBaEHkyxlBEARBEARBEARBEIQW5B9lTa+te0fHVSXZ7LWsPSijdG8P\nx4qSJIPLD3GZaW6EVKZrMHd+qShK13HKEZS4F6fykiH/PijpWvscSi0L76BMzcKWfyzqlPTLUZQ4\nju7GZSh7/7NLx4+8g67hs+Lmsbz1xyG1OvD9pzoO9fVleac/hjtOyBiUuo2exV1+PA1l5KbGoxNk\nMObmvPSrtw9K+mpLqnXsGsldawL6omzSwgLllaX511le1jmUidr5Qprh6s8dEo79Z42O+72B0vCa\napSLNTfzUkY3N3TfPv3+5zpuNa4dywuZhhJhKyJdcjLIfPLjM3RcdgPjzHswH5uliRjT9FqVZWWx\nPOrqpPhb+tec+gHntcuEruw1z+6QMuUdxX3kPzSU5dUUEeelLnBksrTksjrq+kNlcPkJXIZDy6/d\nInBeUteiNL+xmpcx+nWBZCX3EvIub+OyDz/iIJSej/M/9GXu3ubogWu15eXvdDzyLd5BPYo4hgQO\nwFjeuZjLcGIX9FP3k4pclMZ/890f7LXRpETz0kHMZ4+8O0VxMI+am6PEMzue34uBxE3HIQAl9Z+O\n4GX4z497Q8cvvQopxPkjKAOetZwfs3bZEzr22IMy9C6ze7C83V/u13FEa5S++qRzp4LFj+AzrtuJ\n+b/RIKedNBeyn5tb8f5iH4lleb89u1THT6zgkpV/S04xxn2QQSbabQzuq+p8SEGrSrgsbMurKN+m\nkmHqJKaUUv7dMVYLknCPUGcMpZSqqcklrwXp2CkM82nxdT5fnb0MCd8E4q5w/edzLK/Hy5BnDXkP\nDj319dxpKOso3h8t7c1J4HK7gO4orS+txprTdRD/TLlHU9X9JOZlSKlzFnOXj4YGXK8nhmPO6fjE\nDJb3yri5Ol42DY5KZenckdG9E/ZIuy/BYcLKijtCfDAT0oC31r+uY4/oIB3X1nNHr9Wb3tPx24sw\nn70RxdfwNydjT5NFXEh+P3WK5S0jbkM3vonXcauxXGZcWoqy9mlTUJIe0ZrLQ56ehrHl/8w4ZUru\nFWKturOOj9veZC43t8LvkCnruDthR/J+9x2B1GDOYi7fqSuFhKorcWdJ3Mxdxi78CEecsa9gHTLf\njjJ277ggdkzJdaxxfx2EnGrIyJ4s79QGvL8QH5TdU/dUpZQa8Az2mOVk32NhcJy5shnX0M8Df4O6\nNP4f9t4yPosr6h49cXdXAoSEJDghJGhwd3cvXihWKFKstKVAi5YWKcXdXYq7JlhIiLu72/1w7ztr\n7/n35Xd/tw+XL2d92mH2PM/MnHP22fOw115CCNFtXLD4nLixElSXNvM4tVPPGJTpu2tAs2u3jK/F\n4jjcS9hHPMP6Hf2YX+ZLxMo640A3jDrMcxD7NnjX0CeUIte+oKYkX4tk58ze+IdiT++BsVdTZ0wN\nkXOZeuK5a+ny/y+v2xF5i1NjXKulF1/b57/H86OKOL69uZpqdhje4whjRyN4TtRv30xbzY5NI6qd\nd3eBAtS4PR8b795Yc28O4D3LQaUUGn8CipMB9ZFsn150kPlRKhOlgzauBTpaRBLfn76ZMkixfQbB\nfrTmD+a3dhTaXYycg7j24PRV5ldFVBE7twalrmY/rvTl5NdSsXdOhWrm5D/XMb/jc5YrNo8OmsGF\nXci/1O+0vv0JfUkbFD51u4znofh9oHUPzNvXB64zv0N/Yp58uwrPt1/z5syvNrkOl9Yeip3/EbnY\nq6uv6SmiCaElWtUDJUmtQGXtEKjYJSWg7qoEqsXRebi+AT+Dtleaz/OgwmTsSYN7Ym7eWX+D+b07\nge/y68Z/YxBCVs5ISEhISEhISEhISEhISEhIfFHIH2ckJCQkJCQkJCQkJCQkJCQkviDkjzMSEhIS\nEhISEhISEhISEhISXxCf7DljYABOa+yTR+xYZRF4z0uHQOprYFAQ84u6AF47lZN7FRPD/Mx2g9Np\nXR/fa+lmxfxO/HVNsYOfgCtYewy4/tdPcA61fRC4o8vGo7+Jmt957Q5kLbNfpyr2sUcXmF9aJKQY\n240Gr1ktqdV0EmTCioshd1xRwbmGa0YsVey1F3oKTSNsK3jKxSoZzprdwWm18YMEq7Y2l/nKTQSH\nsDARnDorPy43RjnRdWqgD0m6E5fhTM6BlPOpb/9UbCoP2WxaS3bO+wvobRHwHXjxEVdPMb+0x+it\n0GA2+Mv65rzvg1Nn3G/0afRTebzjPfNrUgt8V9rPSKeULx8DGy6PqUkEL0R/hIL4HHbs2VZweH36\ngGOc+YL3iaJ9D9LeQH7Vq9VY5ldWhs/PDsU6MLAxYn5mRILb3BxcVC3dZ4pt15r3H9DTg4ydoT14\n2A16cCk+KhvpUOih2BVFXLo9mvQ4aj0OPYkynvN7jyT9K1zbgKlrpM+lAotSIZEqOB1aI0h/iHWw\n/K9Z7FjiJfQq6Lwax6KuX2R+Oa/Qn8BiJubmx3u8R0e3NXMUOysZY/LntN+ZX14R1gWVRH8TCxlK\nfX0eh4eM6qTY+//CukzO5n2d5n49VLFdGgQrtq4Rl7OdPAw9bcZ3wJql8rNCCBFzEfElhOwhnRvM\nZ35d5/Bx1SR6r8EeEvIr51DbNUF/LzNn8KSfR/EeJC38EHfrTETvs8RrfAyNWiOGGlgjBqt7h1EJ\n15vLNyt2QQn6ZISo9twAT/SkercL8yPoOy7BnBWLXkaWbvjenLhY5pf4CHtco5noAaSW0iYUfNGg\nL6RA4y7wuGvmyftoaBrZHxAjeq/kvVASXiHP6L56tGKfnL+W+f1yDr3T3hz/S7Gd2/N+X0cXHFXs\n+qTHiW1z3sdr1fH1ip0Vi2PpD/BsJ28ay84xt0LsnDEUfg4NeQDbu+G0Yi87ij33w9ApzM+2DvpZ\nXLyEXKpJfgHzs7ZDv42Lz8DHf/7bXeanpff5/g8wPgM5V4te/uzYg213FNvREvtO71ldmd+J9ZAO\n79Ue/QeOrD/H/AbN7K7Yb46gp4m5ShY7sCWkcyP2/7t8uVkqf5ZPb7/B9X2D6yvNLmZ+7Wagl8zb\nfeixYqDLc5HIfeiDk5qbq9g0TgghROPB6HP27iRyAieVLO3en04q9soOE4WmYUwaPGS9SWXHXpy/\notgedpibFRV8r8kuRJ+c+qMQU9Pu8Djl1guxt6wAz9deJaVN83naU6MwGvlRVBTvV7JjwwLF1iby\n7ZG3IpifSzf0b7q/8ZZi0954QghhTHrFZcUgDidd5vtE14XdcOw6juW84v2V9CzIuLYRGkXzOugZ\nppZ2/3gbPdaCpwUrdnkhz+diH2OsrYhctkNtfrH3UtE7lMosN2rBZazbjEVOWJrB+yj9D2heLIQQ\nL7dCxvnYsamK/f2xHczP9O99ip1yHb0e63ThY1iSgrUeeZXM5Q68v2HEBbxn9pqNPmfxLy8zP9qD\n8XNg2A/os5NyJ5ody3iEd6vbT9HjxceF903tu7iXYtP3RX1VnCpNR+65+Q+snWs7uVT8rbeY+xMH\nIb6WZuJ8dX/CX9cfVuwJPZCvunTnve1C/tyj2O59MX/0jE2Y34CfRyk27cvp4NaJ+b15ijwgLwwx\npLyS50E+bq7iU5CVMxISEhISEhISEhISEhISEhJfEPLHGQkJCQkJCQkJCQkJCQkJCYkviE/Smn4b\ni1LxESsGsWPmXraK/W0wyhzVklqrv0Up2Lh2KMls6c21ho/cR5m77iNQakZ247LTU38f/6/XWlWO\nkqbxW+ayYzo6kFN++AKlTkUqis83e9YodtgRSNPp6vLyptUzQAsYT8qlmn3DaQohB7cp9qu7KNl+\n8pGXJC7dPVN8Thg64vq1eJWjKIxDiWb8FZTPWavoSqVEWjE2ElKE2Yd4eW7wSFCRyvPwfF9e4TJn\nrYehfPjuIVDmaBm+uqTXNhBlYHEvUB6olk53bIny1NwYlJ2qKnrFnV0ov/bvDlqc4Wte0msTiJI9\nKkuZdjOG+ZnURum04Oy+/4yYI3h+Zl427FiNAA/Fzovgsm4Utk4ooyxMRAnlg/U/ML/aI0E1cCFy\nzJG7XjA/Kq16dgHWjl8PlB1WFHLZ17KyDHIM86NWMJe+Dv1rv2L7juqt2NlJb5jfvUugIgYPxJwK\nucL92nxN4whihaufM/N7dRLl6j7thcaRHon7bzBmNDtmPwlf+O7UXsXW0uG/oR+5CxrbNz1An9j9\nzz/Mr2kUJqG+OeZ0XVUJKqW3mLujxLddPYxjVtYDdo42WXMtSCzPJ+tXCCHOHEP58fzeKHW1cOMl\n5J5O+F4zI1DaXqqoOBMXgCalew6Ui+pqPs9SbuO8Ghqmp6W/Ap0jt4hTJT9exBox+Qe0lCGL+zK/\nbCKd+3Atxs3Vj48NXS8FsYg9ti68RFZHBxTBxjNAKXr7O2hgXRs1YufQeZWVB6rt9e+59HizqYjp\n6e+xj2nrceqrmYXJvx5T05MKY7HnFMZwiibFmW2I8Qtaj/1f/f6/olYgcppnWzexY9fuIq4sPY6S\nfD2dA8yvpARl3pdOIYdxv/uB+Q36caBiVxSjlN/IkuvZvtkO+luTr8cp9qKJ/XE9NTmtxKylr2J7\nkJJvLS2+L46cgfVXXY08bfFhLhF7Z8Vvit0pCNLp/l9PZ37hVyE/Xk5iue+IxszPyZtLI2sSSYRG\n+ewSl0Ju0BxxyS4Q8u3FaTxnGbwIlLbwQ6ADZeZz+vn13YhlzYLwzPNi+Ry2bY48ZddhUD6nzYRM\n8LIVnCIxlci1J54llBBV0nLvHdafP5EDjs/k+34ekah3t0Wu3virQOZ3bR1kf2PS0xXb8CSX3G7n\n9xk4vgSBCzBHMkIS2LHWU9sq9qu/EM8qSnnsDfgGfg/XgxZB70sIIfoS+tLbg0Rye2wz5mdRFxSq\n7DeI1yu27VFsHdX4LP4d7wbXH4Ly6NHcg/lRKlO7JaAkFafnMb+8KEgF54bgGtwH+TK/a2sRKxu0\nwLw38bBkfgURWeJzwbopyR3sONXvwBZQB2n+0XXFQOZXXY3YeGU53sFKy+8wv66L8My2zd6j2K52\ntsxPWx/7kCG5prcXkB9269CZnWNXA7G7lzUo8AYGXL68uhL83FojQS1Vv4+YtQAlq7AA9LY+/gOY\n34FroMya24Be8/7AWeZnr6J6axrv/nyi2IaG/F1IWx85w6g1QxS7MDGX+cUcwvPVNsQYqCXlDeyR\nMxzdiDny1daxzE9fH88+MwLx0TEYMdAzgtMc3ycgjpj7YF6UZvH3SteeeI95thm5dZslI5hfVhTy\nObs6oIPm54cxP6e2aDWQE478zfw1j2s6Rp/8+UVWzkhISEhISEhISEhISEhISEh8ScgfZyQkJCQk\nJCQkJCQkJCQkJCS+ID5ZVxNDukLnvOcdovVJ129DW5QmOftxGtLc2SgNrd0NXehfbTrE/CaNhErR\nviMo0TN25yVc55ag7JeWa/pORElipREvW10xdJFiD+mO0scjF28zv+drQaXQ1ka5YmLYFeb3w1F0\nlY49hS7SkfeOMT+TGigpbGIAFR3aPV8IIW6uhTLEyG39hMZRhfI7U29OiSkinef9pqHkVUffkPnF\nX8Z9dhiBMuAPf3DVlVenUFpMFZma1KzJ/IoSMS+adURJ4K6dKOHT28vL5lvNxtyqKkNZv3ODFsyv\ntBTd/rOjYhSbljgKIYSnI9RUsp6DqmXdxJH5Gdphfpu7ggZTWcypFG/OgXrUeJjQKGqNANWoRNV1\n3tAOpYKp92IUu0ZH/lyuLv5Jsd2aobS3Rn9eIht/EWWDxbGYqx4j6jO/yL0oAW81D5ScuNMovdY1\n4eXRJSVQK3L0QSf9/HxOe6Pj+3Y3VEZcuvFO6237ouy0iFxrrx+5AsnxeVCwadoR9+E5gHOXzH34\ndWgaoUQBqbMOL/1NjYPyz92LoFVEJHFFiPkbJym2jhGer5khX7O7l4LCueQIOsg7OKqedTEoDtra\n+LxW34FqdnHpUXZOZgFibGAjqBN0+IrTd7ZOggpTShhoSDu+5/F/8a4Zip1wAfMvYD4vOd44frli\nNyFl/dXVnOZz6w7K1ZtPExpF4j9Rih04qy07VpYPWpcWUZFIIecIIUStoVAToSpoNVt1Z37Rd6HU\nRdeElhZfVzlJKLmlimY+E6Fgo1bGWEXouSVlOBZM6GxCCBF7FLHfoZ2HYps4mTE/9/4oxc4nFKzK\nUk51LsvGM6IUkPcnQplfcDCnx2gaS/qiLNvXzY0dGzoXVMr3N3YqdpeVY5jf1q9+Vuxwsk47tG3K\n/GzsME9Gteyo2KZGXAFv48U9iv3it92KvXozqM8Wtfj+VFmJMm19Q5R/p4bw50n33Jdb8T3WTTm1\ns+Fs0NgKEhBTR7Tka/HnP79R7D2LEGt6D+c5YMoNUHiC5iwWmkR7Mlet6nOK2I2zhAJzHWpkNe25\nn64O8gJHV+SUVH1GCCG8AhBvcj6ARlTva77PVpH5Pm5gF/FvcLbmVL8cQo+kVC1KURFCiHFfQUnt\n9GKoVLatx2lHKVn4DI+WuO7Lv/BctuUg7J/JO5GHUlqUEEJ41OJ5o6bxYRuo7bmFnK4UtBD5ppE+\n4jqllQghROpD7K1U9bN9rwDmF3EU+189ouqkVoIsL8Dfxi6IdRu2YN4XJ3PqG1XsTL6I9gWM8i6E\nqN3YA9f9KEax9VQqWY6BmINUaevBFk7zMSFqVy6dcE6ySm3H2M1cfC7U6YKYmRzGr2+wK743LxTv\nkuqWERFHsU79B4A6oq3Lc3e6F76LR07pYMmf8+AJoE3pEGVO+3s459exC9k50/5Ae4rwfWh98Hzn\nr8zPxh9xk6p8bv/hMPNbeQznVVeCUr9myljmF/U38mnHLphX5y5zSnmvrjzeaBpGZtiTagzk7wY5\nH0ARjPob73oW9Tnly9AJ4/rgHtZb+4G830NRAmh8wfURy1MfcIU12yaIqVQxyrkzKPkvovlcLyPq\nSD37IAlcPXUq8wsYit8OWhKK66UlnN5NKVnm85AvRB3nSta1BuLz8j8il/Wf3or5aet8ujZGVs5I\nSEhISEhISEhISEhISEhIfEHIH2ckJCQkJCQkJCQkJCQkJCQkviDkjzMSEhISEhISEhISEhISEhIS\nXxCf7DlDJU0dgrj06bMN4BRaO4Lnl3CaS0ha+UNebc/01YrdonMT5ke5eFMXQS511oz1zG/HiRWK\nHX+CSFu5Qq7sh6GT2Dm0t0jd0eAAz+/rw/wEobBe/wncXIf8Wsxt30+Q00wm/OCeTTnPXM8YHEcD\nWzzLId/2Zn57V58QnxP2bWootq4h71Wg1x73pqsHXu3lZbzHRB1fjH/kwaeKHZ/CZQrDk9G7hfJ+\nD9/nvWlGGgUrdlYWeIcWJuAq1iG8XCGEiCI9TurNAv+9rIz3Q8p8D67v44OQhXNQSdC9IjK9fWei\nH1L2K643/uAc+n+4EK5446mc+9lgAJeq1SR09XDtpk5cji58N56t32TMrchzXFq5dmfw1808rBT7\nyFI+/6qrsRByCtHfxiuNjzXlai4ZuUGxx7VHz4HkaD42VFrbJgCyjjmhqczPbzzu480O9Jl6qOJa\nBy9Bj46MV+ARn1u0nV9rT/SvcGqJdV9SnMj8CqKIHN9noPZGp+I+X/3FJWyrKhADx25Bnyy1JG7y\nG3CQXWqCw7tq0wzmR/m8+6bPV+zmfXicunIIvOqnsyATvZ9Ic2+eyXv4GCRhLpjWxlx6vfUk8/v2\nEGLlqkHo8TH3rzn88wwc8Ec19pCTC3lvmqnbcY+6uohXh2atZn5eRJpb07Cph/0k4QLf72yDwEW+\nvBl7iJstl/i0/og4aeWLez+z4CfmV78n+iPZNoEEafSDc8xP1xhxXc8U/QcKk8BdDzn2gp3T2gfr\nwNMVz4tKogohhEdbxNrCQtyvrS3v1/R40zrF9hsP7vbZhVuZH91LDKyxL6bkcEliR8F7g2gajUgf\ntIfh4exYW0vc2/rFexR7dHe+N4xeNVixD6/A3PcYyHuADA1ETOxMJM2DR3Me+qst6HsXHoPY9H4n\nYtuw3xaxcyZ3hOSnvyc4+PnqviGk10rbWbgeYxveL4Cuq9Dj2FvUvVoK49GPJpr0Jyz4wGWdVx9A\nLnFJwz1njJ1xrXYBvG9QUDzin20gehsVqOTbjchnVJMYrG/J+3+U50IuvP6sYMU2M+M9mkpK0Huo\nPAs5C5WUp/uqEEK8JX0zpm0br9jpz7j8am4EpFl9XXFPjp15jmoQgnma9xax2q82z+N1SY7adwzk\nrK8evsf8mgV93j4XZeXoKaGet1Gn0NOB9ms0MOV9cMpzsIY9h6OP4Z2tvLdk037IBXLe49nYNOZx\nz9QV7zVnlqLvXbsJeNeY+d1Gds7mNehX4jEcsVvd7/DpZuy55iRPrj+Lx4OEG3jHcWqDeNVM1e+r\nLAfPLPs9cowanbk8eNRp3r9Ek/h4DXvSnZOP2TFvZ/RnufAC+5CeNe+TFxqK/KNWLOJNvZm8VwnF\nmrXoJ2JRh++zgW7Yh+6GIQ4Zu+L5TZk5mZ1zdB56fU39C736EqNPMb/4c5BQPkhy7bm/TWR+0Tcg\n6+7UEv2AvMa0Zn5URrysAPv25PWjmF/KLd5bRdMw9UQ+R3NSIYSoLEL+npmPa3x6OpL50WN9uqCH\nWUkaj3uvnmPN9iA9DtV9NQviyM/45QAAIABJREFUEbMt6mFeXPrlsmLbmPE1QWPspjnIN30HNWR+\nj/YivhjqoY9Oi6l8fMydayu2tjb2BtNaVsyvqgrPKPU94nB1Oe+LGPcO+/uA3/7PXrOyckZCQkJC\nQkJCQkJCQkJCQkLiC0L+OCMhISEhISEhISEhISEhISHxBfFJWtPszSjPUsthmhPKk5YOJN78F3BK\nUfQ9SIFefYWSoS5TOzA/u7ooDaVUA2MDXlr6w1SUSLf2hczXlSG41lWnT7NzPtwmpWmPIdV2dvcN\n5td/OqgtTyIiFDtkdQzzm/Ez5DRP/ATpZ6cOvLR0z3qUOffwB5Ug6vhb5udDylM/B8ycUYYfvofT\ni4yIRGBJKsrAHFWSdLQsMzsadBT1+Ow+iXueNgQ0BnXJmZ45ymmz4yBDN3Aoysn1rbnMKIWODo5l\nRoaxY1T6unEPlLBdV5XqFpWiTPnxfpRhlpVziewGfihnC/sAibd88hyEECLjIUqQvdsIjSL5/hvF\ndm3DpSHt23ko9r0fUBbvPYBLX+uZ4plTWd2e0zoxv4JI3FfMizjF9uvPywE7txmn2IfWgVbi0hWl\n9e7a/Pff93uw/gzjQM+i0ohCCJERBRlYHUOEqaaj+b3Hncdaig1FaXjrmVzONSsENJIX668qtoUr\nn+e+o7kUtKax9RooWk83bmbHDB3xPH4csUCxv/7zK+ZH5TsvHQadLGguv2ddIrNtchvlw9VVXILU\n1Qbl4bUbouy9RTNQM9z61GXnVB+EPGL2S5RRXyExXggh/MtBE/vu8B7FrqjIY363lm9S7MoqlNKO\n3sLpSjunQPZyxK94LvUa1mZ+7mrKqgahY4BYWJTH5VdTb6LkuMdc7Ce5YZwSWJ4DOel9m0HdGrZq\nIPN7tBk0PqfnKJFNSslgfg0HgSaccBrx0CYQVCj/8YHsnH2rQGfsQuiBJuZ8H0uNgAS6qTNKeB+s\n+ZH5UUpIcTGeg6lK4j3kFWin9x8hrtHSdyGEsKznID4nqFzw8F587dzehDL1TZexpx2Z/R3zs83A\n3q1DYl3k/hDmN38MxpWu84nj+fx2swPFKD4dc2bNWJS2vzvEqYPL1oNyuOcnjOmiAz8zv+QQ7HEH\nlh1X7NE/DWV+ednIv2ikqOXAx8O+OWhE08aCPtBg5ATmNywuWXwuXL4B2nIPI07ZLsxEPmNF8tei\nOB57wh4i1+u8AjlLRb1c5ldRTMrVH2MOGwVzqpClJdainuVV8W/o2KAB+9uPxK/YU6Cy1B7M6Rxn\nv8P+3iAQNOWTGy8yv0nbZit2dTWuO/11BPMrjMM92gUgVjTz5PGUxS9+6RqBXTN8d4OWwexYVhjm\nY/u2HopdWsjpc9ZNQEuqIjLbvoFcEt2mAfxS7sYodurtGOZXawCefbNgvJ/EnQe1888/OcUwm8Ro\nx9agIVFahhBC1AxCjLX1x70bG3syP7tmkBWPPoycKCMpm/k5+SF2ludibzG04xR4tx58H9ck8iOQ\nNw5dz2OAsTHu91xf0PbKs0uZX/A4UEl2/HBEsW3vcsqZgS2oYPmERrnnN/7u964A+1DUjUuK/eY5\n1q9HP07zNiXvthUVeP7mtjynqDMCsXqyN3IoGieEEOKfE9g/A8l6y0nkc+LWW+SyI74CxadutxHM\nb81BUGD8J8wTmgaVc68s4feS8gJr0ZBIk3cdwV94zD3xPDKe4xyzWtbMr11d0NDSHuJdg1JNhRDC\nLQCfX5CP/KZ5Z7yT5Ifx9zH6HucR6KHYN3fy1gi+HtjH7IPRAuTB73eZn/8IzNWSNLyzxt2PYX66\n5D3Lsxd+o7j0J/+9YdDK/uJTkJUzEhISEhISEhISEhISEhISEl8Q8scZCQkJCQkJCQkJCQkJCQkJ\niS+IT9KaHGtApSH2BS8Xa7JgtGInvgRdJOndLeZXkoaysOFtUJpkXYeXTje0QmmZA6H5nH36O/ML\n34Yy1rAkdMX/7hDoTs//3sDOce8OdYQw0rm9XUuurhN6AiX5U+cMUuy3lzkNydQJ3aKtTVE26OLf\nkvlN/B7lTVa1UC5VVcVL+c58d0R8ThQkg3ZAVQuEEOLhfnSqbj8P9Jasl0nMr7IEZcGWROnH2cWc\n+a2r+FqxX0ajpHDYhK7MrzwfdIDWX6GUsZyo+Vh6cRWJXKJC8n43unTX/2oI8ws/c0H8G1p34gph\nIffeKzZV3VKrBTTthDreXqNRAp50+x3zqz3m86k1WZES/9SQUHbM2hdjWqcvqCgmrlyd6vUWlFfS\nEvzzz58zv2a1UdLsTNSpos+/Z35TBkOpxKUbSoeLklA2rm/JKQ2VRAlKENt/6izmF377oGLXHQnK\nRdgBXr7t2t1LsfXMMT+Sr31kfqmxoIE0m404FLmLK9i824s413z6t0LTWNALc3XtuWPs2I0loDgM\nHY/1YmLCS8x1TUAT6PoD1tvPI+cyv9ELUTZJu+f/sYmrc02aDEqCtgG2BA9CZcoKj2Pn0Hhg5oU5\nsmQhp2plZWDOFaejFHTron3Mr3sTrM2WS2cqdm7uM+bXfghUQx78iLGKSeOqYIfP31LsXy/3ERoF\nmbfvovhz6TAV9Jh4Qi+ic1MIIZ6/RGn8qHXDFPvmmivMj+5xlDI2ax6nooSdBs2s0UTQl2j57ZIp\nm9g5P+6GgsHjDbcUu9kMTmG+/Tv2zIA+GCe3frxEPuE8lBcKkxEDfPtzHkT8DqhXUEqljTef5x8P\nEwou31o1ggl/YK6+u7iHHatnCcpIP3/Eix0XVjG/N9uwf35IRPl2ywCu4LP7JMZ1cAvM4W2/8rL0\nrGdY2z8RivDIZVCpXDmZq4tk3sK8oIpMi/rNZH7rLyDPmLAZ+8nHvTz+m9XBeqb7YqsBzZlf8q0o\nxXbtjuf15uge5jf41+/F50K3zrim8+c4bblDQ8w7UzfQV0szipifhwFV0gG9NukfrkBC4dIJ9JOy\nMk5ZvP8znjMtcae2nTnPm/SJak3qK6x5LS0t5peeh3VVSmjorRpzdbDUF6AL0jyAUpuFEKI0HZ+R\n/A/G85/Xb5hfcBX5/MFC4yglKi65UVwRTd8CzyZ6H3IfmyCey74jeXqjYXifKFONt54eqBQGttg3\nPLt1Z35v94P6594H9AR9a1BqzGtzxahXp7AWXXKxJtTP3dwL5+mbgkZTXs6pGVR5z9wH+XC9SQP4\n9244jD/I/qRnwql+ideRFzlwtsx/hmM7D8WOucD37ed3/lbsRXuguJh0nedp9n6g4rvaggaiVtKq\nKgeFlq6RhcOXMr/MeCjL2vljvjQhNMfzS/j7lxGh6xQWIgasG8M/u0c7xB6qcGrcnr/bdhkXrNhp\nt9AWISQ2lvmNmAwqE1WdurtiLfObNuczLECCF+cwhzss7MyOZRUgnwgYjTxDS5fXeTzaCkoQVe0t\njORULte+yCG8+mKTTwt/wvw+XsT7nncv5LXJ1XjHdFXlIyHrcezIvmuK7WTF1ZVc+2Cd0vYtTYdz\npTOKnFDEDbvaXCGsOAXP6MUVxKsOg7niXc57fIZLTfF/QFbOSEhISEhISEhISEhISEhISHxByB9n\nJCQkJCQkJCQkJCQkJCQkJL4gPklrCtn7h2J79OEqKaHbUUZ36R5K2PoTtR0hhKgiZUKH76Hs1PE3\nrpJy9/1RxS4vAO2nopBTgBrNQ0nXlVGLFfvE3B8Uu6yCl2WXpKBk0rU/Om5nh/LyybZDQSV4ug6l\n1zW8uIpEZQU+b8Av0xX76Jx1zI8qFLVZgjJYHR3+m1hA989HhxFCiJJMlHWqaSbdSMfozDdQu6nm\ngi4iLxzllsVloCSlRHA6QcPBKHuv9RFULscWvORMWxvllh8PP1Bsc9L1PO0JpwzQUkYboixQUhLP\n/Fw7oUytIBHXnfuBK5w07Yyy51tnUUbXZyov5TMgZaxF6fiM/DCuFlBAutU7L9Ks6k/GM5TM1+jM\ny+PSXoPSQJV8KPVECCGqiArOw3BQEMZ+1Yv5ndh3XbE9G2AM81RjPWoN1qKpjYdipz/E2gm7kEhP\nEf7TWyl2/ElCK4tR0ZWa4R5z00Af8xvNy3kjzuM8zx4Yt7BD55hfy0W4x/QQlNJm5HLljkb9Pp+a\ngRBCTJqD618+cCQ7NmvHVMXeOWO3Yo+u78j8StKxnnV0MDfn7lnO/EJ/O6XYtBx18d9fMz9zS5QS\n31oBGmkuKd3UMeXl0SYeKJV3bO2h2I/W7GB+VIGgRyeUwb5WlfQmZmItObSA+klRAldMsW2O0mQL\nU6iy+bvycetQX8NyaQQFUSjN9XLi5dZpRPHjSRiUUbqP52pAXUhJfuJVzMeLLzjNbsECzJHrRPWh\nZ+9pzO/gTysV28QOZbZRB6Aa1NDDg53zkRxz9cEe93QLp4dok7Jxqu5y9/hj5td6CMY38ghikmtH\nTldq5ElUTIgynJ0Pp2Z4DeeKjprG4ADEoi1nlrFjyWQcTzxBPEyJ+If5dVgNyk7NJ8hhtq88xPws\nTTBXvcdhj+weNIX5XX2Fdb+qBqgvvsMQa6d0GsbOGRMcrNhnnyEXW354CfOb2wOUSitCx155iqs/\njWuNtdPWD2Oib8GpeSZuiAEmJshv6vTh6jg9GrZV7BsfPghNojwH+WELb292zDoAc7owEXHeyEml\nBNKxsWI/X4f9pOMPnMIW9w50UJpT2XpztUOv8ThWXgJ71zegctZzc2PnUMUeQweMTcZbTvvoMwV7\n3O19oP11m89p4wlEUSiPqNk4tuP18zSeFsYhrrnZcLqOoYOJ+JxwaOOh2NWVVeyYhQvmVrI5nkdZ\nDqefBy/pSf4CVS0/gudplCbsHoD4Hf+Ur+06g/Euk3AX9BjHIFxPcRanadTywbimP0JeSmlwQgiR\neAX5l4ElaE1VBjzHsrENVuyo96ClvnnPqcn1ZyG/Cd0ARcjYo7wlQ2Uxzwk1iYTTmHOJWZye1eUb\nrJG/vzmg2Lo6OszPowf2kGTyGQXxPA/YugbxdeZS8LO2T+EtLRwsEKPoK82gdaBWuXfk77aJD0Dr\n0dLCu9rUTWOZX/oTqLNaN0SOtn0ep2x//ScUjIsSEIfcszgd5sAfaMfQzBPz5RHJ1YUQYpArp0Rq\nGu62/66gJIQQdRp6KHYpfa+04sq6Dfrjnda2Hu4l4x2nihrb417e/o19SP3uQtmdhYXIqwyIom/i\nWb63tB2IuWRMYn7iWf48s0LwOwClZ92+8JT5dZuEePDqIyhT/iZezE+vFmhxDduADllRwJU9r50F\nJdqvO6cqCyErZyQkJCQkJCQkJCQkJCQkJCS+KOSPMxISEhISEhISEhISEhISEhJfEPLHGQkJCQkJ\nCQkJCQkJCQkJCYkviE/2nEn9AP6jVSzv60Fl3YyegGNVlsV5oI7tICu2tu5sxS5Oymd+tMeJPuFg\nbpn1F/NbdQp8xZEzwbPMegr5wToTOIcwj8joetQHd/vW1qnM7+YlcMxMDdGbpbS8nPmFrwIPL2AY\nvsvEkPdzKSgpUeyd0zcqtronTjt/LjWqaYSeBIcyeAmXCyzNwzhUV4Dr69RWxU0mnOiMp+Baxj/l\nnESzGuglRGXJcmO4NLd7Q8jGOXfBPMt5B/viwdvsnL7Tuih2rcCBil1VxZ9nTg56M9CJlfQigfk1\nmhak2M1iieyhGefWa2mD8Bh9CL0UjF05d72qpFJ8LtTuDs5uUVEUO5ZyHfxHx05Yb2op8ten0WPC\n2xl8/IznfGymbR2v2HoGGE+TZ1xKO/sNJNoNW4Kj7tQenG7KJReC96BqMA1rsbCQ80BLSjBWxUTu\ns6riJfOjXNSsBIxNdjTnPFtFIn5Z1wM/WOsCl0M3sjMVnxOPjiPGLNi7mh37bfxyxZ62Hb2sDAwc\nmN+v+9HXZVGXhoqdePM18zOpjbHr7Res2MamfG2Xl0Mut+uPkOwNPYbv0VZJJbp3gMygri543dGp\nXMa+Vzf0DqJ8944NGzK/r36fTz4PY5CdzGXjEy+Cb2wThJhUpx2Xl7yzHPKTPrwN2n+Gay8ivVjC\n94a4U5DPptztiEthzK/+KEi9xoVibmYXFjK/N9cxPxuRnjE2ql43nmPRx+T2avQc6LhinGI7h/K1\nk/0Sss1uvdCzp85g3uulKB/XF7kHn3HrDZfbbVgP696jN3q7RZ/lceNdAtZ2Kxdw06uqSpjf0Xm7\nFHvqXzwP0AT23kHfhlVDZrBjvYOJTCjpO+Dm24/5JUWfV2x7X8xpc+MzzK9zS4xPEekLtmPpXOY3\nuO03it3d31+x3Xuh98uAwEB2jmMr9AUzJWMSc4bL2fq4or9IuxGQLd0x6Svmt+s2ZEevL0Y8yHzG\n9wljF+x/Dp7I2fT1eb+SJrV5zyFN4vFr9Bkw0OXprHUK9r/Xj+DXfKA/86uoQB8IV5KvxoQcZX6W\nNfCc42+gN5SRLc9TPu5B/zpj0jeo9xD03nl8JYSdM3Mweib++ve3im3u4sL8js5DT6JGDdHbR72H\nR0bhbx2ySeYm8B4pjyMQTwd8hfyK9igTQgjL+vbic4KuCRNVT42bK/Yrtls9PA8dQz7eIb+i150j\n6VvmM3Ao89PWRn4Xefu0YhvYGDO/26sOKjaVVzb1gBSvoTU/x7wu5r6VD/btnHAutx72HDmcPVm/\nukZccvvWsjWKTSWjLTy4HPDd1ejB0nASJJ4znvCcNymUzxNNQt8WMaDtxI7sWHEa5tPwVSR3L+f9\nhUxMMKe/P7ZdsT9ePcv8aN+8i8vRX7BzNx4b/Yaiz9bz9TsV+68Z6A/afkAQO6dOF8T44uIYxY4+\nwHOR389eUuy1Rxcqdm9VT8hvB/yk2M3r4P46TApmfpl/kDVggDnao2lT5qerej/RNDyGoQdh0iWe\nl9P3dCvSC5G+CwjBe5tqaWGdZqniFO0F49CG7GNOPOfNjcV5FeV4TtHXEb8az+DPnY7Xsd1XFTsm\njfd16pqJHMSryb9oWv8/KKJ9y0g8UPdnTb+LfMk2EPHKwI737Rq5lsclNWTljISEhISEhISEhISE\nhISEhMQXhPxxRkJCQkJCQkJCQkJCQkJCQuILQqu6Wl2UA8RHHFfsl388Yse6/LBIsfPyUOpsZsYp\nOm+PgoZk2wxltfpmvHwvk8hZmde2VmwLJy5T9fYPLpH7P9h+BiVmmfmcMtWTlIVZE3nrARvWML+k\nj/gMWqZVtxcvP8rLQ7mUoSHoIXF3eXmrfTOUsJUXoSSqLI+Xb59bh++d+fffQtN4cx7lgVXlnHpT\nVYa/bf0xPnGnON3DuSvuJe8jpAnV0twhhEKVQ0r0mzTnUrdJHzDeydmgVThagorRchGnYGW9R7mY\nqRv8CuJ5qS69Jj0TlACm3IlhfvWGQaa2oACSg+F77jM/XTJXdU1gRz/jn9dgKErX6wSOFprEtUVY\nb04ta7BjVCqTSoxXlXG6l019lPpWV4OOYaKSgisvByVoF6HjdR/L5YCt/FB6+Py3u4rdaCrKRA3N\nuVwgLXk3MsL1hF87yPwKojGmVFbPdwKXDM1PA6XLzh0SsKF79jA/Q0dQZSwJJTPjKZf61iNjXb8P\nlyvWBF4d3azYarnAhsNBLygtRRl0Xg4vp90zBzGV0i8nbv+J+T35EbLY8RmgdlJ5ayE43aFtR8RK\nOzKX0h/z8uhcIoHcaA7GJD00gvm5BYBTtGwgaKRz/+DSgVuIdPiwSd0UuyAym/k1njJRsUtKMHYZ\n4Zxis/8nyIgvP8mlgv8rdk2CNGbDQC7fa0r2rvTboHw6dq7F/FKuQFLScyLiRtZrXh6sa4r5eHQj\nKDR25rz0v/eKPoptZolYS2Uni1K4bLyeKWJjKomNxipawfuLmC90vj2P4vRKH0LBKKvEvkJLgIUQ\nImBesGLf/wky1e2WDWJ+qS9Bh/LpMFFoGs/3/qrYrl14DDz+7THFru2AOHc3jNPTFu7/WbGndgaF\nTE1PW7MMa7uK0IdpLBJCiFQi4f30Ob6r3zKM75lVvMR/yi6s88jHoOI83PuQ+XUmlObcj4gH1r7O\nzK8sH9delgep6ooiFb37NNacoZ6eYnsO4zmgY51g+Bk6Ck1i/QjI6FIqgBBczjfQC+Mblshjfrex\nuD5Kt3fuwOlY5la4LwMD0HzKyjiFNvIaclS75tjjLiwF1U1PRcGKSsW69yXxOGhGG+YX9tdzxXYM\nhGyzVT1OA6BjFfo36G31hjdmfqXZaENw9xBy/LyiIubXZQhk5z/Hvnh31XLFTsvk+VyLecg7tHQh\nvayjwylFH3bcU+wagyBhq6XN/w/atXZ/xQ49vU2x1a0Wckle6UFon1RCOOcFj9cURq7IOdRvWWXp\n+Iwag+vhHEseDyKPYw1XEwqQcxcuzV2ej3VqSKjZ2npcqjr9MfYkTY/jmsGgFvurqIz1ZyAnpDSk\nvj/xPPkFkbL/kAQqS+uenIp4/MANxe7TB2vEb+gw5vfP94jxXv3xnF0agGL4+6Tv2DldR+NYSSpi\noZETp7xnEPqKa3+y58Zx2e9kIkft0RN+8Rc5ZUiPyIrHknyt/y8LmB/Nzy0tmwhNI+wWcrHMxzxW\nWvhhflIaoJpiaOGOuJfxFjkIfW8TQojkm8ghPPtjnVtYNGJ+lFpcUYExyclBm4D8pGR2jr4ZcpXX\n2xHbPiRzP1Oyb9BcJb+Ev6d3+CoY16MDimGFSp7e2AHzJOka8jwbf77PPj+Aax+yaZNQQ1bOSEhI\nSEhISEhISEhISEhISHxByB9nJCQkJCQkJCQkJCQkJCQkJL4gPqnWdJKUz47ZNIsdu74U5bzN5qEz\nd2kpL/OrP3SMYmemoexw5+y9zC8yBTSX9DyUX6/9hasoHL4G6tDSg6B6pOwBLeLHhZPYOYmhKM2i\n5aPj23Zhft9vRNn9w4voxu/Uhpc8V5ah3CkjCZQuSx/e0f7eGpTvdfkBKgxVNmXMb+qu3uJzgpap\nhx/iKgEB3+K785JAXfCbxK8pJxEl1t7d0AE98f0V5ufpj27X+ha0K/5H5udSF2ojztqwyzJRZltd\nzcvF0u+gPNBlNspsU+5wqhtVhfEehjF2Gs1pUmnJuPbs9+jgbdmQlwg7N0NJZUUFSl3tSFmxECrq\nB28a/5/h0p6oMHnz0lf3Nq0VOzsepeY577lCQNwFHLOoC7qRWUNeDp76CjSG3jPw/NJuxTI/Q3t0\nH38bjxLP2tGgepj58/LbF78cVuygJei4n3Q3hvl5DoPyiR6hkqW+5opEdI7p6EAtoMFYXi5bXo5x\niziLcTdxt2B+9vXri88Jcy/QurYvPsCO1R0A6kLMHdA9npx+zvwCScf/F9GgdUXe4Aox/t+CZuGd\ni7FvkdaK+VnVQHy79j3ispEzyjMNbIzYOWbleG4Pf8I+YV+bz81sd1z78KFQHCvL5SWjSw5vUWyq\nHhX66hTzo3Q1v1GgwTj5tWZ+437gdEtNostC0K7iTnMlohfHcL/tFnVW7EMLjzG/qiqUqOscBiWk\nUlUiW2sU1sHAKaCP3TnEKSsxR4iKnBvWqZEjaLxaKsWt/AjQIWlJcU48L7euHYTYU5KCkuI2pn7M\n70Ms4p82oRK0X8r3kuR7UM4JJyXGxj9zuk6TOXxMNQ263tSKaCXlyCcqyFgNHtOZ+ZmaIta1q4ey\nef9uvCzbsi7WxcheyFu2b/mW+bn3hcqVPVGveLcTJdBWJlz1gSoqOQehnLzT4m7Mz9AE95iSEqPY\nug246qCpEygh44dBWWXtvvnMz8Ebn5cXjTX7YPtdwYG/h2/dKjSJzoOhOqWlw+f3xQPIFbOJ+lCA\nVx3mV54LSoiZJ+JzimpPqgrEPKgmc0L9vYUxoDVYeCOfCegNCsLVw/fYOfUJDcDJDXPl7sabzM+B\n0L7NauNa8yI5terodlDlaam+/hFOczEiJf0Nm+C5JEakMD91bqtpuPTCHuRpy+kjRqbIs659v0ex\na/hwJavwGMSfWgagoMUe5zRe+xl4VvYB+OzSHK40W9cFFJToK5hLkQ9AVXB0sGbn+ExGfKC5YmVZ\nKfNLvY8YTde2exc+N22aggqRGwaqi3r/TL+P/MvcB7ldRSF/16BKuJpGn3F4D3Rr2ZIdi7oEGlK7\nqcGKnXCbU7b/vI68p39zqE559ezP/OZ2Ivn/S7yDxT29zPysHJCn6BjgdffgLChldhnG95l9m7EP\nDR2F8XRuxilEJSmIKZQi7NGO0//fXEVu09gL43lnD48BlNbUYT5ypUV9+fvslAWgj1l20jytKZFQ\ncbzHc6WoolRQ/z6cRM5BFRiFEKL3ZFx/RQHmIKUxCSGEtj6NR+D+qami0Q8wJrQTy/PjyLfqBfuw\ncyhluFZv7Gk1Svgau3/4sWI3GYT7pfNFCCGSL+Ed1swHsTf9FadJ1eyL76L0xUyVUlWhijalhqyc\nkZCQkJCQkJCQkJCQkJCQkPiCkD/OSEhISEhISEhISEhISEhISHxByB9nJCQkJCQkJCQkJCQkJCQk\nJL4gPtlzJoJImeUm8p4hRaVEYrEYvKrXGw8zvxp9wNvM+wDOZHD9esxv3Nrhih19ADzEqlIu/Tz7\nF/RRKExBn5ChrcBBvHvtBTtn/NaFih07c61i77x5nvk9Wg3ZtdFbliu2vj7nlabEomfFqz1PFPta\nKOdPrj3zh2Lr6oLX/ZB8jxBCNF0ASWdTU97fRhPIC0dvgZJyLodZUQEOobmzK/l3Lgdn6gCuZEkJ\n+IXmRPZRCN4f5OHmO4r9hvQkEUKI00/w3Do0AD+4+UjwTKur+dgLbciXFRVhPvoNGcHcqEy0lhau\np7KSc4oLE9HbSNcIfR/s/HyZX1YUZMXN3cFRLojlMr8ZbzhPW5MoTga/NSeE93WqKkEfIVMvzFXz\nOjbMj/KPi1PxeY/X/Mn8dEi/CKfu6BkTk6DioSei58D4zZC6LSvAnNLW5jK6esZ4zuFXjyt2g9mc\noxy2FfPD3BccanWPmEoSH14f2aXYjm1rMr+ovyHxXlaC+RGjkkM3dkZ/JisrLt+oCdh7NlPsAd35\nmji/6DfF7rcOfSTsmroy+EXjAAAgAElEQVQzPx0d9JxoZwO+dNRLLkd+eTFk0KnssY0Z7zEx4hf8\n3X0N+gBd+A7yfo/CeR+SyQvBe/Yeir5E68cuZ36dM7DmCgtgm3tzifUj3/yg2G0nQxqztJhz5qPJ\n3Hfthnl/fwPvzUD3pzHbuUTzf0VZLu7DrgXvO+VG9rtywrUe/dsE5hexHz1jaE+w04f4fdD+V45B\nWIstVftiRQHu19TDSrFTb8UodmQM5zwHjkRjrFsr9ys2lSAWQohei3oodlkOeNJqWcx6xr0UO+YK\n+PQVZVyW9+UVcNWDmyP2m9flcyLtCdaHQy+hcfRugp4sOw5/z46N2Qief+xF9DQIu/WB+VVXoJ/A\n2O2bFXvjGC79TWPq8DaY38/PvmR+rRyxnqNJr4wl+zE+Lf14r5/VJyClPbUzcokadrz/UwjpT2Vp\nir4eUxK4xHpVCebWvMlY5+r+FTZN0CvOtSt4/IuCeJ/AWk7wGy40C7ov2reqwY71HN9esfPeIfc0\nVEniZr3Cvqb7Hn5Wjbns9855GAPax6WVP89lS4g8dSbpR1AUi5zK05F/tmMt9HTJisP6C5rM+2FU\nkr3rDZHIdm7E87AB49DzwdQd63T93F3Mb0hb5M3GLtgHfH353Nm/FD2zFh0ZKDSN9HvoJ+g9kvd1\nKi1F3LIzR6zUt+b9U4KnBSt22J94Ng4t+f75aDX2WaumGAcLVS+/nCSs9dw36N/nYIv4WnM4l41P\neY7YFnkF57v682ug/TBoHy81Um5gzdL9RGhpMT+vUbTPCdbv7VXHmV+LhR3F58Kjk+idU13FtcNf\n/oNYlncB66PzGC4Vv+fWEcX+eAmy2kMCeb/I78YPVeyYj5gfXVaOYn5mNTBW//x8VbGtSfzbteU0\nO2f5Ebwjpr1D3hh/5wnzC7mHPpwp5yHV3LUfz2UtSY+w9Jcxil1YyvsQDf4R68rGDs9l7TkeAxJC\neZ9PTcOprYdi0/ciIYTIfIR3P0sLPEOfat7/iUpwG5G44j2Ij2NREfrbRF9CzmDdiPemod+rbYA+\nNf6DkKPrmfF3DbpGwk+j56L3AN5Xku7Ni+eiJ5qdBX/XaFgD+0tVDHpGdf+G965NOIt1H5WCfLVR\nA/69dVy4tLYasnJGQkJCQkJCQkJCQkJCQkJC4gtC/jgjISEhISEhISEhISEhISEh8QXxSVrT18tR\nIpv1mlMpgr5G2ZWNI8q4Wn/Py9RiX4A69OouZEeHb1rF/Ci9prwUtKQawbyk6812lKD9eRayaePa\no4T12IMHqjtBid2AVf0U+6+p85hX66Eo8465/7+XjoVfwn3svAGJuEWDBzC/tAiUwd36/ZZiX1fR\nn3qRMvJhW7YITSOPlGT6DeISny/Wo4y+8RyMXeqDmP/183JD8Xk6RqoppINSsvp9IQOb9jenSQ35\nCiXlWU9QlkjlmmPPhbFzDPRQYpfyAOWeVYG8xL88H6X3Fg6gGRTkRjI/HVKyl0sod5nPrjM/156Q\nS9XRgWReRRGniJlacYlTTUJbD7+jqmXOLy3ZodjdZkJy8PkvR5hf0/mQQL+7GpLJbZdxykX4SZST\nntmENdaLyOOpYWaG0u4nm7cptmNnPu5eE4Pg98s1xS6K46X1vjMxF6mkeuxpLqXtM7yvYme/RGzQ\nM+Rj0Ww+Su1zc7Eu82IzmV8koT+5reLrWRNIj0S5dVw4p5ncfIPSS+PvQPNpvYTT9qIvgS546/EJ\nxabrQwgh+qzDZyRNmKbYHxITmd+lFYjRRvoo/Q2agpJ3xyOcwmJASsqz4xAPaemnEELs/wcSpJPG\n9FRsB18u0WhigOeSdBGUxaDFXzG/0lJQEMzNMec6Ludl/YaGTuJzwcQF5a5vNnFJ6/qz2ip2biSu\n1czBmPlVV0CK1yUYpfFfdwhkfm+3XlBs55Yoi3Vs7s38Xq7HfkXls8M+gi5QvymXkCzNAt3IswM+\n7/jqPcyv0UlIab+OiFFsHxWltc4ExGF6DReXn2N+A9fNVOyk5yiFd2jE6TrvdyA+iM9Aazr9DPKc\nSa/4OB6Ys1uxh/4Cao/PQB4TcjKQq2hpYf1ZGvPxHrR+rmJnRIPK5OAZxPxmdEVZvpMVSvJrEBoM\nlV0WQogHq0FLzcxHHrVgES/xz92IWJFXDGpB4JwFzO/PiVMUO8i7sWJf3HiV+YUT2vuq4+sU+15k\nCPPLzeU0c03CuVNtxX646Q47FkTW4va1RxXbhlBjhBCiW2dQqW/exLWm3H/E/HRJ+fsgImv/9MRz\n5vcmDmuucTbotX4dQJd2789lX+NPIdep0x9xrbqC5zZU3rWUUNTtAjitIOV2jGJPm42x6dmsGfMT\nCENCSxd0gXKVVHMb1drUNKyJZHRlJadBxpzAvmgbiJijb2HA/FL/QU6YkYd8ooY9zwXqzQa1Ii0E\nlPWsUE7bzg7FO49dEOirRg74vNyPGewcPTNck2c35J7hF98zPx1DjGN4GOZL9xE8Hjw5gvjYtC/W\nYu7rNOb3bjvWpvekFrhuG75vJ/2DvdVhmNAoev2A3HP5sHXs2Jj+oKrZBWEMLT1qM7+xwchRN59Z\nptgLBnEpbYd2WFeNpyBHKCzk9OuUe3ifqFkDOUGjGYiNLfJ4Tnl7FXLjhuOwXvIieK4YnYYx6DcE\ntLLixHzmV6Mj6MiPjyPPqe3gwPzu/Iz3jmbjQdd09OI0KUrV+hy4ug9xVJ3PGdlh7uuag0ZkZ8Zb\nKNB1mkWonanvnjG/wni8H5RlI+aYO/LvNXDAGrl6GdLX4hH2Gj83TjG3NgPtirYccW7A6UR332Nt\nUsp/jyZcpty7H/KvqLOIGze2/MP8cgpBU6ynuiYKt/51/9djQsjKGQkJCQkJCQkJCQkJCQkJCYkv\nCvnjjISEhISEhISEhISEhISEhMQXxCdpTVRdqbK4gh2zdkKX5A8XUTKqa8I7Jpt7otyJdq2mNCYh\nhKiqQkkTLQtKeMzLjY1roCS1fX2UGdEST7Ui0akFoAp1XYH66PHbf2N+2yZMV2x/H5SipaVxVZ79\nd1D2RZWGao3hlKE146DeMP071BC2mcipWvlR/PM1DYsGUAJIusRVt9zaomTd0BAlWD49OT2tshJj\nEi5QHu3Uipfnvt0EmhQt3aQdsYUQIuE2unG7tEaJYuxtUI9cA3lpm3U9lAGG/43ScENV2apLQ5Qz\nvzuEbvU1+wYwP2e3Poqtb3ZKsTNDkplf/EmUvelakOvrxpW1CqNyxOdCzW5EjWz1Pnaslh/GLScR\n1+o7vTnzu73qb3xeG5STvv7zBPOz8IFqypiNUB35uI+vxVojMN9DduOzc4pQluxQybv2X19xRrGr\nq3HMt3Vj5qelReYOUSfKieZKMonP0eG9yQQoDYUc3Mb8imLvK3bNEVizmU84xcfYlSsZaRqJFyIU\n26s1p5kUl0GNwckHNIa4m5ym+eIflHkXkXPUdIddk/E8es5FGX7nHF6ybuuH64i/gXWVSkqC7YP5\nWvx1LmgfGYRKMaQlL8Gl0LMwVOzDc7hiXctBWJtnd4Mq6q/DFTmuL4ciVY8f5yh29Pm7zM+qAZ5f\nzYaard9OuYPyeSMLfn0l2SjTTX8AtaGbuzjlgqq91NPHHpkc8pT5hcdhfmauQYyi6i5CCOEQgDLi\nHEJB7jQP5eRV5VXsnJR/EIMt6uHzpvbligoF2Yj9TVuAmhHzmquNOcUj/j0+DOpgQC9eHhx/D3Ek\n+ymoMemE0iqEEN7TebzWNHJSsY4u7+ClyY09PBT7/RaUURs5vWV+1eSZmo5DzlDXk6/FSR1QRr/l\nIkr+E0NuM78FK8YqNqVt5Eci7jm2qUVPEZu+Aq2pvAJ5mqUvnyOD2yHvaDwLlPWYV8eYX+tBoNbt\n2Qyq6OTlfB3Z7MVzWT1skWJPXc1pmGvnQSFo+z+aVYuJPY7y8lpNPdixOHJs0gzQ2ctyuUoKVSlq\nXYCccucZTm3/ejrUVPQtse61VMo5XRphX6R5z+kDmGM0FxZCiF5zEJ9pef/bM5wCT6mrXu1BRUy9\nE8P80sJBuXCxQXwJ8PRkfmY+OBZ9E3uTz6CGzE/P3FB8Trw/gftsOp3vwTTPqioHzas4g6sc2TQn\ndBmicheyn1MpWi2CouW1PVh//ZZxurhFXSgq/fU9KOITVkMpKO8jz0cMSbrj2BTzoCCa54ZZz5Fj\ndlwItZf8JE6t8gvCvZvXxlhp6+kwP4dGiMs5cWR/cuPPslw19zWJzFDsVfN/ncSOmbuAdvfz6J8U\ne/DADsxv1S9TFTtiF+iCeioKW1443k2ta2APubb8EPNrOhK0JAMrzOGQ36G8VqLKh+r2JzFgMT5v\nwb6fmZ/2aeQcHl1AJbu18gDzq8xHjmZLaDNd1ixhfqWl2Lefr8X1Pcvh9Mq2i7k6kKYRFARapVpJ\nLDYS87ZOc7xDqNtbZBB1JUpxsqjF6eYFsVgXNA4n3OFrNvIVcgOaJ6cT+mIdJ/7Z+raI0QkZmC9V\nZZwqOms2qHTapC3H4d08/odvw70398G6DGrL9+OPN0CtCyUUV1MLHvN1TUgbAi7k9H9fy//5TxIS\nEhISEhISEhISEhISEhIS/39B/jgjISEhISEhISEhISEhISEh8QUhf5yRkJCQkJCQkJCQkJCQkJCQ\n+ILQqqaNH1QIu4m+Aul34tgxuzbgVNdpQ7nInH+b+BEymh/2QKbQvimX/vPqAU5wQgh6DljX4Xyu\n0iLImRmb4Rq0tHSIzX9zohLKUfsgveXclcu4WddC/5RVwxYr9uqTO5nfmQXgTDafhB4LVJpUCM4R\njT+DXiC1h7ZgfplvcX3ebccJTWPvVPA4W01qxY5R6b840lulzjh/5leaS7iHhGOtlpOOPAzucK1B\n4C5SKXEhhGjaGX0/wu+C61w7AOOdF8ZlCksI17DRLNzHs195PwdLK3AXvcn9GhpyCbX4h+CMlueB\ni6tvyfnVWrqYT0UJ4DhWFJYxP0s/cPy9Wo0VmkR2Nvj94YdusWO2hGvt7Bus2HFPufSpeS1wrdMe\noV+Eu0q+t6QIHN7st+DBfrj0jvnVboteJfaBWIvXVkKKu2Hneuwcz87o+XRu4S+K7VGXxwPzuuh7\nk3GfXOtALumZfBU9lBKjca31B/EeNrakh1RZKXjizzbwuWNhDl5o66XLhaZxZi4kdWu25vGnogDz\nybQm5BLtfHlfp6hz6J9j6gGpzOJk3sfLqgE4uNq6WLO7vj3I/EYsgBx59Bku+fk/sPXjso9lmZDi\nLSW291Qu1froF/SgqjcUY3Jl2w3mZ0V6MPRfv1SxVw2ZzPzsLSBjHdwXPZXosxNCCJfOmJuOjj2F\nJnH5228Vu3ZfPh8ph1qbxA1tA94jwKYRYtGjDbcU27sTl1c0coQcZPIV0u+qN5fSzvuIfTHyNtYE\nfeYF0by3Ge1tsf0g5NSXbJzC/Eyc8czjziAGPH/E50rPxXjOOuR+U+/zXjIf7uP6qORtl6m8/4CZ\nB9aAg4Nmx1AIIXJy0F/pzmreJyAlB+PY70f0mLCw4P1zXv6Jfi80Zqn3kHLSdyDxOsax1bI5zK9/\nM0iyrlowXrEzSRxusYT3c9DTw3P6uiukvjvU50T2Pr+sUGyaL1VU5DG/bRMhrT1yw3DFnt9/DfP7\n8cA8xS7NQQzQMdBjfuaO6ClnZcXziv+Kq4vQ68bYjPd/iopHj4B/XkMud/JA3lPJhshQ/7YUMrqD\nW/A8bfNF7GvdiMyqnUqaOzkb66xRQ8Qhu1bYI2m+IYQQaTdjFNvEE+NZlsFzSstG6KVVSo4VxfMx\nNCRxoyIf33X3Lpc5NzPCM6N9sFqM5/cef+aDYgevWiU0jfT0a4p9b81Fdsx/KnJs2mvl/Q7enyuJ\nPPfg2e0Vm8ZkIYQozcJcdWztodhxqr3PqQNyUSoHbOuPfKs0WzU+NdGbLfkRYqW2Pn8nofdBc0q7\nQC69+34Xem80nYd5e2/NaebXbDZ6RKY9xLta2F0uLW1sgHy/9zoud/1fkZZ2WbF/n7qDHRu1GFLY\nVjWRi2VG8Our0Rh9IOf1HKTYU+YPYn52jfAZNCYXFkYwv+hL6Elo4YMeQpsXYp3THiZCCBGRiN45\nM7rjmTf/pi3z0zdG7vXrBPTQc7e1ZX7Df0M8zUrA+5FjrfbM7+Fq9EB174+cT9eYx1PaM8Wj/lCh\naTzbvf5/P3YPPdd6LUUuH/X3K+ZXUopnauaI+FiiimdmJNbRPoFvDrxgft69kWdd+BO5Y/cJ5Bny\nnx5YXyd9a8Q5s1pcivzjBaz77AJImKvHkfa38WyDuJ73Op352bbGGq4qxViFXuD9w2hv3HF//CHU\nkJUzEhISEhISEhISEhISEhISEl8Q8scZCQkJCQkJCQkJCQkJCQkJiS+I/9dS2jWG8vLtYytRVjfK\nD+XzbzfdZ34vY2IUu9/8Hopt6c7pSsfn/qDYXZZD0u7a8iPMr+8vKHnPzoDEmLkVaDKhf/AS5T9O\nXlLsr6eiPC7zBZdMTjiN0s2VxyHFu3fGMubXfWE3fNcOSIY2nMzpIQuHrlXsukRKzmsEl5N0a8bL\n2zQNr7oop3265zE71nJWsGJbN0OpvZYWl64ryUDpVvp9lE269/Nlfk3noyyxKAf0mA6zeMm6gbWx\nYtsSilvCBYxBUhaXKXS2JrScx7gGz+6c9lGShtK0tGcooU+9z+VSDa1Q6ubeF3QCA3ML5lddDbnU\nsLMo3fTpy8vGdYx4+aEmkXgfJfixYVz+OfItnoVHTcgo5mcUMD/PgaAY6RNZQXVZe9pj0IgsfUDV\nart0MPPT1sYcKS7AOb7NUXKqLu9Pi4IsdGEJaBW1B3PZ3OIs0DSsJuAaqqs4C9ODUOf0ruO73hzn\nZZZ2lnguDWaj9N93GL93Sh35HHCsjXuxacSl/+5tAAXI1wzzsSiHj7d1Q5R/WtWApN+5w7uYX5d2\nKL2M3I8y0bgMThcsjIP8c/OFoKh+02uGYus/5lvFkh04dn/jLcX2VpFkA0gp8LKRGxR7+tQBzM+S\nlBwfm7NSsYdO7Mr8TGuglNjUCeeYm3Pp1zPzUXo/eJNmKTF1BmOvsarJ9zETZ9BPEq8h9gjVvI2N\ngIxz00lBOKBiGZcT6mSjryHHnBTKpcNLUkE77f4TJDpf7QYl14Q8OyGE0NLB/820q4d1lBeRyfxS\nb2Lt1B0HGU9TVXnw+52gGfgvAE3Zoi4vZdZ/hM8btoFSqLjEZexVSKk6aL56W+yajrL+3CJ+jUuP\nouz9znLQmE2duMSnz2hC5dLBnrZu9Hzmd+gqKKZf9cEeWbj4R+Z34M5fin17FeiHl14i/jvfqcnO\nGTAYVMk/vvlGsf2+5nnFpUVYE7SkesuFC8xv24a55C/Mx+83TWN+/Vp/rdhD22KdmxtxelGbcaAW\nW7XQLK3JidBAsp9zGeK6jfCcWk8B7SP1VgzzW7MAFIx5i7DGxk9ZzfzGdEAOQ+mVto58XVGqHu0a\noG+O/VJ9DTaByIGoBHr2m1Tml/saEtm6ZqAh6Zry3MOmMfaWiP3YC9v3as78DG0xZ3VN8XmXNl/j\nfoTyFCw0j/IixK/G4/k15oQh93QIBBXYtROXBbcj0tqFCdjTPl79wPyazw9W7Me/3FLstNxc5ufY\n1kOxK4ogUU8/O/Mp35tfH8Q+6+AOWoQVoaMJIUQCpWOTPDeA0NGEECImHfducQS5U8AcTrFJuo7P\nI+mqMDXk+Zdb8xric+H6CrSwmPv3T+xYfi6oI8X5eO/Kj+Q5fpo9KCuVldgPTFV718WleC98FI64\ntu7cX8zPsTViQMxRUBunrxyh2MtnbWPnHH+0T7EzP4J2la+iBds3AL2N0mHUOUtVFfLc8L2YH3vC\nDzO/dDL/BpPWEeWVfF+sNYS3CtA04l5DBptSeYQQoudivMMnXMSzeREVzfz8XPFs3r6JUmxKqxNC\nCO8OoMGXkLYgNjacKmrigr8beXjg+m6DMq1vb0xPEaa1kZ/kvMDeYNWAU/TdW+Lz6tiBXl9ZzFt2\nuJJ3VtrC5Ek4p9K198Z7ql0AnoNvMX9P1TH45M8vsnJGQkJCQkJCQkJCQkJCQkJC4ktC/jgjISEh\nISEhISEhISEhISEh8QXxSbWmj09Q3hV7Jowda/09ynaLi1Fa9POopcxv6vrRil2UgtKvrSs59ein\nM1CGOjkPpb75xcXMr1kr0Ku8BoFeFHYQpbk1B3BFhepqlCfN6YOS+XUnvmN+hcko4XLwBs2isrKE\n+WXHvRX/hownCezvqgrUF5Zn4TPsgz2Yn50P6DGWllxlRhO4uQRl7rbNuGIR7fz98T4oHe71XJmf\noR1KuipLUOJpVY+XiOW8RxkmVZypLK1gfhkP8axcuoF+cX4tOvUHtmvAzol5CfqOJVF3SVTRn5r2\nxjMMu4KO+QEzWjO/JFJaSqlBamiREkP/EZgX9NkJIUQRUctpPPRroUk83vazYke/5/PMUA8lzQGk\na3/yLV5q+PEZ/vbthBK7HFU5uOdErB+qKqCmFGlp47fdylKsMRMLlB6XlvKy3+pqzIPEqygHpGom\nQghh1wLl6lRlxqkVLw2888NZxQ6YhvL5YlLiLIQQxg5Q8KIlwJXFfF4mEMWnQRs3Ck0jMxPqAeF/\n32bHbAKx5sJPgfbi3ICvWbeuKGud3w8KLL+e4132o86hDHrqYlCKjpxfy/xC/gYdpek0oj6Xjdib\nE8rnyLTleDYXXiB2+9t3Zn7vCjDncjNQXh9/npeaNxiPMuPEUNC7ilO4AlUKURm7GgLlkWHDOjE/\nK0L9qtlgmNAkfh8HRb0AVYwqjsMe4j0ZiidRx7j6wKmLoCWVVmAOrjnFlQGz00CjSbmHfdahhTvz\nK8nEfC/LwV5TsxVoN2+PHmLn2BNKSMqdGMW+eeM583MgFI6gUaBgFUTxuEvV6pIuYy958o4rcgxe\nCeWOuJOIz85d6zC/0kyUDn8OFcOfh4Ir1WsCp91a+WBfizuLkvwbt/izGbkSNOmQnaAMd/3xe+ZH\n497ivpjrIyf2YH7GTohTj/eBtk3pDWUVPGY1qYnS/YRMUNJsVSpC/qNBF0m9jlLz389eYn6LV034\n1+upquTxn9J06F4Yc+QN8/OejO+1t+8mNIkH60GHj4iIZ8daTcR+kPkMFOtbt18yvx5joZAV9w/m\nrZUzp1JQbDsKdbNvZnC67/HDoE9PWo3Y834/vtezH28T8HgvxppSUWJVFNSoVOxPUxcNUWxDWxPm\nF3sUOapTF+zHarovpVQeXHFCsQfN68X8qPqapuOpEEJcIgp4PqN4/k6pXVSVT63QR1UrzWqCWlBR\nxP0SzmHvoXQj9auQljbyvkKi+JT2Bnuh/3w+n5PvI55ZeIN2W1HCKRK0ZURBJD6bqvQIIcTj35Ev\n1GkJGpe2Hv9/9ai7mLc2Vlj39WfycUwPw7yoEzhaaBJUrSnuHI8BqR9Ax7seCtUaLydO7e6zHJTP\n7DCcQ8dCCCFqtUbri+LiGMXeNJHnNsO+wf2vWYi9NSsfecW6bVwxz4C0O7i8/opiV1VVMb++q6By\naWOPd4t7KzcwP/p+4t8fc9vQnlPYKskc0dbHeku5wfN4+zagpnk2Gyk0jVtL8Q7/5ONHdqz7CNDp\n7p7AfufnxlXG9CyxN9B7OX/9EfPrGtRUsSlNs+ZAnlcl38N+5dUNzz3iyhnF9unBn0Vq3HXFfvE7\ncmEf1WfT2FmUhPyN0v2FEMI+CPe4awEox46WfJ+oZY88yMgEsdxjGG+Dkf4U73H/9r4oK2ckJCQk\nJCQkJCQkJCQkJCQkviDkjzMSEhISEhISEhISEhISEhISXxDyxxkJCQkJCQkJCQkJCQkJCQmJL4hP\najntXn1MsV2IjLEQQgRVgI9lYADe5sAhnLv97SjIVXZp1Eix/dw5Z15LC5fyJAK9KDo04PywU2fB\n1e+eBk66LelRkXCd94Sp1T1YsalkqIUV7++yaQqkIrs1wzXYNHdhfqd3QmZwzt7Nil2Ucpb5WREZ\nYsovS70Zw/yc6geJz4naoyAzS+VThRCiIBb9POoNwPhU5JcyPwMb9JypKge/XN2xyCUYz/fCEkjF\n6enoMD8d8rfOTYx9Aw/wKY3duKR1HXPek+B/4OHO5bytanooduQN9DugPQyEEMK+JeagWw9vxY49\n9Y75VROuvb4FOIQGllwytKKQc5s1ibojIM+XtHwPO1Y7GM/F2BzrQEuLc1WbjYfU+7Pd4H7WburB\n/OhcpT1nipN4/w9twkP3HQze/YezJxVbLd+bRbj/FYXg2Fr7c+6xngn4p+nknNw36cwvpxC9NrLf\ngaNcEMllD21Ir6Wa/bHuI/Y+YX76up+Wt/uvSHmCuRUfw2VSPYaAkxr4LXpRxF7g/UrCtt9X7O+3\nTlfsO6uPMj9t0ivp/HPIbBemcKnk+sPBg/57EdbsrN3oz3XmNy4re+sDuPAZ8XiGL0lPHSF4v6Zl\no39V7GA/3nPhxTTwnHOIrPHMXauYn21T9P+oJzCfN3z1B/MbbU7ks/kW8p8xlMg/V1dzHvqVZeil\nViMHe2RufA7zm7X9K8WO3Is+EGlR95lfJenl4d4FcTzuEu+bEfcS/TY8OyCWhZ3BnCj8yNeEUXfE\n+7J00vvFiktkU5np7FfotxD5jvfpCiJ9ftz64BqsmnIZWdqzLCMFz8WFtxUQdvW9xOfE6UeIgQ1r\ncInZ8lzsf84d0bOjXQnv99K3/SzF7t8S/Zpa5IUwv+xIxOI1Z9D7J/z6Eea35yf0/ejqjzgVngz5\n2Tl/LWHnGBl5KPbOyZDBdq/B+8HVaIQ+DROHg+u/etwo5ufYFAtGTw95X/SNq8yvTpeBiv1kzRbF\nnr2T9006P6aR+FwIeUP6h6l6Qjwnsd3ZEbLGCao+LibO6NHh2gLz4P0N3mfx8ius03okf1X3LaOy\nunlEKtjMDDlUSTrvidZiIuYOXfN2qlzRww59TGgfDpqXCMH3RZ86Dv/67+rrGPB1d/i95ntT8jvM\nv5rrNN9zxqE+9n+iERMAACAASURBVP+ct/y7af6V8BY97Fz9eF7++ihiIu2lY2zH41l1JeZJ/kfs\nha+fcEncTgvQP+3ePvSsaD8T7zjZETwG0j6LWrrItU2ceP8nolAvXt/GPCvfz3PIuh3q4j6c0f/J\nSNWvJD8M9+HSE3GztDSZ+Wnrfr7/jzc1RR7+/jl/Fxr8K3KJW/3Q66ZFL3/ml3AZ+freY4g36y9w\n2elj3yxT7L5r0TOmUxB/p7PyxjrdemW/YpeUIMctyuT99CydkIf1+A45bmEil5WOPgxp7rAc9O3z\nGs97JlneR6+46/vw/tphZCvmZ+WLdWpghHdHi7H8Xfnsd+gF+zl6zniORbyO+pmvxbwwxM7gUbj+\nktQC5mfhjXhblIxjnRvzveBNeIxim5FeW4b2vIcW/bz3J/C7RI1umD93lv/Mzqk5EDmmewDi+o5V\nfM8dOrSjYhfGIGfz+orPzSzSa8rdFtcT0Lcp8yuMQ07j1AG5Q+IVHl+c2tcSn4KsnJGQkJCQkJCQ\nkJCQkJCQkJD4gpA/zkhISEhISEhISEhISEhISEh8QXyyhj8lB+U5o2b3Ycc2jJmn2EWlKAH+/thu\n5re1O8o1C7NQ3mVu5838zixYo9hNa6MUyKMlL/05QUqRHTpAQtK1AaRU8/O5jNvbP84pds/VkOG6\nu3Ir81t2FOW4x+YsV+wmLccyv/6E6nHre8imNZjRgvmd/Z7I/HZEqfCZe1xOzGsiyvNNTf+duvNf\ncHsjpGlbTGjJjhkRiWFdY0gy31l/g/kFjMY1vjwKmkXQdC5PfXkZyui7rgC1IEtVqkrl6gqITKHH\nQNCicoj8nhBCRBM5tZqtMUeyX/HSzWIiad1kKspbqcy3EEI4twBNoLQI36VHJN2EEMLcB6XEcUSi\nMiufl/I1/ipQfC5oa+Oa/Abx0sAn+yFpR2lI7t14uV1BMuhBHZejNPnBjyeZX70xKOcrIvLyFr52\nzM+5EeZ7ctgdxc56jWdZmlnMzinPQ9muoQPKvE1cOIVNWxf3YWgKWT7vSW2YX/oPkNJzboWy2mwH\nXm5MS5lfb4SEdZJKhr1eMJey1DQozc4zgMe2FaN+U+xvf5uk2IWRnBLjNQ1y7tlvUWppbW7G/OLT\nMN8Tb4AO9PTGa+ZnaoDnO2PHd4qto4PS6QlbudxkYSFKNI+vwhgEd+FzruFwzKXJoyBrGRXKx4eW\nNzsEoYw39s5N5lcQBWqOgR3mz9LDvzA/PT0+nzSJsjI816JUTvWjMrg6Bthe601tzvzOLDut2HZE\n8jhtJ98bLE1Q3puag9L6xgN46bQniaeWdVFye2cDZH0dVJKP77diHdgHo+w37TSnPzXph1LxkDOg\n6zQZyMe6mJQ254QiBtQa0ZD50Tlr5wraDN0HhBAi9C+Uivdax/ctTaC0DLHItXVNdsymEWgWYdtx\nHQ2+6cL8bk1EPvFuD/b7D7tuMb+G0yGfnfDuomIb2HBq7MhZoB45N8Wa8K0AlWJsu4nsnHV/gsoU\nSyS3X1/ka8yxA+LNjn2gEbo35nLeVxb/qNhu/liLnj34vb/6E1TJF9GgbQ1sxcv182PIfOIV+v8Z\nVMreyIlTPSy9sF9RqnKLunWZXx6htpi4Y41QeqUQQjQmkuXdpqMUPvlKJPNrSyib7/8BZaUJiYXV\nFZyC9fYgpyn+D+qP4aX1TmQfe7sfeRiljwohRDGZ2/d+hBxwqzGq/I/QBx5tAeWiZkM+UEb6PCfS\nNOwCQccuiOHxp7IUNK/gZUSafPc15ufgiFiSdBn7U1U5f9ZOnbAOrm5BnttmGG8voGuCfTG3GHlM\nSTrinL6K2h5+EPGRzh9nG94Wwn0QchW/lngXotRsIYSw84c8eHUVuFDRR1R7uLeNYmeRfFhNuXNs\nx+OcJlFRgb2w788z2bHkCDxnPUIdt6zLc8rUNNDsfjwFqmR86GXm9zwK7wINCd3y9lP+XKrKMPaG\njlhj926CoqiWQjY1RD7dYSXynvLC28yPSkSb18H4GprzsRZaiMMTfge96/6q7czNyR/75Mv12Ete\nxcQwv8Er+4vPiVfbkYN4uXPqoH1bD8U+tg7v1aN+HML8ynJLFLvgI3Js5x6ezM9Z4O9TG7AvNnPl\nNMD/i723io/q+sL+N3Hi7oFAQhIsEAju7u6uxbVQpEChuLtDgaLFihQrFAjuBCeBhLi7O/lf/D+/\n/ew1b9uLt8Obm/W9WnDWTGbm7LP3PjPrWc/D3ZibanbAPWK0ch6fh9J5eMvISzKu64m/00JDUn/x\nHJ577DrIxGL+os+XH4Px3Xgw7vVen6Vzd93h2J8TC+/eVF+fEoj7sQp0SRJCcOUMwzAMwzAMwzAM\nwzBMmcJfzjAMwzAMwzAMwzAMw5Qh/yprmvkDSgiNNbqN9xjWGv9QLHuC/qBdtf8690jG43ajw3ZB\nAZW5NPgO5ZZqWeO769R5qb5SnuTqizLbOd1QNjx91QjyGAMblJq/3QI3jODYWJJXNweSA3d7lNsV\nF9Mu3ZWboKzMvRHKRxPCaNlbu8mtxd8xeHhH8u9VwyGN2nStvWb6f0Z1oHl/gpZgOVdD+bZLe3y2\njhqOHRaVkefbE+V3CfciSJ5a/hp1CSW9VrWpG0/CnXAZ2zZA6Wb8PZRHW9eiLh9OiutA8O1gGZuX\np6WldborLiTZKEULvPKG5Jl5oBRUKE4PxdlFJE+zM/7/8J9Ky7ff7kI5YIU1/f72Mf+3fD6Psk59\nS+rM4NcV58OqGrq8P17zB8mrPQFlu8WK25qVLb22HT1byDimgMrbVPLzcf2U00VZdYP5Y2T84dgZ\n8hgrP5zTdMX55cku6vJTexAkE7WnwU3k3UHaad3BHuM0NRhjMeISddpovBAyIStvlGq6R9EyYlXm\n9y2oUA/l4k9v0PFYww2l3W9+fS7jhnNakbyI8yjRD3sP14H6Y2hZdtReSByq94FDgl0DKhUys8F1\nn5UM95OvRYhzojPIY1SXjwHL+sh4z/eHSV5eLErAbz1DybeZxjXra44S8vxkPLedPy3D3rEBssml\nJxfL+PN56iRTuRvkluXLuwptkvgUzkiqdEkIISIVJ5jK7zG2MoOpQ4yVKeYU1Q2p0UAqfxJKKXst\nP9S+Plp9gaRVHwaZU9o7rK0129MSXpUbp1Fy27UdPueWPw0keR8P/CVjVU5lUcWW5CW/wFj0VF7P\np73UEc2mIa4/PVOsF3b+1DHp7mkq8dI2Z69tkHFuDJWnWdpg/qk/H+4dMYF3SV7UVczLnop8y9yJ\nShZvL9kp47rTsW44OFNJ0cOVK2QceBaylSZTWsp491XqYPZuq3Ie6+J1h8RTF5LMTxiDVXsOlvHe\ncVSyeOo+5uIzP2yV8c7vFpO8WUchYf88DTJ3TWfGSg2pJF6bFKWhfP76ZTpeOvfDHODYGuP744dw\nkqdKgfWMMR4bdaPSwYJU/C2hyIiMHKizSFoYZAyqZEKVMoVfDiaP8egAacuTM5j7L6y7QvJa9sT8\nYF8Je1RTT7pfU7l0ENJGu1tU7ukxHHul5Exln/uaSuKM9PXFtyTmMvbe1oqzohBCZAZDdhYciv2I\nbnk6935VXK5UEuOpdNmhBGOhRmXMOdG3vpC8h6cwb3k5Yf+ap7jPBByl7no2Ztg/pGZhTnG2pufn\n9QE8d61R9WQc+YLupz/vxVjwGA15aanGey3viPUk7CIkzFbuVGLzaBvmL7etfYU2iQvEe7Kp7k6O\nBR/FfUffftjP/LHmMsnzccHaUFCA+Sv2j08kb8nJtTI+OAWS5n6zu5G81JfYo969hdfwSbn3a9y0\nJnlMieIiemneahlXbU9dYWuMxh4/MQTv3cKCyn1NK+OcBm75VcY331IJlr/oJeOCIrwGe3O6P98+\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H5ZXNFw4hec2MVccPlHKqrlpCCPF4BbqjH/oTMo3QTnAc03RDcrCkspf/YVOf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V0T98G5yysSUsSEHXdZnh9ZM4MiRipjEr0b8uj4s9zdy9MD60tSCvYf3qQthFJKBbwKiVLCTnx2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sDjTuVD/uU5tbqEXew98tINq45xu5jrEc6bcTdhF9Lnyb1GN5qeWJ7vcFrMY8K3qwPJ+2XFNo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\nu01nQ9Xe1pZb5VD17Nx7aFvtMZ6rNl/ZiRbPa3FQcG7s7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D+Nw35z3LfDWV5+FO6nrT/G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FcttHS0fe/+sfrF14P5GI2dDJn/0CPbLcXLiVdu6NJB3v+/0oXl8Xfk/cN/Q+MyUdBmA+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DJnBEEQBEEQBEEQBEEQWhH5ckYQBEEQBEEQBEEQBKEV+VcdRtBEtPurK+MpsnUlSFcPHYN0HTMz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\n63k9oFMXU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LysG+gVcvUBxrePM7SzSWKuKKWUtzjWsD/6OjnV5h4x2z7624jletgsOJjy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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 5.29\n", + " period 01 : 3.33\n", + " period 02 : 2.94\n", + " period 03 : 2.32\n", + " period 04 : 2.80\n", + " period 05 : 2.00\n", + " period 06 : 1.99\n", + " period 07 : 1.84\n", + " period 08 : 1.64\n", + " period 09 : 1.84\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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jp6NVay0Ydf84Qp5dgeTZdiTXtiF57s5Bb8w6c87NzaWiogKtVstzzz3HX//6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\neoJonNUQJiKCn9nMvJlRzJsZRXtXP0dONvBjST3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JJ57A8OHDMXr0aBw6dEiOkkREpBBZeiTJycn44IMPUFZWhvDwcGzYsAF9+/bF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\nRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "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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h/6Kfn+x/5Ev95gY78bncw9IYY2Z9DXvWuo+wV+mpkn0Web/A+4DA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sSwu5SISGwFNNvsaMAuDSIisQUYCzEFcG6KctxzmneicnUo1Q5KIocFdgMSERlogq52gqqDs4uC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\nVKritqU6KFU/gr3JOL1fQc161Y+fH/p7sads2qrdT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AzDMIxpxH6cMQzDMAzDMAzDMAzD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\nS6DfZOu/jreho2ZLSRGRvFW4xxe+D23gGGnkRUQ6+qEFrZ3EuHJrySSSr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ukAACAASURB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ttAb+ZA+0RrpaMa+uXrZM5DFf+zzlpTRLS8/cWXlWPDQIjrKdVsqW26wNZM/rJi6zkdM5IIgf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\nfvw2K375V+tE3paXMbeL5t9hAgm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kcpuTz+sffGDF3A4+LluuiwvnoR7wmjbxrltEXmcH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XrsC7gwubjxaXrl93CVu+kezDmnN3Bp2NkKXIvPr/irji1uPiyvsRhlp/7xOE4zbkXOY1f8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\nYLxlzVh/AOmvddshifG1UlB9KV0wm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7rdSFFkOUt7uUyB4xS76MnJ39k2xpjanTgey5/i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P5WedR2BjabFDzBCVRU1aasKOAaKzDA1IS7qsMpRuntwPq7BVd8p6ufhClMm6/SjNK0iR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0P/inDqkZiaukLl8Rrc348+Y+Pj+j53NqBHZkYhjtHVpffThfOv9uK6I+g1GD9T9wCt+Zt+Pwsl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\nCVrIyAnQJQ/2yfvt64vPNP4/UCun6XiJaMc1AjzN7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XMwxpiSbZgDYjNx7l1Wu/BMnB/X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dP35kLsS7t30u/n3X/eudeNxyeJ6FZspr+dD6bzvxXz75xHiSoS702+p3pMVb+q2ob9PwaZkT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\n7fWLlSmOgz0y3cndNDbCnaVys5S65C6DLKu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TDhpNHVRFfDnyrYOtdP1ZYyDnOU2pqhNu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TsDqi/nwi7HHayP5m9nA3KyS/OOHWKJYcNuPMcQJ7f9/m04E/32NsiDVqzSPHJ2fhufib2r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6yO9RWoTUTba8MsaYkDica/oapDCFhkq5QH8P0mDZDvHIESlrWrAK6ZQtn9Q47cAImdK/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\ndks9qek3w9o8dRb2O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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZswsNSuKuTaM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + " # The quality appears to have increased with more steps." + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file