From 2111c82b2fe80fcc2e61a32fa99df54891c8f82f Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Wed, 30 Jan 2019 16:19:52 +0530 Subject: [PATCH 01/14] Created using Colaboratory --- intro_to_pandas.ipynb | 1630 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1630 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..1ce6ce5 --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,1630 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "01 - intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "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": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# Quick Introduction to pandas" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QvJBqX8_Bctk" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", + " * Access and manipulate data within a `DataFrame` and `Series`\n", + " * Import CSV data into a *pandas* `DataFrame`\n", + " * Reindex a `DataFrame` to shuffle data" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TIFJ83ZTBctl" + }, + "cell_type": "markdown", + "source": [ + "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", + "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "s_JOISVgmn9v" + }, + "cell_type": "markdown", + "source": [ + "## Basic Concepts\n", + "\n", + "The following line imports the *pandas* API and prints the API version:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aSRYu62xUi3g", + "outputId": "51121926-bf3f-4925-de08-52f037d59b06", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "daQreKXIUslr" + }, + "cell_type": "markdown", + "source": [ + "The primary data structures in *pandas* are implemented as two classes:\n", + "\n", + " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", + " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", + "\n", + "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fjnAk1xcU0yc" + }, + "cell_type": "markdown", + "source": [ + "One way to create a `Series` is to construct a `Series` object. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DFZ42Uq7UFDj", + "outputId": "2ba1a4c0-afbd-4a50-e0e0-5eb092ceecea", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "outputId": "daaa3378-29cb-4858-f114-645440020b67", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 4 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "outputId": "9edba847-a185-468b-99d4-7bc25c621d4d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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 \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean -119.562108 35.625225 28.589353 2643.664412 \n", + "std 2.005166 2.137340 12.586937 2179.947071 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.790000 33.930000 18.000000 1462.000000 \n", + "50% -118.490000 34.250000 29.000000 2127.000000 \n", + "75% -118.000000 37.720000 37.000000 3151.250000 \n", + "max -114.310000 41.950000 52.000000 37937.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean 539.410824 1429.573941 501.221941 3.883578 \n", + "std 421.499452 1147.852959 384.520841 1.908157 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 297.000000 790.000000 282.000000 2.566375 \n", + "50% 434.000000 1167.000000 409.000000 3.544600 \n", + "75% 648.250000 1721.000000 605.250000 4.767000 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 17000.000000 \n", + "mean 207300.912353 \n", + "std 115983.764387 \n", + "min 14999.000000 \n", + "25% 119400.000000 \n", + "50% 180400.000000 \n", + "75% 265000.000000 \n", + "max 500001.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WrkBjfz5kEQu" + }, + "cell_type": "markdown", + "source": [ + "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "s3ND3bgOkB5k", + "outputId": "6fd52d16-fbcc-47f8-c707-038ca94d3ec1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 6 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "outputId": "ffb80189-59f0-4f18-9bed-4a34cb1e3dfa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + }, + { + "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", + "outputId": "476deba7-3811-4ac7-ba26-23d62682fa45", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + } + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "outputId": "64d01fd8-5dd6-419b-e423-fdfdbf797eac", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "outputId": "8980072e-1487-4b05-e449-61078aaeda11", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 128 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "outputId": "617816d6-25f9-4565-afeb-86c02645d57f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "outputId": "8ffa6da6-5ced-49e4-e773-6cae080bcc50", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "outputId": "a9be648d-79c9-4816-fa7d-fa87a1bc951c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "outputId": "ef47b816-8f72-4f32-a46b-754ef3a3344c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
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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" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "outputId": "648696c6-f42f-4e56-e689-edd3a6d3d4be", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "cities[\"is contain 'San'\"] = cities['City name'].str.startswith('San') & (cities['Area square miles'] > 50)\n", + "cities" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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", + " is contain 'San' \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "outputId": "1abb28d5-b60a-433d-94e4-54bb22da3cfe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "outputId": "2d5e4581-64b1-487e-9681-a2f75b360bf3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "outputId": "8fa19ced-6169-49c4-ebe8-eac06df27de3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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", + " is contain 'San' \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "outputId": "373839ff-8ced-4188-f797-cdeaf74dbaf6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityis contain 'San'
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "\n", + " is contain 'San' \n", + "2 False \n", + "1 True \n", + "0 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "outputId": "8bee8f5a-618e-438f-f8d9-3ae79829a2e7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])\n", + "cities" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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b/first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..36f1490 --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,2005 @@ +{ + "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": 419 + }, + "outputId": "ac6b730f-f3a4-4f60-b4f6-81b2e526977a" + }, + "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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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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12470-121.639.121.01432.0328.0933.0336.01.783.8
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3642-117.933.927.04566.0620.02045.0664.05.6267.7
12111-121.439.020.0755.0147.0457.0157.02.467.0
..............................
15113-122.338.014.02338.0391.01003.0398.04.2170.5
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13175-121.937.328.01481.0256.0688.0221.05.2240.9
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "12254 -121.5 38.6 47.0 2438.0 804.0 \n", + "12470 -121.6 39.1 21.0 1432.0 328.0 \n", + "7545 -118.4 33.9 41.0 1355.0 349.0 \n", + "3642 -117.9 33.9 27.0 4566.0 620.0 \n", + "12111 -121.4 39.0 20.0 755.0 147.0 \n", + "... ... ... ... ... ... \n", + "15113 -122.3 38.0 14.0 2338.0 391.0 \n", + "3278 -117.9 33.6 23.0 3166.0 411.0 \n", + "13391 -121.9 38.0 18.0 2541.0 355.0 \n", + "5162 -118.1 34.0 40.0 2988.0 690.0 \n", + "13175 -121.9 37.3 28.0 1481.0 256.0 \n", + "\n", + " population households median_income median_house_value \n", + "12254 1148.0 747.0 1.4 141.7 \n", + "12470 933.0 336.0 1.7 83.8 \n", + "7545 655.0 329.0 3.0 205.0 \n", + "3642 2045.0 664.0 5.6 267.7 \n", + "12111 457.0 157.0 2.4 67.0 \n", + "... ... ... ... ... \n", + "15113 1003.0 398.0 4.2 170.5 \n", + "3278 1092.0 345.0 7.9 500.0 \n", + "13391 986.0 346.0 7.2 288.0 \n", + "5162 2144.0 667.0 2.3 189.3 \n", + "13175 688.0 221.0 5.2 240.9 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "HzzlSs3PtTmt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Examine the Data\n", + "\n", + "It's a good idea to get to know your data a little bit before you work with it.\n", + "\n", + "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles." + ] + }, + { + "metadata": { + "id": "gzb10yoVrydW", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "864fb9d9-01ae-40bc-c1eb-a237902bfd2c" + }, + "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": 51 + }, + "outputId": "7940e884-ef55-4d8c-ecf7-ff52ecbeefdb" + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56367.025\n", + "Root Mean Squared Error (on training data): 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "a49b8435-ad05-4208-a96e-c2d48611799b" + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "ef061e76-9c8d-4a10-ebd6-0dc3bcffcebe" + }, + "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": "c25054d0-e19e-4aae-a8a4-d4d4d002551f" + }, + "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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77roL+/fvV7Ntg5pcglt7lxdub8+g2bgjVjJfZcWYfpntam4AQ5Qsg+XfKGU2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\nh97CLUocrG1GR7dPcqj7b0fOoNsb3/x7LHJL7yK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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "t0lRt4USU81L", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This initial line looks way off. See if you can look back at the summary stats and see the same information encoded there.\n", + "\n", + "Together, these initial sanity checks suggest we may be able to find a much better line." + ] + }, + { + "metadata": { + "id": "AZWF67uv0HTG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Tweak the Model Hyperparameters\n", + "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n", + "\n", + "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n", + "\n", + "For each period, we'll compute and graph training loss. This may help you judge when a model is converged, or if it needs more iterations.\n", + "\n", + "We'll also plot the feature weight and bias term values learned by the model over time. This is another way to see how things converge." + ] + }, + { + "metadata": { + "id": "wgSMeD5UU81N", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]]\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label]\n", + "\n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Output a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kg8A4ArBU81Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Achieve an RMSE of 180 or Below\n", + "\n", + "Tweak the model hyperparameters to improve loss and better match the target distribution.\n", + "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination." + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "65a2c07e-70ae-4ce4-ee26-1a6befb47729" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00005,\n", + " steps=200,\n", + " batch_size=10\n", + ")" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.63\n", + " period 01 : 214.42\n", + " period 02 : 204.04\n", + " period 03 : 194.62\n", + " period 04 : 187.07\n", + " period 05 : 181.88\n", + " period 06 : 175.65\n", + " period 07 : 171.73\n", + " period 08 : 168.55\n", + " period 09 : 167.79\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 113.7 207.3\n", + "std 93.8 116.0\n", + "min 0.1 15.0\n", + "25% 62.9 119.4\n", + "50% 91.5 180.4\n", + "75% 135.5 265.0\n", + "max 1631.6 500.0" + ], + "text/html": [ + "
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NhjnjuxDb3MzKHw/xy/4CtUsSQgghgoKEEk2MyaCjT3JswOf6JMc0+Fk4hBDn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y9bMRo0zJkax8wpsfiZfOsz3FjMpU6+2pzPN9sLsNoU/P20zJgcy4MTYogIu3tjPX1BCL+V5cuX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\nw+rVq13eL/a033InarUSMtn1D+hDQvyu+3P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predictionstargets
count17000.017000.0
mean115.8207.3
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25%64.0119.4
50%93.2180.4
75%138.0265.0
max1661.6500.0
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QqNVMSktl7KA2Ab/qL4TwH0dpGTuvmU7lthwSJl9Jy6fu80tBQlV6HN3qOagq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c51/F\nkJJEVva/MbVNu+4xNIWnMGxZBJIH9813I3fq17TJyJJ3u4a7FvRmb0FC59sV5BqnhsPF38V99kh2\nYAzAooTf7yl5AAAgAElEQVRym8zSDU5O5kmEm2HGGBP9MkPn6niVzc2ijwpZ/0UpigKD+kbzwIw0\nUpPF/umrOZJTw4drithzwAZARqqZSROSGXpDLIZA7BlqRXbu3MmJEydYunQpFRUVTJ48mZtuuonZ\ns2czYcIEXnnlFbKzs5k0aRKvv/462dnZGAwGpk6dytixY4mJaULakNCqaTUaHrq9GzV2F/tPlbFw\n7XHm3pYlGtUKgiD4gSqH8uIDuumu1gBSkpUGmzReeb8r7csp5Y8PDQLgwKkySivrLvWnUBSl3u95\n/Hwldodb1XSMi9TqlXElnVbLcw8M5KPtZ/nqQCGVtU7iVBo7FBW/s4zzz/0NQ3ICWcvexNw+/brH\n0J4/jH7bcgA8w2Ygt+3etMlILqg87/3TGAmWdPDxtVRcreO41Rv3mRHjokOcu9njPhVF4esjHj7+\nwonTDd076Jg2yoQlIjROSt0emTUbrSxbVYS9TiIj1cyDs9Lp28MS6KkFJVlW+GZ/FZ9uOMnBo95i\nRFbnCO6+LZkBvaPRiu0tzWLQoEH07t0bAIvFQl1dHbt27eKPf/wjACNHjmT+/Pl06NCBXr16ERXl\n3R7Wv39/9u7dy6hRowI2dyF06XVaHpvci5cW72P7wUKiI41MGd4p0NMSBEFocUJn03ML1FADyK37\n8kFRmD028wdFgYbud1F5tYNzhdVMGd6JR6b04dTZsktbGZ79b/0xrpevNlAjHeNyTe2V0dixfzKl\nD3fe1E71sUNJyfsfcu6ZlzAkxpO1bB5hndpd9xjak3vQ7/wYdAbcI+5BSenYtMm47VCZC4rkbWYZ\nmexTQ0tZgVOlRvJt3rjPHskOEgMQ91lVI7N8k5OjZyXMRpg51sTALH3IFGb3HKhi/uI8CoqdREbo\nePiedG4dkYhOFxrzb05uj8y2nd5Yz7xCB+BdTTJ5QjLduojGn81Np9MRHu7d0pidnc2wYcPYvn07\nRqMRgPj4eKxWK6WlpcTFfRd5HBcXh9Xa8O9LgNjYcPR69X9vJCb61jtH8I1az/8Lj97CU69t49Ov\nzpGaHMVdQ0VhorHEeyCwxPMfWOL5bzxRlAigazWj3LyvAJ1O+4OiQEP3u0gDvLzkW+ItJm7pk8ad\nN7VFp9VSUmG/5n0v50s6Rn0a2ysj2MYOdtbFH3P2qT+jj4+l67I3COvS/rrH0B3ZgX7PZyjGMNyj\n56AkXP8qCwAcVWArABSIauMtSvjA4dFwpMiEzemN++zZxkF4M8d9KorCvhwPH25xUueELhk6Zowx\nERsVGqsjcgvqeGdJPvsO2dBq4bbRicyYmIIlUvwKuFJdncTnX5TyyecllFW40elg5C1xzJ3Vkajw\n5u9bInzfhg0byM7OZv78+YwbN+7S/19tG2ljt5dWVNhVmd/lEhOjsFpDJQi45VH7+f/F1N78+b09\nvLXyEFpZ4Ybu1988urUR74HAEs9/YInn/4caKtKockTavn17NYZpdRpqAHlRfUWBxtzvYsJGmc3J\nqm2nsde5mD0ms1H3vZwv6RhC87AuW82Z/3kBfWw0WUvfILzrdV7BUZT/z955BrZVnm340pZly3vG\nznA22YsssncCIQ6ETCirLR+BMkpLx0dpaekHlBbogLIJBAgmIQkB4uyQPUicvZw9nHjLli1rnnO+\nH4pNEi/JsS2P9/plSzpHj6Rj+bz3eZ77RrN/PdrDm1BMobjH3o8SHut/IYoCpXlgy/VGhoa2BsPN\nXVG2lF6N+5RVxF6N+9Q0sA5QUqrw1fcODp6S0Ovg7lEGhvRsGt0RJTYPqV9fIW1jLpIEvbuZeXB2\nEm2Tbs5otDlSWOTmu/W5pG3IxVYqYTSomTohljsnxBIdqScmJlicXASYLVu28Pbbb/P+++9jNpsx\nmUw4HA6MRiPZ2dnExsYSGxtLXl5e+TY5OTn06dMngFULmgsx4UE8PbM3r3yezvvfHiXEpKN7u5sT\n3QUCgUDgxefT+8zMTJ544gnuu+8+AL788kvOnTsHwJ///Od6Ka65U2YAWR1looC/293Ivow8nG4J\nrUaFyei7GZ9epyHEpPfruQS+4XRL5FhKcbprP4aQtzSNs0+/gCbMTJfUtzB16+TfDhQZ7e5v0R7e\nhGyOxDXxp7UUJGRvd4QtF9Q6iGh3U4KEosB5i44DV4x4ZOgY7eSW2IYXJA6d9vDqZ6UcPCXRvpWa\nZ+aYGNpL1+gFCUlSWLUxl/m/O8K363KJiTLwu1+054/PdBSCxA1k5Th5Z+EFHnn2MEu+zUKjUTF3\negLvvtqDh2YnER0pvv8aA8XFxfztb3/jnXfeKTetHDp0KKtXrwZgzZo1DB8+nN69e3Po0CGsVis2\nm4309HQGDBgQyNIFzYg2cWZ+cVcvVCr4z9JDnMuyBrokgUAgaBb43Cnxhz/8gXnz5vHRRx8BkJyc\nzB/+8AcWLlxYb8W1BGaN6YgkK2zal1ne3XAtVUVm3mgcGR5ioF28mfSTeRUeCz+KG+v2Xqo0qSIk\nSEuJ3VPhdodLYtHaDO6d2KVF+jTUB7VNXLmR/K/XcOaJP6IxB9P1izcJ7lG1MWqlyBLabV+hOXcI\nOSIO99j7IagWs2+yB4ougtsO2iAIb+2N/qwlbskb95lfejXuM95JmLFh2+ZLHQrLNzvZe9yDVgN3\nDtMzvI+uSZgaHjxWzIeLLnL+koMgo5qf3JPIHeNi0OmaxqhJQ3HmfCnL0rLZ/oMFWYG4aD3TJsUx\n5rYoDAbxXjU2Vq5cicVi4amnniq/7eWXX+a5554jNTWVVq1akZKSgk6n45lnnuHhhx9GpVLx2GOP\nlZteCgR1Qde2Efx8anf+u/wwr395gN/f25+4SNFNKhAIBDeDzysHt9vN2LFjWbBgAeB1whbcPBq1\n2puyoShs3He5wv1VxVpWZhwJXhPLykYzIsxGggzaKg0ytWo1Bp0ap7vi4m/b4SyOnS+gX5fYOkvj\naMlUlbgCvpuKFny3ntOP/wFNcBBdFv2H4F63+FeEx4V2cyqazAzkmDa4x9wL+lpcQfc4oegCSG4w\nhEJoK+/oRi25Nu4z3CjRLQBxn8fPe/hynZMim0LrODVzxhuJi2z8x3xWjpMFX15iV3oRKhWMHRbF\nvLtbERHWdGJK6xtFUTh0rJiladkcOOIdxWjXOoi7psQxdECEMPxsxMyaNYtZs2ZVuL3sQsm1TJo0\niUmTJjVEWYIWyoCusdw7sQsLV5/gH6n7+d/7+ld6AUkgEAgEvuHX6b7Vai1vWz558iROp++GiYLq\nmTu+MxqN2u/IzBvNHft2jqk0KrRv52jsTk+VJpeFNle1z1NQ7KrTNI6WSnXJKb6ailpWfc/pR3+P\n2mig82f/JqRvD/+KcNnRbfwMdc555FadcI+cDdpatKi7bN4OCUUGUzQEx9xUwkZWsZaMXD2yoqJN\nuIt2DRz36XApfLPVyc7DHjRqmDxEz+j+OjSNvDvCbpf4amUWX6/OweNR6NoxmJ/ObU2HduLKXRmS\nrLBzbyHLVmZz+rzX0LBH1xDumhJPn+7mRj+O01I4d+6c8KgSNBlG902kqMTJim3neP3LA/xmXj+C\nDMI8WCAQCGqDz9+ejz32GDNnziQ3N5epU6disVh49dVX67O2Zo/TLV0XYVkXkZk3jnVEmI3c1rsV\nU4e0wSMpfplcVkZdpXHc+NpbCtUlp/hiKmpZu4VTj/wWlV5Pl0//hXlAL/8KsJegW/8xaksWUtse\neG67GzS1OImyW6D4ivdncysICvd/H1eRFUg/K3M6x4BGrdAjzkF0cMPGfZ6+JPHFOgcFVoWEaDVz\nxhtIjGncx6UsK3y/o4BPl2RiKfIQHanj/pmJ3HZrhFhkX8Xlltm4LZ+vV+VwJceJSgVD+oczfUoc\nnZKDA11ei+TBBx+8rrvhrbfeYv78+QA8//zzfPLJJ4EqTSDwm2nDkimyudi0/zL//uogT8/sg07b\n+DvrBAKBoLHh82pk8ODBLF++nIyMDPR6PcnJyRgMolWtNlTnKXCzsZY3jnUEGbQEBRvxuNzlBpmV\ndVL4ys2mcdSVn0JTpbr0k6r8Q8oo3LidUz97FpVGQ+eFb2Ae5KejfEkhunULUBfnI3W6Fc/AO8Df\n91xRwJYDpfmg0kBYEuhrv7hzuFUcyTZQ7IRgvUz3uIaN+3R7FFZud7F5v9s78jBAx4SBerTaxr2o\nP36qhA8WXeLU2VL0ehWzpyWQMilOeCFcxVbqYdXGPL5dm0Oh1YNWq2L8iCimTYojMd4Y6PJaNB7P\n9d5FO3fuLBclfI3vFAgaCyqVivsmdKG41E16Ri7vfXOE/5nWo0n4DwkEAkFjwmdR4vDhw+Tm5jJ6\n9Ghef/119u/fzy9+8Qvhal0L6sJToCa0GhXr9l7yLv6LnUSavYv/GaPac+JCYaVml75Q08K5Jmp6\n7c29g6I6Yagq/xCA3HXbOPnQr0CtptPHrxM6pL9fz6sqykW3bgGqUiueHiOQ+ozzf9RCkcGaCc5i\n0OghrE3txj6uUnA17tMjq2gbDW3M9gZN1zifJbForYNci0JMhIo54420jW/cx1xegYuFSzLZvNMC\nwPBBEfzknkSREHGVfIuLb9bmsOb7POwOGVOQmumT47hjfCyR4cJbozFwYxfPtUKE6PARNEXUahWP\n3NmNf6QeYM+JXD5fl8G88Z3F8SwQCAR+4LMo8eKLL/Lyyy+zZ88eDh06xB/+8Af+/Oc/i1ZLP6kL\nTwFfqGrxX2J3Y7NX7x8RaTbQuXUYO4/mVLivuoVzTVT/2nORJJmDp/ObfQdFZSM21fmHWLftIeO+\nJwHo/OE/CBs+0K/nU+Vnolv/CSpnKZ5+E5G6D/O/aMnt9Y/wOEBngrDWoK7dcaAocKFQx9kCHSqg\nU7ST3h2M5FUeHFPneDwKa3a72LDXDQqM6KNjylA9ukbcHeF0yXy9KpulK7NxumQ6tDXx8NwkbulU\n+9jV5sSlKw6Wp2WzaUcBHkkhIkzHPVPjmTAyhmBT4xaaWjpi4SZoDui0Gp64uycvf5bOhvRMwoL1\nTL0tOdBlCQQCQZPBZ1HCYDDQrl07UlNTmTlzJh07dkTdzBaLDcHNegr4QnWL/51Hsmvcvl8XrxgQ\nYtL7bbxZHbmF9ipfe77VeV36SH10jzQWKktOqUrose5MJ+MnT4Es0+mDVwkbNdiv51JlnUW38VOQ\n3LgHT0PuVIvOJrfDm7Ahe8AYDuaEWhtaXhv3abga9xlqlBtsYZKZK7FojZMr+TKRoSpmjzPSIanx\nLloVRWHbDxY+WXyZ3HwX4aFafjavNaNvixTtwcCJ0zaWrcxi9/4iFAVaxRlImRzHqCGRIgK1kVJU\nVMSOHTvKf7darezcuRNFUbBarQGsTCC4OUxGHU/P7MP/LdzLsi1nCQ3WM7JPYqDLEggEgiaBz6KE\n3W4nLS2NdevW8dhjj1FYWChOIGrBzXgK+Ep1wkd1qFUwsk+r8u6EujDehB99JNJP5FDVxLBa5TU8\nvJG67B5pbNTkH1K8ez8Z9z6J4vHQ/8t/ox7kXwyv+uIxtJu/BBQ8w2cit/UzpQO8oxrWTO/oRnAs\nmKJqLUgUO9UcyTLg8KiJCJK4Jc6BvoE+VklS2LDXzZrdLmQZhvTQcscwA0Z9413Ynz5fygefX+TY\nSRtarYrpk+OYcUc8pqDm97fgD4qikH7IytKV2RzN8I6hdUo2MX1KHAP7hjf6tJSWTmhoKG+99Vb5\n72azmTfffLP8Z4GgKRNhNvDMbK8w8cnqE5hNevp1jgl0WQKBQNDo8VmU+OUvf8knn3zC008/TUhI\nCP/+97954IEH6rG05kl1ngK9OkTWyeK7OuGjOkb2TeS+CV2uu+1mjTeh4ihJZVQmSEDddY80NUr2\nHuLEvU8iO110fPdl4u4YQ25usc/bq8/sR7t9Gag1uEfNQ2lViw6X0gIoyQJUEJoExlD/93GVK1Yt\nJ/N+jPtMjnTfTHqoX2Tly3yx1sHFHJmwYBUzxxno2rbxxrYVFrn5bOll1m/NR1FgUN8w7p+VREJs\nyzYW9ngUtv5QwPK0bM5fcgDQt0cod02Jo3uXEDEG0ERYuHBhoEsQCOqV+EgTT93Tm1cX7eOdFUd4\nZlYfOreufUKVQCAQtAR8PjMfOHAgAwd6Z9llWeaxxx6rt6KaO2UjEOknvCaUZV0CB0/n8/m6DFKG\nJ1NS6q51d4I/KRsqIDL05kczqqK6URKAqFADvTpGc+BkLgXFFb0u6qp7pClRsv8IJ+Y+jmx30PGt\nvxI5ebRf22uO7UC7ZyWK3oh7zH0oMW38K0BRvGKE3eL1jQhrA7og//ZxFUmGU/l6rlh1DR73KcsK\nm/e7SdvhwiPBgK5aUkYaCDI0zsWr2y3z7bpcFn9zBbtDpk2ikYfnJNGrW+3FoOaAwymxfks+X6/O\nITffhVoNIwZHkDIpjuQ2LUusbA6UlJSwZMmS8osaX3zxBYsWLaJt27Y8//zzREdHB7ZAgaAOaN8q\nlMem9+CfSw7yryUH+e28fiTFCg8ggUAgqAqfRYlu3bpddyVKpVJhNpvZtWtXvRTWnCkbjZBkhY3p\nmeVdAmU+ClsPXsHpkm7K7PFG4aMyokINPDmjFzERpnobj6hulEQFPDmjF0mxZjRqld+JFM0R28Hj\nnJjzOJLNTof//IXIqeN831hR0BzciPbgRpSgENxj70eJiPevAFkC6yVw2UBjgPA2oKldasGPcZ8a\ngvUSPeKdBOkaJvIvr1Bm0VoH567IhASpuGeMgR4dGmd3hKIo7DlQxEdfZHIlx0lIsIaf39uaCSOj\n0Wgap4DSEFhLPKStz+W79TkUl0jo9SqmjI3hzgmxxMW0LKGyOfH888+TmOidsz979iyvvfYab7zx\nBhcuXOCvf/0rr7/+eoArFAjqhh7to3hoyi289+1RXvtyP7+/rz/RYbUT+AUCgaC54/NZ+vHjx8t/\ndrvdbN++nRMnTtRLUS0Bp1vi4KnK4wYcLu+V5Jsxe7zWE2LxpjNs2HOxwmP6do4hKbZ+Z3irGyWJ\nDDUSc3Usw99EiuZI6ZEMjs95DMlaQvt/vUBUykTfN1ZkNHvS0B7fiRISgWvcA2CO9K8AyQWFF0Fy\ngj4EQhNrnbBRUKrhaLYBj6wi3uymU7SrQeI+ZUVhxyEP32514vJAr44a7h5tJCSocS7uL2Ta+fCL\nSxw4UoxaDbePi2HWnQmYQxqngNIQ5OQ5WbEmh3Wb83G6ZEKCNcy8M54pY2IICxWxnk2dixcv8tpr\nrwGwevVqJk2axNChQxk6dCjfffddgKsTCOqWIT3isZa6SN1witdSD/C7e/thNokIZ4FAILiRWp35\n6nQ6Ro4cyYcffsjPf/7zuq6pReCPGeXNmD0adBqemNkHNUpAFvwGnYZeHaKuS9Yo49ouiLo01myK\nlB4/xfFZ85EKrSS/9jzRd0/xfWNZQrtjGZozB5DDY3GPvR9Mfrb8u0u9kZ+yBEGREBJXK0NLRYHz\nFh3nLN64z84xThLMngbxj7AUy6Suc3LyokSQAe4dZ+CWdiqsNjs6beM6nopLPHzx9RVWbcxFlqFP\ndzMPzU6idWLLvYp2/pKdZWnZbNlVgCxDdKSOeRNbMW54FEHGxvPZCW4Ok+nHkZvdu3czY8aM8t+F\nL4igOTJxYBuKbC5W7brAG4sP8uycvhgayuVZIBAImgg+ixJLliy57vesrCyys2uOlxRUjj9mlDdr\n9qjRBGbBX5a6cfB0PvBjwkak2VAeO3ojdWGs2dSwZ5zh+D2P4ikopN2rzxEza6rvG0tutJu/RHPp\nOHJ0Eu4x94HBz/fPYfUmbKBASDyY/OywuIpbgmM5BgpKtRi0Mt3jvHGf9Y2iKPxwzMPXm504XHBL\nOw13j9aTtus0n6/LpcDqvKlRqLpEkhRWf5/HouWXKbFJJMQZeHBWEgN6h7bIBZmiKBzNKGFZWjZ7\nD3rTnNokGpk+OY5hAyPRalvee9LckSSJ/Px8bDYb+/btKx/XsNls2O32AFcnENQPM0Z1wGpzsf1w\nFm8tP8wv7u6JtiHaBwUCgaCJ4LMosXfv3ut+DwkJ4Y033qjzgloK/phR1pXZY0Mv+G9M3Sjzzujd\nKdrvcZTmiv3UOa8gkW+h3cu/JXZeiu8buxzovv8cdfZZ5PgOuEfNAZ0fx4miQGk+2HJApfYmbBhq\nN85zfdynh25xThqiMcFqk1m8wcnRsxIGHcwaZ+DWW7QsWn/yumPvZkah6oof9lt47e0MLmY6MAWp\neWBmIlPGxaDTtrwTU1lW+GF/EUvTssk4bQOgW+cQpk+Oo3+vlinQtBR+9rOfMWXKFBwOB48//jhh\nYWE4HA7mzp3LzJkzA12eQFAvqFUqHpjcleJSN4fO5PPRyuM8fMctqMV3nUAgEAB+iBIvvfQSAIWF\nhahUKsLCwuqtqJbCtWaUlmInep0ap7vileWmaPZYXerGwVP5OEdLTe411QVOt1TeraJcuszxmY/i\nzs2n7Yu/JvYnM2reQRkOG7oNC1HnZyK16YZn2D2g8WMaS1Gg+DI4ikCt9Rpaao3+vyC8cZ8ZeXoU\nRUXbCBftIhom7nNfhpul3zspdUDHJA2zxhmIDFVXe+zdzChUbbmS7eCj1Ex+2F+ESgXjR0Qx965W\nhLdAfwS3W2bTzgKWr8om84q3S+zWPmHcNSWOrh2FM31LYOTIkWzduhWn00lIiPczNxqN/PrXv2bY\nsGEBrk4gqD+0GjXzU3rw6hf72HEki7AQPTNHtxzfLIFAIKgOn1cx6enpPPvss9hsNhRFITw8nFdf\nfZWePXvWZ33NHklWkBVvC0GwUUtshJ5ShxtLsbNa74drF7eNcXFfnWfGzY6jNEXKRln2ZXjHCVpL\nxYxf9Ca6ggLa/Olp4h6a5fvObEXo1i1Abc1D6tAPz+A7/TOklCWvf4S71CtEhLWuVcKGJMPJPD1Z\nxTq0aoVb4hxENUDcZ4ldYen3Tg6c9KDXwvSReob20pVfcWosx16pXWLJt1l8syYHj6TQp3sY981I\noH3blnPcl1Fql1izKY9v1uRQUOhGq1Ex5rZIUibFtWgfjZbI5cs/+gtZrdbyn9u3b8/ly5dp1apV\nIMoSCBoEg17DkzN68dKn6azadYFQk55Jg/yM7RYIBIJmiM+ixD/+8Q/eeustOnf2tj4fPXqUv/71\nr3z22Wf1VlxzRpJl/rxgDxdzSspvKyh2UVDsYnTfVkwc2KZSweHGxW1jmZW/keo8M+pqHKUpce0o\ni9lawPCv3kZXXEjerDkM/Pk8n/ejsuahW7cAla0IT7fbkPpN9M+Q0uP0ChKSyzuqEZroHd3wE7tb\nxZEsAyUuDSF6ie4NFPd5+IyHxeudlNgV2iWomTPeSHT49fUH+tiTZYUN2/L57KvLFFo9xETpuX9m\nItMmtyYvr6TmHTQjCovcfLsuh7QNeZTaJYwGNXdOiGXqhFiiI4UDfUtkzJgxJCcnExMTA3h9RcpQ\nqVR88skngSpNIGgQzCY9v5zVm/9buJcvN54iLFjPkB5+xncLBAJBM8NnUUKtVpcLEgDdunVDo2l8\nV+ibCp+vzbhOkLiWg6cLmDmmU6UdEDf6NDSGWfnKqM4zoymOo9wM144ThBRbmLr0XczFhewaMokL\nnYYw1u3bKIuq4Aq69R+jctjw9BmH1GOEf4KEywZFl0CRwBQFwbG1StjIt2k4ltOwcZ92p8LyzU72\nHPOg1cAdw/SM7KNDra5YfyCPvaMZJXyw6CJnztsx6NXMnZ7AnRPjMOjVLcon4Uq2g+Wrc9i4NR+3\nRyHUrGXu9AQmj4khJLjlxp0K4JVXXuHrr7/GZrNx++23c8cddxAZWTtzXYGgqRIdFsQvZ/Xh5U/T\n+XDlMUJMOnq2jwp0WQKBQBAw/BIl1qxZw9ChQwHYvHmzECVqidMtse9kXpX3F1TRYt7YZuVromzs\nJBBRpI2JsnGC4JIipi59l1BrAT8MGs++W8eg9nGcwHPpNLo1H4DbhXvQVOTOA/0rwl7o9ZAAMLeC\noHC/X4eiwDmLjvMWHSoVdIlxkhDq8Xs//nLivIfU9U6KShSSYr3dEfFR1asgDX3s5ea7+GRxJlt3\nWwAYOSSSe+9u1eK6AU6fK2Xpyix27i1EViAuRk/KpDhG3xaFQd94OrkEgWPatGlMmzaNK1eusGzZ\nMubNm0diYiLTpk1j/PjxGI2187YRCJoaSTEhPDGjF/9I3c+byw7x6zl96dBK+LUJBIKWic+ixAsv\nvMBf/vIX/vd//xeVSkWfPn144YUX6rO2ZktRiZPCEleV94cHGyptMfdnVv5az4lAoVEHJoq0sREW\nYqCVysHwpe8QVpTP3lvHsnfQeMC3cQJ1Zgalm74AWcIzbAZyci/fn1xRwJYLpXneMY2w1qAP9vs1\nuCU4mm3AYtdi1Mp0j3diNtRv3KfTpfDNNic7DnlQq2HSYD1j+uvQaGruOGioY8/plFmWlsWyVdm4\nXAodk008PCepRZk2KorCgaPFfLvuDHsPFALQvk0Q06fEMaR/hE+fl6DlkZCQwPz585k/fz6LFy/m\nxRdf5IUXXmDPnj2BLk0gaDA6tw7nf+7szn+WHeKfiw/yu3v7kRDl//9ogUAgaOr4LEq0a9eODz74\noD5raTGEhRiIqmLmHaBPFS3mQQYtYSH6SgWNssVtZZ4Tt/VOZOqQNlV6TtS3aWZDR5E2NtSFhUz8\n8m30hXmk9x/ND4MnlN9X3TiB0y3hPpFO5P5vQa3BM3oecqIfIzqKDNbL4LR6jSzD2oDWf5HK6lBz\nJNuA06Mm0uThltj6j/s8nSmRutZBvlUhPkrNnPEGkmL9f9L6OvYURWHrLgsfL84k3+ImIkzH/9zX\nipFDIisdKWmOSLLCjj0WlqVlc+a8HYBet5iZPiWO3t3MLWpcReA/VquVFStWsHTpUiRJ4pFHHuGO\nO+4IdFkCQYPTt3MM90/qyoK047yWeoDf39efCHPL8t0SCAQCn0WJHTt28Mknn1BcXHydMZUwuvSf\n6l8/zZAAACAASURBVGbeW8eGMHdcp+tuK3W6+XztSY6fL6iyw6Jscfv5uowKnhMrtpyh1O6q4DnR\nVEwzmzLufAvH73kU/ZXLWCbfzul+E1CXVJ+sUva5BJ/dywzDUexo2ZEwgSEJHfF5WS57oPAieOyg\nM0FYkjf60w8UBa4UazmZq0cB2kW4aFvPcZ9uj0LaDheb97lBBWP665g4SI9W23gWuKfO2vhg0SWO\nn7Kh06q4+/Y47r49niBjy+gCcrpkNm7LZ/mqbLJzXahVMHRAOA/NbU+U/1NBghbG1q1b+eqrrzh8\n+DATJkzg5Zdfvs6vSiBoiYzo3Yoim4tlm8/w+pf7+e28fpiMLS82WiAQtFz8Gt+YP38+8fHCIbgu\nuHbmvcDqICxET99O0cwd37lcEChbnG49eAWHq/KoxajQHxe3/npONBXTzKaKO7+Q4zMfxZ5xhrif\nzuHWF37JKI9cY1dK6vqTmE5sY2bYWYokHa/k9+b85VIuKKd8+1w8Dq8gIbvBGAbmBL8TNirGfTqJ\nMtVv3OeFLIlFax3kWBRiwlXMGW+kbULjWehbitx8+tVlNm7LR1FgcP9w7r8nkfjYlnFFq8TmYdXG\nPL5dl0OR1YNOq2LCqGhSJsaSEGckJsZMbm5xoMsUNHJ++tOf0q5dO/r160dBQQEfffTRdfe/9NJL\nAapMIAgsdwxpS1GJkw3pmfxryUF+OasP+hY48ioQCFomPosSiYmJ3HnnnfVZS4vCl5n3G0WDG4kI\nMfD8AwMwm7xmevlFpX55TtQkYAAt2gviZvBYijgxez72Y6eIvf8e2rzwS1QqVY3jBE6XhzYXtjI2\n7Bx5HgMv5fchy+N9vE9mps4SsF7yjm4Ex4Ap2u+EjeviPg0S3ePqN+7TIyksWVfMt5vtyAoM76Nj\nyhA9el3j6I5wu2W+WZvD4m+ycDhl2iUF8dCcJHreYg50aQ1CvsXFN2tyWP19Hg6njClIw923x3H7\nuFgiwsSVPIF/lEV+WiwWIiIirrvv0qWq/98JBM0dlUrF3HGdsZa62XM8h3dWHGH+9B6ic1UgELQI\nahQlLl68CMCAAQNITU1l4MCBaLU/bta6dev6q64FUNUitTrRoIwimxO701MuSoSFGIiswqviRkPF\nmkwzF64+wYkLFjHWUQs8RcUcn/M4pUcyiLl3Om3/+mvf5utlCfX2ZYzVnyPTbeLlvN4UyD860d8o\nLFWgtABKsgAVhCZ6uyT8JM+m4fjVuM+EUDcdo+o37vNyrsSitU4u58lEmFXMHmegY+vGERmpKAq7\n9xWx4MtMsnKcmEM0PDCrNeNGRKNpAb4RFy/bWb4qh807CvBICpHhOmZNS2DCyGhMQUKkFNQOtVrN\n008/jdPpJDIyknfeeYe2bdvy6aef8u6773LXXXcFukSBIGCo1Sp+dkc3bHY3+07m8c6Ko/x8aje0\n9Z27LRAIBAGmxrP/+++/H5VKVe4j8c4775Tfp1KpWL9+ff1V10zxxViyOtGgjBuFBq1Ghcmoq1SU\nuNFQsToBQ6/TsP1wVvnvYqzDdzzWEk7MfZzSg8eInn0n7V7+HSpfhBzJg3brYgwXj3JeCuX/8npS\nIl8fJ1llUoeiQEk22AtApYHw1l4fCT9QFDhboONCoR61Sqn3uE9JVti4182aXS4kGUYNCGL8ADVG\nfeNY7J+/ZOfDRZc4eKwYjQamjo9l5p3xhAQ3DsGkPjl+qoRladns3lcEQGK8gZTJcYwcHIlOJ06M\nGxvZuU527C2kXesg+nQPDXQ5NfL666+zYMECOnTowPr163n++eeRZZmwsDAWL14c6PIEgoCj06p5\n/K6e/HPJQfYcz8Hh8vDY9J6iY1UgEDRrajzD3rBhQ407Wb58OSkpKXVSUHPGH2PJ6kSDMm4UGlI3\nnOJiTkmFx7VvFVrBULE6s01ZqTzq0afxgRaMVGIjY94T2PYdIeqe20n++3O+CRJuJ7rvP0eddQY5\nLpktDKQkK6fCwypN6pAlsGaCqwQ0Bq8godFX2LY6XBIcyzZisWsaJO4zu0Bm0VoHF7NlQoNVzBxr\nYMSt4Y3Cj8Ba4mHRssus+T4PWYF+PUN5cHYSSQnGmjduwiiKwt6DVpalZXM0w/sd0rlDMHdNjuPW\nPmEtJlGkqWApcrNtt4Utuy1knLYBcGufsCYhSqjVajp08I4Hjh07lpdeeonf/OY3jB8/PsCVCQSN\nhyCDlqdn9uatZYc5dCaf11P388SM3piMzV8YFwgELZM6+XZbunSpECV8wB9jSYNOU2XXg0YNo/sl\nkTI8mRxLafnV86rGPUrsbjySUqEN/0azTYNegyIrON2VL0hrHB9owUi2Uk7c+yQlew8SNX0S7V97\n3jdBwlmKbsNC1HmXkJK64hkxk7tUGtxqPfsy8rAUO4gwG7mtdyumDmlzw5O6oegCeJygD4bQJFD7\nJxhdG/cZZfLQtR7jPmVFYct+Nyu3u/BI0L+LlpSRBkzGwC94PR6FVRtzSV1xhRKbRGK8gQdnJ9G/\nl/8jME0Jj0dh6+4ClqVlcyHTAUD/XqFMnxxHt84hItazEWEr9bBjbyFbd1k4dKwYWQG1Cnp3MzN8\nUCRDBzSN6JMbj6mEhAQhSAgElWDQafjF3T1575uj/HA8h1cX7ePpWb0JNfl34UEgEAiaAnUiSlwb\nESqoHH+TMZxuCZu98vhPc5AOSVb44we7yzsuurSJqHLcI6/QXqmYcK3Z5qerT7DtmpGNytDrNISI\nf4YVkEodZNz/NCW79xM5dTzt//knVBofVvalVnTrPkZdlIPUvg+eISmg1qCBCiaoSa1u6CRw26Ho\nojf6MygCQuL9MrRUFLhs1XIqzxv3mRzpok14/cV95hfJfLHWwZnLMiFBKmaMMdCzQ+O44rPvsJUP\nF13i0hUHpiAND85OZPKYGHTa5juq4HBKrN2czzdrcsjNd6FWw4jBEUyfHEe71kJ0bCw4nTJ7DhSx\neVcB6YeseDze/7VdOgQzfFAEQ2+NaPJmo0L4EgiqRqtR88id3QkyaNh84AqvfJbOM7P6EBnavLv3\nBAJBy6NOVgXipKJmajKWvFE0KCpxYimuXJQotLnZmJ5Z/nu+1cn2w1kY9ZpKo0Ojw4Mq9yK4huMX\nLDW+BodLYvmWM8JX4hpku4OTD/yS4u17ibh9DO3/8xdUWh/+rIoL0K9bgKrEgqfrYKQBkyvEdlaZ\n1OG0QlEmoEBIHARF+iVISDJk5OrJLvHGfXaLcxBpqp9xDUVR2HHYwzdbnbjc0LODhhmjjYSYAv+d\nkZnlYEHqJfYcsKJWwYRR0cxNSSAstGkv8qrDWuzhu/U5rFyfS4lNQq9XcfvYGO6cGEtsdMuINm3s\neDwKB45a2byzgN37inA4vX+bbZOMDB8UybCBEcTFNN3Pat++fYwaNar89/z8fEaNGoWiKKhUKr7/\n/vuA1SYQNEbUahX3T+pKkEHL6t0XeenTdH41pw9xomtVIBA0IxrHpcoWgD/JGDU9vircnoqCBMDg\nHgnV+kD4YqpZhvCV+BHZ4STjoV9h3bqb8Ikj6fDmX1Hrav6TUlmy0K3/GJW9BE/vMUg9R/kmKigK\n2POhJMf7+NDWYPAvlrL0atynzaXBfDXu01hPcZ+WYpkv1zvJuCARZIB5Ew307awNuIhpK5VY/M0V\nvluXi0dS6NE1hIdmJ5Hcpvme4OXkOfl6dQ7rtuThcimEBGuYdWc8U8bGEmoW/wYCjSwrHD1ZwpZd\nFnbssVBc4v0uj4vWc/u4CIYPiqRtUlCAq6wbVq1aFegSBIImh0qlYubojpiMOpZtPsPLn3o7JpJi\nQwJdmkAgENQJ4my0gajOWLIyA0ODTkOvjtHXdUTUhCRDQqQJl0cu9yLo2zmah6Z2p6DAVuV2/ggg\ntfWVqCpxxJckEl/2fSXPhuSWGkwskZ0uTv7sWaybdhI2bhgd334Jtb7mK+yq3AvoNixE5XLgvvV2\n5K6DfXo+RZGh+Ao4CkGthbDWoPNvkZJn03Asx4Akq2gV6qZjtIv68C9UFIW9xz0s2+TE4YKubTXM\nHGsgLCSw4xCSrLBhaz6ffnUZa7GH2Gg9D8xMZHD/8IALJfXF2QulLF+VzdbdFmQZYqL03DkhlnEj\nojAahLAYSBRF4cx5O1t2FbB1t4V8ixuA8FAtt4+LYcSgSDq1NzW7YzMxMTHQJQgETRKVSsXUoe0I\n0mv4fN1JXvk8nadm9qZDq+btfSQQCFoGdSJKhIQIpdYXrjWWvFY0uDEZo4xx/ZP8EiXAu0D/44O3\nYnd6yhf6mmryrSVZ5qtNp7E53D7tv8pYymr2X1niyIxR7Vny/Rmfkkh82nexk0iz//uoDbLLzalH\nfkvR+m2EjRpCp3dfQW2o2WtDdfkUuu8/B1nCfdvdyO37+PiEEkXnT4DDClqjV5DQ+D5iICtw7pq4\nz66xTuLN9RP3WVwqs3iDkyNnJAw6mDnWwMBuge+OOHKimA8WXeLsBTtGg5p5d7Xizomx6JthxKWi\nKBw5UcLSldnsO2wFvK3/KZPjGHZrJFpt81rkNjUyrzjYsquALbssXM72CsGmIA3jhkcxfFAE3bua\n0Yi0E4FAUAXjBrQmyKDlw5XH+Pui/Txxd09uaRcZ6LIEAoHgpvBZlMjNzWXlypUUFRVdZ2z55JNP\n8tZbb9VLcc2Na40lfekOiAw1EuXnCEdhiRO70+NzJ8ONiSA/1urtvLiRSmMp/dh/WeLIiQuF18WX\nVpdE4u++/dmHv8huD6cf/T2FazYTOnwgnT54FbWxZpFGff4w2q1LABWekXOQW3f17Qk9Lii6gFty\ngd4MYYkVvCeqw+WBozlGCq/GffaIdxBiqJ9xjQMnPSzZ6KDUAR2TNMwaZyAyNLCL/pw8Jx9/mcn2\nPYUAjBoayX13tyIyovkZtsqywq59hSxbmc3Js6UAdOscwl1T4ujXMzTgwlBLJq/AxdbdFrbsLODM\nBTsAer2KYQMjGDYogn49QtE1Q4FMIBDUD7f1TMCo1/LOisO8vvggj6Z0p2+nmECXJRAIBLXGZ1Hi\nkUceoUuXLqL1sg6o0sCwksdVNfJRlWjgTydDdYkgoSY9fTrHcPBUvk9dHf7uPzO3pNLbffWs8DfN\npC5QPB7OPP4clrSNmG8bQKePXkMdVLMDtvrkHrS7VoBWj3vUPJT4ZN+e0FXqTdhQJIKiErCrw/0y\ntCxyqDmSZcAl1W/cp82usPR7J/tPetBpIWWkntt66VAHcBHscEosXZnN16uycbkVOrc38fCc1nTu\nEBywmuoLt1vm+x0FLE/LLr/yPqhvGNOnxNOlGb7epoK12MP2PRa27LJwNMP7fafReCNXhw+KZGDf\nMIKMYoRGIBDUjv5dYnjynt78+6uDvLn0MA/fcQtDuscHuiyBQCCoFT6LEiaTiZdeeqk+axFUQlUj\nH4qisH5vxdGOazsZyvwazGGVew9UZ3BZZHMx8dbWzBzdsdaeD9XtX67iYr2vnhX+ppncLIokcfqJ\nP1LwzTrMg/rS+ePX0ZhqFiQ0R7aiTV+NYjDhHvsTlCgfRT1HEVgvAwqYEwiJb4P92kjQ6mpVINOq\n5XQDxH0eOeNh8QYnxaUKbePVzBlvJCYicFd8FUVh804LC5dkkm9xExmu4757WjFiUCTqZtYSX2qX\nWP19Lt+sycVS5EarUTF2WBQpk+NIShBxcYHAbpfYta+QLbssHDhqRZK8OmL3LiEMHxTBkAERhIYI\nKyeBQFA3dG8Xya9m9+WNLw/w/jdHsTs9jOmXFOiyBAKBwG98Pjvq3bs3p0+fpkOHDvVZj+AGqhr5\nkGQZlUpVqT/FjT4OMRFB9OoQVcFroTqDy9BgPUEGrc9dHZVR3f7VqsqFCV87PfxNM7kZFEnizNMv\nULB8NSEDetF54RtoTDWYTCoKmn1r0R7ZgmIKxT3uAZQwH1orFQVK88CW6x3TCGsNet89WyQZTuQa\nyCnRorsa9xlRD3GfdqfC11uc/HDUg0YNt9+mZ1RfXUAX/hlnbHyw6BIZp23otCruuSOe6VPi6u1q\ndF2YtNYGS5Gbb9fmsGpjLqV2GaNBzbRJsUwdH0tUMxxLaey43DLpB61s2VXAngNFuNzeL7YObU0M\nHxzBsIER4nMRCAT1RsfEMJ6d25fXUvfz6ZoM7E4Ptw9pF+iyBAKBwC98FiW2bNnCggULiIiIQKvV\nikzxBuZGcaA6f4rP12VcN/KRY7FX6rVQ3XhIYYmLPy/44aaMI6vbf2JMyHWeEmX46lnhb5pJbVFk\nmbO/epH8JSsJ7teDLp/9C01IDS3xsox29zdoTu5BNkfhHvcAhIT78mTe7ginFdQ6CG8DWt/FlVKX\niiPZRmwuNaEGiW7xTozauvePyLjgIXWdk8IShaQYNbMnGEiIClwbeoHFxadLL7NxWwEAQweEc//M\nRGKj606YupaqzFvr22D1craDr1flsHFbPm6PQniolrumxDNpdDTBJnH1vSGRJIVDx4vZsrOAnemF\nlNq9wl9ivIHhgyMZNjCCxHjRrSIQCBqGNnFmfntvf/7+xT6+2nSGUqeHGSM7CC8hgUDQZPD5TPa/\n//1vhdusVmudFiPwnxvFCn+9Fq4dD8m3Oq57fF0YR1Y1fvJj+oZvSST+7NuffVSHIsuc+81L5KV+\nQ3DvbnT57N9ozFV3LTjdEkXWUuIPfYfm4hHkiHjcY++HIB86HWSP1z/CbQdtEIS39kZ/+khuiYbj\nOQYkRUViqJsO9RD36XQrfLvVxfZDbtRqmDBIz7gBOjSawJz0uNwy36zJYcm3WTicMu1aB/Hw3CR6\ndDHX6/M2tMHqybM2lqVls3NvIYoC8bEGUibFMvq2qGaZHtJYURSF46dK2LLLwrYfLBRZvQk20ZE6\nJoyMZvigSJLbBIlFgEAgCAjxkSZ+N68/f0/dT9rOC9idEvdO6BxQfyeBQCDwFZ9XPYmJiZw6dQqL\nxQKAy+XixRdfJC0trd6KE1SkppZxf70Wyjoupg5tx58+/AFLScVtb8Y4srqODn+SSGrat0avQ3K5\n665DQlE4/79/I/ezZZh6dKHLov+gDat8sVt25fxwRhY/0e2ltbGALH0soeMeRGP0YfTF44TCCyC7\nwRAKoa18TtiQFThboOPi1bjPW2IdxJklf16qT5y5LPHFWgf5RQrxkd7uiNaxgemOUBSFnemFfJya\nSXaei1CzlgdnJzF2eFS9Ryk2lMGqoigcOFLM0rRsDh3zeol0aGti+pQ4BvcPF5GRDcj5S3Y27yxg\nx54iruR4hdvQEC2TRnuFiK4dg5udX4lAIGiaRIUZ+d28fryWup/v92XicHp46PZb0FYTDS8QCASN\nAZ9FiRdffJFt27aRl5dHmzZtuHjxIg899FB91ia4Bl9bxn31WrhR3LA7PRRWIkjAzRtHViek3Ixn\nxbX7iIkOJtdHI8iaUBSFC3/4OzkfLyGoWye6fPEm2vDQKh+fuuEU2/ee5ddRB+lssLLPEcm/Lndl\nxNZLNV85d5VA0SXv6IYpGoJjfE7YcHngaLaRQoeGIJ1M97i6j/t0exRW7XSxKd0NwOj+OiYO0qPT\nBmYRdu5iKR8susTh4yVoNDBtYiz3TE0g2NQwAkl9G6xKksL2PRaWp2WXR0f27m7mrslx9LzFLK7C\nNxBZOU627Cpgy24LFzO9QkRQkIZRQyIZPjiCXreEog3Q34BAIBBUR2iwnmfn9uWNxQfZeTQbu9PD\noyk90Deg95FAIBD4i8+ixKFDh0hLS+O+++5j4cKFHD58mLVr19ZnbYJr8LVlvCavBa1GxefrMiqI\nGynDk+vcODJQs/c3g6IoXPzzG2R/mEpQl/Z0TX0LXWTVfhBOt8Spk5d4LmYfbXU2tpfG8rblFiTU\nNV85t1ug+AqggtBEMIb5XGeRXc2RbG/cZ3Swh64xTrR1fL5xMVti0RoH2RaF6DAVsycYSU4IzElN\nkdXNouVXWLspD1nxxio+OCuJxAZOmagvg1WnS2bD1ny+XpVNdp539GbYwAhSJsfRoW3dpcgIqqag\n0M22Hyxs3VVAxplSAHRaFYP6hTFicCSTxiRitZYGuEqBQCCoGZNRxzOz+vCfZYc4cDqfNxYf4Bd3\n9yLIIPyHBAJB48Tnbye93use7na7URSFHj168Morr9RbYYIfuRmfCEuxg+jwH9M3qhM3qhMzAHIs\npZV2O1TVCdHQs/c3i6IoXPrrv8l65zOMHdvR9cv/oouKqHabkpxsfmHcSbzWztqSRD4u6oSC9wpq\nlVfOFQVsOVCaDyrN1YQN3xaeigKZRVpO53vjPttHumhdx3GfHklh3Q8u1v/gRlZgWG8dU4bqMega\n/sqwx6OQtiGXL76+QqldIjHBwEOzk+jX03cBpy6pa4PV4hIPqzbm8u26XKzFHnRaFRNHRTNtUhwJ\nsfVj1Cn4kRKbh517vRGeh48XIyveZKA+3c0MHxTJoH7h5V04BoO4yigQCJoOBr2GJ+7uxbsrjrA3\nI5e/f7GPp2f2ISRIF+jSBAKBoAI+ixLJycl89tlnDBgwgAcffJDk5GSKi6tvl//b3/7G3r178Xg8\nPPLII/Ts2ZNnn30WSZKIiYnh1VdfRa/Xs2LFCj7++GPUajUzZ87knnvuuekX1pyorU9EmV9Dh3ZR\nFBfZaxQ3Xnh4YPnPZcaRfTpFISsKz723s0K3A1BlJ4RHUupk9r6hYhcVRSHzb//lylufYGzfhq6L\n30YXE1XtNqrCHOJ3fIpaa2eZtS1LipOBHxfulV45V2QoygRXMWj0ENYGtL7FBXpkyCiL+9RcjfsM\nqtu4zyt5Ep+vcXI5TybCrGLWOAOdWgfmysreg0V8lHqJzCtOgk0aHp6TxKTRMQFvm68Lg9W8Ahcr\n1uSwdlMeDqdMsEnD3bfHcce4WMLDxAljfeJ0yvxwoJDNOy3sO2TFI3lHnrp2DGb4oAiGDogQn4FA\nIGgW6LRq/ielOwvSjrPtUBavfJbOL2f1IcIsRG+BQNC48Hm18cILL1BUVERoaCjfffcd+fn5PPLI\nI1U+fufOnZw8eZLU1FQsFgvTp09nyJAhzJ07l8mTJ/Paa6+xZMkSUlJSePPNN1myZAk6nY4ZM2Yw\nfvx4wsN9iFBsIdS2ZbzMr8Go11JMzeJGSamrgvnkV5tOs76Kbgegyk6Icf2Tbmr2vqFHPy6/9h6X\n//khhnZJdF38Nvq46Gofr8q7hG79J6hcdnab+7Mks6LnRIUr55Lbm7DhcYDO5O2QUPsmtFjtCumX\ngih1qwk1SnSPc2Kow7hPSVb4fq+b1btcSDIM7KZl2nADRkPDCwCZVxx8lHqJvQetqFUwaXQ0c1Ja\nEWpuHG2n1Zm31sTFTDvLVmWzeWcBkgRRETpmT0tgwshogoLElfj6wu2ROXCkmC27Cti9rwiH0yvm\ntUsKYtigCIYPiqi3CFmBQCAIJBq1mgen3EKQXsu6vZd4+bO9/Gp2X2LCgwJdmkAgEJRT41n+0aNH\n6datGzt37iy/LTo6mujoaM6ePUt8fHyl291666306tULgNDQUOx2O7t27eKFF14AYPTo0Xz44Yck\nJyfTs2dPzGZvskG/fv1IT09nzJgxN/3imgo1dQPUVcu4r+JGmZhRXWdF+oncKkcG9mXkMXVou5ua\nvW/I0Y/L//yAzH+8i6FNoleQSIit9vGqK2fQff8ZSG7cQ1Lo0b4v40JOVX/l3O2Aogve6E9jOJgT\nfDa0zCnRkHFWwSOrSQxz0yGqbuM+cywyi9Y4uJAtExqs4p4xBrolN7wAYCv1kLoii5Xrc5Ak6HmL\nmYfnJNE2qXGeOPlj0nrsZAl/f/s823bnA5CYYGD6pHhGDIlAp22c/ipNHVlWOJrhjfDcvsdCic2b\nShMXo+eOQZEMHxRBm8TGeWwJBAJBXaJWqZgzrhMmo5YV287x0qd7eWZ2XxKjgwNdmkAgEAA+iBLL\nly+nW7duvPXWWxXuU6lUDBkypNLtNBoNJpP3hH3JkiWMGDGCrVu3lntTREVFkZubS15eHpGRkeXb\nRUZGkptb+UK4ueFPN8C1LeMFVgdhIXr6dvKvZdxfcaP6zorKb/fe58Du9NRaSGmo2EWAK29+zKVX\n/os+KYGuS97GkFi5yFaG+sJRtFu+BMAzYhZym+5oqCHe1FkM1kteQ4jgWDBF+SRIyAqcyddzqUiH\nRk2dx33KisLWA26+2+bCI0HfLlruGmnAZGzY7ghJVliedpl3PzmLtcRDXIyeB2clMbBvWJNOm5Bl\nhb0Hi1i6Mpvjp2wAdOkQzPQpcdzaO0zESNYDiqJw+lwpW3ZZ2LrbQkGhNzUmIkzLHeNiGD44kk7J\npiZ9XAkEAkFtUKlUpAxvj8mg5YsNp3jls3Sentmb5ISq08UEAoGgoahRlPj9738PwMKFC2v1BOvW\nrWPJkiV8+OGHTJgwofx2Ram89byq268lIsKEto6jBmJizHW6P194b/mhSrsB9HotKSM7EhFqwKj/\n8SN6fGZf3l1+iF2HsygodnDknIVvdlzgoand0dSQQV32+h6f2RdTkJ6dh6+QV2gnOjyIwT0SKt2H\nOSyImIggciz2CvuLDjeCSkVuJfcZ9BratY7g8Q4xPj/XtVzJs1FQhehhKXag0euIqUTd9/czPPP6\nR1z8678xJsUzZP1CTO1bV/t415FdODZ/AVodpmk/RdumYsdG0jU/K4qCvSALW9FFUKkJbd0RQ2hk\nhW0qw+5S2HlSIa8YzEYY0llFmKnuUhhyLR4+WFrE8XMuzCY1D9wZyq3dG/6qcfqhQv713ilOnbUR\nFKThkZ8kM3NaEgZ90+0ecLtl1m3O4fOlFzl7wZvWMHRAJPNmtKZXt6YttNREIL5HAc5dtLFuUw7r\nNudy6Yr3OykkWMvUCfGMGxFLnx7haDQ3/74H6vU1JI39NWZkZDB//nweeOAB7r33Xk6fPs3zzz+P\nSqWiXbt2/OlPf0Kr1QqvKoGgCiYMbIPRoOXjVcd5ddE+npzRiy5tqjf1FggEgvqmRlHivvvuNo+o\ncgAAIABJREFUq/Yk+pNPPqnyvi1btvD222/z/vvvYzabMZlMOBwOjEYj2dnZxMbGEhsbS15eXvk2\nOTk59OnTp9qaLJa6jWWLiTGTm1u9aWdd43RLbDuQWel9q3acI237uQrGkQtXn2D74azyx+VY7KzY\ncoZSu6vakYYbX1/Kbe2YPLD1dVf1CwpslW7bq0NUpd0OvTt6PRcqu8/ulHh/+SHmjutMym3tGNu3\nFWcvFxESrCcxOqTK5ypDcktEmqse/ZBc7gqfV9lr9NUYM+v9L7jw/N/RxcfQOfW/2Mzh2Co5Bsr2\nF5OZjnHfahR9EO4x9+EMSoDqjhlFgZIsb+ynWgthrbE6ddVvc5VCu5qjV+M+Y4I9dIl1Emaqm2NU\nURR2HvHwzRYnTjf0aK9hxhgDZpOnQf8GsnOdfPxlJjv2FgIwZWwcd98eS2S4DmtR9cdHY8XukFi7\nOY8Vq3PIt7jRaGDUkEhSJsfRNikoIN8zDUlDv77cfBdbdxewZZeFsxe8QoRBr2bYQK9HRN8eoeh0\nXnGroKDkpp+vuX9+UPlrbEwiRWlpKX/5y1+u69D8+9//zs9//nNGjhzJm2++SVpaGmPHjhVeVQJB\nNYzo3Yogg5Z3VxzhtS8PMD+lR/l5nUAgEASCGkWJ+fPnA96OB5VKxeDBg5Flme3btxMUVPWV1eLi\nYv72t7+xYMGC8hOBoUOHsnr1aqZNm8aaNWsYPnw4vXv35rnnnsNqtaLRaEhPTy/vzmjOVDcaIV9t\nFinrnDhxoZBSh7vSRTrUbqTB13n46pIGnG6JrQev4HBVHCnYl5FHyvBklm4+w/ZDV3C4vMZyRr2G\n23rGM3tspyoNK2vjoSFJMp+vy/BpFCZ7wWKvIBEbRdfFb2NMrtgh8eNoTQ6jleNMDz2PTR2EevxD\nqCOrHvFwuiWsxXaiyEPtKQWtwZuwoanZzV9R4NLVuE+ADlFOksI8dRb3WVQik7rOyYkLEkY9zJ1g\noF8XbYNeubc7JL76LosVq3NwexS6dAjm4blJDB0Y32QXfEVWN9+tyyVtYy4lNgmDXs0d42K4c2Ic\nMVG+JasIfKPI6mb7nkK27Crg2EmveKXRwIDeoYwYFMmAPmEEGYVhaHNFr9fz3nvv8d5775Xfdv78\n+XL/quHDh/P5558THR3d4r2qBIKauLVrLEa9hjeXHuI/Sw/xs6ndGHhLXKDLEggELZQaRYmyKxIf\nfPAB77//fvntEyZM4NFHH61yu5UrV2KxWHjqqafKb3v55Zd57rnnSE1NpVWrVqSkpKDT6XjmmWd4\n+OGHUalUPPbYY+UnEs2Z6kwnb+RiTvVX+XxJs6gt1SUNlJQ6cFYiSJTV9Pnak9d1dgA4XBLr92ai\nUqkq7e4o60xIGZ4M+B67+OE3R3wyxsz5dCnnf/8K2uhIui5+m6AObSvdX+qGU6zfc5GfhJ1kgjmT\nLE8QL+f1pne6lbnjKooSZSLGuUv53D/EhDpCR6ZVRXy7Nmh8ECQ8MpzIMZBr06LXyHSLcxJeR3Gf\niqKQfsLDsk1O7E7o0kbDzLEGws0NNyIhywqbdhSwcMllLEVuoiJ0/OSeRIYPimiy4wzZuU6+Xp3D\n+i15uNwK5hANs1MSmDwmhtCQxpEU0hwotUvsSi9kyy4LB45akWWvJUuPriEMHxTJkP7hmMX73SLQ\narVotdd/1p07d2bTpk2kpKSwZcsW8vLyWrRXlUDgDz3bR/HLWX3455IDvPP1EexODyP7JAa6LIFA\n0ALx+UwuKyuLs2fPkpzsXSxeuHCBixcvVvn4WbNmMWvWrAq3f/TRRxVumzRpEpMmTfK1lEaJr2MD\nZVTXDeAvvqRZ3CyVdVZUn+Zh4Ni5/Cr3ty8j97rujqpMP194eCAlpa5q31enW2Ln4StVPM+PXSS5\nX6zg3LP/hzYynK6L/0tQp+Qq93cgI5tHI45xmymbC+5gXsnrTaFsqLIrJXXDKc6cz+YX48IJC9Kw\n9oiNL3YXM7a/psa0EJtLxZEsI6VuNWFGiW51GPdZXCrz1UYnh05LGHRwzxgDg7o3bHfEidM2Pvj8\nIifPlqLXqZh5ZzzTJ8dhNDTNK9pnL5SyLC2bbT9YkGWIjdYzbWIsY4dFYzA0XS+MxoTLLbP3YBFb\ndlnYe6AIl9v799Ax2cTwQRHcdmsEURGiC0UAv/nNb/jTn/7E0qVLGThwYKW+VIHyqoLGNf7SEhHv\nf83ExJiJizXzx3d38PGqE6i1Wu4a7buJui/7FwQO8f4HFvH++47PosRTTz3FAw88gNPpRK1Wo1ar\nW8SYRU1Ul6DhkZRqhYobEzUAarMUvXGkoSqBxF/hpCaqE1a6tolg2w1dEtdSUOy8rrujpghQp1si\nx1Jaae1FJU5yCysabsKPXSTq9Rs5+8xf0ESE0TX1LUxdOlRZm7WohPt1e+lrzCfDGcqr+b0oVXTX\n7e9agcbplpBLC/nN5Eg0avh0h5UNx7y+JzWN1uSUaDieY0BWVCSFuWlfh3GfB056+GqjA5sDOiSq\nmTXOSFRYwy2a8y0uFi65zKYdBQAMGxjBfTNaERtdvwJafaAoCoeOl7A8LZt9h60AtEsKYvqUOG67\nNaJOTBRbOpKkcOhYMZt3FbArvZBSu7dTKDHBwIhBkQwbFEGrOGOAqxQ0NhISEnjnnXcAr49VTk5O\no/CqgpbhQ9KYEe+/74QZNPzm/9l778Cozjvd/zO9aCTNqEtIdETvCNFEFdhgY4MdbHBJHDvFSfzL\nlpvfZrP35qZsySbrZBNns0k2cU1s3G2MbYxNs0UTvaNGlYT6zGg0o6nnnPvHgFCZGSQhAYL384+N\nztGZ95w5Mzrv836/z/PIVJ59/QgvfniSBrub1QXDr3sBQ7wHNxdx/W8u4vp3JZZI021RorCwkMLC\nQpxOJ4qiYLMJp16IPpm+4gMRy9+gfWuE3eXjt+8co9YeeXIdiaR4A9NGp7aJG9EEkm89OLnbfgs9\nJZrnxIpZQzh5zo7TE4g69ivVHbEjQBuQJJljZ5qijj3RYiDVGjklxBZvRN6yg/N/9xM0CRbGvP47\nzONjVC4EfGQceItsYxPHfDZ+bZ+IX9F0OF6HqhRFIdhcx2OzLHgDMr/d6uRE9dVzjtZaIytwpklP\ndbMOjUphXLqPNEvfxH22+hTe3eHncFkIrQbun69n3mQd6htUHeEPyHywuY53PqrDH5AZPtjEU4/k\nMC7XckNevy+RZIV9h5y8u6mOinPhScuEMRZWL09n6oSEAdt6cqugKAqlZzx8sdfB7gMOml0hAFKT\n9dy1MGxYOTTHJK6zICrPPfcckyZNYuHChbz77rvcf//9d6xXlUBwPWQmx/GDR6fx7BtH+HD3Bbw+\niXVLR92wZweBQHBn021Rorq6mp///Oc4HA7+8pe/8NZbb5GXl8fQoUP7cXi3NrEm0+19IKL5G1zB\noNOw/XB1jwSJuRMyeOyu0R1W4KMJJBWXXJy/5Oryc0VReHTp6G6/ZiQ6e05YzDreLzrHv75yIKog\nATA1N7Vt7LFMP5tcfrYfvtRl7HD1Whp0GmZNyOSDorNdfr/AUcHFf/svNBYzo1//HXETx0Q/Ga8b\n3bZXUNtrOGccwrPVQ5HoKNp0qEpRZGipwSI34/BI/Gqzg2pnqMP+kVpr/CEVJ+sMuHwazDqZ8Rk+\n4vR9065x6lyIt7b5cXkUBqerWbfMSJrtxlRHKIrCnoNOXnqjmoamAIkJWr72SDaL5iWj6avyjxtE\nICizY7ed9z+po6bOj0oF+dMSeWB5BrkjusbRCrqPoiicr/RSVOxg5z4HDU3h74mEeC13L0ph/qwk\nRo+IQz3A7hlB/3PixAl+/vOfU11djVarZfPmzXzve9/jn//5n/ntb3/LjBkzWLhwIcAd6VUlEFwv\nKVZTmzCx9VAVrf4QT94z5roXsAQCgeBadFuU+OEPf8ijjz7a5gkxdOhQfvjDH/KXv/yl3wZ3qxNr\nMh2JgyUNrJwzlHhzx17oWOJGJIx6DQ8u7Gg8GesY7QWJ9uw6XsuXFo6M2crR0hqgqt5Ndpqly7jb\nc8Vz4rUtZTF9Mq6kb7Q3rIzlTaFWXU0jaU/ntognV46n1RvoULExr/ksGX/4HWqzidGv/ReWyeOi\njguPE92Wl1C7mpBGziA97x4W7Tgb3WhTDkFzFQRbkTVGNpX5uggS0LW1xulVc7LOQLBd3Ke2D/7W\n+/wKG4r87DsVQqOGFXP0LJymu2FiwLmLrfz5tSpOlbnRalSsujuNNSszMZsGlm+Ep1Vi844GPvys\nHkdzCK1WRWFBMqvuTmdQpmgduB5q6v3sLA5HeFZeCrermYxqFs5JYv6sJCaNjRdtMIKYTJgwIeIz\nx9tvv93lZ7eDV5VAcDNItBj4/iPT+PVbR9lzshZfIMTT949H1w+eKwKBQHCFbosSwWCQJUuW8NJL\nLwGQl5fXX2MaMPQkQQPA4fbzoxf2MWNMWof2g56KG76AxE9f2k+zO4AtXs+YIUkszcvu0TGuHKfB\n6SU7tWtZfSAU4l9fOUR1gxtZCYsDg1It/O8vT0OvjXzbxBJGEuN0fPfBSWSlWrqIILG8KSIJEtC1\nLUKj6VixwZ5iLjz9W1RGA6NffQ7LtAlRr4OquQHdlpdRtTYTGl+ANHUpmsvpIJFSRwj5obkSpAAX\nnCr+sK2GOqcfo14NqAgEpS4ihqJAZbOWs016VPRt3Gd5ZYg3tvhxtChkpahZt8xAVsqNeXhwuoKs\nf6+Gz75oRFEgb0oiTzw8aMD1/tudQT78rJ7NOxpo9cqYjGpWL0/n3sJUkoShYq+xOwLs2u/ki2J7\nW/uLTqti1nQr8/NtTJuUiEEvVuAEAoHgVsJi0vG9tVP47TvHOVzeyK/fOsb/9+BEjHqRdCQQCPqH\nHn27uFyutt7e8vJy/P6eTYJvN3qToOF0B7q0H/RU3LhyHAB7S4DdJ2o5WFqPQa/BFyWiMypRXMn/\n5eWDVDV42v4tK+GWlH95+SA/fSq/7eftzTNjiSstrUHiTLqoVRmRvCkmjUzmaHkD9paubSDREkcM\nOg36g4co/9YPUOl0jP7rb4jPmxz19FVNl9BtfRmVv5XQ1KVIE+Z3OV4HP4iAJyxIKDIn6lT850c1\nbeakvkDYmG/OhAweb9daE5KhpN5AYx/HffqDCh/tCrDrWBC1CpbO1FGYp0d7A1abgyGZj7c28OYH\nNbR6ZTLT9Xx1bTZ5k639/tp9SXWtj/c/qWPHbjuhkII1QcsDKzK4e1EKcWbx8NUbWtwh9hx0UlRs\n52SpG0UBtRqmTkhgXr6N/KlW4sxixU0gEAhuZYx6LX+7ZhJ/2HCSw+WN/PL1I/ztQ5OJM1475lwg\nEAh6Srefur/zne/w0EMP0dDQwMqVK3E4HPzHf/xHf45tQBBpMm02ajt4SkSifftBX8SD+oPRJ7ka\ntQopQsmBUa8htZMBI1xu2WgnSLSnqsFDS2sAs1HbxVRz0sgUbPH6HokIV8eojliZoFGrIl6Xzm0R\nV3Du2EP51/5/VBoNuX/5NfH5U6O+pqruHLrtr0IwQHDW/cijZkTdFwCvE1rC/hZBcwYvF52OmJZS\netHZ9v+egIoTtUa8fRz3ea5G4vVPfTQ2K6TbVKxbZiQnvf8neoqicPCYixdfr+JSnR+dHlIGB/AZ\nnLy5q5nyhr4xUO1vys56eG9THcWHnCgKZKYZWHV3OgvnJqHX3dpjvxXx+SU++7yej7dc4vBxFyEp\nfI+PGRlHQX4Sc/KsWBPEg6xAIBAMJHRaDd9aNYEXPz7NnpN1/PzVw/yvhyf3ewy9QCC48+i2KDFs\n2DBWr15NMBikpKSEBQsWcPDgQWbPnt2f47vliTSZ1mpUvLGtgoMlDTjckSsHOrcfXBE3dh6riVrt\nYLMYoh7vCka9hjijFkeLv62FQG/Q8vGu8132nTMxI+LEvrTS2eVnnbeXVTq7mGpuP1RNTpoloigR\nTUToTOfKhGjpHu09Ka7Q/EUx5U9+D9RqRr30KxLmRBcZ1FWlaL94HRSFUMEa5KETow9KUcBTD61N\noFJDYg4OjypqVciV91bRxlPaEI77zEkMMCw5eN1xn8GQwubiADsOBUGBhdN03D1Lj07b/9URlZe8\nvPh6NYdPuFCrYWSujgapEUkTnoBey9D1ZqMoCodPuHhvUx0nSsKi4cihZlavSCd/mnXAmXHebIIh\nmSMnXBQVO9h3uBn/5UqhoTkm5s+yMTfPNiDjXwUCgUBwFa1GzVP3jsNo0LL9UDU/e/UQ31s7hZRE\n080emkAguI3otijx9a9/nfHjx5Oens7IkeEJYSjU1djvTqXzZPqRwlxWzhnKj17Y19Zq0Z7OlQNX\nxI1VBcN47bNySi44cLrDwsKkEUkUzsjBYtLx05f2x2zzCAQl/umxaeh1mrZqg6SkOAL+EIdKGy6L\nFR2jRDsTDMVuLfAHQlG9I1p9QRZNzeLYGXsHEWFVwTDqHa0dvRm6QbQKis40fV5M+RN/D7LMqJd+\nReL8/AhHC6M+exTt7ndBrSG46BGUrFHRB6DI4KoGfwto9ATiBuH0KJgM2qgtN0kJJuyBRGrtejQq\nhfHpPlL7IO6zsl7i9U/91NplkhNVrF1qZHhW/1dHuD0h3thQw8fbGpBlmDwunse+lMkfPz6K2tW1\n6qOzCenNRpIUdu138N6mOs5XhhNupoyPZ/WKDCaOsYi4yR4gyQqnSt0UFdvZc9CJ2xO+rzPSDNy9\nKJ1pE+PIyRIPqgKBQHA7oVapeGxpLmaDlo/2XOBnfw0LE5nJIo1KIBD0Dd0WJaxWKz/72c/6cyy3\nHfFmPTPGpPWo/cBs0PG1e8d18Gpov9+12jz0Og1JiSbMhqtvbWcTyGgTe0mWL1d41MU8r6NnmmJU\nCfi5a+ZgHlo86nJEqJ73i87yo+f3tbV5TM3teYl/F2+HdrQUH6bs0e+iSBKjXngW68Lo1Tvqkr3o\n9n+EojcSXPQ4Strg6C8qhaD5IoR8KFoT7xwNUHz6UNt5mI26LqKE2WSkcP4sat16zDqZCRk+zNcZ\n9ylJCu9ua+GDHV5kBeZO0nHPXD0GXf9OpiVJ4bMvGnntvUu0uCUy0gx89eFB5E1JpMHpvWalSLT3\n60bh98ts3dnIhs311DcGUKtg3kwbq5enM3zIzR3bQEJRFCrOt4YjPIsdOJqDACRZdaxclkxBvo2R\nQ82kpSXQ0NByk0crEAgEgv5ApVLx4IIRmA1a3tpxhn9/9RB//9AUhmSIuF2BQHD9dFuUWLp0KR98\n8AFTp05Fo7k6oc3KyuqXgd0u9KT9oD3tJ+HtBYpVBcMoOnaprVS6M76AxPtFZyOWz8ea2AO8sa2i\nW74Wx880XdM7IlpEaKwS/2hCTCxa9h+l9LG/QQkEGfmnn2NdMi/yjoqC5vgOtEe3oRgtBAu/gmLL\niH7gkA+clSAHwZjIG/vdfLq/usN5NLn85KRZaPWFcLT4GDkkk7xpk9FodKRZQuSmXn/cZ02TxPpP\n/VQ3yFgtKh4uNJA7uP8NGI+dbuGF9ZVcqPJhMqr58ppB3FuYiu6y30Isc9Zr+Yf0Ny53iE3bGvh4\nSwMudwi9TsXdi1K4/650MtJEO0F3qbzkpWivg537HNTUh99nS5yGpfOTKchPYtxoi2h5EQgEgjuM\n5bOGYDJo+cvmUn6x/jB/u2YSo7IHlsm1QCC49ej27Ka0tJSNGzditV794lGpVOzYsaM/xnXb0N32\ng0hcqVxobyY5erAtqiBxhd6Uz8eK8+y6r8zUXBt7T3atqGhfARLrmO3HGOk8u1NN4T50gtJHv4vs\n8zNt/a/RzotSIaHIaA58grZkD0qclUDhE5CQHOMEW8ItG4oMcWn4dVYOlp6PuGurL8QPvzKDqmYd\n9d64cNxnip9BCdcX9ynLCjsOBflkbwBJhoJpJu7KU2My9O8ksLbez0tvVlF8qBmVCpbMS+bRB7Ow\nJXY0KYxlztpd/5C+pqEpwAeb69hS1ITPL2OJ07Dm3gxWFKYKk8VuUt/oZ+c+B0XFjrZWF4NeTUG+\njYJ8G1MmJKC7XqVNIBAIBAOahVMHYTRoeP7D0/zy9SM888BEJgyP8VwlEAgE16DbosTRo0fZv38/\ner2+P8dz29K5SqE7VQGdKxeaXH52n6i95mv1pnw+VpxnJO6aGfa4iFUBEuuY7ccY6TyvZZjoPnqK\n0keeQW71MuK//5XM1csil47LEto9G9CcPYycmEqw8AkwJ0Q/sVY7uGsBFSRkgzGBZkdr1PNwe0OU\nN5lpCRrQa2TGZ/hJNF5f3GeDQ2b9Zz4u1MrEm1U8tMTAgpnWfi2N93ol3v6olg8+rScUUhgzMo6v\nPZLDiKHR76HeVgH1NReqvLy/qY6ifXYkCZJtOtatzmTp/BRMxlvD1+JWxukKsnt/OMKzpCKcuqPV\nqMibkkhBvo28KYkYDeI6CgQCgeAqs8ZlYNRr+f37J/jN28f45n3jmTEm7WYPSyAQDFC6LUpMmDAB\nv98vRIkYdEdo6G5VQE8qFzrTm/J5i1mPQa/Gd40qDAgnfGQkxV2zAqQ7Jf7draZoj+d4CaVrv4Pk\nbmX4cz8l+b6lkQcqBdF+8SaaqhLk5GyCSx4HQ5RJtqKAuw68dlBpwJoDOnPM87AmxrNk7kxaggas\nRolx6T7019FZISsKu44F+WhXgGAIpuRqeWCBgThT/1VHyLLCjt12/vpONY7mEClJOr7y0CDm5tmu\naQB5PVVAfcGpMjfvbarlwFEXADlZRlYtT6cg3yZW869Bq1di7yEnO4sdHD3lQpZBpYIJYyzMn5XE\nrGlW4i393yYkEAgEgoHLlJEp/N2ayfzmnWP8fsMJngiMoWCSaOsWCAQ9p9tPnXV1dSxevJgRI0Z0\n8JR49dVX+2VgA4metB90tyqgp5UL7elN+fw7n5/pliAB4ShRoC1NI1pFRndK/OtjVCFEqvhoPVVO\nydrvILncDP/Nj0l54O7Igwz60W1/FXXdOeSM4QQXPgK6KEKNLIXbNQJu0BjCgoTmqvgW6TyGDR7E\n7OmT0Wo15FgDDEu6vrhPu0vmjS1+KqokzEZYW2hgSm7/thyUVLh5/rUqKs63oterWHt/JqvuTsdg\n6NmE/lpeJX2JLCscONrMe5vq2lb1x4yM44EV6UyflIhaeBxExR+QOXSsmaJiBweONhMMhQ1YRw0z\nU5CfxNw8K0k2IToLBAKBoPuMGWLjH9ZN5VdvHOHFj0vw+SWW5uXc7GEJBIIBRrdFiaeffro/xzGg\n6a7Q0JOqgESLIaqZZDSMeg3zJmX2qHxekmVe+6yMz49cirhdo1aRYNbidAexxRuYmpuCAvyfP+3t\nlv/DtUr8e2KY2FpSQclD30JyNDPslz8k5Uv3RD4pfyu6ra+gbqpGyhlLqGANaKJM8KXg5YQNP+jj\nwi0b6q6CzpXxHi1vYviIkYweMRRZlhib5iU9vvftGoqiUHwyxAdFfvxBGD9Mw5olBuLN/bfS32gP\n8Mpb1RQVOwAoyLfx5TWDSEm6dSekwZBM0V4H739SR+UlHwB5UxJZvTydsaMsN3l0ty6SpHD0lIui\nYgfFh5x4feF7NTvTyPxZNubNtJGZbrzJoxQIBALBQGZYZgL/+Og0nn3jCOu3ltPqD3Hf3KE3e1gC\ngWAA0W1RYubMmf05jgFBpPaMnggN3fVYgPDq85ghSTE9JGwWA80eP7Z4A2MG21h3OUO6J7yxrYLt\nhyMLEgCSrPDdNVMw6TUkWgy88/kZtvbA/6Fzib/JoMXrDxGSFDTq8HlOGpEccQztKz685ecoeejb\nhOxOhv7in0hdd3/kAXua0W19GXVzA9KIqYRm3R9RZAAg6IXmSpBDYLKBJYNo7pQatZoHFo4md7Qe\nd0CLSScxMcN/XXGfzW6ZN7f6KbkgYdTDuqUGpo/RXrNtIhLdaR3y+2Xe31zHux/XEggojBhi5qlH\nsm/pSb3XK/HpF41s/LSeJkcQjQYWzkli9fJ0Bg8y3ezh3ZLIskJJhYeiYju7DzhxtYQASE3Wc/ei\nsGHl0BxTr+4zgUAgEAgiMSjVwg8em86z6w+zYec5Wn0hnnl46s0elkAgGCCIpuFuEKs9oydCQ0+q\nAvxBiaV52Rwsrccf7LoSn5xg4P8+kYfXH+p1L393fSu+OFLN43eN6ZX/wxW0GhVbDlZ1uIaTR6Wg\nAo6daQJArQJZgaR4A9NGp7ZVJ3grzlOy5mlCjXaG/Nv3SXvsgYivoXI1odvyEiqPk9DYOUjT7wKV\nGn9QosHRCioVqVZTeIw+V7hlAwUs6WBKiipIANhb1ZyuMxKUVaRfjvvU9LKYQVEUDpeFeHeHH68f\ncnM0PFRowBbf8wN2p3VIURR27Xfw8pvVNNqDWBO0fOPRQSyam3TLtjs4XUE+2tLApm0NeFoljAY1\nK5emsXJZGqnJt25Fx81CURTOV3opKg5HeDY0hSusEuK1LF+cyvxZNnKHx92y77dAIBAIBj5pVhM/\neGw6v3zjCJ8dqMTZGuDxpblYTCIBSyAQxEaIEt0gVnvGgwtGdFto6I7HQvtJZpMr+sTX4wuycff5\na8ZmxqK7vhXHztjbVuJ74v/QnkjXcNvB6g77yJeLDiaPSmmruvCdq6TkoW8RrG9i8E+/R/oTayIe\nX2qoRrf5z6h8bkJTliBNWICkKKzfUsru4zVtfhlGvYavL05japYSFiEScsAQH/XcFQUuOnWcs+tQ\nAaNS/GRdR9xnS6vMO9v9HD8jodfBlxYZmDWhd9URcO3WoTPnW3l+fSWnyz1otSpWL0/nS/dmYDbd\nmmkKtfV+NmyuY9vOJgJBhYR4LY+szuTuRanCeDECNXU+ioodfFFsp7om/Nk0m9QsnptEQX4SE8fG\no9EIIUIgEAgENwZbvIHvPzKVP2w4yf5TdVRUOvnmfePJzbHe7KEJBIJbGPGUfw26Ux1wLaGhPdfy\nWOg8yZSi2BX4AnKXtonulPC3J1blRnuuCA49qfRoT0+TRPacqOXBBcPR1NVTsuZpgrUVRRxwAAAg\nAElEQVQN5Pzob8n42tqI+6vqL+DZ8Vfw+wnOvBd5dD4Ab2wt7yB8aNSwNi+OqVkKngDEpQ8DXfR+\n+qAEJfUGmlq1GC7HfSZcR9znsYoQ72z34/YqDM9Ss3apkeTE3ntHxLqu+0820nhBz47ddhQF8qcm\n8pWHs8lM61kqy42i7EwLL7x2jt37HcgKpKXouf+udJbMS+6x8ebtTpMjwM59DnYWO6g43wqAXqdi\n9gwrBfk2pk9KRK8T10wgEAgEN4d4s57/9fAUdhyv4dVPSvjFa4dZPX8Yy2cNQS1aBwUCQQSEKHEN\nulMdEEto6CwUxIpR9AclDpXW92h8h8saWVUwnPeLznYr/aM9sSo32nNFcOhOpUckepok4gtIvPX6\nHib/7j8IXKoj+5+eIfObj0XcV1Vdju7z9aBIhOY9iDxsMtD1Wpr1Kr6z2MrYLAPnG4O8stfD9x/X\nEW2K3uJXc7LWgC+kxmaSGJvuQ9/L4oJWn8J7n/s5VBpCq4H7CvQUTNFd9x/mSNdVkcHvNOC0Gzgv\n2xk8yMhT67KZNC7hul6rP1AUheOnW3h3Ux1HT7YAMDTHxAPL05mTZxMr/O1ocYfYc8BJ0T47J0vd\nKAqo1TB1QgIF+Tbyp1lv2eoXgUAgENx5qNUqHi4cTXaSmT9+cJJ3Pj9LyQUHX1s5nsQ40YYpEAg6\nIkSJa2AyaLFaDDjc0asDIgkNWo0qZq9/pBjFZre/R2kbEBZG1n9Wxq52hpjXMp9sT3tBpcnli7hP\ne8HhWpUekehuRcYV4lqc5LzyRwLOJrK//y2ynnki4n7q88fR7nwb1GpM9z2FP35w27b21zItXsPf\nLLWRadVy6IKP//m8maCkRG03qXVpKWvUIysqBl+O++ytfnD6fIg3t/pxeRRy0tWsW2okPalvVrHb\nJ7QoCgQ9WrwNJuSgBrVG4avrBrFicdotN7mXZIW9B52893EdZy6EV/qnTbJyb2EKU8bHCwPGy3h9\nEvuPNFNUbOfIiRZCUri/aeyoOAryk5gzw0pigujTFQgEAsGtS26OlR9/NY/nPzrNsTNN/PiFfXxj\n5TjGDk262UMTCAS3EEKUiEJ7b4dIggR0rQ5oLzS8tqWsWzGh7TEZtG1mj93FajFQctERcduV9pJY\ntBdU7C4fWw5WcayiKargEKvSIxrdrcgAiHM3c9+7f8TS3ETCt54g62+eirifumw/2uKNoNMTXPQY\nuuHjoaGlbXuixUBSvJ5ks8IzS2zEG9V8fMzNOwfcKISNQju3m8gKlDfqqXHp0KgVJqT7SImTrjnm\nSPgCChuL/Ow9GUKjhuWz9SyarkPTh0aDBp2GOJOehsYQrQ0mQq06QMFg9TNitIaVS9N7ddyetgF1\nl0BQZvuuJjZ8Uk9NvR+VCmZPt7J6RTpzZmbQ0O79u1MJhmQOHw9HeO4/0oz/shfKsMEmCvKTmDfT\nJow+BQKBQDCgiDfr+e6XJvHpvkre+fwMz75+hJVzh3Lf3GHCgFkgEABClIhKZ2+H9iQnxK4O6G1K\nhdcf6pEgATBmiI09UWJDr7SXZHfjOAadhszkOB5fNhr/omtPSiNVesSifYWF3eVDFUF8MXtcrHz3\njyQ2N3EwbzGWmYWM6XwgRUFzsgjt4c9QDHEEC7+MkpQVcXwP5KeQN0hCpYIXdzZTVOZt2z41N7XD\nufmCKk7WGWjxa4jTS0zI8GPS9S7us6IqxBtb/NhdCpkpah5ZaiArte9L65ucfi6Ugas+HlChNQcx\np3rRGGQCkgF/UOqRqNCdJI/e4GkN8cn2Rj78rB6nK4RWq2Lp/GTuvzudQRnRPT3uFCRZ4WSpm6Ji\nO3sOOPG0hoWwzDQDBbNsFOQnkZ0prpMgOl6vhF6vvuWqogQCgeAKapWKu/MHMyonkT+8f5IPdp2n\n9KKTb9w3Hlv8rel3JRAIbhxClIhALFHBatHzf5+YQbw5+mplb1MqEi0GknvQ5pCTZmHtkhGUXnT0\n2HwyFj0VHLpD+wqLs9XNPPv6kQ7bTa0t3Pvu/2B1NnJ4+kL2z7qL5MupH20Ta0VBc+hTtKd2opgT\nCS59AiUhpeuLKQp4GpgzWCYgqfjvbU6OXgy3phj1GuZOzOggKNlbNZyqMxCSVaTHB8lNCfQq7jMQ\nVPh4d4Cio0HUKijM07F0ph5tH08UJElh844GXn33Eq1eLWqdhCnViy7uaiqIo8UfMw0lEtdK8ugp\ndkeADz6r59MdjXh9MmaTmtXL07l3aRpJ1ju77UBRFMrPtVK0186u/U4czUEAkqw6lsxLpiDfxoih\nZtHKIuiCoig0NAU4Xe7hdLmbkgo3F6t9zJyayD8+E7syTiAQCG42I7IS+fGTebz0cQkHyxr40Qv7\n+Nq945g0IvlmD00gENxEhCgRgViigssTwOsPxRQlYnko6HUaLObIE7JYbQ5qNcidgh8q6918sOtC\nr8wnbzTtWwKGD0rscH2MrW7ufe9PJDnqOTqlgOI5y0Gl6ijgyDLa4g/QVBxETkghWPgExCV2fSFF\nBtcl8LtArUOfNJin12hpcLSCSkWq1dR2TRQFLjh0nHeE4z5zU/xk9jLu83yNxPrPfDQ6FdJsKtYt\nNTI4o++v/ZGTLl54vYrKah8mo5rk7CCSydNlzD0VpHpb3ROJqhof72+q4/M9dkKSgi1Rx5qVGSxb\nkEqc+da4H28WF6u9FBU72LnPQW19+P63xGlYtiCFgnwbY3MtfdriIxj4SJLC+SovJeXuyyKEhyZH\nsG27Xq9i/GgLi+aIB3qBQDAwiDPq+PbqCWw7VM0b28r59VtHWZ4/mNXzh6PtzaqQQCAY8AhRIgK9\njb68QixxwReQeL/oXNSV50hGkpNGJnOkrB6HO9hl/53Havj5t+Z0+Z1I7SX95RUQi2gtAVNGpbD1\nYDUGr4d73/8TyU21HJ88lz0F93Jlht12raUQ2p1vo7l4Ejkpi+CSL4MxruuLySFwVkLICzoTJOaA\nWosByE6L77BrUILT9QbsrVoMWpnx6b2L+wyFFDYXB9h+KAgKLJiqY/lsPTpt304sqy55+eXvz7D/\nSDMqFSydn8wjD2Tx8b5zbDng6bJ/TwWp3lb3tKf0jIf3Pq5l35FmFAWy0g2sWp7OwtlJ6C5HVN6M\ne/BmU9/op6jYwZ6DpZw5H36vjAY182fZmDcziSkT4tFpxUOYIIzXJ1F2xkNJRbgSovSMB5//6neT\nNUHL7OlWxoyKY+woC8NyzGj7+PtGIBAI+huVSsWS6dmMHJTI7zecYFPxRcoqnXzz/vGkJJpu9vAE\nAsENRogSEeht9GV7VhUMZ+exS/gCXSe6sVae27c5NDi94eV8lYodh6ojvo4vIPHm1nKeundcVPNJ\nSZJ5bUtZB2Fg0ohkCmfkkJRg7NfJYbSWgMXTB7FsrBXbT57D1ljDyYmz2TX/Ptov+U/NTcFACN32\n9ahrziCnDyW48FHQd+2vD/lawX4O5CAYEiEhE1SRJ3od4z5DjE339yrus6peYv1nfmqbZJITVKxd\namT4oL69lq1eibc21vDhlgZCIYVxuRaeWpfN8CFhgaA3aSiR6K0QpygKh467eG9THSdL3QCMGmZm\n9Yp0Zk61tq3695dfxa2KsznI7gMOvtjroPRMWIjQalXkTUlk/iwbMyYnYjTcGaKMIDZNjgAl5R5O\nV4QrIc5XejtUxWVnGhk7Ko4xoyyMHWUhI1Uv2noEAsFtw5CMeH70RB6vbC6l+FQdP3lxP0+uGMvU\n3NSbPTSBQHADEaJEFK53suduDbQ553fmWivPkizzzudn2iZwtng9ep0afzDy8UouOtq8FyId84WN\nJ7sIA9sPX2L74UskR5kctl/RBnq1uh2rJeDU8UrWffoy3toq4h68F+09D5N81tHxWs/NQrflZdSN\nlUjZowkVPAzaCK0vATfOc9UgSxCXCuYUovVg1FyO+1QUFUNsAYbaeh73KUkKWw8E+Wx/AFmGORO1\n3DvXgEHfdxMFSVbYvrOJv757iWZXiPRUA49/KYs5M6wdJiS9SUOJRE+FOElS2LnPwfub6jhfFTYQ\nnTohgQdWpDN+tKXLpKmv/SpuRTytEsWHnHxRbOf4qRbksJ7IxLHxzM+3cc9d2fi9kWN3BXcGsqxQ\necnX1oZxutxNfePVGGitVkXu8HAFxNhRcYweaSHBIv5MCwSC2xuTQRuOCR1i47XPyvjtu8cpnJHN\nmoUjRSWhQHCHIJ52onC9k73raQHpPIGztwSi7guxTQ39QYm9J2qi/m7nyWH7Fe0mlx+jXg2o8Aek\nHq9uR2sJ0Pl9zH7zz3hrL2JZvYLhz/5vxht0HUv7g63otryI2lmPNGwyoTmrQR3h+nsd0FKDolJB\nwiAwRvCZACQZKhr11LTo0KoVxqb7SO5F3Gdtk8Rrn/qoblBIjFPxcKGB0UP69mN0qszN8+srOXvB\ni0Gv5pHVmTz16Ahcrtaov9MX5qTdEeJ8fomtRU1s2FxPQ1MAtRrmz7Kx6u50hg2O/Prd8asYqPgD\nMgeONlNUbOfQMRfBUDixJXe4mXn5SczNs7WZeiZYdDQIUeKOwh+QqTjn4XS5h5KKsBBxJV0Fwn4i\neVMSw5UQIy2MGGpGrxMP4AKB4M5DpVIxf3IWw7MS+P37J9hyoIryymaeXjWe9D42XxcIBLceQpS4\nBr2d7PW2BSTWBC4asUSOZrc/3AZyDa5MDt/5/EyHMbdvP+np6nYkYUYX8HHPB8+TXnuR8xPz+HTQ\nfGzP72sTO9JsZmixo9/yEiq3g9DoWUh5y7u2YigKuOvAaweVBuvQ0Ti7WisA4A2qOFlrwB3QYNFL\njO9F3KcsK2w/FGDTngCKoiIQasDlq+VgeTIjc/qmBaGhKcArb1Wzc58DgAWzk3jswSxSkvQYbkCp\nfywhzuUOsWlrAx9trafFLaHXq1ixJJX7lqWRnhrbY6U7fhXdia29VQiFFI6ecrGz2EHxYSdeX/gz\nkjPISMFMG/Pyk8hME/FmdyLNrmBbBcTpCg9nz7cSkq5+12SmGZg5NZGxoyyMGRnHoAwjamFsKhAI\nBG1kp1r4v1/J49XPyth5vIafvLifJ5aPYebY9Js9NIFA0I8IUaIf6U0LSKwJXDRiiRyJFgOpVhP1\njtjChKPFR4PT2y1BpLtpDJ2FGW0wwPIPXiSj5gLluVPYtuBBFLW6g9jx6PREdFtfRuVtITRpEdKk\nRV1bMWQZXFUQcINGD9bB6Mzx4GnpMoYmj4bT9eG4z4z4IKN6EffZ6JRZ/5mP8zUyshKiNXCOoOTE\nE6BPWhB8fon3NtXx/qY6AkGFkcPMPLUumzEjLb0+5vXQXoirb/Tzwaf1bPmiCX9AxhKnYc3KDO5Z\nkkpiQvdiPa/XOPZWQJYVSio8FBXb2b3ficsdAiAtRc+KJTYK8pMYki2Mue4kFEXhUp0/3IpxOZ7z\nUt3Ve1yjgeGDzZe9IMKVELbEOzsKVyAQCLqDQa/hyXvGMnaIjVc2l/KHDSc5fcHBuiWj0N8hJtkC\nwZ2GECX6kd60gCRaDNji9RFbNpLiDUwelcKxiqZuixwGnYZZEzL5oOhszNe1xRtBUboliHQ3jQGu\nCjPHTl4i/90Xybp0jnO5k9m27GGUTtUFjopydE1HUAV8hGasQBo7u+sBpSA0V0LIB7o4SMyO2Nah\nKHDeoeOCQ4dKBbmpfrISQtccb3tkRWH3sSAf7QoQCAEqJ67Wsyh0PE5PIzOvjlFhZ7GDl9+qpskR\nxJao4+kvZbFgdtJNXz29UOXlvU11FBXbkWVISdLx6F1ZFBYkYzL27Dz7wjj2ZqAoCucueikqtrNz\nn4NGezj9JjFByz1LUpmXb2P0iDhhOniHEAzKlJ7xXBYhwpUQrpar3wVmk5qpExIYezkVY9SwOAwG\n0YohEAgEvWX2hAyGZsbzhw0n+fzIJc5UN/OtVRPITI6QwCYQCAY0QpS4AcRqAWnvo6DVqHjn8zO0\n+iN7HUwbncojhbn4F/UsVvHJleNp9QY4XNZIkytyT/vU3BRSbeaoK9rt6cnqtkatZu3cwUz5n1/j\nrjqDcXEBW8auQOkkJIw32Pm26QQEZYJzHkAeMbXrwYLesCAhh8BohfjMiIaWQQlO1xmwe7UYtTLj\nM/zEG3oW9+lokXlji5/ySgmzEe6ereKVzWVEavroiUhzhYpzHp5fX0VJhQedVsWD96Tz4D0ZPZ7w\n9yWKonCqzM17m+o4eMwFhFsSHliezryZSdcVO9hXKSE3gkt1PoqKHRTttVNdG/4smE0aFs9LpiDf\nxsQx8Wg0Qoi43XF7Qm0ixOlyDxXnWwm0a2dLTdZTkG9rM6XMGWRqS5sRCAQCQd+QmRzH//nydF7f\nVsH2Q9X85KX9PL5sNHMnZt7soQkEgj5EiBI3iUgRiWajjsp6d5d9jXoN8yZltk3geupzodFcrdiw\nu3xsOVgVsdpCo1ZHXdFuT09Wt2Wfn/Kv/QPunfuwLi0g579/hvXlgx2EjzxjPd9JOgWo8M59GM2w\n8V0P5G+B5ipAAUs6mJIiChIun5qTdQb8ITVJl+M+e7IQrygK+06F2PCFH38Qxg3VsGaJAYNe4cM9\n19+CYHcGefWdarbtsgMwa7qVr6wZRMZN9CCQZYX9R5p5d1MdZZfjK8eOimP18gymT0rok6qNvkoJ\n6S8a7QF27XNQVOzgzIWwoahep2LODCsF+UlMm5QgDAhvYxRFob4xcDmW00NJuZuL1VcFXLUKRgyz\nMHKoqa0SIiVJfxNHLBAIBHcOOq2Gx5eNZuxgGy9uOs3zH52m5IKDR5flYtSLqYxAcDsgPsn9QIcU\niSgTr0gRidEqFOKMWh5cMOK6zRQNOg2ZyXE8vmx01GqL9ivadpcPgz68LRCUery6LfsDlH/j+zRv\n303ikrnk/O7faAnITBqRzPbDlwBYYK7ha9YS/IqGHSmLWdxZkFCUsJmluw5Qhds1DAldXktRFC65\ntJQ36FGAobYAQ3oY9+nyyLy51c/p8xJGPTxcaCBvrLatPP96WhCCQZmNn9Xz1sZafH6ZodkmnlyX\nzcSx8RH37849dL0EQzKf77Hz/id1VNeE7728KYk8sCK93/ws+iIlpK9wuUPsORAWIk6VuVEUUKth\n2sQECvJt5E+1YjLdOsKJoO+QJIXzld7LVRDhVAy7M9i23aBXM3FsPGNGxjFulIXcEXEMGWyloaGr\nb41AIBAIbgwzxqQxOCOeP244wa4TtZytcfH0/RPISbs5HlwCgaDvEKJEHxKp+iFShGZPEzZiRX5e\nC39QoqbRgxSUOkxuo00OI61oAz2eIMvBEBVP/4DmLTtJWJDPoXVf58+vHGq7LjlpFvKlMlYbS3HL\nOranFrLk7lkdD6Io0FILPgeotZCYA7quZoKSDAfOKpxvMFyO+/STbO5+3KeiKBwpD/HOdj9eP4zK\n0fBwoQFbfEcRqDctCIqisO9wMy++UUVdQ4B4i4YnHs6hcH5KxFLvaPfQMw9FaGfpJa1eiU8/b2Tj\np/XYnUG0GhWL5yax6u50cgbd3maNXp/EvsPhCM8jJ11Il2+TcbkWCvJtzJlhIyFefC3ebni9EqVn\nwxUQp8s9lJ314PNfbcWwJWqZPcPK2JHhVoyhOebralcSCAQCQf+QZjXxg8em8/aOM3y6v5J/eeUA\n6wpHsWBylvB4EggGMOLpuw+JVP0QKZ2hpwkbvUko6DC5bfGTFB9ZIIlGZ9GiJ4KIHAxx5tv/hHPz\n5yTMm8nhx7/FZ8fq27Y3uXws5jSr4i8QMsbDoi+zLCWj00GkcLtG0ANaY1iQ0HR1rr8a9wkWg8T4\n9J7FfbpbFd7Z4eNYhYReCw8sNDB7ohZ1hD9sPW1BuFDl5fn1VRw/3YJGAyuXpvHQfRlY4qJ/7KLd\nQ2aTnlVzh3b7vCLhbA7y4ZZ6Nm1rpNUrYTSouW9ZGiuXpd3WpejBoMyhEy6K9trZf7SZQCB8fwwf\nbKJgVhLzZtpu6/O/E2lyBDqkYpyv9CK3+1rIyTK2xXKOHWUhPVUvHmYFAoFggKDVqFm7ZBRjBtt4\n/qNTvPJJKSUXHHzl7jGYDGJqIxAMRMQnt4+IVf1wqLShQzpDrIjESPQmoaC7Aklfo4RCnH3mhzg+\n2kb8nOkM+fN/8Pxfj7RtV6HwRGIZhZZL1MtmTEufQm9N7ngQKQDOi+H/6i2QkB2uq+9Eo0dDyeW4\nz2FpkB3n61Hc5/EzId7e5sftVRiWpWZtoZEUa88Fm864WkKsf/8Sn+5oRFbC7QBfXZtNdqYx5nFj\n3UN7T9SwfGZOr1o5aup8vL+5nu07mwiGFBLitTyyOpPli1NjCiQDGUlWOFnSwhd7Hew95MTTGi6J\nyEo3UJAfjvAcdI33QzAwkGWFyku+Dq0Y9Y1X04t0WhWjR4YjOa8IEfGW2/O+FwgEgjuJKaNS+MmT\nM/nDByfZd7qe8zUtPL1qPEMzurb5CgSCWxvxZNZHxKp+sLf4+evmUp5YMQaNWh0zIjE7NQ6PN4TT\n4yeplwkFrf4gRUcvRdzW2/jKK8TyOlAkibN/82PsGz/DMnMKuS//J00B2q6LBpmnbaeZY67nQsDC\nL+yT+UfFRFr7gwRbwVkJihQ2s7SkdzG0vBr3qUetUhid6mfScBMN3eyI8foV3vvcz8GSEFoNrJyn\nZ/4U3XUbOoZCCp9sb+D1DTV4WiUGZRj46tpspk9K7Nbvx7qHGp3eHrfwnDnfyrsf17L3oBNZgfRU\nPavuTmfR3GQM+tvPtFFRFMrOtlJUbGf3fgeO5nBcY7JNR2FBMgX5SQwfYhIr4gMcv1+m/LynrQqi\n9IynTXQCiLdoyJuS2GZIOWKIGZ0wKRUIBILbkqQEI/+wbiobdp7joz0X+NdXDvLQ4pEUTs8Wf+8F\nggGEECX6iGtVP+w6UYvJqG2rUujsT2C1GIgz6fD4gjjcfqwWPZNGJHW73eIKkizzry8fxB+MHIHZ\nm/jKK8eN5ZehSBJn//6nNL33CZbpkxj919+giTOTqJdISjDQ0tLK3ySdYIrRTqk/kWebJmKKj+/Y\nluJrBtclQIH4jLAo0YmABKfrjDi8ml7FfZZcCPHmFj/NHoWcNDVrlxrJSL7+CcvhEy5eWF9FVY0P\ns0nDV9cOYvniVHTa7h871j2UYjV1q4VHURSOnWrh3Y/rOHY6bMo3fLCJ1SvSmT3ddltGWV6o8lJU\nbGfnPgd1DeEV8niLhmULUyjItzFulKVPEkQENwenK0hJuYeSinAlxJkLrW1eIACZaQbypyYyZlS4\nEmJQhkE8iAoEAsEdhFaj5sEFIxidY+VPH55i/ZZySi44+OqKsVhMXVt/BQLBrYcQJfqIWNUPV2hf\npdDZn+CTfRfZcfhqdYPTHWD74Uuo1SoeXTq62+N4bUs5NfbWqNtt8YYe+1NA9HYQSVZYNn0QLf/8\nS+xvfUTc1PGMfu05NJY4IHxd8kcmMv3iHsYYmjniS+I39gkEFA1zrrSlKAq0NoKnAVRqSMgBQ1cn\n5Q5xn+YQY9O6H/fp8oTY8IWPI+XhTpC7Z+lZPF133ZP06lofL71RxYGjLtQqWLYwhUdWZZKY0PM/\ngrHuoVkTMmNWt0iywt4DTt7dVMvZC14AJo2NZ/WKdCaPi7/tJml1DX527nNQVGznQlU4utFoULNg\ndhIF+TYmj0sQRoUDEEVRuFTrD7diVIQrIWrqrop0Gg0MH2wOt2GMimPsSAvWRPHAKRAIBAKYMDyZ\nnzw5k//54CSHyxu5+OI+vnn/BEYO6l7FqkAguHkIUaIPeXjxSFp9IXafqI24PVKVgkGnIdFiYO/J\nuoi/s+t4LV9aOLJb7Rb+oMSRssaY+4wZbOtx60Ysr4PPD1UiPfsc404U4xs6jMl/fQ5NfDtBwetm\nXeBz1IZmDgYz+a09l4R489W2FEUGVw34m0GtA2tO2NiyHYoCl1xaKhovx30mBRhi7V7cpyTL/Hnj\nRcouxAMGwMvoIS0snjE0YvpFd/G0Sry1sYaPtjQQkhQmjLHw5Npshg2+vrjLaAkfT64cj93u6bK/\nPyCzfVcTGzbXU1vvR6WCOTOsrF6ezshhcdc1llsNZ3OQXfsdfFHsoOxM+FpotSrypyZSkJ/EjMmJ\nGAyiTH8gEQzKnLnQyunLlRAl5R5c7lDbdrNJzdQJCW2tGKOGxYn3OAI3IkJYIBAIBgJWi4HvrZ3K\nh7vPs2HXOf79r4d4cMFw7sofHNHEXCAQ3BoIUaIP0ajVPH7XaEovOiKW4EdL0WhwevEFIkdY+gIS\nDU4v2anXzmBudvtxuqObZ+p1atYt7bnJZVSvA0Vhzo4NjDtRTGNKFhuXfYWqg3U8UnhZkXY70W15\nEXWLHSk3j1FTlvPPrcGrD85yKOwfEfSC1hQWJNRXb0l/UMLR4qcpkEiDR49WrTAu3U9Sp7jPaLGn\nwZDCr16vp64pbKTpC13CF6xm90kFsynUK8NPSVbYWtTEq+9ewtUSIi1FzxMPDWLWdGufVCNES/jQ\ndHLwdHtCfLK9kQ+31NPsCqHTqli2MIVVd6WRmX77GDh6WkPsPRiO8Dx+ugVZAbUKJo+LZ16+jdnT\nrcSZxdfYQMHtCVFyuQKipMJD+VkPwdDVWIzUZD3zJ9jaDClzBpmuSzy83eluDLVAIBDcSajVKu6b\nN4zcHCt/3HiSt3acoeSik6fuHUuCWaRtCQS3IuJpvo+JVYIfNUVDuUaE5bW2X8Zk0JJo0eN0ByJu\nnzshA3MvopIieh0oCnOKNjLh+B6akjPYuPrr+I3mthYVo6cJ3daXUbW6CE2YjzSlEINKRZrhcql1\nyA/NF0EKgiEBErLCrRtcfdAurXIzeeIkkqx6An4PeSMV4vRXJyixYk+r6hVe+9RHo9OCrHjxBM4i\nyVcrDXpj+HmytIXn11dx7qIXo0HNow9kcd9daej7wUQvWsJHkyPAxk/r2byjEZ9fxmzS8OA96dxT\nmIbtNilj9/tlDhwNCxEHj7sIXZ605o6Io2CmjbkzbbfNud7OKIpCXUPgsheEh8Et/A0AACAASURB\nVNMVbiqrfW3b1SoYmmO67AURTscQ0aw942alLAkEAsFAYMwQGz95ciZ//vAUx8828eMX9vHN+8Yz\nerDtZg9NIBB0QogS/UC0EvxoKRqpNjNGvRpfoKtho1GvIfUappTtJ+fRBImcNAuPxKiSiFX+20Vo\nURRm7fyISUd2Yk9KZ+Pqb+A3hVsFHC0+vFXniN//Fip/K86xi1FPmI+hfRVBwAPNleHWDXMKxKV2\nSNh4Y1sFJdVB5uXPQq/XUXrmPPuPnKRxWlaHB+3ID+TVVNfHU9cUj6KAL1iLN1gFdLy2PTH8rG/0\n8/Kb1ew+4ARg4ZwkHn8wiyTbjZtAna/08MJrF/hij52QpGBL1PHQfZnctTAFs2ngl2uHQjIHjzVT\nVOyg+JATnz/8fg0eZKQgP4l5M21kpPXcC0Vw4whJChXnPG1eECXlHhzNwbbtBr2aiWPj21oxcofH\n3Rb37s0iVlvd9aYsCQQCwe1CglnP366ZzObii7zz+Vl+sf4w988bxr2zhwoTbIHgFkKIEv1AtBL8\naBh0GuZMzGTbweou2+ZMzLjmg2XnyXl7bBYDsydlsnreUEKSQlNzKyaDFq8/RKLFgFaj6lb5b5vQ\nUtrAyM3vM+XwFzhsaWx84Bv4zFdbS2ZaPaTtXY8SCrLeN4GPtygk7dt79Zj+ZmipCe8cnwUma4fx\n+gISrUoii+cNJyRJ7Nx3mLMXwufW/kE70gO5RmXGbBhObaMZW7yKBxfpeOHjOrwRkkiitdJ0GItf\n4t2P6nj/kzqCIYXc4WaeWpdD7ogb59VQUuHmvU117DvcDMCgDAOrlqezYFbSgI85lGWF0+Vuiood\n7D3opLkl7CWQlqLnnkIbBflJDMk23eRRCqLR6pUoOxOugDhd7qHinAev7+pnzZaoY/YMK2NHWRg7\nMo6hOWZhPtqHxIoQ7m3KkkAgENyOqFUqls8awqgcK3/ccIL3i85RetHJ11eOw9oL83eBQND3CFGi\nH4lWgh+JdUtGoVapOFTagKPFjy3ewLTRqRGrK9pXNQBRV8tsFgM/fjKPIdk2/uvNwxwua6DJ5Uet\nAlmBpHg9cSY9lfXutt+JVv57RWiZe2ALdQe240/PYOO9X8Nrjm/bZ7qxgW+ZTyGH4LdN49jvS207\n5tYDVUzOkBifpoBKA4nZoO84uQ+E4FiNkZHDh+Nye/h89wEcza627e0ftDs/kBu1WRh1WahUavyh\ner5yTzo5afqet9IQnix/UWznr29foskRJMmq4/E1WczPT7ohqrqiKBw85uK9TXWcKgu/N+NGx3Pf\n0lTypiQOaGVfURTOXvRStDcc4dnkCK+kJ1l13FOYSkF+ErnDzbddWsjtQKO9XStGuZsLlV7kdp1l\nwwabGTXM3NaKkZ6qF+9jPxIrQrg7oqtAIBDcaYwclMiPvjqTFz8+zeHyRn78wj6+vnI844d1jaAX\nCAQ3FiFK9ILOokBfuJ53p7oikqnZ6MG2qKtlzR4/Xn+IFzae7DAxvzKRsLcEsLdEbveIVP5b/as/\nUffcCwTS0vn0oadpVZnbBI67kxp51HQSlVbLf7sms993VazQa+BrC6yMT1OQ1TrU1iGg7dj60OxT\nc6rWgF9SU1dfz7bdBwkGQx32af+gfeWB3NGiIk4/HK3GgiwHcPvPkWjxk2YbAvS8labsrIfn11dR\ndsaDTqtizb0ZrF6RjsnY/2XQoZDCzn123ttUx8XLvffTJiawekU6C+dm0tjovsYRbl2qa3wUFdsp\nKnZw6XLEo9mkYcm8ZArybSwsyMJhH7jnd7shyQqV1d42U8rT5R4amq5+V+i0KsZcNqMcO8rC6BFx\nDB9mo6Gh5SaO+s6iV/5FAoFAcIdjMel45oGJbDlYxZvbKvjVG0dYMXsIqwqGCYNggeAmIkSJHtBZ\nFDDoNYCCLyCTfA3X8+5GtsWqrojkobD7RC1GvSZieoct3ojJoGXviZoen2vn8t9Lv32R6mf/SDAl\nlTfveRK3KvxzWYG74ip53FSBojdRn7eGPW9Wth0n0aTmu4U2hqXqKKnxk5yTQ2o7QaJz3OewpACX\nzlV3ESSg44O2TqMmI2koUjD+cnVEI97ABRQkJo8a1LZfd1tp7I4Af3nnEjt224FwrOZXHhpEWkr/\nrzb6/BKffdHExk/raWgKoFbD/Fk2Vi9PZ2hO+DoPxBXnRnuAnfscFBXbOXvBC4Ber2JunpWC/CSm\nTUxoa0HRagbe+d1O+P0y5eeuChClZzy0eq9+p8RbNMycmsiYkWFTyhFDzAO+feh2oKeiq0AgEAjC\nz1RLZ+QwclAif9hwgo/2XKC00snT940nKeH2STATCAYSQpToAZ1FgfZCQLS2h55EtsUSLmKZmgVD\nkeNEp+am4PWHaHB6e3aidKxKqPnvV6j62e/QDcrgo9XfwK2+4iGh8ED8eR5MOE+zbECz+AlM1nSS\nEuppcvnJSdLy3UIbyRYNRWWtfHQ8wE++dlVwkWQobTBQ79aiUyuMS/dhM8tkX+NBu9Ep8/oWH5fq\nE1EI4vGfISg52o4baXobTewJBGU+2FzPOx/V4vPLDM0x8dQj2UwYHR/hKH2LqyXER1vr+XhrA26P\nhF6v4p4lqdx3V9oNEUP6A1dLiN0HHBQVO9paTzQamD4pgXn5NvKnWDEJc8ObjrM5yOmKsBnl6XI3\nZy+2IrX7GslMNzBrupWxlyshsjIMA1IYu93pqX+RQCAQCK4yLDOBHz0xk5c/KWF/ST0/emEfT907\njikjU2720ASCOw4hSnSTWKJAezq3PXQnsq07wkUsUzNJhswkM4GQ3GUSH5IUUq0m6h09EyauVCXU\n/s+rVP7Lc+gz00l9/tdUfXwRABUKjyeWc5elmrqQkZ83TeHvtFbSLpcU19c28PSiRIw6NW8faOHj\nYx4KZ2S3XZfWgIqTdUY8ATXxBonxGX6M2nBfSbQHbUVR2H0syMadfgIhQOXE1XoWhY5VFUfKm/jS\nQinmw7miKOw96OSlN6upbwyQEK/lq2uzWVKQjKafPRvqG/1s2FzPlqJGAgEFS5yGh+/LYMWSNBLi\nB95H0uuVKD7ipGivg6OnXG2T23G5FubPsjF7um1AntftgqIoVNf6LydihCshauqvfpdoNDBiiJmx\noyyMGRluybCKyNUBRU/8iwY6ZWVlfPvb3+aJJ57gscceY//+/fzqV79Cq9ViNpv5xS9+QWJiIn/+\n85/55JNPUKlUPPPMMyxYsOBmD10gENyCmI1anr5/PGOH2li/pZzn3j7GsrwcvrRwBFqNqAgUCG4U\nYqbQTWKJAu1p3/YQS8jYeayGVQXDMRu03RIuYpmaQVg0+dFX89pSNa62L8CsCZl8UHS2W+dpsxiY\nPiYsiNS98AYXf/yf6NJTGPPW71HlDCJpZx1Ol5dv2EqYZ66jMhjHvzdORmNJbKusWDs7CdwhJFnh\n99scnLWrKJyR3Vbp0ODWUNJgQJJVZCUEGZkSIJIO0P5B29Ei8+YWP2WVEiYDrJyl4i+flqF0/bVr\nOs+fu9jKC69XcaLEjUYD99+VxpqVmcSZ+3eF8XxlK+9tqmPnPgeyDKnJeu5blsb/Y++9o+M6z3Pf\n357eMIOZwaADRAfYO8EG9iqLEqlOFSuWfJKsOF5O4sTH8bWT+Pqk+MjJufGN73Esyz62IoqyClVs\nURIpSiLEAlHsINHBijrADMr0tu8fg0oCIEiC/futpUVo6rcLNvb3fO/7PGuW2dFp76zVzXA4xpGT\nPew96OLLE92EQvEjkT/JQFmplSULrCTZbl5kqmCQcDhGwznfQCtGdb2HXs9gGYRBr2TOdHPcD6LI\nRGGOEa1W3HgJbn98Ph8/+tGPWLRo0cBj//zP/8xPfvIT8vLy+PnPf85rr73Gxo0bef/999m+fTse\nj4cnn3ySpUuXolTeWddZgUBwc5AkiRWzMshPj7dzfHToAnUXu3j+K1NIT7p5iWsCwb2MECXGyZVE\ngX6Gtj2MJWQEQlFe3VXL0+uLx5U1r1UrKcm2sq+ydcTXdnnippYjTcSf2zQVnz80kL4xGokmDf/w\n3HwSDBraf/sG577/AmqHnZLXf44uLxuAeYVWpp45xFx9J3UhMy90zMArq1lTlIRWpYDeFhR+NyiV\nRMwZPLw+d0AkicnQ0KnmQpcGhSQzOTlASsLIrSf9yLLMoaoI7+wNEgjB5Bwlj67SotPK/OHg1TnP\nd/eE2fZ2C7s/6yAmx1sKvvZ4JhlpN65/UJZlTtV4eOv9No5WxpNEJmXq2LwxhaXzbbdVROKVfE+i\nUZmT1b0DEZ79ngMZqVrKSm0sLbWSkSp6MW82vZ7IgCFldb2H+jM+wpFBuc5h1zB7mjkezVloIitd\nd0cnuAjuXTQaDS+++CIvvvjiwGNWq5Wuri4Auru7ycvLo6KigrKyMjQaDTabjYyMDOrr6ykuLr5V\nQxcIBHcAWckmfvDsPF75qJZ9la38/a++YM28TB5YkoteK6ZMAsGNRPyGjZOxnM6HMtSM0WLSYk3Q\njJpwUX3ejdPtG1W4cPUGaGzqJi/DglatZOvaIg7XthMIxS577VgRcErl8HaI9w+eZe/xy8WNeSXJ\ncUHilbc5+91/QWW3UvL6/0ZfkEMwHKXH3cPW6H7U+k6qI3Z+0jkFQ4KJRUVJPL4iF7ovQMgDSi0k\nZqNRqknWxz87FIHTbTq6Akr06hhTUwKYtCPVOQzS443x+p4gp89E0arhsdVaFkxRDfS2j9d5PhyJ\nsXOPk9feacXnj5KRpuW5JzKZM90y5vdfD7GYzBdHu3nr/VbqzviAeDvDQ/elMGe6+bbqzx+rfUgh\nSdQ0ePm8ws2+Q266euKtMnarmrXL7SwrtZGbrb+ttuduRpZl2pyhvioID9X1Xi40BwaeV0iQk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KQRqlkh6nilCXjmhEQq2C9SuSeHBDCmnJt9+k+2JLgPIKF+UVblra4uek0aBkTZmdslIrU0sS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\nVGRkMBbL2WsKOBzw1H3JmEzQxxlIZIQVs7lnrXJwOEJ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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "M8H0_D4vYa49", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality." + ] + }, + { + "metadata": { + "id": "QU5sLyYTqzqL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Is There a Standard Heuristic for Model Tuning?\n", + "\n", + "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n", + "\n", + "That said, here are a few rules of thumb that may help guide you:\n", + "\n", + " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n", + " * If the training has not converged, try running it for longer.\n", + " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n", + " * But sometimes the exact opposite may happen if the learning rate is too high.\n", + " * If the training error varies wildly, try decreasing the learning rate.\n", + " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n", + " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n", + "\n", + "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify." + ] + }, + { + "metadata": { + "id": "GpV-uF_cBCBU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Feature\n", + "\n", + "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n", + "\n", + "Don't take more than 5 minutes on this portion." + ] + }, + { + "metadata": { + "id": "YMyOxzb0ZlAH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "ecfd8b4a-9619-4778-e603-bc827ec26458" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00005,\n", + " steps=1000,\n", + " batch_size=5,\n", + " input_feature=\"households\"\n", + ")" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 226.59\n", + " period 01 : 216.24\n", + " period 02 : 206.77\n", + " period 03 : 198.22\n", + " period 04 : 190.35\n", + " period 05 : 183.71\n", + " period 06 : 178.61\n", + " period 07 : 174.29\n", + " period 08 : 171.27\n", + " period 09 : 169.40\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 112.0 207.3\n", + "std 85.9 116.0\n", + "min 0.2 15.0\n", + "25% 63.0 119.4\n", + "50% 91.4 180.4\n", + "75% 135.3 265.0\n", + "max 1359.3 500.0" + ], + "text/html": [ + "
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SQghRrrBgExGhpfcVDg8xe9rPClHT7FlnOHDN3WR+uZrgXl3puPIDAtq29M3O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\nMAXXbAxVUGTXsPukmQMWE4oCbaOs9Ig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cMKaV2hQLgiAIbYJISnRwaSk2MlNtVDSQmJCl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PH6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\nfoP3Jqo4cjRId0c/g11DKNEINrM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mean119.5207.3
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4F9DJylfIylfo31OH2dj4m+Ljp8s5mV7OhNERhAQ3z3T3ohIHmz/PJTzUwIQx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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 59ee2593969ef92fb8e9d40a4325cddcf653513f Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 2 Feb 2019 19:47:49 +0530 Subject: [PATCH 03/14] Rename first_steps_with_tensor_flow.ipynb to asg2_first_steps_with_tensor_flow.ipynb --- ...tensor_flow.ipynb => asg2_first_steps_with_tensor_flow.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename first_steps_with_tensor_flow.ipynb => asg2_first_steps_with_tensor_flow.ipynb (99%) diff --git a/first_steps_with_tensor_flow.ipynb b/asg2_first_steps_with_tensor_flow.ipynb similarity index 99% rename from first_steps_with_tensor_flow.ipynb rename to asg2_first_steps_with_tensor_flow.ipynb index 36f1490..95b3f6e 100644 --- a/first_steps_with_tensor_flow.ipynb +++ b/asg2_first_steps_with_tensor_flow.ipynb @@ -2002,4 +2002,4 @@ ] } ] -} \ No newline at end of file +} From a4affc74a0d8d56dac5f1551d6ca613f59b1d353 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 2 Feb 2019 19:48:33 +0530 Subject: [PATCH 04/14] Rename intro_to_pandas.ipynb to 01_intro_to_pandas.ipynb --- intro_to_pandas.ipynb => 01_intro_to_pandas.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename intro_to_pandas.ipynb => 01_intro_to_pandas.ipynb (99%) diff --git a/intro_to_pandas.ipynb b/01_intro_to_pandas.ipynb similarity index 99% rename from intro_to_pandas.ipynb rename to 01_intro_to_pandas.ipynb index 1ce6ce5..900bf07 100644 --- a/intro_to_pandas.ipynb +++ b/01_intro_to_pandas.ipynb @@ -1627,4 +1627,4 @@ "outputs": [] } ] -} \ No newline at end of file +} From 9ba94cda2277d5c28e2e5d3ce5900ce3b6345b63 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 2 Feb 2019 19:49:31 +0530 Subject: [PATCH 05/14] Rename asg2_first_steps_with_tensor_flow.ipynb to 02_first_steps_with_tensor_flow.ipynb --- ...ith_tensor_flow.ipynb => 02_first_steps_with_tensor_flow.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename asg2_first_steps_with_tensor_flow.ipynb => 02_first_steps_with_tensor_flow.ipynb (100%) diff --git a/asg2_first_steps_with_tensor_flow.ipynb b/02_first_steps_with_tensor_flow.ipynb similarity index 100% rename from asg2_first_steps_with_tensor_flow.ipynb rename to 02_first_steps_with_tensor_flow.ipynb From b555d6f59e1637bc6c248c3aa4c6571ebd985bcf Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 2 Feb 2019 20:11:33 +0530 Subject: [PATCH 06/14] Created using Colaboratory --- 03_synthetic_features_and_outliers.ipynb | 1366 ++++++++++++++++++++++ 1 file changed, 1366 insertions(+) create mode 100644 03_synthetic_features_and_outliers.ipynb diff --git a/03_synthetic_features_and_outliers.ipynb b/03_synthetic_features_and_outliers.ipynb new file mode 100644 index 0000000..0478867 --- /dev/null +++ b/03_synthetic_features_and_outliers.ipynb @@ -0,0 +1,1366 @@ +{ + "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": 439 + }, + "outputId": "312936a7-cbca-4401-d054-5894bd9bdf12" + }, + "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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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "273 -116.6 34.2 14.0 6438.0 1719.0 \n", + "12296 -121.5 38.5 18.0 700.0 169.0 \n", + "1882 -117.3 33.9 35.0 3623.0 841.0 \n", + "8577 -118.5 34.5 5.0 15341.0 2527.0 \n", + "6308 -118.2 34.0 41.0 1576.0 339.0 \n", + "... ... ... ... ... ... \n", + "2484 -117.6 34.1 26.0 3271.0 595.0 \n", + "2787 -117.7 34.1 16.0 1458.0 295.0 \n", + "14336 -122.1 37.4 27.0 3410.0 1156.0 \n", + "5317 -118.1 33.8 37.0 3242.0 698.0 \n", + "9125 -119.0 35.3 19.0 7092.0 1517.0 \n", + "\n", + " population households median_income median_house_value \n", + "273 1586.0 691.0 1.6 67.4 \n", + "12296 260.0 128.0 2.9 152.9 \n", + "1882 2721.0 766.0 2.2 86.9 \n", + "8577 7270.0 2320.0 6.1 236.2 \n", + "6308 1252.0 302.0 2.0 98.1 \n", + "... ... ... ... ... \n", + "2484 2259.0 566.0 4.0 132.0 \n", + "2787 912.0 331.0 3.6 160.4 \n", + "14336 2314.0 1086.0 3.5 165.6 \n", + "5317 1080.0 629.0 3.9 432.5 \n", + "9125 4101.0 1436.0 2.1 74.8 \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": 955 + }, + "outputId": "e30af6d2-42de-4df4-9183-4746caf340b8" + }, + "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=0.05,\n", + " steps=1000,\n", + " batch_size=10,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 189.60\n", + " period 01 : 151.64\n", + " period 02 : 133.24\n", + " period 03 : 131.75\n", + " period 04 : 133.61\n", + " period 05 : 134.01\n", + " period 06 : 136.28\n", + " period 07 : 135.80\n", + " period 08 : 135.88\n", + " period 09 : 133.84\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 206.0 207.3\n", + "std 95.0 116.0\n", + "min 46.6 15.0\n", + "25% 168.7 119.4\n", + "50% 202.8 180.4\n", + "75% 231.8 265.0\n", + "max 4531.2 500.0" + ], + "text/html": [ + "
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9gKGeutHdCnTmFCUl4s3rC/G3p+pw2M18eUlRosMRERmxysdmYjKhuhIiIiKD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\nY1wqeZnxbD9YSUVdK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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i5Ul3zf5QYvW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Leaz2oYMQcBf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 975 + }, + "outputId": "32665418-6b24-41a9-ea19-13fa7360517d" + }, + "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": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.73\n", + " period 01 : 189.65\n", + " period 02 : 169.09\n", + " period 03 : 153.50\n", + " period 04 : 140.57\n", + " period 05 : 133.65\n", + " period 06 : 131.20\n", + " period 07 : 130.55\n", + " period 08 : 130.99\n", + " period 09 : 130.68\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 192.5 207.3\n", + "std 87.1 116.0\n", + "min 46.2 15.0\n", + "25% 158.3 119.4\n", + "50% 189.5 180.4\n", + "75% 216.1 265.0\n", + "max 4159.7 500.0" + ], + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean192.5207.3
std87.1116.0
min46.215.0
25%158.3119.4
50%189.5180.4
75%216.1265.0
max4159.7500.0
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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 130.68\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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4bkcO00d2D3RIQgghRJsmlRJ+UtOYsjUTEgDWIycAMA7odzYpoTFgd6lwulWE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\n1feT0FC1AJFA9CbbaDs89rNkl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9ZOcukKHFsKNPLYUqDeilBPA2eMt9H+Sis8lNW+FuBA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\n9eu5+uqrARg7dizr1q0LV3mt3rBhw3j22WcBaNeuHTU1NWq/0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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZjQrZ8mcHFiU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Identify Outliers\n", + "\n", + "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values. Ideally, these would lie on a perfectly correlated diagonal line.\n", + "\n", + "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n", + "\n", + "Do you see any oddities? Trace these back to the source data by looking at the distribution of values in `rooms_per_person`." + ] + }, + { + "metadata": { + "id": "P0BDOec4HbG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 574 + }, + "outputId": "4afcd3ef-4336-4a7e-aaff-f17dcd27f24b" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "plt.figure(figsize=(16, 9))\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "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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NM6GXXp+0/LjDIxM6sG+r/F6PervbFx+bnInmtA4AAAAA6xdhtAB+r0e7t7Xo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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ClBJR9oDpLss", + "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": "aQyNg9wspLOE", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "ee57c00a-67b1-41cc-d68a-e07c1479d1d3" + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 1)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist(log=True)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jByCP8hDRZmM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "s0tiX2gdRe-S", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "e7dd4990-8e47-4c0c-9ce5-85d87a454f89" + }, + "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": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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MRiYqEBGpQOhABAANK2pgNhnUv5DwLUVEVJiEf7w27TqlydxNNBrj0BwRkQqEDkRaZs1x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\nSDNlsIn0EGGatX5EKdlExUXIQDTyj0V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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kMQD0Uq3RqTX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n", + "\n", + "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:" + ] + }, + { + "metadata": { + "id": "POTM8C_ER1Oc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "12766f41-f136-47e1-95eb-4b7ab6d0e33b" + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "9l0KYpBQu8ed", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Clip Outliers\n", + "\n", + "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n", + "\n", + "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n", + "\n", + " clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n", + "\n", + "The above `clipped_feature` will have no values less than `0`." + ] + }, + { + "metadata": { + "id": "rGxjRoYlHbHC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "4429ab4f-ddd6-422c-c679-54c90ce661c1" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = california_housing_dataframe[\"rooms_per_person\"].apply(lambda x: min(x, 4))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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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": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "51d7b811-1c10-4585-c96c-071ae99d60fc" + }, + "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": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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spKZfQ6FODzOAQp0eqenXsHEPn2gSkXPUdOMQi0T4dPt5VFaxGwcREbVsTEoQ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JSf/vsgb/y+n87u8eJ33ZpUWkZ9LhtEWsai0j+Y4GhrmGMpIeJ8//jnyOW0Ub/VlxYhqtd5sNuF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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "0ee25f32-d49b-4f92-c865-03d77ad77916" + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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GoiyOfjDlBq7UOFIkVMu8gOxutcljQzCaVCwNmm5C0SRePtCPiaS0qIvNYsK3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\no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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From db0341aa9eafb19ca453c4ef5601e454e0c1fdeb Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 9 Feb 2019 02:24:28 +0530 Subject: [PATCH 07/14] Created using Colaboratory --- 04_validation.ipynb | 1555 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1555 insertions(+) create mode 100644 04_validation.ipynb diff --git a/04_validation.ipynb b/04_validation.ipynb new file mode 100644 index 0000000..dd950f3 --- /dev/null +++ b/04_validation.ipynb @@ -0,0 +1,1555 @@ +{ + "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": 317 + }, + "outputId": "256acaa0-7238-4ae1-df49-9f3540375705" + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 33, + "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 35.6 -119.6 28.6 2631.0 537.1 \n", + "std 2.1 2.0 12.6 2145.4 415.0 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1455.0 297.0 \n", + "50% 34.2 -118.5 29.0 2115.0 432.5 \n", + "75% 37.7 -118.0 37.0 3135.0 644.0 \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 1426.5 499.3 3.9 2.0 \n", + "std 1137.4 379.9 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 788.0 282.0 2.6 1.5 \n", + "50% 1160.0 408.0 3.5 1.9 \n", + "75% 1720.0 603.0 4.7 2.3 \n", + "max 35682.0 5189.0 15.0 52.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 33 + } + ] + }, + { + "metadata": { + "id": "JlkgPR-SHpCh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "7cb7ed10-8dcf-459d-b710-1814afbd5515" + }, + "cell_type": "code", + "source": [ + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "training_targets.describe()" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 12000.0\n", + "mean 205.7\n", + "std 115.2\n", + "min 15.0\n", + "25% 118.8\n", + "50% 178.9\n", + "75% 262.5\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 34 + } + ] + }, + { + "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": 317 + }, + "outputId": "90ac504b-c3bc-4451-e222-639a1ca437b3" + }, + "cell_type": "code", + "source": [ + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_examples.describe()" + ], + "execution_count": 35, + "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 35.6 -119.6 28.5 2674.1 545.0 \n", + "std 2.1 2.0 12.6 2260.7 436.7 \n", + "min 32.5 -124.3 2.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1473.0 295.0 \n", + "50% 34.2 -118.5 28.0 2165.5 437.0 \n", + "75% 37.7 -118.0 37.0 3185.2 658.0 \n", + "max 41.9 -114.6 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 5000.0 5000.0 5000.0 5000.0 \n", + "mean 1437.0 505.8 3.9 2.0 \n", + "std 1172.7 395.5 1.9 1.3 \n", + "min 6.0 2.0 0.5 0.2 \n", + "25% 791.0 281.8 2.6 1.5 \n", + "50% 1185.0 413.0 3.6 2.0 \n", + "75% 1725.2 610.0 4.9 2.3 \n", + "max 28566.0 6082.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "metadata": { + "id": "oVPcIT3BHpCm", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "de79bbef-1f97-4cb2-ef4f-5d3c46e9eb70" + }, + "cell_type": "code", + "source": [ + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "validation_targets.describe()" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 211.2\n", + "std 117.7\n", + "min 22.5\n", + "25% 121.9\n", + "50% 184.1\n", + "75% 270.7\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + } + ] + }, + { + "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": "15606ebc-315d-4dad-bb84-d5e15b7b7ce7" + }, + "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": 37, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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FNJ75plvToKTg4s6WDCzJPvAEuzmARgghRM8opXhns01dfRynywxTKOTy3y9G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H/3gmKr52eSbjc0wsQ8uNxgfkry5KsB3QG03+ixvR0zNe3h+w7aCuJYoFPFTitlu1/hSqvOcX+F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\nL3LZ2RyzvvTR1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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", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # 1. Create input functions.\n", + " training_input_fn = lambda : my_input_fn(features=training_examples, targets=training_targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda : my_input_fn(features=training_examples, targets=training_targets, shuffle=False, num_epochs=1)\n", + " predict_validation_input_fn = lambda : my_input_fn(features=validation_examples, targets=validation_targets, shuffle=False, num_epochs=1)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # 2. Take a break and compute predictions.\n", + " training_predictions = np.array([item['predictions'][0] for item in linear_regressor.predict(input_fn=predict_training_input_fn)])\n", + " validation_predictions = np.array([item['predictions'][0] for item in linear_regressor.predict(input_fn=predict_validation_input_fn)])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zFFRmvUGh8wd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "4c3248eb-ce28-4b34-b762-92a839c7d0fc" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n", + " learning_rate=0.00003,\n", + " steps=500,\n", + " batch_size=8,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 58, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 215.67\n", + " period 01 : 198.14\n", + " period 02 : 183.70\n", + " period 03 : 173.74\n", + " period 04 : 168.16\n", + " period 05 : 164.94\n", + " period 06 : 164.46\n", + " period 07 : 164.38\n", + " period 08 : 164.88\n", + " period 09 : 166.29\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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w6/Z+lFaWStghERFR08QAU09d21ijfesWuByXhavx6s0msjBsgaHOg5BfXoCD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\nCoFgGN19XtjdXjhcHth7vZhdmIN1i20z3hYGGCISLYVchgV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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "I-La4N9ObC1x", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Xyz6n1YHbGef", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i1imhjFzbWwt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "ea110519-ef7b-4d39-e659-45460ee45c1f" + }, + "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": 54, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 215.70\n", + " period 01 : 198.61\n", + " period 02 : 184.44\n", + " period 03 : 175.04\n", + " period 04 : 168.12\n", + " period 05 : 164.80\n", + " period 06 : 164.28\n", + " period 07 : 164.72\n", + " period 08 : 165.81\n", + " period 09 : 166.52\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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tjJ0QlX4FN7MUnxhMRETUVDDA1FF/32awNtXDscgkJGfUZZ+klyBAwPa43ais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\n6A1YXFaxSyOiQY4BhiiCZIKAW2dn4SczRqKm0Y4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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "65sin-E5NmHN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 5: Evaluate on Test Data\n", + "\n", + "**In the cell below, load in the test data set and evaluate your model on it.**\n", + "\n", + "We've done a lot of iteration on our validation data. Let's make sure we haven't overfit to the pecularities of that particular sample.\n", + "\n", + "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n", + "\n", + "How does your test performance compare to the validation performance? What does this say about the generalization performance of your model?" + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "08ba4e4a-bf63-46fe-e5d3-d8fc41bcad05" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_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 = np.array([item['predictions'][0] for item in linear_regressor.predict(input_fn=predict_test_input_fn)])\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): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 60, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 162.173\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 c096b9590fbf47e87a822dd4be192999df418f23 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 9 Feb 2019 15:59:44 +0530 Subject: [PATCH 08/14] Created using Colaboratory --- 05_feature_sets.ipynb | 1512 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1512 insertions(+) create mode 100644 05_feature_sets.ipynb diff --git a/05_feature_sets.ipynb b/05_feature_sets.ipynb new file mode 100644 index 0000000..18e5309 --- /dev/null +++ b/05_feature_sets.ipynb @@ -0,0 +1,1512 @@ +{ + "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": 1225 + }, + "outputId": "9d021e27-39c8-4717-afae-4659bff36620" + }, + "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 28.5 2629.3 536.8 \n", + "std 2.1 2.0 12.6 2123.5 415.4 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1457.8 295.0 \n", + "50% 34.2 -118.5 29.0 2127.5 435.0 \n", + "75% 37.7 -118.0 37.0 3160.0 651.0 \n", + "max 42.0 -114.3 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1428.6 498.8 3.9 2.0 \n", + "std 1149.2 379.5 1.9 1.3 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 789.8 280.0 2.6 1.5 \n", + "50% 1170.0 410.0 3.5 1.9 \n", + "75% 1729.0 606.0 4.7 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": 379 + }, + "outputId": "2bda4fbf-410d-412e-b562-66de77a4f2b6" + }, + "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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target-0.1-0.00.10.10.0-0.00.10.70.21.0
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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 0.0 \n", + "housing_median_age 0.0 -0.1 1.0 -0.4 \n", + "total_rooms -0.0 0.0 -0.4 1.0 \n", + "total_bedrooms -0.1 0.1 -0.3 0.9 \n", + "population -0.1 0.1 -0.3 0.9 \n", + "households -0.1 0.1 -0.3 0.9 \n", + "median_income -0.1 -0.0 -0.1 0.2 \n", + "rooms_per_person 0.1 -0.1 -0.1 0.1 \n", + "target -0.1 -0.0 0.1 0.1 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "latitude -0.1 -0.1 -0.1 -0.1 \n", + "longitude 0.1 0.1 0.1 -0.0 \n", + "housing_median_age -0.3 -0.3 -0.3 -0.1 \n", + "total_rooms 0.9 0.9 0.9 0.2 \n", + "total_bedrooms 1.0 0.9 1.0 -0.0 \n", + "population 0.9 1.0 0.9 -0.0 \n", + "households 1.0 0.9 1.0 0.0 \n", + "median_income -0.0 -0.0 0.0 1.0 \n", + "rooms_per_person 0.0 -0.1 -0.0 0.2 \n", + "target 0.0 -0.0 0.1 0.7 \n", + "\n", + " rooms_per_person target \n", + "latitude 0.1 -0.1 \n", + "longitude -0.1 -0.0 \n", + "housing_median_age -0.1 0.1 \n", + "total_rooms 0.1 0.1 \n", + "total_bedrooms 0.0 0.0 \n", + "population -0.1 -0.0 \n", + "households -0.0 0.1 \n", + "median_income 0.2 0.7 \n", + "rooms_per_person 1.0 0.2 \n", + "target 0.2 1.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "RQpktkNpia2P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n", + "\n", + "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n", + "\n", + "Use this information to try removing features. You can also try developing additional synthetic features, such as ratios of two raw features.\n", + "\n", + "For convenience, we've included the training code from the previous exercise." + ] + }, + { + "metadata": { + "id": "bjR5jWpFr2xs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jsvKHzRciH9T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g3kjQV9WH3pb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "varLu7RNH3pf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters." + ] + }, + { + "metadata": { + "id": "DSgUxRIlH3pg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 639 + }, + "outputId": "8c012e04-3f21-4346-f6f4-8a08d04861da" + }, + "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=0.03,\n", + " steps=1000,\n", + " batch_size=32,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 113.29\n", + " period 01 : 107.56\n", + " period 02 : 101.99\n", + " period 03 : 97.35\n", + " period 04 : 93.92\n", + " period 05 : 90.85\n", + " period 06 : 88.79\n", + " period 07 : 87.27\n", + " period 08 : 86.26\n", + " period 09 : 85.36\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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RRoV/jPKCXCbBD3uu4dZ97Y3uBEHA6BbD0dKmGcJTIrAtag97xBAREUGPBUxO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\nIjnba6+9xn/+53/y3//937jd7kQPR4A9e/bQ1tbGnj176OrqwmazkZ+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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": "7b3004ac-b464-45e7-e5c4-9a82f28d3edf" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Yx80r6vM6L7WYmAqgp39M9Du58KmS0OxsQ65m2GY1wWJmsX13H37w86OS9cCZ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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": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44, 2), range(34, 46, 2))\n", + " selected = pd.DataFrame()\n", + " selected[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " \n", + " return selected\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pZa8miwu6_tQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "PzABdyjq7IZU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n", + "\n", + "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`." + ] + }, + { + "metadata": { + "id": "xdVF8siZ7Lup", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U4iAdY6t7Pkh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "7398f00c-651d-489e-c4ca-5d5b7c07c691" + }, + "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": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 226.59\n", + " period 01 : 216.40\n", + " period 02 : 206.30\n", + " period 03 : 196.31\n", + " period 04 : 186.44\n", + " period 05 : 176.70\n", + " period 06 : 167.14\n", + " period 07 : 157.74\n", + " period 08 : 148.57\n", + " period 09 : 139.72\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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t2c97Xx9m76FS7vrpeCKc9nbbLw2ax8xhE3l+5xskF6WSVZHNTeOvYVbklH49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\njh+wdkg2rpECxg3SqdQl2ahJclGXZPP5HA4nhy+W8cbRq7S0d2MNC2D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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 7e647f6aed23a95767b2b2b9fc58ad8c2fc72b37 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 9 Feb 2019 16:58:03 +0530 Subject: [PATCH 09/14] Created using Colaboratory --- 06_feature_crosses.ipynb | 1572 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 1572 insertions(+) create mode 100644 06_feature_crosses.ipynb diff --git a/06_feature_crosses.ipynb b/06_feature_crosses.ipynb new file mode 100644 index 0000000..cba125f --- /dev/null +++ b/06_feature_crosses.ipynb @@ -0,0 +1,1572 @@ +{ + "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": 1225 + }, + "outputId": "fe341c57-8730-4ee4-aac0-4806cdd77299" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2657.5 543.0 \n", + "std 2.1 2.0 12.6 2214.9 428.7 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1465.0 297.0 \n", + "50% 34.2 -118.5 29.0 2127.0 435.0 \n", + "75% 37.7 -118.0 37.0 3162.2 651.0 \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 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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": 622 + }, + "outputId": "f14ebb1a-5b68-4ba1-96fd-7b1003e89427" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 299.32\n", + " period 01 : 202.80\n", + " period 02 : 111.48\n", + " period 03 : 135.97\n", + " period 04 : 114.12\n", + " period 05 : 174.68\n", + " period 06 : 128.59\n", + " period 07 : 125.25\n", + " period 08 : 115.15\n", + " period 09 : 121.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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JO+64g4iICFeVddHif76p3fF7wphMJgb2tFNdU8vGXbnuLE1ERKTFMBnHL6lp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\n8ABm8+EZxXa7naKiIl8dXgSQhWnn8cLBUrYdKvjWADOSPB4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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", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0FfUytOTNJhL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "c1838d94-4f0d-4ec3-9b90-8ffc8c13b54f" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 168.50\n", + " period 01 : 142.27\n", + " period 02 : 125.86\n", + " period 03 : 114.71\n", + " period 04 : 106.83\n", + " period 05 : 100.96\n", + " period 06 : 96.52\n", + " period 07 : 92.96\n", + " period 08 : 89.97\n", + " period 09 : 87.47\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RC3P06KFG9Vu69EukpaXWu/2tt157XCE9dixgiIiIWpD09DQcPLi/UX0XLFgIFxfXerd/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\nIy8vL92HQwm//OUv8fzzz+Ovf/0r7r33Xjz44IMsL4LYvn07jh49ilg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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 = long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xZuZMp3EShkM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "c04e86a6-5d9c-43e9-c2e7-dae76daff17f" + }, + "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": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 162.21\n", + " period 01 : 133.89\n", + " period 02 : 116.83\n", + " period 03 : 105.57\n", + " period 04 : 97.60\n", + " period 05 : 91.86\n", + " period 06 : 87.42\n", + " period 07 : 83.83\n", + " period 08 : 81.01\n", + " period 09 : 78.60\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+1p14u3l0xsPGnSOJYSTlJRu5QiGEEKJ+kgBTi9ydrOkT6ElSej6HI5MAUKvU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\nTqUuyUZNksvC8vkDnGrtp6axj5pmNyO+8wuBy0tsFOdYWFl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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 42542aa8274b88bb5a90de748345c8fb45260576 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 9 Feb 2019 17:22:39 +0530 Subject: [PATCH 10/14] Created using Colaboratory --- 07_logistic_regression.ipynb | 1637 ++++++++++++++++++++++++++++++++++ 1 file changed, 1637 insertions(+) create mode 100644 07_logistic_regression.ipynb diff --git a/07_logistic_regression.ipynb b/07_logistic_regression.ipynb new file mode 100644 index 0000000..3f331a7 --- /dev/null +++ b/07_logistic_regression.ipynb @@ -0,0 +1,1637 @@ +{ + "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": 1225 + }, + "outputId": "10c71569-62f5-4243-9524-8a6b3ea397d7" + }, + "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 2640.9 538.8 \n", + "std 2.1 2.0 12.6 2195.2 423.6 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1462.0 296.0 \n", + "50% 34.2 -118.5 29.0 2124.5 433.0 \n", + "75% 37.7 -118.0 37.0 3137.0 648.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1423.8 500.2 3.9 2.0 \n", + "std 1129.2 386.4 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 788.8 281.0 2.6 1.5 \n", + "50% 1166.0 408.0 3.5 1.9 \n", + "75% 1705.2 605.0 4.8 2.3 \n", + "max 28566.0 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": 741 + }, + "outputId": "4b307e0f-bc9c-421c-efde-74a558c79c3f" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 0.45\n", + " period 01 : 0.45\n", + " period 02 : 0.45\n", + " period 03 : 0.45\n", + " period 04 : 0.44\n", + " period 05 : 0.44\n", + " period 06 : 0.44\n", + " period 07 : 0.44\n", + " period 08 : 0.45\n", + " period 09 : 0.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IzUoSHCGEEOI2k5mZwa+//lKvc+fOfRpvb58bPr9o0TvNFVazapVbNQghhBDixt55ZzFx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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": 450 + }, + "outputId": "fda8cdcb-4ec0-44fa-aa60-52e2a18aac35" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "plt.hist(validation_predictions)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([9.000e+00, 2.882e+03, 1.825e+03, 1.990e+02, 5.300e+01, 1.700e+01,\n", + " 9.000e+00, 4.000e+00, 1.000e+00, 1.000e+00]),\n", + " array([-0.43400019, -0.12780265, 0.17839489, 0.48459243, 0.79078997,\n", + " 1.09698752, 1.40318506, 1.7093826 , 2.01558014, 2.32177768,\n", + " 2.62797523]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "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", + " \"\"\"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", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VM0wmnFUIYH9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "d3c92abe-385b-4aad-abfe-18b12e44d8ad" + }, + "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": 13, + "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 : 0.53\n", + " period 08 : 0.55\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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lpREQEFC9PGbMGJo1a4aLiwvR0dEcOHCArVu3MmDAAAA6depEWloalZWV9ipR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\nocheSpQpgnCjd2zTl2RmkuTBrF4SG3WSuMwso5+e+IhAslP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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": 51 + }, + "outputId": "1a3b7208-0a64-4c0c-bede-1190e0251e3d" + }, + "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": 14, + "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": "96694471-f180-4509-a9f3-07d3bb3a93b7" + }, + "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": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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6WkJCQtHpdOza9SVWqw2z+dK/o8TE9CY1dRWKolBWVmrvVr9dGnHKlGkcPXqIpCTDddWQ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\njdERw5nTdwYe0v0KIcRFJISvw67j3y1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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": 656 + }, + "outputId": "00ded91e-188f-4482-9959-90bcbb464c10" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.59\n", + " period 01 : 0.58\n", + " period 02 : 0.56\n", + " period 03 : 0.56\n", + " period 04 : 0.55\n", + " period 05 : 0.54\n", + " period 06 : 0.54\n", + " period 07 : 0.53\n", + " period 08 : 0.54\n", + " period 09 : 0.53\n", + "Model training finished.\n", + "AUC on the validation set: 0.75\n", + "Accuracy on the validation set: 0.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ZfDWhDQk0ulXyj43vOrscERGRVlGYkWYeHTkdo86fI9Y9rNy72dnliIiInJXC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\n1+RxuPw4ObWnCPYIws/Vtwdf0Q91mDv4a/q71LU18HDiEqK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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 3bc11bcd40f420cf71230031fbe85b84c980cd64 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Sat, 9 Feb 2019 17:40:04 +0530 Subject: [PATCH 11/14] Created using Colaboratory --- 08_sparsity_and_l1_regularization.ipynb | 1141 +++++++++++++++++++++++ 1 file changed, 1141 insertions(+) create mode 100644 08_sparsity_and_l1_regularization.ipynb diff --git a/08_sparsity_and_l1_regularization.ipynb b/08_sparsity_and_l1_regularization.ipynb new file mode 100644 index 0000000..e12bd94 --- /dev/null +++ b/08_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": 1225 + }, + "outputId": "c5ca1522-992f-478c-cc73-7964ba32bd43" + }, + "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 2643.8 540.4 \n", + "std 2.1 2.0 12.6 2167.6 421.0 \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 2127.0 433.0 \n", + "75% 37.7 -118.0 37.0 3142.0 651.2 \n", + "max 42.0 -114.3 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1432.9 502.3 3.9 2.0 \n", + "std 1165.7 385.3 1.9 1.2 \n", + "min 8.0 1.0 0.5 0.1 \n", + "25% 792.0 282.0 2.6 1.5 \n", + "50% 1166.0 409.0 3.5 1.9 \n", + "75% 1729.0 607.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gHkniRI1Ijna", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bLzK72jkNJPf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_buckets(feature_values, num_buckets):\n", + " quantiles = feature_values.quantile(\n", + " [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n", + " return [quantiles[q] for q in quantiles.keys()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "al2YQpKyIjnd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + "\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"households\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"longitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"latitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"housing_median_age\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"housing_median_age\"], 10))\n", + " bucketized_total_rooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_rooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n", + " bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_bedrooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n", + " bucketized_population = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"population\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"median_income\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"rooms_per_person\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"rooms_per_person\"], 10))\n", + "\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n", + "\n", + " feature_columns = set([\n", + " long_x_lat,\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_total_rooms,\n", + " bucketized_total_bedrooms,\n", + " bucketized_population,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hSBwMrsrE21n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Calculate the Model Size\n", + "\n", + "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works." + ] + }, + { + "metadata": { + "id": "e6GfTI0CFhB8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def model_size(estimator):\n", + " variables = estimator.get_variable_names()\n", + " size = 0\n", + " for variable in variables:\n", + " if not any(x in variable \n", + " for x in ['global_step',\n", + " 'centered_bias_weight',\n", + " 'bias_weight',\n", + " 'Ftrl']\n", + " ):\n", + " size += np.count_nonzero(estimator.get_variable_value(variable))\n", + " return size" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XabdAaj67GfF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Reduce the Model Size\n", + "\n", + "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n", + "\n", + "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n", + "\n", + "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?" + ] + }, + { + "metadata": { + "id": "G79hGRe7qqej", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Task 1: Find a good regularization coefficient.\n", + "\n", + "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n", + "\n", + "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n", + "\n", + "Again, the model will train on the entire data set, so expect it to run slower than normal." + ] + }, + { + "metadata": { + "id": "1Fcdm0hpIjnl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " regularization_strength,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " regularization_strength: A `float` that indicates the strength of the L1\n", + " regularization. A value of `0.0` means no regularization.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 7\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on validation data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " # Compute training and validation loss.\n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9H1CKHSzIjno", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "outputId": "5c5d6f3a-8c94-41ae-9bd7-fa5d203a9e67" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.05,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=0.1,\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)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.35\n", + " period 01 : 0.31\n", + " period 02 : 0.29\n", + " period 03 : 0.28\n", + " period 04 : 0.27\n", + " period 05 : 0.27\n", + " period 06 : 0.26\n", + "Model training finished.\n", + "Model size: 801\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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bAGdnZ6qqqmhokGmQHwV4OjBnXAglFbV8sSulTWPZ6WxZFBENwKcJX1BVX2WK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\nzVqYOXbsGNu2bQNgwYIF9PX1MTAwgN0e+KF65ZVXQv+enp6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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 cf41d70b20bdf137ccc5186516e6b792bf5c1619 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Mon, 18 Feb 2019 15:37:50 +0530 Subject: [PATCH 12/14] Created using Colaboratory --- 09_intro_to_neural_nets.ipynb | 1178 +++++++++++++++++++++++++++++++++ 1 file changed, 1178 insertions(+) create mode 100644 09_intro_to_neural_nets.ipynb diff --git a/09_intro_to_neural_nets.ipynb b/09_intro_to_neural_nets.ipynb new file mode 100644 index 0000000..be9adf1 --- /dev/null +++ b/09_intro_to_neural_nets.ipynb @@ -0,0 +1,1178 @@ +{ + "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": 1225 + }, + "outputId": "78626e6a-396c-465b-dca2-05993c25a15b" + }, + "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 2629.1 539.1 \n", + "std 2.1 2.0 12.6 2183.1 421.2 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1449.8 296.0 \n", + "50% 34.2 -118.5 29.0 2118.5 435.0 \n", + "75% 37.7 -118.0 37.0 3142.2 648.0 \n", + "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1428.4 500.9 3.9 2.0 \n", + "std 1146.5 384.3 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 788.0 281.0 2.5 1.5 \n", + "50% 1166.0 410.0 3.5 1.9 \n", + "75% 1720.0 605.0 4.7 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": 656 + }, + "outputId": "e9cfa5b6-bf1d-4e83-c4d2-ae67b3e90a7b" + }, + "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 : 168.57\n", + " period 01 : 161.43\n", + " period 02 : 151.47\n", + " period 03 : 141.02\n", + " period 04 : 132.65\n", + " period 05 : 120.15\n", + " period 06 : 118.78\n", + " period 07 : 109.28\n", + " period 08 : 112.21\n", + " period 09 : 108.46\n", + "Model training finished.\n", + "Final RMSE (on training data): 108.46\n", + "Final RMSE (on validation data): 106.32\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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69KW6fusmGy9t41zyBVSoeLZmS/rW74GNsTUAqZm3WbEzgrBLyRgZqhncsQGd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\nMUF8umk/s97ZSnNrmwerFfl+CjAiIl3M5YljubXfzTS02nl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+ "text/plain": [ + "
" + ] + }, + "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": "dd359d5e-f70a-446d-bd06-841a63c3b8c4" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "# YOUR CODE HERE\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 107.19\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 6a9a6bf8d0d261e7135af95cf39f9d9cf97a9c38 Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Mon, 18 Feb 2019 15:55:30 +0530 Subject: [PATCH 13/14] Created using Colaboratory --- 10_improving_neural_net_performance.ipynb | 1748 +++++++++++++++++++++ 1 file changed, 1748 insertions(+) create mode 100644 10_improving_neural_net_performance.ipynb diff --git a/10_improving_neural_net_performance.ipynb b/10_improving_neural_net_performance.ipynb new file mode 100644 index 0000000..8a91f06 --- /dev/null +++ b/10_improving_neural_net_performance.ipynb @@ -0,0 +1,1748 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "improving_neural_net_performance.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "jFfc3saSxg6t", + "FSPZIiYgyh93", + "GhFtWjQRzD2l", + "P8BLQ7T71JWd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"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": 1225 + }, + "outputId": "f522eb8b-869d-4653-c637-e1d75b7418da" + }, + "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 2623.9 534.4 \n", + "std 2.1 2.0 12.6 2169.8 417.5 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1446.0 295.0 \n", + "50% 34.2 -118.5 29.0 2115.0 431.0 \n", + "75% 37.7 -118.0 37.0 3139.2 645.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1416.5 496.1 3.9 2.0 \n", + "std 1113.1 380.1 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 783.0 279.0 2.6 1.5 \n", + "50% 1163.0 406.5 3.5 1.9 \n", + "75% 1704.0 599.0 4.8 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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": 775 + }, + "outputId": "26252ad4-11aa-4b1e-aa20-8ad7ddf34afc" + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 156.61\n", + " period 01 : 143.80\n", + " period 02 : 128.29\n", + " period 03 : 115.05\n", + " period 04 : 108.51\n", + " period 05 : 110.58\n", + " period 06 : 109.64\n", + " period 07 : 104.83\n", + " period 08 : 104.72\n", + " period 09 : 105.76\n", + "Model training finished.\n", + "Final RMSE (on training data): 105.76\n", + "Final RMSE (on validation data): 105.75\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IWBDaA0qFATYfS0ZGbrkOKyUiImobGGBaiZu5C8Z3DURxTQnWJ27V2m1lbmqE\nZ8b3QL1KxLe74lBbp2pkJCIiImKAaUXBXUfD3bwrLuZdwZnsGK0+Xw9rjOnnjKz8Cmw8lqyjComI\niNoGBphWJBEkmOs9E8YyOTYn7UBORa5Wf0SAO5xsTHAsNhMXrzf+ZX5EREQdGQNMK7MytsDMblNR\nq67D6vgfUa+u1/QZyKT40wQfGMgkWLUvEUVlNTqslIiISH8xwOhAPztfDLbvj/SyTOy5eUirz9nG\nFDNGe6C8qg7f7YmHuu1tEiMiImpxDDA6Mt1rAmyMrXA4/TgSC69r9QX4OaGPhzUS0opw8Hy6jiok\nIiLSXwwwOiKXyTHfZxYkggRr4zegvPbOnakFQcC88d1hbmKIbSduIvV2qQ4rJSIi0j8MMDrkYtYZ\n4a7BKKktw/8St2htrTZTGOLZMG+o1CK+3RmH6tr6RkYiIiLqWBhgdCzQZSS8Ornjcn4corPOavX5\nuFoiZGAX5BRV4ccj1x8wAhERUcfDAKNjEkGCp71nwESmwNbru5FdkaPVP2WkG7rYmeLU5WzEJOY+\nYBQiIqKOhQFGD1jIO2FWj2moU9djVdx61KnqNH0yqQR/muADQwMJVu9PRGFptQ4rJSIi0g8MMHqi\nj01PDHUchMzybOy8uV+rz8HKBLMCvVBZU48Vu+OhVnNrNRERdWwMMHpkqmc47BS2OJYRjbiCRK2+\n4b0d0K+bDZIyirH3bJqOKiQiItIPDDB6xEhqiPk+MyETpIiK34TS2ju3WhcEAXNDusNCaYSdp1Jw\nI7NEh5USERHpFgOMnumsdMIE93EoqytHVMImra3VpsYGeC7cG6LYcNfqqhpurSYioo6JAUYPBXQe\nhh6WXogvuIbjt05r9XXrYoHx/i7IL6nGukNJOqqQiIhItxhg9JBEkCCyxwyYGphgR/JeZJZna/VP\nHOYKVwcznIm7jTNxt3VUJRERke4wwOgpcyMl5vSYjnpRhR/i1qP2nq3V3jAylCLq4DXkFlfpsFIi\nIqLWxwCjx3pZe2Ok8xDcrsjB9uQ9Wn22FgpEjvVCda0KK3fFQaVW66hKIiKi1scAo+cmuYfC0cQe\nJzPP4HJenFafv489Bnnb4UZWKXZFp+qmQCIiIh1ggNFzhlIDzPeZBZlEhnWJm1Fcc2f7tCAIiBzb\nDdbmcuw5k4qkjGLdFUpERNSKGGDaAEdTe0z2CEVFXSWi4jdBLd65XKSQy/BcuA8AYMXuOFRU1z1o\nGCIionaDAaaNGOk0BD2tuiOx6DqOZpzS6vNwNsfEoa4oLK3B2gPXtL47hoiIqD1igGkjBEHAnB4R\nMDNUYteNA0gvu6XVHzrEBZ7jvMdxAAAgAElEQVTO5riQmIvoK9kPGIWIiKh9YIBpQ5SGpni6xwyo\nRBVWxa1HjapW0yeVSLAw3BvGRjKsP3wdOYWVOqyUiIioZTHAtDE9rLwwuvNw5FbmY0vSLq0+a3Nj\nPB3cDTV1Kny7Kw71Km6tJiKi9okBpg2a4D4OzqaO+Dn7PC7mXtHqG+Rth6G97JF6uwzbT93UUYVE\nREQtiwGmDTKQyDDfZxYMJAZYn7gFRdXa26dnBXrB1sIYB86m41p6kY6qJCIiajkMMG2UvYktpnmG\no7K+CmviN2htrTY2kmFhuDcAYN3hJH5LLxERtTsMMG3YUMdB8LXpievFN3Eo7bhWn7ujOYb7OiIz\nrwJHYzN1UyAREVELYYBpwwRBwKzuU9HJyBx7Uw4hpSRdq3/KSDcojGTYcSoFpRW1DxiFiIio7WGA\naeNMDUww13sGRFHE6rj1qK6v1vSZKQwxeYQbqmrqseXEDR1WSURE9GQxwLQDXhYeCHIZhfzqQmxK\n2qnVN8rPEZ1tTRF9ORs3skoeMAIREVHbwgDTToS5joWLsjPO3f4FMbcvatqlEglmB3kBANYdSoJa\nzdsMEBFR28cA005IJVLM85kJQ6khfry2HQVVhZo+r86dMNjHDmm3y3DqcpYOqyQiInoyGGDaEVuF\nNSK8JqFaVY3V8T9CpVZp+qaP8oCRoRRbT9xEeRXvWE1ERG0bA0w7M9i+H/rZ+uJmSRoOpP6kabdQ\nGmHC0K4or6rDDn5DLxERtXEMMO2MIAh4qtsUWBh1wv7Un5BSkqbpC+rfGfaWChy7mIn0nDIdVklE\nRPR4GGDaIYWBccPWaojYeG275lt6ZVIJZgV5QhSB/x1OgihyQS8REbVNDDDtlKeFOwbZ90NGeRai\nM89p2nu6WqGflw2u3yrB2fgcHVZIRET06Bhg2rGJ7uMhl8qx++YBlNdWaNpnjPaAgUyCTUeTUVVT\nr8MKiYiIHg0DTDtmbqREqFsQKuursPPGfk27dSdjhA52QUlFLXafTtVdgURERI+IAaadG+k0BI4m\n9jiTfQGppXfulRQyqAuszeU4HJOB7IKKRkYgIiLSPwww7ZxUIkWE16TfFvTu0CzoNTSQYuYYT6jU\nIhf0EhFRm8MA0wF4Wrihv10fpJfdws9Z5zXtfTyt0dPNEvGpRYhNytNhhURERM3DANNBTPYIhZHU\nELtuHEB5XcMlI0EQMCvQC1KJgA0/JaOmTvWQUYiIiPRDiwaYpKQkBAYGYt26dVrtp06dQrdu3TSP\nd+3ahalTp2L69OnYvHlzS5bUYXUyMsd41yBU1Fdi982DmnZ7SwWCB3ZBQWk19p9Na2QEIiIi/dFi\nAaayshJLliyBv7+/VntNTQ1WrFgBGxsbzfOWLVuG1atXIyoqCmvWrEFxcXFLldWhBTgPg73CFqcz\nzyG99JamPWyICyyURth3Nh25xVU6rJCIiKhpWizAGBoaYuXKlbC1tdVq/+abbzBr1iwYGhoCAC5d\nuoRevXpBqVRCLpejb9++iI2NbamyOjStBb1Jdxb0yg1liAjwQL1KjQ1Hruu4SiIiooeTtdjAMhlk\nMu3hU1JSkJiYiEWLFuE///kPACA/Px+Wlpaa51haWiIvr/EFpRYWCshk0idf9G9sbJQtNrau2dj4\n4UJBP5zJ+AVx5XEY7TYEABA6whSn427j1+R8pOVXon8POx1Xen/teW7aMs6L/uLc6C/OzeNpsQBz\nPx988AHeeuutRp/TlO28RUWVT6qke9jYKJGX175vdBjaORi/ZF3Bul+3wV3uDoWBAgAQMdId8TcL\n8c3WS/jXgkEwkOnXGu+OMDdtEedFf3Fu9BfnpmkaC3mt9hsqJycHN2/exN/+9jdEREQgNzcXc+bM\nga2tLfLz8zXPy83NveeyEz1ZFvJOGNd1DMrrKrAn5ZCm3dnWFKP7OiGnqAqHLqQ3MgIREZFutVqA\nsbOzw5EjR7Bp0yZs2rQJtra2WLduHXx9fXHlyhWUlpaioqICsbGx6N+/f2uV1WGN7jwctgprnLx1\nBhllWZr2ScNdoVQYYPfPqSgsrdZhhURERA/WYgHm6tWriIyMxPbt27F27VpERkbed3eRXC7H4sWL\nsWDBAsyfPx8vvPAClEpeF2xpMokMEZ4NC3o3Je3QXLpTyA0wbZQ7auvU2HQsWcdVEhER3Z8gtsHv\nkG/J64Yd7brkyitr8WveVTzdYwYGOfQDAKhFEe9H/YKbWaV4baYfurtY6LjKBh1tbtoKzov+4tzo\nL85N0+jFGhjST1M9w2EgMcD2G3tRVd/wHTASQcDsIC8IAP53JAkqtVq3RRIREf0BA0wHZym3QEjX\n0SirLcfem4c17a4OZhju64jMvAocjc3UYYVERET3YoAhjOkyEjbGVjiR+TMyy7M17VNGukFhJMOO\nUzdRUlGrwwqJiIi0McAQDCQyTPOcALWo1lrQa6YwxOQRbqiqUWHr8Rs6rpKIiOgOBhgCAPS07oFe\n1t5ILk5BTM6vmvZRfo7obGuK6CvZuJFZosMKiYiI7mCAIY1pnhNgIJFhe/IeVNU3fAeMVCLB7CAv\nAMC6w0lQq9vcpjUiImqHGGBIw9rYEkEuASipLcP+lCOadq/OnTDYxw5pt8tw6nJWIyMQERG1DgYY\n0hLUZRSs5JY4disa2RU5mvbpozxgZCjF1hM3UV5Vp8MKiYiIGGDoDwylBpjmGf7bgt6dmgW9Fkoj\nTBzqivKqOuw4dVPHVRIRUUfHAEP36GXtDR+r7kgqSkZs7mVNe2B/Z9hbKnDsYibSc/gNkkREpDsM\nMHQPQRAwzXMCZIIU25L3oLq+BgAgk0owK8gTotiwoLcN3oWCiIjaCQYYui9bhTUCXUahuKYEB1J/\n0rT3dLVCPy8bJN8qwdm4nEZGICIiajkMMPRAwS4BsJRb4GjGKeRU5GraZ4z2gIFMgk3HklFVU6/D\nComIqKNigKEHMpQaYqpnOFSiSmtBr3UnY4QOdkFJRS12n07VbZFERNQhMcBQo3ytfdDD0guJRddx\nKe+qpj1kUBdYm8txOCYDWfkVOqyQiIg6IgYYapQgCJjuNRFSQYot13ejVtVwU0dDAylmBnpCpRax\n/ggX9BIRUetigKGHslPYYEyXESiqKcbB1KOa9j4e1ujpZon41CLEJuXpsEIiIupoGGCoSUK6jkEn\nI3McST+B3MqGsCIIAmYFekEqEbDhp+uoqVPpuEoiIuooGGCoSYx+W9BbL6qw+fouzSUje0sFggd2\nQUFpDfadSdNxlURE1FEwwFCT+dn0gpeFB+ILruFyfrymPWyICyyURth/Lh25xVU6rJCIiDoKBhhq\nMkEQMMNrIiSCBFuv70KtquGmjnJDGSICPFCvUmPDkes6rpKIiDoCBhhqFnsTOwR0HoaC6iIcTjum\naR/YwxbdOnfCr8n5uHyjQIcVEhFRR8AAQ802vmsgzA3NcCj9OPKrGsKKIAiYHeQFiSDgxyNJqKtX\n67hKIiJqzxhgqNnkMjmmeISiXl2PLdd3adqdbU0xuq8TcoqqcOhCug4rJCKi9o4Bhh5JP7s+8Ozk\nhiv5Cbian6BpnzTcFUqFAXb/nIrC0modVkhERO0ZAww9EkEQEOE1CRJBgs1JO1H324JehdwA00a5\no7ZOjU3HknVcJRERtVcMMPTIHE3tMdJ5CPKrC3Ek/aSmfWgvB7g5muF8Qi4S04p0WCEREbVXDDD0\nWEJdg6A0NMXBtKMoqGoIK5LfFvQKAP53JAn1Ki7oJSKiJ4sBhh6LscwYk91DUaeuw9bk3Zp2Vwcz\nDPd1RGZeBY7FZuqwQiIiao8YYOixDbTvC3fzrriUdxXxBdc07VNGukFhJMOO6JsoqajVYYVERNTe\nMMDQY/t9Qa8AoWFBr7oeAGCmMMTkEW6oqlFh6/EbOq6SiIjaEwYYeiKclY4Y4eyP3Kp8HL1rQe8o\nP0d0tjVF9JVs3Mgs0WGFRETUnjDA0BMT5hoMUwMTHEj9CUXVxQAAqUSC2UFeAIB1h5OgVou6LJGI\niNoJBhh6YhQGxpjkPh616jpsTd6jaffq3AmDfeyQdrsMpy5n6bBCIiJqLxhg6Ika5NAPrmZdcDH3\nMhIL79yZevooDxgZSrH1xE2UV9XpsEIiImoPHjnApKamPsEyqL2QCBJEdGtY0LspaSfqf1vQa6E0\nwsShriivqsP2Uzd1XCUREbV1jQaY+fPnaz1evny55u/vvPNOy1REbV4XpTOGOg1CTmUujmVEa9oD\n+zvD3lKB4xczkZ5TpsMKiYiorWs0wNTX12s9Pnv2rObvosjFmPRgE9xCYGKgwL7UIyiuadh9JJNK\nMCvIE6LYsKCXP0NERPSoGg0wgiBoPb77F84f+4juZmKgwES3cahV1WJ78l5Ne09XK/TzskHyrRKc\njcvRYYVERNSWNWsNDEMLNYe/4wC4KDsjJudXJBXd+SK7GWM8YCCTYNOxZFTV1DcyAhER0f01GmBK\nSkpw5swZzZ/S0lKcPXtW83eixkgECWZoFvTugEqtAgBYmxsjdLALSipqsft0qm6LJCKiNknWWKeZ\nmZnWwl2lUolly5Zp/k70MC5mnTHEcQBOZ53HiVunMbrLCABAyKAuiL6SjcMxGRjW2wGO1iY6rpSI\niNqSRgNMVFRUa9VB7dgEt3G4mHsFe1MOo59dH5gbmcHQQIqZgZ74cusVrD+ShMUz+vASJRERNVmj\nl5DKy8uxevVqzeMNGzZg4sSJePnll5Gfn9/StVE7YWpognC3EFSrarA9eZ+mvY+HNXq6WSI+tQi/\nXMvTYYVERNTWNBpg3nnnHRQUFAAAUlJS8Omnn+L111/HkCFD8O9//7tVCqT2YZjTIHRWOuFCTiyS\ni1MANCwKnxXoBalEwMaj11FTp9JxlURE1FY0GmAyMjKwePFiAMDBgwcREhKCIUOG4KmnnuIZGGoW\niSBBhNckANBa0GtvqUDwwC4oKK3BvjNpuiyRiIjakEYDjEKh0Pz9/PnzGDx4sOZxU9YrJCUlITAw\nEOvWrQMAXLx4ETNnzkRkZCQWLFiAwsJCAMCuXbswdepUTJ8+HZs3b36kN0L6z83cBYMd+iOzPBun\nMu98KWLYEBdYKI2w/1w6courdFghERG1FY0GGJVKhYKCAqSnp+PixYsYOnQoAKCiogJVVY3/oqms\nrMSSJUvg7++vaVu1ahU++ugjREVFwc/PD5s2bUJlZSWWLVuG1atXIyoqCmvWrEFxcfETeGukjya5\nj4exTI49KQdRWttwOwG5oQwRAR6oV6mx4cj1h4xARET0kACzcOFCjB8/HuHh4fjLX/4Cc3NzVFdX\nY9asWZg0aVKjAxsaGmLlypWwtbXVtH3xxRfo3LkzRFFETk4O7O3tcenSJfTq1QtKpRJyuRx9+/ZF\nbGzsk3l3pHeUhqYIcw1GVX01dibv17QP7GGLbp074dfkfFy+wcuTRETUuEYDzMiRIxEdHY3Tp09j\n4cKFAAC5XI6///3vmD17dqMDy2QyyOXye9pPnjyJkJAQ5OfnY8KECcjPz4elpaWm39LSEnl53JHS\nng13GgwnUwecvR2DmyUN614EQcDsIC9IBAHrj1xHXb1ax1USEZE+a/R7YLKysjR/v/ubd93c3JCV\nlQVHR8dmH3DEiBEYPnw4Pv74Y6xYsQJOTk5a/U25wZ+FhQIymbTZx24qGxt+SV9L+9PAWXjn6CfY\ndmMXPgh6AxKJBDY2SoQOc8XuUzdxOj4H08d43fM6zo1+4rzoL86N/uLcPJ5GA8zo0aPh6uoKGxsb\nAPfezHHt2rXNOtjhw4cRFBQEQRAQHByML7/8En5+flo7mnJzc9GnT59GxykqqmzWcZvDxkaJvLyy\nFhufGljBDgPt++L87Vhsv3QEI5wb1koF93PC8V8ysOHwNfTuagFLsztn8Tg3+onzor84N/qLc9M0\njYW8Ri8hLV26FA4ODqipqUFgYCA+//xzREVFISoqqtnhBQC+/PJLJCQkAAAuXboEV1dX+Pr64sqV\nKygtLUVFRQViY2PRv3//Zo9Nbc8k91DIpUbYffMAymsrAAAKuQGmjXJHbZ0am44l67hCIiLSV42e\ngZk4cSImTpyI7OxsbN++HbNnz4aTkxMmTpyIoKCg+65x+d3Vq1exdOlSZGZmQiaT4eDBg3jvvffw\n7rvvQiqVQi6X46OPPoJcLsfixYuxYMECCIKAF154gfdZ6iDMjZQIdQ3C1uQ92HljP2b3mAYAGNrL\nASd+zcL5hFyM7FOEHi4WOq6UiIj0jSA2ZdHJXTZv3oyPP/4YKpUKMTExLVVXo1rytBtP67UulVqF\nDy58htsVufhb/xfQ1awLACAluxTvrYmBo40J/jlvAGRSCedGT3Fe9BfnRn9xbprmkS8h/a60tBTr\n1q3DlClTsG7dOvzpT3/Cvn37Hv5CooeQSqSY4TUJIkRsvLYDarFh95GrgxmG+zoiM68Cx2IzdVwl\nERHpm0YvIUVHR2Pr1q24evUqxo4diw8//BBeXvfuDCF6HJ4W7uhv1wcxOb/iTNYFDHUaBACYOtIN\nv1zLxY7omxjobYff1pITERE1HmCeffZZdO3aFX379kVhYSFWrVql1f/BBx+0aHHUcUz2CMWV/Hjs\nvLkffWx7wcRAAaXCEJOGu+F/h5Ow5Xgy3phnpesyiYhITzQaYH7faVRUVAQLC+2FlLdu3Wq5qqjD\n6WRkjvGuQdievBe7bh7AzG5TAACj/Bxx8lIWTl+5jcS0QlgpDHRcKRER6YNG18BIJBIsXrwYb7/9\nNt555x3Y2dlh4MCBSEpKwmeffdZaNVIHMcp5KOwUtjideQ7ppQ0BWSqRYHZQw2XLrzb9irp6lS5L\nJCIiPdFogPnvf/+L1atX4/z58/j73/+Od955B5GRkTh79izvGk1PnEwiQ4TXxIYFvUl3FvR6de6E\nUX5OSLtdhu0nU3RcJRER6YOHnoFxd3cHAIwZMwaZmZl4+umn8dVXX8HOzq5VCqSOpbulJ/xseyO1\nNB1ns3/RtM8I8ICDtQkOnk9HYlqRDiskIiJ90GiAEQRB67GDgwOCgoJatCCiqR5hMJQYYOeNfais\na7hthJGhFItn9YUgCPh+bzwqq+t1XCUREelSk74H5nd/DDRELcFC3gnjugaivK4Ce1IOadq7uVgi\nbIgLCkprsP5Ikg4rJCIiXWt0F9LFixcxatQozeOCggKMGjUKoihCEAQcP368hcujjiqgy3CcuX0B\nJ2+dgb/DQHRWNtz5PGxIV1y+UYCfr95GHw9r9O9uq+NKiYhIFxoNMAcOHGitOoi0GEhkmO45Ecsu\nfY9NSTvwat/nAQAyqQQLw73xf6suYO3Ba/BwNkcnUyMdV0tERK2t0UtITk5Ojf4hakneVt3ga9MT\nN0tScf52rKbdwcoEEQEeKK+qw6p9iWjm7byIiKgdaNYaGKLWNtUjHAYSA2y/sReVtVWa9oC+TvBx\ntcSVmwU4/muWDiskIiJdYIAhvWZlbIFgl9Eoqy3Hj1d2atolgoBnxveAiVyGjUev43ZhpQ6rJCKi\n1sYAQ3ovsMsI2ClscTD5BOIKrmnaLZRGiAzuhto6NVbujodKrdZhlURE1JoYYEjvGUgNMN9nJqQS\nKaISNqKstlzTN7CHHQZ72yEluxR7z6TpsEoiImpNDDDUJnRWOmFmr4koqy3HuoTNWgt3Z4/1goXS\nCLuiU5GSXarDKomIqLUwwFCbEdZtDLpZeOBqQQJOZZ7RtJvIDbAgtAfUooiVu+NRU8cbPhIRtXcM\nMNRmSAQJnvaeAROZAtuS9yC7IkfT593VEkH9O+N2YSW2HLuhwyqJiKg1MMBQm9LJyByzekxDnboe\nq+LWo059555IU0e6wdHaBD/F3sLVmwU6rJKIiFoaAwy1OX1semKIw0Bklmdj1439mnZDAykWhnlD\nKhHw/b4ElFfV6bBKIiJqSQww1CZN85oAW4U1jmacQkLBnRs7utgrMXGYK0rKaxF18Bq/pZeIqJ1i\ngKE2yUhqiHneMyERJFibsBHltRWavnGDu8DDyRwXEnNxLj6nkVGIiKitYoChNsvFrDPC3YJRWluG\n/yVu0ZxtkUokeDasB4wMpIg6lITC0modV0pERE8aAwy1aYFdRsKzkxsu58chOuucpt3WQoGZgZ6o\nqqnH93sToOalJCKidoUBhto0iSDBXO+noJAZY+v13bhdkavpG97bAX08rJGQVoQjMbd0WCURET1p\nDDDU5lnIO2Fm96moU9dh9V1bqwVBwNxx3aFUGGDL8RvIzCt/yEhERNRWMMBQu9DXtjf8HQYgozwL\ne24e1LSbmxhiXkh31KvUWLknHvUq3vCRiKg9YIChdmOa5wTYGFvhSPoJJBZe17T7edlgWG8HpOeU\nY2d0ig4rJCKiJ4UBhtoNucwI831mNWytjt+I8ro7W6tnjvGEtbkc+86m4fqtYh1WSURETwIDDLUr\nLmadEeo6FiW1pVifuFWztdrYSIZnw7wBEfhuTzyqauofMhIREekzBhhqd8a6jIJHJ1dcyruKn7PP\na9q9OnfCuMEuyCuuxsaj1xsZgYiI9B0DDLU7v2+tNpYZY0vSLuTctbV60nBXdLY1xclL2bh4PU+H\nVRIR0eNggKF2yVJugZndpqBWXYfV8T+i/ret1TKpBAvDvSGTClizPxGlFbU6rpSIiB4FAwy1W/3s\nfDHIvh/SyzKxN+Wwpt3ZxhRTR7qjtLIOaw4k8oaPRERtEAMMtWsRXhNhLbfE4bTjSCpK1rQHDeiM\n7l064eL1fERfztZhhURE9CgYYKhdk8vkmOczE4IgYE38RlTUVQIAJIKABaHeMDaSYv1P15FbXKXj\nSomIqDkYYKjdczV3wfiugSiuKcGPd22ttjKXY05QN9TUqvDdnnio1byURETUVjDAUIcQ3HU03M27\n4mLeFZzNjtG0D/axQ/9uNki+VYL959J0WCERETUHAwx1CL9vrZZL5dh0fSdyKxu2UAuCgKdDusPc\n1BA7TqUgPadMx5USEVFTMMBQh2FlbImZ3SajVlWL1XEboFKrAACmxgZ4ZnwPqNQiVu6OR129SseV\nEhHRwzDAUIfS394PA+z6Iq0sQ2trdS83KwT0dUJmfgW2nripwwqJiKgpGGCow5nRbRKs5JY4lHYM\n14vuhJWIUR6ws1Tg0IUMJKQW6rBCIiJ6GAYY6nCMZXLM83nqt63VG1BZ17CF2shQioVh3pAIAr7f\nl4DK6jodV0pERA/CAEMdkpt5V4R0HYOimmJsuLZNs7XazdEMYUNcUFhag/8d5g0fiYj0FQMMdVgh\nLqPhZu6CX3Iv4fztWE172JCucHVQ4kzcbVxIzG1kBCIi0hUGGOqwpBIp5nrPhFxqhI1J25FXWQCg\n4YaPz4Z5w1AmwdoDiSgqq9FxpURE9EctGmCSkpIQGBiIdevWAQCys7Mxb948zJkzB/PmzUNeXsN3\ncezatQtTp07F9OnTsXnz5pYsiUiLtbElZnSbjBpVLVbH/6jZWu1gZYKI0R6oqK7Hqn0JvOEjEZGe\nabEAU1lZiSVLlsDf31/T9tlnnyEiIgLr1q1DUFAQVq1ahcrKSixbtgyrV69GVFQU1qxZg+Li4pYq\ni+geA+37or9dH6SWpmN/6hFNe4CfE3q6WuJqSiGOXczUYYVERPRHLRZgDA0NsXLlStja2mra/vnP\nfyI4OBgAYGFhgeLiYly6dAm9evWCUqmEXC5H3759ERsb+6BhiVrEDK/JsJRb4EDqUSQXpwBo+Jbe\n+eN7wEQuw6ajycguqNBxlURE9LsWCzAymQxyuVyrTaFQQCqVQqVSYf369QgPD0d+fj4sLS01z7G0\ntNRcWiJqLQoDY8z1fgoAtLZWWyiN8HRId9TWq/HdnnjUq9S6LJOIiH4ja+0DqlQqvPbaaxg8eDD8\n/f2xe/durf6mrDWwsFBAJpO2VImwsVG22Nj0eFpybmxseiO9ehy2xu/DzrQ9eNn/GQDAeBslEjKK\ncfyXWzh+KRszg7u3WA1tFf/N6C/Ojf7i3DyeVg8wb775JlxcXPDiiy8CAGxtbZGfn6/pz83NRZ8+\nfRodo6iossXqs7FRIi+PN/TTR60xNyNth+OXW1cRnX4B7qbuGGjfFwAwbbgrLl/Pw4bDSXCzV8LN\n0axF62hL+G9Gf3Fu9BfnpmkaC3mtuo16165dMDAwwMsvv6xp8/X1xZUrV1BaWoqKigrExsaif//+\nrVkWkYZUIsU875kwkhpi47XtyK9quKWAQm6ABaHeUIsiVu6OQ00tb/hIRKRLgthC+0OvXr2KpUuX\nIjMzEzKZDHZ2digoKICRkRFMTU0BAO7u7vi///s/HDhwAN9//z0EQcCcOXMwYcKERsduydTKVKy/\nWnNuzmbHICphE9zMXfBXvz9DKmm4ZLnhp+s4dCEDAX2dEDm2W6vUou/4b0Z/cW70F+emaRo7A9Ni\nAaYlMcB0TK05N6IoYlXcevySewnjXYMQ6hoEAKirV+Ffq2OQmV+BVyJ80cvNqlXq0Wf8N6O/ODf6\ni3PTNHpzCYmorRAEAU91mwwLo07Yn3IEN0tSAQAGMimeDfOGVCLgh30JKK/iDR+JiHSBAYboARQG\nCs3W6tVxG1BVXw0AcLFXYtJwV5SU12LtgUR+Sy8RkQ4wwBA1wtPCDWNdAlBQXYhNSTs07eMGucDD\n2Rwx1/JwNi5HhxUSEXVMDDBEDxHqGgQXZWecvx2LmNsXAQASiYBnw7xhZCjFusPXUFBSreMqiYg6\nFgYYooeQSqSY5zMThlJD/HhtOwp+21pt28kYs8Z4oqpGhe/3xkPNS0lERK2GAYaoCWwV1ojwnIhq\nVTXWxG/Q3LV6WG8H9PGwRmJ6MQ5fyNBxlUREHQcDDFETDXboDz+bXrhRkopDaccBNOxWmjeuO5QK\nA2w9cRO38sp1WyQRUQfBAEPURIIgYGb3qehkZI59qYeRUpIOADAzMcS8cd1Rr1Jj5e541NXzho9E\nRC2NAYaoGUwMFJjrPaRyDHUAACAASURBVAOiKGJ13HpU/7a12s/TBiN8HZCRW46d0Sk6rpKIqP1j\ngCFqJi8LDwR2GYn86kJsTtqlaZ8x2hM2neTYfzYNSRnFOqyQiKj9Y4AhegRhbmPRRemEs7dj8EvO\nJeD/t3fn0VGdZ57Hv7d2qapUWtBekkDCIAQYzGJArLGxHe/xCrHB9sTjnh6nZyZ9nKQ9TrzEzukz\n+ExOpxP7OB3bSTAe2yR434jtdsAswkDALBIgEEhoX0tVpaX2O39ISCoEWAJJVSWezzk6UlW9dfVK\nz71XP733vfcCcUYd//WWIlDglY/K6PYGItxLIYQYvyTACHERdBodDxV9H4NGz5vH3qHN4wDgCnsi\nNy3Mo8Xp4c3/PB7hXgohxPglAUaIi5RuTuPuKbfRHehmfdlbhNSeybu3L5lEbpqF7Qfr2V/eHOFe\nCiHE+CQBRohLUJx5NbNSZ3Ci/RSf955ardNqeOTWInRaDX/afBRnpy+ynRRCiHFIAowQl0BRFO4r\nvAubIYGPTn1GlavnYnbZqRbuXlGAu8vP+k/lho9CCDHSJMAIcYksejMP9J5a/cfSN/AEvACsnGdn\nWl4S35xoYdvB+gj3UgghxhcJMEKMgMLkK7g2dxnN3a1sOt5zarVGUXj45mnEGXW8+cVxmhxdEe6l\nEEKMHxJghBght+bfQI4li5L6PexrOghAcoKJNddPwesP8spHRwiF5FCSEEKMBAkwQowQnUbHQ9Pv\nQ6/R8+bRt3F4ei5mt7AonfmFaZyodfLp11UR7qUQQowPEmCEGEEZ5jTuuuJWugLdvFa2kZAaQlEU\n1t4wFZvFwHvbTlHV4I50N4UQIuZJgBFihC3JWsCVE6ZT3l7Bf57+CgBLnJ6Hb5pGMKTy8kdl+PzB\nCPdSCCFimwQYIUaYoijcX3g3NoOVD05u5rSrBoAZ+SlcO8dOXUsnf/lbhZxaLYQQl0ACjBCjwGIw\ns7ZoFSE1xB/L3sAb7LmY3d3fKSAjOZ7/3FfDi+8epqPbH+GeCiFEbJIAI8QomZY8hWtyltLU1cLb\nvadWG/Vafrx6NlNzEtlX3sxTr35NaWVbhHsqhBCxRwKMEKPotoIbybZksqNuN980HwZ6Tq3+yfev\n4q7l+bi7/PzqrW/Y+OVx/IFQhHsrhBCxQwKMEKNIr9HxX6bfh16j440jm2j3OgHQaBRuXjSRJ9bO\nJT0pjr/uruaXr+2ltqUzwj0WQojYIAFGiFGWaU7nzsm30Bno6ju1+oxJmQk881+uZtmsLKqbOnj2\nT3v4cl+NTPAVQohvIQFGiDGwNHsRM1Kmccxxgi+rt4W9ZjRoeejGQn54x0wMOg2vf1bObzYdxCV3\nsRZCiPOSACPEGFAUhTXT7sFqsPBBxWaq3bWD2sydmsqzDy9gWl4SBypaeeoPuzl0sjUCvRVCiOgn\nAUaIMWI1WFg7bRVBNcgrhzZQ464b1CbJauSx1bO59zuT6ez2829/PsAbn5fjD8iF74QQYiAJMEKM\noekpU7lx4kpaPG08v/e3fFb5t7A5MdBzF+vvLsjlyQfnkZkSzxd/r+HZ9XupaeqIUK+FECL6SIAR\nYozdkn89j876AWZ9PO+f/JR/2/cSzV2DDxXlplt56qH5fOeqbGqbO3l2/V4+31NNSCb4CiEE2mee\neeaZSHdiuLq6Rm9yo9lsHNXli4s3nmqTFj+BhZnzaPU4ONJWzs76PVj08eRYs1EUpa+dTqth1uQJ\n5KVbOXSylX3lzZysczFtYhImgy6CP0G/8VSX8UZqE72kNkNjNhvP+5qMwAgRIRa9mYen389DRd9H\nq2h589g7vHTwjzi9rkFtZ18xgecevpoZk5I5fKqNp17dzTfHWyLQayGEiA4yAnMWScXRazzWRlEU\nsi2ZzE+/irqOBo60lbOrfi8pcclkmtPD2poMOhZMT8ccp+dgRSslpQ24On0U5iWh00buf5HxWJfx\nQmoTvaQ2QyMjMEJEuSRTIj+c/TD3TvkevpCfVw+/zp9K36TL3xXWTqMoXDcvh6cenEd2qpm/7a/l\n2T/toarBHaGeCyFEZMgIzFkkFUev8V4bRVGYmJDDVakzqXRXU9Z2jD2N+8m2ZDIhLiWsbYLZwNIr\nM/F4gxysaGX7wXoMOi352Qlhc2jGwnivSyyT2kQvqc3QXGgERgLMWWSlil6XS20sBjMLM+ahVbQc\nbj3K1w1/p9PfxRWJ+Wg12r52Wo2GmQUpTMpMoLSyjX3lzRyvcVI0MZk449hN8L1c6hKLpDbRS2oz\nNHIISYgYo9VouXHSSn4894dkxKextWYH/2fPv1PpOj2o7ZUFKTz7g6uZVZDCkSoHT736NX8/1hyB\nXgshxNiREZizSCqOXpdjbRKNNhZlzscX8nG49Si76vcSUkMU2CaiUfr//zAatCwoSsdmNnCgopVd\nZY043J4xmeB7OdYlVkhtopfUZmhkBEaIGGbQ6rn7itv4X1f9AzZDAp9WfsH//fsLNHQ2hrVTFIXv\nzLHz9EPzyUmz8NWBen7xxz2cqh98WrYQQsQ6GYE5i6Ti6HW51yYlLplFWfNwet2UtR2jpH4PRq2R\nvAR72MRda7yBJTMz8QeCHKhoZceherQahcnZtlGZ4Hu51yWaSW2il9RmaGQERohxIk4XxwNFq3hk\nxlqMWiObjn/Ab795hTaPI6ydXqdh1TVX8Njq2Vji9by99STPv7mfVqcnQj0XQoiRJSMwZ5FUHL2k\nNv0yzOksyJxLU1czR9rKKanbS6IxgWxLZtgoS1piHEtmZtLo6ObwqTa2H6onNdFEdqplxPoidYle\nUpvoJbUZGhmBEWIcSjBY+W8zH+L+wntQCfHakY28cngDbl/4XastcXp+eMcMHrqxkGAoxO/eL+WV\nj8ro9gYi1HMhhLh00XE3OCHERVEUheKs+UxJKuC1so1803yYCmcl9xfezcwJRWHtls3K4gq7jd9/\nWMbOww0cr2nnH26dTkG2LYI/gRBCXBw5hHQWGdaLXlKb84vXx7Egcy4mnZHSlqPsbtxPu8fJlKR8\ndJr+/1POTPANhlQOnmhl+6EGACbbbWgucoLveKiL1x+kotbJnqNNnKpz0ery0OkJEFRV9FoN2gje\na+pSjIfajFdSm6G50CEkRVVVdbS+cXl5OY8++igPPfQQa9asAeC1115j3bp17N69G7PZDMAHH3zA\n+vXr0Wg03Hvvvdxzzz0XXG5z8+jd9yU11TqqyxcXT2ozNHUdDfyp7E1qO+pJMSXzQNEqJidOGtTu\naJWDlz8qw+H2MjnbxiO3FpGaGDfs7xdrdVFVlVaXh4paFydqnZyodVLd2EHoArtCs0lHosVIotVI\nosXQ87XFSJLV2Pu1gQSzIaI31TyXWKvN5URqMzSpqdbzvjZqh5C6urp47rnnWLRoUd9z7733Hq2t\nraSlpYW1e/HFF9m0aRN6vZ67776b6667jsTExNHqmhDjWpYlg5/O+x98fOpzPq/awq/3/Y5rc5dx\nS/4N6AeMxhTmJfHsw1ezfvMx9h5t4uk/7Gbt9VNZNCMjgr0fef5AiNONbk7UOqnoDSztHf3/+eq0\nCpOyrEzOtlGQ1XOqeXuHt+fD3fu5w4fD7aW2pfO830cBrGYDSb2BJtE6MOT0hx5LvP6iR7uEEP1G\nLcAYDAZefvllXn755b7nVq5cicVi4cMPP+x77sCBA8ycOROrtSdlzZkzh3379nHNNdeMVteEGPd0\nGh23F9zIzAnTWF+2kS9Ob6Ws9RgPFq3Gbs3qa2c26fnvt09nR34K/++Lcl7+qIxDJ1tZc/1U4k2x\nOUXO2eHlRK2rJ6zUOamsdxMIhvpet5kNzJ2SSkG2jcl2G3npVvS6oY2ceP1BnAMCTV/QGfC4vrWT\nqsbz/2et1ShhgaZnZMcwYITHSJLFSJxRO+Y35hQilozaHkqn06HThS/eYhl86mZLSwvJycl9j5OT\nk2lulvu4CDES8m0T+d/zf8S7FR+zvXYXz+/9LbdMup6Vecv7bkWgKApLrszkihwbL39Yxq6yRo7X\nOHnk1iKm5ET3SGgwFKK2ubPvUFBFrZPm9v5r3WgUBXuamcnZtr6PFJvpooOBUa8lLSmetKT487ZR\nVZVub5D2Di+Os0Zx2geEnsoGN8HQ+a+SbNBrBoQcw4DDVf2PbRYjRr32vMsQYjyLun+xhjIlJykp\nHp1u9DbaCx1zE5EltbkYVv5n5oMsrZ/L73a/zvsnP+Wo8xg/XPgQGZbUvlapqVZ+9aNU3vr8GH/5\nopzn39jHPddOYfX1U791bsdY1aWjy8fRKgdHK9s4UtlG+WkHHl+w73VLnJ5509IpnJjEtInJXJGT\nNKZ35h4o71teD4VU3F0+Wp0e2lyeAZ+7aXP1fN3m9HC8pp0L7RbNcXqSE0ykJJhItplIsZlITuj5\nCCgaMmWbiVqyP7s0EQ8waWlptLS09D1uampi9uzZF3yPw9E1av2RiVXRS2pzaey6PB6f/yM2HnuX\nfU0H+fHmX3Ln5FtYkrUgbETihrl2JqVZePnDMjZ+Uc7u0gb+4bYi0s8z6jBadQmpKg2tXWFzV+pb\nw7f9rAlmJmcnUJDVczgoPTk+bH5Jh6ubjrMXHGWsBg3WCfHkTTj37zcQDOHq9PWM4PSO3jgGjup0\neGlzdlN9jsNWigILizK4fekk0i5igrYYPbI/G5qITOIdqlmzZvHzn/8cl8uFVqtl3759PPHEE5Hu\nlhDjkkVv5gfT72fWhOm8Vf4ebx17h4MtpawpvAebMaGv3ZScRH7xg6t5/bNj7Cpr5Jk/7OG+665g\nyczMUZuX4fEFOFV35swgFyfrnHR6+i+2ZzRomZaX1DPZNttGQXYCZpN+VPoSTXRaTd+IyoX4/EHa\nO/sPU7W5vOw+2kRJaQO7jzSydFYWtxZPJMl6/tNShYglo3Ya9eHDh1m3bh21tbXodDrS09MpLi5m\n586dfPPNN8ycOZPZs2fz05/+lM2bN/Pqq6+iKApr1qzhtttuu+Cy5TTqy5PUZmS1e528fuQvHGkr\nx6yLZ3XhncxJu3JQu5LSBl7/7Bjd3iDzCtN48LtTw4LDxdRFVVWanZ6+kZWKGifVzR1hh0pSE019\n81YKsm1kp5rRakb+NGV/KEBdRz3V7lpqO+rRarQkGm0kGW0kGhNJNNqwGa1h19OJFSkpFj7ZVsF7\n207S6OhGr9Nw7Rw7Ny7MxRpviHT3LmuyPxuaC43AjOp1YEaLBJjLk9Rm5Kmqyle1Jbx74mP8IT/z\n06/i3im3E68PP5zR3N7Nyx+VcaLGSZLVyCO3FFGYlwQMrS7+QJDKBnfYtVdcnQNPZdYwMdMaFlhs\n5pH/A+sL+qntqKfaXUO1u5bT7lrqOhsIqaELvk9BwWqw9Acbk41E4+APgza6RoTO1CYYCrHjUAPv\nbz+Fw+3FZNByw9W5XD8/J2JzhC53sj8bGgkwwyArVfSS2oyexq5mXivbSKXrNIlGG2um3cO05Clh\nbYKhEB+XVPHB9kpUVeW7C3O5Y2k+mRm2QXVxuL39oyu1zt4zbvp3NYkWQ39YsdvITRv6qcxD5Ql4\nqe2o53RvWKl219LQ1RQWVvQaHdmWLHKt2eRYs7Fbs1BVlXavE4fXSbvHSbs3/MMfOv89pMz6+AGj\nN70jOKaBj22YdGN3COfsbcYfCLJlfx0flVTi7vJjidNz08I8rpmTjUHOZhpTsj8bGgkwwyArVfSS\n2oyuYCjIZ1Vb+KTyc0JqiOX2Yr5XcBMGbfhISEWtk99/WEpzu4e8dCs/eWAeTc1uTtQ4qahzcaLG\nSasr/FTmnHRL2KnMyQnGEZ1L0x3opsZdx+neoFLtrqWxqxmV/t2bQaPH3htUzgSWjPg0tJqh/+FW\nVZXOQFdfsHGcCTYDHju87fiC579EfJzO1Bdmkow2bGeN6iQZbcTp4kbk93O+bcbjC/D53ho2f32a\nbm+ARIuBWxdPYumVmVF3NeHxSvZnQyMBZhhkpYpeUpuxcdpVw/qyt2joaiItfgIPFq1mYkJuWJtu\nb4A3Pi9nx+GGQe+3xOl7DwMlMDnbxsSMBIyGkfvvvsvfFRZUqt21NHW3hLUxaY3k9IaUM4ElLT61\n79o3o0lVVTxBD44BozfnGs3pCnSfdxkGjb430CSGjd4kDTh0ZdGbvzXkfNs209HtZ/PXp/libzW+\nQIjURBPfW5LPgqJ0NBq5iN5okv3Z0EiAGQZZqaKX1Gbs+IJ+Pjy5mS+rt6FRNNyQdw03Trx20GjF\n7iON7CxtJMli6D+VOWlkRg8A3L6OvpByJrS0etrC2sTp4vpGVM58nhCXMiZh5VJ4g75Bozc94aad\ndk/P4w7/+W9doFN6JhufPR9n4GhOQXYWrRe4/cEZzg4vH5VUsWV/LcGQSvYEM99bms+cKRPkasCj\nRPZnQyMBZhhkpYpeUpuxV+44wWtlf8bhbSfXms2DRavJMKeHtRmpuji97rDJtdXuWhze9rA2Zn08\nuVb7gJEVOymmpHH7R9Yf9OP0uQaP5vQFn3Zcvo6wQ2UD2UwJFGfMZ0n2QhKNtm/9fi3t3Xywo5Id\nh+tRVZiUaeXOZQUUTRy/v+NIkf3Z0EiAGQZZqaKX1CYyugPdbCr/kF0Ne/vusbTCvrhvhGO4dTkz\nSXZgUKl21+D0hS/DarCQa7UPGF2xk2i0yR/SswRDQVw+94Bg094zF8fTTnn7CTr93WgUDVelzmRF\nzmImJeR96++wvrWT97adYs/RJgAKcxO5c1kBk+3fHoLE0Mj+bGgkwAyDrFTRS2oTWd80H+bNo2/T\n4e9kSmIBa4vuJdmUdMG6qKpKm8dxVlipxe0Pvz5uotEWNl8lx5o9pBEDcWHWJAOfHv6KrTU7qevs\nma+UY81muX0x89Jmof+W076rGty8u+0kBytaAZhVkMIdy/LJTZdL4F8q2Z8NjQSYYZCVKnpJbSLP\n5XPzxtFNHGo5gklr4t4pt3PzzOW0tHT0XJyuuzVscm21u5bOQPjl/5NNSX0hJcdqJ8eaRYJB/iCO\nhjPbjKqqHG+vYEvNTg42l6KiYtGbWZy1gKXZC0kyXfimneXV7byztYLyGicAV09L43tL88lIPv9N\nLcWFyf5saCTADIOsVNFLahMdVFVlV/1eNh3/AE/Qy8z0QjxeHzUddXQHPGFtJ8SlDAgrPR8WvTlC\nPb/8nGubae12sK22hJ11u+kMdKFRNMyaMJ0VOUsosE087+E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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": 656 + }, + "outputId": "b345f30d-6aa9-4613-c302-41ebacc7c28b" + }, + "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=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 231.58\n", + " period 01 : 209.63\n", + " period 02 : 163.93\n", + " period 03 : 118.06\n", + " period 04 : 113.66\n", + " period 05 : 109.62\n", + " period 06 : 104.76\n", + " period 07 : 99.09\n", + " period 08 : 92.70\n", + " period 09 : 86.36\n", + "Model training finished.\n", + "Final RMSE (on training data): 86.36\n", + "Final RMSE (on validation data): 86.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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TSTIS2Lo7zd2xRERErigVmFrIbrNzd4c7CPQKxFbvMIvWJeryahERqVVUYGop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\nTFyam8SkeUlciTo3uVnpbFhZyQYqicVidPeOUN/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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": 656 + }, + "outputId": "8bdc7c43-cc0b-48ca-de1a-0800ed4670e9" + }, + "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": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 79.49\n", + " period 01 : 72.80\n", + " period 02 : 72.26\n", + " period 03 : 77.04\n", + " period 04 : 70.08\n", + " period 05 : 71.00\n", + " period 06 : 68.47\n", + " period 07 : 67.53\n", + " period 08 : 66.93\n", + " period 09 : 67.70\n", + "Model training finished.\n", + "Final RMSE (on training data): 67.70\n", + "Final RMSE (on validation data): 68.13\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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YdKRQuUpJsUZQtLi0h1DXXk9xY6nakYQQQohBRwqVqxRmdCfA15WaEm9Ahn+E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\nx1+oGUMX0E9g6AgafTfdY2by2grJaysEQKvSEOETPnHGJdYQhb+nn+I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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": "dc3461f2-fd27-4df0-f2de-f93df6650c7c" + }, + "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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+ "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": 656 + }, + "outputId": "a72555df-d2bd-4fe2-f23a-ca9c8f24ad5d" + }, + "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.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 213.99\n", + " period 01 : 138.02\n", + " period 02 : 115.73\n", + " period 03 : 114.19\n", + " period 04 : 112.43\n", + " period 05 : 110.55\n", + " period 06 : 108.16\n", + " period 07 : 105.04\n", + " period 08 : 101.07\n", + " period 09 : 96.50\n", + "Model training finished.\n", + "Final RMSE (on training data): 96.50\n", + "Final RMSE (on validation data): 95.85\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RvzHANIGa26m9unycOFMS4GqIiIhaPwaYJtDL1AMyyCAzWZGSVhTocoiIiFo9\nBpgmoJQp0CukB0RtGX47nRXocoiIiFo9BpgmUrOsQHbladgclQGuhoiIqHVjgGkivmUFTFYc4TAS\nERGRXzHANJFO+g7QyLSQGQuQnMZlBYiIiPyJAaaJiIKIqNCrICgrkJJ1mssKEBER+REDTBOKslTN\ngymVZSOn0BHgaoiIiFovBpgmVLMuksxUgJRULitARETkLwwwTcisDkGYKgyioRApadZAl0NERNRq\nMcA0sf7hkRBkHhwtSIXb4w10OURERK0SA0wT62uuGkbyaPNxKssW4GqIiIhaJwaYJnaVuRcECFXL\nCnAeDBERkV8wwDQxjVyNboauEHQlSDqdE+hyiIiIWiUGGD/oFxYJQQAynGmwl7sCXQ4REVGrwwDj\nBzW3U4uGAhw9zWUFiIiImhoDjB90M3SBUlRB5PNgiIiI/IIBxg9kogx9zL0gqh1IOpMR6HKIiIha\nHQYYP4kKrVpWoFjIQl4RlxUgIiJqSgwwftLXt6yAFSlpnAdDRETUlBhg/CRCEwaTwgTRWIiUVC4r\nQERE1JQYYPxEEISq26nlLhzJOw2Pl8sKEBERNRUGGD+quZ3apclFWnZpgKshIiJqPRhg/KhP9bpI\noqkAKWm8nZqIiKipMMD4kV6pQyddR4j6IiSn5QW6HCIiolaDAcbP+oVGQhAlpJWehrPCHehyiIiI\nWgW/Bpjjx49jwoQJWLduHQDgl19+wc0334x58+bhnnvuQUlJCQDgvffew+zZszFnzhx8//33/iyp\n2dXcTi0Y83EsvTjA1RAREbUOfgswDocDS5cuRVxcnG/b888/j2effRZr167FwIEDsX79emRkZGDb\ntm346KOPsHLlSjz//PPweDz+KqvZ9TJ1h0yQQzRyHgwREVFT8VuAUSqVWLVqFSIiInzbzGYziour\neiFKSkpgNpuxb98+jBw5EkqlEhaLBZ06dcKJEyf8VVazU8gUuCqkB0RtGZLSMwNdDhERUasg99uJ\n5XLI5XVP///+3//D3LlzYTQaYTKZsHjxYrz33nuwWCy+fSwWC/Lz89GnT58Lntts1kIul/mrdISH\nG5r0fEO6RuNo0e8o8J4B5HKEmzVNev62pKnbhpoG2yV4sW2CF9vmyvgtwNRn6dKlePPNNzF48GAs\nW7YMH3300Xn7SJJ00fMU+XFtofBwA/Lzm/aZLV2UXQFU3U69+0A6RsZ0bNLztxX+aBu6cmyX4MW2\nCV5sm8ZpKOQ1611Ix44dw+DBgwEAw4YNQ3JyMiIiImC1nn3Ufm5ubp1hp9ago749dHIdZMYCJKcV\nBLocIiKiFq9ZA0xYWJhvfku5MAGRAAAgAElEQVRSUhK6deuGoUOHYteuXaisrERubi7y8vLQu3fv\n5izL70RBRFToVRCUFTicnQ5vI3qZiIiI6ML8NoSUnJyMZcuWITMzE3K5HNu3b8fTTz+NJUuWQKFQ\nwGQy4bnnnoPRaERCQgLmzp0LQRDw1FNPQRRb3+Np+loisT/3V5SrcpGRW4Zu7Tn2SUREdLkEqTGT\nToKMP8cN/TUuWVxRgn/8+Cw8xWGY0SEB0+K6N/k1WjuOGQcntkvwYtsEL7ZN4wTNHJi2LERlQoQ6\nHKKhCClp1osfQERERBfEANOM+oVFQpB5cLI4DRWu1vOwPiIioubGANOMapYVkAxWHM/gsgJERESX\niwGmGV0V0hMiRMiMBUhJ5bICREREl4sBphmp5Wp0N3aFoCtBcnpOoMshIiJqsRhgmlm/0EgIApBT\nmYHisopAl0NERNQiMcA0s5p5MDKTFYe5OjUREdFlYYBpZl0NnaESVRCNBUhJLQp0OURERC0SA0wz\nk4ky9LH0hqh2IiUzo1GLVxIREVFdDDABEGWJBADYFdnIzLcHuBoiIqKWhwEmAM7OgylACufBEBER\nXTIGmAAI14QiRBkC0ViA5FQuK0BERHSpGGACQBAE9A+LhCB34/eCdLjcXFaAiIjoUjDABEjf6nkw\nXn0+TpwpCXA1RERELQsDTIBEmnsBAESjFcmcB0NERHRJGGACRK/QoYu+E0R9MZLT8gNdDhERUYvC\nABNAUaGREEQJmc7TsDkqA10OERFRi8EAE0BRtW6nPpLGp/ISERE1FgNMAPUwdYdcUFQtK8B5MERE\nRI3GABNAClGOq8w9IGrLkJyRyWUFiIiIGokBJsBqnspbKstGTqEjwNUQERG1DAwwAVazLpJotCIl\nlcNIREREjcEAE2Adde2hl+shMxYgObUg0OUQERG1CAwwASYIAqJCr4KgrMSx/Ay4Pd5Al0RERBT0\nGGCCQM08GI82D6eybAGuhoiIKPgxwASBmgBTtTo158EQERFdDANMEAhRmdBOEwHRUIgULitARER0\nUQwwQaJfaCQEmRfpZemwl7sCXQ4REVFQY4AJEjXDSILRymUFiIiILoIBJkj0DukJESJkpgIc5rIC\nREREDWKACRJquQo9TN0gam1ITs8JdDlERERBjQEmiERZIgEBKEIm8oq4rAAREdGFMMAEkdq3U6dw\nHgwREdEFMcAEkW7GzlDL1BBNVqRwWQEiIqILYoAJIqIgoo+lN0RVOY5kn4HHy2UFiIiI6sMAE2Si\nqoeRKjV5SMsuDXA1REREwYkBJsj0NUcCAGRGK1J4OzUREVG9GGCCTLg2FBaVGaKxEMmp1kCXQ0RE\nFJQYYIJQVGgkBLkbaSUZcFa4A10OERFR0GGACUI1t1PDYMWx9OLAFkNERBSEGGCCUB9zbwDVz4NJ\n5TwYIiKic/k1wBw/fhwTJkzAunXrAAAulwuLFy/G7NmzMX/+fJSUlAAAtmzZglmzZmHOnDnYuHGj\nP0tqEXQKLboaOkM0FCP5dG6gyyEiIgo6fgswDocDS5cuRVxcnG/bhg0bYDabsWnTJkydOhX79++H\nw+HAW2+9hdWrV2Pt2rVYs2YNios5bBJliYQgSMj3ZKKgpDzQ5RAREQWVyw4waWlpDb6vVCqxatUq\nRERE+LZ99913+MMf/gAA+OMf/4jx48fj0KFDiI6OhsFggFqtxqBBg5CYmHi5ZbUaNfNgZMYC3k5N\nRER0DnlDb95xxx344IMPfK9XrFiB++67DwDwxBNP4MMPP7zwieVyyOV1T5+ZmYkffvgBL730EsLC\nwvDkk0/CarXCYrH49rFYLMjPz2+waLNZC7lc1uA+VyI83OC3czdWiKU/lIcU8JqsOJldilkTAl9T\nMAiGtqHzsV2CF9smeLFtrkyDAcbtrnsL7969e30BRpKkS76YJEno0aMHFi1ahBUrVmDlypXo16/f\neftcTJEfV2oODzcgPz84noDbO6QnDnuPIfHIaeTmRUIUhECXFFDB1DZ0FtsleLFtghfbpnEaCnkN\nDiEJ5/yDWTtcnPteY4SFhSE2NhYAMGLECJw4cQIRERGwWs8+sC0vL6/OsFNbVrOsQLkqB+m5/EMn\nIiKqcUlzYC4ntNQ2atQo7N69GwCQkpKCHj16ICYmBklJSbDZbLDb7UhMTMSQIUOu6DqtRV9L1bIC\nvJ2aiIiorgaHkEpKSvC///3P99pms2Hv3r2QJAk2m63BEycnJ2PZsmXIzMyEXC7H9u3b8a9//QvP\nPvssNm3aBK1Wi2XLlkGtVmPx4sVYsGABBEHAwoULYTBwXBAAOujawaAwwGaqmsg7La57oEsiIiIK\nCoLUwKSTefPmNXjw2rVrm7ygxvDnuGGwjUuuOfxf/JyTCNfh4XjjnulQKfw3eTnYBVvbUBW2S/Bi\n2wQvtk3jNDQHpsEemEAFFDqrr/kq/JyTCOitOJ5RjOieoYEuiYiIKOAanANTVlaG1atX+17/97//\nxfXXX48HHnigzsRb8p+a58GIJivnwRAREVVrMMA88cQTKCgoAACkpqZi+fLlePTRRzFs2DA8++yz\nzVJgW2dSGdFB2w6ioQjJpxt+Pg4REVFb0WCAycjIwOLFiwEA27dvR3x8PIYNG4abbrqJPTDNKCo0\nEoLoRU55JorLKgJdDhERUcA1GGC0Wq3v559//hlDhw71vb7SW6qp8XzDSEYrDnNZASIiooYDjMfj\nQUFBAdLT03Hw4EEMHz4cAGC32+F0OpulQKp6Iq9MkEFm4vNgiIiIgIvchXTXXXdh6tSpKC8vx6JF\ni2AymVBeXo5bbrkFCQkJzVVjm6eSKdHT1A2/e08h5VgOJKkfe8CIiKhNazDAjB49Gnv27EFFRQX0\nej0AQK1W429/+xtGjBjRLAVSlb6WSPxefAp2eQ4y8+3oHKEPdElEREQB0+AQUlZWFvLz82Gz2ZCV\nleX76tmzJ7KyspqrRsLZdZFEYwGSOYxERERtXIM9MOPGjUOPHj0QHh4O4PzFHD/88EP/Vkc+XQyd\noJFpYDdZkZJWgPjruga6JCIiooBpMMAsW7YMn332Gex2O6ZNm4bp06fDYrE0V21UiyiI6GvpjYOe\nJBzPy4TLfQ0U8ra7rAAREbVtDQ4hXX/99Xj//ffx6quvoqysDLfeeivuvPNObN26FeXl5c1VI1Wr\nuZ3aq8vH72dKAlwNERFR4DQYYGp06NAB9913H7788ktMnjwZzzzzDCfxBkBfSyQAVN1OzefBEBFR\nG9bgEFINm82GLVu2YPPmzfB4PLjnnnswffp0f9dG5wjTWBCqtsBqLERyqhVzxvQOdElEREQB0WCA\n2bNnDz7++GMkJydj0qRJeOGFFxAZGdlctVE9okIjsad8LzLtmbA5KmHUKgNdEhERUbNrMMDceeed\n6N69OwYNGoTCwkJ88MEHdd5//vnn/VocnS/KfBX2ZO6FaCzAkbQiXNevXaBLIiIianYNBpia26SL\niopgNpvrvHfmzBn/VUUXFGnuDQECRJMVKamFDDBERNQmNRhgRFHEQw89hIqKClgsFqxcuRLdunXD\nunXr8O677+LGG29srjqpmlahQTdjZ6RJZ5B8PBeS1JfLChARUZvTYIB55ZVXsHr1avTq1Qvffvst\nnnjiCXi9XphMJmzcuLG5aqRz9LVEIs2WAZuQg5xCBzqE6gJdEhERUbNq8DZqURTRq1cvAMD48eOR\nmZmJ2267DW+++SbatePQRaD0NVc9D0ZmsnJZASIiapMaDDDnDk106NABEydO9GtBdHE9TF2hFJUQ\njQU4zABDRERtUKMeZFeDcy2Cg1yUI9LcE6LGjqM52XB7vIEuiYiIqFk1OAfm4MGDGDNmjO91QUEB\nxowZA0mSIAgCdu3a5efy6EL6WiKRXHAUbk0eTmaWoE9X88UPIiIiaiUaDDBfffVVc9VBl6hmXSTR\nZEVKWhEDDBERtSkNBphOnTo1Vx10idprI2BUGlFiLEBKWgFuHNUz0CURERE1m0uaA0PBQxAERFmu\ngqBw4XRJJuzlrkCXRERE1GwYYFow3zCSwYojaUUBroaIiKj5MMC0YGfnwRTgcBpvpyYioraDAaYF\nMyoN6KhrD5mhCElp+YEuh4iIqNkwwLRwUZZIQPSiyJuNvCJHoMshIiJqFgwwLVztYaQUzoMhIqI2\nggGmhesd0gMyQQaZ0YoULitARERtBANMC6eUKdErpAdEXSmOZObA4+WyAkRE1PoxwLQCUdWrU1eq\ncpGWXRrgaoiIiPyPAaYVqDMPhsNIRETUBjDAtAKdDR2hlWshMxUgOa0g0OUQERH5HQNMKyAKIvpa\nekNQliO1MBvOCnegSyIiIvIrBphWomYYCQYrjqbzdmoiImrdGGBaib7mSACAzGjF4VQGGCIiat0Y\nYFqJUI0Z4ZowiMYiJKdZA10OERGRX/k1wBw/fhwTJkzAunXr6mzfvXs3+vTp43u9ZcsWzJo1C3Pm\nzMHGjRv9WVKrFmW5CoLMjfzKbBSUlAe6HCIiIr/xW4BxOBxYunQp4uLi6myvqKjAu+++i/DwcN9+\nb731FlavXo21a9dizZo1KC4u9ldZrdrZ26mtSOHq1ERE1Ir5LcAolUqsWrUKERERdba/8847uOWW\nW6BUKgEAhw4dQnR0NAwGA9RqNQYNGoTExER/ldWqRZp7QYAA0ViAwwwwRETUivktwMjlcqjV6jrb\nUlNTcfToUUyZMsW3zWq1wmKx+F5bLBbk5+f7q6xWTSPXoLuxC2T6EqSk58ErSYEuiYiIyC/kzXmx\n559/HkuWLGlwH6kR/+iazVrI5bKmKus84eEGv53b3wZ3uRqptnQ4FbkorfCid5eQQJfUpFpy27Rm\nbJfgxbYJXmybK9NsASY3NxenTp3CX//6VwBAXl4e5s6di/vvvx9W69m7ZvLy8jBgwIAGz1VU5PBb\nneHhBuTnt9z1hLqougEAZKYC7DmYAZPaf0GvubX0tmmt2C7Bi20TvNg2jdNQyGu226jbtWuHHTt2\nYMOGDdiwYQMiIiKwbt06xMTEICkpCTabDXa7HYmJiRgyZEhzldXq9DB2hVJUVk3k5bpIRETUSvmt\nByY5ORnLli1DZmYm5HI5tm/fjjfeeAMhIXWHNNRqNRYvXowFCxZAEAQsXLgQBgO71S6XTJQh0twL\nyd4jOJGfgwqXBypF6+mFISIiAgBBasykkyDjz2631tCt913GHmz6fQsqU/tjfuwkjLimQ6BLahKt\noW1aI7ZL8GLbBC+2TeMExRASNZ+o6ufBKEIK8eH2ozhymksLEBFR68IA0wq100YgRGWCJrQYkiTh\n9Y9/Q2q2LdBlERERNRkGmFZIEAT0NV+Fcq8TN8aHotLlwSsbDiG7wB7o0oiIiJoEA0wrNSDiagDA\nz/btSJjYGWVOF15e/ysKbVwjiYiIWj4GmFYqOqwfJnUbizynFQfcX+APozqi0FaBl9f/ilJHZaDL\nIyIiuiIMMK3YH3rGY2SnOGSWZeOEcgcmXNse2QUOvLLhEJwV7kCXR0REdNkYYFoxQRCQEHk9YtsN\nRKotHVbzHgyLjkBaTine3JwEl9sb6BKJiIguCwNMKycKIuZFJSA6rB+OFZ2Ap8sBDLjKgiOni/Du\nlhR4vAwxRETU8jDAtAEyUYYF/W9FpLk3frOmwNDnCPp0NeHA8Xx8+NWxRi2gSUREFEwYYNoIhUyB\ne6JvQ3djV+zPO4guA0+jazs9dv+WjU3fnwx0eURERJeEAaYNUcvVuC/mT+ioa48fs/fi6rh8tLNo\n8eXedHy573SgyyMiImo0Bpg2RqfQYtGAuxCmCcV3Wd9j6OgymA0qbPzuJH44lBXo8oiIiBqFAaYN\nMqkMeGDAXQhRmfB15tcYO8EDvUaBNV8dxYFjeYEuj4iI6KIYYNqoUI0F9w+4C3qFDl9lfoEpk+VQ\nymVYuSUFR9IKA10eERFRgxhg2rD2uggsHLAAKpkK27I/w4wpGgDA65uTuPgjEREFNQaYNq6roTPu\njbkDMkGGr/M+xR8mG7j4IxERBT0GGELvkB64O/o2eCUJO4s+xfQJIShzuvCv//6KghIu/khERMGH\nAYYAAP1C++CO/reg0uPC/+xbMHlUCIpKqxZ/tHHxRyIiCjIMMOQzMCIat/adDbvbgUPeLzD62hDk\nFHLxRyIiCj4MMFRHXMdYzL7qDyipLMVJ9de49hojTueU4o2Pf4PL7Ql0eURERAAYYKgeY7uMwLQe\nE1FQXog8yy5c08eAo+nFeOczLv5IRETBgQGG6jWl+wSM6zISuY48lHf6CZHddDj4uxVruPgjEREF\nAQYYqpcgCLix93QM6xCLjLJMKK46gK4dNNjzWzY27uLij0REFFjyQBdAwUsQBNzcdxacngoczPsN\nkdFKVFT0w1f70mHQKDBlaLdAl0hERG0UAww1SBRE3N7vJlS4K3C48BiujlWi4qee2LjrJHQaBUbF\ndAx0iURE1AZxCIkuSi7KcVf0PPQy9UByUTKihmdAp5FjzVdHsf8oF38kIqLmxwBDjaKUKXFvzO3o\nYuiEXwsPYtBoK5QKEe9uTUEKF38kIqJmxgBDjaaRa7Ao5k6010Zgf+FexI0tBQC8+XESTmVx8Uci\nImo+DDB0SfRKHe4feBdC1WbsK9qNEeMqUOn24NWNh5Bl5eKPRETUPBhg6JKFqEy4f8DdMCoN2Fey\nE6PGeFDmdOHl9Vz8kYiImgcDDF2WcG0o7h9wF3RyLX6x78CIkUBRaQX+xcUfiYioGTDA0GXrqG+P\n+wb8CUqZAr9WfoPrrhWRW+jAK+u5+CMREfkXAwxdke7GrvjzNXdAEAQcEb/BgBgRp3O5+CMREfkX\nAwxdsUhzL9x59Vx4JA/StTvRL0rGxR+JiMivGGCoSUSH9cP8qD+i3FOBfMsu9O4pw8HfrVj95VEu\n/khERE2OAYaazJD2A3FTn5koc9lR1nEPunQS8WNSDjZ8d4IhhoiImhQDDDWpEZ2G4oZeU1FSWQKp\n1z5EhIvY/nMGtu09HejSiIioFWGAoSY3sdsYTO42DgXlBdD2OwBziICPvz+F73/NDHRpRETUSjDA\nkF/M6DkZozoNQ64zF6EDkqDTCfhw+zEu/khERE2CAYb8QhAEzIn8A65tPwiZjjPoHHsESiWwcksK\nUlK5+CMREV0ZBhjyG1EQMbfvHMSE9Ue6Iw294k5CELx4c3MSTmaVBLo8IiJqwfwaYI4fP44JEyZg\n3bp1AIDs7GzcfvvtmDt3Lm6//Xbk5+cDALZs2YJZs2Zhzpw52Lhxoz9LomYmE2W4o/8t6GPujVTH\n7+g7IgOVbjde3XAImVz8kYiILpPfAozD4cDSpUsRFxfn2/bqq68iISEB69atw8SJE/HBBx/A4XDg\nrbfewurVq7F27VqsWbMGxcXF/iqLAkAhU+Du6PnoYeyKk87DiB6RA3u5C8vX/wpriTPQ5RERUQvk\ntwCjVCqxatUqRERE+LY9+eSTmDx5MgDAbDajuLgYhw4dQnR0NAwGA9RqNQYNGoTExER/lUUBopar\ncF/Mn9BJ3wG/VxxC9HArikor8PJ/f4XNzsUfiYjo0vgtwMjlcqjV6jrbtFotZDIZPB4PPvroI8yY\nMQNWqxUWi8W3j8Vi8Q0tUeuiVWixaMCdCNeE4oTrAPpfV4jcIieWb/iViz8SEdElkTf3BT0eDx55\n5BEMHToUcXFx2Lp1a533G/PEVrNZC7lc5q8SER5u8Nu527pwGPBUyEN4YufLOOX4GdHXjkTSz8Db\nW1Lw1F1xUCkable2TXBiuwQvtk3wYttcmWYPMI899hi6deuGRYsWAQAiIiJgtVp97+fl5WHAgAEN\nnqOoyOG3+sLDDcjPL/Xb+QkAlFgYvQDLE9/GSdce9O4/HMkpwDPv7cXCG6+GTKy/Y5BtE5zYLsGL\nbRO82DaN01DIa9bbqLds2QKFQoEHHnjAty0mJgZJSUmw2Wyw2+1ITEzEkCFDmrMsCoB2uggsGnAX\n1HIVsvU/odtVDvx6worV247Cy3WTiIjoIvzWA5OcnIxly5YhMzMTcrkc27dvR0FBAVQqFebNmwcA\n6NWrF5566iksXrwYCxYsgCAIWLhwIQwGdqu1BV0MHXFfzJ/wxsFVKLT8hI7dh+PH5BzoNAr8cVxv\nCIIQ6BKJiChICVILXCbYn91u7NZrfkcKj+OdQx9AFGRQpg9DfpYas0b3xLS47nX2Y9sEJ7ZL8GLb\nBC+2TeMEzRASUX2iLJG4o/8tcHld8HTbB3NYBT7+/hR2HeTij0REVD8GGAoKAyKiMTdqDpweJ+SR\nv0BnqsTa7cfwCxd/JCKiejDAUNAY2mEI5lx1PcrcZTBcfQAqXSXe3ZKC5NSCQJdGRERBhgGGgsqY\nLsMxvcdklLhKYBlwCIKismrxx0wu/khERGc1+3NgiC4mvvs4ON1OfJvxA9oPSUbmvqvx6sZDuNvt\nhShJ0KsV0GuqvpQKkXcrERG1QQwwFHQEQcDM3tPgdJfjp+yf0eVaOdL39sMr/3fwvH3lMhF6jdwX\naGq+dOf8bKj1s1Yth8jQQ0TUojHAUFASBAE3970RFZ4KHMg7hKhRKowwz0R+vgNlThfKyl0oc7pg\nd1Z9L7BV4Ey+vZHnBnTqmpAjh0GjhK5WCNJpFGd7ebRng5BcxhFXIqJgwQBDQUsURNzW748o91Qg\npeAozpS/Bp1cA51RB12oFgaFFu0VWugUOugUWqhlesglFeBRAm4FPJUKuCpkcJZ7UOZ0obRW4KkJ\nP/lFzkY/+VellNUavpJDr1VCr1bUCT++0FMdkNRKGYe4iIj8gAGGgppclOPOq+fh49+3IKc8F8XO\nUhRVlCDLntPoc2jkGujUWugMWugUWnSQ66BXVP2sVWihgBqiRwHJo4LXpYC3Ug6nU4K93I2y8qqg\nU+qoDj/lLmQX2lHp8jbq2jJRqHdYy6BVwKBVwqhVwKBTwlj9s16ruOBaUEREdBYDDAU9pUyBm/vO\nqvPkSo/XA4fbCbvLUf1lr/ruPud1rfczy4vhljyNuqZclEMn10Jn0kIXpkWIQodO1aFHp9BCLeoh\nq+7tkSrl8Liqenvs5Z46vTw1X8VlFciy2tGYvp6GAo5Bq4RRp4RBq4BRp4RWJWcPDxG1SQww1CLJ\nRBkMSj0MSn2jj5EkCRWeyuqgc37Aqfu6altRRXGje3sECNDI1dBptdCZqoa1utSEHrkOWrkGcqgg\neJSQ3Ep4K5VwlctQ5vSg1O6CzVGJUkclbA4XbPZK5BQ4Lhp4ZKJQFWa0yuqgUyvkaM4JPzolVApZ\no39fRETBjAGG2gxBEKCWq6CWqxAKc6OPO9vbY0fZhUKP++xrh8uBokvo7dHINTAYddCH6hGi1KOz\nQgeDUg+dXAs5NBDdVYHHXaFApVOOMqe7KujYXSh1VqLU7kJesRPpeWUXvZZKIfP13hi1Z3tyzu3t\nMWg5cZmIghsDDNFF+KO3p8xlR1mlHaUuO8oqy1DqKkO+owBSIwaZdHIt9GY99BE6RCj16KnUwaDQ\nQyPTQO7VVA1ruWoCj4hSp7sq7Dgqq3t5XDidUwqPtxHXUsvrDTi1e3oqJMBT4YKGw1lE1IwYYIj8\n4HJ6e7ySFw63syrQVNpR6iqrDjm1v5f5Qk+eI/+igUeAAJ1CC32EHgaFDp2VVd/1Ch1UggaipIbg\nVsFbqYCrQoEKh4hSpws2hwul9rOBpzHDWXKZCJPubO+OSV8VcEw6VfU2BUx6FYxaJTQq3p1FRFeG\nAYYoSIiCCH11uGivu/j+XskLu8uB0soylLnsdb77Qk/NtopS5NhzG1WDTquFwaSHXqlHuEIHffVw\nlgIayDwqSB4lPJUKuMsVcDoEuLxAXoEdNkclSuyVyMgrg9vTcNypCjs1Aafqe83P527nrehEVB8G\nGKIWShTESxra8ng9KHM5UOYqqzf0nP1e1uhb1UVBhEltgLabFmalAV2VehgUeqhELeTe6t4dlxKu\ncgXKHSJKHR7Y7FVBx2avRHruxYeylHKxTsCp08NTPYxV8zPDDlHbwQBD1EbIRBlMKgNMKkOj9nd7\n3bXm6pwznFUr8Di8DlidBcgsy77oOXUGLQyhBoQo9OhSHb5UghZy31BW1dydCqccZXZvVa9OWdVQ\nVmPm7SgVYlW4OSfwnP2ugrF6mEut5P/8EbVk/C+YiOolF+UIUZkQojI1uF/N83kqPZUorSyDrbIM\npZWlvqGs2q9tlWWNHs5S69UwWqqGsjoq9dAr9FCLWsglTXXPjgLuCiUqHDLYHRJK7W6U2Ctgs1ci\nrRFhR6WQwahTnJ2jUx10zAYVLEYVQo1qWAxqqJS89ZwoGDHAEFGTUMqUCNVYEKqxXHTfmt4dW03Q\nqf6q89pV9Tq/5OJ3Zyl0ChjMVT067WuHHa8aokcNr0sJT4UCFQ45HA6h6lk7ZZUocVTiVJatweUk\ndGp5VZgxqqu/q+r8HKJXQRQ5bEXU3BhgiKjZNbZ3Bzg7Wblu2Cmt7tmpCjo1r8+UZsFzkefviDoR\nhhA9jEo9wpUG6JU6qIXqnh2PClKFGi6nEo4yOYpsbhTaypFT5Ljgc3ZEQYDZoPSFGnNN702tkMMn\nJhM1PQYYIgpqlzJZWZIkON3Os8NVrrLzenlqwk6uIx8ZZVkXPpkKMHTSw9wzBN3UIdDLjFBKWghu\nLbzlKlTYlSgtFVFkq0SBrRwnMkvw+5mS+k+llJ3tvTGoEXpOL47ZoIZCzocGEl0KBhgiajUEQYC2\nepHOdrqIi+5f4ams1ZtT9b24ogRF5cVVX9VLSaSXnqn3eNEowhxuQidVCK5WmaARDZB7tIBLA3e5\nCs4yBUpKvCi0VaKotBxZVvsFazHpqnpxLHV6cFTV29QwahXsxSGqhQGGiNoslUwJlSYUYZrQC+4j\nSRLKXHYUlhehqFa4KawoRnF5MQrLi3GqJK3+eTpyQBWhhLlLCPqoQ2BUGKGCHqJbC6lSjUqHEo5S\nOYptbhTaKpCRV4rUbHD56xAAABNDSURBVFu9dchlYq3JxdU9OKbavTqccExtCwMMEVEDBEHwDWF1\nQ5d69/F4PVU9NxUlKCwvQnF5CQorzvbiFJUXI8eRV/8FtIDepINFHYIeqhDoZAYovDqIbg085SqU\n21UoswkoKq1Ega0CR04XXbDW2hOOLUYVuncKgV4pQ/tQLcJMaq5tRa0KAwwR0RWSibJad2D1qHef\ncncFiiuqemxqQk1RddApLi9Glj0X6aWZ5x8oAqJZREh7E7qqTDApTVALesi9OqBSDZdThfIyBUps\nEgptFcgtqrWwZ+LZ88lEAeEhGrS3aKu+QrW+nw0cnqIWiAGGiKgZqOUqtJe3Q3tdu3rfrxmqqum1\nOTfoFFUU41TJ6fqHqlSAsp0C5m5m9FP9//buPbatu+7j+Nt3x9fYju0017Zptz69rKXdHp6WFhhs\nIEBaYbeU0gB/PEio4g/QuFRlo0ybQBkXobFqwNikqggt0HEZArpxK6q0tsA6lS3aelvS5urEiRM7\ncZzUl+cPu27Slj3butR2+3lJUZrj45Pv6amaT36/7zk/L26LB2vOidNcTWLUzGTMRnQ0w+BoksHR\n5CVvd9jMhGcFmwWFP4d8VVgtmpaS8qQAIyJSBmZPVTXRcNl98lNV8QvBZtbn0dQYY6lxIpebqnKA\n0+ugeXkNPmsAe86DYdrF9ISdeMzC0Gh+WYeL+28MgN9jnzNacz7gVLttGDVqIyWkACMiUiHyU1U+\nAlX/eYXz6cxMsdF42pzk9FAPQ8lhhpJRziR66cqdnfsGH/hqq1nlqMFt8mFJu8mmnEzFbcRGjERi\nKTq7RunsGp3zNqvFSNjnuOyUVJVNP1pk/ulfmYjINcRmslLrDFHrDBEMulnjTRRfy2QzRFOjxUBT\n/DwV5Xjs1NwDWcFUZ6KmJcBSewAH1RjPuUgnq5gYsxEdyRIZTdJzmQf8eZ3WOaEm7M+P2tRU2zEZ\n1Ugs7wwFGBGR64TJaCLsCBJ2BC95LZWeZnhqpBhqIslhhqaGGUoOXzot5QZ7tY2Fq2qotvixZc9P\nSVWRiFkYGjnHiZ4xjveMXfT9DYR8FxqJZ/fduKvUSCxvjQKMiIhgN9todNfR6K6bs/18c3FxxGbq\nwsjNwGSEnuysO6dMQA146tyssNfgMlVjybjJTDmZGrcxNmoiMjLNwMiljcROu/mywSbsq8JiViOx\nXEoBRkRE/qPZzcUt1QvnvJbNZYmlxgsjNdHCaE3+z6/HL3q4nwMMDgOBpX6WWgM4DN78lNRkYUoq\nCt2DCU73X9pIHPDaWRBw0hhy0VzrpinsIlhdpSbi65wCjIiIvC1Gg7HYVPxf/hvmvHYuc+6SfptI\nMsrQ1DAnxk/MPVA1WPxmmqtq8Jp92LNemHYyPVHF+KiF4ZEML78+wsuvjxTfYreaaArnw0xz2E1T\n2M2CgEMP67uOKMCIiMg7zmKysMAZZsFlnnszlZ4qhJpCr82sqamBzOCFHa1ALTgbHNxor8FtCGCc\n9pIaczA0aOZkzxgnZvXZmE1GGoJOmsLu4khNY9ClZ9lcoxRgRETkqqoyV9HsaaTZM3dphlwuR3wm\ncaGRuDg1FaV3spfs+VvAHWBYbKB5ZRCfKYTlXDXTcSejETO9QxN0DybgWH5XgwHqAk6awq58sCmM\n2jjslqt81vJOU4AREZGyYDAY8No8eG0elvpa5rx2LptmcDJCb6Kfnol+ehP99E30E8kU7pCyAo0Q\nXOKlxhrGlvaRnnAzHrXTPzBFX3SSQ52R4vFqvPZ8mKl101wIN9Uu21U8W7lSCjAiIlL2LEYzje56\nGt31rC9sy+ayRKdG6S0EmvznPk4lCj02JiAMrvoqFtvDOHIBspNuJkaq6O8/x4snhnnxxHDxe3id\n1rl9NbVugl67bu8uUwowIiJSkYwGIyFHDSFHDWtDNxW3x2cS+UBTCDU9E32cmThDju78DgEwB80s\nsYdwGWowpLwkYw6GBy5tFq6ymWmadffT+WZhPZCv9BRgRETkmuKxulkeuJHlgRuL21LpafonB+gp\nBps++icG6cv1F94EBo+BRnuAalMQ80w1qXEHIxHjJQ/ls5iNNARd+amn2nxfTUPQqefVXGUKMCIi\ncs2zm20s9i5ksXdhcVsmm2EwOXRhpCbRR+/EANFUNL+DA1gEoRs9BCxBrGk/5xIuxoZtnB2Iz1n8\n0mgwUFfjKExBXeir0bpQ80d/syIicl0yGU3UuxZQ71rAu1kH5O+EGk3FCo3CfYVg00/X5GngdL5Z\nuB48TTZqbGEcWT+ZSQ/xETuR/iS9w5O88MqFW8FD1VX5nppadzHceJ3W0pzwNWZeA8yJEyfYvn07\nn/3sZ9m2bRsDAwN89atfJZPJEAwG+c53voPVauXZZ59lz549GI1G7r33Xu655575LEtEROSyDAYD\ngSo/gSo/a4Iri9snZiZnjdIU7oJK9pDjbL5ZOASWsIkFtiAuQwCmvCRjVUT64V/Hp/jX8QvNwtUu\nKzc2+2kMOmmp87BwgQebnlXzls1bgEkmkzz00EOsX7++uO3RRx9l69atfOQjH+H73/8++/bt4+Mf\n/zi7d+9m3759WCwW7r77bm6//Xaqq6vnqzQREZG3xGV1ssy/lGX+pcVtM5kZ+iYG6Z3oK97e3T8x\nQCRbGIHx5T/qbX48xhpM015S4y6ig1mOdA5ypHAck9FAQ8jFkjovLfUeWuq91Ojup//XvAUYq9XK\nE088wRNPPFHcduTIER588EEAbr31Vp566ikWLVrEqlWrcLvdAKxdu5ajR4/ygQ98YL5KExERuWJW\nk5VF3iYWeZuK2zLZDENT0TkjNb2JfrqnC7d2u/MfC2xevIYQxik/8aiDvp4MZwYT/OVofjeP00pL\nnYcl9V4Wa5TmsuYtwJjNZszmuYefmprCas3P/QUCAYaHh4lGo/j9/uI+fr+f4eFhREREKo3JaCou\nofDfrAXyfTVj0+PFUNOT6OfsRA9nUifzq1UGwRYy0mAN4cgGmYl7Ge6z8dLJaV46GS0cV6M0FytZ\nE28ul3tL22fz+RyY5/F2tWDQPW/Hliuja1OedF3Kl65NeQjh4QYuLJ2Qy+UYScY4MdLFycLH67Gz\n+emnKmAJ1FicBK0LME8HiA876OtOzxmlqXbbWNbsY1mzn2UL/SxprL6uRmmuaoBxOBykUinsdjuR\nSIRQKEQoFCIajRb3GRoaYs2aNW94nFgsOW81BoNuhocT83Z8eft0bcqTrkv50rUpX8Ggm1zSwtKq\nG1jacAM05JdL6Jvop2v8LN3xs/nPk6eAU+ADq89ArbUGZzZIOuElOmDn8CspDhfuejIZDTSGXLTU\nF0Zp6ip/lOaNAvhVDTAbNmzgueeeY/PmzTz//PNs2rSJ1atXc//99xOPxzGZTBw9epSdO3dezbJE\nRERKzmI0s9DTxELPhZ6a+EyC7vGzdMXP0jV+hjOJXkYyw/lRmsVQvcSG3xzGPO1ncsRFb9803YMJ\n/vJi/v1ep5XFhV6alnovC2vd18zq3Ibcm5mzeRteeeUV2tvb6evrw2w2Ew6H+e53v8uOHTuYnp6m\nrq6Ob3/721gsFvbv38+TTz6JwWBg27Zt3HHHHW947Pn8jUK/sZQvXZvypOtSvnRtytfbvTaZbIaB\nyQhd8bPFYBNJDs3Zx2vx4coFySS8jA5WMR61Qy6/9MHFozRL6rwEyniU5o1GYOYtwMwnBZjrk65N\nedJ1KV+6NuXrnbw2yXNJuuM9c0LNVHqq+LrZYMZvDmGZCZAcdTHUZyOdshdf9zqt+UBT5ym7UZqy\nmUISERGRd5bD4piz9lM2l2U4Gc1POxVCTd/EADlDPwTAEgCf2Y07FyI74SUWcXD05BRHCytzV8oo\njQKMiIjINcRoMBJ2hgg7Q/zPgpuB/GKWPYneYqB5PX6GgZnT+V6aheBcZMRnDmKdCZCMuenpm6R7\nMM5fXsyHluIoTaE5uBxGaRRgRERErnF2s42lvhaW+lqA82s+jdEdP1MMNT2JPtKGCPjB6ocqkwMP\nIXKT1flRmlPJOaM0TWEXi+u8vHt5mCX13qt+TgowIiIi15n8mk8+AlU+1oXzjy45l03nnxpcuOOp\nO36WSKob7EAzOJoNeM0BbOcCpGJuevoTdA3EefVMjIf/991X/RwUYERERASL0VxcGuHWxo0AjE8n\n6I6fLYaaM/EexgxR8IPFD06jjWU160pSrwKMiIiIXJbX5mZ1cAWrgyuAy9/GPcNkSWpTgBEREZE3\nxWQ00eCuo8Fdx6b6/ylpLcaSfncRERGRt0EBRkRERCqOAoyIiIhUHAUYERERqTgKMCIiIlJxFGBE\nRESk4ijAiIiISMVRgBEREZGKowAjIiIiFUcBRkRERCqOAoyIiIhUHAUYERERqTgKMCIiIlJxDLlc\nLlfqIkRERETeCo3AiIiISMVRgBEREZGKowAjIiIiFUcBRkRERCqOAoyIiIhUHAUYERERqTgKMLN8\n61vforW1lS1btvDvf/+71OXILI888gitra3cddddPP/886UuR2ZJpVLcdttt/OpXvyp1KTLLs88+\nyx133MGdd97JgQMHSl2OAJOTk3zhC1+gra2NLVu2cPDgwVKXVNHMpS6gXPzjH//gzJkzdHR0cPr0\naXbu3ElHR0epyxLg8OHDnDx5ko6ODmKxGJ/4xCf40Ic+VOqypODxxx/H6/WWugyZJRaLsXv3bp55\n5hmSySQ//OEPef/731/qsq57v/71r1m0aBH33XcfkUiEz3zmM+zfv7/UZVUsBZiCQ4cOcdtttwHQ\n0tLC+Pg4ExMTuFyuElcmt9xyCzfddBMAHo+HqakpMpkMJpOpxJXJ6dOnOXXqlH44lplDhw6xfv16\nXC4XLpeLhx56qNQlCeDz+Th+/DgA8Xgcn89X4ooqm6aQCqLR6Jx/TH6/n+Hh4RJWJOeZTCYcDgcA\n+/bt473vfa/CS5lob29nx44dpS5DLtLb20sqleLzn/88W7du5dChQ6UuSYCPfexj9Pf3c/vtt7Nt\n2za+9rWvlbqkiqYRmP9AKyyUnz//+c/s27ePp556qtSlCPCb3/yGNWvW0NjYWOpS5DLGxsZ47LHH\n6O/v59Of/jR/+9vfMBgMpS7ruvbb3/6Wuro6nnzySV577TV27typ3rEroABTEAqFiEajxa+HhoYI\nBoMlrEhmO3jwID/60Y/46U9/itvtLnU5Ahw4cICenh4OHDjA4OAgVquV2tpaNmzYUOrSrnuBQIB3\nvetdmM1mmpqacDqdjI6OEggESl3ade3o0aNs3LgRgGXLljE0NKTp8CugKaSC97znPTz33HMAdHZ2\nEgqF1P9SJhKJBI888gg//vGPqa6uLnU5UvCDH/yAZ555hl/84hfcc889bN++XeGlTGzcuJHDhw+T\nzWaJxWIkk0n1W5SB5uZmjh07BkBfXx9Op1Ph5QpoBKZg7dq1rFixgi1btmAwGNi1a1epS5KCP/zh\nD8RiMb74xS8Wt7W3t1NXV1fCqkTKVzgc5sMf/jD33nsvAPfffz9Go35fLbXW1lZ27tzJtm3bSKfT\nfPOb3yx1SRXNkFOzh4iIiFQYRXIRERGpOAowIiIiUnEUYERERKTiKMCIiIhIxVGAERERkYqjACMi\n86q3t5eVK1fS1tZWXIX3vvvuIx6Pv+ljtLW1kclk3vT+n/zkJzly5MjbKVdEKoQCjIjMO7/fz969\ne9m7dy9PP/00oVCIxx9//E2/f+/evXrgl4jMoQfZichVd8stt9DR0cFrr71Ge3s76XSac+fO8Y1v\nfIPly5fT1tbGsmXLePXVV9mzZw/Lly+ns7OTmZkZHnjgAQYHB0mn02zevJmtW7cyNTXFl770JWKx\nGM3NzUxPTwMQiUT48pe/DEAqlaK1tZW77767lKcuIu8QBRgRuaoymQx/+tOfWLduHV/5ylfYvXs3\nTU1Nlyxu53A4+NnPfjbnvXv37sXj8fC9732PVCrFRz/6UTZt2sQLL7yA3W6no6ODoaEhPvjBDwLw\nxz/+kcWLF/Pggw8yPT3NL3/5y6t+viIyPxRgRGTejY6O0tbWBkA2m+Xmm2/mrrvu4tFHH+XrX/96\ncb+JiQmy2SyQX97jYseOHePOO+8EwG63s3LlSjo7Ozlx4gTr1q0D8guzLl68GIBNmzbx85//nB07\ndvC+972P1tbWeT1PEbl6FGBEZN6d74GZLZFIYLFYLtl+nsViuWSbwWCY83Uul8NgMJDL5eas9XM+\nBLW0tPD73/+ef/7zn+zfv589e/bw9NNPX+npiEgZUBOviJSE2+2moaGBv//97wB0dXXx2GOPveF7\nVq9ezcGDBwFIJpN0dnayYsUKWlpaeOmllwAYGBigq6sLgN/97ne8/PLLbNiwgV27djEwMEA6nZ7H\nsxKRq0UjMCJSMu3t7Tz88MP85Cc/IZ1Os2PHjjfcv62tjQceeIBPfepTzMzMsH37dhoaGti8eTN/\n/etf2bp1Kw0NDaxatQqAJUuWsGvXLqxWK7lcjs997nOYzfpvT+RaoNWoRUREpOJoCklEREQqjgKM\niIiIVBwFGBEREak4CjAiIiJScRRgREREpOIowIiIiEjFUYARERGRiqMAIyIiIhXn/wBL8H77ML14\nFwAAAABJRU5ErkJggg==\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": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "fa92bca4-37da-493f-d295-58765aa273a2" + }, + "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": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 118.73\n", + " period 01 : 107.95\n", + " period 02 : 106.07\n", + " period 03 : 104.70\n", + " period 04 : 103.03\n", + " period 05 : 101.80\n", + " period 06 : 101.59\n", + " period 07 : 101.19\n", + " period 08 : 100.73\n", + " period 09 : 100.57\n", + "Model training finished.\n", + "Final RMSE (on training data): 100.57\n", + "Final RMSE (on validation data): 99.35\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RHOyDxeLW7D4XKzzcv1X7DwtO5q/f/J1d5bsZnnwdyz7axYFjVaRe3tVBEV6a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\nwpEwVe6jvH1+L+3+Dix6C2snf4E1RSuxGMxxfe8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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "P8BLQ7T71JWd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1hwaFCE71OPZ" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "djKtt4mz1ZEc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Dw2Mr9JZ1cRi" + }, + "cell_type": "markdown", + "source": [ + "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances." + ] + } + ] +} \ No newline at end of file From 5295289dbb9e65c92ad710987a86f5159729611d Mon Sep 17 00:00:00 2001 From: SHUVANKAR ROY Date: Mon, 18 Feb 2019 16:22:40 +0530 Subject: [PATCH 14/14] Created using Colaboratory --- ...classification_of_handwritten_digits.ipynb | 2921 +++++++++++++++++ 1 file changed, 2921 insertions(+) create mode 100644 11_multi_class_classification_of_handwritten_digits.ipynb diff --git a/11_multi_class_classification_of_handwritten_digits.ipynb b/11_multi_class_classification_of_handwritten_digits.ipynb new file mode 100644 index 0000000..7b90020 --- /dev/null +++ b/11_multi_class_classification_of_handwritten_digits.ipynb @@ -0,0 +1,2921 @@ +{ + "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": 233 + }, + "outputId": "6e807114-c445-4663-d32c-7d1772319bde" + }, + "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": [ + "
" + ] + }, + "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": 973 + }, + "outputId": "f2ba5fe4-9bb4-49d2-a647-4f92a9205c1b" + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=2000,\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": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 3.97\n", + " period 01 : 3.54\n", + " period 02 : 3.51\n", + " period 03 : 3.36\n", + " period 04 : 3.44\n", + " period 05 : 3.32\n", + " period 06 : 3.22\n", + " period 07 : 3.38\n", + " period 08 : 3.25\n", + " period 09 : 3.15\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.91\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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PLkaPHkdYWDdGjx7L0aNH+PzzT3jxxVd+td+WLZvo2jWMP//5cX78cWtLz7q2tpZXX30T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\nnYnH66W+pZuK2nYOD4X5tkPNbDvUDAzOcD+3Z56RFI3JFFp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+ "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": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, as well as a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: An `int`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TOfmiSvqu8U9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n" + ] + }, + { + "metadata": { + "id": "evlB5ubzu8VJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 346 + }, + "outputId": "0b2172d1-5d56-4999-8ca9-3dd7cb0c5808" + }, + "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": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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10000.0 10000.0 \n", + "mean 0.0 0.0 0.0 \n", + "std 0.0 0.0 0.0 \n", + "min 0.0 0.0 0.0 \n", + "25% 0.0 0.0 0.0 \n", + "50% 0.0 0.0 0.0 \n", + "75% 0.0 0.0 0.0 \n", + "max 0.0 0.0 0.0 \n", + "\n", + "[8 rows x 784 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "metadata": { + "id": "PDuLd2Hcu8VL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "e3bfe2f0-f05f-42e8-9e09-2be95c35d6dc" + }, + "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": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 5.04\n", + " period 01 : 3.58\n", + " period 02 : 3.19\n", + " period 03 : 2.54\n", + " period 04 : 2.87\n", + " period 05 : 1.95\n", + " period 06 : 2.45\n", + " period 07 : 1.93\n", + " period 08 : 2.03\n", + " period 09 : 1.73\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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apWLR9TE42dvw2eYMThea9u5qV1tn5vW/gb+MfpyRvsM4W1PA8sQPePXwcjIr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\nncWD78HX0eeKt1tYV8zW3F3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nCXcJYfHge4j1ijL7kK+5cm2vsWeU3zDsNLaklKZzoPAw\nVY1VRLr37fQrR7sjazqmLUWKszAZY+VZrVIxvL83xZX1JGWVkZRVxrBIL+y0MqNVbUMzJ/IqcbbT\noLKSIX9btZYItzD2FxwiqSSVYT6DunxzU31LPauOr+GbrHW06HVc32cGCwbMwdlCbyQz96tS+7iG\nMcg7hqyKbFLKjnOkKJFQl+BuP2Xn5cg5WoqzMCFj5lmlUhga6U11ffO5KSczShjSzwsHO+t4K5W5\nVdQ08u3ubN79LpWtCbkUltcxrL+31dw85GbriqvWhcNFx8ioOMlVfsM7/ZKN9LIM3jy6goyKkwQ5\nBbB4yD0M9Rlo0ZmwLHHucNE6ExcwkmZ9Myml6ewrSMBg0BPhGtatZgXrDDlHW7A4/+tf/+L111/n\n888/x93dnYiIiDaXleLcPRk7z4qiMKiPJzq9gaMZJSQcL2ZQhCfODr3nZpnC8jq+2pHFynVpnMir\nxMneBn9PR1JOlVFb38LAPtbz2FmwcyBVTdWklKZT2lDGEO/YDsXWqGviq4zv+DJjLU36JmaETeGO\n6Hm42Vq+W7TUuUOtqIjyiKSvWx+Ol2WSVJpKatkJ+rr1wcmm57zu1mAwUN9Sj7OjPfX1zZYOx6La\nK86KwWAwmGKn+/btY8WKFbz33nuUl5dz4403sn379jaXLy6uNur+vb2djb5NcSlT5nnDvhy+3J6F\nk70Nj80bQqhfz37d5+nCatbvy+FgehEGA/i42TNjdAhXx/rh4urA46/v5ExxLTeOC2fW1ca7CetK\ntehbeO3w25yqOs2cftczKXhsu8tnVWTzn7RVlNSX4ufgw+3R84z+lq8rYQ3njvqWer44sZYDBYfR\nqmy4qd91jA0YbTV/lF1Os76FsoZySurLKK0vPffvhjJK6s/906BrwN3elTi/UVwdMMoq/iizBG/v\nts9pJivOOp2OxsZGHBwc0Ol0jBkzhp9++gm1uvVhLynO3ZOp87z96Bk+3ngcO1s1S+YMJjLYzWT7\nsgSDwcCJ3ArW7ztN0slS4NxEIdfGhTKiv8/5a8ze3s6cOFnCPz4+RGlVA3fM6M+EIYGWDP0iFY2V\nPH9wGbXNdTwy5D76uV86Stasa+b7Uz+w5fROACaHjGNW+HRsrGQyjV9Y07njcFEin6V/RV1LPTGe\nA7h1wC1WMTuYwWCgqqmG0oafC+/PRbfk558rG6swcGlp0aps8LL3xM3WlVPVOdQ3N6BSVAz2imF8\nUBz93CK6zR8gxmCR4nyhVatWkZCQwIsvvtjmMlKcuydz5PlAWiHvfZeKWqXw4I0DGRTR/Wf4MRgM\nHMsqZf3eHDLPVALn5r++Ni6U2FbelvZLnvNLa/nnfw9T29DMgzcMZHh/63n0JrPiFMuOvIOjxoE/\njHwEd7v//SGVU5XLf9K+oKC2EC97TxZGzTXqI1jGZG3njorGSv6b9iVpZSdwsnFkwYCbGewda/L9\nNumaftXxll7U/TbrLx2SVlBws3XFy94DT3sPvOw88bL3OP+zs43T+WPb2c2GDSm72HlmL2dq8gHw\nc/BhXFAcV/kNw17T86eWtWhx3rx5M++88w4rV67E2bntQFpadGjk0RnRhoS0Qv754QF0egOP3Tqc\ncVbUNXaGTqdn19EzrN6aQU7BuQIwMtqXWyZHEhXu0aFtnDhdzp+W70GnN/DsojgGRlz5Y0zGsjFj\nOysPr6KvRxjPTn4UBYWv0zbwdepG9AY90/tO4NbBN2Jngndi92R6g55NGTv4b+I3NOuamRgex11D\n52JvY3dF2yyrr6CopoSi2lIKa0oorC2h6Od/VzZUtbqevY0dfo7e+Dh54evkhY+jJz6O3vg6eeHl\n4N7pkRCDwcDxkpP8kLmDvXmH0el12GpsGRc6iul9xxPqFtTl37E7M2lx3rVrF8uWLeP999/Hza39\n4UjpnLsnc+b5+Olylq1OpLFJx+1WNqx7OU3NOnYn5bNx/2lKKhtQKQqjon2YeVUoQT5Ol13/13lO\nPlXKsi8T0dqo+MOCYYRYyfSbBoOB/6St4kDBYYb6DKKkroTcmrO427pxW9QtDPDoZ+kQL8uazx35\ntYV8lPIZuTVn8bTz4Pboee2OQNS31FNSX37uuu/PXW/pz8PPZfXltBh0l6yjUlR42LnjZfdz92vv\ngZe95/mfHTT2Rhl6bi3P1U01/HT2ALvO7KO8sQKACNcwxgfGMcRnIBoTvi7WEizSOVdXV7NgwQI+\n/PBDPD0vPwwpxbl7MneeswuqeGXVMWrqm5k7qS8zrgox2767oq6hhW1H8vgxIY+q2iY0ahXjBvsz\nY1QI3m4dH7ZrLc/7Ugp497tUXB21/HHh8E5tz5SadM28fOhN8mrOAhDnP5Kb+13XbYYprf3c0aJv\nYf2pzfyQsw2AaaET6e/e9+eie274+ZciXNtS1+o2nGwcfx52/rnw2nvgaXeuELvZuppl3un28qw3\n6EkuSWPnmb2klZ0AwNnGiTEBoxgbeBUedu4mj88cLFKcV61axRtvvEF4+P/+qnvhhRcICAhodXkp\nzt2TJfJ8tqSWl1cdpSxMNzcAABvBSURBVLy6kevGhHLjuD5WdxNJZW0TPx7MZduRPOobddjbqpk0\nNIhpI4Nxdez8Y2Ft5fnHg7l8tiUDH3d7/njbcFy6sG1TKK0v59uTGxjhO4SBXtGWDqdTusu5I6si\nm49SP6e0oeyS7zQqzfli+8u/f+mAPe3csdN0fTjcWDqa56K6Enaf2cfe/IPUtdSjoDDQK5rxgXH0\n9+jbrZ8Dt/gNYR0hxbl7slSeSyrqeenzoxRV1DN5WCALpkVaxYxWxRX1bDxwmt2J+TS36HFxsGHa\nyGAmDQ3Cwa7rQ3Lt5fmrHVms25tDqJ8z//ebodjb9qyhP3PrTueOhpYGtuftQafXnSu8PxdhF62z\n1Retzua5SdfMocKj7DzzE6erzwDgY+/FuMDRjPYf0S2n35TiLEzGknmurGnk5VVHySuuJS7Gl7tm\nRllswoy84hrW78vhQGoReoMBL1c74q8K4eqB/mhtrnyIsL08GwwGPtiQzu7EfKJC3fndLYOx0Vj3\nidmaybnDPK4kzzlVuezM20tC0VFa9C3YqGwY4TuE8YFxhLh0nxvIpDgLk7F0nmvqm1n25TGyzlYx\npK8XD9wQg40Z7/rPzKtk3d5sjmWde0Y50NuRmaNDGRXlg1plvAJ5uTzr9Hre/DqZo5kljIryYdH1\nMVYxktAdWfqY7i2Mkeea5lr25SewK28vJT8P74e6BDMhcAzDfAZZ3TP0vybFWZiMNeS5oamFN75K\nIi2nnKhQdx66aaBJh3YNBgNJJ8tYvy+HE7nn7ijtG+jKzLhQBkV4mqQodiTPTc06Xl51lIy8SqYM\nC2LBNPPMf9zTWMMx3RsYM896g560sgx2nfmJ5JJ0DBhwtHEgzn8k4wJH42Vvne9GkOIsTMZa8tzc\nouPttSkcySgh3N+F388djJO9cf9q1usNJBwvYv3eHE4X1cD/t3fvUVXX+f7Hn5sNmzuCJMhF8A6C\ninjXvE1pN8smszCT6nSZWjZrzjTOnPxZTc0004yd1TmdstNYVmN2o0zLptJuXhMlBUFRUdFUQEQQ\n5X7Zl98fZicrzYS9v182r8darBYsZL/322+++Hwv7w8wqHck14xOoH+PcLcG4YX2ub6plb+/nmvK\nMZ8dhVmOaW/nrj5XNZ5gY9kWNpXlUNdajwULAyL7MzFuLCmRSaa6Fq9wFrcxU58dTievfLSHTTvL\niesWzNyMIYSHtH3YRavdyaadR/l4y2EqqhuxWGBEchTXjE702PPFP6fP1bXNph3z2RGY6Zj2Zu7u\nc6vTTl5FAetLsjlYcwiAyIAIxsWNZkzMCEJtPz1fwN0UzuI2Zuuz0+Xizc/28fm2ErqFB/D7mekX\n/fxvY7OdddvLWP3VYU7VteBrtXDpoBiuGpVAdIRn7wz9uX0285hPszPbMe2tPNnnI7WlbCjN5qvy\nPFqcrfharKRHpTEhfgy9whIMu/yjcBa3MWOfXS4X7288yMovvyY8xMbcmenEXXLhW+7VNLTw+dYS\nvsgtob7Jjr/Nyi/S45gyvAcRocaMnbyYPh8oq+E/38w7PfI0I42kBO8Y3OBuZjymvZERfW5obWRL\n+TY2lGZzrOE4AD1CYhkfP4bh0en4Wz07J0DhLG5j5j6vzjlM1hf7CQn044Gb0+gVE3be76861cTq\nnMOszy+jxe4kJNCPKcPjuWxYPMEBxt71ebF9NuuYTzMz8zHtTYzss8vloqh6PxtKsymo3IXT5STQ\nN4DR3YczPm400cFRHqlD4SxuY/Y+b8gv45+r9uDvZ+U3Nw4mOfGHq8eyyno+3nyIzbuO4XC6iAzz\n58qRCYxPi8W/HZ5Rbg9t6bNZx3yaldmPaW9hlj5XN53ky7IcvizbQk3L6XqSI/oxPn4MgyIHuHWU\nqcJZ3KYj9HnrngoWrSzEYrEw54aBDOl7ehenA2U1fLT5EHl7j+MCYiKDuGZ0IqNSog0bZnIube2z\nWcd8mlFHOKa9gdn6bHfayT9eyIbSbPadPABAuH8XxsWOYmzsKLfso61wFrfpKH3eeaCKhct34HC6\nmDauF3sOVbP7UDUAvWLCmDomkSH9LjHt4I726LPGfF6YjnJMd3Rm7nNZXTkbSrPZUr6NZkcLPhYf\n0rsN4upek4kJjm6311E4i9t0pD7vPXKS/1lWQGOzHYDUnhFcM6YnyQnufUa5PbRHnzXm88J0pGO6\nI+sIfW6yN5FTnsf60k0crT9GStck7h9yV7v9/POFs351lk6jf49w5t06lI0FRxkzMJqe3c9/g5i3\nsVgs3H5VEnUNrWzfX8lLH+7SmE+R8wjwDWBC/BjGx43mcG2JR5+N1q/N0qn0iArhlsn9Ol0wn2H1\n8eG+61PpF9+FnN0VvPnpPkxy8kzEtCwWC4lhPTy6j7TCWaSTsflZ+c2MwcR1C+bz3BL+lX3I6JJE\n5HsUziKdUHCAH7+7eQiRYQGsWH+AddtLjS5JRL5D4SzSSUWE+vO7jNMbhLy6uohtRceNLklEvqFw\nFunEYiKD+e1Nadh8rSxaWUjR4WqjSxIRFM4inV7v2DDunz4Ql8vFM+8WcPiYuR9vEekMFM4iwsBe\nkdw1dQCNzQ7+++18jp9sNLokkU5N4SwiAIxO7c4tl/fjVH0LT2Vtp6a+xeiSPK6mvgWHU4+WifE0\nhEREvjVlRA9qGlr4MPsQ//1OfqcY89nc4mDL7mOsySvlUHkt/XqEc+fVyUR39eye3SLf5d3/14nI\nzzZ9Qm9O1bewseAoC5fv8Noxn6WV9azNK2XTznIam+1YLJAYHcq+Iyd57JWvmDW5H+MGx5h+tKt4\nJ4WziJzFm8d8ttqdbNtbwdq8MvYeOQlAlxAbU4b3ZEJaLF3DAth15BTPLcvnlY/3UHCgituvSiYk\n0Nj9vKXzUTiLyA+cGfP5VNZ2cnZXEBpoY9aUfh12FXn8ZCPrtpexoaCM2oZW4PTGJ5PS40jre8lZ\nW4ROHBpPVJiNxR/sYlvRcYpLT3H3tSmk9OxqVPnSCWlXKmkT9dkzjOpzfVMrf389l9Lj9dwwoTfX\nje3p8RoultPpIr+4krV5Zew8UIULCA7wZfzgWCYOiT3nNeUzvXY6XXy0+RDvbzyIw+niqpEJ3DCh\nt1ee4jeC/u3QrlQicpHOjPl8Yuk2Vqw/QFiQHxOHxBld1nmdrGtmQ34Z6/LLOFHTDEDfuC5MSo9l\nRHIUfr7WC/o5Pj4Wrh3bk9ReXXlhZSGrcg6z6+sT/GpaKrGXBLvzLYho5Sxtoz57htF9PlpVz99e\ny6W+qZU5vxzEsKRuhtXyY1wuF7sPVbM2r5S8fZU4nC78bVbGpnZn4pBYEqLPvUL5vh/rdVOLnbc+\n38f6/KP4+fow87K+TEqP67Cn+c3A6GPaDM63clY4S5uoz55hhj4fKKvhP9/Mw+F0MTcjjaQEz22f\ndy51ja18ueMoa7eXcexEAwDx3UL4xdA4RqdEX9RjYOfr9baiCv758R7qm+yk9Ynk364ZQFiwrU3v\nobMywzFtNIWzuI367Blm6fPOg1X8zzsF2Px8eHDW0J+1Im0vLpeLA2U1rMkrJWd3BXaHE1+rDyMH\nRDEpPY4+sWFtWtH+VK+ra5tZ/K9d7D5UTViQH3dOTWFwn8iLfr3OyizHtJEUzuI26rNnmKnPmwvL\neeGDXXQJtjE/cxjdwgM98rpNLXY2Fx5jbV4phyvqAIiOCGRSehyXDoppt8edLqTXTpeLT3KOsHx9\nMXaHi8uHxXPTpD7Y/C7seraY65g2im4IE5F2Mzq1O7UNrbz5+T6eytrO/NnD3Hpqt6SijjV5pWQX\nltPU4sDHYmFYUjcmpccxIDHCkOevfSwWrhqVQErPCBatLOTzbSXsOVTNr6al0iMqxOP1iPfRylna\nRH32DDP2+d11xXyYfYjE7qHtPuaz1e5g657jrMkrZX/pKeD0/tMT02IZnxZLRKh/u73W9/3cXje3\nOnhnzX6+yC3F12phxqS+TB4e7xVDW9zJjMe0p2nlLCLt7rtjPp9bsYN/n9H2MZ/HqhtYl1fGxh1H\nqWtsxQIM7N2VXwyJY3DfSKw+5nvG2N/PyuwrkhjUO5JXPtrNW5/vY0dxJXdOTXHrLxHi3bRyljZR\nnz3DrH12OJ08t3wn2/dXMnJA1EWN+XQ4nWzfV8XavBIKv64GICTQj/FpMUwcEkeUh65pn9GWXp+q\nb+GVj3ZTUFxFSKAfd1ydzND+5nrszCzMekx7klbOIuIWbRnzeaKmifX5ZazPL+Nk3entKfvHd2FS\nehzDkqI65CSuLsE2/n3GYL7ILeXtNftZuHwHE4fEMvOyfvjbdLOYXDiFs4i0ic3Pym9mDObvr+fy\neW4JYSG2c475dLpc7Dp4gjV5peTvr8LpchHob+XyofFMTI8lvlvHv5nKYrFw+bB4khPCWbRyF+u2\nl7Hn8EnunZZCz+5hRpcnHYROa0ubqM+e0RH6XF3bzBNLt1FV08TtVyWdNeaztqGFjTuOsi6vjIqT\njcDp7Rl/MTSOkQOiCLCZZ53Qnr1utTtZvr6Y1TlHsPpYuGFCb64amYCPj24W6wjHtLvptLaIuF1E\nqD+/y0jjb6/l8urqIkICbYQG+bF2eylb91Rgd7jw8/Vh3KAYJqXH0Ssm1OvHX/r5+pBxWT8G9opk\n8Ye7WLa2mB3FVdxzXQpdwwKMLk9MTCtnaRP12TM6Up/PjPlsbnV8+7WYyCAmDYlj7KDuBAeYe29k\nd/W6tqGFJauKyN17nCB/X267KomRA6Lb/XU6io50TLuLVs4i4jG9Y8O4f/pAXv5wN/3iw5mUHkdy\nQrjXr5J/SmiQjftvGMiGgqO88dle/vF+ITuKq5g1pX+7PiMu3kFHhIi0u4G9IvmvX48zugzTsVgs\nTEiLpX+PcF5YWciXO8vZW3KSe65LpW9cF6PLExPpeM8qiIh0cN27BjE/cxhTxyRSebKJv7+Wy8qN\nB3E4nUaXJiahcBYRMYCv1YcbJ/bhP2alEx5q472NB1nwet63d7NL56ZwFhExUFJCBH++cyQjB0Sx\nv/QUj72cw6adRzHJvbpiEIWziIjBggL8uHdaKndfOwCAxf/azaKVhTQ0tRpcmRhFN4SJiJiAxWJh\n7MAY+sWH8+IHu8jZXUFx6SnuvjaFpIQIo8sTD9PKWUTERLqFB/Lgren8clwvqmtbePKNPN5dV4zd\noZvFOhOFs4iIyVh9fJg2rhf/b/ZQLgkP4MPsQzyxdBvlJxqMLk08ROEsImJSfeK68Ni/jeTSgd35\nuryWx17JYX1+mW4W6wR0zVlExMQC/X2569oUBvWJ5NVVRfzz4z0UFFdxx9XJhASaYxRqq91BY7OD\nxmY7jS12GpvsNLZ88/mZj+983tTiYNiA7owZ0A1fq9aIP0bhLCLSAYwcEE3fuC68+MEucvce50DZ\nKe66NoXUnl0v+mc6nE6aWhw/GaanPxzfBKudhmY7Tc2O0/9tsWN3/PyVfEFxFZ9sDua2q5LoFx9+\n0e/BW2njC2kT9dkz1GfPMXuvnU4Xq3IOs2L9ARxOF1eM6MGI5KjTK9bmHwnUlm9Wq812GpodZ4Xr\ndzcn+Tn8bVYCbVYC/X3/7+MnPg/wtxLk70uAzReLBT7dVsqq7K9xARPSYpgxqa9pzgR4yvk2vnBr\nOO/du5c5c+Zwxx13MHv27PN+r8K5Y1KfPUN99pyO0uuvy2tYtHIXx37GTWK+Vh+C/K0EnCtQ/b/5\n3PZ/nwfYfE+H6nfCtT32o+7WLZTN20tYsqqIkuN1hAb5MfOyfoxOje40m6QYEs4NDQ3ce++99OzZ\nk6SkJIWzl1KfPUN99pyO1OvmFgefbTtCQ5P97IC1nR24Ad8Erp+vea7vnumz3eHk061HeH/jQVpa\nnQxIjCDzyiS6dw0yukS3M2TLSJvNxosvvsiLL77orpcQEenU/G1Wpo7paXQZbeJr9eHqUYmMSI7i\ntU/2UlBcxR9fyuHaMYlcPTrRVL9QeJLbrzk/++yzRERE/OTK2W534OtrdWcpIiJiYi6Xi007jvLC\nih2cqGkirlsI989IY1DfS4wuzeNMc7d2dXX7PlzfkU5NdWTqs2eoz56jXnvGufrcPyaUx+8ayYr1\nB/g8t4T5z3/JpQO7c/NlfQkNshlQqfuc77R25zxfICIiphXo78usKf15+LbhJEaH8uXOcua/sJkN\nBZ1nAIvCWURETKlXTBgP3z6MWy7vh93p4pWP9rDgjTzKKuuNLs3t3HZae+fOnSxYsIDS0lJ8fX1Z\nvXo1zz77LOHhethcREQujNXHhykjejAsqRtvfLaP3L3HefTlHK4encC1Y3pi8/POe5U0hETaRH32\nDPXZc9Rrz7jYPm/fV8nrnxZRVdNMVHggmVcmkdrr4qekGUnXnEVExCsM6XcJj989iitH9qDyVBNP\nZW1n0cpCTtU1G11auzLN3doiIiIXIsDmS8Zl/RiT2p0lq4rYsusYO4qrmDGpDxOGxOLjBRPGtHIW\nEZEOKSE6lIcyhzH7iv64cPHq6iL+9to2SirqjC6tzRTOIiLSYfn4WLhsaDx/vWc0IwdEUVxaw2Ov\nfMU7a/bT3HJxG3uYgcJZREQ6vPAQf+67fiAP3JxG1zB/Pt5ymIcXb6GguNLo0i6KwllERLzGoN6R\nPH73KKaOSeRkXTNPv1PA/67YQXVtx7phTDeEiYiIV/H3s3LjxD6MSonm1dVFbC06zs6DJ5g+oTeX\nDY1vly0v3U0rZxER8Urx3UKYd+tQ7rg6GauPhTc+28dfXt3KoXLzP8eucBYREa/lY7EwIS2Wv94z\nmjGp0XxdXsufl3zFm5/to7HZbnR556RwFhERrxcWbOOe61KZO3MIUeGBfLr1CA8v3kLu3uNGl/aj\nFM4iItJppPbsyp/vGsm0S3tS29DCwuU7eGZZAVWnmowu7Sy6IUxERDoVP18rvxzfm1Ep0SxdXcT2\n/ZXsPlTNL8f3YvLweKw+xq9bja9ARETEADGRwfzhlnTumjoAP18fsr7Yz+P/3MqBshqjS1M4i4hI\n52WxWLh0UAxP/Go04wbHcLiijr++upXXPimiocm4G8YUziIi0umFBPpx5zUDeHBWOt0jg/git5SH\nFm/mqz0VGLGzssJZRETkG0kJEfzpzpHcMKE39Y12nn9vJ0+/U8Dxk40erUPhLCIi8h2+Vh+uG9uT\nx+8eSWrPCHYcqOKRxVvI3lnusRoUziIiIj8iOiKI32UM4Vf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OHZOjJBERKUSWjiQxMREff/wxSktLERYWho0bN6Jv3764dOkSFi5ciE8//VSO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\nkwAAAABJRU5ErkJggg==\n", 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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": 1172 + }, + "outputId": "aeb873d7-7727-4779-ce84-5b3a3ee051ff" + }, + "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": 27, + "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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Nx7GmJ85qKeJcDDKEwmLQbt31wxaRaqwpbhQU6/cMHb3iteM0J+FyvXeY4rt5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gJbWhUKYA/cV4t7kz5HrjnRJL3PW4D1/OTvNot+8UUXl6iHA77WntNy3CbTuON+CZfY98WQfD+p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YE+nXy3Xg54e9bGgIea6zGuOWNFUe9M4+sdlpR0xELit5Y4+Ic9+Muo3d9Rjr3PvniLi2Erzr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ju+5LVDIcTR7OzFqt+5HU947fn34jHeL0VEln37HnyOHpyrym5rUP1S/LqmTTwJUY3D7Hqt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ZjiCNmuVPqWv1b1OT12GpyKlJuY5FbesA0idXUgqYmzI41YXULE5P/1ja71addplsyu+E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\nuE7KODJWIYKPI6TTKyT99cI/veG0C+8CB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ELsyxnDMAzDMAzDMAzDMIwpxP9iL55+/Q9O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\n4TPbVLtlM2Y48Zvv4Xyvu26lasdpsN0DSGfzDdTfiXVV6tRDb5NzK2yKO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TmU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\ntYt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Esq+sPwLN+4wbjVfx8cP48/WV2krWNQaEQu8YFBks2rVfQJ2B3Pvgo9hvaV8Zf9Ky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\nfsSJE0pRFyA8ROqLeT397rMPOvFLX39UtJt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BJxk+1oAI6cLEtWnYaanmlVLRLjwfzgWs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529kDssHo9z/M5iiU9dLvtR5acYB+Oz0Pc+OnhQtMvfiLGTSo5RVeukzCX9Enzfad++0on9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\nMRHn5efI+43gBCnZYIcPXt/7WuRa4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kb9+zUirncYa9hFvwZkL8gRccMN8jp8jcbXsa5S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XKXfMMz5FfAX6f+/33hGvVT6AP8Z244lLZBmMdV6s4SCS/vJ7jDGm/b06px1KMpwRSw7D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/Iq9kh8/s20NfcEwjzo70HM5xsRUVcVrpdbsZvrvz3rSzIiHP0/HIKkrWpfG/ZpndW4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\nvTia2lmfK2ZhXtkhr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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": "d4Vl6HnDOm76", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1155 + }, + "outputId": "dceb8789-59a4-421c-e7b1-361be826b691" + }, + "cell_type": "code", + "source": [ + "weights1 = classifier.get_variable_value(\"dnn/hiddenlayer_1/kernel\")\n", + "\n", + "print(\"weights1 shape:\", weights1.shape)\n", + "\n", + "num_nodes = weights1.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights1.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(10, 10), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "weights1 shape: (100, 100)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "xVSPmgbTOw42", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 162 + }, + "outputId": "6915b99c-5a26-4fb2-f42e-98903cb244ac" + }, + "cell_type": "code", + "source": [ + "weights2 = classifier.get_variable_value(\"dnn/logits/kernel\")\n", + "\n", + "print(\"weights2 shape:\", weights2.shape)\n", + "\n", + "num_nodes = weights2.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights2.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(10, 10), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "weights2 shape: (100, 10)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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