From cf0e5f0ca9edb069f7592c67ebc56895b25901ae Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Mon, 28 Jan 2019 23:26:20 +0530 Subject: [PATCH 01/11] Quick Introduction to Pandas programming exercise solved!!! --- intro_to_pandas.ipynb | 1902 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1902 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..a1327bd --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,1902 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# 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": "526094d0-c58d-4f7b-e10a-3baa6694ebb3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "# from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'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": "deab94e0-5309-4acc-9680-b0b7403bb393", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "outputId": "05fc187e-e9ff-490d-d117-068715b55009", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": "8d644b5b-e454-46d8-8ef5-a445bdbe77e2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean -119.562108 35.625225 28.589353 2643.664412 \n", + "std 2.005166 2.137340 12.586937 2179.947071 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.790000 33.930000 18.000000 1462.000000 \n", + "50% -118.490000 34.250000 29.000000 2127.000000 \n", + "75% -118.000000 37.720000 37.000000 3151.250000 \n", + "max -114.310000 41.950000 52.000000 37937.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean 539.410824 1429.573941 501.221941 3.883578 \n", + "std 421.499452 1147.852959 384.520841 1.908157 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 297.000000 790.000000 282.000000 2.566375 \n", + "50% 434.000000 1167.000000 409.000000 3.544600 \n", + "75% 648.250000 1721.000000 605.250000 4.767000 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 17000.000000 \n", + "mean 207300.912353 \n", + "std 115983.764387 \n", + "min 14999.000000 \n", + "25% 119400.000000 \n", + "50% 180400.000000 \n", + "75% 265000.000000 \n", + "max 500001.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 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": "4e3bff09-68bd-4fd9-a53e-8f14dc0dccef", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 6, + "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": "7fbb825a-6946-49b2-8f4c-2c7933e87bba", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 399 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 7, + "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": "be2ecb53-2513-4c15-f9e6-e337fc066a26", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 109 + } + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\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": "6e3ef9ec-095a-440a-e542-a9a83f52b3c2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 9, + "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": "f89fe7cd-ebb9-47b5-f9a4-d4a60173ff02", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 130 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 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": "ac9a6983-6672-4a78-9ccf-02ea38529a1d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "outputId": "1bcd0fb7-0db3-400c-eb1d-886d4efd38eb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "outputId": "0386defc-5d96-4a4e-c022-f8bd6794b093", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "outputId": "19fd284c-4b13-4751-85e9-b882aee82a2e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": "1bb53a82-d04c-4780-d316-62885291f0fb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities['Named after a saint and big'] = (cities['City name'].apply(lambda x: 'San' in x)) & (cities['Area square miles'] > 50)\n", + "cities" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityNamed after a saint and big
0San Francisco85246946.8718187.945381False
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2Sacramento48519997.924955.055147False
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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", + " Named after a saint and big \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "outputId": "1af06058-90d5-4526-ca27-a039914c6496", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityNamed after a saint and bigIs wide and has saint name
0San Francisco85246946.8718187.945381FalseFalse
1San Jose1015785176.535754.177760TrueTrue
2Sacramento48519997.924955.055147FalseFalse
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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", + " Named after a saint and big Is wide and has saint name \n", + "0 False False \n", + "1 True True \n", + "2 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "outputId": "3e4a1394-8bfc-4ebc-ce76-93a6b475d46f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "outputId": "5a26bbc6-30a6-497c-b06b-0a4e323a97b7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "outputId": "11a9ed73-2d0c-4ed8-c2a1-73fcfd655756", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityNamed after a saint and bigIs wide and has saint name
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0San Francisco85246946.8718187.945381FalseFalse
1San Jose1015785176.535754.177760TrueTrue
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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", + " Named after a saint and big Is wide and has saint name \n", + "2 False False \n", + "0 False False \n", + "1 True True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "outputId": "43adab71-cc1b-4419-e98a-bf29932bcce5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityNamed after a saint and bigIs wide and has saint name
2Sacramento48519997.924955.055147FalseFalse
1San Jose1015785176.535754.177760TrueTrue
0San Francisco85246946.8718187.945381FalseFalse
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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", + " Named after a saint and big Is wide and has saint name \n", + "2 False False \n", + "1 True True \n", + "0 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "outputId": "af2e7fee-e987-44e6-d488-471d00539274", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([0, 1, 2, 3, 4])" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityNamed after a saint and bigIs wide and has saint name
0San Francisco852469.046.8718187.945381FalseFalse
1San Jose1015785.0176.535754.177760TrueTrue
2Sacramento485199.097.924955.055147FalseFalse
3NaNNaNNaNNaNNaNNaN
4NaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "1 San Jose 1015785.0 176.53 5754.177760 \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " Named after a saint and big Is wide and has saint name \n", + "0 False False \n", + "1 True True \n", + "2 False False \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "outputId": "595ea205-1fa3-4924-daa0-910837bfd46a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
City namePopulationArea square milesPopulation densityNamed after a saint and bigIs wide and has saint name
0San Francisco852469.046.8718187.945381FalseFalse
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "4 NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "\n", + " Named after a saint and big Is wide and has saint name \n", + "0 False False \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "2 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "2l82PhPbwz7g" + }, + "cell_type": "markdown", + "source": [ + "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n", + "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n", + "in which the index values are browser names).\n", + "\n", + "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n", + "sanitizing the input." + ] + } + ] +} \ No newline at end of file From acb6109379ccf515ef05a12ba44126e3580a5510 Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Tue, 29 Jan 2019 01:57:15 +0530 Subject: [PATCH 02/11] First Steps with Tensorflow programming exercise solved! --- first_steps_with_tensor_flow.ipynb | 2002 ++++++++++++++++++++++++++++ 1 file changed, 2002 insertions(+) create mode 100644 first_steps_with_tensor_flow.ipynb diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..24e3fe8 --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,2002 @@ +{ + "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 + } + }, + "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", + "outputId": "56765ffc-da5a-4ee4-e4c9-f8cab48757df", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + } + }, + "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": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count17000.017000.017000.017000.017000.017000.017000.017000.017000.0
mean-119.635.628.62643.7539.41429.6501.23.9207.3
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min-124.332.51.02.01.03.01.00.515.0
25%-121.833.918.01462.0297.0790.0282.02.6119.4
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": 5 + } + ] + }, + { + "metadata": { + "id": "Lr6wYl2bt2Ep", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Build the First Model\n", + "\n", + "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n", + "\n", + "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n", + "\n", + "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference." + ] + }, + { + "metadata": { + "id": "0cpcsieFhsNI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 1: Define Features and Configure Feature Columns" + ] + }, + { + "metadata": { + "id": "EL8-9d4ZJNR7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n", + "\n", + "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n", + "\n", + "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n", + "\n", + "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n", + "\n", + "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:" + ] + }, + { + "metadata": { + "id": "rhEbFCZ86cDZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the input feature: total_rooms.\n", + "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n", + "\n", + "# Configure a numeric feature column for total_rooms.\n", + "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "K_3S8teX7Rd2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument." + ] + }, + { + "metadata": { + "id": "UMl3qrU5MGV6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 2: Define the Target" + ] + }, + { + "metadata": { + "id": "cw4nrfcB7kyk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:" + ] + }, + { + "metadata": { + "id": "l1NvvNkH8Kbt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the label.\n", + "targets = california_housing_dataframe[\"median_house_value\"]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4M-rTFHL2UkA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 3: Configure the LinearRegressor" + ] + }, + { + "metadata": { + "id": "fUfGQUNp7jdL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n", + "\n", + "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. " + ] + }, + { + "metadata": { + "id": "ubhtW-NGU802", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Use gradient descent as the optimizer for training the model.\n", + "my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n", + "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + "\n", + "# Configure the linear regression model with our feature columns and optimizer.\n", + "# Set a learning rate of 0.0000001 for Gradient Descent.\n", + "linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-0IztwdK2f3F", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 4: Define the Input Function" + ] + }, + { + "metadata": { + "id": "S5M5j6xSCHxx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n", + "the data, as well as how to batch, shuffle, and repeat it during model training.\n", + "\n", + "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n", + "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n", + "\n", + "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n", + "\n", + "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n", + "the size of the dataset from which `shuffle` will randomly sample.\n", + "\n", + "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor." + ] + }, + { + "metadata": { + "id": "RKZ9zNcHJtwc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wwa6UeA1V5F_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** We'll continue to use this same input function in later exercises. For more\n", + "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)." + ] + }, + { + "metadata": { + "id": "4YS50CQb2ooO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 5: Train the Model" + ] + }, + { + "metadata": { + "id": "yP92XkzhU803", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n", + "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n", + "train for 100 steps." + ] + }, + { + "metadata": { + "id": "5M-Kt6w8U803", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = linear_regressor.train(\n", + " input_fn = lambda:my_input_fn(my_feature, targets),\n", + " steps=100\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7Nwxqxlx2sOv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 6: Evaluate the Model" + ] + }, + { + "metadata": { + "id": "KoDaF2dlJQG5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's make predictions on that training data, to see how well our model fit it during training.\n", + "\n", + "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n" + ] + }, + { + "metadata": { + "id": "pDIxp6vcU809", + "colab_type": "code", + "outputId": "0c0ddbca-f25f-4ad6-9ecf-e5d9494bfeb4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + } + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn = lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56367.025\n", + "Root Mean Squared Error (on training data): 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "outputId": "785f5875-bcdd-4fea-a5a7-101b22de797e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "outputId": "68db9d0d-08ed-47bf-d3a1-5be70ba61b17", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "calibration_data = pd.DataFrame()\n", + "calibration_data[\"predictions\"] = pd.Series(predictions)\n", + "calibration_data[\"targets\"] = pd.Series(targets)\n", + "calibration_data.describe()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 13 + } + ] + }, + { + "metadata": { + "id": "E2-bf8Hq36y8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n", + "\n", + "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n", + "\n", + "First, we'll get a uniform random sample of the data so we can make a readable scatter plot." + ] + }, + { + "metadata": { + "id": "SGRIi3mAU81H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "sample = california_housing_dataframe.sample(n=300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "N-JwuJBKU81J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red." + ] + }, + { + "metadata": { + "id": "7G12E76-339G", + "colab_type": "code", + "cellView": "both", + "outputId": "701566fd-ef81-4857-fcf5-31e08a57d201", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + } + }, + "cell_type": "code", + "source": [ + "# Get the min and max total_rooms values.\n", + "x_0 = sample[\"total_rooms\"].min()\n", + "x_1 = sample[\"total_rooms\"].max()\n", + "\n", + "# Retrieve the final weight and bias generated during training.\n", + "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n", + "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + "# Get the predicted median_house_values for the min and max total_rooms values.\n", + "y_0 = weight * x_0 + bias \n", + "y_1 = weight * x_1 + bias\n", + "\n", + "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n", + "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n", + "\n", + "# Label the graph axes.\n", + "plt.ylabel(\"median_house_value\")\n", + "plt.xlabel(\"total_rooms\")\n", + "\n", + "# Plot a scatter plot from our data sample.\n", + "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n", + "\n", + "# Display graph.\n", + "plt.show()" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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tjoiIgmMgD5PUkZeCIEhu/kr0YzLD2UiX6H0iIkp1KkHpSSUJJJY1c0OtyetfDQ0Alq7e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\nFcGg6/wY7v3peNidHbDaHCjI1UOrUWP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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", + "outputId": "4fa51873-7111-43cd-ebb7-d50e7d729b17", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 997 + } + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.0001,\n", + " steps=100,\n", + " batch_size=1\n", + ")" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.63\n", + " period 01 : 214.43\n", + " period 02 : 206.05\n", + " period 03 : 196.42\n", + " period 04 : 187.87\n", + " period 05 : 181.90\n", + " period 06 : 175.66\n", + " period 07 : 172.63\n", + " period 08 : 171.74\n", + " period 09 : 168.29\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 111.0 207.3\n", + "std 91.5 116.0\n", + "min 0.1 15.0\n", + "25% 61.4 119.4\n", + "50% 89.3 180.4\n", + "75% 132.3 265.0\n", + "max 1593.2 500.0" + ], + "text/html": [ + "
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CCCH8kn/9cq2FrqbKYfGGw6zbnkFuoQUFyC20sG57Bos3HPZ8oB4wbkBzBnVt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l3xNLz08ZT6NWkOnWsqDYlA4dslxjb2+ldi5BEATh2iwsdPLD51by0982cfyshx+8eIDf3VxJ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\n8Rfw8rbiM28G87d2xVl1+jwglwuOljoJbGU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predictionstargets
count17000.017000.0
mean116.9207.3
std96.4116.0
min0.115.0
25%64.6119.4
50%94.0180.4
75%139.3265.0
max1676.8500.0
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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 167.30\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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vhr+erpF+DtM1RPCM6pdK7vEyvtl7ipWf5XLNJV2DHZIQQggRkiQpIYQQPlb0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\nREFBwXU/LN7e3uTn55tYUc1xdXW1X6q/c+d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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", + "outputId": "062151bd-572e-48ce-d0e3-27b1f77ba579", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 997 + } + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "train_model(\n", + " learning_rate=0.00001,\n", + " steps=3000,\n", + " batch_size=1,\n", + " input_feature='population'\n", + ")" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 220.00\n", + " period 01 : 204.96\n", + " period 02 : 193.27\n", + " period 03 : 184.85\n", + " period 04 : 179.95\n", + " period 05 : 177.12\n", + " period 06 : 175.96\n", + " period 07 : 176.09\n", + " period 08 : 177.22\n", + " period 09 : 178.99\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 150.2 207.3\n", + "std 120.6 116.0\n", + "min 0.3 15.0\n", + "25% 83.0 119.4\n", + "50% 122.7 180.4\n", + "75% 180.9 265.0\n", + "max 3750.2 500.0" + ], + "text/html": [ + "
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Xey8gAb7/fjh4pIxnlhyhsNhB8ohI/jK9FXq9fwWJzldDCkp4m7c+a6pex23/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\nQ2K/eD7acphvfj7F+IFtfd0kIYQQwqs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Q2TMTdZDny8rrLTKH82SUChjbX0lkiPd/fj5d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WsRBXrX5/51g4JIT9Nx3aBgr7b8FQjak+v6RNIvIZTvs0o4c7GUAYnhvg5JIBAIBJ0EIUq0grqm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\nR1HV1M2mPY3sq3Dy/DtHeW1bNQv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predictionstargets
count17000.017000.0
mean117.8207.3
std94.6116.0
min0.215.0
25%65.1119.4
50%96.2180.4
75%141.8265.0
max2940.2500.0
\n", + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 176.12\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6g8k/FWkq7Xp25lmwufREh7jok2jHLB+xbcqU1F78fKCYZZsOMfCseLq2l2oc\nQgghxG/JTAnhKSMpCQkh2paandnsmngLruJSuj3zoJcSEirG75a6ExJh0TjSb2y5hISquGdH1JaC\nweLe0NJPCYljFUa2HbFic+npGu2gfwdJSLRFxy/jkGocQgghRP0kKSGEEG1Q9U+7yJp8K67Scro/\nN5eEGROa36jiwvh1Jobs71CjEnGk3wjhMc1vt0F9O9z7RziqwRzmLvlpMLdM38eHocKuAjO7Cy0Y\ndHBOexvdY5yyXKMNk2UcQgghxOlJUkIIIdqYqh2/sGvyrShlFXR/4RHip41vfqNOB6b172M4+BNq\nfBecl90AoS00Vd1Z466wodghJAYiO4O+5acl1Drd5T7zKk2EWRQGJ9US207KfQr3Mo7YX6txHMqT\nahxCCCHE8SQpIYQQbUjV9p/ZPeU2lMpqerz0OPFTrmh+o/YaTGvfQX90L0qnZJyjZ4ElpPntNoSt\nHEoPgaZAWHsIb++XDS2Lqg1szQ2hymGgQ4STgR1thJik3Kdwcy/j6CPLOIQQQoh6SFJCCCHaiMot\nP7J76u0oVTX0fPkvxE0Y2/xGayowrXwTfWEOSvf+uEZMA2MLLJvQNKgqgIoj7iREZBcIbaGlIr8J\nY3+xiZ/zrKgapMTbSYmXcp/iZP26x3iWcSzZeNDf4QghhBABQ06bhBCiDajc/AO7r5mNUmOj56t/\nJfaqjGa3qasoxrziDfTlBbhSLsR18YSWWTahqe5kRE0R6E0Q3R0sYb7v9zccCuw4ZuVwmRmrUWVg\nJxsdIlwtHocIHrKMQwghhDiZJCWEEKKVq/h2G7un34Fmt9Pr9aeI/V1as9vUlRzFtPINdNVluAak\nopw3FnQt8JWiutzLNewVYApxV9gwWnzf72+U2/RszQmhrNZAbKiLwUm1hFtkSr44vbplHKomyziE\nEEKIOpKUEEKIVqxi4xayp9+J5nTS61/PEDNuVLPb1OUfxLTqLbDV4Dz/cpT+I1tmHweXzb2hpasW\nLJEQ1RX0Rt/3exxNg9xyIz8csWJXdHSPcXB2eztSUVk0VL/uMQw/V5ZxCCGEEHUkKSGEEK1U+Veb\nyZ5xF5qi0Ovf84jOGNHsNvU5uzCtnQ8uJ65LJqKmXND8QBvCXuku+ak6oV08RHRsmZkZx1FUyCqw\nsLfIglEPAzrY6Bot5T5F400eKcs4hBBCiDqSlBBCiFaobP0msq+7B03TOOvNeUSnDWt2m/p92zH+\n9wNAh3Pktajd+zc/0DPRNKgphvIc978jOrmTEi2cCahx6NiaG0JBlZEIi8KQzrVEh8rUe9E0IRYj\n142tW8bxiyzjEEII0aZJUqK5XjeqAAAgAElEQVSNszsVCkprsDsVf4cihPCSsrVfs+e6ewA46+3n\niRp1SbPbtG9dj+mbT8Fkxjn6OrROZzW7zTPSNKjKg6p89waa0d3AGun7fn+joMpd7rPGqadTpJNz\nO9mwGKXcp2ieft3qlnFUyzIOIYQQbVrLLsYVAUNRVRau28v27EJKKuzERFgYmBzPlNReGPSSqxIi\nWJWu+oq9N85BZzBw1vz/I3LY+c1rUNMw/LAW+8//RQsJxzlqJlp0e+8EezqqAhW54KgGgwWiuoDB\n5Pt+jw9Bg/3FZnLLTeh1Gn0SbCSGSwJXeM/kkb34eX8xyzYdYlByPF3bh/s7JCGEEKLFya/PNmrh\nur2s2ZJLcYUdDSiusLNmSy4L1+31d2hCiCYq+eJLd0LCaCT5P39vfkJCVTFuXoLx5/+ii4zDkX5j\nyyQkFId7/whHNZjD3DMkWjghYXfp2HHUSm65iVCTyuCk2qBJSLhcGl9td3DgaHDE25bJMg4hhBBC\nkhJtkt2psD27sN77tmcXyVIOIYJQydI17Lv5AXQmEykLXibioiHNa1BxYfz6Iwx7vkeNbk+7qXdB\neLR3gj0dR427woZih5AYiOzsXrrRgspq9WzJtVJuMxDfzsWgpFramYNjucbRQoX/W1jL5xscbN3t\n9Hc4ogH6dYthxK/LOBbLMg4hhBBtkCQl2qDyKjslFfZ67yuttFFeVf99QojAVPz5Kvbe+md0Visp\nC14m/IKBzWvQacf05X8wHNqJmtAV52W/R9+uBaaV28qh7BBoCoS3d/+vBTe01DQ4XGrih6NWXIqO\nnrF2+ibaMQbBN6Wqaqzd4uDFhbXkFatcdI6RKy6x+Dss0UCTfq3GsXzTIQ7mVfg7HCGEEKJFBcGp\nlvC2yDALMRH1n6xGh1uJDJMTWSGCRdGnK9h3+1wMoVZ6f/APws8/t3kN2mswrXkH/bF9KJ1ScI6a\nBeYQ7wR7KpoGVQVQccSdhIjq4p4l0YJcCuzMt7C/xIzZoDGgo43OUa6gKPdZVKbyyie1LP/GQbsQ\nHTf+zsqEkVYspiAIXgC/XcaRhdMlyziEEEK0HZKUaIMsJgMDk+PrvW9gchwWU8tOlRZCNE3Rx0vZ\nf+cjGMLbkbLwVcIGn9O8BqvLMa38N/qiXJQeA3CNuAaMPt7LQVPdyYiaItCbILq7ex+JFlReo7H1\nSAhF1UairApDkmqJCgn8H4WaprHpZyfPf1DDwWMqA84yct+0UHp3kz2sg1HdMo4jhdUs+eagv8MR\nQgghWoycufzK5nBRUFpDZJilTfwon5LaC3DvIVFaaSM63MrA5DjP7UKIwFb44WIO3PsEhshwen/4\nCu369wHce8aUV9kb/VmmqyjCtOYddNXluHoPRRmSATof560VF5TngKsWTCG/7h/Rsl9LeZUG9hzQ\nUFQ9naMcdI9xog+CCQYV1SofrbWTdVAhxALT0y0MTDaiC4apHeKUJo3sxU/7S1i+6RCDkuPo1j7C\n3yEJIYQQPtfmkxJ1pTF/3FdMYWltmymNadDrmTY6mQnDezbpB4wQwn8K3v+Mg/f/FWN0JCkLX6Xd\n2SnNKvOrKz6Kae276OzVuM4dhXL2cN/v5eCyQVkOqE6wRkJ4B98nQY6jarC3yMzRChNGA/RrbyO+\nXXBs8rtjj4vML23U2CC5s4Epoy1Ehbfe76u2JMRi5PqxvXnuwx94c1kWj8w6D1MwbGoihBBCNEOb\nT0rUlcasU1caE2Da6GR/hdViLCYDCdGh/g5DCNFABe9mcvCBv2GMiaL3R68R2vcsoOmfZbq8A5jW\nvw9OB84LrkBNbmYZ0YawV7qXbGgqtIuH0LgW3dDS5tSxM99Cpd1AO7PCsD5GbFWBn5CotWt8tt7O\n1t0uTEa4ariZi/qb0MvsiFalb7cYRgzsxPrtR1jyzQGuvrSnv0MSQgghfKpNp9+lNKYQIpjkv7XQ\nnZCIi6F35uuehERTP8v0OVmY1r4LigvXsEm+T0hoGtQUu5dsaBpEJLmTEi34o7qkxsCW3BAq7QYS\nw5wM6mQjPCTwf9Rn57iY934NW3e76Jyo555rQrlkgFkSEq3UpBE9iY2wsnzTYQ4ck2ocQgghWrc2\nnZSQ0phCiGCR98YCDs2dhykhlj6f/JPQ3v/b/6Upn2X6vdsw/vcD0OlwjrwWtVszN8k8E02Dqjyo\nynfvGxHdDawtt15e0+BgiYkfj1lQVEiOs9M7wYEhwL8FnS6NRV/Z+ednNiqrNdIvMHPHpBASogM8\ncNEsdcs4VE3jLanGIYQQopVr08s36kpjFtdzMi+lMYUQgeLY6/8h5y8vYkqMo/fHrxPSq9sJ9zf2\ns8zwy0aMW1egmUNwps5Ai+/sy/BBVaA8F5zVYLRAZBcw+Liqx3GcCmQVWCipMWIxqvRLtBNhDfwf\neTn5CgtW2Sgo1UiI1jHtMiudE9vG3j/PPvssW7duxeVycfPNN3POOefw4IMP4nK5MBqNzJs3j/j4\neBYvXsz8+fPR6/VMnjyZSZMm+Tt0r5FlHEIIIdqKNp2UqCuNefw67DrBXhrz+B34hRDB6+jL75D7\n9D8wdUigz8evY+3R5aTHNPizTNMwbF+NcecGtJBwnKNnoUUl+nYAigPKDrv/3xwGEZ1A33KfrZV2\nPTvzLNhceqJDXPRNtBPoH+2KorF2i5PV3ztQVRh2rolxF5kxGdvGUo1vv/2WPXv2sHDhQkpLS7nq\nqqu44IILmDx5MmPHjuX999/n7bffZvbs2bzyyitkZmZiMpmYOHEiaWlpREVF+XsIXjNpRE9+2lfM\n8k2HGXhWPN07SDUOIYQQrU+bTkrA/0pj/rivmKKy2qAvjVnfDvwXD+jEFUO7tOpqIkK0Rkde/DdH\nnn0dc6f29P74dazdkk752DOW+VVVjJsXY9i7FTU8FufoWRAW7dsBOGp+3T9CgZAYCEtssf0jNA2O\nVRrZU2RG06BrtINu0c6W3L6iSQpKVRasspGTrxIZpmNqmoXkzm3rq/q8886jf//+AERERFBbW8uj\njz6KxeJOskdHR7Nz50527NjBOeecQ3h4OACDBg1i27ZtpKam+i12bwuxGPn92N7M+/AH3lqWxSPX\nSTUOIYQQrU/bOtOpR11pzJsnhLDvYHHQl8asbwf+xRv2U1PraBPVRLzl+Jkmwfx+EMFJ0zSOPP8v\njr7wBubOHemT+TqWzh1P+5zTlvlVXBi//hjD4V9QYzrgTJ0JIWG+HURtGVQeAzR3uc8QHydAjqOo\nsKfITF6lCaNeo0+indgAL/epahrf/Ohk6UYHThcMTjFy1QgLIZYAz6L4gMFgIDTUXRUqMzOTSy+9\n1PO3oigsWLCA22+/naKiImJiYjzPi4mJobCw/g1fjxcdHYrR6JvP9fj4cJ+0+fPhMr745iBrth9h\n5ti+Xu+jNfHFMRCNI8fA/+QY+J8cg8Zp80mJOlazMehLY55pB/4Jw3vKD+wzqG+mycDkeKak9pKZ\nJqJFaJrGkXmvc/TFN7F07UTvj1/HktShwc8/qcyv045p/QL0eftRE7vhHDEdzFYfRP4rTYPqQqgp\nAp0eIju7l220kFqnjp15FqocBsIsCv0S7YSYtBbrvynKKlUWrrGTnaMQaoVr0qwMOEu+ntesWUNm\nZiZvvfUW4E5IzJkzhwsvvJChQ4eyZMmSEx6vaQ07zqWlNV6PFdwnoIWFlT5p+/ILuvDdz3l8sm4v\nvZMiZRnHKfjyGIiGkWPgf3IM/E+OQf1Ol6iRX1lByO5UKCitOanMn1QTab66mSbFFXY03DNN1mzJ\nZeG6vf4OTbQBmqaR+/Qr7oRE9870zvxnoxISJ7FVY1r9Nvq8/ShJvXGOmunjhIQKFUfcCQm9CaK7\nt2hCoqjaXe6zymGgQ4STgR1tAZ2Q0DSNbbudPLeghuwchT7dDNw/PVQSEsCGDRt4/fXXeeONNzzL\nMx588EG6du3K7NmzAUhISKCoqMjznIKCAhISEvwSr6/VLeOQahxCCCFaIznzCSJnuoov1USaR2aa\nCH/SNI2cJ14i7/X3sPboQu+PX8fcoRk/sKrLMa15B31FEUrPgbguvNK3G0wqLig/DC4bmEIhMsld\n+rMFqL+W+zxcZkav0+gdb6d9hKtF+m6q6lqNT9bb2bHHhdkEk1ItXNDPiC7QN71oAZWVlTz77LO8\n8847nk0rFy9ejMlk4s477/Q8bsCAAcydO5eKigoMBgPbtm3joYce8lfYPtenWwwjB3biy+1HWLzx\nABOGSzUOIYQQrYMkJYJIfftF1P09bXRyq64m0hIaMtMk2Jf4iMCkaRqHH3uB/Dc+wNqrmzshkRjX\n5PZ05YWY1sxHV1OOq+/FKIMucy+l8BWXzV1hQ3WBNdK9h4Qv+zuOwwW/FFgpqzVgNaqc3d5OmCWw\nryLvOuhi4Vo7FdUa3TrouSbNSlyUTFyss3z5ckpLS7n77rs9tx09epSIiAhmzJgBQM+ePXnssce4\n9957ueGGG9DpdNx+++2eWRWt1aSRPflpfzFffHuYQclSjUMIIUTrIEmJINHQq/j17cB/8YCOXDH0\n5DKC4kQy00T4g6ZpHJo7j4K3PyIkuQe9P34NU3xsk9vTFR/BtPZddPYaXAPTUPoN823FC3ule8mG\npkK7BAiNbbEKG+U2d7lPh6InNtRF74TALvdpd2os+drOpp9cGPQw9iIzIweZ0OtldsTxpkyZwpQp\nUxr02IyMDDIyMnwcUeCwmo1cP7YP8z7YLtU4hBBCtBqSlAgSDb2KX98O/Ekdo2SzlQaQmSaipWmq\nyqE/P0vB/ExC+vSi98JXMcXFnPmJp6A7th/T+vdBceK88ErUs4Z4Mdrf0DSoLYGqfEAHEUlgbZmr\ntpoGRyqM7CsyowE9Yhx0jgrscp8Hjyl8sMpGUblG+1g90y6z0ClePlNE4/XpGs3IQZ34cpss4xBC\nCNE6SFIiSDT2Kv5JO/CLBqlvpsnA5DjP7UJ4i6aqHPzT0xS+/xmhfZNJWfgqptioJrenP7wT44aP\nAXANm4LatZ+3Qj2ZpkFlHthK3ftGRHYGU4jv+juOS4XsQgsFVUZMBo2+iTaiQwJ3uYZL0Vi12cG6\nrU7QYMQgE2MuNGM0BnAGRQS8SSN68tO+YpZ/e0iWcQghhAh6kpQIEnIVv2XUN9NEXlvhbZqicOC+\nJylauITQc3rT+8NXMEZHNrk9/Z6tGDd/DgYTzhHT0Dr48MqpqkB5LjirwWh1JyQMJt/1d5xqh46d\neVZqnHoirO5ynxZj4FbXyCtWWLDKzpFClZgIHVPTrPTsJJ8novmOX8bx5rIsHpVlHEIIIYKYJCWC\niFzFbzky00T4iqYo7L/nLxR/vIx2A/qS8sE/MEY1/SqnYecGjNtWoVlCcabOQItL8mK0v+FyuCts\nKA53qc+IJNC3zA+hgioDuwssKJqOpEgnPWIdBOpWDKqmsWG7k+WbHLgUOL+vkSuHWbBaAjRgEZRk\nGYcQQojWQpISQeRUV/HtToXi8hq5qi9EgNNcLvbf9RjFn62g3aCzSXn/ZYyRTawWoGkYtq3C+MvX\naKEROEfPQotsRgnRM3BUV0DpAdAUCImBsMQW2dBS1WB/sZncchN6nXu5RkKY4vN+m6qkQuXD1Tb2\nHVEJC9ExaZSFs3vIV63wDVnGIYQQojWQM6UgVHcVX1FVFqzJZnt2ISUVdmIiLAxMjmdKai8MLXT1\nUgjRMKrTxf7ZD1OyZDVhQ/qT8v5LGMLDmtiYgvHbxRj2bUONiMM5eha0a/p+FGdUW0Z54TH3XhLh\nHSAk2nd9Hcfu0rEz30KFzUCoSaVfexvtzIG5XEPTNL7PcrHov3bsTji7h4FJqVbCQmV2hPCdk5dx\nDMFklIsTQgghgoskJYLYwnV7T9hjorjC7vl72uhkf4XlM3anIvs8iKCkOl3su+0hSpetI/yCgSS/\n9yKGsHZNa0xxYtzwMYacLNSYjjhHzQRrE9s6E02D6kKoKUKnN6BFdnIv22gBZbV6duZbcCp64sNc\npMTbCdQl85U1Kpnr7Py8X8FigqlpFob0NqIL5HIgotU4fhnH518fZOIIWcYhhBAiuEhSIkjZnQrb\nswvrvW97dhEThvdsNT/cFVVl4bq99c4IESLQqQ4n+255kNIV6wm/aDDJ776IIbSJlSocNkzrF6DP\nP4Ca2B3nyOlgspz5eU2hqVBxBOyVYDAR1b0PpRUu3/R1fLca5JSZ2F9iQgf0irXTKdIVsOU+f97v\n4uO1dqpqNXp2MjA1zUJMRIBmT0SrVbeM44vN7mUcPTrKMg4hhBDBQ86cglR5lZ2SesqDApRW2iiv\nqv++YFQ3I6S4wo7G/2aELFy319+hCXFaqt3B3hvnULpiPRGXnE/yu39vekLCVo1p9dvo8w+gdO6D\nc9QM3yUkFCeUHnQnJEyhEN0do8X3JT9dCuzMt7C/xIzZoHFuRxtJUYGZkLDZNRausfH2Uhs2h8bv\nLjFzy9VWSUgIv7Cajfx+bB80Dd5anoXTFbj7rgghhBC/JTMlglRkmIWYCAvF9SQmosOtRIb56MdK\nCzvTjBCbw/dXboVoCtVmZ8+Ncyhfu5GI4ReS/NZz6EOsTWusugzTmnfQVxSj9BqM64IrQO+jmVBO\nm7vChuoCaySEd2yRDS2r7Dp25lupdeqJsir0TbRhDtBvqH1HFD5cbaOkQqNTvJ5pl1loH9s6ZqaJ\n4NW7azSpgzqxTpZxCCGECDIBesonzsRiMjAwOf6EPSXqDEyOazVLN840I6S0wi5vYhFw1Fobe264\nn/L1m4gceRFnvTkPvbVpiUJdWQGmtfPR1VTg6ncJysDLfJcksFdCRa57DUW7BAiNbZGERF6lkexC\nM6qmo0uUg24xzoAs9+l0aaz41sF/tzlBB6PPM5F2vhmjIQCDFW3SxBE9+VGWcQghhAgyMs80iE1J\n7cXoIUnERljR6yA2wsroIUmtaq+Fuhkh9YkOtxJ9ivuE8Belxkb2dfe4ExKjL+Gst55rekKiKBfT\nyn+7ExKD0lEGpfsmSaBpUFMM5TmgARFJ0C7O5wkJVYPsQjO7CizodHB2exs9YgMzIXG0UOHFhbWs\n3+YkNlLH7IkhjBlqkYSECCjHL+N4c9kvsoxDCCFEUJCLzEHMoNczbXQyE4b3bLVVKc40I8RqNlLZ\nxLalmofwNld1Ddmz7qZy4xai0ofT6/Wn0VvMTWpLd2wfpvULQHHiHDoetddgL0f7K02DymNgKwO9\nESI7g8n3+0fYnO5yn5V2A+3MCv3a2wk1BV65T1XV+HKbk5XfOlBUuOgcI5dfYsFikmSECEzHL+NY\n9PUBJo1oPRcqhBBCtE6SlGgFLCYDCdGh/g7DZ+pmfmzPLqK00kZ0uJWByXFNnhFyumoeBr1MHhJN\no1TX8P2Ue6ncuIXosSPp+epT6M2mJrWlP/Qzxq8zAXBdOhW1S19vhvo/qgLlueCsBqPVnZAwNC3m\nxiipMfBLvgWXqiMxzElyvANDAP6nV1Sm8sFqGwePqUS00zFllIXe3eRrUwS+umUcKzYfZnBygizj\nEEIIEdDk7Eo0S0vMNvD2jJC6ah516qp5AEwbndzseEXbo1RVs3v6nVR9v4OYK0bT4x9Pojc17eNV\nn/09xs1LwGjCOXI6WvseXo72Vy6He0NLxQHmMPeSDR8n5TQNDpWaOFjqLveZHG+nQ3jgVdfQNI1v\nd7pYvMGOwwkDzjIyYYSFdiEBFqgQp1C3jOPZD7bz5rJfeOz68zAZZUagEEKIwCRJCdEk/pht4I0Z\nIWeq5jFheE9ZyiEaxVVRRfb0O6na+iMdp4yj07yH0Rmb8NGqaRh+/grjD2vQLKE4R81Ei+3k/YAB\nHNXuGRKa4t7Msl2Cz/ePcCqQVWChpMaIxajSL9FOhFX1aZ9NUVGt8tFaO1kHFUIsMD3dwqAU388e\nEcLbeneNZtSgJNZuy5VlHEIIIQKaJCVEkwTrbIMzVfMor7K36qUwwrtc5ZXsnjab6u07iZ0whgHv\nPEtxaW3jG9JUDFtXYsz6Bi00EufoWWiR8d4PGKC2DCqPuv8d3gFCon3Tz3Eq7Xp+zrNgd+mJCXHR\nJ9FOIOb+duxxkfmljRobJHc2MGW0hajwAFxXIkQDTRzRkx/3F7Fi82EGJcfTs2Okv0MSQgghTiJn\nW6LRzjTbwO4M3N2+z1TNIzJMqnmIhnGVlrNrym1Ub99J3OQr6PHiY+ibMkNCVTB+swhj1jeoEXE4\nMm70TUJC06Aq352Q0OkhqqvPExKaBkcrjGzLtWJ36egW7eCcDoGXkKiuVVmw0sa7X9hwuuCq4WZu\nHG+VhIQIehazgevHuKtxvLUsS6pxCCGECEhyxiUarSGzDQJVXTWP+gxMjpOlG6JBnCVl7Jp8KzU/\nZhE/bTzdX3gYnaEJ7x2XE+N/P8SwfztqbCec6X+Adj64kqmpUJHrLvtpMEN0dzC3834/x1FU2F1o\nJrvQgkEP53Sw0y3GGXD7R2TnuPjzPwrZuttFl0Q991wTyiUDzOgDLVAhmqhuGcex4hoWbTjg73CE\nEEKIk/h0+YbNZuPyyy/ntttuY+jQocyZMwdFUYiPj2fevHmYzWYWL17M/Pnz0ev1TJ48mUmTJvky\nJJ9rC2Um62YbFNeTmAiG2QberuYh2hZncSm7ptxG7S97iJ9xNd2efgBdU/ZRcdgwrX8fff5B1PY9\ncI6YBiYf/LejOKE8B1w2MIVCZJK79KcP1Tp1/JxnodphINyi0C/RjjXAyn06XRrLNjrYsMOJQQ8Z\nF5pJHWLCoJdkhGh9PMs4vjvMoBRZxiGEECKw+PTM9LXXXiMy0v3F99JLLzFt2jTGjBnDCy+8QGZm\nJuPHj+eVV14hMzMTk8nExIkTSUtLIyoqypdh+URbKjNZN9vg+D0l6gTDbANvV/MQbYezsJhdk2+l\ndvd+Eq6bRNe/zkHXlCvqtVWY1s5HX5qH0qUvrksmgcEHH8dOm7vChuoCa5R7DwkfzwAoqjaQVWBB\nUXV0jHDSK85BoP3Oz8lXWLDKRkGpRkK0jtumxBBuDtwZXkI0l8Vs4Pdj+/DMgu28tSxLqnEIIYQI\nKD77tbxv3z727t3LiBEjANi8eTOjRo0CYOTIkWzatIkdO3ZwzjnnEB4ejtVqZdCgQWzbts1XIflU\n3caPxRV2NP638ePCdXv9HZpPTEntxeghScRGWNHrIDbCyughSUE126CumockJERDOAqKyJp4C7W7\n95P4h2uanpCoKsW08g13QuKsIbiGTfFNQsJeCWUH3AmJdgk+T0ioGuwvNvFznhVNg94JdpLjAysh\noSgaqzY7eOnjWgpKNYada+Kea0Lp0cns79CE8LmULtGMGizLOIQQQgQen82UeOaZZ3j44YdZtGgR\nALW1tZjN7hO/2NhYCgsLKSoqIiYmxvOcmJgYCgvr30AxkLXFMpMy20C0JY68QnZNugXbvkO0v3k6\nnR+5u0kJCV1ZPqY189HVVuI6+1KUc0d7P1GgaVBb4t7UEp17uYYlwrt9/IbDBb8UWCmrNRBiUumX\naCPMEljLNQpKVRasspGTrxIZpmNqmoXkzlKASrQtE4f35Md9vy7jSI6nZydZxiGEEML/fHJGtmjR\nIs4991w6d+5c7/2aVv/J6qlu/63o6FCMPph2GB8f3qTnHSuqpqTy1Bs/Gswm4uN8u6ncmTR1bA2R\n5LOWG8aXY/O31jq2YBpXbW4em6fcim3fIXrc9wd6P3XfaRMSpxqb6+hBala9BfYaLJdeScSQkV6P\nVdNUqo4dxFZViN5oIqJLCqYQ73321De2okqNbdkaNid0jIbzehowG8O81mdzqarG2u9qWLiqCocT\nLh4QwrXjImgXcuJEwWB6TzZWax6baJwTlnEsl2UcQgghAoNPkhLr168nJyeH9evXk5eXh9lsJjQ0\nFJvNhtVqJT8/n4SEBBISEigqKvI8r6CggHPPPfeM7ZeW1ng95vj4cAoLK5v0XMWpEBN+6o0fFYez\nyW17Q3PGFuhkbMEnmMZlz81j16SbsR86Qoc7ryf2jzdTVFR1ysefamy6o3swrf8AVAXXRVdh7zoI\nvP0aqIp7Q0tnDRitqJGdKatSoco7/fx2bJoGR8qN7Cs2owE9Ypx0jnJSXuqV7ryirFLlwzV29uQo\nhFrhmjQr/XsZqKmqpua4wxhM78nG8sXYJMkR3OqWcazdmsuiDQeYNDJ4ll0KIYRonXySlHjxxRc9\n/3755Zfp1KkT27dvZ+XKlVx55ZWsWrWKYcOGMWDAAObOnUtFRQUGg4Ft27bx0EMP+SIknwr2jR+F\nECez5xwla+ItOHKO0vGPN9LpvpuatGRDf/AnjBs/AXS4hk9F7dzH+8G6HO4NLRUHmMMhshPofLfB\nrkuF3YUWCquMmAwafRNtRIeoPuuvsTRNY3u2i0/X26m1Q59uBiaPshDRrnVtOixEU00c3pOf9hXL\nMg4hhBABocXO0O644w4WLVrEtGnTKCsrY/z48VitVu69915uuOEGrr/+em6//XbCw4PzCkxr2PhR\nCOFmO5RL1tU34cg5Sqf7bibp/publpDY/R3GDR+DwYhz9EzfJCQc1VB6wJ2QCI117yHhw4REtUPH\nttwQCquMRFgVhiTVBlRCorpW470Vdt5faUdRYVKqhRuusLaphER1jULm0jx+ymqdsz9E81nMBq4f\n2xtNg7eWZ+F0Kf4OSQghRBvm812+7rjjDs+/33777ZPuz8jIICMjw9dh+Jxs/ChE62A7kMOuSbfg\nOJpP0gO30fHO3ze+EU3D8NN/Me5Yi2Zph3P0TLSYjt4PtrYMKo+6/x3eAUKivd/HcQqqDOwusKBo\nOpIinfSIDazqGlkHXXy01k5FtUa3DnquSbMSF9V2khGqqrFuYzH/+eQo5RUuMkbGkXqpD953olVI\n6RLN6MFJrNmay2cbDjBZlnEIIYTwE9l63MvqykwKIYJP7b5D7Jp8K85jBXT+8x10uH1W4xvRVAxb\nVmDctQmtXRTO0bPQIhtzzmYAACAASURBVOK8G6imQXUB1BS7Z0VEdgaz7zbTVTX44aDKnnwrBp17\nuUZCWOBcWbU7NJZstLPpJxcGPYy7yMyIQSb0gZQx8bHsfdW8sSCHvQdqsJj1TL+6I79LT/B3WCLA\nTRjekx/3FbPyu8MMlmUcQggh/ESSEkIIAdTuOciuybfgzC+i86N30+HmaxvdhqYoGDd+iuHADtTI\neJyjr4NQL5fj1FSoOAL2SjCY3QkJo8W7fRzH7tKxM99ChQ1CTSr92ttoZw6ccp8HjyksWGWjuFyj\nQ6yeaZdZ6BjfdmaplZU7eS/zCOs2lgAw7IJoZk7qRFyM2c+RiWBQt4zjmQXb+feyLB677jws5rbz\n348QQojAIEmJVsDuVGTJiBDNULN7H7sn34azsJguf7mP9n+Y2vhGXA5qF3+A4cAvqHFJOFNngMXL\ns6YUp7vChssGplB3QkLvu//mS2v1/JJvxano6BwLXSNqMQbIagiXorFqs4N1W52gwYhBJsZcaMZo\nbBuzI1wujWVrC/ho8TFqalW6JYXwh+lJ9EsJzn2ZhP+kdInmsvM6s+r7HP6zejc3jOvr75CEEEK0\nMZKU8IPfJhEam1Soe3xYqIlFGw6wPbuQkgo7MREWBibHMyW1FwZ9gPxyECLA1WTtZdfkW3EVl9L1\nqT+ReN2kxjfiqMX05fu4Cg6hduiFc/hUMHl59oKz1p2QUF1gjXLvIdGEzTcbQtMgp8zE/hITOqBX\nnJ3/Z+/OA6Oqz/2Pv2efyb7vYScEgsiuqMiOCCogexAV1Lr2tr3ealuvVlt/7a3d721dKkUURTYp\noILsKIgssohsCTtJyJ5JJslkzpw55/z+mKIsIZkkk0yW7+svspyTZxIyOeeZ7/fz9O9u5YoJzgFV\nUKqwdJNEXrFKVJiOOeOsdEvuOA3Zw8ccLFyaQ16+REiwgR88kMr4ETEYDB2jISP43/SR3cnOKefL\nbwvo0zmKYX0TAl2SIAiC0IGIpkQLUlSV5dtOX9VECLKaqK5xY69019tUuPZ4i9mAy/39vu5Sh/Td\nWNLMsWkt9rgEoa1yHsv2NiTsFXR57RfEPXB/w09SU4lp63vo7QUY0/pTPXgyGPz81CpVQkUuoEFw\nnHfKRjM1JDwKnCy2UFJtxGxQyUiQCLeqjZo+4m+qpvHFIZkNX7nxKDC0j5HJd1qwmgNfW0soLJZ4\nZ3kuew9WoNPBXSNjyLw/ibAQ8adcaBqjQc8TkzN4+Z39vLcxi65JYSREiXwsQRAEoWWIK5kWtHzb\n6e+aBuBtIpQ6pKverqupcO3xVzYkrnQou4RpI7qLrRyCUIfqIyc5OedplHIHXf/4IrFzJjf8JJV2\nzFsXo6ssQ0kbQujEOVSXVvuvSE2DmlKoKgJ03nGfFj9nVFyhStJxrNBKjawnwqrQJ96FuZX8lShz\nqCzb7OJMnkqITceMMRb6dmslxTUzSVL5aH0BazYUIns0evcM5tHMVLp1FjeNgv/ERQbx0IR03lp3\njDfXHOWFBwdhMorrCEEQBKH5dYwrulZAkhUOZRf79Lm1NRUacry90kVFlSSmgAjCDVR9c5ys2U+j\nOKro+udfEjvzngafQ2cvwLT1PXQ1lXhuGoFy8xh0/tw2pWlQmQ+uctAbvfkRJpv/zn+Ngkoj2cVm\nVE1Hpwg3XaLkVjHuU9M09p/wsOZzCUmGvt0MzBhtJSSoFRTXzDRNY/fX5SxenktJmUxUhImHZiYz\n/JbIVrFyRWh/bukTz4kLZXzxTT4rtp1h7nix6lIQBEFofqIp0UIqqiTKrlgVUZfamgoNOT4y1Ep4\niHc/uyQr5JdUo8jKd+cRgZhCR1Z18ChZmc+gVDnp9r+vEDNtYoPPoSu6iGn7EnRuF57Bd6P0vs2/\nRaqKNz9CdoLR6m1IGEz+/RqXv5QGp0vMXHKYMOg1+sa7iAluHeM+K50qq7ZJHD2rYDHB7HEWBqcb\nO8QN+YXcGhYuzeHoySqMBh33T4xn+qQEbDbx3C00rzlj0ziT52DrwVzSO0cyqFdsoEsSBEEQ2jnR\nlGgh4SEWosIsV23XuJErmwqNOX5AWgxGg46lW7I5lF1MqUPCatYDOiS3cl12hZjeIXQUlfu/IWvu\nf6DWuOj+t18TPeWuBp9Dn5eN8fNloCrIt09D7dbfv0V6JG9DQnGDJRTCkkHXPMG1Ltk77rNSMhBs\nVuibIGEztY5xn0fPeli5VaKqRqN7soHZ4yxEhbX/AN+qag/L1uazYVsxqgqD+oWxYE4KSfHWQJcm\ndBAWk4EnJmfw63e/5p31J+icEEJMePOt0hIEQRAE0ZRoIRaTgQFpsVdlQtzIgLQYAIrszu8aBXUd\nbzUbcMsKkaFWBqTFMGt0j1ryJ9Tv/n05u0LVNPQ6nZjeIXQIlXsPk/XAf6C6JHq8/v+Iundsg8+h\nP3cE45cfgV6PZ2Qmakov/xbprvY2JDTVG2YZHNdsgZZlTgPHCy14VB3xoTJpMW4MreDX3iVprN0p\nse+4B6MB7htuZnh/E/p2vjpCUTW27Srl/VWXcFR5SIyzsGBOCoNvDg90aUIHlBwbQua4NBZvOMlb\n647xfOZAjK3hCUIQBEFol0RTogXNGt0D8GZG2CtdRIZasFmNFNtrkGRv08Bi0nPyop3/fnvPdY2C\n64/3NiGmDO9GldN91YhRX/Indn9bIKZ3CB2C46sDZM/7MZrbTY+3fkvUxNENPoc+ay/GfZ+CyYw8\n6gG0+C7+LbKmHCovef8dmgS2CP+e/980DS7YTZy3e8d9psVKJIZ6mqv30SBn8hSWbXZR5tBIjtWT\nOd5CQnT7X7118nQVCz/I5cwFJ1aLnnnTk7h3XBwmk7gJFAJneL9Ejp8vY9+JItbsPMf0kd0DXZIg\nCILQTommRAsy6PVkjk1j2oju322X+OjzM+QWfZ/WL8nqVW9f2yi49vjL2y2CLN//KH3NnxDTO4SO\nwLFrP9kP/hhNUejxj98ROWFkw06gaRiObMd4ZDuaNQR5zINoUYn+K1DToLoInKWgM3gnbJiD/Xf+\nK8gKnCi0UFZjxGJUyYiXCLOq9R/YzGSPxmd73Hx+UAYdjB1iYtxQM0ZDK+iUNKOycpklq/LYsbsM\ngDtvjeTBGclER5oDXJkggE6n46EJ6ZzPr2T9ngukd46gb9foQJclCIIgtEOiKdHMastrsJgMxEUG\nNXoix+Xjb6Qh+RO1EdM7hPai4vM9ZM9/FlSVHgt/T+S44Q07gaZi3L8eQ9ZetOAI3GMfhjA/XpRr\nKjjyQKoEg9kbaGm01H9cIzhceo4VWpA8eqJsHnrHS7SGvmNescLSTRIFpSox4Toyx1vpnNgKCmtG\nskfl0y3FrFiXT41LpWsnG49mptInLSTQpQnCVWwWI49PzuA3Sw6w8OPjvLJg6HWZV4IgCILQVKIp\n0UwUVWX5ttN15jU0dSLHjfiaX2E1G2pdLVFb0KYgtDXl23dzasF/AdBz0R+IGH17w06gKhi/XI3h\n/BHUiDjkMQ9BUJj/ClRkb36ExwWmIG9DQu//m3FNg/xKI6eKzWhAl0g3nSPlgG/XUFWN7QdkNu51\no6hw201G7rnDgsXUvldHHDrq4J9Lc8grkAgJNvD4vFTGjYjB0BrmrwpCLbomhjFjVA+WbT3F258c\n5z9n9W/3GS+CIAhCyxJNiWZybdBkbXkNTZ3IUZcr8yfKHC4sZu/NzpWBmJqmsfVA3nXHDkiLEVs3\nhDatfMsuTj36U9DrSVv0R8JH3tqwE3jcGD9fhuHSKdTYVORR88Dix/R5ucbbkFA9YI2A0MRmCbRU\nVMguMVNYacKo1+gTLxEVFPhxnyXlKh9udnE+XyUsWMesMRbSu7TvP0cFRRKLluWy/3AFeh1MGBVD\n5tQkQkPa9+MW2odxg1M4cb6Mb86Usv6rC9xzW5dAlyQIgiC0I+JqqBnUtS3j2m0YDZnI0ZBGwZX5\nFQazCcUtA1y1lURRVXQ63XXBmZcbGoLQFtk3fs7pHzyPzmAg7b2/EHbHkIadQKrBtP199MUXUZN6\nIt85G0x+3OMvOaAiD9AgJB5sUc3SkHDKOo4VWKh2Gwi1KGTES1gDPO5T0zT2HPOwbqeEW4abexqZ\nNtJCsK39vurqkhQ++rSQtZ8VIns0+qSF8GhmCl07ie1xQtuh0+lYMKk3L7+znzU7z9GrUwQ9U5on\njFcQBEHoeERTohnUtS3j2m0Y107UiAixEGwz4XTJ2CulJjcKLCYDsTHBFBdXAly1/aO24E2xQkJo\ny8o2bOfM4z9DZzaTtuQvhA0b1LATOCsxbX0XfXkhSpeb8Nx2Pxj89DSpad4wy+oiQOfdrmEJ9c+5\nr1FSbeBEkQVF1ZEUJtMjxk2gdwc4qlVWbJU4cV7BZoG5d1kY2MsU2KKakaZpfLnfzuLleZTaZaIj\nTTw0M5k7hkaiE0vfhTYoNMjM4/dl8LulB3lr3TFenj+UEFv7/R0WBEEQWo5oSjSDurZlhAdbsF0x\nKeNGjYHaAjKh9uDMpqovOFMQ2oKyj7dw+qkX0Fst9Hr/r4TeMqBhJ6gsw7xlMboqO0qvW/AMmQg6\nP41k1DSozAdXOeiN3oaEyY/bQf5N1eB8mYmL5Wb0Oo30OImEUI/fv05DfXPKw6rtLpwuSOtkYPZY\nC+Eh7Xfc5fkcJwuX5nIsqwqjUce0SfFMm5SAzSqavkLblpYaweQ7urJm5zkWfXqCH067STTZBEEQ\nhCYTTYlmUNe2DHuVxK8W778u9PLaxsC1b18bnBkRYqF/WgyZY3t+d47WojkaJ4JQl9I1Gznzw5fQ\n26z0+uB/CR1yc4OO15XlY9r6HjpXFZ5+o1D6jfLflgpV8eZHyE4wWr0NCYP/X110e+B4oZVylwGb\nSSUj3kWIJbDbNWokjdU7JA5meTAZYeoIM7f3M7Xbm5jKKg/L1ubz2bZiVA2G9A9n/uwUEuNEcLDQ\nftwzrAtZF8s5fLqELQdyGTc4NdAlCYIgCG2caEo0kyu3ZZQ6XFd9rLbQy/pcG5xpr5LYfjCP07kV\nvPTw4FbRmPBl4ogg+FvJ6g2c/Y9fYgi20evDvxMysG+DjtcVXcC07X10sgt5yCTU9AaGYtbFI3kb\nEorbu1UjLNl/qy+uUFHjHffpVvTEBHtIj5UwBrgfmJ3jYdlmiYoqjU7xeuaMtxIX2T6fBxRVY8sX\nJXyw+hKVVQpJ8RYWzElhUL/wQJcmCH6n1+t47N4+/HLRPlZuP01aSgSdE5pnK5ogCILQMbTPK8RW\n4PK2jJceHkxESO0heYeyS5Dk+pPw6wrOzCmqYunm7DqPzS+p9unrNNXlxkmpQ0Lj++bL8m2nm/1r\nCx1T8YpPOPvDlzCEBtNr+esNbkjoc7MwbVkMHjfy7dP925BwV4P9nLchERQNYSl+b0hoGuSWGzl8\nyYpb0dEtyk1GfGAbErJHY83nEm/9y0WlU2PCrWaemWFrtw2JE6eqeO5XJ3nzvRxkWePBGcn85de9\nRUNCaNciQiw8ek8fPIrGG2uPUiMFfpuYIAiC0HaJlRLNrEbyUFHlrvVj14Ze3khdwZkAh06VMHO0\nctVWiatWLVRKRIU276oFXyeOCIK/FH+4lnP/9SqG8FDSl71OcL/0Bh2vP3sY4+5/gd6AZ9Rc1GTf\nVi35pMbuzZAACE0Cm/9T6j0qZBVbKK4yYjKo9ImXiLSpfv86DXGxUOHDTS6K7BpxkToyx1tJjW+f\nv/dldjfvrbrE51+VATByWBTzpicRFenHSS2C0Ird1C2au2/pxIa9F1myKYvH7unTbrdmCYIgCM1L\nNCWaWV2hl5GhVsJD6t9rHB5iISLEgr2q9sZERZX7uubGtds9GrNlpCEaMnFEEJqq6P3VnH/uNxgj\nw+m1/HWC+/Zq0PGGE19h/Ho9mtmKPOoBtLjOfqlL0zSoKvRO2dAZIDwFzMF+OfeVqt06jhVYccp6\nwqzecZ8WY+DyIxRFY8vXMlv2uVE1GN7fxKTbzJiM7e8GRZZVPt5cxMqPC3BJKt0623g0M5XePUMC\nXZogtLipd3YjK6ecPccK6d05kuH9kgJdkiAIgtAGtc/1tK3I5dDL2gxIi/Fp9YDFZKB/WswNPx4V\ndnVzQ5IVDmYV1fq5vm4Z8ZUkKxTZndgsRqLCam+w+Np8EQRfFL67ytuQiI4kfdVbDWtIaBqGw1u9\nDQlbCPL4R/zWkEBTceSc8jYkDGaI7NosDYmiKgMHcm04ZT0p4TL9k1wBbUgU2VX+b1UNm/a6CQ3W\n8fhUK1PutLTLhsSBIxX86KUTLFl1CZNJx5MPduK1F9NFQ0LosIwGPU/cl4HNYuSDzdlcKqkOdEmC\nIAhCGyRWSrSAK0Mv7ZUuIkOtDEiL+e79vsgc25PTuRXkFFVd97ErmxuKqvL+xizKKpu2ZaQ+tYVa\nBllNta4I8bX5Igj1KfjnMi6++AeMMVGkr3yDoF7dfT9YVTHu/xRD9j60kEjcYx+G0Cj/FKbIUJGD\n2+MCU5B3wobev//nVQ3OlJrJqzBh0Gn0iXcRF9L8WTE3rkdj9xGZT750I3tgULqRqSMs2CztrxmR\nX+hi0bJcvv7GgV4Hk8bEMntKIiHB4k+oIMRE2Jh/dzqvrznKG2uP8uKDgzGLv/mCIAhCA4grqhZw\nOfRy2ojujR6VadDreenhwSzdnM2hUyVUVLmJCru+ubF822m+PFpww/NEhFj8smqhtu0hpQ6J1LgQ\nnC5Po5svgnAjBf/4gIsv/xlTXDTpK9/E1rOr7wcrHoxffoThwlHUyHjk0Q9BkJ/S4uUa74QN1YM1\nMhaXMcZ/40T/TfLoOFZoweEyEGRSyUhwEWwO3OqI8kqVZVskTuUoBFkhc7yVfj3a35+TGpfCR58W\nsHZjER6PRt/0EB7NTKVzii3QpQlCqzI4PY5RA5LZfiiPZVtP8eCEhmX8CIIgCB1b+7uKbMUsJkOT\nVigY9Hrm3ZXOzNFKrc2NusImLwu2mZq8aqGur+N0eXjp4cHUSJ5GNV8EoTb5r79Hzqv/iykhlvQV\nb2Dr0cX3g2U3pi8+RH/pNGpsJ+TRD4DZTzeVkgMq8gANQuIJSeyMq+T61UxNYXfqOV5kRVZ0xIV4\nSIuVMAZo452maRzM8rB6h4TLDb27GJg5xkJYcPvaCahpGrv22nl3ZR6ldpnoSBPzZ6Vw25AIEeQn\nCDcwe0wPTuVWsOPwJXp3iWJIelygSxIEQRDaCNGUaINu1Nyob0oHgNMlI8lKk5oF9YVa1kgeEWop\n+M2l/3uH3N/+HXNiPOmr3sTaNdX3gyUnpm3voy/JQUlOw3PnLDD6YTqCpnmzI6qLvKsiwlLBEurX\nG1ZNg5xyE2fLTOiAHjESyWEefy/C8FmlU2XJBolvTnswm2DGaAu3ZBjb3U36uYtOFi7N5Xh2FSaj\njhn3JHD/pHisFtFgFYS6mIwGnpySwSuL97N4wwk6J4QSFyFWFQmCIAj1E02JdqSuSR+X2SulJmdK\n+GOiiCD4Iu/PC8n7/ZuYkxO8DYnOKb4f7HRg2vIu+ooilK798Nx2v39yHjTNO+7TVQ56I4R3ApO1\n6ee9gqzAySILpU4jZoNKRoJEuDVw4z5PnPewansx5ZUqXRL1zBlnJSaifa2OcFR5+PBfl9i0owRV\ng6EDwpk/K4WEOPF8Jgi+SowOZt74Xvzz0xO8tfYoP39gEEZD+3quEARBEPxP/KVoBS5PsGjqVIy6\nJn1c5o+mgT8mighCXTRNI/f3b3kbEqlJ9F79jwY1JHSOUsyfvY2+oghPr1vx3D7NPw0J1QPlF7wN\nCaPVO2HDzw2JKknPgVwbpU4jETaFwSk1AWtISG6NVdtdLFznotKpMuk2M09Ps7WrhoSiavxr/SWe\n/vkxPtteQmK8hZf+swc//2F30ZAIoNdee41Zs2Yxbdo0Nm3aBMB7771HRkYG1dXfT3hYt24d06ZN\nY8aMGaxcuTJQ5QpXuP2mRIZlJHAuv5LVn58NdDmCIAhCGyBWSgRQbRMsBqTFMmt0Dwz6xl30Xw6V\n3HUkH5f7+iaHv5oG/pgoIgi10TSNvNfe4NJfF2HpnEz6yrewpCT4fLyu7BKmre+hc1XjuXk0yk0j\n/RM86ZG8gZaKGyyhEJYMOv/enBc4jGSXmFE1HZ0i3HSNkgO2XeN8vsLSTS5KKzQSo/U8PSsKm9EV\nmGKayfHsKt7+IIfzOTXYrHoenpnMxLGxmAIV2iEAsGfPHk6dOsXy5cux2+1MnToVp9NJaWkpcXHf\n5xQ4nU7+/ve/s2rVKkwmE9OnT2fcuHFEREQEsHoBYN5daZzNd/DZvoukd46kX/foQJckCIIgtGKi\nKRFAtU2wuPx25ti0Rp3z8qSPKcO78eHmbE5etGOvlPzeNPDHRBFBuJamaeT+5m/k//1dLF1T6b3y\nTcxJ8T4frys8j2n7+yC7kYfeg9rrFv8U5q72NiQ0FYJiIDjWrxM2FBVOl5rJd5gw6DX6xruICQ7M\nuE+PorFpr5ttB2TQYORAE3ffaiYx0URxcftoSpTa3by7Io+de+0A3D06nun3xBEVYQpwZQLAkCFD\n6NevHwBhYWHU1NQwZswYQkND+fjjj7/7vG+++YabbrqJ0FDvJJ2BAwdy8OBBRo8eHZC6he9ZzUae\nnJzBq+99zcJPjvPKgqFEhoqVR4IgCELtRFMiQOqaYHEou4RpI7o36SY/yGLkkXv6IMkKBrMJxS37\nrWkgyVdP/xChloI/aJpGzq/+SsFb72Pt1on0VW9hTqh7O9KV9DknMe5cDqqK547pqF37+aewGrs3\nQwIgNAls/n0VtkbWcbzQQqVkIMSskJEgYTMFZtxnQanC0k0SecUqUWE65oyz0i25/TQbZVll3aYi\nVn1SgEtS6dEliEfnpnLHrQkUF1cGujzh3wwGA0FB3r8rq1at4s477/yu8XClkpISoqKivns7KiqK\n4uK6J1ABREYGYTQ2z//r2Fg/jRpuB2JjQ3nkvr689a9vWfxZFr9+4jYM+uZf+iV+BoEnfgaBJ34G\ngSd+Bg0jmhIBUt8Ei6aGUV5mMRmIjQm+6oL72qaCrxRVZemWUxzOLqG8yj/bTQQBvA2Ji7/8E4UL\nP8TasyvpK97AHB/j8/H6M4cwfrUG9AbkUQ+gJff0R1He6RrOUtAZIDwFzMFNP+8VSp0GThRa8Kg6\nEkJlesa4CUQmnKppfHFIZsNXbjwKDO1jZPKdFqzm9jNZY//hChYty6WgSCIs1Mgjc1IYfUc0+ha4\nSRIaZ8uWLaxatYpFixb59Pma5lszz253NqWsG4qNDRXNrWsMTYthf1osB7OLeWftt0y+o2uzfj3x\nMwg88TMIPPEzCDzxM6hdXY0a0ZQIkEBMsGhKhoWiqvxq8dfkFFV99z5/bDcRBE3TuPDCaxQtXomt\nVzfSV7yBKdb3/ceG47sxHtiAZrYhj34ALbZT04tSVXDkgrsKDGbvhA1/jBL9N02D83YTF+wmdDpI\ni5VIDA3MuM8yh8qyzS7O5KmE2HTMHGMho1v7+dOQV+DinWW5HDjiQK+He8bGMntKIsFB7ecxtkc7\nd+7kzTffZOHChbWukgCIi4ujpKTku7eLioro379/S5Uo+ECn0zF/YjoXChys+/Ic6Z0i6NUpMtBl\nCYIgCK2MeHk7QAIxweJyhkWpQ0Lj+6bC4vUn6538sXRz9lUNiSsdyi5p8uQQoWPSVJULP/8fb0Oi\ndw/SV73le0NC0zAc2uxtSNhCkcc/4p+GhCJD+XlvQ8IU7J2w4ceGhKzAt/kWLtjNWI0aA5NdJIW1\nfENC0zT2HZf5wwdOzuSp3NTdwE/nBrWbhkRNjcJ7K/P48YsnOHDEQd/0EP70cm8eyUwVDYlWrrKy\nktdee4233nqrztDKm2++mW+//RaHw0F1dTUHDx5k8ODBLVip4Itgq4nH7+uLDh1vrTuGw+kOdEmC\nIAhCKyOuzAKoJSdY1JVh8eXRAk5cKGNgr7haV01IssKhUyW1HgtQ5sftJkLHoakq55/7DcVL1xCU\nkUavZa9jivYxr0FVMe77BMOp/aihUchjHoZQP7z6Jtd4Ay1VD1gjIDTRr4GWDpeeY4UWJI+eqCAP\nveMkApEPW+lUWblN4thZBYsJZo+zMDjdiC5Qoz78SNM0vthj590VedgrZGKjzTw8K5lhgyLaxePr\nCNavX4/dbufHP/7xd++75ZZb2Lt3L8XFxTz22GP079+f5557jmeffZZHHnkEnU7H008/fcNVFUJg\n9UgJZ+qdXfno87Ms+vQE/zG9H3rx+ygIgiD8m2hKBFBLTrCoK8MCoKzSfcOtGBVVEuVVN35lIyLY\n0izbTYT2S1MUzj37KiUrPibopnTSl/0dY2S4bwcrHoxfrsJw4RhqZALymIfAFtL0olwOcOQBGoTE\ngy3Kbw0JTYN8h5FTJWY0oEukm86RgRn3efSsh5VbJapqNLonG5g9zkJUWPtYNHf2gpO3P8jh5Olq\nzCYds+5LYOrdCVgs7ePxdRSzZs1i1qxZ173/mWeeue59EyZMYMKECS1RltBEd9/amZMXyzlyppRN\n+3KYcIsfVrYJgiAI7YJoSrQCLTHBoq4MiyvVNvkjPMRCdB3H9m+m7SZC+6QpCmd/8gqlq9YT3L8P\nvZb+DWNEmG8HyxKmzz9En38GNa4z8qgHwGxtYkGaN8yyusjbhAhLBYv/Xm1VVMguNlNYZcKo1+gT\nLxEV1PLbnVySxtqdEvuOezAa4L7hZob3N7WLVysdlR4++NclNn9egqbBrYMimD8rmbgY0SwVhNZC\nr9Px6D19eHnRPj76/AxpqRF0S/LxuV8QBEFo18TLR+2YJCsU2Z243J46MyyudHnyx5XqOjY1LoTM\nsX6YdCB0CJrHw+GHn/M2JAbdRK9lr/vekJCcmDYvRp9/BiWll3eFhD8aEpWXvA0JvREiuvq1IeF0\n6ziYZ6WwykSoRWFwSk1AGhJn8hT++KGTfcc9JMfq+clsGyMGmNt8Q0JRNNZvLebpXxxj044SkhOs\n/PLZHjz/dDfRPTqrXwAAIABJREFUkBCEVig82Myj9/ZBVTXeXHsUp8sT6JIEQRCEVkCslGiHrp2y\nERtpo1/3aKaP7AbAoeziG656uNHkj+kju5F1sZy84ipUDXRAUmwwLzw4UIwDFXyiyh7OPvPflH28\nhZAhN9Pr/b9iCPVx20V1Baat76KvKEbp1h/PsCmgb+LqHNUDFbkgO8FohfBUMJiads4rFFcbOFlk\nQVF1JIXJ9Ihx09LTJ2WPxmd73Hx+UAYdjB1iYtxQM0ZD225GABzNquSfH+RyPreGIJue+bOTmTg6\nDqOx7T82QWjPMrpEMXFYZz796gLvfnaSJyZniLwXQRCEDk40Jdqhy1M2Liuy11yVFzFtRHeWbMxi\n99GC64690eSPVTvOXjV9QwPyiqtZteOsGAcq1Et1y5x56hfY128navgQui76I4Zg37Ys6RwlmLYs\nRlddgSd9GMrgCaBrYiPMI0HFRe+kDUsohCU3/Zz/pmpwrsxETrkZvU4jPU4iIbTlXw3MK1ZYukmi\noFQlJlxH5ngrnRPb/jarkjI3767IY9c+OwCj74hm3rQkIsL911ASBKF5TRnelayccvafLKJ3l0hG\n9k8OdEmCIAhCAImmRDtT15SNK/Mi5k9MJ8hq9Gnyh6/nFITaqG6Z04//jPKNnxN6+2CGfPwP7E7f\ntjDoSi9h2voeOqkaT/+xKH3vbHr4pLvKu0JCUyEoBoJj/RZo6XJrHLlkpdxlwGZSyYh3EWLR/HJu\nX6mqxvYDMhv3ulFUuO0mI/fcYcFiatuvRLpllbWfFfLRp4VIbpUeXYN4bG4qad2CA12aIAgNZNDr\nefzeDF5+Zx8fbjlFj6RwUuL8EFgsCIIgtEkNakpkZ2dz8eJFxo4di8PhICxMBBS1NnVN2bBfMbqz\nIZM/fD2nIFxLldycfux5yrfsJGz4UHq+8yeMwUHgrKz3WF3BOUw7PgDZjXzLfahpQ5peUI0dKvO9\n/w5NApuPI0h9UFGjZ89FDZdsICbYQ3qshLGFe3Ul5SofbnZxPl8lLFjHrLEW0ju37d6zpmnsP1zB\nomW5FBa7CQ8z8tjcVEbdHoW+pffDCILgN9HhVhZM6s3/ffQtb6w9yksPDcFiFi9wCIIgdEQ+X60u\nXryYTz75BLfbzdixY3n99dcJCwvjqaeeas762hRJVpp9tGd96pqyUVtehC+TPxp6TkEAUF0Spx79\nKRXbdhM24lbSFv0Bvc23YEr9xeMYd64ENDzDZ6B2ualpxWgaVBVCTRnoDBCeAmb/vMKuaZBbYeRs\nqRmAbtESqeGeFh33qWkae456WLdLwi1D/55Gpo2yEGRt2zftefku/vlhLoeOOjAY4N7xccy6L5Hg\nIHHjIgjtwYCesYwdlMKWA7l8sCWbBRN7B7okQRAEIQB8bkp88sknrFixgoceegiA5557jtmzZ4um\nBNcHS0aFWRiQFsus0T1aPATy8qSMKzMlLrtRXkQgzim0b2qNi+wF/4Xj8z2Ej76Nngt/j97qW/NK\nf/ogxj1rwGBCHjEHLen6LUUNK0YFR65324bBDOGdwGhu2jn/zaNCVpGF4mojJoPK7b30ILVsfoSj\nWmXFVokT5xVsFph7l4WBvdp2voKzRmHFx/l8srkIRYGb+4TyyJwUUpNtgS5NEAQ/mzGqB6dyK9h1\nJJ8+nSO5NSMh0CUJgiAILcznpkRwcDD6K26w9Xr9VW93ZNcGS5Y6pKuCJVva5VyIy3kRMRHe6Ru1\n5UU09px1ZVAIHZvidHHq4f/EsWsfEWOH0+Pt36G3+NYEMBzbhfHgRjSzDXn0PLTY1CYWI3sDLT0S\nmIK9KySaOrXj36rdOo4VWHHKesKtCn3iJWLDQiiuPX6lWXxzysOq7S6cLkjrZGD2WAvhIW33eVlV\nNb7YU8Z7K/OwV3iIizEzf1YKtwwMF+n8gtBOmYx6npicwcuL9/Puxiy6JoYRHyW2hAqCIHQkPjcl\nOnXqxN/+9jccDgebNm1i/fr1dO/evTlraxNaYwjktXkR3btEU1lR49dzBnKLitB6Kc4ash/8MZW7\nDxBx1wh6vPU/6M0+vGqvaRgObcZ4bCdaUBjymIfQIuKaVoxcAxU53tGf1kgITfBboGVhpYGsYguq\npiMlXKZbdMuO+6yRNFbvkDiY5cFkhKkjzNzez9Smb9zPnHfy9gc5ZJ2pxmzSMXtKIlMmxGMxt90m\niyAIvomPCuKhu3rxj4+P8+baY/xi3iBMRvG7LwiC0FH43JR46aWXeO+994iPj2fdunUMGjSIuXPn\nNmdtbUJrDoG8nBdhNRupP1awYedszVpDtkdHpFRVkz3vx1TuPUTkpNF0f/036E0+PMWoKsa96zCc\nPoAaGo089mEIaWIApcsBjjxAg5B4sEX5pSGhanCm1ExehQmDTqNPvIu4EN8mifhL9kUPy7ZIVFRp\ndIrXkzneSmxk2714r3DIfLD6Elt2lqJpMGxwBA/PTCYuRmTVCEJHcmtGAscv2Nl1JJ+VO06LceOC\nIAgdiM9NCYPBwPz585k/f35z1tPmiBDI1qOlsz1E8+N7SmUVWQ/8iKr93xB17zi6/e3XvjUkFBnj\nrlUYLh5HjUpEHv0g2JowFk7TwFkK1UWg00NYClhCG3++K0geHccKLThcBoJMKn0TXASZW27cp1vW\n+HS3m13fyOj1MOFWM6MHmzC00QkUiqLx2fZiPlyTT7VTITXZyqOZqfTr7Z+fV0emqhrHT1WRGGch\nNjbQ1QiC7+aOTeNMXgVbvs6ld+dIBvQU/4EFQRA6Ap+bEn369LlqabBOpyM0NJS9e/c2S2FthT9D\nIMVNbtO0VLZHawo2bQ08jiqy5v6Q6gPfEjXlLrr/7yvojD48tcgSph1L0RecRY3vgjxyLph9m85R\nK031jvt0VYDeCBGdwNiE813B7tRzvNCKrOqIC/GQFivRkiuLLxYqfLjJRZFdIy5SR+Z4K6nxbfc5\n4tsTlSxcmsPFPBdBNgOPzElhwqhYjMa22WBpLTwejV37yli9oZCcPBcjh0Xx6i+iA12WIPjMYjbw\n5OS+/Pq9r1n06QleWRBKVJh/nscFQRCE1svnpsTJkye/+7fb7earr74iKyurWYpqa5oaAilucpvO\nKXnYdeRSrR/zd7ZHaws2DSRPuYOszGeoPnyc6OkT6fbnX6Iz+PB9dlVj2rYEfWkeSko6njtngqEJ\nEyNUD1Tkguz0NiLCO4HB56e3G9I0uFhu4lyZCR3QI0YiOazlxn0qisaWr2W27HOjanBnfxMTbzNj\naqM378WlbhYvz2X31+XodDD2zmjm3p9ERFjbnhYSaJKksnVXCWs+K6K41I1eDyOHRZF5f1KgSxOE\nBkuJC2HOmJ68tzGLN9cd4/nMAeJaSBAEoZ1r1FW72WxmxIgRLFq0iB/84Af+rqnNaWoIpLjJbboP\nN2fjcqu1fsyf2R6tMdg0UDz2Ck7OfhrntyeJmXUvXf/w3z41JNRKO6aNC9E7SlC6D8Bz6+SmTcTw\nSN4JG4oMljAIS/Ju3WgiWYGTRRZKnUbMBpWMBIlwa+3/x5pDYZnKh5td5BSqRITomD3OQs/Upjda\nAsEtq6zZUMhH6wtwuzXSugXx6NxUenYNDnRpbVpVtYcN24r5ZEsxjkoPZpOOiWNimXxXnMjkENq0\nEf2TOH7Bztcni1i76xz33ymC1QVBENozn69wV61addXbBQUFFBYW+r2gtqwxIZDiJrfpJFnh5EX7\nDT8eEWLxW7ZHaw42bUlyaTlZs5/CeSyb2MwpdHntF+h8eCVLV1FM9Zr30FeW4+lzO8rAu5oWQOmu\n8q6Q0FQIioHgWL8EWlZKeo4VWHB59ETYFPrEuzC30K+hqml8eUTmk11uPAoMSjcydYQFm6XtrY7Q\nNI19hyp4Z1kuhSVuIsKMPD4vmZHDotC30SyM1qCsXObjTYVs3FFCjUslOMjA9HsSmDQ2Vqw6EdoF\nnU7HwxPSOZ/v4NPdF+jVKZKMLlGBLksQBEFoJj43JQ4cOHDV2yEhIfzlL3/xe0EdTUNvckXuxPXq\n+h4CpHeO9Nv3SgSbglxq5+TMJ6k5cZq4B6fR+TfP+9aQKMnFtG0JmuTEM2AcSsbwpjUQauzeDAl0\n3tUR1iZO7Pi3fIeRUyVmVE1Hpwg3XaPkFtuuUV6psmyLxKkchSArzL3LSr8ebXN1RM6lGhZ9mMvh\nY5UYDDD5rjhm3pdIkE08bzVWfqGLNZ8Vse3LUjwejchwEzPvS2T8iBjxfRXanSCrkScm9+W37x/g\n7Y+P88qCoYQHmwNdliAIgtAMfL7a/e1vf9ucdXRYvt7kityJG6vre2g1G8gc19NvX8ufwaZtkVxc\n6m1IZJ0lbv5MOr/606sCcG9El38G046loMhYx86iIrFv44vQNKgqhJoy0BkgPBXMTV+doqhwusRM\nfqUJg16jb7yLmOCWGfepaRoHszys3iHhckPvLgZmjrEQFtz2fredNQrL1+bz6dYiFAX6Z4TySGYq\nKYkirK6xzl10snp9Ibv321E1SIyzMOXueEbeFoXZ1Pb+jwiCr7olhTFtRHdWbD/Nwk+O85OZN6Nv\nqS6xIAiC0GLqbUqMGDGizpuOHTt2+LOeDseXm1xJVliyMYvdRwu++5jInfheXd/DO/olEmTx73Lm\npgabtlXuwhJOzngC1+nzxD86h06v/KdPDQn9xWMYd64EwDN8FuZ+t0JxZeOKUBVw5Hm3bRjM3gkb\nhqa/clYje8d9VkkGQswKGQkSNlPLjPusrtH4aLvEN6c9mE0wY7SFWzKMPn1vWxNV1dixu4wlq/Io\nd3iIjzEzf04KQ/uHt7nH0hpomsbx7Co++rSQQ0cdAHTtZGPaxARuHRzRZkfBCkJDjR+aysmLdo6c\nKWXDngtMGtYl0CUJgiAIflZvU2Lp0qU3/JjD4bjhx2pqavjZz35GaWkpkiTx1FNPkZ6eznPPPYei\nKMTGxvL73/8es9nMunXrePfdd9Hr9cycOZMZM2Y07tG0UTe6yZ0+shtLt2RzKLu41lUAl48RuRMt\n2yhoarBpW+TOL/I2JM5eJOHxB0h96Ue+NSROfY1x7zowmJBHZqIlNiGsTJG9gZYeCUzBEJ7StIDM\nfyutNnCiyIJH1ZEQKtMzxo2hhV58PnHew/ItEpVOjS6JeuaMsxIT0fZe+T51rpqFH+SQfdaJ2awj\nc2oikyfEi1fxG0FVNQ4cqeCjTwvJOlMNQEavEKZNSqB/Rqho8Agdjl6nY8Gk3ry8aB//+uIcvTpF\n0iM5PNBlCYIgCH5Ub1MiOTn5u3+fPn0au90bKOh2u3n11VfZsGFDrcdt376dvn378thjj5GXl8eC\nBQsYOHAgmZmZ3H333fzpT39i1apVTJkyhb///e+sWrUKk8nE9OnTGTduHBER/tkf3hpdmwtxo5vc\npVuya331/0odKVyxLoFoFDQm2LQtcl8q5MSMJ5DO5ZD49EOk/OIZn26MDMd2Yjy4Cc0ShDx6HlpM\nSuOLkGugIsc7+tMWCSEJTQ601DQ4bzdxwW5Cp4NesRKJYZ4mndNXklvj410SXx31YNDDpNvMjBxo\nanPhj+UOmQ8+usTWXaVoGtw+JIKHZqYQGy32fTeUx6Oxa38Zq9cXkpPnAmBI/3DunxhPeo+QAFcn\nCIEVFmTmB/dm8Ptlh3hr7VFeXjCUYKsIdRUEQWgvfM6UePXVV/nyyy8pKSmhU6dO5OTksGDBght+\n/sSJE7/7d35+PvHx8ezdu5dXXnkFgFGjRrFo0SK6du3KTTfdRGhoKAADBw7k4MGDjB49urGPqdWq\nLxfiypvcuqZyXKkthCu2ZDhnR2kUtBQpt4CTMx5HupBH0o8WkPzck/U3JDQNw8FNGI/vQgsKQx77\nEFp4XOOLcDm8WzbQICQebFFNbki4FThRaMFeY8Rq9I77DLW0zLjP8/kKSze5KK3QSIzWkzneQlJs\n21pp4/FobNhezLI1+ThrFDolW3lsbip900MDXVqbI0kqW3eVsOazIopL3ej1MHJYFFMnxtMp2Rbo\n8gSh1UjvHMm9t3Vh3ZfneWf9SZ6e2lesHBIEQWgnfG5KfPvtt2zYsIF58+axZMkSjh49yubNm+s9\nbvbs2RQUFPDmm28yf/58zGbvK2jR0dEUFxdTUlJCVNT3Y56ioqIoLq77ZjwyMgij0f8X8bGxzXtB\n/faab69a+XA5FyLIZuaxKTdd9bn5JdWUVd54osRlt9+cREpS/atKmvux1UZRVBZ9fIw9R/MpLq8h\nNsLGrX0TWXBvBgY/ro8PxGNrKYF8bM7zuXw709uQ6PniM/R8sf4VEpqq4Nq8Avn4XvSRcQRNexJ9\nWOR1n+fL49I0DWfJJZyOXHR6PaEpPbGEXn+uurjcHuwOicgwC1az9+murErjcLaG0w2JETC0hwGz\nMbhB563LjR6bx6Pxr+2VfLKzBoBJdwRz/5hQTMa2c1EdGxvK19/Y+es/TnPuopOQYCM/ebwHk+9O\nwmhoO4+jNi39u1ZZ5WH1p3ms/DiP8goZs1nPtHuSmD0llcR4/4aCtufnSKFjue/2rmRdLOdgdjHb\nDuYxZlATVuAJgiAIrYbPTYnLzQRZltE0jb59+/K73/2u3uOWLVvGiRMn+OlPf4qmfR8cd+W/r3Sj\n91/Jbnf6WLXvYmNDKW5s+J4PJFnhy2/yav3Yl99c4u6hqVetIlBkhajQ2idKAESFWhjYK5Z7h3Wq\nt+7mfmw3cu32kyJ7Det2nsVZ4/ZbOGegHltLCORjc13I5eT0J3DnFZD80yeIfPJhSkqq6j5IkTHu\nXIkh5wRqdDLS6HnUSMbrQi19elya6h336aoAvQktIhWHywgu374fN1qVdOfg3pwptaABXaJkOkfI\nVNh9OqVPbvTY8ksVlm6UuFSiEhWmY844K92SdZTb6/metiIe1cSf3sjiqwPl6HQwfkQMmVMTCQ8z\nYS9rO4+jNi35u1ZWLvPxpkI27iihxqUSZDMw/Z4EJo2NJSLMBMgUF8t++3rN8dhEk0MIFL1exw/u\ny+CXi/axfNspeqaE0yle/H8UBEFo63xuSnTt2pUPPviAwYMHM3/+fLp27Upl5Y0vdI4ePUp0dDSJ\niYn07t0bRVEIDg7G5XJhtVopLCwkLi6OuLg4SkpKvjuuqKiI/v37N+1RtUIVVRJlN2gw1JYLUddE\nidv7JvDAXb1adbhiXdtPRDhn6+Y6e5GTM57EnV9Iys+fJumH8+s/yO3CtGMp+sJzqAndkEdmgqmR\n24pUjzc/Qq4Bo8078tPg81MVAMu3nb7qd6e82kONLobTpVaMeo0+8S6igpp/u4aqaXxxSGb9bjeK\nCkP7GJl8pwWrue2sKpDcKms2FLJ6QyFut0p6j2AenZtK985im1RD5Be6WPNZEdu+LMXj0YgMNzLj\n3kTuGhlDkE08FwqCryJDLTx6T2/+svIIb6w9xi8fHvzdSjhBEAShbfL5WfxXv/oV5eXlhIWF8ckn\nn1BWVsbjjz9+w8//+uuvycvL44UXXqCkpASn08nw4cPZuHEjkydPZtOmTQwfPpybb76Z//7v/8bh\ncGAwGDh48CC/+MUv/PLgWpPwEAtRYbWvfLhRLkRdEyUM+qZtf2junIeGNmGE1qHm9HlOznwSuaCY\n1Bd/ROKT83w4qArTtiXoyy6hpPbGM3wGGBoZQOaRoPwiqDJYwiAsCXQN+79+bUMsNCSYkcMGExkR\nhr28gjF99ITZmn8qRJlDZdlmF2fyVEJsOmaOsZDRre1cOGuaxp6D5byzLI/iUjfRUWYemJbIiFuj\nxD7uBjh30cnq9YXs3m9H1SAhzsLUCfGMvD1KTCcRhEbq1z2Gu4amsnFfDks2ZvPYvX0CXZIgCILQ\nBD5fIc+cOZPJkyczadIk7rvvvno/f/bs2bzwwgtkZmbicrl46aWX6Nu3L88//zzLly8nKSmJKVOm\nYDKZePbZZ3nkkUfQ6XQ8/fTT34Vetid1rXwYkBZTa2OgOSZK1Be26S+NacIIgVVz6hwnZzyBXFRK\np1f+k4THMus/qKoc09bF6B2lKD0G4bnl3saP6XRXQUWud+tGUAwExzYq0PLKhlhqUgK3D+2P2WTi\n5OlzHDxynNu7DSXM1nwNMU3T2H/Cw5rPJSQZbupuYPooKyFBbedGPievhoVLczlyohKjQceUCXE8\nNb8n1dU1gS6tTdA0jePZVXz0aSGHjnpHZ3ftZOP+ifEMGxyJoY1NWRGE1mjaiO5k51Tw1bEC+nSJ\n5PabEgNdkiAIgtBIPjclnn/+eTZs2MDUqVNJT09n8uTJjB49+rusiWtZrVb++Mc/Xvf+d95557r3\nTZgwgQkTJjSg7LaprpUPdfHnRIlrl7VfDtsE/JbzAI1rwgiB48w6w8kZT+IpKaPzqz8lfsGseo/R\nlRdh2vouOqcDT8ZwlAHjGj8Vo6YMKgsAHYQlg7XxM+jDQyxEh1vp1KkrfdN74PEo7Nx7kHMX84gO\na96GmKNK4Z1PXRw7q2A1w5xxFgalG9vMyoJqp8Lytfl8urUIVYUBfcN4ZE4KyYlWgoKMVFcHusLW\nTVU1Dhyp4KNPC8k64/1mZfQK4f6J8QzoG9Zm/h8IQltgNOh5fHIGr7yzjyWbsuiWFCbyTgRBENoo\nn5sSgwYNYtCgQbzwwgvs27ePdevW8fLLL7Nnz57mrK9daY6VDw3R0jkPjW3CCC3LefwUJ2c+iaes\nnM6//RnxD02v9xhdcQ6mbUvQuWvwDLwLJeOOxn1xTYOqQm9TQmfw5keYm9iA0xkZM/xWLLZQHJVV\n7Nj9NeUOb/5NczbEjp7xsGpHCZXVKt2TDcweZyEqrG0sz1dVjW1flvL+R5eocHiIjzXzyJwUBt8c\nLm6kfeDxaOzaX8bq9YXk5LkAGNI/nPsnxpPeIyTA1QlC+xUXYeOhCem8ufYYb6w5xl+ejQ10SYIg\nCEIjNGiDs8PhYMuWLXz22Wfk5OQwa1b9r6YK1/PnyoeGaOmch0A3YYT6VR/NImvWU3jsFXT5/QvE\nzZ1a7zG6S6cxff4hKDLysCmoPQbV+nn15paoCjjyvNs2DBaISAVD7SuvfFVeo+d4oQWLTU9NdTlf\n7T2Io7Ka6LDma4i5JI01OyX2H/dgMsJ9w80M729C30Zu5rPPVPP20hxOn3NiMeuZe38S990V57e8\ng+bOrwkkya2ydWcpazcWUlTiRq+HkcOimHJ3PJ1TbIEuTxA6hKG94zlxwc7nhy/xf8sPM298zzbz\n/CsIgiB4+dyUeOSRRzh16hTjxo3jiSeeYODAgc1Zl9AMApXzEKgmjFC36iMnOTn7KZSKSrr+8UVi\n50yu9xj9haMYd60CwHPnbNRO14eL+ZRbosjeQEtFAnMwhKU0PosC74KL3AojZ0q9TY3u0RIp3UyM\n7j24WW+Iz+QpLNvsosyhkRyr5+lZUVj0Lr9/neZQXiGzZFUe274sA2D4LZE8OCOZmKimNYYua6n8\nmkCodnrYsK2EjzcX4aj0YDbpmDgmlsl3xREXI/JyBKGlzRnTk9ziKj4/lEuQWc+MUWJFpiAIQlvi\nc1PiwQcf5I477sBguP7C/u233+axxx7za2GC/4mcB+GyqsPHyJrzDIqjim5/+SUxM+6p9xh99n6M\nez8Gkxl5ZCZaQrdaP6++3BLZWQX2s96VErZICElofBYF4FEhq8hCcbURk0ElI14iwuYd99lcDTHZ\no7HhKzdfHJJBB2OHmBg31ExivIni4tbdlPB4ND7dWsSKdfk4a1S6pNh4dG4KGb38uxe7pfJrWlJZ\nucwnm4v4bHsxNS6VIJuB6fckMGlsLBFhjZw4IwhCk5lNBn40/WZ+t/QgG/ZeJDLUwtjBqYEuSxAE\nQfCRz02JESNG3PBjO3fuFE2JNkLkPAhVB74lK/MZlOoauv3fr4i5/+66D9A0DEe/wHh4C5olCHnM\ng2jRybV+an25JTOGxVJeXOBd2hCSAEFRTXos1W4dRwus1Mh6wq0KfeIlLEatSeesT16xwtJNEgWl\nKjHhOjLHW+mc2DYaeoePOfjn0lxy812EBBv4wQOpjB8Rg8Hg36XOLZ1f09zyiyTWfFbI9l2lyB6N\nyHAjM+5N5K6RMQTZ2s7jEIT2LMRm4uXHhvHsX7/gwy2niAixMDg9LtBlCYIgCD5oUKbEjWha894E\nCP4jch46tsr935A19z9Qa1x0//urRE8eX/cBmorhwEaMJ3ajBYcjj3kILfzGQWJ15Zbc1s2AqTof\nnV6PFpYClqa9Ml9YaSCr2IKq6UgNd9M1WqY5Jy2qqsb2AzIb97pRVLjtJiP33GHBYmr9e5cLiyXe\nWZ7L3oMV6HRw18gYMu9PIizEL38CrtPS+TXN5dxFJ6vXF7J7vx1Vg4Q4C1MnxDPy9ii/ZW4IguA/\nCdHB/GTGzfzP0oP84+PjhAWbSUuNCHRZgiAIQj38ckUq0tnbHpHz0PFU7j1E1gM/QpMkerzx/4i6\nZ2zdB6gKxq/WYjh7CDU8FnnMQxBc96jO2nJLjHp4+I5wbuthQ9MbiezaG3ul0ujHoWpwptRMXoUJ\ng04jI95FbEjjz+eLknKVDze7OJ+vEhasY9ZYC+mdm+eG3p8kSWX1hgLWbCjELWv07hnMo5mpdOvc\nvL/7gcqv8QdN0zieXcXq9YUc/NYBQJdUG9MmxTNsUKTfV5UIguBfnRNCeXpqX/668gj/u+oIP583\niOSY4ECXJQiCINSh9V9VdyCtKaW+NdVSn7ZUa6A4dn9N9rwfo8ky3d/6H6LuHlX3AR4Z484VGHJP\nokYnI4+eB9b6L+quzS0Jtep4ZkwkPePNlFRDTOduGK1BUFnZqMfh8ug4XmDBIRkIMqn0TXARZG6+\nlVqaprHnqId1uyTcMvTvaWTaKAtB1tZ9Y6ppGru/Lmfx8lxKymSiIkw8NDOZ4bdEtkgTuS3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UV+k9/qHhW5NgdbjmRg0PlPyhrKi6KmqoKmFhnqSjCTNYIRXe60dSb7/U9Je+F/0EZH0mPV3wnp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oxt5Th8nJKWDuzKlIkoRV42eCHfTaVKU4Kats3B1n88EEqLBirp41Cw2jBkZ2dsf4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\nsu/3h9i8tYm1RU42ljTib99BoSDPzMxCK1M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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From fa3954c3175f471d7dbbac95159531133a0da332 Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Tue, 29 Jan 2019 02:07:52 +0530 Subject: [PATCH 03/11] Synthetic Features and Outliers programming exercise solved! --- synthetic_features_and_outliers.ipynb | 1498 +++++++++++++++++++++++++ 1 file changed, 1498 insertions(+) create mode 100644 synthetic_features_and_outliers.ipynb diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb new file mode 100644 index 0000000..bc89b21 --- /dev/null +++ b/synthetic_features_and_outliers.ipynb @@ -0,0 +1,1498 @@ +{ + "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 + } + }, + "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", + "outputId": "e59f619b-7066-478c-9929-ecb0236dce39", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + } + }, + "cell_type": "code", + "source": [ + "# from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn.metrics as metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "11066 -121.0 37.7 40.0 3012.0 616.0 \n", + "5520 -118.2 34.1 48.0 2514.0 595.0 \n", + "674 -117.0 32.6 27.0 1710.0 282.0 \n", + "5321 -118.2 34.7 36.0 1338.0 250.0 \n", + "9839 -119.7 34.4 35.0 1402.0 369.0 \n", + "... ... ... ... ... ... \n", + "11802 -121.3 36.0 31.0 372.0 68.0 \n", + "7313 -118.3 33.9 37.0 3107.0 903.0 \n", + "11145 -121.0 39.0 17.0 4786.0 799.0 \n", + "11747 -121.3 38.0 42.0 1824.0 277.0 \n", + "12170 -121.5 38.5 34.0 1717.0 354.0 \n", + "\n", + " population households median_income median_house_value \n", + "11066 1423.0 595.0 2.6 100.6 \n", + "5520 2484.0 601.0 3.1 142.5 \n", + "674 1089.0 297.0 4.7 151.9 \n", + "5321 709.0 250.0 3.6 101.4 \n", + "9839 654.0 385.0 2.6 318.8 \n", + "... ... ... ... ... \n", + "11802 479.0 67.0 3.6 200.0 \n", + "7313 3456.0 734.0 2.2 147.5 \n", + "11145 2066.0 770.0 4.0 185.4 \n", + "11747 720.0 309.0 5.2 183.7 \n", + "12170 848.0 306.0 2.5 87.0 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "I6kNgrwCO_ms", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll set up our input function, and define the function for model training:" + ] + }, + { + "metadata": { + "id": "5RpTJER9XDub", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VgQPftrpHbG3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \n", + " Returns:\n", + " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", + " after training the model.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label].astype('float32')\n", + "\n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + " \n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Create a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", + " \n", + " return calibration_data" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FJ6xUNVRm-do", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Try a Synthetic Feature\n", + "\n", + "Both the `total_rooms` and `population` features count totals for a given city block.\n", + "\n", + "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n", + "\n", + "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n", + "\n", + "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n", + "the final RMSE should be.)" + ] + }, + { + "metadata": { + "id": "isONN2XK32Wo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**." + ] + }, + { + "metadata": { + "id": "5ihcVutnnu1D", + "colab_type": "code", + "cellView": "both", + "outputId": "37ae1e61-eaf2-4958-98be-eb6ebaad93b6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 997 + } + }, + "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=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.84\n", + " period 01 : 190.53\n", + " period 02 : 170.42\n", + " period 03 : 153.05\n", + " period 04 : 140.40\n", + " period 05 : 134.32\n", + " period 06 : 131.19\n", + " period 07 : 130.81\n", + " period 08 : 131.12\n", + " period 09 : 131.21\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 190.9 207.3\n", + "std 87.8 116.0\n", + "min 43.5 15.0\n", + "25% 156.4 119.4\n", + "50% 187.9 180.4\n", + "75% 214.7 265.0\n", + "max 4189.2 500.0" + ], + "text/html": [ + "
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rT9WB33tIWCucXDm9A+eeIwmJ5qyu3s2jz+Xx4v8KCDJruetfnbnkwgR0uraV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\nhoQTQnQ7Ekp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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", + "outputId": "044968a7-63f1-4904-961f-4387d00e66fc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.76\n", + " period 01 : 190.49\n", + " period 02 : 169.70\n", + " period 03 : 152.67\n", + " period 04 : 140.37\n", + " period 05 : 134.73\n", + " period 06 : 131.20\n", + " period 07 : 130.43\n", + " period 08 : 130.65\n", + " period 09 : 131.71\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 198.5 207.3\n", + "std 90.3 116.0\n", + "min 46.9 15.0\n", + "25% 163.1 119.4\n", + "50% 195.5 180.4\n", + "75% 223.0 265.0\n", + "max 4310.9 500.0" + ], + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean198.5207.3
std90.3116.0
min46.915.0
25%163.1119.4
50%195.5180.4
75%223.0265.0
max4310.9500.0
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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 131.71\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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xtHcbggON/u6SEEII0eDJI+xmqLDEftrElPnFNgpL6pa0sjoVIykkIHGSqqpk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iUkoO6EBFRP1fA1GS4pBkWWOf3KvwBo+/yj1QtoWxXaXzei6Aw4u4+68KCKApCd4BkEWj3FVWpy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\nXoIiixWzx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NByhIeH89BDD/HQQw+xbNkyXnnlFebOncvevXubOzShhQv0NXLDgChWbEnn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\nYrPZMJlM1NXVERERQU5ODr1796a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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", + "outputId": "578dfd24-bc3b-44e1-ea33-aee004e5accd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 421 + } + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title('Identifying Outliers')\n", + "plt.xlabel('Predictions')\n", + "plt.ylabel('Targets')\n", + "plt.scatter(calibration_data['predictions'], calibration_data['targets'])" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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wYsD+hcypnhMKl6QRbhlAySgzAAR978pxgzJqzRoRSUtxAUyuKhx++Tk6tLS7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\n", + "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", + "outputId": "ca010d01-df82-47ee-e0ec-cef81707cce4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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0n2csi8mARVWTE74vV3uYcjVNJnI9u3T2cRGR4COiTW8fRXAod3nbYQXY0dQG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\nNavfPotZz8QEIqIkCRuIIr1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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", + "outputId": "6493f965-747b-4c25-8405-0f3698ab5595", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(60, 6))\n", + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 9, + "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", + "outputId": "c7774ece-a423-416a-f1bf-ae0278cb0fd9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + } + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "california_housing_dataframe[\"rooms_per_person\"] = california_housing_dataframe[\"rooms_per_person\"].apply(lambda x: min(x, 5))\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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3KSJihuiIEH45+mwq3D7mLd2GYWgYh4iInN6UlBCRoKiq98LJhkr3TU6o1TAL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\nMxQSCyCJiIiIiIiIKAY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YRe36Gg/z+It/6VKcLaWWf8wcbZ7JYw/cgEiUzyqMSIrAiTMB0BaKLLRXMEsy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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", + "outputId": "ded63b37-9d94-41d4-8f4f-f35c479838b3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Zl5lb47Z1mXk4XWqwJiISSJecdRIRbex8svpXduXtD3Q4IiIifqekhEgLVlTq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\ns6Ggjo0FdVTW2Pszxa+y4qOp5M8LMDLHI95TBaELPC4Hd35iMt//7U4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FyJ2YhBLvnSOw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\nqS43G4GhJEdOmm0ZJ05nmlRuqcuhrsbHpupMk0pRu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oIxNlYW4HXZ5sSYzNB1hv79dTqe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\nI7ndbtxzzz3YunUrNm/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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "outputId": "6ec05f07-ccd8-4ec9-dd09-7b572a9650b6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + } + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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B28crai+nN+kwMBQsJqOsYbOYDPAHI7rcuQ6bCe3zHQVL9kpO5nlg7XzVFc/B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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 48aef176e6a119f8e2212524dd5721fe671c7245 Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Tue, 29 Jan 2019 02:09:27 +0530 Subject: [PATCH 04/11] Validation Set programming exercise solved! --- validation.ipynb | 1567 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1567 insertions(+) create mode 100644 validation.ipynb diff --git a/validation.ipynb b/validation.ipynb new file mode 100644 index 0000000..a52a04d --- /dev/null +++ b/validation.ipynb @@ -0,0 +1,1567 @@ +{ + "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", + "outputId": "983b6c63-6985-4084-933c-e009a660ba8c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + } + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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 28.5 2660.4 541.2 \n", + "std 2.1 2.0 12.6 2222.4 424.0 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1471.0 299.0 \n", + "50% 34.3 -118.5 29.0 2135.0 435.0 \n", + "75% 37.7 -118.0 37.0 3154.2 649.0 \n", + "max 42.0 -114.5 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1438.0 503.7 3.9 2.0 \n", + "std 1176.7 388.7 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 794.8 283.0 2.6 1.5 \n", + "50% 1170.0 411.0 3.5 1.9 \n", + "75% 1719.2 606.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "JlkgPR-SHpCh", + "colab_type": "code", + "outputId": "ab49fe90-d2f2-41d6-ae81-ad1ce74312e2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "training_targets.describe()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 12000.0\n", + "mean 207.2\n", + "std 115.8\n", + "min 15.0\n", + "25% 119.4\n", + "50% 180.1\n", + "75% 265.9\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "5l1aA2xOHpCj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "fLYXLWAiHpCk", + "colab_type": "code", + "outputId": "74efb60e-6f99-4baa-a189-a53c228e88c4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + } + }, + "cell_type": "code", + "source": [ + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_examples.describe()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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 -119.5 28.8 2603.4 535.1 \n", + "std 2.1 2.0 12.5 2074.1 415.4 \n", + "min 32.5 -124.3 1.0 12.0 3.0 \n", + "25% 33.9 -121.8 18.0 1432.0 293.0 \n", + "50% 34.2 -118.4 29.0 2115.5 430.0 \n", + "75% 37.7 -118.0 37.0 3142.0 648.0 \n", + "max 41.9 -114.3 52.0 32054.0 5290.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 5000.0 5000.0 5000.0 5000.0 \n", + "mean 1409.2 495.4 3.9 2.0 \n", + "std 1075.3 374.2 2.0 1.2 \n", + "min 8.0 2.0 0.5 0.0 \n", + "25% 777.0 276.0 2.6 1.5 \n", + "50% 1159.5 404.0 3.5 1.9 \n", + "75% 1727.0 604.2 4.7 2.3 \n", + "max 15507.0 5050.0 15.0 41.3 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "oVPcIT3BHpCm", + "colab_type": "code", + "outputId": "953de292-c121-4473-812d-3373af974058", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "validation_targets.describe()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 207.6\n", + "std 116.5\n", + "min 15.0\n", + "25% 119.8\n", + "50% 180.8\n", + "75% 262.7\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "z3TZV1pgfZ1n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Examine the Data\n", + "Okay, let's look at the data above. We have `9` input features that we can use.\n", + "\n", + "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense will get you far.\n", + "\n", + "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data." + ] + }, + { + "metadata": { + "id": "4Xp9NhOCYSuz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "gqeRmK57YWpy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's check our data against some baseline expectations:\n", + "\n", + "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n", + "\n", + "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n", + "\n", + "If you look closely, you may see some oddities:\n", + "\n", + "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n", + "\n", + "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n", + "\n", + "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n", + "\n", + "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source." + ] + }, + { + "metadata": { + "id": "fXliy7FYZZRm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Plot Latitude/Longitude vs. Median House Value" + ] + }, + { + "metadata": { + "id": "aJIWKBdfsDjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n", + "\n", + "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`." + ] + }, + { + "metadata": { + "id": "5_LD23bJ06TW", + "colab_type": "code", + "cellView": "both", + "outputId": "80077838-4773-4149-df6d-0497f1750803", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 518 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(13, 8))\n", + "\n", + "ax = plt.subplot(1, 2, 1)\n", + "ax.set_title(\"Validation Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(validation_examples[\"longitude\"],\n", + " validation_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n", + "\n", + "ax = plt.subplot(1,2,2)\n", + "ax.set_title(\"Training Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(training_examples[\"longitude\"],\n", + " training_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n", + "_ = plt.plot()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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eWoqTGFjc85r+3KCBaWj8w60FbNuToOJAEq8HFszMoShf/skJIbKbrml84cYc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MPGMfNTgi8+LHDU8JexvCVY2c4dH8dVxHGaN0pnhtsr8GffhYdPKSqB5cyM2ZOQKygoKPggU6/A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kgURfQVXHZ2J3irPc7Le+J0DSneOujSkzMJIiiEsLfXYXvn9NRofntXkW/+IEM4yTR0/9Ax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\nHWlsxyIejwEaw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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(training_examples, training_targets[\"median_house_value\"], batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, training_targets[\"median_house_value\"], shuffle=False, num_epochs=1)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value\"], shuffle=False, num_epochs=1)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # 2. Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = [_['predictions'][0] for _ in training_predictions]\n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = [_['predictions'][0] for _ in validation_predictions]\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zFFRmvUGh8wd", + "colab_type": "code", + "outputId": "2fc47f91-26f1-472a-d8fa-de0e9c8b82e6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + } + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n", + " learning_rate=0.0001,\n", + " steps=150,\n", + " batch_size=10,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 217.09\n", + " period 01 : 199.34\n", + " period 02 : 186.14\n", + " period 03 : 176.33\n", + " period 04 : 169.32\n", + " period 05 : 167.63\n", + " period 06 : 167.25\n", + " period 07 : 167.61\n", + " period 08 : 169.12\n", + " period 09 : 168.28\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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bKV0aF/ulsbFfGpu7U2YF5idr165lwYIFzJw5s0TvL8lF31JSsu821i35+rqT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\ngnIQgiDgpqvysKykGFlpOmwuP4sn39iHhgsupUs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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", + "outputId": "1ba2240d-5af7-49e0-e6c3-f48be5b9fd5c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + } + }, + "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": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 217.12\n", + " period 01 : 199.42\n", + " period 02 : 184.91\n", + " period 03 : 176.06\n", + " period 04 : 170.57\n", + " period 05 : 167.85\n", + " period 06 : 167.40\n", + " period 07 : 168.28\n", + " period 08 : 169.16\n", + " period 09 : 169.64\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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8rz0+isjEnWxKiGB856HUcfa4Kfu41dyssZGbS+NivzQ29ktjc2NsVmB+tWnT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\nkaijACMiIiJRRwFGREREoo4CjIiIiEQdBRgRERGJOgowIiI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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": 35 + }, + "outputId": "4e6bec7a-d8a7-446b-d4e8-c0320c5d6309" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "#\n", + "# YOUR CODE HERE\n", + "#\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(test_examples, test_targets[\"median_house_value\"], shuffle=False, num_epochs=1)\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = [_['predictions'][0] for _ in test_predictions]\n", + "\n", + "test_root_mean_squared_error = math.sqrt(metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print('RMSE on test data: %0.2f' % test_root_mean_squared_error)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RMSE on test data: 161.07\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "yTghc_5HkJDW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "_xSYTarykO8U", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "fa413d2a-f044-41af-e801-bafc1dafff69" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(\n", + " test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 161.81\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file From 05da0f0a00f11efa8df9dbdc9bcd30ac1ae8074c Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Fri, 1 Feb 2019 01:51:15 +0530 Subject: [PATCH 05/11] Feature Set Programming Exercise solved! --- feature_sets.ipynb | 1606 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1606 insertions(+) create mode 100644 feature_sets.ipynb diff --git a/feature_sets.ipynb b/feature_sets.ipynb new file mode 100644 index 0000000..21ca84d --- /dev/null +++ b/feature_sets.ipynb @@ -0,0 +1,1606 @@ +{ + "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": 1224 + }, + "outputId": "30131abf-5106-43a6-b37a-b5fb056c8d61" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2640.1 536.7 \n", + "std 2.1 2.0 12.6 2178.4 418.7 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1457.0 296.0 \n", + "50% 34.2 -118.5 29.0 2123.0 431.0 \n", + "75% 37.7 -118.0 37.0 3150.2 647.0 \n", + "max 42.0 -114.3 52.0 32054.0 5290.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1423.7 498.8 3.9 2.0 \n", + "std 1138.0 382.3 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 786.0 280.0 2.6 1.5 \n", + "50% 1167.0 407.0 3.6 1.9 \n", + "75% 1720.0 603.0 4.8 2.3 \n", + "max 35682.0 5050.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "hLvmkugKLany", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Develop a Good Feature Set\n", + "\n", + "**What's the best performance you can get with just 2 or 3 features?**\n", + "\n", + "A **correlation matrix** shows pairwise correlations, both for each feature compared to the target and for each feature compared to other features.\n", + "\n", + "Here, correlation is defined as the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient). You don't have to understand the mathematical details for this exercise.\n", + "\n", + "Correlation values have the following meanings:\n", + "\n", + " * `-1.0`: perfect negative correlation\n", + " * `0.0`: no correlation\n", + " * `1.0`: perfect positive correlation" + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 383 + }, + "outputId": "b0186af2-d785-4333-cfd6-0fed58821cd5" + }, + "cell_type": "code", + "source": [ + "correlation_dataframe = training_examples.copy()\n", + "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n", + "\n", + "correlation_dataframe.corr()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms \\\n", + "latitude 1.0 -0.9 0.0 -0.0 \n", + "longitude -0.9 1.0 -0.1 0.1 \n", + "housing_median_age 0.0 -0.1 1.0 -0.4 \n", + "total_rooms -0.0 0.1 -0.4 1.0 \n", + "total_bedrooms -0.1 0.1 -0.3 0.9 \n", + "population -0.1 0.1 -0.3 0.9 \n", + "households -0.1 0.1 -0.3 0.9 \n", + "median_income -0.1 -0.0 -0.1 0.2 \n", + "rooms_per_person 0.1 -0.1 -0.1 0.1 \n", + "target -0.1 -0.0 0.1 0.1 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "latitude -0.1 -0.1 -0.1 -0.1 \n", + "longitude 0.1 0.1 0.1 -0.0 \n", + "housing_median_age -0.3 -0.3 -0.3 -0.1 \n", + "total_rooms 0.9 0.9 0.9 0.2 \n", + "total_bedrooms 1.0 0.9 1.0 -0.0 \n", + "population 0.9 1.0 0.9 0.0 \n", + "households 1.0 0.9 1.0 0.0 \n", + "median_income -0.0 0.0 0.0 1.0 \n", + "rooms_per_person 0.1 -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 0.1 0.0 \n", + "population -0.1 -0.0 \n", + "households -0.0 0.1 \n", + "median_income 0.2 0.7 \n", + "rooms_per_person 1.0 0.2 \n", + "target 0.2 1.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "RQpktkNpia2P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n", + "\n", + "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n", + "\n", + "Use this information to try removing features. You can also try developing additional synthetic features, such as ratios of two raw features.\n", + "\n", + "For convenience, we've included the training code from the previous exercise." + ] + }, + { + "metadata": { + "id": "bjR5jWpFr2xs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jsvKHzRciH9T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g3kjQV9WH3pb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "varLu7RNH3pf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters." + ] + }, + { + "metadata": { + "id": "DSgUxRIlH3pg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 658 + }, + "outputId": "b92fba0b-8edc-4102-efbf-5e840aeee5e1" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = ['latitude',\t'median_income', 'rooms_per_person']\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=0.05,\n", + " steps=2000,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 106.97\n", + " period 01 : 103.01\n", + " period 02 : 95.93\n", + " period 03 : 90.24\n", + " period 04 : 91.13\n", + " period 05 : 86.68\n", + " period 06 : 84.49\n", + " period 07 : 87.69\n", + " period 08 : 86.07\n", + " period 09 : 83.92\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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lVGCuMf07BXPniNYUFlnJOhyO1bDyzbEV9o4lIiJSpVRgrkH9OgYzdXhrCtL9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\nEqJ8rluc3ux5Y7SYOFxzjN0X9tNl7MbD0Z3l0Yu5I3Qmeq1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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": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "acb48c86-83b6-4c79-ff42-96cc7b8209c2" + }, + "cell_type": "code", + "source": [ + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 165.99\n", + " period 01 : 125.51\n", + " period 02 : 116.69\n", + " period 03 : 116.07\n", + " period 04 : 115.80\n", + " period 05 : 114.94\n", + " period 06 : 114.50\n", + " period 07 : 114.10\n", + " period 08 : 113.27\n", + " period 09 : 112.70\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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KiIgEjQJMC0qPTcFv+nHFleiKvCIiIkGkANOC6tbBxHcpI7ewgvxiNXYUEREJ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\n0U1MGJfl87xrjAGGiIholCUnNY9kYnNyU9Sou5o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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RidI9YhKOiY2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Make Better Use of Latitude\n", + "\n", + "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n", + "\n", + "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco." + ] + }, + { + "metadata": { + "id": "hfGUKj2IR_F1", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "outputId": "5a470a8f-d08e-4bcb-9a5d-2ff18c9eba5f" + }, + "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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f6chJZK5srcfut86JSq3u3Ner6viy7tmzIyXre0wXqJkZ4mPY/dZHupyL0j0n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\nB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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "6N0p91k2iFCP", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Try creating some synthetic features that do a better job with latitude.**\n", + "\n", + "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n", + "\n", + "Or you could break the space into 10 different buckets. `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n", + "\n", + "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n", + "\n", + "What's the best validation performance you can get?" + ] + }, + { + "metadata": { + "id": "wduJ2B28yMFl", + "colab_type": "code", + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 658 + }, + "outputId": "5988ab0c-6178-4b8c-c978-604c8a0dbef7" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#\n", + "def make_new_features(source_df):\n", + " lat_ranges = zip(range(32, 42), range(33, 43))\n", + " new_features = pd.DataFrame()\n", + " new_features['median_income'] = source_df['median_income']\n", + " new_features['rooms_per_person'] = source_df['rooms_per_person']\n", + " for r in lat_ranges:\n", + " new_features[\"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 new_features\n", + "\n", + "new_training_examples = make_new_features(training_examples)\n", + "new_validation_examples = make_new_features(validation_examples)\n", + "\n", + "train_model(\n", + " learning_rate=0.05,\n", + " steps=2000,\n", + " batch_size=5,\n", + " training_examples=new_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=new_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 : 85.21\n", + " period 01 : 84.27\n", + " period 02 : 83.13\n", + " period 03 : 82.64\n", + " period 04 : 82.22\n", + " period 05 : 81.83\n", + " period 06 : 81.53\n", + " period 07 : 81.36\n", + " period 08 : 81.14\n", + " period 09 : 80.97\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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OSrZ6p/acrDrwaK/5OFrY892pDezNOKBaLCGEEMKUSKFyEzp7OzJvcncqq/Qs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\nRejgMS9lEznNXWOceLWFxo5RYGHjw1tL2JCRBCg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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "pZa8miwu6_tQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "PzABdyjq7IZU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n", + "\n", + "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`." + ] + }, + { + "metadata": { + "id": "xdVF8siZ7Lup", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U4iAdY6t7Pkh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "c16127b8-b714-4c62-c826-f995c5d3f584" + }, + "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": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 227.67\n", + " period 01 : 217.45\n", + " period 02 : 207.29\n", + " period 03 : 197.23\n", + " period 04 : 187.29\n", + " period 05 : 177.50\n", + " period 06 : 167.86\n", + " period 07 : 158.40\n", + " period 08 : 149.16\n", + " period 09 : 140.16\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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SpUvRaDRMmTKFu+66q9Z1N5WzkPRRUlnCusTN7Es5hFajZVirAYT5DKG6SsO6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\nmCI3aAlJRERuqOlTnbEtRzCwXj/cXWy4hKZwLmA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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From fda9737fe1f30616f0e426c9fadbd8df7eb5f00b Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Sun, 3 Feb 2019 04:06:49 +0530 Subject: [PATCH 06/11] Feature Crosses Programming Exercise solved! --- feature_crosses.ipynb | 1642 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1642 insertions(+) create mode 100644 feature_crosses.ipynb diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb new file mode 100644 index 0000000..57651ba --- /dev/null +++ b/feature_crosses.ipynb @@ -0,0 +1,1642 @@ +{ + "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": "python3", + "display_name": "Python 3" + } + }, + "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": 1224 + }, + "outputId": "060da32d-85ee-4c3f-8206-d1a6b4730d87" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 207.7\n", + "std 116.1\n", + "min 15.0\n", + "25% 120.0\n", + "50% 180.7\n", + "75% 265.5\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "oJlrB4rJ_2Ma", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "NBxoAfp2AcB6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hweDyy31LBsV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## FTRL Optimization Algorithm\n", + "\n", + "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)." + ] + }, + { + "metadata": { + "id": "S0SBf1X1IK_O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1Cdr02tLIK_Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "f878a21a-2e6f-45ff-809e-a292b72df7ef" + }, + "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 : 193.16\n", + " period 01 : 115.34\n", + " period 02 : 129.42\n", + " period 03 : 110.33\n", + " period 04 : 173.00\n", + " period 05 : 153.73\n", + " period 06 : 129.44\n", + " period 07 : 126.82\n", + " period 08 : 140.67\n", + " period 09 : 115.00\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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SQgghRCvntQRmx44dHD16lA8++IDS0lJmzpzJ4MGDmTdvHpMmTeL5559n1apVzJgxg0WL\nFrFq1SrPjo/jx4+nQ4cO3gpNCCGEEK2c16aQBg4cyAsvvAC4GrBVVVWxc+dOxo4dC7j6hfz3v/9l\n37599O3bF5PJhF6v58orr/T0TxFCCCGEqI/XRmA0Gg0GgwGAVatWMWLECLZv345OpwNcXVELCwsp\nKioiPDzc87jw8HAKCwsbfe6wMINX5w8bKxoSviXnxj/JefFfcm78l5ybS+P1VUibNm1i1apVvPnm\nm0yYMMFzfUP75zVlXz1vVm5HRZm8uspJXDw5N/5Jzov/knPjv+TcNI3PViFt27aNV199lSVLlmAy\nmTAYDFRXVwOQn5+P2WzGbDZTVFTkeUxBQQFms9mbYQkhhBCilfNaAlNRUcEzzzzDa6+95inIHTJk\nCBs3bgTgiy++YPjw4fTr14/9+/dTXl6O1Wplz549XHXVVd4KSwghhBBtgNcSmPXr11NaWsof//hH\nUlNTSU1N5be//S1r165l3rx5WCwWZsyYgV6vZ8GCBdx6663ccsst3H333ZhMMi8ohBBCXKwtW75q\n0v1eeOE5cnKyG7z9L3+5v7lCanatspmjN+cNZV7Sf8m58U9yXvyXnBv/5c1zk5ubw6JF/+bxx5/x\nyvO3pMZqYFplKwEhhBBC1O/555/m4MGfGD58IBMmTCI3N4d//3sxTz75dwoLC6iqquI3v7mDoUOH\nc889d3D//Q/w9ddfYbVWkpFxkuzsLP7whwUMHjyUKVPG8tlnX3HPPXcwcOA17NmzC4vFwtNP/4vI\nyEj+/vdHyMvLpW/fy9m8eRNr1qxvsdcpCYwQQgjhJR9uTuOHQwXnXK/RqHA4Lm4CZGBPM3PHdG3w\n9htuSGX16g/p1KkLGRknWLz4DUpLS7j66kFMmjSV7OwsHnnkLwwdOvyMxxUU5PPPf77Ijh3f8fHH\nHzF48NAzbg8ODuaFF17hlVdeYuvWzcTFJVBbW8Prry/l22+38eGH713U67lYksCcpqiqhIKCXMyq\nWF+HIoQQQlyyyy7rDYDJFMLBgz/xySerUanUlJeXnXPfyy/vD4DZbKaysvKc2/v1u8Jze1lZGSdP\nptO3bz8ABg8eikbTsv2dJIE5zafHN7KnYB9PDnuU4ACDr8MRQgjRys0d07Xe0ZKWqk8KCAgA4Msv\nP6e8vJxFi96gvLyc225LPee+pycg9ZXHnn27oiio1a7rVCoVKpWqucNvlHSjPk2YvgMOxUlWRY6v\nQxFCCCEuilqtxuFwnHGdxWIhNjYOtVrNN99sxm63X/Jx4uMTOHz4ZwC+/37HOcf0NklgTpNgjAMg\ns7LhJWVCCCGEP0tO7sThw4ewWn+ZBho1agzffbeNe+/9HUFBQZjNZt56a8klHWfIkOFYrVZ+97tb\n2bdvLyEhoZca+gWRZdSnKbAV8tiOZxkYfSW/7n29V44hLp4sCfVPcl78l5wb/9UWzk15eRl79uxi\n1KixFBYWcO+9v+Pddz9q1mPIMuomigyKQK8NJEtGYIQQQohGGQzBbN68iXffXY6iOPn971t20ztJ\nYE6jVqlJ7pDA0eJ0ah12dJoAX4ckhBBC+CWtVsvf//6kz44vNTBn6dQhEafiJMea6+tQhBBCCNEA\nSWDO0jEsEUBWIgkhhBB+TBKYs3Q6lcBkVkoCI4QQQvgrSWDOkhASg1qllhEYIYQQwo9JAnOWAE0A\nscHRZFfm4lScvg5HCCGE8Io5c6Zhs9lYvnwpBw7874zbbDYbc+ZMa/TxW7Z8BcD69ev45puvvRZn\nQ2QVUj0SjfFkV+aSbyskNjja1+EIIYQQXpOa+usLfkxubg6bNm1k1KixTJ7ceKLjLZLA1CPBFAd5\nrkJeSWCEEEK0Jr/5zY088cRzxMTEkJeXy0MPLSAqykxVVRXV1dXcd9+f6dWrj+f+//jH3xg1aiz9\n+1/BX//6ALW1tZ7GjgBffLGBVas+QKNR07FjFx588K88//zTHDz4E2+9tQSn00mHDh2YPfs6Fi9+\ngf3791FX52D27LmkpEzhnnvuYODAa9izZxcWi4Wnn/4XMTExl/w6JYE5TW6xlfQCK4mmeMDVUmAg\nV/g4KiGEEK3V6rRP2Vuw/5zrNWoVDufFbYR/hbkvs7pObfD2ESNG8+23W5k9ey7btn3DiBGj6dKl\nGyNGjGL37h945523+cc/nj3ncRs3bqBz5y784Q8L+OqrL9i0aSMAVVVVPPfcS5hMJu6++3aOHUvj\nhhtSWb36Q2655Xb+85/XAPjxxz0cP36MV155k6qqKubPv54RI0YBEBwczAsvvMIrr7zE1q2bmTt3\n3kW99tNJDcxp1n13gsff2kmIOgKQpdRCCCFaH1cCsw2A7du/YdiwkXzzzVf87ne38sorL1FWVlbv\n406cOE6fPv0AuOKKAZ7rQ0JCeOihBdxzzx2cPJlOWZml3scfOvQz/ftfCUBQUBAdO3YmMzMTgH79\nXIMBZrOZysrKeh9/oWQE5jRRoUEoChQU24kMiiCrIgdFUVq8RbgQQoi2YVbXqfWOlnizF1Lnzl0o\nLi4kPz+PiooKtm3bQmSkmUceWcihQz/z8sv/rvdxigJqtevvnfPU6JDdbuf5559h6dJ3iYiI5IEH\n/tjgcVUqFad3V6yrs3ueT6PRnHac5mnBKCMwp0mOcTWNOplXQaIxDmudDUtN/ZmqEEII4a8GDx7G\n668vZvjwkZSVWYiPTwDgm2++pq6urt7HJCUlc+jQQQD27NkFgM1mRaPREBERSX5+HocOHaSurg61\nWo3D4Tjj8T179mbv3t2nHmcjOzuLhIQkb71ESWBOlxx9KoHJryTBXQdTIY0dhRBCtC4jR472rBJK\nSZnCBx+8w3333U3v3n0oLi7ms88+OecxKSlT+Omn/dx77+/IzDyJSqUiNLQDAwdew2233cxbby1h\n3rxUXnzxeZKTO3H48CFefPE5z+P79etPjx49ufvu27nvvrv57W/vISgoyGuvUaU011hOC/LWsJui\nKPzxpW8J0mm4aU4HXvnfW0zuNJ4pncZ75XjiwrSF9vNtkZwX/yXnxn/JuWmaqChTg7fJCMxpVCoV\nXeJDKbBUEalzLZ+WQl4hhBDC/0gCc5YuCaEAlFlUmHRGsqQnkhBCCOF3JIE5S5f4DoC7kDeekupS\nrHabj6MSQgghxOkkgTmLewTmZH6Fa0deZBpJCCGE8DeSwJwlJiIYvU7jWolkdCUwmZWyEkkIIYTw\nJ5LAnEWtVpEUbSK32Ep0kKtXQ1ZFro+jEkIIIcTpJIGpR1K0EUWB6vJAAjU6smQERgghhPArksDU\nw72hXUZBJfHGOPJthdQ67D6OSgghhBBuksDU44yWAqY4nIqTHKtMIwkhhBD+QhKYesRGGAjQql0r\nkYyulgKyEkkIIYTwH5LA1EOjVpNoNpJdaCXO4CrkzZQN7YQQQgi/IQlMA5KiTTicCg6bEbVKLSMw\nQgghhB+RBKYBydFGALILq4gNjia7Mhen4vRxVEIIIYQASWAa5CnkzXe1FLA77eTbCn0clRBCCCFA\nEpgGxUca0ahVZORJSwEhhBDC30gC04AArZr4yGAyCyqJD5aWAkIIIYQ/kQSmEUkxJmrrnGjtrg7V\nMgIjhBBC+AdJYBrh3pE3v7CWyKAIsipyUBTFx1EJIYQQQhKYRrgTGFchbxzWOhuWmjIfRyWEEEII\nSWAakWg2osLVUiDB5NqRN7NC6mCEEEIIX5MEphGBOg0xEQYyCiqIN8YCsiOvEEII4Q8kgTmP5BgT\nVTUOgpzhgBTyCiGEEP5AEpjzcNfBlJaoMOmMZMkIjBBCCOFzksCcR5K7kDfPtSNvSXUpVrvNx1EJ\nIYQQ7ZskMOfh7ol0Ml925BVCCCH8hSQw52HQBxDVQe9aieQp5JWVSEIIIYQvSQLTBMnRJiqr7ISo\nogDIqsj1cURCCCFE+yYJTBO4O1NXlAYQqNGRJSMwQgghhE9JAtME7kLejIJK4o1x5NsKqXXYfRyV\nEEII0X5JAtMEngQmv5JEUxxOxUmOVaaRhBBCCF+RBKYJQoN1hJkCXSuRjK6WArISSQghhPAdSWCa\nKDnaRGlFDWFaVyGvtBQQQgghfEcSmCZKOrUfTE2FAY1KIyMwQgghhA9JAtNE7pVI2QU2YoLNZFfm\n4lScPo5KCCGEaJ8kgWmi5LNaCtiddgpshT6OSgghhGifJIFpojBTIMaggDNaCmTKNJIQQgjhE15N\nYI4cOcK4ceNYsWIFAD/88AM33HADqamp3HnnnZSVlQHwxhtvMGfOHK699lq++eYbb4Z00VQqFckx\nJgot1UQFRgPSUkAIIYTwFa8lMDabjYULFzJ48GDPdU8++ST/+Mc/WL58OVdccQUffPABmZmZrF+/\nnnfffZfXXnuNJ598EofD4a2wLol7Gslhdf0rhbxCCCGEb3gtgdHpdCxZsgSz2ey5LiwsDIvFAkBZ\nWRlhYWHs3LmT4cOHo9PpCA8PJz4+nrS0NG+FdUnchby5BbVEBkWQVZGDoig+jkoIIYRof7yWwGi1\nWvR6/RnX/b//9/+4++67mThxIrt372bmzJkUFRURHh7uuU94eDiFhf5ZHOteSp1RUEGiMQ5rnQ1L\nTZmPoxJCCCHaH21LHmzhwoW8/PLLDBgwgKeffpp33333nPs0ZUQjLMyAVqvxRogAREWZ6r0+IsKI\nQa8lu8jKuEGd2Fu4n3J1Cd2jEr0WizhTQ+dG+JacF/8l58Z/ybm5NC2awBw+fJgBAwYAMGTIENat\nW8egQYNIT0/33Cc/P/+Maaf6lJbavBZjVJSJwsKKBm9PjDJyJNOC0eEq5D2QfYxkXWevxSN+cb5z\nI3xDzov/knPjv+TcNE1jSV6LLqOOjIz01Lfs37+f5ORkBg0axJYtW6itrSU/P5+CggK6du3akmFd\nkOQYEwqgqg4FpJBXCCGE8AWvjcAcOHCAp59+muzsbLRaLRs3buSxxx7j4YcfJiAggNDQUJ544glC\nQkKYO3cuN910EyqVir/97W+o1f67PY17JVJxMZh0RrKkJ5IQQgjR4ryWwPTp04fly5efc/37779/\nznWpqamkpqZ6K5RmlRRz2o688fH8XHIYq91GcIDBx5EJIYQQ7Yf/DnX4qdhwAzqtmozTduSVaSQh\nhBCiZUkCc4HUahWJZiPZRVZiDbGA7MgrhBBCtDRJYC5CUowJh1MhoLYDAFkVuT6OSAghhGhfJIG5\nCO5C3rISLYEaHVkyAiOEEEK0KElgLoI7gckosBJvjCPfVkitw+7jqIQQQoj2QxKYixAfFYxGrSIj\nv4JEUxxOxUmOVaaRhBBCiJYiCcxF0GrUxEcFk1lQSVywq5BXViIJIYQQLUcSmIuUHG3CXudE73A1\nosyUDe2EEEKIFiMJzEVKPrWhnc0ShEalkREYIYQQogVJAnOR3IW8WQU2YoLNZFfm4lScPo5KCCGE\naB8kgblICWYjKhVk5FWQaIzH7rRTYCv0dVhCtKg8az7Pbn+VfPneF0K0MElgLlJggIbYiGAyCiqJ\nP9VSIFOmkUQ7813OD/yQvY/X/vc21XXVvg5HCNGOSAJzCZKjjVTXOjAqEYC0FBDtT5olHYB8WwHL\nD65EURQfRySEaC8kgbkE7jqY2opgQJZSi/aluq6azMpsuoQn0yW0Ez8W7mdTxje+DksI0U5IAnMJ\n3CuRcgtqiQyKIKsyRz6BinYjvSwDp+Kkb3RPbu1zE6E6Ex8f28DhkjRfhyaEaAckgbkEiWZXAnMy\nv4JEYxxWuw1LTZmPoxKiZaRZjgNwWVRXQgNN3NY3FZVKxZs/vUNptcXH0Qkh2jpJYC6BQa/FHBbE\nybwKEozuQl6pgxHtw1FLOipU9IjoAkDn0I7M7jaNSruVJQeWY3fW+ThCIURbJgnMJUqKNmGtriNU\nEwXIjryifbA77JwszyDBGEdtzS+/RkbGD2Fg9JWcLM9k5ZGPfRihEKKtkwTmEiVHGwFwWE9tbCeF\nvKIdOFGeSZ3iwOiMJvVvn/P1XtfIo0qlYl7PWcQbY/k2Zyff5fzg40iFEG2VJDCXyF3IW1ioYNIZ\nyZIRGNEOuJdPV5WEAPDul0dIy3bVf+k0Om7vczNB2iA+OLKGjPIsn8UphGi7JIG5REnRpxfyxlNS\nXYrVbvNxVEJ4l7uANzdDT6BOg1NRWLxmP2XWWgCiDBH8utf1OJwOlhxYTqXd6stwhRBtkCQwlyjE\noCM8JJCT+RUknNqRV6aRRFvMb4xTAAAgAElEQVTmcDo4Xn6SKH0UFgtc2cPMnFFdsFTW8uraA9Q5\nXD3B+kRexqRO4yipLuWtA+9KrzAhRLOSBKYZJEebKKusJVzrLuSVlUii7cqszKbWUUsosQD07hxB\nytVJXNUjisOZFlZtOea576SOY+kT0ZNDpUf59PgXvgpZCNEGSQLTDNzTSFSFApBVkevDaITwLnf9\ni7MiDHAlMCqVilsmX0ZshIEvfshk58/5AKhVaub3up5IfTgbT25mX+FPPotbCNG2SALTDNwtBUqL\nteg1gWTJCIxow9z1LwVZQeh1GjrFuRL3oEAt98zqi16n4a0NB8kqrATAEGDgjsvnE6AOYNnPH0jn\naiFEs5AEphm4VyJl5lcSb4wl31ZIrcPu46iEaH5OxUma5QThgWEUFkK3hA5o1CrP7bERwdw65TJq\n7U4Wrd6Prdq1mV28MZZ5PWdT7ajm9f3LqK6r8dVLEEK0EZLANIMORh0hhgBPIa9TcZJjlWkk0fbk\nWvOpqqsiXO0qWO+eGHrOfQb0MDNpUBL5pVW88enPOE/1B7s65kpGJgwlz5rPO4ekc7UQ4tJIAtMM\nVCoVSTEmisqqiQqMBmQlkmibjp6aPlIqIwDokRhW7/1mjejMZclh/JhWxGf/PfnL9V2n0Dm0I3sK\n/sfmzG3eD1gI0WZJAtNM3HUwmmrXJ1JpKSDaIncBb3GOgQCtmo6xpnrvp1GruXN6byJCAlm79TgH\njhcDoFVrua3PTYToTKw9tp4jpcfqfbwQQpyPJDDNxJ3AVJYGoVFpZARGtDmKopBmOU6IzkRerkKX\nuBC0moZ/hYQYdNw1sy8ajZrXPvmJQksVAKGBIdza5yYA3jwgnauFEBdHEphmkuQu5C2wERNsJrsy\nVzbuEm1KQVURFbWVRGnjUVDRPbHDeR/TKTaEmyZ0x1pdx6LV+6m1OwDo2qETs7pOpcJeyX8OrJDO\n1UKICyYJTDOJCtUTFKjlZJ6rpYDdaadAlouKNsS9fFptiwRoUgIDMKJfHCP6xZFRUMnyjYc9xbuj\nEoZyVXR/0ssz+OjoOu8ELYRosySBaSYqlYrkaCP5JTaig2IAyJRpJNGGuOtfSvOC0ahVdIk7dwVS\nQ24c351OsSa+PZDHljM6V88hLjiGbdn/ZUfuLq/ELYRomySBaUbJMSYUIMDu+mQqLQVEW5JmSceg\nNZCTpaJjjIlAnabJjw3Qqrl7Zl+MQQG8u+mop3N1oEbH7X1vJkir5/3Dq8mskJ8ZIUTTSALTjNwt\nBarLggFZSi3ajuKqUkqqS4nWxeNwNn366HThIXp+N733OZ2rzYZI5ve6HruzjiX7l0nnaiFEk0gC\n04zcK5FyCmqIDIogqzJHNusSbcKxMtf0kbb6wupfznZZx3DmjDy3c3XfyF5M6jiW4upSlv70nhTA\nCyHOSxKYZhQTbkAXoOZkXiWJxjisdhuWmjJfhyXEJXMX8Fbkm1AB3RKaXv9ytpRrkhhQT+fqyZ3G\n0yu8BwdLjrA+/ctLDVkI0cZJAtOM1GoVSWYTOUVW4oJjAWROX7QJaZZ0AjWBZGaoSTAbMegDLvq5\nVCoVv2mgc/Wve99AhD6cDSe+Yn/Rz80VvhCiDZIEppklR5twKgqBjnBAduQVrV95bQX5tkJiAuOp\nc1z89NHpGupcHRxg4Pa+qQSotbz98/sU2Iou+VhCiLZJEphmlhRjBKC2wvWvFPKK1s69fDqwNgqA\nHs2QwEDDnasTTfHc0GM2VXXVLNm/jBpHbbMcTwjRtkgC08zchbyFBU5MOiNZMgIjWjl3AlNZFAJA\nt2ZKYKDhztXXxA5gRPxgcqx5vHtolRTDCyHOIQlMM4uLDEarUXEy37Ujb0l1KVa7zddhCXHR0izH\nCVBryT6pISbcQGiwrlmfv6HO1bO7TaNTSDK78n9kS9a3zXpMIUTrJwlMM9Nq1MRHGcks+KWQV6aR\nRGtls9vIqcwjRh9PdU3z1L+czd25Ory+ztV9b8IUYGR12qeekSAhhABJYLwiOdpEncNJMBGA7Mgr\nWq9jZSdQUDDYzUDz1b+cLcSg4+6ZfdFoVGd0ru4QGMqtfW4E4I0Dy2VbAiGEhyQwXpB8qjO1o9L1\nb1ZFri/DEeKiuUc9bCWu+hdvjMC4uTpX9zinc3W3sC7M7DKZilpX5+o66VwthEASGK9IinatQCop\n0qDXBJIlIzCilUqzpKNWqck+qSMiJJCIUL1Xj9dQ5+rRicMZYO7H8bKTrE771KsxCCFaB0lgvCAx\nyohapSIjv5J4Yyz5tkJqHXZfhyXEBamuqyGjIovYoDisNsWroy+na6xzdWxwNN9kfcf3eXtaJBYh\nhP+SBMYLdAEaYiMNpxKYOJyKkxyrTCOJ1uVEeQZOxUmwIxrw7vTR6RrqXK3XBnJH35vRa/S8e+gj\nKY4Xop2TBMZLkqNN1NgdhKhcze/kl61obdz9j2otrsSlpRIYaKxzdRTze12H3Wlnyf5l2GSLAiHa\nLUlgvMS9oZ1S5Sp+lJYCorVJs6SjQkVuRiAhhgBiwg0tevyzO1c7nK4O1ZdH9SYleQxF1SUs/fl9\n6VwtRDslCYyXuFcilRcHolFpZARGtCp2Zx3p5RlEB0VjKXPSPbEDKpWqxeM4vXP1yq9/6Vw9pfME\nLgvvzk/Fh9iQvqnF4xJC+J4kMF6SaHatRMrMtxETbCa7Mlc+KYpW42R5JnXOOkKUGKBlp49Od/7O\n1WGsP7GJA0UHfRKfEMJ3JIHxkqBALdFhQWTkV5BgjMPutFNgK/R1WEI0iXv/l7qyMMB3CQz80rk6\n8KzO1caAYG471bl66c/vU2gr9lmMQoiWJwmMFyXHmLBW1xGude1iminTSKKVcBfw5mcHERSoJSHK\n6NN4YiOCua2eztVJpgSu6zGLqroqlhxYRq10rhai3ZAExovchbyq6lBAWgqI1sHhdHC87ARR+kiK\nipx0SwhFrW75+pezNdS5enDsVQyLH0R2ZS7vHlotnauFaCckgfGipFOFvNbSIECWUovWIasyhxpH\nLWHqOMB7/Y8uRkOdq+d0+xUdQ5L4IX8P32R/58MIhRAtRRIYL3KPwOQU1BIZFEFWZY58OhR+z13/\n4qwIB3xb/3K2hjpXB6i13NbnJowBwXx0dB3HLCd8G6gQwuskgfEiY1AAESGBnMyrINEYh9Vuk266\nwu+5E5jC7CB0AWrPlgD+4uzO1UWnOleH6Ttwa58bURSF/xxYTllNhY8jFUJ4kyQwXpYUbaLMWktk\noGs79swKqYMR/supODlmSSc8MIy8PIUucaFoNf73a+L0ztUvr/mlc3X3sK7M6DqZstoK/nNgBQ6n\nw8eRCiG8xau/mY4cOcK4ceNYsWIFAHa7nQULFjBnzhzmz59PWZlrNOKTTz5h9uzZXHvttaxcudKb\nIbU496dXTY27kFfqYIT/yrMWYK2zEaHxv/qXs7k6V8eSkX9m5+qxiSO4wnw5x8rSWZP2mY+jFEJ4\ni9cSGJvNxsKFCxk8eLDnug8//JCwsDBWrVrF5MmT2bVrFzabjUWLFrF06VKWL1/O22+/jcVi8VZY\nLc5dB1NdFgxAthTyCj/mXj6tskUA0M2PExhwda7uGHNu5+qbes4hxmDm66zt7Mrb6+MohRDe4LUE\nRqfTsWTJEsxms+e6r7/+ml/96lcAXHfddYwdO5Z9+/bRt29fTCYTer2eK6+8kj179ngrrBbnHoHJ\ny3di0hllBEb4NXf9S0luMBq1is5xIT6OqHEBWk0Dnav1pzpXB/LOoVVkV0o3eCHaGq8lMFqtFr1e\nf8Z12dnZbN26ldTUVO677z4sFgtFRUWEh4d77hMeHk5hYdvZsbaDMZCQYB0Z+ZUkGuMpqS7FKh10\nhR9SFIU0y3FCAkxkZyl0ig0hMEDj67DOKyK0/s7V0cFmUntdR63Tzuv7l2GzV/k4UiFEc9K25MEU\nRaFTp07cc889LF68mNdee41evXqdc5/zCQszoNV67xdrVFTzrrroltiB3YcKGBWeyM8lh6nUlNIx\nKrpZj9FeNPe5Eb/IqyigrLaCyzr0IV+B/j3MTX6/fX1eoqJMFFbUsvSzn/nP+oM8fucQNBo146MG\nU1iXz9qDG3nv2CoeGPZb1Cr/K0r2Jl+fG9EwOTeXpkUTmMjISAYOHAjAsGHDeOmllxg1ahRFRUWe\n+xQUFNC/f/9Gn6e01HsjGFFRJgoLm3f5ZWy4ayO7unJXHcz+rDSi1fHNeoz2wBvnRvxiZ84BAJzl\nrrqXhAhDk95vfzkvw/tEsz+tkN2HC1m88keuH9sNgLExozmUd5w9OftZ8cPHTOo0zseRthx/OTfi\nXHJumqaxJK9FP4qMGDGCbdu2AfDTTz/RqVMn+vXrx/79+ykvL8dqtbJnzx6uuuqqlgzL69yFvLXl\nrn4yWRUyHy/8j7uA15IXjEoFXeNDfRzRhTm7c/X3B3/pXH1L73mEBXbgs/Qv+an4kI8jFUI0B68l\nMAcOHCA1NZU1a9awbNkyUlNTmT59Ot988w033HADmzZt4o477kCv17NgwQJuvfVWbrnlFu6++25M\nprY1rOZOYIoK1eg1gWRJTyThh9Is6Ri0QWRlqkgymzDoW3SAtlmc3rn6zfWnda7WBXN731Q0ag1L\nf3qPoirpXC1Ea6dSWuHe9t4cdvPGsJ6iKPzhhW0YDTqiBuwhvTyD50YsRKcJaNbjtHUy5Oo9pdUW\nHv7uCToHd+Onr7sw7qoE5o3r3qTH+uN52XWogMVrDxAdFsQj8wd6krHvcr7nnUOrSDDGsWDAXeg0\nOh9H6l3+eG6Ei5ybpvGbKaT2SqVSkRRtIr/ERowhBqfiJMcq00jCf7iXTwfURAH+vYFdU1zV08yk\na87tXD0k7mqGxl1NVmUO7x9eI73JhGjFJIFpIe5ppCCHa4Mw6Uwt/Im7/qWy0PV96u8b2DXFrJH1\nd66+tvsMkk2J7MzbzbbsHT6MUAhxKS46gTlx4kQzhtH2JcW4CnjrKl1/IGRDO+FP0izpBGp0ZJ5Q\nExthIMTQ+qdWGutcfXvfVIwBwaw6+gnHy06e55mEEP6o0QTmlltuOePy4sWLPf9/9NFHvRNRG+Ue\ngSktCkCj0sgIjPAbFbWV5NkKiNUnUGNXWv300eka61z9m9434lScvLF/OeW1UosgRGvTaAJTV1d3\nxuUdO34ZbpW54wsTHW4gUKchM99GTLCZ7MpcnIrT12EJwbFT9S/6Wlfbj+5tKIGBhjtX9wjvyvQu\nkyirLefNA+9I52ohWplGExiVSnXG5dOTlrNvE41Tq1Qkmo3kFtmID47D7rRTYGs7LRNE6+Uu4LUW\nu/oetbUEBhruXD0uaST9o/py1HKctcfW+zhKIcSFuKAaGElaLk1ytAmnohCsuAp5M2UaSfiBNMtx\ntGotWSc1RIbqCQ/Rn/9BrVBDnatTL7uWaIOZzZnb2J3/o4+jFEI0VaMJTFlZGf/97389X+Xl5ezY\nscPzf3Fh3HUwTpvrk26mbGgnfKyqroqsylziguKxVSltcvTFrfHO1akEanSsOLSKnMo8H0cqhGiK\nRhOYkJAQFi9e7PkymUwsWrTI839xYZJjXO9ZRbHrE64U8gpfO2Y5gYKCoc7VXLQtJzDg6lz923o6\nV8cER5N62XXUOmp5++f3pR5GiFag0b3Cly9f3lJxtAuxEQa0GjVZ+TVE9oggqzIHRVFkak74jLv+\npabUNSrYllYgNaRXx3DmjOzCyi3HeHXtAf50Q380ajVXmPsyKOYqduTtYkvWt4xNGuHrUIUQjWh0\nBKayspKlS5d6Lr///vtMnz6dP/zhD2d0kBZNo9WoSTQHk11YSUJwHFa7DUtNma/DEu1YmiUdtUpN\n1kkdocE6zGFBvg6pRaRck8SAHlEczrSw8utjnutndp1CcICBT49vpKS61IcRCiHOp9EE5tFHH6W4\n2LX5U3p6Os8//zwPPvggQ4YM4R//+EeLBNjWJEWbqHMohKgjAciskDoY4Ru1jlpOVmQSGxRLeYWT\n7okd2s1oYEOdq426YGZ1nUqt086HR9bKdhFC+LFGE5jMzEwWLFgAwMaNG0lJSWHIkCFcf/31MgJz\nkdyFvFS7C3mlDkb4RnpZBk7FickZA7T9+peznd65+q31hzydq6+JGUC3Dp3ZX3SQfUU/+ThKIURD\nGk1gDAaD5//ff/89gwYN8lxuL5/Umpu7kNdWEgxAthTyCh9x9z+yW1yJS3uofzlbbEQwt06+jBq7\ng0Wr92OrrkOlUnF9j1loVRpWHvmY6rpqX4cphKhHowmMw+GguLiYjIwM9u7dy9ChQwGwWq1UVVW1\nSIBtTUJUMGqVitx8ByadUUZghM+kWdJRoSI3M5BgvZa4qGBfh+QT9XWujgk2Mz55NJaaMj49/oWv\nQxRC1KPRBOb2229n8uTJTJs2jbvuuovQ0FCqq6uZN28eM2bMaKkY25QArYa4yGAyCipIMMZRUl2K\n1W7zdViinalz1pFefpLooGiKS510S+iAuh2Pqp7eufrznRkATEwejTkoki1Z33KyPNPHEQohztZo\nAjNy5Ei2b9/Ot99+y+233w6AXq/nz3/+MzfeeGOLBNgWJccYqbU7CdO6es/IfjCipWVUZGF31hGq\nap/1L2dzd64ODdaxdls6+SU2AjQBXN9jFgoK7x1eLXvDCOFnGk1gcnJyKCwspLy8nJycHM9X586d\nycmRP7oXK+lUIa+mJhSQHXlFy0srde3/4igPBySBAVfn6hvGdaPO4WT5F65+ST3Cu3J1zJVkVmSz\nNfu/vg5RCHGaRjeyGzNmDJ06dSIqKgo4t5njsmXLvBtdG+VeiVRd5qo5yKrI9WU4oh06WuYq4C3M\nDiIwAJKijT6OyD8M7Gnm2/157D9ezI6f8xncO4ZZXafyU9Eh1h3/nP5RfQjTS7InhD9odATm6aef\nJjY2lpqaGsaNG8cLL7zA8uXLWb58uSQvlyDRbEQF5Oep0GsCyZIRGNGCnIqT45YTROkjyct30DU+\nBK3mgvq6tlkqlYqbJnRHp1Xz/ldHqayyY9IZmdF1CjWOWlYe/cTXIQohTmn0t9b06dN58803+fe/\n/01lZSU33ngjt912G+vWraO6WpYWXqygQC3R4QYy8q3EG2PJtxVS67D7OizRTmRV5lDtqCFMHQfI\n9NHZojoE8athnaiw2Vm1xbVL7+DYq+gS2ol9hQfYVyh7wwjhD5r0sSs2Npa77rqLDRs2MHHiRB5/\n/HGGDRvm7djatOQYE1U1dYTrzDgVJzlWmUYSLcPd/4hKqX9pyISBicRHBbN1Xw5HMi2oVCpu6DkL\njWdvmBpfhyhEu9ekBKa8vJwVK1Ywa9YsVqxYwZ133sn69eu9HVub5q450NnDAFmJJFqOO4Epzg1G\nq1HROS7ExxH5H61GzfyJPQFYvvEwdQ4nscHRjE8eRWmNhc/SZW8YIXyt0SLe7du389FHH3HgwAEm\nTJjAU089Rffu3VsqtjbNXchbW+5KZGRDO9ESFEUhzXKcsMAOZOc46BofSoBW4+uw/FLXhFBG9Y9j\ny485bPw+gymDOzIxeQy78n/k68ztXB1zJYmmeF+HKUS71egIzG233cbBgwe58sorKSkp4a233uKh\nhx7yfImL515KXVygRaPSyAiMaBF5tgKsdhtR2ngURaaPzmf2qC6EBOv45NsTFJTa0GkCuL7HTBQU\n3j30EU7F6esQhWi3Gh2Bca80Ki0tJSws7IzbsrKyvBdVO2AMCiAyVE9Gno2YjmayK3NxKk7UKlkN\nIrzH3f9IbYsA2mf/owsRrA/g+rFdef2Tn1nxxRHum9uPy8K7c1V0f3bl/8jW7P8yKmGor8MUol1q\n9K+lWq1mwYIFPPLIIzz66KNER0dz9dVXc+TIEf7973+3VIxtVnK0iQqbHbM+BrvTToGt0NchiTbO\nXf9SkmdEpYIu8aE+jsj/XXNZNL07hXMgvYTvDxYAMLvbNIK0Qaw79jmWmjIfRyhE+9RoAvOvf/2L\npUuX8v333/PnP/+ZRx99lNTUVHbs2MHKlStbKsY2K+lUZ2p9nWs1SKZMIwkvctW/pGMKMJKV6SQ5\n2kRQYKODsALX3jCpE7oToFXz3ldHsVXbCdGZmNllMtWOGlYdkb1hhPCF847AdOnSBYCxY8eSnZ3N\nzTffzMsvv0x0dHSLBNiWJZ9aieSodBfyyoZ2wnuKq0uw1JQRrUvA4ZT6lwthDjMwbUhHyq21rPrG\nNQ03OG4gnUM7srdwP/uLfvZxhEK0P40mMKqzutPGxsYyfvx4rwbUnrhXIpUWBgKylFp419FT00fa\n6khAEpgLlXJNEnGRwWzZm01adhlqlZobesxCrVLzweG11DhqfR2iEO3KBVWMnp3QiEsTagwk1Kgj\nK7+GyKAIsipzzug3JURzSit1jRxUFLhG/LolSP3LhdBq1Nw8sQcAb39+iDqHkzhjDOOSRlJaY2F9\n+pc+jlCI9qXRCfC9e/cyatQoz+Xi4mJGjRqFoiioVCq2bNni5fDavuRoE/87VkyXoBgOlPyEpaZM\nmsUJr0izHCdIqycjQ0V8pAGTQefrkFqd7okdGNEvlq37cvnyh0wmDUpmUsex7M7fx+bMbQyMvoIE\nU5yvwxSiXWg0gfn8889bKo52y53AGBTXstbMimxJYESzK622UFRdQmdjN36yKzJ9dAnmjOrK3qNF\nfLw9nat6monqEMR1PWayeN9/eO/wahYMuEu2QxCiBTT6UxYfH9/ol7h0yadWIik213busiOv8IZj\np+pfAmujAKl/uRTGoACuH9uN2jon73x5BEVR6B3RgwHmfpwoz2B79k5fhyhEuyAfE3zM3ROpvEgP\nQLYU8govOFrmSmAqC10JsyQwl2ZQr2h6dQzjf8eK2XXYtX/T7G6/Ikir5+NjGyirKfdxhEK0fZLA\n+FhEiJ5gvZacPAcmnVFGYIRXpFnS0al1ZJ7QYO4QRJgp0NchtWquvWF6oNWoeffLI9iq6wgNNDG9\nyySqHdV8dHSdr0MUos2TBMbHVCoVyTEmCkqriDPEUVJditVu83VYog2pqK0kz5pPXFACVTVOuiXK\n6qPmEB1uYOqQZMqstazeegyAoXHX0Ckkid0F+/ip+JCPIxSibZMExg+494MxqVyFvLIfjGhOx8pO\nAGCoMwMyfdScJl2TTGyEga/3ZHMs59TeMD1nn9obZg21sjeMEF4jCYwfcBfycqqQN0umkUQzcjdw\ntJW4Rl6kgWPzCdC69oZRgGWfH8bhdBJvjGVs4giKq0vZcOIrX4coRJslCYwfSDo1AmMtDQakJ5Jo\nXmmWdLQqDVkntXQw6ojqEOTrkNqUHklhDOsbS2ZBJV/+kAXApE7jCNeHsSnjG7Irc30coRBtkyQw\nfsAcFkSgTkNeHug1gWRJTyTRTKrqqsiqyCHOEE9FpYPuiR1kR20vmDumK8agANZuP05RWRWBGh3X\ndZ+BU3Hy3qHVOBWnr0MUos2RBMYPqFUqks1GcottxAbHkm8rpNZh93VYog04XnYSBQWjMwaQ6SNv\nMQYFcN2YrtTanbzzhWtvmD6Rl3GF+XLSy0/yXc73vg5RiDZHEhg/kRRjQlEgVB2JU3GSY5VhZ3Hp\n0k5tYFdd6qp/kQJe7xnSJ4aeSR3Yd6yYPUdce8PM6TYNvUbP2mMbKK+t8HGEQrQtksD4CfdKJE2N\n6w+NrEQSzSHNchy1Sk1uhg5jUACxkcG+DqnNUqlUpE7sgVaj4p0vj1BVU0eHwFB+1SWFqroq2RtG\niGYmCYyfcK9EqrKcKuSVlUjiEtU6ajlZnkVsUCwlFgfdEkJRS/2LV8VGBDNlcEcslbWs3upa/TU8\nfhDJIYnsyv+Rg8VHfByhEG2HJDB+IjbCQIBWTWFeABqVRkZgxCU7UZ6BQ3EQgqv+RaaPWsbkQclE\nhxvYvDuL9Nxy194wPVx7w7x/eLXUtwnRTCSB8RMatZqEKCPZhTZiDGayK3Nl5YK4JEdP1b/UlYUB\nksC0lNP3hnn780M4nE4STXGMThhGUXUJG2VvGCGahSQwfiQ5xoTDqRCmNWN32imwFfo6JNGKuQt4\n87P0BOo0nsahwvsuSw5jaJ8YMvIr+Wq3a1uEyZ3GExbYgS8zviGnMs/HEQrR+kkC40eST/2BCbC7\nPinLhnbiYtU560gvO0lMUDT5hXV0iw9Fo5Yf95Y0d0xXgvVa1mw9Tkl5NXptINf1mIFDcfD+Ydkb\nRohLJb/R/Ii7kLem3JXIZMqGduIiZVRkY3fa6aCKBWT6yBdMBh1zx3Slxu7gnS9dxbt9I3vRP6oP\nx8pOsCN3l48jFKJ1kwTGj8RHGtGoVZQU6ABZSi0unrv/kVIZDkgC4yvD+sbSPbEDe48WnbY3zK8I\n1OhYk/YZFbWVPo5QiNZLEhg/EqBVExcZTHZeDZH6CLIqc1AUxddhiVbIXf9SmB2EVqOmU2yIjyNq\nn1QqFTdP7IFG/cveMGH6DkzrnIKtrorVaZ/6OkQhWi1JYPxMcrSJ2jonkYHRWO02LDVlvg5JtDJO\nxckxywki9RHk5DroHBdCgFZ+1H0lLjKYyYOSKa2oYe02V2I5MmEISaZ4vs/bw6GSoz6OULS0Omed\nr0NoE+S3mp9x18Ho61xLXzMrpA5GXJjsylyqHdVEauNQkOkjfzB1SDLmsCA27c7kZF6Fa2+YnrNR\noeKDw2uwy94w7caPhQd4YNvfWLp3pa9DafUkgfEz7pYC9krXv7Ijr7hQ7ukjrBGANHD0BwFaDakT\ne6AosPTzQzidCkmmBEYlDqWgqoiNJ7/2dYiiBWzJ/JY39i+nxlHL+iObf/lZFRdFEhg/k2g2ogIs\nBYEAZEshr7hA7gLektxg1CoVXeKl/sUf9O4YzuDe0ZzMq+CrPVkATO00gQ6BoXxx8mvyrAU+jlB4\ni1Nxsvrop6w8+jFGXTDXdZ8JwHuHV8t00iWQBMbPBOo0xEQYyM6rw6QzygiMuCCKopBmSadDYChZ\nWQ6SY0zodVpfhyVOuY1OEPAAACAASURBVG5Mt7P2htEzt/t0z94wUrTf9tgddt766V2+ytxKtCGK\nPw24hxEJgxnfZTh51nw2ZXzj6xBbLUlg/FBytImqGgfRgTGUVJditdt8HZJoJfJtBVTarZgDEnA4\nZfrI34QE67h2dFeqax28t8lVvNsvqg+XR/bmqOU4O/J2+zhC0Zysdhsv/fgGewr+R5fQjiwYcDeR\nQa6tDeZdPoMQnYkNJ76SXdcvkiQwfijpVB1MkOKqYZD9YERTufsfaapc3ztSwOt/hl0eS7eEUHYf\nKeTHo0UAzO0+HZ1Gx5q0T6mstfo4QtEciqtKeG73Yo6VpXOF+XJ+3/92ggMMntuDdQbmdPsVdc46\n3j+8RkbfLoJXE5gjR44wbtw4VqxYccb127Zto0ePHp7Ln3zyCbNnz+baa69l5UqpzHavRHJaXf9m\nyTSSaCJ3/UtZnms3564Job4MR9RDfcbeMIeprj21N0ynCVjtNtakfebrEMUlyqjI4p+7F5FvK2BM\n4nB+03seAZoAAGrtDj797gTpOWVcab6c3hE9OVyaxvd5e3wcdevjtQTGZrOxcOFCBg8efMb1NTU1\nvP7660RFRXnut2jRIpYuXcry5ct5++23sVgs3gqrVXD3RCov1gPSE0k0jbv+xRgQTGamQkJUMMag\nAF+HJeoRH2Uk5Zokistr+Hi7e2+YoSQa49iRt4sjpcd8HKG4WD8VH+Zfe16loraSOd1+xexu01Cr\nXH9qbdV2nvvgR1ZvPc4TS7+nzuHkuu4z0KkDWJ32KZV2GX27EF5LYHQ6HUuWLMFsNp9x/auvvsq8\nefPQ6Vzb5e/bt4++fftiMpnQ6/VceeWV7NnTvjNRgz6AqA56srNBrwkkS3oiiSYori7FUlNGTGAi\ntXWKTB/5uWlDOvL/27vvAKnrM/Hj7+/0ndnZ3itsYSlbYCmKAopUKwhSBUuM5cTkkuglxiSX3OV+\n8UzO6F3EGmMBpUkRGwoiigIidZeylWV777uzZdrvj2EXNygI7O7sLM/rH2TKd55hnJlnvp/n8zzB\nfga2f1NCUWUzapW6uzfMmuyNWGV3isfZU7afFzNew+l08OPkZUyNntR9XUNLB//91iFySxrxN+up\nqLWw7esiAr0CuDluJi3WVjn7dpH6LIHRaDQYDIYelxUUFJCVlcWNN97YfVlNTQ0BAQHdfw8ICKC6\nWgqaYkLNtLbZCPUKpdJSTac0uhIX0LV8pOsIAqT+ZaDTaV29YRxOJ29sy8bhcBLrE82UqGuostSw\nXXrDeAyn08n7pz7hrax38FIb+OmYBxgdktJ9fWWdhT+tOkhJdStT0yP5z/sm4GfW88HeQmob25ka\nNYlI73D2lcvZt4vRr/srn3zySX7729+e9zY/pJDJ39+IRqPurbDOERxs7rNj/1Aj44I4mF2Nvy6U\nwpYi2rRNRAYOcXdYbjcQXpuBqqTA1VukvcEfsDJxdBQBPobz36mXyOtyaaYGmzmQU8MXh0s5kFvD\nzZPiuNdvPhm1x/i48DNmjLiWCHPoZT2GvDZ9y+aw8/I3b7Hr9F5CTIE8MeURInzCuq/PK2ngqbcP\n09DSwdKZSSyemYSiKNxz80ieXXuYLXtO8/hd41lx9V38Zsef2ZC3hb/M+k13zYz4fv2WwFRWVnLq\n1Ckee+wxAKqqqli2bBk/+clPqKmp6b5dVVUVo0ePPu+x6uv7bltxcLCZ6urmPjv+DxVkdi2xdTaa\nAMgszsXXEejOkNxuoLw2A9Wximy81AZO5dkJ9ffC3mGlurrvz9zJ63J5br92CAdOVPLGhydIjPDB\n36xnfvxt/P3YKp7fu5qfjr4fRVEu6djy2vStNls7f89cRVZ9LjHmKP4l7V60Habuf/OThfX8bWMG\nHZ12ls0cxg3pkdTUuCaQTx0bzXu78/nqaBlffFPIiCGBTImayOcle3jrwFZujpvpzqc2YJwvAe+3\nbdShoaHs2LGD9evXs379ekJCQli9ejVpaWlkZmbS1NREa2srhw4dYty4cf0V1oDVtZW6tc617U4a\n2onzaehopLqtlnCvaNo6HLJ85EF8vfXccX08bR121nzq6g0zOjiZ5MAR5MjulAGroaORZw69QFZ9\nLsmBI/hZ+kP46M5+2R7MruKZ9Uew2hw8OGcUN6RH9bi/SqWwbEYSCvD2jlxsdge3xs2WzswXoc8S\nmGPHjrF8+XI2b97Mm2++yfLly79zd5HBYODRRx/lvvvu495772XFihWYzXLK09ekcxV6lalRK2rp\nBSPOK/9M/xevTtfuPklgPMuU0RHER/pwIKuKjPwaFEVhoexOGbDKWir4nwMrKW0pZ1Lk1TyQchd6\nta77+l1HSnl+yzHUKhU/W5jGhBHfvQwYG2ZmyugISmta+exQKV4aAwuGzcEmnZl/kD5bQkpOTmbV\nqlXfe/3OnTu7/3v27NnMnj27r0LxWDEh3hzNryXOK5jSlnIcTkf3djwhvq1rKFxrrWvukSQwnkWl\nKNw9azj/8fo3rPo4h//6sT+BXv7cHDeTzXkf8G7eh9w5YoG7wxRATn0+L2e+QZutnTlxNzIj9vru\nJT6n08n7ewvZ/MUpvL20/HxhGkPDzz+LbN6UOA5kVbHlywKuGhlKWtAoUoJGkllzgn3lB5gYMb4/\nnpZHkm/DAayroZ2PKgirwyrtpsX3ymsoQKfSUlykxt+sJ8i3f4p3Re+JCvFm5oRoapva2fqVKyHt\n2p2yp/wbcutPuTlCcaDiMCuP/J1Ou5W7Ry5m5pCp3cmLw+lkzY5cNn9xikAfPb9eln7B5AXAbNQx\nd3IcbR023vk8H0VRXL1h1Do2531Ac2dLXz8tjyUJzAAWe6YORt3h+jUtDe3Ed2mxtlLWWkGEMYqW\nVjtJ0X6XXPQp3Ou2a4cS5Gvg4/3FFFe1uHrDJLl6w6yVycVu43Q6+aTwM147sQaNSsuKtPuYEJbe\nfb3N7uCV906w42AJkUEmnlg+jvBA0w8+/vVjIogK9ubLjHJOlTW5OjPHzaLVZmFT3vt98ZQGBUlg\nBrCuMzCW+q5CXmloJ86V33AaAJPdtc4uy0eeS69Vs2ymqzfMm9uycDidDPWNYXLk1VRYqmRysRs4\nnA7W5Wzh3fyP8NP78oux/0JSQEL39R2ddv7vnQy+PlFJQqQvv7ozHX+z/qIeQ61SceeMRADe2p6N\nw+nk+qhriTFHsr/iEFl1ub36nAYLSWAGMH+zHm8vLdUVrn4AUsgrvktXA7v2OtfcI0lgPFtqfCDj\nh4eQX9bE50dc7/nb4md/a3JxzQWOIHpLp72TlzPfZHfpXiK9w3ls7AoivcO7r29ps/KXtYc5VlBH\nanwgjy4efcnjO5Ji/LlqZCgF5c18lVGOSlF1d2Zem71Jmpl+B0lgBjBFUYgNM1NTZyfQEEBJS5lU\npYtz5DUUuHaqFWrx9tISHmi88J3EgLZkeiJeejXv7MqnsaUDL41X9+TidTK5uF80d7bw7OGXyKw5\nQZJ/Aj9Pfwh/w9kfB3VN7Ty5+iCnypqYOCqMR+aloNdeXoPVhVMT0GvVvPN5PpZ2KzHmKKZGT6K6\nrZaPT396uU9p0JEEZoCLOTPY0V8dQqvVQkNHo5sjEgNJu62d4uZSIo2RNDTZpP5lkPDz1jP/unja\nOmzdvWHSQ1IZGZhEVn0uByqPuDnCwa3KUs3/HFxJYVMxV4WN5eG0H+Gl8eq+vqymlf+36iDltRZm\njo/mvltGoFFf/tepv1nPLdfE0myxsuXMkM+bh87EX+/H9qLPKWupuOzHGEwkgRngugp5NdauQl6p\ngxFnnWosxIkTb4er/iVRlo8GjetHRxIX4cP+k1UcO1V7ZnfK7WhVWjbmvofF2ncdya9kBY2FPH3w\neWraapk9ZBrLRyxEozrbcSS/rJEnVx+kvrmDBdfHs+iGBFS9+KNh5vgYQv292HmwlJLqFgwaPYuS\n5mJ32lmTvQmH09Frj+XpJIEZ4LoKeTuaXBXt0pFXfFtX/xdboytxSZIEZtBQqRTumpWESlF48+Ns\nOqx2grwCuGnodJqtLWzJ/8jdIQ46R6uP8b+HX8Jia2Np0nxujZvV44zmsVO1/GXNYSwdNu69cTg3\nXh3b62c8tRoVS6Yn4nA6eXt7Dk6nk5SgkYwOTuFU42n2lO3v1cfzZJLADHDBfl546dXUVri6PJZK\nIa/4lryGUygolBcbMOjURId4uzsk0YtiQs3MHB9NTWM77+85DcC06ClEmML4quzr7h1o4vLtKvmK\nVzJXoSgqHky5m2sjr+px/b4TFfzvOxk4HPDI7SlMTovos1hS44NIiw8kq6iBb7JcIwUWDLsNg1rP\nlvyPaOyQ+VYgCcyAp1IUYkLMVFU5MWu95QyM6NZpt1LYVEyEMZyqWiuJUX6oVFL/MtjMmTSUQB89\n274uoqT6TG+Y4fMBWJO9UXrDXCaH08GmvPfZkPMu3joTPx/zEMlBI3rcZseBYl7ZegKdVsWji9IY\nMyy4z+NaPD0RjVph3c48Ojrt+Ol9uS3+RtpsbWzM3drnj+8JJIHxALFhZpxAoDaEuvZ6WmXtWwCF\nTUXYnHZ8Fde2zmHRvm6OSPQFvU7NnTOTsDucvPmxq0dInG8skyKuory1kp1Fu90dosey2q28fnwN\nnxZ9QagxmMfGPkKMz9mhi06nk81fnOLtHbmYTTp+tTSdpBj/fokt1N/IrAkx1Dd38MG+0wBMjrya\nIT4xHKw6yvHa7H6JYyCTBMYDdO1E0tkDAOkHI1y66l/sTa4P1KTo/vlgFf1vdEIQY5OCyStpZPdR\n1/t/TvyNmHXefHh6OzVttW6O0PO0Wi08d/TvHKw6SpzvEH4x9mGCvAK6r3c4nKz6OJv39pwmxM+L\nJ5aPJSa0fwcN3zJxCP5m19m3qnqLqzdM0jxUiop12ZvptHf2azwDjSQwHqBrJ5K12ZXIlMgykuBs\nAlNV4oVWo2JIuExxH8yWTh+GQadmw2f5NLZ2YtQauSPhVqwOG2ulN8xFqW2r568HnyevoYAxwSn8\ndPT9eGvPtv632hy88O4xdh0pIybEm18vSyfEz+s8R+wbep2aRTckYLM7WftpHgBR5ghuiJ5MbXsd\nHxbs6PeYBhJJYDxAWKARnUZFQ5WrPbXMRBJ2h51TjacJ9QqhvNJKfIRPr/ShEAOXv1nPvClxWDps\nrNvp6g0zNnQ0IwKGcbIuh0NVR90coWcobi7lfw4+R4WlihuiJ/Oj5DvRqs92z23rsPHshqMczK4m\nKdqPXy5Nx9f74kYD9Kbxw0MYHuPHkbwaMvJdZ9puGjqDQIM/nxZ/QWlLudticzf5xPMAapWK6BBv\nKisU9Go9JTIT6YpX1FxKp8NKgCoCJzI+4EpxQ3oUQ8LM7DteyfGCum/1htGwIXcrFmubu0Mc0E7U\nZvPMoRdo7mzhjsTbmJ94Kyrl7NdgU2snf377MCcL6xmTGMQvFqVhNGjOc8S+pygKS2cMQ6UorNmR\ng9XmQK/WsSjpdhxOB29nbbxie8NIAuMhYsLM2B0QpAuh0lItczGucF3zj2h1rdlLAnNlUKkU7p49\nHEWBVR9n02m1E2wMZPaQ6TR3trD11DZ3hzhg7Sn7hhcyXsPhdHBf8jKmRk/qcX11Qxt/Wn2Qwspm\npqSF8/DtyWg1lzcaoLdEBXtzQ3oklfVtbD9QDMCowOGMDUnjdFMRX5buc3OE7iEJjIfoqoPxcgbg\ncDoob5WW0leyrvqX2jIjapVCfITsQLpSxIaZmTEumqqGNt7fWwjA9JgphJlC+bJ0H6caC90c4cDi\ndDr54NQnvJW1AS+1gZ+MfoAxISk9blNS1cKfVh+kqr6NmyfGcvfs4ahVA+vrce7koZiNWt776jT1\nzR0AzE+8DS+NgXfzt12RY2YG1iskvldXAuNo8QFkpMCVzOF0kN9Y4BrwWWZnSJgZvW5g/FIU/WPu\n5KEE+Oj5aF8hpTWtaFQaliTNw4mTNVkbsTvs7g5xQLA77KzO2sCHp3cQaAjg0bEPE+83pMdtcoob\n+O+3DtHY0sniaYnMvy5+QM4TMxq0zL8ung6rnQ2fuQp6ffVm5sTfRLu9nXdyrrzeMJLAeIiIIBNq\nlUJTrQGQkQJXsrKWCtps7QRrIrE7nLJ8dAUy6DTcOWMYdoeTVduycDidJPgN5ZrwCZS1VrCzWHrD\ntNnaeSHjNfaVHyDGHMVj41YQagrpcZsjuTU8ve4IHVY79986kpnjo90U7Q8zKTWcoeFm9p2oJKe4\nAYBrIyYQ5xvL4epMMmtOuDnC/iUJjIfQalREBpuoKFOjVtTSC+YK1rV8pLIEAlL/cqUakxjMmMQg\nckoa+SrDtRNlbsJNeGtNfFCwndq2OjdH6D4NHY08e+hFTtblkBw4nJ+lP4SPrmebga8yy3luUyYK\n8JP5qUwcFeaeYC+C6kxBL8Bb23NwOJxnesPMP9MbZgvttg43R9l/JIHxILGhZqxWCNQHUdpSfsVW\nnl/pugp4Gyq8UYDEKKl/uVLdOWMYep2a9Z/l0WTpxKQ1Mj/xVqwOK+tytlyRvWHKWir4nwMrKWkp\nY1LEVTyQcjd6ta7HbbZ9XcSrH5zES6/msSVjSI0PdFO0Fy8+wpdJKeEUV7Ww64irlCDCO4wZMddT\n39HABwWfuDnC/iMJjAfpmkzt7QzE6rBSZal2c0SivzmdTvIaCvDV+VBYbCcqxBujQXvhO4pBKcDH\nwLzJcbS221i/01UXMT50DMP9Ezlem8W+kkNujrB/5dTn89dDz1Pf0cBtcbNZnDQPtepsfZjT6WT9\nZ3ms/ywPf7Oex+9MJyHS834AzL8+Hi+9ms1fnKLZ4urGO3vINIK8Avms+MsrpkZSEhgP0t3Gus31\nhpOGdleeKks1zdYWwvRR2OxS/yJg2tgoYkPN7DlWwcnTZ3rDJM1Fo9LwzJ6/89uv/sTfDr/Chpx3\n2V26l9z6fJo6mwfd2ZkDlUdYeeTvdNqt3D1yMbOG3NCjGNfucPCPD0+y7esiwgKMPLFsLJHBnjm9\n3dekY84kV+K6+QvXGVmdWttdyH2l9IZxb4cecVGig71RFGip84IgKG4pZTxj3B2W6Edd9S+a9iAA\nkiSBueKpVAp335jEH984wJsfZ/Of900gxBjMj0YtZW/VfoobysmqzyWrPrfH/YwaL8JMIYQaQwgz\nhRB25s8Ag3+P5m4DndPpZEfR52zJ/xCD2sD9KcsZHpDY4za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A/KF2sfrjtdFp1XjpNRzK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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", + " #\n", + " # YOUR CODE HERE: bucketize the following columns, following the example above:\n", + " #\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\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", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0FfUytOTNJhL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "c12a6d9e-86fd-47c9-b3b8-3b4a44f88ffb" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 169.41\n", + " period 01 : 143.13\n", + " period 02 : 126.63\n", + " period 03 : 115.35\n", + " period 04 : 107.33\n", + " period 05 : 101.44\n", + " period 06 : 96.94\n", + " period 07 : 93.40\n", + " period 08 : 90.44\n", + " period 09 : 87.99\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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R1OSGk1Wew5asHWZHEhERcUoqME5m+qgYajI6gsOFz499SZW9yuxIIiIiTkcF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\nPB4PP/nJT0ilUnzqU59i06ZN/POf/8Rut7N3715GR0e57rr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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": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "3c207c6e-e26c-47b7-fd01-843c6b857127" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 169.35\n", + " period 01 : 142.99\n", + " period 02 : 126.41\n", + " period 03 : 115.21\n", + " period 04 : 107.17\n", + " period 05 : 101.37\n", + " period 06 : 96.89\n", + " period 07 : 93.34\n", + " period 08 : 90.41\n", + " period 09 : 88.09\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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DUoFxIDarlRuGn7bEwKFlGIZhcioRERHHowLjYHp18ifKO4raIn+Si1LYX5Bs\ndiQRERGHowLjYCwWC9NHfD8LY2ihRxERkXNRgXFAMeHe9AqPoiY/lIzSLLZn7zI7koiIiENRgXFQ\nU4dHU5sRA4aVpYdXUq2FHkVEROqpwDioUH93hnaNpvp4BwoqC9mQ8Y3ZkURERByGCowDu2ZoFNac\nGKi1syJ1NRU1FWZHEhERcQgqMA7Mx7MdY/tEU50ZSVlNOauOfmV2JBEREYegAuPgJg6IwLno1EKP\nq9M2UHyyxOxIIiIiplOBcXBuLnauHhRNVXoM1XXVLEv90uxIIiIiplOBaQFG9gnH+2Q0RoU7mzK3\nkV2WY3YkERERU6nAtABOditTr/x+oUfqWHJ4hdmRRERETKUC00IMiA0i1DmKuhPt2ZW7l9Tio2ZH\nEhERMY0KTAthPX2JAeATLfQoIiJtmApMCxIb6UsXv2hqCwNIKU5lX/4BsyOJiIiYQgWmBbFYLEwb\nEU11+g8LPS7XQo8iItImqcC0MJEhXiR0jKYmL4yssuNsOZ5odiQREZFmpwLTAl13ZRR1mZ2gzsp/\nD6+kqrba7EgiIiLNSgWmBQrycWNEbAzVxyMoOlnM+oxNZkcSERFpViowLdRVQzpiy+8ENU6sOLKG\n8upysyOJiIg0GxWYFsrL3ZmJfaOpzoyioqaCL46uMzuSiIhIs1GBacHG9b8C19IYjCoX1qZtpLCy\nyOxIIiIizUIFpgVzcbZz7eBoqtNjqDFq+FwLPYqISBuhAtPCDYsPxbc2hrpyDzZnbSez9LjZkURE\nRJqcCkwLZ7dZmTb81BIDBoYWehQRkTZBBaYV6NclgA5u0dSW+LAn71sOFaWaHUlERKRJqcC0Apb6\nhR67APDpoc+10KOIiLRqKjCtRNcIH+KCo6ktCCK15Bi78/aZHUlERKTJqMC0IlOHR1OT3gkMC5+l\nLKe2rtbsSCIiIk1CBaYVuSLQg0ExMdTkhpNdnsvm49vNjiQiItIkVGBamWuHRWFkdYI6G58f/pKq\n2iqzI4mIiFx2KjCtjJ+3C6Pjo6nOiqC4qoQPD36iA3pFRKTVUYFphSYP6ohTfhcob8+W4ztYeXSN\n2ZFEREQuKxWYVsjD1YkpA6OoONgbe607Sw+vZPvxnWbHEhERuWxUYFqpsQlXEBseQum3vbAZTizY\n/zEpRUfMjiUiInJZqMC0UnablXuu6UGoRzDlB+OpNQze3DOfnPI8s6OJiIhcMhWYVszNxc5vpsXj\nWRtKVWo3yqrLeX33O5RVl5sdTURE5JKowLRyft4uPHBDT6xFEdRlR5FTnsdbe96juq7G7GgiIiIX\nTQWmDegY7MUvr4ql6mgnLCVeYJcEAAAgAElEQVQhHCpK5V8HFun0ahERabFUYNqI3p0DuHF0Z8oP\n9sBe6cvW44msOLLa7FgiIiIXRQWmDRnbL5xRvTtw4tt47LXu/Df1C7YeTzQ7loiIyAVr0gKTnJzM\nmDFj+OCDD864fsOGDXTp0qX+8pIlS5g6dSo33HADCxcubMpIbZrFYuFnYzrRMyKU0n29sRnO/HP/\nQg4VpZodTURE5II0WYEpLy/nmWeeYdCgQWdcf/LkSd566y0CAgLq7zd37lzmz5/PggULeO+99ygq\nKmqqWG2ezWrll1fHEu71v9Or39r9HjnluWZHExERabQmKzDOzs68/fbbBAYGnnH9G2+8wc0334yz\nszMASUlJxMXF4enpiYuLC3369CExUbs1mpJrOzsPTOuJV10IVYe7U1ZTzutJ71JaXWZ2NBERkUax\nN9mG7Xbs9jM3n5qayoEDB3jggQd48cUXAcjLy8PX17f+Pr6+vuTmNjwb4OPjht1uu/yhvxcQ4Nlk\n23YUAQGePH33YB59rZa64xXkBKcw/8A/eXz4/TjZnMyOd15tYWxaIo2L49LYOC6NzaVpsgJzLs89\n9xyPP/54g/dpzKm9hYVN90VsAQGe5OaeaLLtOxJP51O7k+b8pwZX1wr2c4iXN7zLzO43YbFYzI53\nlrY0Ni2JxsVxaWwcl8amcRoqec12FlJ2djaHDx/mt7/9LdOnTycnJ4dbb72VwMBA8vL+9/X2OTk5\nZ+12kqYTH+PPzWO6UJ4ci63Sl23ZO1mW+qXZsURERBrUbDMwQUFBrFq1qv7yqFGj+OCDD6isrOTx\nxx+npKQEm81GYmIi/+///b/miiXA6L7h5BRW8OWuWjx7bmHZkVUEuPnTP7iP2dFERETOqckKzN69\ne5k9ezYZGRnY7XZWrlzJq6++Svv27c+4n4uLCw8//DB33nknFouF++67D09P7RdsbjeOiiG3qIKk\nb3vjHreVD/YvxKddezr5RJkdTURE5CwWowV+n3xT7jdsy/slT1bV8vw/E0krP4JLtx242tvx2773\nEeTuGLv02vLYODKNi+PS2DgujU3jOMQxMOL42jnbeOCGnrS3hHLycHfKayqYt/tdSqt0erWIiDgW\nFRg5Q3uPdvxmWjxOJRHUZkWTV5HPm3veo7q22uxoIiIi9VRg5CzhgR7ce20PatI7YSkK5XDxET44\nsFCrV4uIiMNQgZFz6hHlx63ju1D+XSy2Cl+2Z+/i89QvzI4lIiICqMBIA0b0CmNC/0hK9/fCXuPO\n8iOr2Zy13exYIiIiKjDSsGkjoukbHUbpt6dWr/7Xgf+QXJhidiwREWnjVGCkQVaLhV9M6U5Hn1DK\nD8RTZxi8ted9jpflmB1NRETaMBUY+UntnGzcP60nvtYwTqbEUlFTwetJ73CiqtTsaCIi0kapwEij\neLs788AN8TiXRlCbGU1eZQFv7tbp1SIiYg4VGGm0MH937ruuB7WZnaAwlNSSoyzY/zF1Rp3Z0URE\npI1RgZEL0r2jL7eN70rFoVhsFX7syEni88M6vVpERJqXCoxcsGHxoUweGEXp/nhsNR6sOLqGbzK3\nmR1LRETaEBUYuSjXXRlF/07hlH3bG5vRjn8d/A8HCw6ZHUtERNoIFRi5KFaLhTsndyPa79Tp1YYB\nb+99n+Nl2WZHExGRNkAFRi6ak93GrKlx+NlCOZnSg4qaSuYlvavTq0VEpMmpwMgl8XJz5jc3xONS\n1oHazBjyKwt4c/d8qnR6tYiINCEVGLlkIX7uzLo+jtrMGCgII7XkGO/v/0inV4uISJNRgZHLoksH\nH+6Y1I2KlFis5X7szNnN0sMrzY4lIiKtlAqMXDaDe4Rw9eAoyg7EY6v24Iuja9mUudXsWCIi0gqp\nwMhldc3QSAZ1ueLU6dV1zvz74GIOFHxndiwREWllLrrAHDly5DLGkNbCYrFw+8RudAoIpfxAr1On\nV+9ZQGbpcbOjiYhIK9JggbnjjjvOuDxv3rz6vz/55JNNk0haPCe7lVlTexLgFMbJlB5U1lby+u53\nKak6YXY0ERFpJRosMDU1NWdc3rx5c/3fDcNomkTSKni4OvGb6fG4lkdQkxFDQWUhb+yeT1VtldnR\nRESkFWiwwFgsljMun15afnybyI8F+bjx66lxGMdPnV59tCSN977V6dUiInLpLugYGJUWuVCdwtvz\n88nd60+v3pW7hyUpK8yOJSIiLZy9oRuLi4v55ptv6i+XlJSwefNmDMOgpKSkycNJ6zCwezC5RZV8\n8nUNHj238uWxdQS4+jEkbIDZ0UREpIVqsMB4eXmdceCup6cnc+fOrf+7SGNNGRRBTmE5m76twT1u\nCx8e/ARfVx+6+XY2O5qIiLRADRaYBQsWNFcOaeUsFgszJ3Qlv7iS5AO9cOm+jb/v+YCH+95LqEew\n2fFERKSFafAYmNLSUubPn19/+cMPP+Saa67h/vvvJy8vr6mzSStjt1m57/o4gtqFcfLQqdOr5yW9\nQ/FJnV4tIiIXpsEC8+STT5Kfnw9AamoqL730Eo8++iiDBw/mz3/+c7MElNbF3cWJ39wQj1tlBNXp\nnSg8WfT96tU6vVpERBqvwQKTlpbGww8/DMDKlSuZMGECgwcP5qabbtIMjFy0gPau3D+1J5acGIyC\ncI6eSGP+tx/q9GoREWm0BguMm5tb/d+3bt3KwIED6y/rlGq5FNFh3tw1JZbKlO5Yy/xJyt3LpynL\nzI4lIiItRIMFpra2lvz8fI4dO8bOnTsZMmQIAGVlZVRUVDRLQGm9+nUN5Ibhnb5fvdqT1cfWsyHj\nm59+oIiItHkNnoV01113MWnSJCorK5k1axbe3t5UVlZy8803M3369ObKKK3YhAEdyC6sYMO31bjH\nbeHjg5/h5+JLd78uZkcTEREHZjF+YlGj6upqTp48iYeHR/11GzduZOjQoU0e7nxyc5vurJWAAM8m\n3b6craa2jlcWJrE/7zAu3bbTzm7nob73EuYRcsb9NDaOSePiuDQ2jktj0zgBAef/zrkGdyFlZmaS\nm5tLSUkJmZmZ9X+ioqLIzMy87EGlbbLbrNxzbRwhruHfr159kteT3qX4pL7tWUREzq3BXUijRo0i\nMjKSgIAA4OzFHN9///2mTSdthpuLnd9Mi+dP71dTll5OYfh3vLH7XX7T5x7a2ZzNjiciIg6mwQIz\ne/ZsPvvsM8rKypg8eTJTpkzB19e3ubJJG+Pn7cL903oy+5/V1LlWcIx05u/7N3fFzcBquaB1R0VE\npJVr8LfCNddcwzvvvMPLL79MaWkpt9xyC7/4xS9YunQplZWVzZVR2pDIEC/uvroHVYe7YynzZ3fe\nPj459LnZsURExME06r+1ISEh3HvvvSxfvpzx48fzpz/9ydSDeKV169M5gBtHdqb8QDy2Kk/WpG1g\nffoms2OJiIgDaXAX0g9KSkpYsmQJixcvpra2ll/+8pdMmTKlqbNJGzY24QqyiypYt7cat55b+Dj5\nMzoEBtPROcrsaCIi4gAaLDAbN27kP//5D3v37mXcuHE8//zzdO7cubmySRtmsVi4eUwn8ooq2bu/\nCtfu2/jLxjeZEjWecREjdEyMiEgb1+D3wHTt2pWOHTsSHx+P1Xr2L4znnnuuScOdj74Hpu2oOFnD\n8/9MJL0snfY99lJplBLvH8uM7jfiancxO56gz4wj09g4Lo1N4zT0PTANzsD8cJp0YWEhPj4+Z9yW\nnp5+GaKJNMy1nZ0HpvXkuQ+qyd/hil/ctyTl7SNr+xzujptJiHuQ2RFFRMQEDc7DW61WHn74YZ54\n4gmefPJJgoKC6N+/P8nJybz88svNlVHaOF8vF56YmUBcRBj5O+NxLuxETnkeL2x/lcSc3WbHExER\nEzQ4A/O3v/2N+fPnEx0dzerVq3nyySepq6vD29ubhQsXNldGEbzcnXnml4N4Y1ESK7ZaaRfgQV3U\nXv6x9wOOdLiSa6ImYrPazI4pIiLN5CdnYKKjowEYPXo0GRkZ3Hbbbbz22msEBWnqXpqXzWZl+qgY\n7rm2BxSFUrq7P654s/rYel7b9XdOVJWaHVFERJpJgwXGYrGccTkkJISxY8c2aSCRn5LQNZDHZ/Yj\n0DWIgh39cK0MI7kohdnb5nC0JM3seCIi0gwu6FzUHxcaEbOE+bvzxG396B0VQsHuHthzulJ4soiX\ndszj64wtZscTEZEm1uAxMDt37mTEiBH1l/Pz8xkxYgSGYWCxWFi3bl0TxxM5PzcXO/ddH8fyzUdZ\nvN6CvdgDp857+NfB/3CkJI3pna/ByeZkdkwREWkCDRaYFStWNFcOkYtitViYPKgjHYO9eHPJPk7s\ndMOn5142ZW0lozSLu+Jm4OPS3uyYIiJymTX4RXaOSl9k1zb91NjkFVcwd/FejuYU4dMtmUqPo3g4\nufPz2Fvo4hvTjEnbFn1mHJfGxnFpbBqnoS+y0/exS6vh7+3KY7f2YWiPcAq/7YolI47y6gpe3fU2\nq459RQvs6iIich4qMNKqODvZuGNSV24b35WTWeFU7E/AGVc+OfQ5/9j3TyprKs2OKCIil4EKjLQ6\nFouFEb3D+N2tffAmmKKdA3CtCWRnzm5e3P4a2WU5ZkcUEZFLpAIjrVZ0qDdP3Z5A15AgCnb2wrko\nmuPlObyw/VWScveaHU9ERC6BCoy0al7uzjx8Uy/GJ0RQnNyJuiO9qK6r5a0977MkZQV1Rp3ZEUVE\n5CKowEirZ7NauXFUJ351TSwUhlG2ewAueLLy6BrmJb1DaXWZ2RFFROQCqcBIm9G/WxCP39aXQNcg\nCnf0x7UyhP0FybywbQ7HTqSbHU9ERC6ACoy0KWEBHqctQdATe24X8isLeWnHPDZnbTc7noiINJIK\njLQ5PyxBMHV4NKWpkdQc6guGlQX7P+bDg59QU1djdkQREfkJKjDSJv2wBMGDN8bjXB7CiV0DcK3z\nYUPGN7yc+CZFJ4vNjigiIg1QgZE2rUekH0/dnkCH9sEUJPbFpewKUkuO8vy2V/iu8LDZ8URE5Dya\ntMAkJyczZswYPvjgAwCysrK4/fbbufXWW7n99tvJzc0FYMmSJUydOpUbbriBhQsXNmUkkbP4t/9+\nCYLYKyjc1x1LZiylVWXM2fUWa9M2agkCEREH1GQFpry8nGeeeYZBgwbVX/fyyy8zffp0PvjgA8aO\nHcu7775LeXk5c+fOZf78+SxYsID33nuPoqKipoolck5nLEGQ2YHK/QnYjXYs+m4J87/9Nydrq8yO\nKCIip2myAuPs7Mzbb79NYGBg/XVPPfUU48ePB8DHx4eioiKSkpKIi4vD09MTFxcX+vTpQ2JiYlPF\nEjmv+iUIbumDNyEU7xyAa40/27N38Zftr5FTnmd2RBER+Z69yTZst2O3n7l5Nzc3AGpra/nXv/7F\nfffdR15eHr6+vvX38fX1rd+1dD4+Pm7Y7bbLH/p7DS3fLeZqjrEJCPCkS7Q/Ly7YwZ6dzrTvkkIm\nKby441XuH3gHfULjmjxDS6PPjOPS2Dgujc2labICcz61tbU88sgjDBw4kEGDBrF06dIzbm/M8QaF\nheVNFY+AAE9yc0802fbl4jX32Nw/tQeL1qWwcquVdkEenIzYx/Mb5jEpciwTO47GatEx8KDPjCPT\n2DgujU3jNFTymv1f4Mcee4yIiAhmzZoFQGBgIHl5/5uaz8nJOWO3k4hZzliCoCCcsr39ccGTZalf\n8ubu+ZRXN12RFhGRhjVrgVmyZAlOTk7cf//99dfFx8ezZ88eSkpKKCsrIzExkX79+jVnLJEG1S9B\n0C6IwsQEXE4Gszf/ALO3v0pGaZbZ8URE2iSL0UTniO7du5fZs2eTkZGB3W4nKCiI/Px82rVrh4eH\nBwDR0dH84Q9/YMWKFfzjH//AYrFw6623cvXVVze47aacdtO0nuMye2zKK2v4x+ffsvO7XDyjDlPj\n/x1OVidu6TqNhODepuUym9njIuensXFcGpvGaWgXUpMVmKakAtM2OcLY1BkGy745yifrD2P3zcEl\nZi81VDEyfCjXxUzGZm26g8sdlSOMi5ybxsZxaWwax6GOgRFpyawWC1MG/7AEQSilu/vjUteetekb\neWXnWxSf1D9IIiLNQQVG5CLUL0HgHUJhYj/alYeTUpzK7G2vcLj4qNnxRERaPRUYkYv0wxIEQ2LD\nKdobiyWrOyVVJ3g58Q3Wp2/SEgQiIk1IBUbkEjg72fj5pG7MGN+VkxkRnDzQD5vhxEfJn7Jg/8dU\n1VabHVFEpFVSgRG5RBaLhZHfL0HgZYRSsmsALjV+bDm+g5d2zCWvosDsiCIirY4KjMhlEh3mzZO3\nJ9A5KITCnX1wLulIWmkmL2ybw/78ZLPjiYi0KiowIpeRt7szv/1ZL8b160jxga7UHYujouYkc5P+\nwYoja6gz6syOKCLSKqjAiFxmNquVm0Z/vwRBfgfK9yXgjBtLD6/g7T0LqKipMDuiiEiLpwIj0kTq\nlyBwDqEocQAuJ4PYnbePF7a/SmbpcbPjiYi0aCowIk0oLMCDJ2Ym0DsylMKkeOz5MeSU5zF7+xw+\nS1lORU2l2RFFRFokFRiRJubmYue+6+O4/soYSlNiqEnpjZPhwhdH1/L0Ny+wMWOzjo0REblAKjAi\nzaB+CYLp8TiXhVGwbRDuRbFU1pzk3wcX89zWl9lfoDOVREQaSwVGpBn1iPLj6Z/3Z2DXUPKSr6Ak\ncQjtq6LJLDvOa7v+zrykdzhelm12TBERh2c3O4BIW+Pr5cLdV8cyul84H60+xKFdLtg9ggnonsq+\n/APsL0hmaOhAJkeOxcPZ3ey4IiIOSTMwIiaJDvXmsVv78KtrYmlvCyBraxzWIwm4WbxYn7GJP2ye\nzapjX1FdV2N2VBERh6MZGBETWSwW+ncLoncnf1ZtT2fppiPkbvHBN/I4dYHJfHLoczZkbOa66EnE\nB/TAYrGYHVlExCGowIg4ACe7jYkDIxgSF8KnG1P5apcV46g/Qd3SKeAQb+9dQEz7SKbGXEUHr3Cz\n44qImE67kEQciJe7M7eN78Iff96fHh2Cyd4bRcXuIbSv7cCholRmb5/D+99+RNHJYrOjioiYSjMw\nIg4oLMCDh27sxZ7D+Xy05hCZO9xp5xuCd6dDbDm+g8Sc3YztMJwxESNoZ3M2O66ISLNTgRFxYHFR\nfnTv6MP6pCw+3XCYnC3t8b4iG1vYdyw7soqvM7dydfQE+gf3wWrRhKqItB0qMCIOzma1MrJ3GAO6\nBfH5N0f4cruVmgx/AjpnUuqdzIL9H7Mu/Wumxkyhk0+02XFFRJqFCoxIC+HmYueGkTGM6B3GonUp\nbDtgx+IcQHBsGmknDvPyzjeJD+jBtdGTCHTzNzuuiEiTUoERaWEC2rtyz7U9GJNexIerD5G60xUn\nz2D8uh0mKXcve/P2Mzx8MBM7jsbNyc3suCIiTUI7zUVaqE7h7fn9bX25+6rueFoCOL41HuuxvrSz\nuLMmbQN/+OYF1qV9TW1drdlRRUQuO83AiLRgVouFgbHB9OkcwMptaSz7xk5+ti++UVlU+yez8LvP\nWJ+xietiJtPDr5u+CE9EWg0VGJFWwNnJxlWDO3JlzxA+2XCYDUk2jKMBBHVLJ4dDvLF7Pl19OnF9\npymEeYSYHVdE5JJpF5JIK+Lt0Y7bJ3bjqTsS6BYWTPaeaE7uGYJ3XTgHCr/jua0v88/9iyg+ecLs\nqCIil0QzMCKtUIcgT357Uy+SUvL5eM0hjm/3wNU/BPfoQ2zK2sqOnF2MixjFqCuG4WxzMjuuiMgF\nU4ERaaUsFgu9YvzpEenLup0ZfLbRTu4WH7w7ZGMJTWbp4RVszNjMtdET6RvUS8fHiEiLogIj0srZ\nbVbG9LuCQT2CWfr1EVbvsFGb4U9A5wxKLN/x7rf/Zm3610ztdBVR3hFmxxURaRQVGJE2wt3FiZtG\nd2JknzAWrU1hx34nLM6BBPU4xpGSI/x1x1z6BsZzTfRE/Fx9zY4rItIgFRiRNibIx437ro/j4LFC\nPlx9iKOJbjh5B+PbJYUdOUkk5e1j1BXDGBcxEle7i9lxRUTOSWchibRRXTr48MTt/bhzcjfcawPJ\n3toHW3ofnHDhi6Nr+cM3s9mQsVlfhCciDkkzMCJtmNViYUhcCP26BLJi6zGWb7FRetwPv+hMKn2T\n+fDgYtanb+L6TlPo5tvZ7LgiIvVUYESEds42rhkayZXxoSxen8KmPTYMpwCCu6WRSQqv7fo7sX5d\nuT5mMsHuQWbHFRFRgRGR//HxbMedk7szpu8VfLj6Ow7udsHuHoxf98Psyz/A/oJkhoYOZHLkWDyc\n3c2OKyJtmAqMiJwlItiTR27uzc7v8vh47SGyt3ngGhCOa9R3rM/YxLbsRCZ0HM3w8CFmRxWRNkoF\nRkTOyWKx0KdzAD2j/VizI50lXx8hb6sP7SOOUxuUzCeHPmdD+jfc1mcqke2isVp0ToCINB8VGBFp\nkN1mZVz/DgyOC2HJxlTW7rRRm+ZPYJd0CjjES5vext/Vj6GhAxgUkqBdSyLSLCyGYRhmh7hQublN\ntxBdQIBnk25fLp7GxjFk5ZexcG0Kuw7lYXUpIzwum0LrYWqMGuwWG70DezIsbBBR3hFansBk+sw4\nLo1N4wQEeJ73NhWYH9EPlePS2DiWb48U8OHqQ6TnloKtmpCYAup8j1JSWwBAqHsww8IGkhDcR1+I\nZxJ9ZhyXxqZxVGAugH6oHJfGxvHU1RmkZJey5KtD7DtSCBi4+5fgF5VNPkeoow5nmzMJQb0ZFjaQ\nKzzDzI7cpugz47g0No3TUIHRMTAictGsVguDe4bSKcST7IJyvtqVycY9WRzb6g1OEYR2LqDaK5Wv\nM7fwdeYWOnp1YGjYQPoGxuNsczI7voi0YJqB+RG1YselsXFMPx6X6ppath/MZe3ODA6lFwMGXiFF\neHc4Tr5xDAMDV7srA0P6Mix0IEHugeaFb+X0mXFcGpvG0QyMiDQbJ7uNQbHBDIoNJj2nlHW7Mti0\n105alg/WdpGEds6j3D2VtWkbWZu2kc7toxkWPoie/t2xW/VPkog0jv61EJEmEx7owa3jujBtRDRb\nvs1m7c4Mju1xAUsoPmGFuIdnklyUQnJRCp7OHgwJ6c/g0AH4ufqYHV1EHJwKjIg0ORdnO8N7hXFl\nfChHjp9gbWIGW/fbKUz3w+4WRUjnPE5YU1lxdA0rj64l1q8rw8IG0t2vi74gT0TOSQVGRJqNxWIh\nMsSLyMle3Dg6hk17j7NuZwZpu9zBEo5/RAHOIenszd/P3vz9+Lr4MCR0AINDE/ByPv++cBFpe3QQ\n74/owCrHpbFxTJc6LoZhkJxWxNqdGew4mEttnYGz1wmCYnIpdkql2qjGarHSK6AHw8IG0al9lL4g\nr5H0mXFcGpvG0UG8IuKwLBYLXTr40KWDD8VlVWzcnclXuzJJS/QE2xUERhZgDThGYs5uEnN2E+QW\nyLCwgQwI7oObk5vZ8UXEJJqB+RG1YselsXFMTTEudYbBvtQC1iZmkJSSh2EYuPqU4B+dQ6HtCLVG\nLU5WJ/oGxTMsbCARnldoVuYc9JlxXBqbxtEMjIi0KFaLhbgoP+Ki/CgoqWR9UiZfJWWStt0b7BEE\nR+dT53uEzVnb2Zy1nSs8wxgWOpB+wb1pZ3M2O76INAPNwPyIWrHj0tg4puYal5raOpIO5bFuZ8b/\nli0IKMI3Mpt8jmJg4GJzoX9wH4aFDSTUI7jJMzk6fWYcl8amcTQDIyItnt1mpW+XQPp2CSS7sJyv\ndmaycY8zaVt9wKkjIZ3zqPI8wvqMTazP2ES0d0eGhg2kd2BPnPQFeSKtjmZgfkSt2HFpbByTmeNy\n9rIFdXiFFOHVIYt8Iw0ADyd3Bob0Y2joQALc/EzJaRZ9ZhyXxqZxNAMjIq3SuZctcCI9yxerSyQh\nnfMot6ay6thXrDr2Fd18OzMsbCA9/Lphs9rMji8il0AFRkRahXMuW7DbDSxhtA8vwD0sk/0Fyewv\nSKZ9O28Gh/ZnSGh/2rfzNju6iFwE7UL6EU3rOS6NjWNy1HExDOO0ZQuyqaqpw+5eSnCnXE64pFJV\nV4XVYiXOvzvDQgfSxTem1S1b4KhjIxqbxtIuJBFpc863bEH6Lg+wXoFfRAFOQWkk5e4lKXcv/q5+\nDA0dQP/gvni307IFIo5OMzA/olbsuDQ2jqkljcvZyxbU4ex9gsDoHIqdjlBj1GDBQkevDsQHxNIz\nIJYgtwCzY1+0ljQ2bY3GpnE0AyMiwpnLFpSUVbHh+2UL0hO9wBZBYGQ+zgG5HCk5RmrJUT5NWUaw\nexDx/rHEB8TSwTNc3/gr4iA0A/MjasWOS2PjmFr6uJy9bAFgryIw4gSuAbkUGOnUGDUAtG/nTc/v\ny0yn9lEOfyZTSx+b1kxj0ziagREROY/Tly0oPHGSXd/lkvhdHgdS21Gb4gfWGHxCT+AdWkBRdVr9\nF+W52l3p4deN+IBYuvl2xsXezuyXItKmNGmBSU5O5t577+X222/n1ltvJSsri0ceeYTa2loCAgJ4\n8cUXcXZ2ZsmSJbz33ntYrVamT5/ODTfc0JSxRETOycezHSP7hDOyTzjlldXsTsknMTmXPYfbUZju\nA5ZIPAJO4H9FMWW2NLZlJ7ItOxG71U5Xn07EB/Qgzr8bns4eZr8UkVavyQpMeXk5zzzzDIMGDaq/\nbs6cOdx8881MnDiRl156iUWLFnHttdcyd+5cFi1ahJOTE9OmTWPs2LG0b9++qaKJiPwkNxcnBsYG\nMzA2mOqaWvYdKWRnci67DuVxZIc3cAUu3mUEdiym2j2Lvfn72Zu/HwsWorw7Eh9waleTv2vb+vZf\nkebSZAXG2dmZt99+m7fffrv+ui1btvD0008DMHLkSN555x0iIyOJi4vD0/PUfq4+/7+9e41t86z7\nOP71KY5P8SGxnXPWJGOl6drSrfCsWwHBGBJIq9iAltLAKyQ08QI0EFXZKAgE6gQSgk0DxJCmIrTC\nxvFhdAOxoj6i3YGUroXhC1kAABRRSURBVM2a9LCkSRMnsRM7TnxKE/t5Ydck29hS1sZ28/tIk9p7\nt+/+L13O+tt1X4fNm+np6eEDH/jAtSpNROSKWMwmNnXWsamzjmw2x7mRaXrOhOk5E2bohBNowmxP\nUr9mFoN7jFenBzk/PcBvzv0vTc6G4ryZZmejJgGLXCXXLMCYzWbM5qWPT6VSVFXlj7qvra0lHA4T\niUTw+XzFe3w+H+Fw+E2f7fXaMZuv3eS5N5s0JKWlvilPq61fgsEabt/ckt8sLxTn2MkQx06N8Wrv\nNBDAYLmJpo4E1roIY4kLjMyG+PPgX/HbfWxp2siW5k2sretYkUnAq61vKon65u0p2STe/7T4aTmL\noqLR5NUup0gzw8uX+qY8rfZ+cVqM3Lm5iTs3NxGOpTh+NkLPmTBn+2Pk+nxgbMffMoOjfpJYepin\nzz7H02efw2Gxc3PtOjb4u3in70aqTFVXvbbV3jflTH2zPGWzCslut5NOp6murmZ8fJxAIEAgECAS\niRTvmZiYYNOmTStZlojIVeH32LhrSwt3bWkhnpzjxNkIx89GODVQRfiCFwzteIIzeJtjzGSHOTb2\nEsfGXsJitLCu9iY21nWxvu6dOCz2UjdFpOytaIDZunUrzzzzDNu3b+fZZ59l27ZtbNy4kQceeIB4\nPI7JZKKnp4e9e/euZFkiIlddjb2KbRsb2baxkfTcPKdenaLnbJgT56oYGHMDrdi9swRuiJO2jRSP\nNDAajHS617DRv54N/nX4qr2lbopIWbpmG9mdOnWK/fv3MzIygtlsJhgM8r3vfY89e/aQyWRobGzk\nu9/9LhaLhUOHDvHYY49hMBjYvXs3d99995s+WxvZrU7qm/Kkfrky8wtZ+odj9JwJc/xMmNjsHABV\njiT1a2bI1oSYnB8r3t/iairsBLyeBkfwiiYBq2/Kl/pmed7sFZJ24n0NfanKl/qmPKlf/nvZXI7B\n0AzHz+ZXNIUm8/P7jFUZGtpnMHsnmMyOsJBbAKDOVsvGuvwZTe3utrc8PVt9U77UN8ujAHMF9KUq\nX+qb8qR+uXpCk4n8yMzZCK+OxvMXTZcIts1iD0SYYpi5bH7ExmVxcnPdOjb6u7jJ24nFZHnd89Q3\n5Ut9szwKMFdAX6rypb4pT+qXa6N4rMGZMH1DMRayOTBk8TXO4GmKMm0aJrmQAMBqqmJd7Vo21nXR\nVbsWu8UGqG/KmfpmecpmFZKIiCzPfz7WwMLUiBtow1WXwN82TcJ8keMTL3N84mWMBiM3eTvZUNfF\n+xy3Aq8fmRG5HmgE5jWUisuX+qY8qV9W1muPNZhJXgJy2GpSBNfEmXeGmLw0Xry/zlbLjZ72/D/e\ndq1qKhP6uVkejcCIiFwn3uxYg8ETdqAec3WahvZZbHUxwnMXORp6kaOhFwGorfYVw8yNnnZqbb43\n/wNFypRGYF5Dqbh8qW/Kk/qlPORyOYYnZos7AQ9PzF7+N7hq09Q1JTA4p4jmRkkvpIuf81V7F43Q\ndFBb7dV5TStAPzfLo0m8V0BfqvKlvilP6pfyFI6lGJ5M8lLvGH1D0eJ+M5DD6Uvhb0pidEWJ5kZJ\nLaSKn/NaPcXRmRs9HdTZfAo014B+bpZHr5BERFYZv8fGuhsDbO6oJZfLMRFL0T8Uo28oSv9QjIGT\ndqAO6MThSRNoSWJ0TRGdD/HCWA8vjPUA4LG6l7xy8tvqFGikLCjAiIhc5wwGA0GvnaDXzns3NpLL\n5QjHUvQNxegfitE/HGXgpA2oJR9oMvibk5hqpojNj/Li+HFeHD8OgLuqZtEITTsBu1+BRkpCAUZE\nZJUxGAwEvHYCiwJNZDpdHJ3pH4oyeKoa8AEd2N1pAs0pTO4pprMhXhr/Fy+N/wuAmirXohGaDoIK\nNLJCFGBERFY5g8GA32PD77GxbUMjAJHLIzTD+VAz2GvjcqCx1WQItiQxu6PEsiH+OXGCf06cAMBV\n5VwyKbjeHlCgkWtCAUZERF6nzmPjDo+NOzY0ABCZThVGZ/KhZrD38ghNOzZXhmBrCnNNlOlsiJ6J\nl+mZeBkAp8XBjZ52Or3tvMPTQb0j8JZnOIkshwKMiIi8pTq3jbqbbdx+cz7QTE6ni6Mz+RGaasAL\nrMHmyhBoSWFxR5nOhTgePsnx8EkAHBZ7cYXTjd52GhxBBRr5ryjAiIjIFat1V7PV3cDW9flAMxVP\n0z+cnz/TPxTjwiv/DjTVzgzBljQWT5R4LsS/wqf4V/gUAA6znU7PGm70dtDpaafJWa9AI8uiACMi\nIm+br6aa27rqua2rHsgfRtk/FKV/OEbfUIwLp5O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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "AFJ1qoZPlQcs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Feature Crosses\n", + "\n", + "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n", + "\n", + "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n", + "\n", + "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model." + ] + }, + { + "metadata": { + "id": "-Rk0c1oTYaVH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train the Model Using Feature Crosses\n", + "\n", + "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n", + "\n", + "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`." + ] + }, + { + "metadata": { + "id": "-eYiVEGeYhUi", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]),\n", + " 1000\n", + " )\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": 640 + }, + "outputId": "149574d1-9972-4bc4-9620-5c6409f77692" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 163.20\n", + " period 01 : 134.97\n", + " period 02 : 117.89\n", + " period 03 : 106.69\n", + " period 04 : 98.81\n", + " period 05 : 93.16\n", + " period 06 : 88.76\n", + " period 07 : 85.20\n", + " period 08 : 82.32\n", + " period 09 : 79.95\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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asyOJiIg4PRUYJ9CnYyBt3DriKG/BtpOJnCzLNjuSiIiIU1OBcQIWi4Wpw6Kp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\nxrJraLr6AwyPzANNYJnHXh7CXRMhaZ+kZ/oc3dO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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": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "af0e1a26-9c69-4261-8a10-960f9111a382" + }, + "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 : 163.23\n", + " period 01 : 134.85\n", + " period 02 : 117.75\n", + " period 03 : 106.45\n", + " period 04 : 98.48\n", + " period 05 : 92.69\n", + " period 06 : 88.35\n", + " period 07 : 85.01\n", + " period 08 : 82.06\n", + " period 09 : 79.82\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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SoqkxaliRuMbsSCIiIg5PBcYBDOsRindVBEaZF5tPbSejJNPsSCIiIg5NBcYB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\nYY5PnOD4xIn8+TAQctXRWrmGVk8zLd41+G2V2hw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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ymlHJ-vrhLZw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Try Out More Synthetic Features\n", + "\n", + "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?" + ] + } + ] +} \ No newline at end of file From 5a59863e74db84ff2d15e3136be49b10c2175b38 Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Mon, 11 Feb 2019 20:42:40 +0530 Subject: [PATCH 07/11] Logistic Regression Programming Exercise solved!!! --- logistic_regression.ipynb | 1731 +++++++++++++++++++++++++++++++++++++ 1 file changed, 1731 insertions(+) create mode 100644 logistic_regression.ipynb diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb new file mode 100644 index 0000000..4ca9fe6 --- /dev/null +++ b/logistic_regression.ipynb @@ -0,0 +1,1731 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "logistic_regression.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "dPpJUV862FYI", + "i2e3TlyL57Qs", + "wCugvl0JdWYL" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "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": [ + "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": 1224 + }, + "outputId": "39a8676e-6d22-469a-9ef4-12e1c018c707" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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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 28.5 2660.3 541.2 \n", + "std 2.1 2.0 12.5 2235.3 425.6 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1461.0 297.0 \n", + "50% 34.2 -118.5 28.0 2127.0 434.0 \n", + "75% 37.7 -118.0 37.0 3150.2 648.0 \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 1438.0 502.9 3.9 2.0 \n", + "std 1156.5 387.5 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 793.0 282.0 2.6 1.5 \n", + "50% 1170.0 410.0 3.5 1.9 \n", + "75% 1738.0 606.0 4.8 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Validation examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value_is_high\n", + "count 5000.0\n", + "mean 0.3\n", + "std 0.4\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 1.0\n", + "max 1.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "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": 768 + }, + "outputId": "5a7f2065-790c-4656-9bff-22286417e229" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\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 : 0.44\n", + " period 04 : 0.44\n", + " period 05 : 0.44\n", + " period 06 : 0.44\n", + " period 07 : 0.44\n", + " period 08 : 0.44\n", + " period 09 : 0.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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SB/M6bL3yO5ez44BbA1ynjG7Di0+2xMzUhNW7YpjzwzHiU4znNkBVVQndc4mN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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": "l17K_1E58k5s", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "18ff0916-934e-43cd-b693-18df48df732a" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "dPpJUV862FYI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to display the solution." + ] + }, + { + "metadata": { + "id": "kXFQ5uig2RoP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "251965b9-cd80-46f4-f0c8-f8c2e2f6011d" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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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", + " \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": 640 + }, + "outputId": "20fe57ed-121c-4f1e-db05-d17bc21eed2f" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.59\n", + " period 01 : 0.58\n", + " period 02 : 0.56\n", + " period 03 : 0.56\n", + " period 04 : 0.54\n", + " period 05 : 0.54\n", + " period 06 : 0.54\n", + " period 07 : 0.54\n", + " period 08 : 0.53\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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kyZNZsWIFpaWljBgxgoSEBIYMGcJLL73En//8Z12F12jZWhozd7QX8GBAcGpG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\nNX1gCwraHQ7WfnCM7EIT14+N4XvzUl0SPKxdbfzPwbVUtlZ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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": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "d65ecd4c-1c67-4161-a6d5-126e8f3c0c6d" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.60\n", + " period 01 : 0.57\n", + " period 02 : 0.57\n", + " period 03 : 0.56\n", + " period 04 : 0.55\n", + " period 05 : 0.54\n", + " period 06 : 0.55\n", + " period 07 : 0.54\n", + " period 08 : 0.53\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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KSop59tnVuLi40tzcTEtLC7/61dOMHTuu+/0vvbSOmJi5TJo0mV//+n9pa2tjwoRJ3eu/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\n+ycmZtLumx4/mnzFihXMnz8fnU7H3Xffzfbt2+nr6yMlJYXg4OAxt9v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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i-Xo83_aR6s_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n", + "\n", + "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n", + "\n", + "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC." + ] + }, + { + "metadata": { + "id": "DKSQ87VVIYIA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "outputId": "a9287f84-50af-42f6-ac47-fe64fab26e77" + }, + "cell_type": "code", + "source": [ + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "AUC on the validation set: 0.74\n", + "Accuracy on the validation set: 0.76\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "47xGS2uNIYIE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n", + "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n", + "obtain the true positive and false positive rates needed to plot a ROC curve." + ] + }, + { + "metadata": { + "id": "xaU7ttj8IYIF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "b3267042-0eb7-4a1c-e980-6196bff181b7" + }, + "cell_type": "code", + "source": [ + "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + "# Get just the probabilities for the positive class.\n", + "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n", + "\n", + "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n", + " validation_targets, validation_probabilities)\n", + "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n", + "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n", + "_ = plt.legend(loc=2)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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UQgKk4xVfKWkqY3lWJkX15wjw8GdhymxuCkvVuizRQySEheiE/At1vLU1B5NR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\nQrBnEIvNc0kJSdS6LCGEcCoSwjfgQkU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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": 677 + }, + "outputId": "d44af227-bee3-406e-9d82-2d8b28a43aa5" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000001,\n", + " steps=10000,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.55\n", + " period 01 : 0.53\n", + " period 02 : 0.52\n", + " period 03 : 0.52\n", + " period 04 : 0.52\n", + " period 05 : 0.52\n", + " period 06 : 0.52\n", + " period 07 : 0.51\n", + " period 08 : 0.51\n", + " period 09 : 0.51\n", + "Model training finished.\n", + "AUC on the validation set: 0.77\n", + "Accuracy on the validation set: 0.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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xmLGy2AhfmM3A/uPiWmcm+ERCJVMioeAgTGaTxNERERH1PyxmrGzSaB3kMgGJ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\nT08PHo/njNesWLEiU73x+Xz09/czNDSUqd6I5IO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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": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "9b1c1966-a02d-47a5-b817-1d300c35dfb1" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000003,\n", + " steps=20000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.50\n", + " period 01 : 0.48\n", + " period 02 : 0.48\n", + " period 03 : 0.47\n", + " period 04 : 0.47\n", + " period 05 : 0.47\n", + " period 06 : 0.47\n", + " period 07 : 0.47\n", + " period 08 : 0.46\n", + " period 09 : 0.46\n", + "Model training finished.\n", + "AUC on the validation set: 0.80\n", + "Accuracy on the validation set: 0.79\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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i7nszxcbGUV5eSnFxEbW1tezfvwd//0CeffZPpKef5e23/3bD5YxGUKkUAAzfzhxptVpe\nf/0vfPzxp/j5+fP0079pc1xFUbj6zo86nbZ1fWq1+qpxuuf2kDIz0wkjAuMJdQvmaNEJiuqLrV2O\nEEII0aaEhIm8994/mDRpCtXVVYSFhQOwd+9udDrdDZeJjIwiPf0cAMePpwLQ0FCPWq3Gz8+f4uIi\n0tPPodPpUKlU6PX6a5YfMGAwJ04c+3a5BvLz8wgPjzTXtyhhpjNUiooFsbMxYmRL1nZrlyOEEEK0\nacqUaezYkczUqTOYM2c+a9f+H088sZTBg4dQXl7Oli0br1tmzpz5pKWdYdmyX5Cbm4OiKHh5eTN6\n9Fh+8pMf89FH73P//Ut4883XiYqK4fz5dN5887XW5YcNG07//gNYuvSnPPHEUn7+88dxcTHfzZoV\nY3fN8ViJOafn2pv+MxqN/CX1TS7X5rNizBOEuYeYrQ5xPXNPzYrOkb7YLumNbZK+mC4gwKPN52Rm\nppMURWFBbCIAmzO/snI1QgghRO8lYaYLBvn2J9YritNlaeTU5Fq7HCGEEKJXkjDTBYqisCBGZmeE\nEEIIa5Iw00X9ffvQzzuOsxXnyajKtnY5QgghRK8jYaYbfH/sTLKVKxFCCCF6Hwkz3SDOO5qBvv24\nUJXB+YpL1i5HCCGE6FUkzHSThd/NzmQld9sVDYUQQghxcxJmukmUZwRD/QeRWZ3D2YoL1i5HCCGE\n6DUkzHSjBTGzgSvHzsjsjBBCCGEZEma6UbhHKCMC47lcm8fpsrPWLkcIIYToFSTMdLMFMbNQUNic\nmYzBaLB2OUIIIUSPJ2GmmwW7BXFL0AgK6os4UXLG2uUIIYQQPZ6EGTOYFzMTlaJiS9Z2mZ0RQggh\nzEzCjBkEuvozLngUxQ0lHC06Ye1yhBBCiB5NwoyZzImeiVpRk5S9A71Bb+1yhBBCiB5LwoyZ+Ln4\nMCF0DGWN5RwuSrV2OUIIIUSPJWHGjBKjp+Og0rA1aydag87a5QghhBA9koQZM/J28mJSWAKVzVUc\nLDhi7XKEEEKIHknCjJnNjpqGo8qB5OydtOi11i5HCCGE6HEkzJiZh6M7UyMmUt1Sy/78Q9YuRwgh\nhOhxJMxYwMzIKTirnfkqZzdNumZrlyOEEEL0KBJmLMDNwZXpEROp09azN++AtcsRQgghehQJMxYy\nPXISrhoXdlzeS6Ou0drlCCGEED2GhBkLcdG4MDNyCg26RnZd3m/tcoQQQogeQ8KMBU0Jn4C7gxu7\nclOo09ZbuxwhhBCiR5AwY0HOGidmR02jSd/Ezsv7rF2OEEII0SNImLGwSWEJeDl6sCc3hdqWOmuX\nI4QQQtg9CTMW5qh2IDF6Bi0GLV/l7LZ2OUIIIYTdkzBjBeNDx+Dj5M3+/ENUNVdbuxwhhBDCrmnM\nufJVq1Zx6tQpFEVhxYoVxMfHtz43ffp0goODUavVALz66qsEBQW1u0xP4aDSMDdmBp+m/5fk7F3c\n2/92a5ckhBBC2C2zhZkjR46Qk5PD2rVrycjIYMWKFaxdu/aa17z//vu4ubl1aJmeYlzwLXyVvZsD\nBUeYGTkVPxcfa5ckhBBC2CWz7WY6dOgQM2fOBCAuLo7q6mrq6to/4LUzy9grtUrNvJhZ6I16tmXv\nsHY5QgghhN0yW5gpKyvDx+f72QZfX19KS0uvec1zzz3Hfffdx6uvvorRaDRpmZ5kdPAIglwDOVx0\njJKGMmuXI4QQQtglsx4zczWj0XjN17/+9a+ZNGkSXl5eLF26lOTk5JsucyM+Pq5oNOpuq/OHAgI8\nzLZugPuG3crfDn3A7sK9PD7uIbOO1dOYuzeic6Qvtkt6Y5ukL11ntjATGBhIWdn3sw0lJSUEBAS0\nfn3bbbe1/nvy5MlcuHDhpsvcSGVlQzdWfa2AAA9KS2vNtn6AOOc+hLmHsD/nCFOCJxLsFmTW8XoK\nS/RGdJz0xXZJb2yT9MV07YU+s+1mmjBhQutsS1paGoGBgbi7uwNQW1vLo48+SktLCwBHjx6lb9++\n7S7TU6kUFfNjZmPEyJas7dYuRwghhLA7ZpuZGTlyJIMHD2bx4sUoisJzzz3HunXr8PDwYNasWUye\nPJl7770XJycnBg0axJw5c1AU5bplrEWnN6DTGywyVrz/ICI9wjlecprE2gLCPUItMq4QQgjREyhG\nUw5MsWHmmp5b+dERfD1d+NUdQ1AUxSxjXC2t/Dz/OPVP4v0H87P4B80+nr2TqVnbJH2xXdIb2yR9\nMZ1VdjPZO38vF05eLOWbrAqLjDfItx+xXlGcLksjpybXImMKIYQQPYGEmTYsmhgDwJf7Mk06q6qr\nFEVhYWwiAJszvzL7eEIIIURPIWGmDRGB7kwYFkp2US0nL1nmGjD9fPrQzzuOsxXnyajKtsiYQggh\nhL2TMNOO+2f3RwHW78/CYKFDixa0zs5cf90dIYQQQlxPwkw7IoM9GTs4iNySOo6ft8yViOO8oxnk\n258LVRmcr7hkkTGFEEIIeyZh5iYWTYhBpSisT8nCYLDU7MxsADZnJVvkeB0hhBDCnkmYuYkgX1fG\nDwmmoKyeI+eKLTJmlGcE8f6DyazO4WzFeYuMKYQQQtgrCTMmWDghGrVKYUNKFnqDZS6k1zo7kymz\nM0IIIUR7JMyYIMDbhYnxIRRXNnLoG8vMzoS5hzAyMJ7LtfmcLkuzyJhCCCGEPZIwY6KF46PRqBU2\nHsiy2G0O5sfMQkFhc+ZXGIyWGVMIIYSwNxJmTOTr6cyUYWGUVTdx4EyhRcYMdgtidPAICuqLOFFy\n2iJjCiGEEPZGwkwHzB8fhYNGxaaD2Wh1lpkpmRs9E5WiYkvWDpmdEUIIIW5AwkwHeLs7MW1EGBU1\nzew7VWCRMQNd/RkXfAvFDSUcLTphkTGFEEIIeyJhpoPmjYvCyUHN5kPZtGj1FhlzTvQM1IqapKzt\n6A2WGVMIIYSwFxJmOsjTzZEZo8Kprmthz4l8i4zp5+LDhNCxlDVVcLgo1SJjCiGEEPZCwkwnzBkb\nibOjmqTDOTS3WGamJDF6Gg4qDVuzdqI16CwyphBCCGEPJMx0gruLA7NHR1DToGXn8TyLjOnt5MWk\nsAQqm6s4WHDEImMKIYQQ9kDCTCfNHh2Bq5OGrYdzaGy2zEzJ7KhpOKodSc7eSYtea5ExhRBCCFsn\nYaaTXJ0dSBwbSX2Tju2puRYZ08PRnanhE6huqWV//iGLjCmEEELYOgkzXTBzVDjuLg4kH8mlvsky\nMyUzI6fgrHbmq5zdNOmaLTKmEEIIYcskzHSBi5OGuWMjaWzWkXzEMrMzbg6uTI+cRJ22nr15Bywy\nphBCCGHLJMx00fSR4Xi6OrA9NZfahhbLjBkxEVeNCzsu76VR12iRMYUQQghbJWGmi5wc1cxLiKa5\nRc+2I5ctMqaLxoVZkVNp0DWy6/J+i4wphBBC2CoJM91g2ohQvN0d2Xksj+p6y8zOTA4fj7uDG7ty\n91OnrbfImEIIIYQtkjDTDRw0ahaMj6ZFa2Dr4RyLjOmscSIxahpN+mZ2Xt5nkTGFEEIIWyRhpptM\nig/Fz9OJ3Sfyqay1zFlGE8MS8HL0ZE9uCjUttRYZUwghhLA1Ema6iYNGxcIJMWh1BrYcyrbImI5q\nB+ZET6fFoGV7zh6LjCmEEELYGgkz3Wj8kGACvJ3Zd6qA8uomi4yZEDoGHydv9uUfoqq52iJjCiGE\nELZEwkw30qhV3DohBp3eyKaD2RYZ00GlYV7MTHQGHcnZuywyphBCCGFLJMx0s4TBwQT7unLgTCEl\nVZa5BszY4FH4u/hxoOAI5Y2VFhlTCCGEsBUSZrqZSqWwaGIMeoORTSlZFhlTrVIzP2YWeqOebdk7\nLDKmEEIIYSskzJjB6IGBhPm7cTCtiMJyy1wD5pag4QS7BnK46BglDWUWGVMIIYSwBRJmzEClXJmd\nMRph44FsC42pYn7sbAxGA0lZMjsjhBCi95AwYyYj+wcQGejOkbPF5JXWWWTM4QFDCHMPIbX4BIX1\nxRYZUwghhLA2CTNmolIUbpsUixHYaKFjZ1SKivkxszFiZEvWdouMKYQQQlibhBkzGtbHj5gQT1LP\nl3K52DJX6I33H0SkRzgnSk6TV1tgkTGFEEIIa5IwY0aKonD7pBgA1u+3zOyMoigsiE0EYHPWVxYZ\nUwghhLAmCTNmNjjGlz7hXpy8VEZWYY1Fxhzk249Yr2jOlJ0lpybXImMKIYQQ1iJhxsyuzM7EAvDl\n/kyLjbkwdjYAmzKTLTKmEEIIYS0SZixgYJQPAyK9+Sazgkt5lrl/Uj+fPvTz6cO5igtcqrLMLi4h\nhBDCGiTMWMjtky07OwO0zs5sltkZIYQQPZiEGQvpG+7NkBhfzuVUkp5jmfsnxXpFM8ivPxerMjlf\ncckiYwohhBCWJmHGgm676tgZo9FokTEXxHx37Mw29Aa9RcYUQgghLEnCjAXFhnoyvI8/F/OqScuu\nsMiYUZ4RDA8YQlbNZd46+T7VzZa53o0QQghhKRJmLGzRxCvXnflyX5bFZmd+NPBuhgcM4WJVJi8f\n/RsXKzMsMq4QQghhCRJmLCwq2INR/QLIKqzhdEa5RcZ00bjwkyFLuLPPAuq09aw+8R5fZe/GYDRY\nZHwhhBDCnCTMWMGiSTEoXLkqsKVmZxRFYXrkZJ4Y+XO8nDzZkLmVd09/Qr22wSLjCyGEEOYiYcYK\nwgPcGT0wkJziWo5fKLPo2LFe0TwzehkDfPryTfk5Xj66Wq4SLIQQwq5JmLGSRRNjUBRYn5KJwUKz\nM9/xcHRn6fBHmRczi8qmKl4/9g/25R2y2CyREEII0Z0kzFhJiJ8bCYODyS+tJzW9xOLjqxQV82Nm\nsXTYozhrnFl74Us+PvsZTbpmi9cihBBCdIWEGSu6dUI0KkVhQ0oWBoN1ZkUG+vXjmdHLiPGMIrX4\nJH9NfYvC+mKr1CKEEEJ0hoQZKwr0cWVifDCF5Q0cPltktTp8nL15YuTPmR4xiaKGEv5y9E2OFB23\nWj1CCCFER0iYsbIF46NRqxQ2pmSj01vvVGm1Ss2dfRfykyFLUClqPjn7OZ+l/xetXmu1moQQQghT\nSJixMn8vFyYPD6WkqpGD31hvduY7IwKH8rvRvyLMPYSUgq957fg/KGu0zPVwhBBCiM6QMGMDFiRE\no1Gr2HTAurMz3wl0DeB/Rj3O+JDR5Nbm8/LR1ZwuTbN2WUIIIcQNSZixAT4eTkwbEUZ5TRP7TxVY\nuxwAHNUOPDDwbn404G50Bh3vnvmE9ZeS5GaVQgghbI6EGRsxLyEKR42KzYdy0OpsJzAkhI7mt7f8\nikAXf7Zf3sPqE+9R1Vxt7bKEEEKIVhJmbISXmyPTR4VTWdvMnpO2MTvznTD3EJ4e/WtGBAwlozqL\nl4+s5nzFJWuXJYQQQgASZmzK3LGRODmq2XIoh2at7czOALhonHl0yI+4q++t1OsaeOvk+2zL3ik3\nqxRCCGF1EmZsiIerI7NuCaemvoXdx/OtXc51FEVhWsREnhj5C7ycPNmUmcw7pz+iTltv7dKEEEL0\nYmYNM6tWreLee+9l8eLFnD59+oavee2111iyZAkABoOBZ599lsWLF7NkyRIyMjLMWZ5NShwTiYuT\nhqTDOTQ266xdzg3FekWxfPRvGOjbj7Pl53n5yGqyqi9buywhhBC9lNnCzJEjR8jJyWHt2rW89NJL\nvPTSS9e95tKlSxw9erT16507d1JbW8vnn3/OSy+9xF/+8hdzlWez3JwdSBwdQV2jlp3H8qxdTpvc\nHd345bBHWBAzm6rmat44/g578g7IzSqFEEJYnNnCzKFDh5g5cyYAcXFxVFdXU1dXd81rXn75ZZ54\n4onWr7Ozs4mPjwcgMjKSgoIC9HrbOnbEEmaNjsDNWUPykcs0NNnm7AxcuVnl3JiZPD78J7honPnP\nhQ18mPZ/NOmarF2aEEKIXkRjrhWXlZUxePDg1q99fX0pLS3F3d0dgHXr1jFmzBjCwsJaX9OvXz8+\n+eQTHnzwQXJycsjNzaWyshJ/f/82x/HxcUWjUZvr2yAgwMNs627PndP78q+kcxw4W8z9iQOsUoOp\nAgJGMjgiljcOfcDxktMUNRbz5PifEukddvOFuzSudXoj2id9sV3SG9skfek6s4WZH7p690NVVRXr\n1q3jo48+orj4+zs0T5kyhePHj/PAAw/Qv39/YmNjb7rborKywWw1BwR4UFpaa7b1t2fcgAC+3HOJ\n9XsvkTAwEHcXB6vUYTo1S4dhnFREAAAgAElEQVT8hA0ZW9mZu4/l21/hvv53MDZklFlGs2ZvRNuk\nL7ZLemObpC+may/0mW03U2BgIGVlZa1fl5SUEBAQAMDhw4epqKjggQce4PHHHyctLY1Vq1YB8MQT\nT/D555/z/PPPU1NTg5+fn7lKtGnOjhrmjYuisVlP8hH7OLhWrVJzR98F/HToj1Erav51bi2fpn8h\nN6sUQghhVmYLMxMmTCA5ORmAtLQ0AgMDW3cxzZkzh6SkJP7973/z9ttvM3jwYFasWEF6ejrLly8H\nYN++fQwaNAiVqveePT5tRBhe7o7sSM2jpqHF2uWYbHjAEJ4ZvYxw91AOFBzh1WN/p7RBblYphBDC\nPMyWFEaOHMngwYNZvHgxL774Is899xzr1q1j+/btbS7Tr18/jEYjd911F++++25rsOmtHB3ULEiI\nplmrZ+vhHGuX0yEBrn48NWopE0LHkFdXwCupqzlV+o21yxJCCNEDKUY7P5fWnPsabWFfplZnYPl7\nh6ht0PLKzxPwdneyaj2d8XXhMT47vw6tQcuMiMksipuLWtW1g7ZtoTfietIX2yW9sU3SF9NZ5ZgZ\n0T0cNCoWjI9GqzOQdMi+Zme+MzZkFL+95XECXf3ZmbuPv514V25WKYQQottImLEDE4eG4O/lzJ6T\n+VTU2Oc1XMLcQ/jdLb9mZGA8mdXZ/PnI30ivuGjtsoQQQvQAEmbsgEatYuGEaHR6I5vtdHYGwFnj\nzCODH+Dufoto1DXx9skPSMraLjerFEII0SUmh5nvrt5bVlZGamoqBoN8AFnS+CHBBPm4sP9UAWVV\njdYup9MURWFq+ASeGPkLvJ282JK1nX+c+pC6FrlZpRBCiM4xKcz86U9/YuvWrVRVVbF48WLWrFnD\nypUrzVyauJpapeLWiTHoDUY2Hsy2djldFuMVyTNjljHIrz/nKi7w56N/I7PafmedhBBCWI9JYebs\n2bPcfffdbN26ldtvv53Vq1eTkyMfPJY2dmAQof5uHDxTRLEZr3xsKe4Obvwi/mEWxiZS3VzDG8ff\nYXduitysUgghRIeYFGa++3DZs2cP06dPB6ClxX4u4tZTqFQKiybGYDAa2ZiSZe1yuoVKUTEnega/\nGv5T3DSufHFxIx988780ys0qhRBCmMikMBMTE8O8efOor69n4MCBrF+/Hi8vL3PXJm5gVP8AwgPc\nOZxWTEFZzznOpL9vH54Zs4w4rxhOlp7hL0ffJL+u0NplCSGEsAMmXTRPr9dz4cIF4uLicHR0JC0t\njYiICDw9PS1RY7t6+kXzbuTEhVLeWneG0QMC+cVtQ6xdTrfSG/Rsykxm++U9OKg03NvvdhJCR1/3\nOlvtTW8nfbFd0hvbJH0xXZcvmnfu3DmKiopwdHTkjTfe4C9/+QsXLlzotgJFxwzv6090sAdH00vI\nLamzdjndSq1Sc1ufefxs6INoVA78b/p/+N9z/6FFblYphBCiDSaFmRdffJGYmBhSU1M5c+YMzz77\nLG+++aa5axNtUBSF2ybFArB+f6aVqzGP+IDBPDN6GREeYRwqPMqrx96mpKHU2mUJIYSwQSaFGScn\nJ6Kjo9m5cyf33HMPffr06dV3s7YFQ2N9iQvz5MTFMrKLaqxdjln4u/jy1MhfMjFsHPl1hbxy9E1O\nlJyxdllCCCFsjEmJpLGxka1bt7Jjxw4mTpxIVVUVNTU98wPUXiiKwu2tszM948ymG3FQO3Bf/zt4\ncNBiDEYDH3yzhi8ubkQru52EEEJ8y6Qw8+STT7Jp0yaefPJJ3N3dWbNmDQ899JCZSxM3MzDKh34R\n3pzOKCcjv2ffuHFM8EieHv1rglwD2Z2bwrKklRwsOIreoLd2aUIIIazMpLOZABoaGsjKykJRFGJi\nYnBxcTF3bSbpjWczXe385Upe+fQEg6N9eGrxCGuXY3ZNumaSsrazr+AQWr2WQFd/FsTMZkRgPCpF\ndn1amz1sM72V9MY2SV9M197ZTBpTVrBjxw5WrlxJcHAwBoOBsrIy/vSnPzFlypRuK1J0Tv9IHwZF\n+5CWXcmF3Cr6RXhbuySzctY4cUffBdw9fC7/e3wDBwuO8GHap4Tl7ObW2DkM9huAoijWLlMIIYQF\nmRRmPvjgAzZu3Iivry8AxcXFLFu2TMKMjbh9Uixns4/x5b5Mnr5/RK/4MPd19ea+/ncwM2IKW7K2\nk1p8gndOf0SMZxS3xs2hn0+ctUsUQghhISbNyzs4OLQGGYCgoCAcHBzMVpTomLgwL+Lj/DifW8W5\nnEprl2NRAa5+PDR4MSvGPMGwgCFk1eSw+sS7vHXifXJqcq1dnhBCCAswaWbGzc2NDz/8kPHjxwOQ\nkpKCm5ubWQsTHXPbpBhOZ5Tz5f5MBkb59IrZmauFugfz2NAfk1OTy8aMbaRXXiQ99SLD/AezIDaR\nUPdga5cohBDCTEwKMy+99BKrV69m48aNKIrC8OHDWbVqlblrEx0QHezJiL7+nLhYxpnMCuLj/Kxd\nklVEeUbwqxE/5UJlBpsyt3GqLI3TZWe5JWg482NmE+DaO38uQgjRk5l8NtMPZWRkEBdn/eMSevvZ\nTFfLLanjuQ+PEB3swbMP3tKjZ2dM6Y3RaCStPJ2NmdvIrytEpagYHzKauTEz8XaSG6Wag71tM72J\n9MY2SV9M1+V7M93I888/39lFhZlEBLozekAg2UW1nLxYZu1yrE5RFIb4D+SZ0ct4ZPAD+Lv4klLw\nNc8deoX/XtxEbUvPuq+VEEL0Vp0OM52c0BFmtmhiDIoCX+7PwiA9AkClqBgVNIw/jHmKBwbcjYeD\nO7ty9/PcoZfZnPkVjbpGa5cohBCiCzodZnryLgx7FurvxrhBQeSV1nHsvNyY8WpqlZrxoaN5LuFp\n7u67CEeVI1uzd/DcwVfYnrOHFn2LtUsUQgjRCe0eAPzFF1+0+VxpqXxQ2qpbJ8Tw9dkSNqRkMapf\nACqVBM+rOag0TI2YQELoaPbkprD98l7WZySxK3c/c6NnMD50DBqVScfGCyGEsAHt/sY+duxYm88N\nHz6824sR3SPI15XxQ4JJOVPIkXPFjBsspyXfiJPakcTo6UwKS2Dn5b3sykth7YX17Li8l3kxsxgT\nPFJukSCEEHag02cz2Qo5m+nGSqsaWfHeYfy9nHnxp2NRq3rWh7I5elPTUstX2bvZn38InVFPsGsg\nC2ITGR4wRHarmsiet5meTnpjm6QvpuvyvZnuv//+636Zq9VqYmJi+OUvf0lQUFDXKhTdLsDbhUnx\nIew5WcChb4qZGB9i7ZJsnqejB3f1u5XpkZPYmrWTw0WpfPDNGiI9wlgQO4dBvv0k1AghhA1Sr1y5\ncuXNXlRYWIhOp+POO+9k5MiRlJeX069fP4KDg/nwww9ZtGiRBUq9sYYG8x206ebmZNb1m1tEoDu7\njueRU1zLtJFhPerYGXP2xkXjQnzAIEYFDaOupZ70yoscLT7B+coMAlz98XX2Mcu4PYG9bzM9mfTG\nNklfTOfm5tTmcybNzBw7doyPPvqo9euZM2fy2GOP8d5777Fz586uVyjMwtfTmSnDw9h5LI+UM4VM\nHR5m7ZLsSpBrAI8MeYDZtdPYnJXMmbJzvHH8HQb59WdhbCKRHuHWLlEIIQQmnppdXl5ORUVF69e1\ntbUUFBRQU1NDba3s67Nl8xOicNCo2HQgm+KKBmuXY5fCPUL5efzDPDVqKX29Yzlbfp5Xjr7JB2fW\nUFRfbO3yhBCi1zPpAOAvvviCv/71r4SFhaEoCnl5efzsZz/Dz8+PhoYG7rvvPkvUekNyAPDNbUzJ\nYn1KFg4aFXdOjmXmLRF2v8vJWr0xGo2cr7zExsxt5NTkoqAwNngU82Jm4ufie/MV9HA9ZZvpiaQ3\ntkn6Yrr2DgA2+Wymuro6srOzMRgMREZG4u3t3W0FdoWEGdMcTS9hTfJ56hq1xIV58si8gYT42e+d\nz63dG6PRyOmys2zOTKagvgi1omZC6FjmRE/Hy8nTanVZm7X7ItomvbFN0hfTtRdmTDoAuL6+nk8+\n+YTNmzeTmppKeXk5Q4YMQaOx/oXF5ABg04T5uzEhPoTy6ia+yapg/+lCHNQqYkM97fIMHWv3RlEU\ngt0CmRg2lkBXf3LrCjhXcYF9+Ydo0jUT4RGGo9rBavVZi7X7ItomvbFN0hfTtXcAsEkzM08++SRB\nQUGMHTsWo9HIwYMHqays5NVXX+3WQjtDZmY6LjW9hP/96jw1DVriQj15eN5AQv3ta5bG1nqjN+g5\nVHiUrdk7qWquxlntzMzIyUyLmIizxtna5VmMrfVFfE96Y5ukL6br8m6mH//4x/zrX/+65rElS5aw\nZs2arlfXRRJmOqe2oYVPd1zk67PFaNQqbpsUQ+KYCLu5uJ6t9qZFr2V//iG+ytlNnbYedwc3EqOm\nMSksAYdeMFNjq30R0htbJX0xXXthxqRPrsbGRhobv7+zcENDA83NzV2vTFiNh6sjP7t1MEtvH4qr\ns4Yv9mSwas0x8kvrrF2aXXNUOzAjcjLPJ/yOBTGz0Rn0/PfSZlYe/gsp+YfRG/TWLlEIIXock89m\nevvttxkyZAgAaWlpLFu2jNtuu83sBd6MzMx0XV2jls92XOBQWjEatcKiiTHMGRtp07M09tKbOm09\nO3L2sifvAFqDFn8XPxbEzGZU0LAeed8ne+lLbyS9sU3SF9N1y9lMhYWFpKWloSgKQ4YMYc2aNfzP\n//xPtxXZWRJmus+Ji6X8a9t5qutbiAr24NF5AwkPdLd2WTdkb72pbq5hW/YuDhR8jd6oJ9QtmAWx\nicT7D7LLA7DbYm996U2kN7ZJ+mK6bgkzP3Sj42isQcJM96pr1PL5zosc/KYItUrh1gnRzB0XhUZt\nW7MI9tqb8sYKkrJ28HXRMYwYifKM4La4ufTz6WPt0rqFvfalN5De2Cbpi+m6fMzMjdj5zbZFG9xd\nHPjJgkEsuyseD1cHvtyfxYv/SiW3RI6l6Q5+Lr4sGXQPfxj7JCMChpJTk8vqE+/x/05/THF9ibXL\nE0IIu9TpMNOTpsbF9Yb18efFn4xl4tAQLhfX8cLHR9mQkoVOb7B2aT1CsFsQPxm6hKdv+RV9vGM4\nU3aWF4+8zn8ubKBOW2/t8oQQwq60u5tpypQpNwwtRqORyspKTp8+bdbiTCG7mczvdEY5n2xLp7K2\nmYhAdx6dP5DIoLan+yyhJ/XGaDRyqiyNLy9toayxHFeNC3NjZjI5LAGNyvoXpuyIntSXnkZ6Y5uk\nL6br9DEz+fn57a44LMz6d2GWMGMZDU06/r37IvtOFaJWKcxPiGLB+GirHUvTE3ujNejYl3eQrdk7\naNQ1EeDix+195hPvP9huZkJ7Yl96CumNbZK+mM4sBwDbCgkzlvVNZjkfb0unoqaZ8AA3Hp0/iKhg\ny8/S9OTe1LXUk5S9g/35hzAYDfT1juWOvguI9Ai3dmk31ZP7Yu+kN7ZJ+mK6Lt+byZbJvZksK9DH\nlUnxoTQ0aTmdWcH+U4XoDAb6hHmjtuCduHtybxzVjgz2G8DIwHgqmys5V3GRgwVHKGusIMozwqZv\nj9CT+2LvpDe2Sfpiui7fm8mWycyM9aRlV/BxUjrlNU2E+bvxyPyBxIRY5o7Rvak36RUXWXdpM/l1\nhTiqHJgZNZWZkVNwUjtau7Tr9Ka+2BvpjW2SvphOZmY6SRJz+wK9XZgUH0Jji47TGeXsP12AVmeg\nb7iX2a8e3Jt64+/ix4TQsfg6e5NRnU1aeTpfFx3D3cGNUPdgmzqepjf1xd5Ib2yT9MV0MjPTSZKY\nTXcup5KPks5RVt1EiJ8rj8wfSFyol9nG6629adI1sT1nDztz96E16IjwCOPOPgvo6xNn7dKA3tsX\neyC9sU3SF9PJzEwnSWI2XYC3C5OGhdDUrOd0Zjkppwtp1uqvzNKY4Yyn3tobjUpDf98+jAkeSV1L\nPecqLnC46Bj5dYVEeITh5uBq1fp6a1/sgfTGNklfTCczM50kiblzzl+u5MOkc5RWNRHse2WWpk9Y\n987SSG+uyK65zH8vbiazOhu1omZK+HjmRs/A1UqhRvpiu6Q3tkn6YjqZmekkScyd4+/lwuT4UJq1\nes5kXJmlaWzW0TfCu9uuSyO9ucLbyYuEkFsIcQ8mu+YyZyvOc7DgCA4qByI8wix+Z27pi+2S3tgm\n6YvpZGamkyQxd92F3Co+TDpHSWUjQT4uPDxvIP0ivLu8XunN9bR6LXvyDrAtexdN+iYCXf25o88C\nhvgNtNhBwtIX2yW9sU3SF9PJzEwnSWLuOj8vZyYNC0WrM3Amo5wDZwppaNLRr4uzNNKb66lVauK8\noxkfOppmfQvpFRdJLT5JRnU2Ye4heDqZ/+KG0hfbJb2xTdIX08nMTCdJYu5el/Kq+WfSOYorGgj0\nduHheQPoH+nTqXVJb26uoK6ILzO2cLb8PAoKCSG3sCA2ES8n810LSPpiu6Q3tkn6YjqZmekkSczd\ny9fTmcnxIej0xitnPJ0poq5RS/9OzNJIb27Ow9GdMcEjifGMJLcun3MVF9hfcBij0UiUZwRqlbrb\nx5S+2C7pjW2SvphOZmY6SRKz+WTkV/Nh0jkKyxsI8Hbm4bkDGRBl+iyN9KZj9AY9hwqPsjnzK2q1\ndfg4eXNr3BxuCRrerQcJS19sl/TGNklfTCczM50kidl8fD2dmTwsBL3ByOmMcg6cKaKmocXkWRrp\nTceoFBWRnuFMCBsLwPnKS5woOc3Z8vMEuQXi69y53X0/JH2xXdIb2yR9MZ3MzHSSJGbLyCyo4cOk\ncxSU1ePv5czDcwcwMNq33WWkN11T3ljBhoytHCs5BcCIwHhui5uLv4tfl9YrfbFd0hvbJH0xXXsz\nM2YNM6tWreLUqVMoisKKFSuIj4+/7jWvvfYaJ0+eZM2aNdTX1/O73/2O6upqtFotS5cuZdKkSe2O\nIWGmZ9Dq9Gw8kM3Ww5cxGI1MHRHG3VPjcHHS3PD10pvukVmdw38vbiK75jIaRc3UiInMiZ6Oi8al\nU+uTvtgmrV5LSJAPZWV11i5F/IBsM6ZrL8zc+JOiGxw5coScnBzWrl1LRkYGK1asYO3atde85tKl\nSxw9ehQHBwcAvvzyS2JiYnjqqacoLi7mwQcfZNu2beYqUdgQB42aO6fEMbJfAB8mnWPPiXzOZJTx\n0LyBDL7JLI3ovFivKP5n1FKOlZxi/aUkdlzey+HCVObHzGJC6FizHCQszEtv0FNYX0x2zWWya3LJ\nqcmlsL6YEI9A5kcnMsx/sE3dnFSI7mC2MHPo0CFmzpwJQFxcHNXV1dTV1eHu7t76mpdffpknnniC\nt99+GwAfHx/Onz8PQE1NDT4+3bMfX9iPmBBP/vjgaDYdzCbpUA6vfX6SycNCuWdaH1ydzfZ27dUU\nReGWoOHE+w9mT24KyTm7WHthPXvzDnJH3wUM8u0vH342ymg0UtVcTVbN5SvhpTqX3No8Wgza1tc4\nqhyI9Awntzaf98/8ixjPSG7rM58+3jFWrFyI7mW2T4eysjIGDx7c+rWvry+lpaWtYWbdunWMGTOG\nsLCw1tfMnz+fdevWMWvWLGpqanj33XdvOo6Pjysajfn+99jetJYwn5/dOYwZY6NY/fkJ9p0q4Gx2\nBY/fM5xRA4JaXyO96X4PBN/K/KFT+feZTezMOsA/Tn3IsOCBLBl2J5HeYTdfAdIXc2rQNpJRkcOl\n8mwuVmRzqTyLqqaa1ucVRSHCM5Q+ftH08Y2mr1804Z4hqFVqCmqK+PzMJg7nHeeN4+8wKnQo9w1d\nZHJfhfnINtN1Fvuv7tWH5lRVVbFu3To++ugjiouLWx/fsGEDoaGh/POf/yQ9PZ0VK1awbt26dtdb\nWdlgtpplX6Z1eTmpWfGjkWw+mM2WQzmsfP8wE+NDWDy9D1ERvtIbs1G4PfpWxviPZt3FzZwqOsfp\nopeYEDqG+bGz8XRs+xevbDPdR2/QU1Bf1Drjkl2bS3F9CUa+/13q7eTF8IAhRHtGEuUZQaRHOM6a\nq8740EJF+ZXfkaEBwSzpt5iJQeNZn7GFYwVnOF7wDWNDRrEgZjY+zl2/zYjoONlmTGeVY2YCAwMp\nKytr/bqkpISAgAAADh8+TEVFBQ888AAtLS1cvnyZVatW0dzczMSJEwEYMGAAJSUl6PV61GrZb99b\nadQqbpsUy8h+AfxzyzlSTheSllXB7348mkAPR2uX16OFuYfw+PCfkFaezrpLW0gp+JrU4pMkRk1n\nWsREHNQO1i6xxzAajVQ0VZJdk9t6rEtubT7aq3YXOakd6esdS5RnBNFekUR7RuDt1PG70cd4RfKb\nET8nrTydDRlbOVyYSmrxSaaGTyAxaprV7rguRFeY7Wym48eP89Zbb/HRRx+RlpbGiy++yGeffXbd\n6/Ly8li+fDlr1qzhww8/pKysjKeffpr8/HweeeQRkpOT2x1HzmbqPXR6A0mHcth4IBuVSuHR+QMZ\nOyjo5guKLtMb9Bwo+JotWdup09bj6+zDori5jAocds3xNLLNmKZB20hO7ZWDc7+beanVfn+mkYJC\nqHsw0Z5XQku0ZyTBboFdusDhjXpjMBo4UnSczZlfUdlchYvGhcSoaUwJn4CjhFWLkG3GdFY7NfvV\nV18lNTUVRVF47rnnOHv2LB4eHsyaNav1NVeHmfr6elasWEF5eTk6nY5ly5aRkJDQ7hgSZnqftKwK\n3tnwDQ1NOu6YHMv8hCg5QNVCGrSNJOfsYk9uCjqjnhjPSO7su5AYryhAtpkb0Rv05NcVts64ZNfk\nUtxQcs1rfJy8r4QWr0iiPCKI9AzHSd29M4/t9Uar17I3/yDJ2bto0DXi7eTFgpjZjA0Z1a1XiBbX\nk23GdFYLM5YgYaZ3atAZ+eN7B6moaWZSfAhLEvt36S7comNKG8rZkJHEidIzAIwKHMaiuHkMiIzs\n1duM0WikvKni+91F1bnk1eWjNehaX+OsdiLSM6J1xiXaM8KsN//8jim/zxq0jXyVs5s9eSloDTpC\n3IJYFDeXIX4D5T8MZiKfM6aTMNNJ8iazXQEBHlzMKmP1f06TU1zLoGgffnnbUDl928IuVWXx34ub\nuFybh0alYXL0WJwMLjipHXHWOOGkdsJJ7fiDv51w0lz5t0ZR2/WHZIO2ofVaLt/NvNRp61ufVykq\nwtyCrxzn4hlJtFckQa4BVpnt6Mjvs8qmKpKytnOoMBUjRuK8Yritzzxiv52BE91HPmdMJ2Gmk+RN\nZru+601Ti473Np7l5KUywvzdWHZ3PP5enbt6regcg9FAavFJNmRspaq5ukPLqhRVa9BxVl8VfjRX\nBZ/rwlA7QUljvoCkM+jIryu8ck2X6lxyai9T0lB2zWt8nX1aZ1yunF0UhmM37y7qrM78PiuoK2Jj\n5jbOlJ0FYFjAEG6NnUOwW6A5SuyV5HPGdBJmOkneZLbr6t4YDEY+23mRncfy8HJzZNnd8UQHm3/a\nXlxLq9fS6FhLUVklzfpmmvUtV/7Wfffvlmseb9I1X/dYs775ml0ynXF1QHJSO30bkq4OSG0EJc21\noUpRFPJqC1pnXPJq89EZ9a3juGicifKI+P5YF8+Idk9bt7au/D67VJXFhowkMqtzUCkqEkJGMy9m\nZqfOphLXks8Z00mY6SR5k9muG/Vm+9FcPt95EQcHFT+7dTAj+gZYqbreqzu2Gb1BT4vh2/DzbeBp\n+jboXBN+bhiGWr4NSdc+dvUpzp2hUlSEu4e0zrhEe0YS6OpvVwfHdrU3RqOR02Vn2ZCxleKGEhxU\nDkyPmMSsqCmdvpeXkM+ZjpAw00nyJrNdbfXmxIVS3t2UhlZrYPHMvsy6JcIK1fVetrrNGIyGG4ag\npusC0vczRDqDjhD3YKI9Iwh3D7P7U5W7qzd6g57DRalsydxOdUsNbg6uzImewaSwBBxUcsxaR9nq\nNmOLJMx0krzJbFd7vckqrGH1F6epqW9h5qhwFs/oi0plvweZ2hPZZmxXd/emRd/CntwDfHV5N426\nJnydfVgYm8gtQcPtasbK2mSbMV17YUa9cuXKlZYrpfs1NLSYbd1ubk5mXb/ovPZ64+PhxC0DAjib\nXcmpjHIuF9cxvI+/nLptAbLN2K7u7o1apSbOO4bxoWMwGA1cqLzEidIznC5Lw9fZlwAXP7s+U81S\nZJsxnZubU5vPSZhph7zJbNfNeuPq7MC4QUFkF9VyJrOCb7IqGNHHH2dHmQY3J9lmbJe5euOodmSQ\nX3/GBI+iQddIesVFjhYf51JVFiFuQXKQ8E3INmM6CTOdJG8y22VKbxw0asYMDKKyrpkzGeWkppcw\nKNoXTzfbOFW2J5JtxnaZuzeuDi4MCxjCsIAhlDdVkl55kQMFRyiqLybcPRQ3uefTDck2YzoJM50k\nbzLbZWpvVCqF4X38UatVHL9QxuGzRUSHeBLoLWdfmINsM7bLUr3xdPRgTPBI+nrHUFRfQnrlRfbn\nH6K2pfbb2zS0/YHUG8k2YzoJM50kbzLb1ZHeKIpC/whvgnxdOHa+lMNpxfi4OxEVbLvXBLFXss3Y\nLkv3xs/Fl/GhYwhxDyavNp+zFRfYn38YvUFHpEcYGjnzCZBtpiMkzHSSvMlsV2d6Ex7gTv9IH45f\nKOVIegk6vYEBUT5ykGI3km3GdlmjN4qiEOIWxKSwcXg6epJVk0NaeToHC47goHYg3D2015/5JNuM\n6STMdJK8yWxXZ3vj5+XMyH4BnMks5+TFMooqGhjWxw+1qnf/Qu0uss3YLmv2RqWoiPKMYGLoOBxV\nDlysyuR02VlSi0/i6ehOsFtgr/1PhWwzppMw00nyJrNdXemNu4sDYwcFcSmvmjOZFZy/XMWIvgE4\nOqi7ucreR7YZ22ULvdGoNPT1iWV86Bh0Bh3nKy9xvOQ0aeXn8Hfxw9/Fz6r1WYMt9MVeSJjpJHmT\n2a6u9sbJQc24wUGUVDZyJrOC4xdKGRrnh7uLfV/l1dpkm7FdttQbJ7Ujg/0GcEvQCOq09ZyruMiR\nouNkVecQ4haEl1PvuUJyIx8AABx7SURBVLeaLfXF1kmY6SR5k9mu7uiNWqViZP8A9AYjJy6W8fXZ\nYvqEe+Hn6dxNVfY+ss3YLlvsjZuDKyMChzLUfyDljRWkV14kpeBrShvKCPcIw9Wh5591aIt9sVUS\nZjpJ3mS2q7t6oygKg6J98fFwIjW9lENpxQT5uhAW4N4NVfY+ss3YLlvujZeTJ2NDRhHrFUVhXRHn\nvj2du17bQKRHOI7qnnttKFvui62RMNNJ8iazXd3dm6hgD+LCvDh+oYTDacVo1Ap9w7167UGJnSXb\njO2yh94EuPgxPnQMwa4B5NTmcbbiPCn5hzFiJMIjHI2q5x3XZg99sRUSZjpJ3mS2yxy9CfRxYVic\nP6cyyjh+oYzK2maGxvrJTSo7QLYZ22UvvVEUhVD3ECaGjcPdwY3M6hy+KT/HocKjOKmdCHcP6VGn\nc9tLX2yBhJlOkjeZ7TJXbzzdHBk9IIjzl6s4nVlOZkE1w/sE4KDpOb88zUm2Gdtlb71RKypivCKZ\nGDYOtaLiYmUGp8rSOFZyCrWiorq5hpqWOhp1jWgN2ivLqNR2N5tqb32xpvbCjGI0Go0WrKXbmfPW\n6XJrdttl7t40teh4b+NZTl4qIyzAjd/cNQw/Lzkw+GZkm7Fd9t6b6uYatmbv5EDB1xiMhjZf56x2\nxkVz9R+XH/z9w8evfY2DSmPRQGRLfTEYDTTrW2jWN9Osa6ZZ30KTvrn16yv//vYx3ZXHv3usWddM\ni0HL9IiJjAoabpb6AgLavmq7XE9aiBtwdtTw+B1D+WznRXYey+PFf6Wy7O54ooN7zymjQtgSLydP\nFve/nekRk7hYmUGjvolGbSON+iYatE006Rtp1DW1/qlsrqKwvhkjHfv/ulpRdzoIffeYpXaD6Qy6\nK+Hi22DRfJOw8V0wufJ8S+sy372+5dsZrs5yVDlQ01LXTd9dx0iYEaINKpXCA7P68f/bu/OoJu98\nDeDPm50kLGEJS0AExKq4gUvrrlVbx07bUWuxKu29f8xMr2fuudPbmVMvbceZOx2n9Jye25nR02ln\n6WnpdMQqbZ2OrdNFXCquIBTcEReCkCABTAhLSO4fCciiFJXw5oXnc05PIHnf8A0/kj7+ttcYFoRt\nX53Hq38rwrOPTcTU1EixSyMasYzaSBi1A3sPensaWnuEHKfLeYuvnbc8pqG1qWsI606o5arbBKG+\nocjYHoY6W1OPQNLi6xXpHjR6BBXffS5Pxx3X1kmAALVcBbVcjSCFBgZ1KNRyNdRyNTQKdddjN7/3\n3qe5xTEahRoqmQpyESdoM8wQfYelMxIQGarBW7vK8Yf8Ujy1OBVLpieIXRYRfQeZIPMFiLvfr8bl\ndvUbeG5/fwsaW5tQ47Dcce/Q7V5LZ5AIVgUjKijS970Kal/Y6HxcrVDdfEyuhkah6RNOVDKl5OYX\n9YdhhmgA0sdG4YV1GfjdjlJ88OV5WBtakPngGK50IhrmFDIFglV6BKvubu8pj8fTb++QQgO0tbih\n6RFGuvWC+IKKklcZ7xd/O0QDlBQbgpeypuGNHaX44vhV1DU68aNH06BWDb+9L4hocAiCAI1CA41C\nA8MtHg+kCcBSxvWmRHcgMiwI2eszMD7RgOLzdcj5oAiN9laxyyIiGtEYZojukFajxHNPTsHcSbG4\nVHMDr7x3AmarODP4iYiIYYborijkMvz78nFYMT8Z15tasPn9Ipy6VC92WUREIxLDDNFdEgQBj84e\njR89NgHtrg783/YSHCitFrssIqIRh2GG6B49MCEGP1uTDo1Kjnd2n0H+/ouQ+MbaRESSwjBDNAjG\nJoThxaenwxgWhE8PXcKf/nEK7a7bb7lORESDh2GGaJDEhGuR/fQ0jDGF4vCpWry+rRh2571tD05E\nRN+NYYZoEIVoVfj5U1MxY5wR56oa8Zv3jqPW1ix2WUREwxrDDNEgUyrk+PHjaVj+QCJqbU785r0T\nuFDVKHZZRETDFsMMkR/IBAFPLEzBM8vuQ3OLC6/9vRhHT9eKXRYR0bDEMEPkRwummvDTJydDIRfw\nx0/KsfvwZa50IiIaZAwzRH42MSkC2eunITxEjR0FFXj387NwdXClExHRYGGYIRoC8UY9XsyajlHR\neuwvqcbvdpTC2eoSuywiomGBYYZoiBiC1di4LgNTUiJQXlmP375/AvVNLWKXRUQkeQwzRENIo1Lg\nP1dNxuKMeFRZHfj1e8dRYeZKJyKie8EwQzTEZDIB6x4ai6cWp6LJ3obf5J7Amx+Xoaae+9EQEd0N\nhdgFEI1US2ckIMGox/a9F3DsjAUnzloxd3IMHpu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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From aaecf78a9395064fcaa9fb0f667fc97f5c97a3d6 Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Sat, 16 Feb 2019 17:46:42 +0530 Subject: [PATCH 08/11] Intro to Neural Networks Programming Exercise solved!!! --- intro_to_neural_nets.ipynb | 1234 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1234 insertions(+) create mode 100644 intro_to_neural_nets.ipynb diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb new file mode 100644 index 0000000..d1cb6de --- /dev/null +++ b/intro_to_neural_nets.ipynb @@ -0,0 +1,1234 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_neural_nets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "O2q5RRCKqYaU", + "vvT2jDWjrKew" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "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": [ + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2I8E2qhyKNd4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pQzcj2B1T5dA", + "colab_type": "code", + "outputId": "e08b0378-a23f-4fa5-f2c3-ace1bbdb2c9e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1244 + } + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 204.9\n", + "std 116.4\n", + "min 15.0\n", + "25% 118.1\n", + "50% 177.1\n", + "75% 261.9\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RWq0xecNKNeG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Building a Neural Network\n", + "\n", + "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n", + "\n", + "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n", + "\n", + "`hidden_units=[3,10]`\n", + "\n", + "The preceding assignment specifies a neural net with two hidden layers:\n", + "\n", + "* The first hidden layer contains 3 nodes.\n", + "* The second hidden layer contains 10 nodes.\n", + "\n", + "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n", + "\n", + "By default, all hidden layers will use ReLu activation and will be fully connected." + ] + }, + { + "metadata": { + "id": "ni0S6zHcTb04", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zvCqgNdzpaFg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural net regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U52Ychv9KNeH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `DNNRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2QhdcCy-Y8QR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train a NN Model\n", + "\n", + "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n", + "\n", + "Run the following block to train a NN model. \n", + "\n", + "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n", + "\n", + "Your task here is to modify various learning settings to improve accuracy on validation data.\n", + "\n", + "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n", + "\n", + "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n", + "\n", + "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n" + ] + }, + { + "metadata": { + "id": "rXmtSW1yKNeK", + "colab_type": "code", + "outputId": "9f40e9e9-93fa-40b4-97c9-c6e6e424957e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 805 + } + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.0075,\n", + " steps=5000,\n", + " batch_size=10,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 172.55\n", + " period 01 : 149.08\n", + " period 02 : 218.00\n", + " period 03 : 112.26\n", + " period 04 : 125.67\n", + " period 05 : 106.90\n", + " period 06 : 113.98\n", + " period 07 : 109.01\n", + " period 08 : 102.11\n", + " period 09 : 114.69\n", + "Model training finished.\n", + "Final RMSE (on training data): 114.69\n", + "Final RMSE (on validation data): 115.13\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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EBDB37lymTp1KeHi4t0ITPlRd6yCnyEZqQhgGvc6z3a24ybZsIzQghNTwAe2e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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", + "outputId": "8a9ac727-566b-4651-f385-3e3dc480484d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=2000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 173.33\n", + " period 01 : 170.53\n", + " period 02 : 169.55\n", + " period 03 : 168.45\n", + " period 04 : 167.75\n", + " period 05 : 167.47\n", + " period 06 : 168.93\n", + " period 07 : 165.46\n", + " period 08 : 163.39\n", + " period 09 : 160.79\n", + "Model training finished.\n", + "Final RMSE (on training data): 160.79\n", + "Final RMSE (on validation data): 156.39\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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wkLAjhBCinpCAIwBwsLGgb2gT+oY2Ia9Qy/ELNzh6Lo2zl7O4nJrHj79dopmn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\nl1NZ18SgnjGYzcYsOSo4LqSNtTEpF+NSNsbk7lysZiu9o3p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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "c6diezCSeH4Y", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Evaluate on Test Data\n", + "\n", + "**Confirm that your validation performance results hold up on test data.**\n", + "\n", + "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n", + "\n", + "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)." + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "outputId": "54fe17e3-aac5-4259-e263-be92a361200e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "# YOUR CODE HERE\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 156.27\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", + "outputId": "99c1840a-0758-4159-ac9c-756099b43821", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 156.27\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file From a36612464801294fcfb0b0bc87b3d0b752b36ffe Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Sat, 16 Feb 2019 20:56:13 +0530 Subject: [PATCH 09/11] Sparsity and L1 Regularization Programming Exercise solved!!! --- sparsity_and_l1_regularization.ipynb | 1180 ++++++++++++++++++++++++++ 1 file changed, 1180 insertions(+) create mode 100644 sparsity_and_l1_regularization.ipynb diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb new file mode 100644 index 0000000..0cd9e1c --- /dev/null +++ b/sparsity_and_l1_regularization.ipynb @@ -0,0 +1,1180 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "sparsity_and_l1_regularization.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "yjUCX5LAkxAX" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "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": [ + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "3V7q8jk0IjnW", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pAG3tmgwIjnY", + "colab_type": "code", + "outputId": "7adf76af-d491-451e-e1f8-f044052ea24c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + } + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value_is_high\n", + "count 5000.0\n", + "mean 0.3\n", + "std 0.4\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 1.0\n", + "max 1.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gHkniRI1Ijna", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bLzK72jkNJPf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_buckets(feature_values, num_buckets):\n", + " quantiles = feature_values.quantile(\n", + " [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n", + " return [quantiles[q] for q in quantiles.keys()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "al2YQpKyIjnd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + "\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"households\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"longitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"latitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"housing_median_age\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"housing_median_age\"], 10))\n", + " bucketized_total_rooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_rooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n", + " bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_bedrooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n", + " bucketized_population = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"population\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"median_income\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"rooms_per_person\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"rooms_per_person\"], 10))\n", + "\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n", + "\n", + " feature_columns = set([\n", + " long_x_lat,\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_total_rooms,\n", + " bucketized_total_bedrooms,\n", + " bucketized_population,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hSBwMrsrE21n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Calculate the Model Size\n", + "\n", + "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works." + ] + }, + { + "metadata": { + "id": "e6GfTI0CFhB8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def model_size(estimator):\n", + " variables = estimator.get_variable_names()\n", + " size = 0\n", + " for variable in variables:\n", + " if not any(x in variable \n", + " for x in ['global_step',\n", + " 'centered_bias_weight',\n", + " 'bias_weight',\n", + " 'Ftrl']\n", + " ):\n", + " size += np.count_nonzero(estimator.get_variable_value(variable))\n", + " return size" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XabdAaj67GfF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Reduce the Model Size\n", + "\n", + "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n", + "\n", + "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n", + "\n", + "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?" + ] + }, + { + "metadata": { + "id": "G79hGRe7qqej", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Task 1: Find a good regularization coefficient.\n", + "\n", + "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n", + "\n", + "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n", + "\n", + "Again, the model will train on the entire data set, so expect it to run slower than normal." + ] + }, + { + "metadata": { + "id": "1Fcdm0hpIjnl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " regularization_strength,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " regularization_strength: A `float` that indicates the strength of the L1\n", + " regularization. A value of `0.0` means no regularization.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 7\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on validation data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " # Compute training and validation loss.\n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9H1CKHSzIjno", + "colab_type": "code", + "outputId": "6a6cbe11-5d8a-46c7-9c1e-45ed3a15aeab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 732 + } + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=1.0,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.36\n", + " period 01 : 0.31\n", + " period 02 : 0.29\n", + " period 03 : 0.28\n", + " period 04 : 0.27\n", + " period 05 : 0.26\n", + " period 06 : 0.26\n", + "Model training finished.\n", + "Model size: 537\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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33RTRYxIetl3Yc/EA6aUXTD6+EEIIYQzSzJhQSJAHwYHuJF8sYWeC6aebLHQW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\n2nCkqg0AkGHXS+Fmdk4aNCr+V010NfwOISJKAItRjRUL0rF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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", + "outputId": "166f78b5-ba4f-48f7-ea8a-fe3f3928b024", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " regularization_strength=0.1,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.32\n", + " period 01 : 0.30\n", + " period 02 : 0.28\n", + " period 03 : 0.27\n", + " period 04 : 0.26\n", + " period 05 : 0.26\n", + " period 06 : 0.25\n", + "Model training finished.\n", + "Model size: 765\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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YqZRYu+cykjKKpS7JaDra++KFkGcgCAKWn1+NpKJkqUsiIqJ2iGHGCFwcbOoe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\ndtw3ywqFnA3DRBQeGGaI6Ib0GhVWLUrC9/wNw/t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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 283d009dcb549711fae21dfb39e26b5f920ce9de Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Sun, 17 Feb 2019 17:14:49 +0530 Subject: [PATCH 10/11] Improving Neural Net Performance programming exercise solved!!! --- improving_neural_net_performance.ipynb | 2057 ++++++++++++++++++++++++ 1 file changed, 2057 insertions(+) create mode 100644 improving_neural_net_performance.ipynb diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb new file mode 100644 index 0000000..09664ec --- /dev/null +++ b/improving_neural_net_performance.ipynb @@ -0,0 +1,2057 @@ +{ + "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": "python3", + "display_name": "Python 3" + } + }, + "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": [ + "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", + "outputId": "a15673a8-ea4f-4dcb-b0ad-e2d10021b1db", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1244 + } + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 208.4\n", + "std 117.1\n", + "min 15.0\n", + "25% 119.5\n", + "50% 180.9\n", + "75% 267.1\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "NqIbXxx222ea" + }, + "cell_type": "markdown", + "source": [ + "## Train the Neural Network\n", + "\n", + "Next, we'll train the neural network." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6k3xYlSg27VB", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "De9jwyy4wTUT", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural network model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "W-51R3yIKxk4", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " my_optimizer,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A tuple `(estimator, training_losses, validation_losses)`:\n", + " estimator: the trained `DNNRegressor` object.\n", + " training_losses: a `list` containing the training loss values taken during training.\n", + " validation_losses: a `list` containing the validation loss values taken during training.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor, training_rmse, validation_rmse" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KueReMZ9Kxk7", + "outputId": "30aa2bc1-d4ee-4a7e-bc43-a83e52d1bcb5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 805 + } + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 168.69\n", + " period 01 : 168.24\n", + " period 02 : 168.78\n", + " period 03 : 168.10\n", + " period 04 : 166.35\n", + " period 05 : 165.46\n", + " period 06 : 163.86\n", + " period 07 : 162.48\n", + " period 08 : 147.25\n", + " period 09 : 133.89\n", + "Model training finished.\n", + "Final RMSE (on training data): 133.89\n", + "Final RMSE (on validation data): 136.94\n" + ], + "name": "stdout" + }, + { + "output_type": 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FqlWrmDZtmve5p59+2nv/nnvu4eqrryYrK4v169fz+uuvU1FRwfXXX8+MGTNa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\n/PPPAwDq6+uRkpKCZ599Fmbz/3xjYH19PSZPnuzwtZqaOkT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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", + "outputId": "4ac20144-0912-4d8d-9558-4b5245db5baf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "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", + " normalized_dataframe = pd.DataFrame()\n", + " normalized_dataframe[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " normalized_dataframe[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " normalized_dataframe[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " normalized_dataframe[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " normalized_dataframe[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " normalized_dataframe[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " normalized_dataframe[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " normalized_dataframe[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " normalized_dataframe[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return normalized_dataframe\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=0.002),\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 : 173.07\n", + " period 01 : 111.49\n", + " period 02 : 95.61\n", + " period 03 : 79.55\n", + " period 04 : 75.29\n", + " period 05 : 73.17\n", + " period 06 : 71.83\n", + " period 07 : 70.98\n", + " period 08 : 70.48\n", + " period 09 : 70.01\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.01\n", + "Final RMSE (on validation data): 69.82\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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0VAjlJR7Y3RuxO3sfhWWFZkcSERGp01RgqsGJVXnd8xvhMBzszPjN5EQiIiJ1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\nJbtENSzhRVPHdXsCZictj0wvjIAiRC9ZEFpEQe2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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_", + "outputId": "27b3776e-3489-44a1-b929-d5c91ce3d763", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n", + " steps=2000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 172.11\n", + " period 01 : 115.07\n", + " period 02 : 104.18\n", + " period 03 : 89.90\n", + " period 04 : 80.19\n", + " period 05 : 75.96\n", + " period 06 : 74.27\n", + " period 07 : 72.85\n", + " period 08 : 72.11\n", + " period 09 : 71.48\n", + "Model training finished.\n", + "Final RMSE (on training data): 71.48\n", + "Final RMSE (on validation data): 71.31\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6FgUYJ0joG4rFbKLgYCCgxR1FRETamwKME/h6ezCwVxDZh8HubSejKJM6R52r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\nt3Y/Fw3pyX+NuYXXNr/FovTXuDXxOlL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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "MrwtdStNJ6ZQ" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Optimizer\n", + "\n", + "** Use the Adagrad and Adam optimizers and compare performance.**\n", + "\n", + "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n", + "\n", + "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "61GSlDvF7-7q", + "outputId": "632bbf61-1cb9-4ac7-ef4e-8ae12063f05c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "_, adagrad_training_rmse, adagrad_validation_rmse = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\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": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 113.30\n", + " period 01 : 104.06\n", + " period 02 : 89.75\n", + " period 03 : 78.38\n", + " period 04 : 73.74\n", + " period 05 : 72.26\n", + " period 06 : 71.57\n", + " period 07 : 71.13\n", + " period 08 : 70.89\n", + " period 09 : 70.60\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.60\n", + "Final RMSE (on validation data): 70.52\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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LvxtHcuNZFL/MpJQiIiINnwpMPQsL8uKusRGUlFXy5ry95J9zFLrVYmVG11/R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\n7xJWmh1LRER+gQqMNHs3DG5PcIAHibt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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "n9OtJOBDFIUx", + "colab_type": "code", + "outputId": "458b2e37-c496-4305-c302-dd2451946f69", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "_, adam_training_rmse, adam_validation_rmse = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(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": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 118.74\n", + " period 01 : 91.92\n", + " period 02 : 73.15\n", + " period 03 : 71.37\n", + " period 04 : 70.49\n", + " period 05 : 70.41\n", + " period 06 : 69.63\n", + " period 07 : 70.40\n", + " period 08 : 69.08\n", + " period 09 : 68.92\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.92\n", + "Final RMSE (on validation data): 68.68\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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45plnTigr8+bN4/bbb8disfie2759O6NHjyYyMpKQkBAmTJhAZmamv2L51eS0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\n/2p0BrTlA+dkAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "mccx4QkjNWYr", + "colab_type": "code", + "outputId": "3c9a4f07-05e1-43ca-af99-ff512a009f03", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376 + } + }, + "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_rmse, label='Adagrad training')\n", + "plt.plot(adagrad_validation_rmse, label='Adagrad validation')\n", + "plt.plot(adam_training_rmse, label='Adam training')\n", + "plt.plot(adam_validation_rmse, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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XX32JkpLiO4qvLkkiF0IIcVe2bduCt7cPu3bdetI0h7KyMlas+P6Wz58+/SE6depyw+Ov\nvfYWVlY6U4RmUo1yi1YhhBANQ25uDqdPn+Sll17h+++XMmbMBAAOHTrARx+9j5OTM87OLnh5eVNe\nXs4bb/yTtLRLFBUVMWPGo/Tu3ZeDB8Mvn+tC8+YtcHBwICgohB9+WE5hYSHz5r3Mzp2/s2vXDior\nK+nZszczZjzKpUupzJv3IhYWFvj5+V8X20cfLeTcuRjee+9tOnToyP79+0hPT+O1197khx+Wc+rU\nSUpLSxkzZjyjRo3hjTf+Sf/+A8nJyebYsSNkZ2eRkHCeKVOmM3LkGCZMGMXSpSv497/fwcXFlbNn\nT5OamsIrr7xO27bt+OCDdzl+/BgtW7YiIeE8r732Jp6eXnX+O6jTRB4VFcXs2bN56KGHmDZtGhcv\nXuSll16ivLwcrVbLu+++i6urK2vXruXbb79FrVYzadIkJk6cWJdhVVNcVMaxwxfwaGaHSqUyW7tC\nCGFKP+6M4eCZSya9Zrd2bkwa4FfrOTt3bqdXrz50796TBQteJy3tEq6ubnzxxSfMmzefNm38ee65\nJ/Hy8iYvL5d77unBsGEjSUq6wLx5L9K7d18+++xj5s37F61bt+Hxx2fRrVt3AM6di+G///0Zb29n\ndu78nUWLvrqcJ0Zz331TWLXqBwYOHMKkSfezfPkSYmKiqsU2Zcp0Tp06wXPPvcjGjetITU3h888X\nU1paioeHF0888QwlJcVMmjSGUaPGVHvtuXMxfP75Yi5cSOTVV/+PkSOrHy8tLWXhwk9YvXoVmzdv\nQKvVcuzYEb76ahlxcbHMmDHVBL+BW1NnibywsJD58+fTs2dP43MffPABkyZNYvjw4Xz33Xd88803\nzJkzh08//ZRVq1ZhYWHBhAkTGDx4MA4ODnUVWjVRJ1LZuyOGsHGdaOnvYpY2hRDiz2L79i08+OBM\nNBoNoaED2bFjK5MnVw3c2rSpGiUHBgZTUlKCra0dp0+fZO3an1Gp1OTm5gCQmnoRf/92APTo0YuK\nigoA/PzaYGlpCYBOp2POnEfRaDRkZ2eTm5tLfHwcoaGDAAgK6sr+/ftqjbV9+w6oVCqsrKzIzc3h\nb3+bgVarJTs767pzO3XqgkajwdXVjYKC/OuOBwQEAeDq6s6pUyeJj4+jQ4fOqNVqWrf2w8PD8066\n847UWSK3tLTkyy+/5MsvvzQ+9+qrr2JlZQWAo6MjJ0+e5OjRo3Tu3Blb26rKLsHBwURERDBgwIC6\nCq0ad287AOKi0yWRCyEarUkD/G46eja1S5dSOXXqBJ988gEqlYri4mJsbQ1MnjwNtfp/S7AURQGq\nFsXl5uby6adfkZubyyOPTL/umlfPjFpYWACQlJTEihXfsXjxd+j1eqZPn2S8rkqlvvxz5U3j1Wqr\nrhcZeZiIiEN88sl/0Gq1DB7c97pzNZr/FWq5En/txxXU6v/Fbs4Z3jpb7KbVatHpqi8K0Ov1aDQa\nKioq+P777xk1ahTp6ek4OTkZz3FyciItLa2uwrqOm6ctBjsrzsekU1l58z8EIYQQVbZv38LYsRP5\n9tv/smTJ9/z3vz+Rm5tLUtIFXFxcSUiIR1EUIiMPA5CdnY2npxdqtZrffttJWVkZAE5Ozpw/H09F\nRQUHD4Zf105WVhaOjo7o9XrOnj1DSkoKZWVlNG/egjNnTgEQEXHoutepVGrj6P5qOTnZuLm5o9Vq\n2bPnNyoqKo2x3Clvbx/Onj2DoijEx8eRknLxrq53O8y+2K2iooLnn3+eHj160LNnT9atW1fteE2f\nfK7l6Kg3aVm7th09OPzHeYrzy2nR2tlk1xU1q62urjAd6WfzaMr9vGvXdhYsWFCtD8aPH8cff+zi\nH/94ln/+8//w8vKieXMfbGysGDt2FI899hjR0acZP348Xl6erFjxLc899yyvvPICPj4+tG3bBltb\naxwc9FhZWeDqaouTU3vs7e144olZhISEcP/9k/n44/d44403+Pvf/87+/b/j7++PpaW2WiwODjoU\npYL581+mf//+6PWWuLraEhY2kBUrlvP0048xaNAgQkP788kn76HTWWBvb01lZbHx3IICNRqNGldX\nWzQaNS4uBuN5rq622Ntbo9NZ0Ldvd9avb83s2TPo0KEDfn5+uLrameXvQ6XcSua8Cx9//DGOjo5M\nmzYNgOeffx4fHx+efPJJAMLDw1mxYgULFy4E4KWXXmLIkCGEhobe8JppaXkmjTEno4jvvwynSzcf\neg8079RUU+Pqamvy35+4nvSzeUg/m8aBA/tp1qw5np5evPPOGwQGhjBkSJjxeGPo59LSUnbs2Mqw\nYSMpKipi6tQJ/PjjGrRa04yXa/tAYNYR+dq1a7GwsDAmcYCAgADmzp1Lbm4uGo2GiIgI/u///s+c\nYeHr54yFpYa4qHR6DWgtq9eFEMKMFEXh//7vOfR6GxwdnQgNHVjfId02S0tLzpw5xapVK1CrVTzy\nyN9MlsRvps5G5CdOnGDBggUkJSWh1Wpxd3cnIyMDKysrDAYDAK1bt+af//wnmzdv5uuvv0alUjFt\n2jT+8pe/1HptU38yc3W15bsv93PuTBqTZnTF2c1g0uuL/2kMn6z/DKSfzUP62Tykn+tpRN6pUyeW\nLVt2S+eGhYURFhZ28xPrUEt/F86dSSMuOl0SuRBCiEajyW/RqigKpZlZNG/ljFqtIj46vb5DEkII\nIW5Zk0/kuXt2c3DmoyipF/Bq7kBaSj75uQ1vU3whhBCiJk0+kWvs7KGykrwD+40bwsTJqFwIIUQj\n0eQTub5DB9Q6HfkRh433kMdHZ9RzVEII0Xg05DKmt2rOnEeJjY1h48Z1/Pbbr9cdHzGi9pX0V8ql\n7t+/j19+WXXHcdyJJp/I1RaWOHULoSwtDcvcS7h62JKckE1J8d3t8iOEEE1FQy1jeieGDx9Fv343\n3sekJleXS+3Roxdjx06oi9BuSMqYAs49e5D++17yDh+ipX8IaSl5nD+XiX9H9/oOTQghGrSGXMb0\npZee4777plwu2lLM1KkT+f77n3jrrX9dF8MVX3/9BQ4ODowePZ7XXpvLpUuptG/fwXj84MFwvvrq\ncywsLLC1teVf/3r7unKpsbHnmDPn7/z443/ZsWMrAH379mPatId4441/1lgC9W5IIgccg4NQabXk\nRxzG94khHNgdR3x0uiRyIUSj8XPMeiIvHTfpNYPcOjPOb2St5zTkMqb9+oWyd+/vBAYGc/BgON26\n9aCgIL/GGK518OB+ysvL+eKLbzh58gSrVq0AIC8vj1dffR0vL2/mz3+F8PA/riuXCpCcnMSmTev4\n8sulADz66IPGSm3XlkCVRG4CGmtr9J06U3AkEkN5LnYOOhJiM6kor0SjbfLfPgghxA015DKmvXvf\ny/ffL+Xxx5/i999/Y+DAITeM4VpxcXF07twFgI4dOxkrdzo4OLBgwetUVFSQnJxESEi3Gl8fHX2W\njh07G3d369w5wPhB49oSqHdLEvllhqAQCo5EUhAZQcs27Th68AIXzmdJERUhRKMwzm/kTUfPptbQ\ny5ja2tri4uJGQkI8J04c4x//+L9biuFy1MZrX/0e3nprPu+++wG+vi1ZuHBBLb2jqlYErKyszHi9\nm5VIvV0y3LzMEBAIajV5EYfxvXwbmmwOI4QQN9bQy5gC3Htvf779drFxdHyjGK519bWPHz9KaWkp\nAAUF+bi7e5CXl0dExGFjgr62XKq/f1tOnDhOeXk55eXlnDp1En//tnfQyzcnifwyjcGAvm17SuLj\ncNaVo7O2ID46wySfloQQ4s9o+/YtjBgxyvhYpVIxbNhItm/fwqOPzmbu3Bd44YWncXOrWm/Uv/8A\n9u37naeeegxra2vc3Nz45psvmTVrNi+//A9efPEZWrTwrTZiBWjfvj3W1noee2wGO3ZsZfTocbz/\n/gImTryfDRvW8swzc8jLq3kv9nvv7c+OHVuNhVhuFMO1evToTWlpCXPmPMqOHVtxdXUDYNy4iTz2\n2EzeeecNpk59gOXLl6BSQXl5GXPnvmB8vaenF3/5y1ieeOJRHn98FqNGjcbDw/PuOvwG6ryMaV2o\ni6IpaWl5ZP+6k0vfLcV18lSOlDTjzPEUxk4PwsPb3qTtNWVS/MA8pJ/NQ/rZNP4MZUzrWm1FU2RE\nfhVDUDCoVORHHLpqel02hxFCiLp0pYzp44/PIjc3t1GWMa1PstjtKloHB3StWlMUHUVzJy1arZq4\n6HR69G9V36EJIcSfVvfuPenevWd9h9FoyYj8GobgEFAUSk4epVlLJ7IzCsnKKKzvsIQQQogaSSK/\nhiE4BKBq9XqbK3uvy+p1IYQQDZMk8mtYurph1aw5hadP0sxbj0ol1dCEEEI0XJLIa2AI6QoVFVTE\nnMLTx57UpFwK80vqOywhhBDiOpLIa3Blej3/8CF821xevR4jq9eFEKIm5ixjGhMTTULC+Vs6NyMj\nnXfeeeOGx+uj5GhdkEReA0tPLyw8PCg4cZwWLewA+Z5cCCFuxJxlTH/7bSeJiQm3dK6zswvPP//y\nDY/XR8nRutDkbz/LKMri91N76ObYDZ22alN8lUqFbXBXMjeuR5MUg5OrDRfisygtKcfSqsl3mRBC\nGJmijOmcOY8SHNyVgwfDUavVDBs2go0b16NWq/nww8+MbZ07F8OaNT/z2287cXR05F//mkePHr1x\ndHSkV6++LFy4AK1Wi1qtZv78tykoKGDu3Bf4+utl3HffGEaPHsfevb9TWlrKhx8uYteuncTGnmP8\n+Em88cY/8fLyJiYmGn//trz44jxiYqJ5441XMRhsadeuA9nZWbz88j/rqadvrMlnpTNZUfxwZi0n\nnKN4tPODaNRVWwMagkPI3Lie/IhDtPQfwuF950mMy6R1O7d6jlgIIa6XtvIH8g4dNOk1bbt2w3Xi\n5FrPMUUZU6gaPX/22dc89tgMcnNzWbToK2bPfoTY2Bg8PLoC0Lq1H92796R//4F06NCJ8vJyevTo\nRY8evTh4cD9PP/0P/P3b8dVXn7N16yZ6977XGGdFRQXNm/syZcoDvPrqSxy6pq/Onj3Na6+9iaOj\nE2PHDicvL49vvvkPDz00i379Qpk370V0Op1J+9dUmvzUeg+PrgR4tOdExhl+jFpt3FvdqoUvWicn\nCo4ewbeVIyC7vAkhxLW2b9/CoEFDq5UxBa4rYwoYS4g+9tgM3njjn9VKiHbo0BGoSuht2lQVF3Fy\nciI/P7/W9q+8ztHRmS++WMScOY+yffsWcnKuL096dfnQgoLq1/X2boazswtqtRoXF1cKCvI5fz6e\nLl0CAOjT597rrtdQNPkRuUat4eles3h567vsSQ7HWefEEN9QVCoVhuAQsrdvQ5+VgI2tFfExGVRU\nVKLRNPnPP0KIBsZ14uSbjp5NzZRlTK8ulHI7ZT612qpSpx9++B5Tpz5Ijx69+P77ZRQVXb+RV23X\nvbZQi6Io1cqkXl1etaGRjAToLayZHTADRysH1sRu4mBKJACG4KrpnKoa5S6UlpRzMbHmIvRCCNHU\nmKqM6e1QqVTXlQwFyMnJxtvbh9LSUvbv30t5efldvz9vbx9jKdP9+/fd9fXqiiTyyxys7JkdMANr\nrY5lp38kKisGa782aGxtyY+MwNfPCZDV60IIcYWpypjejoCAID744F0OHTpQ7fnx4+/jpZeeY968\nFxg//j42bVp/02n5m3nggZl8+ukHPPPMHBwdHavNMjQkUsaU6iXyorJi+OTI11hqLHgmeDaanzeR\ns/s3vJ57kRWb0rC00jLtsR4NepqlIZNyhOYh/Wwe0s/mUV/9fOLEcXQ6HX5+bVi27BsUReGBB2aY\nPQ6QMqa3xd/Rj2ntJ1JUXsyio4uhU3sACo9E0KK1M/m5JaSn3t2nPCGEEA2fpaUFb789n8cfn0Vk\nZARjxoyv75Bq1OQXu9XkHo9gsoqzWRu7mW/U+xiv05EfcRjfhwcRfeoScdHpuHrc+NOREEKIxq/q\nVral9R3GTTX5EfnFjAK+XHOc/KLqiy6GtAilt1d3EotSSGpuS3lmBu7afNQalXxPLoQQosFo8ok8\nNjmXtbtj+fePRykq+d8qR5VKxX3+Y+jk3I4I91IAio9H4NPCkYxLBeRmF9VXyEIIIYRRk0/kPTt5\nMKBrM+Iu5vLJz8cpK//fbQ0atYaHO06lso0vZRpIDd9NS//LRVRkcxghhBANQJNP5GqViicnBRLU\nxoXT57P4fM1JKiorjcd1WisevaeTAAAgAElEQVRmhTzCRR8Dlhm5ZJWcAKRGuRBCiIahySdyAI1G\nzd9Gd6Rdcwcio9NZsukMlVfdlWdvZUvbe0cCEBO+Fjs3Cy4mZlNcdPubGQghxJ+NOcuY3qqIiEPM\nnfs8AC+++Mxtx3R1udRXX32JkpLiugnUBCSRX2ah1fDE+C609LRl7/EUftwZU20LP69ufUGjxi+x\nmFjdaRQFzkuNciGEMGsZ0zvx9tsLb/s1V5dLfe21t7CyapgFU0BuP6vG2krL3ycG8PZ3EWw9mIiN\ntQWjevkCoNHboG/fEdcTxym1SAT8iDpzkbadPeo1ZiGEqE/mLGMaHR3Fxx8v5KOPPgdg8eL/YGtr\nh69vS7766nMsLCywtbXlX/96u1qMI0YMZMOGHbcck4eHZ7Vyqa+88hJLl64gPz+Pt976F2VlZajV\nal58cR4qlarGEqjmJIn8GrZ6S569L5C3lkfwy+5YbHRaBgT7AFWlTQtPHGdkpRt/6PJJjKsgr6gA\nW2ubeo5aCNHU7dt5jtgzl0x6zVbt3Og1oHWt55izjGmbNv6kp6eRl5eHra0te/bsZsGChRw/foxX\nX30dLy9v5s9/hfDwP9Dr9dfFeqsxLV68vFq51Cu++upzRo4czcCBQ/j11+0sXvwfZs78a40lUG1t\nzbfXiEyt18DJTsdzkwOx01vw3dYo9p9MAcAQGAwqFZ5x2dg316Kq1LBk92rKK+9+c34hhGiMzF3G\ntHfvewkP30dKSgpWVpa4urrh4ODAggWvM2fOo0RGHq523avdbkzXOnv2NEFBIQAEB3clOvosUHMJ\nVHOSEfkNuDvpeea+QBZ8H8nXG05jbaUlwM8F6zb+FEVHMWj0A6yPOkdeYiXfnVnFA+3vk/3XhRD1\npteA1jcdPZtafZQx7dcvlJ9++pGcnGz69RsAwFtvzefddz/A17clCxcuuGG8txvT9VTG15WVlRtL\nnNZUAtWcZERei+butjw1oQsatYpFq08QlZiNITgEFAVDSjTWegvsczw5cDGC9XFb6ztcIYQwq/oo\nY9qxY2fi42PZt28v/fsPAqCgIB93dw/y8vKIiDh8w+veTkw1lUtt374DERGHADhy5DDt2rW/7fjr\ngiTym/Bv5sDssZ2prFT4cNVRcnyqpnwKIg/j28YFdZkWz7IWbI7fwd6k8HqOVgghzKc+ypiqVCo6\ndQqgoCAfD4+qxcbjxk3kscdm8s47bzB16gMsX76EjIzr9/q4nZhqKpf6yCN/Y/PmjTz55N/YuHE9\nM2f+9bb7rC5IGVNurUTegdOpfLHmJAa9BU9kbqPyYhIWT7zG5vUxtAl2Zqv1TxSVF/O3Lg/R0bmd\nSeP7M5Gyj+Yh/Wwe0s/mIf0sZUxN4p727kwf2pa8wjL2V7pDRQUO2fFoLdRciivkr50fQqNS89WJ\n5STkXajvcIUQQjQRkshvQ/8gb8b3a0Wk1huA3MMHad7KmZysIhzLXXmo4xTKKsr47Og3ZBRl1nO0\nQgghmoI6TeRRUVEMGjSI5cuXG59bunQpHTt2pKCgwPjc2rVrGT9+PBMnTmTlypV1GdJdG96jBd37\ndiLN0oGCkyfw8jEAEBeVTqBrJ8a3GUVuaR6Lji6msKywnqMVQgjxZ1dnibywsJD58+fTs2dP43Or\nV68mIyMDNze3aud9+umnLFmyhGXLlvHtt9/ecL/ehkClUjGxf2uKW3VAW1lB5O/hqFQYa5SHNuvD\ngGZ9SSm8xBfHv6VM7jEXQghRh+oskVtaWvLll19WS9qDBg3i6aefrna/9dGjR+ncuTO2trbodDqC\ng4OJiIioq7BMQqVS0WviUADsEk5RodNy6WIe+XklAIz1G0GQa2disuNYdmoFlUplbZcTQggh7lid\nJXKtVotOV32TeYPBcN156enpODk5GR87OTmRlpZWV2GZjK55c7QuLvgXJZFUUJXAr5Q2VavUPNhh\nMq3sfTl86Shrz22uz1CFEEL8iTW4nd1u5W44R0c9Wq3mpufdjtqW9t9IQZ9eJK9ei59NHiVFDhw4\nkEjokLbGGYeXQx9n7o532Zawi2Yu7oS16W/SmBurO+lrcfukn81D+tk8pJ9vrN4TuZubG+np/7tx\n/9KlSwQGBtb6mqws0y4iu9N7FDXtOgNrGWKTxk9ljlRmF/GflUcYF+pnPOdvnR7mvUOf8k3Ej1iU\n6eji2tGEkTc+cj+oeUg/m4f0s3lIPzfw+8gDAgI4fvw4ubm5FBQUEBERQdeuXes7rFuia9Uajb09\nxSeOEhzkhRoV+8IT2HYw0XiOi7UzjwU8jIVay+KT3xOfm1CPEQshhPizqbMR+YkTJ1iwYAFJSUlo\ntVq2bNlCr1692LdvH2lpacyaNYvAwECef/55nn32WWbOnIlKpeLxxx83a/m3u6FSqzEEhZCzayct\nDUWcAFy1Gv67Ixq9Tkvvzp4AtLBrxoxOU/ni2Ld8dvQbnguZg6veuX6DF0II8acgW7Ryd9M2BadO\nkrTwXexDB7Ittw1FhWUcU1dSVFrJ42M7EeTvajx394U/WBH1C27WLjwb8jgGy6ZXx1ymyMxD+tk8\npJ/NQ/q5gU+tN3Z6/7ao9TYUHInA18+Z8rIKpvVphVar4rM1Jzl9Pst47r0+PRncvD+XitL54vgS\nSituv/KPEEIIcTVJ5HdJpdViCAykPCsLL9tSAEoyi3hiXBcUReGjn44RdzHXeP5fWofR1T2Q2Jzz\nfHvqB7nHXAghxF2RRG4ChuCqxXk2iSex0mmJj0mng68jf/1LR0rLKvj3j0dJTq/aklatUjOt/STa\nOLTiSNpxfonZUJ+hCyGEaOQkkZuAvmNHVFZWFEYepoWfMwV5paSl5NG1nRsPhrUjv6iM91ccIT2n\nCAALtZZHOz+Ah407OxN/59fEPfX8DoQQQjRWkshNQG1hiU3nAMoupeJzeZO6uKiqe+PvDfBiUqgf\nWXklvP/DEXIKqqbf9RZ6ZneZgZ2lLT9FryPy0vH6Cl8IIUQjJoncRGyDQwBwuHQWjVZt3K4VIKx7\nc0b0bEFqVhH/XnGEwuKqRW7O1o7MDpiBhcaCb0/9l9ic+PoIXQghRCMmidxEbLp0QaXVUhR5CB9f\nR7LSC8m5age6cfe2on+QNwmX8vlw1TFKyioAaGbrzSOdplOhVPL5sSWkFjb8feaFEEI0HJLITUSt\ns0bfoSOlSRdo7m4BQFxUhvG4SqVi2mB/7mnvRvSFHBb9coLyiqoV6x2d23J/23EUlBWy6MjX5JXm\n18t7EEII0fhIIjehK6vXnbJiAapNrwOo1SoeGdmBzq2cOR6bwVfrT1FZWbUfTy+vexjmO5D04kw+\nO/YNpRWl5g1eCCFEoySJ3IQMAYGgVlN+4hAePvakXMihsKB6QtZq1Mwe2wk/H3sOnL7Ed9uijBXf\nRrQcQnePEM7nJvLNyf/KPeZCCCFuShK5CWlsbbH2b0txbCzNvfUAnI/JuO48KwsNf5/QhWZuBn6N\nTOLn3VUjeJVKxZR242nn2IZj6SdZGbX2lsq6CiGEaLokkZvYldXrrkVVFdCunV6/Qq+z4Jn7AnFz\ntGbDH+fZHF5VFU2r1vJI52l42XiwO2kfOxJ3mydwIYQQjZIkchOzCapK5KpTh3F00XMhPouy0ooa\nz7W3seS5+wJxtLXix19j2H00GQBrrTWzA2bgYGXPLzEbOJx6xGzxCyGEaFwkkZuYhaMjulatKTp7\nhhYt7KgoryQxLvOG57s4WPPMfYEYrC34dvMZDp25BICjzoHZATPQaXQsPbWC6MsL6IQQQoirSSKv\nA4bgEFAU3MtTAYi/wfT6Fd4uNjw9KQBLCw3/WXeSk5cTv7fBk1mdp1OJwhfHvyWlILXOYxdCCNG4\nSCKvA1duQ7OMisDGYEl8TAaVlbWvQG/paceT47sAKj75+TjnknIAaOfUhmntJlJUXsSnRxeTU9K0\na/IKIYSoThJ5HbB0c8OqWTOKTp+kRUsHSorLuZiYc9PXtW/hyGOjO1JWXskHK49yIa1qY5juniGM\nbDmEzOIsPju2mOLykrp+C0IIIRoJSeR1xBDcFaW8HA91FgDx0dffhlaTIH9XHh7ejoLict5fcYRL\n2VUV08J8B9LL8x4S85JYfPI7KiprXkAnhBCiaZFEXkcMl29Ds4k/iqWVhrjo9Fu+J7x3Z0/uH9iG\nnPxS3v8hkuz8ElQqFZPbjqWDU1tOZpxhRdQvco+5EEIISeR1xdLLGwt3d4pOHKWZryN5OcVkphXc\n8usHd2vGX3r7kpZdzPsrjpBfVIZGrWFmp6k0M3ixN/kA4SmH6/AdCCGEaAwkkdcRlUqFISgEpaQE\nT6uqBH6lRvmtGt2nJQODfUhKK+DDlUcpKa1Ap9XxaJcHsVBbsPrcRorKi+sifCGEEI2EJPI6dGX1\nukPSCdRq1Q13ebsRlUrF/YPb0KOjO+eSc/nk52OUlVfipHNkSIv+5JXmszl+R12ELoQQopGQRF6H\ndL6+aB2dKDl+GO/m9qSn5pOXc3sjaLVKxYzh7Qlo7czJ+Cy+XHeSykqFQc3746Rz5NfEPVySGuZC\nCNFkSSKvQyq1GkNQMJWFhXjZlgE33xymJlqNmsfGdMK/mQOHzqaxdMsZLNRaxvqNoEKp4Kfo9aYO\nXQghRCMhibyOXVm97pR2FrhxEZWbsbTQ8NSELrRwt2X30Yus2nWOINfOtHFoxYmM05zMOGuymIUQ\nQjQeksjrmHUbfzQGWyqOHcDVw5bkhGxKisvu7FpWWp6+LwAPJz2bwhMIP5XKRP/RqFDxU/RayivL\nTRy9EEKIhk4SeR1TaTTYBAZRkZtLM2cVigLnz924iMrN2Okt+fukACy1albsjMFR60of7x6kFqbx\n24V9JoxcCCFEYyCJ3AxsQ6pWrztnnwNu/za0a7k5WDOiZwtyCkpZsyeOka2GoNdaszFuO3ml+Xcd\nrxBCiMZDErkZWLdrj9raGtXJcOwcrUmIzaC8/O62WA3r3hw3R2t2HL5AVpbCiFZDKK4oZu25zSaK\nWgghRGMgidwM1BYW2HQJoCI9neYelpSXVZJ0Pvuurmmh1TB1sD+VisLyrWfp49kdTxt3/rh4kITc\nCyaKXAghREMnidxMrqxedy1IAO5+eh2gcytngv1dib6Qw8HT6Uxo8xcUFFZGr5V92IUQoomQRG4m\nNp26oLKwwOpMODq9BfExt15EpTaTB/pVLXz7NYbm+pYEuHYiNieew6lHTBC1EEKIhk4SuZmorazQ\nd+pMWXIyzb2sKSooIzU5966v62JvzchevuQWlLJ6Tyzj/EaiVWv55dxGSipKTRC5EEKIhkwSuRnZ\nXp5edytLAUwzvQ4w9J7muF9e+FaYa8GgZveSXZLD1vO/muT6QgghGi5J5GZk0yUQNBpsYg6htVDf\n0XatNbHQqpk62B9FgeXbohjcvD/2lnZsT/iN9KI7v2ddCCFEwyeJ3Iw0Njbo27Wn/Hws3t4GsjOL\nyMq49RrltenUypkQf1diLuRw+EwWY/yGU15Zzi8xG0xyfSGEEA2TJHIzu7J63YMMAOKjM0x27ckD\n22CpVbPy1xg62neilX0LjqQdJyorxmRtCCGEaFgkkZuZITAIVCrs4yNRqUz3PTmAs72OUb19yS0s\nY/WeeCa2qdqHfWXUWioq724DGiGEEA2TJHIz09o7YO3Xhopzp/HwNJCanEtBfonJrj+kW3PcnfTs\njLgARfb08OxKckEKe5LDTdaGEEKIhkMSeT0wBIWAouBhWbUv+vkY002vVy18a2Nc+Day1VB0Gh3r\nY7eQX2aa7+OFEEI0HJLI64Eh5HKN8uRjgGmn1wE6tXSma9uqhW8nowoY1nIgheVFbIjdZtJ2hBBC\n1D9J5PXAwtkFqxa+cPYozi7WXDifRWmJaWuJTx7YBksLNT/+GsM9Lvfgrnfl96Q/SMq/aNJ2hBBC\n1C9J5PXEEBwCFRV42ZRQWaGQGGfa+72d7HSM6uVLXmEZ6/YkMr7NKBQUVkXJPuxCCPFnUqeJPCoq\nikGDBrF8+XIALl68yPTp05kyZQpPPfUUpaVVW4iuXbuW8ePHM3HiRFauXFmXITUYV3Z5c0o7C0Cc\niTaHudrQe5rj4aRnZ+QFDGXedHRuR1T2OY6knTB5W0IIIepHnSXywsJC5s+fT8+ePY3PffTRR0yZ\nMoXvv/+eFi1asGrVKgoLC/n0009ZsmQJy5Yt49tvvyU7++5KfDYGlp5eWHp6oT1zEIOtJedjMqio\nqDRpG1rN1Tu+nWWc30g0Kg2/xKyntKLMpG0JIYSoH3WWyC0tLfnyyy9xc3MzPhceHs7AgQMBCA0N\n5Y8//uDo0aN07twZW1tbdDodwcHBRERE1FVYDYohOARKS/F2UCgtqeBiouk/wHRs6UTXdm6cS8ol\n+lw5/Zv1JqM4ix0Ju03elhBCCPOrs0Su1WrR6XTVnisqKsLS0hIAZ2dn0tLSSE9Px8nJyXiOk5MT\naWlpdRVWg3JllzeXnFgA4qJMdxva1SYP8MPKQsOqXefo59EPWwsDW8/vJKv4zz/zIYQQf3ba+mr4\nRguubmUhlqOjHq1WY9J4XF1tTXq9W6G4dCLVzRXVmf3oWrUkITYDF5cgVCqVSdtxdbXl/iFtWbLh\nFLsi05kaOJbPDy5j04WtPNVzpknbutV4RN2TfjYP6WfzkH6+MbMmcr1eT3FxMTqdjtTUVNzc3HBz\ncyM9/X8LvS5dukRgYGCt18nKKjRpXK6utqSl5Zn0mrfKOiCYkm1b8HJWE5tUzOkTF3H1MP0fbK8O\nbmzZH8/GfXGE+HWlua0PexMOcY9LN/wcWpq8vRupz75uSqSfzUP62Tykn2v/IGPW28969erFli1b\nANi6dSt9+/YlICCA48ePk5ubS0FBAREREXTt2tWcYdWrK6vXXQoSAdNvDnPF1QvfvtsWxYQ2fwFg\nVfRaKhXTLrITQghhPnU2Ij9x4gQLFiwgKSkJrVbLli1beO+993jxxRdZsWIFXl5ejBkzBgsLC559\n9llmzpyJSqXi8ccfx9a26Uyh6Fr7obGzw3B2PxqvMcRHp3PPvXUzQu7g60S3dm4cPHOJ5PNedHMP\n5mBqBH9cPEhvr+510qYQQoi6pVIa4e4gpp5iqe9pm9RlS8j5bRdnev+VpNQSpv6tO3YO1nXSVmZu\nMS9/GY6FVs0LD7bnvaMfYKm24NUez6O3qJs2r1bffd1USD+bh/SzeUg/N6CpdVEzQ3DVVwnuZSlA\n3U2vQ9WOb3/p40t+URk7wzMY2mIA+WUFbIrfXmdtCiGEqDuSyBsAfdt2qPV67GIOABBfB7u8XW1w\n12Z4OuvZFZmEn2UgLjondl3YS0rBpTptVwghhOlJIm8AVFothoAgNJkXcXW25OKFHIoKS+usPa1G\nzbTB/ijAD9tjGeM3kkqlkp+i18k+7EII0chIIm8grmwO40EmigLnz5m2iMq12vs6cU97N2KTc8lN\ndqSdYxtOZZ7lRMbpOm1XCCGEaUkibyD0HTuhsrTE/nzV9rTxdfg9+RX3DWiDlaWGn36LZVjzYahV\nan6KXkdZpWlLqgohhKg7ksgbCLWlJTadu2CVEou9nQWJcZmUlVXUaZuOtlaM7t2S/KIy9h0qoK93\nT9KKMtiVuKdO2xVCCGE6ksgbkCur1z0t8ygvr+RCfFadtzmoqw9eLjb8FplEZ30PbCz0bI7fQU5J\n077VQwghGgtJ5A2ITZcAVFotjslV9cLNMb1u3PENWLUjgZEth1JcUcLac5vqvG0hhBB3TxJ5A6Kx\ntkbfvgPWCSewttYSH5NBZWXdryJv38KR7h3cibuYR0WaD94GT/anHCI+N6HO2xZCCHF3JJE3MIbg\nEFSAp76Y4qIyUpJyzNLupFA/rCw1/PxbHCObjwBgZZTswy6EEA3dHSfy+Ph4E4YhrrAJDAKVCuf0\ns0Ddbw5zhaOtFWP6tKSguJwjRyoJcutCfG4CB1MizdK+EEKIO1NrIn/44YerPV60aJHx51deeaVu\nImritLZ2WPu3xXDuMBYWauKi0s22ScvAEB+8XWz47UgyXe3uxUKtZc25jRSXF5ulfSGEELev1kRe\nXl79fuL9+/cbf5YdwOqOITgENZV42JaTm11MZnqBWdrVatRMG1K18G3tzlQGNetPTmkeW87/apb2\nhRBC3L5aE7lKpar2+Orkfe0xYTqGoMs1ynPiAIiPzjBb222bO9KjgzvxKXnocv1xtHJgZ8Ju0grN\nF4MQQohbd1vfkUvyNg8LJyd0rVphe+4AKrWqTquh1WRiqB86Sw1rfksgrNlQypUKfo5Zb9YYhBBC\n3BptbQdzcnL4448/jI9zc3PZv38/iqKQm5tb58E1ZYagrhTHxuJup5CSkkd+bjEGO51Z2r6y8O2H\nnTFEn9Tj596SY+knOZ0ZRXsnf7PEIIQQ4tbUOiK3s7Nj0aJFxn+2trZ8+umnxp9F3TEEBwPgWngB\ngPgY805tDwjxwdvVhj1HL9LDYQAqVKyKWktFZd1uGyuEEOL21DoiX7ZsmbniENewdPfA0tsH+3MH\noJkPcVHpdAr2Nlv7V0qdLvg+kq27c+nV/R72Xgxnd9IfhDbrY7Y4hBBC1K7WEXl+fj5LliwxPv7h\nhx8YPXo0Tz75JOnp5v3etikyBIdgVZKLk62a5IRsSorLzNp+2+aO9OzozvmUPJwLu2Ct1bEhbht5\npflmjUMIIcSN1ZrIX3nlFTIyqqZ04+LiWLhwIS+88AK9evXijTfeMEuATZnt5SIqbmUpVFYqJMTW\nbY3ymkwK9cPaSsP63y8y0HsAReVFrI/dYvY4hBBC1KzWRJ6YmMizzz4LwJYtWwgLC6NXr15MnjxZ\nRuRmYOnjg4WrGw7xhwHz7fJ2NXuDFaP7tKKguJyLUa542LizN/kAiXnJZo9FCCHE9WpN5Hq93vjz\ngQMH6NGjh/Gx3IpW91QqFYbgEPT5qRis1Zw/l0lFufn3Ph8Y4o2Pqw17jqbS23EACgoro9bIpkBC\nCNEA1JrIKyoqyMjIICEhgcjISHr37g1AQUEBRUVFZgmwqbtSRMWdTMpKK0hKyDZ7DBq1mmlD2gLw\n+95yOjt34FxOHBGXjpk9FiGEENXVmshnzZrF8OHDGTVqFLNnz8be3p7i4mKmTJnCmDFjzBVjk6Zr\n2QqNgwOOF6qKl8TVw/Q6gH8zB3p29OB8ah7epV3RqjT8ErOB0orSeolHCCFElVoTeb9+/dizZw97\n9+5l1qxZAOh0Ov7xj38wdepUswTY1KnUagxBIdhmJWBlqSI+2nxFVK41KbQ11lYatuzJpLdnb7JK\nstl2fle9xCKEEKJKrYk8OTmZtLQ0cnNzSU5ONv5r1aoVycmy2MlcbINDUKPgrs2jML+USxfz6iUO\ne4MVY/q2orCknJzY5thb2rItYRcZRVn1Eo8QQoibbAgzYMAAWrZsiaurK3B90ZSlS5fWbXQCAGv/\ntqgNBpwuniTBvjtx0em4e9nVSywDgr35/ehF/jiWzti/hLI5ZS2rz21gZqdp9RKPEEI0dbUm8gUL\nFrBmzRoKCgoYMWIEI0eOxMnJyVyxictUGg2GwCDK9u5D49SD+Oh0evRrVS+xVC188+ft7yI4FG6B\nb+fmRFw6xr1Z52jj2LpeYhJCiKas1qn10aNHs3jxYj744APy8/OZOnUqjzzyCOvWraO4uNhcMQqq\nVq9rlArcrIrISi8kO7Ow3mLxb+ZAr04eJKYW0LKy6pbEldFrqVTMf2ucEEI0dbdUxtTT05PZs2ez\nadMmhg4dyuuvv06fPrLftjnp23dArdPhnH4WqL/V61dMvLzj2669hYS4BJGUf5G9yeH1GpMQQjRF\nt5TIc3NzWb58OePGjWP58uX89a9/ZePGjXUdm7iK2sISmy4BOKacQqWqn13ermZvY8nYywvfyhL9\n0WmsWBe7hcKy+pspEEKIpqjWRL5nzx6efvppxo8fz8WLF3n77bdZs2YNM2bMwM3NzVwxissMwSFY\nVpbgrCsj5UIuhQX1ew93aLA3zdwMhB/PoZtjbwrKCtkQt61eYxJCiKam1sVujzzyCL6+vgQHB5OZ\nmck333xT7fhbb71Vp8GJ6mw6dUGl1eKSE0e6pT/Rp1IJ6Nas3uK5svDtreURnD5kj2t7Z3Yn/UFv\nr+54GTzqLS4hhGhKak3kV24vy8rKwtHRsdqxCxcu1F1UokZqnQ59p864HjtMdJu2nDicROcQH9Tq\n+tv3vo2PA707e7D3eAoDOvQiTVnHT9HrmBP4iOzHL4QQZlDr1LparebZZ59l3rx5vPLKK7i7u3PP\nPfcQFRXFBx98YK4YxVUMQVXT6y3sS8nNLuZ8TP1XoZvY3w9rKy1//FGJv30bzmRFcyz9ZH2HJYQQ\nTUKtI/J///vfLFmyhNatW7Njxw5eeeUVKisrsbe3Z+XKleaKUVzFEBBIqlqNT9pRYjVdOXbwAi39\nXes1JjsbS8bd24rvtkWhTe2I2vocP0evp4NTWyw0FvUamxBC/NnddETeunXVJh8DBw4kKSmJBx54\ngE8++QR3d3ezBCiq0xgM6Nu2xyLuBN4+BpITc0hLqZ8tW6/WP8iL5m4GDh8rJtChK+nFmexM/L2+\nwxJCiD+9WhP5td9xenp6Mnjw4DoNSNycoWtXAFqpUwA4dqj+1ytcXeo0/qgHBgsbNp/fSXZJTj1H\nJoQQf263dB/5FbJ4qWGw694Ttd4G3YEtODhZE3PqEoX5JfUdFn4+9vTp7ElSSiltNN0prShldcym\n+g5LCCH+1Gr9jjwyMpL+/fsbH2dkZNC/f38URUGlUrFr1646Dk/URK3T4RA6gMwN6/CzK+BQppoT\nkcnc07dlfYfGhP6tiYhKIzJcjVdPLw6mRnCvT09a2beo79CEEOJPqdZEvnnzZnPFIW6Tw4BBZG3Z\nhMPRbVi5j+BkZDLBPZuj1WrqNS47G0vG9WvF8q1R2GQGgC6ZVVFrea7r46hVtzUBJIQQ4hbU+n9W\nb2/vWv+J+qO1t8euV712Ds4AACAASURBVG+UtIv4eagoLiwj+uSl+g4LgP6B3jR3N3DsmIK/oQPn\n8xIJv3i4vsMSQog/JRkiNWKOQ8JApcItZjcqVdWit6trxtcXtVplXPh26ZQvlmoL1sRuoqhcKuYJ\nIYSpSSJvxCw9PLEJDEIdf5YW3joy0wpIOp9d32EB4Pf/7d15dFv1nf//590kWZsl71vsrBCyQVhL\nIIRAQlg6rG2ThqTM0n7bofTM9NB2aKYU+E63cH4z325Mp/1O6Y/JDE1amEJnYRkoS1pCCISG4Oy7\n49iOd1m77vL9Q47jkIQsOJIlvx/n6Ei6vlLe/uRar7vpfetLuXpWLW3tNpOMSxhIR3l+38v5LksI\nIYpOToPctm0efPBBlixZwvLly9m9ezdtbW0sX76cpUuX8ld/9Vek0/m9EEihKVt0EwDjOjcB8N6G\n/H8V7YhPXDsJr1tn64YQYVeIV1p+z6FIe77LEkKIopLTIH/55ZcZGBhg9erVfPvb3+bRRx/lhz/8\nIUuXLuXJJ5+kqamJp556KpclFbySyVPwTJ6Cq3kdVZUe9u/upq9ndFxKNOh1cde8iSSSUBqZjeVY\n/Gj9/49pm/kuTQghikZOg3zfvn3MmjULgMbGRg4dOsT69eu5/vrrAZg/fz7r1q3LZUlF4chWeVNq\nHwCb327NYzXHmndRPU3VAba+52ZqYAa7e/bzn3tezHdZQghRND7062cj7bzzzuOJJ57gnnvuYf/+\n/bS0tJBIJHC5XACUl5fT2dl5yvcJh70j/jWrysrAiL5fLlUsmEvvM0/hvPcygVl/xvb327n5zpl4\nSkZHn/MvLb6Ir/xwLV1bJlN9QQf/c+BVLp8wkwtrpuW7tKJWyMt0IZFxzg0Z55PLaZDPmzePjRs3\ncvfdd3P++eczceJEduzYMfTz0z3jurd3ZHcdV1YG6OzMf7/yjyJ4/SIS//ILxhs9bI74WfvyDi66\nojHfZQFQ5jW45sJaXt/Uxi3TF9Cl/IofrvsFKy7/MkGX/HGeC8WwTBcCGefckHH+8BWZnJ+1/uUv\nf5nVq1fzyCOPEIlEqK6uJpnMfi2po6ODqqqqXJdUFAJXXokWDFK++UV0XWXzO63Ytp3vsoZ84trJ\nlAXdPPdyLx8rm8dAOsqqLb/CdkZPjUIIUYhyGuTbtm3j61//OgCvv/4606ZNY86cObzwwgsAvPji\ni8ydOzeXJRUN1XARun4hWjzC+FCaaCTF3h35v1b5Ef4Sgy/cNgNFUXjrdR9TSiezpWc7r7T8Pt+l\nCSFEQctpkJ933nk4jsMnPvEJfvrTn/L1r3+dL33pSzzzzDMsXbqUvr4+br/99lyWVFRC116H4nZT\ns/NVYHR9FQ2y3y3/049PIxLLkNg5g4Dh59ndz3EgMrrqFEKIQpLTY+SqqvK9733vuOm/+MUvcllG\n0dJ8PkrnzsN56UXqJikcao3QcShCdV0w36UNue2aSWzc2sG7O7u4qnYeG53/4vHmf+OBy/4Kj+7J\nd3lCCFFwpLNbkQkvvAFUlfrB3uaj4VrlwymKwp/fcgEVpR7eWGdxUenldCa6+dWOZ/NdmhBCFCQJ\n8iJjlFcQuOxyAi3vEQpo7NnWSTQyunqc+zwGf3n7DDRNYfMbldR761nf/g5vtW/Md2lCCFFwJMiL\nUHjRTShAY2wntu3w/ruH8l3ScSbUBll83RSiMQtr74W4NRert/87nfHufJcmhBAFRYK8CHkam/BO\nm07Zzj/gdqtsefcQmYyV77KOc93F9Vw6tYq9+20m2leRstI83vxv0sJVCCHOgAR5kQovugnNsWii\ng1TSZMf7Hfku6TiKovBnN02lKlzCxvVupvimc2DgIP+x54V8lyaEEAVDgrxIeadNxz2ukartr6Cq\no+da5R9U4ta59/YZ6JrKrvXjKHOX8dKB19javePULxZCCCFBXqwURSF84024zTgNnhh93XFa9vbm\nu6wTaqwOcPfCKcTjoB64GE3ReGLraiLpsd2SUQghTocEeRELXHIZelk5NbtfB+C9DS15rujkrrmw\njiunV9OyX6fRvlRauAohxGmSIC9iiq4TXngDgVgHlV6Tlr299HTF8l3WCSmKwvJF51Nb7mXLhhD1\n7gls6dnO71rW5rs0IYQY1STIi1zp3HmoXh91rRsA2DzKGsQM53Flj5e7dI3WjZPx6X5+u/t59kdG\n754EIYTINwnyIqd6PISunU959w58bocd73eQTGTyXdZJ1Vf6Wb7ofBIxDb11NpZj8YvmJ0mao6up\njRBCjBYS5GNA6PoFqLrGuL6tmKbNlj+OvgYxw101s5arZ9XSvt9HrT1TWrgKIcSHkCAfA/TSEME5\nV1F96F0MDd5/pxXLGt0nkd298DwaKn3seaeWcqNGWrgKIcRJSJCPEeEbbkTHpD5zkFg0ze5tnfku\n6UO5DY2/vH0GbsOgc9NUXGq2hevh+Oi5xroQQowGEuRjhKumFt+FF1F34E0ge63y0dggZrjach9/\neuNUUlEPrvaLSFlpftH8pLRwFUKIYSTIx5CyG2+mxIxSo/XR2T5AR2sk3yWd0hXTqpk/u57OfWWU\nW5OkhasQQnyABPkYUjJ5Cp5Jk6nbvw6ATRtG71fRhlty/WQaq/0cfHcCAS3ESwdeY0v39nyXJYQQ\no4IE+RhTduNNhJIdlOop9u7oZKB/9H+ty9A17r19BiWGm77m6aiKyr9sWSMtXIUQAgnyMcd34Wxc\n1TXUt72N48Dmd1rzXdJpqQp7+fObLyAdCeDqnM5AJsq/bFkjLVyFEGOeBPkYo6gq4UU3Ut2/G7dm\nsXXTITLpwjh57JLzq1hwaQO9e+oImPVs7dkhLVyFEGOeBPkYFLxyDkbQT33vFtIpi22b2/Nd0mn7\n1PzJTKgt5fB75+FRvNLCVQgx5kmQj0Gq4SJ03QLqu5tRFYfNb7eO+q+iHaFrKn95+3R8uo/YjulY\njsXj0sJVCDGGSZCPUaFrr8OtO9QmDtDfm2D/7u58l3TaKkpL+IuPTyPdW46rZwpdiW7W7Hgm32UJ\nIUReSJCPUZrfT+nca2g4/C6QbRBTSC6aXMFNVzTSv3sCJVY5b7VvZH3bO/kuSwghck6CfAwLL1yE\n34xQbnXTur+P7sPRfJd0Ru64ZiKT68P0vj8dHYM1O34jLVyFEGOOBPkYZpRXELjscho6BrfKR/G1\nyk9E11S+cOt0fGopyT3TpIWrEGJMkiAf48KLbqI8fhAfCXY2dxCPpfNd0hkpC3r4X38yDbOrFr1/\nHAcGDvLbPc/nuywhhMgZCfIxztPYhO+C6dR3bsKyHLa8O7qvVX4iMyaWc8uc8QzsPB+XFeDlA69L\nC1chxJghQS4I33gTtZFd6IrF+++2YpmF1y3t9qsnMLWhgsjWGShIC1chxNghQS7wTpuOr6GWut6t\nJGIZdm49nO+SzpiqKnz+1ukElEoyLedJC1chxJghQS5QFIXwopto6NsKOGwugGuVn0ip383nb52O\n2d6EGq2SFq5CiDFBglwAELj0cgJBN1WxA3QdjtLW0p/vks7KBU1hbrt6IrEd09FsD8/ufk5auAoh\nipoEuQBA0XXCCxcxrvd9oPAaxAz38TnjmT6ulviOGdiOLS1chRBFTYJcDCmdO4+wGiOY6WHvzi76\nexP5LumsqIrC5/5kGqVOPWbbBLoS3azeLi1chRDFSYJcDFE9HsLXXse47s0AbH6ncLfKg14Xn791\nOlbreSiJEBs6pIWrEKI4SZCLY4SuX0B18iBuO8m299pJpwq3S9p540Lcdc1kEjtmodj6YAvXznyX\nJYQQI0qCXBxDLw1ReuWVNPQ2k0lbbN3Ulu+SPpJFVzQys2Ecqb3SwlUIUZwkyMVxym64kfr+HaiO\nxeZ3WrHtwvsq2hGqovDZj08jlJmA1VnHgYFWaeEqhCgqEuTiOK7aOkKzplEb2cVAf5J9Owv7imL+\nEoMv3D4Dq2U6pHy8fOB1mqWFqxCiSEiQixMqW3QzDX1bgML+KtoRk+pK+eS8qSR3zgJHZdWWNfSn\npIWrEKLwSZCLEyqZMoWKxkrKYq20Heyns73wQ2/hpQ3MbphM+kC2heuqrdLCVQhR+CTIxUmFF91E\nY38zUBxb5Yqi8Oc3TyWUPB+rr0JauAohioIEuTgp/0WzqQ7Y+NJ97Np6mFg0le+SPjKvx+CLd8zE\n3j8LMm5p4SqEKHg5DfJYLMZ9993H8uXLWbJkCWvXrmXbtm0sWbKEJUuW8NBDD+WyHHEKiqpSdsON\nNPRtwbYdmjcW3rXKT2R8TZBPz5tBavdMbNvm8fefJCEtXIUQBSqnQf6b3/yGCRMmsGrVKn7wgx/w\n7W9/m29/+9usWLGC1atXE41Gee2113JZkjiF4Jw5NDidGHaK5ndbMTNWvksaEdfOrufS+mlk2ibQ\nlexmjbRwFUIUqJwGeTgcpq+vD4BIJEIoFKK1tZVZs2YBMH/+fNatW5fLksQpqIaL8uvnU9e3nWTC\nZMeWjnyXNCIUReGeG6dSFr8QO1oqLVyFEAUrp0F+yy23cOjQIRYuXMiyZcv42te+RjAYHPp5eXk5\nnZ3SQnO0CV17HeOSe1Acm/feKsxrlZ9IiVvni7fPwt43Gyyd1dulhasQovDoufzHnn32Werq6vj5\nz3/Otm3b+OIXv0ggEBj6+ekGRDjsRde1Ea2tsjJw6pnGqsoA8evmsHPDXjqUSUT7Ukw8r/Ls324U\njXVlZYAvfPxj/ONLPTD5PZ7YtprvLPgaupbTP41zYjSNczGTcc4NGeeTy+mn1caNG7n66qsBmDp1\nKqlUCtM82ve6o6ODqqqqU75Pb298ROuqrAzQ2Vn435M+lzxXz2fcy4/SEZjE6y/tIBD2nNX7jMax\nvmhCmCvqLmZDZzf7aOHn63/NnVM+nu+yPpLROM7FSMY5N2ScP3xFJqe71puamti0aRMAra2t+Hw+\nJk2axNtvvw3Aiy++yNy5c3NZkjhNRkUlDTMnUZro4MDuHnq7R3ZlKp8URWH5DedTEb0UO+nl5RZp\n4SqEKBw5DfLFixfT2trKsmXLuP/++3n44YdZsWIF//AP/8CSJUtobGxkzpw5uSxJnIHwjTcxbrBt\nayFfq/xE3C6Ne2+7CGffbLAVnmheLS1chRAFIae71n0+Hz/4wQ+Om/7kk0/msgxxljyNTTSN87Er\nEWX7pjauuGYCbo+R77JGTH2Fj3uuuYLHN3RD0zaeaP4l983+LKoifZOEEKOXfEKJM1J+08009G/F\ntBy2/LGwr1V+IlfOqGFOzcew+irZ3reLlw+8nu+ShBDiQ0mQizPinTadCYE4mp1h84YDWFbxXXTk\n7gXnU9F/BU7azbO7n2dTZ3PRfOVOCFF8JMjFGVEUhaobFlIb2UUsZrJ3R2Ffq/xEXIbGfbddinPg\nImzH4Webn+DHf/xnWgaKo0WtEKK4SJCLMxa47HLGkw21TW/uz3M150ZNmZc/m3s1qffnYPVVsK13\nJ9/b8H1WbfkVfan+fJcnhBBDJMjFGVN0nYbrrqYi1sLhjhjtrcUZbJdfUM1Xbr+Gush8UtsvwY4H\neLP9bR5e9yj/uecFknKhFSHEKCBBLs5K6TXX0BTfBcB7bxXvZUAvaArz4D2X8vn58wm2Xk96zwzS\nKZXn9r3Mw+se5Q+t67Hs4riQjBCiMBV+H0qRF6qnhPFXzmB7cw97tjtEI0n8wbPr9jbaKYrCZVOr\nmD2lgrWbGnlmXSOJwA4G6vby5Pan+V3L77lzyi1MKzsfRVHyXa4QYoyRLXJx1soWLGTcwHYcFDa/\n05rvcs45XVOZf3EDKz93NR+fuBB7y7WYhxtoj3Xwj5se50d//L8clBPihBA5JkEuzpoeCjFleg2G\nmWDLOy1k0mNjF7PHpXPr1RNY+Rfzuab8RjJbrsLqL2d77y6+u+H7rNoqJ8QJIXJHglx8JBWLFtEQ\n2U7ahO3vt+e7nJwK+lzcvfA8vrVsIbO1jw+eEOfnzba3efiNR/nPPS+SNFP5LlMIUeQkyMVH4q6r\nY0qDgeJYbHpj75hsnFIV9vL5W6fz4B03Myl6y7AT4l7ioTdW8ofW9dhO8TXOEUKMDhLk4iOru2kh\n1QN7iURNDuzpyXc5edNUE+ArSy7hr6//OJXtN5FpncRAKsGT25/mW2/+H7mimhDinJAgFx+ZZ/IU\nJvsjAGz6/e48V5N/0yeU8dA9V/IXl9yOb98CzM562uMd/OOmn/ODjf+X1mjx9agXQuSPBLn4yBRF\nYfyN8wgl2mlti9PTGct3SXmnKgpXTKvmu38+n09Nvgt91zys/nJ29O3kO2/9H/5FOsQJIUaIBLkY\nEf6LZjOB7FfQNr2xJ8/VjB66pnL9JQ2s/NNF3FjxSexdl2PH/axvf5uH3lgpJ8QJIT4yCXIxIhRV\n5bz5F1OSibBjWxeJeDrfJY0qJW6d2+dO5HtL/4QrXZ/E3DeDTErjuX0v8eAfvscfDskJcUKIsyNB\nLkZMaM5VNCb2YjsKzW8fyHc5o1Kp381nbriA/337J5iWvJNM6yRi6SRPbnuaR974e7bICXFCiDMk\nQS5GjOpyMe2KCWh2ms0bWoryWuUjpbrMy323z+brCz5NQ/ctmJ31dCY7eWzTz/mHDT+VE+KEEKdN\nglyMqMrrr6M+uodkRmFX89hqEHM2JtQG+friq/jSZcsIH1qA1V/O7oHdfGf99/nF5jVyQpwQ4pQk\nyMWI0vx+pk0tBcfmj6/vHJMNYs6UoijMmFjO3y1byGemfAZ3y5XYCR9vd77DN/+wkmd3viAnxAkh\nTkqCXIy4hpsXUBlvoSfq0HZQtihPl6oozJlRy8q7b+O2qntQDs7CTKu82PIyf7v2e/xeOsQJIU5A\nglyMOKOikvNrslvi7/5uS56rKTyGrnLj5U08+qklXFuyDLttMgkzwS+3P8031/5/NHdty3eJQohR\nRIJcnBOTb5lHINnFgbYUkb5EvsspSF6PzqfmTeW7t93DxdYnsTrr6cl08Y/vPc7KN38iJ8QJIQAJ\ncnGOlIyfwCRfP6Dwx1e25rucghbyu/nsjZfw0ILPMil6C1Z/OQfie/nO+u/zs3d/SX8qku8ShRB5\nJEEuzpnpiy7DZcbZvqOXdMrMdzkFr7bcx/23zeMrl32eip5rsBM+NvW+yzd+/z2e2vocKUua8Agx\nFkmQi3MmMHMmTU4bpqOx5c1d+S6naExuCPHwXbfw+alfwNt1MVZG5ZW2V3jgte/wyv51ckKcEGOM\n9vDDDz+c7yLOVHyE23/6fO4Rf0+R/VqVnzjbD2To6Ygw82MT8PtlrEeCoijUlPlYcMF0/PHJ7Gjp\nJ+0+zNa+rfx+/7skMylSmRQe3YNLc+W73KIlnx25IeOcHYOT0XNYhxiDKq/6GLWv/ButahN7m1up\nrg7mu6SioqoK117YxFXT/4z/3LCdl1pfJhJu4entvx2ax3C8lBtVNARqOa+ikcll46j0VqAqskNO\niGIgQS7OKUXXmTm7htat8MdXt/Gx6y7Id0lFydA17rhyGouSU/jvjdvYG2mhLdpGXO3B8UZoV/bR\n3ruPt3vXAaA4Gn7KqCqpZnxpA9Oqmxgfqseje/L8mwghzpTiFGDrrc7OgRF9v8rKwIi/pzjKTib4\n9feepcdTwzULJlFWE6Syxo+ua/kurWgdWaYzpkVbd5xdHZ3s6mqhNdZGr9lJxuhD8URR1GP//A3L\nT0irpN5fy+TycUyvGU+ltwxFUfL0m4xu8tmRGzLO2TE4GQlyZCHJheZVz7D2YABHyYa3gkNZUKO2\nMUzNxCpqG0rxB2VrcKScapmOJ01auiJsaz/A3r5W2hPtDDjdOO5+FCNzzLyKZeB1yqhwV9EYrGdq\nVSMXVDXiNuTYu3x25IaMswT5KclCcu5ZiQQta56m40AvnXGNfk8lA+5ynGHHaUt0m6oKN7WTqqmb\nVEVFtR9Nk+O4Z+Nsl+n+aIod7W1s6zxAy0Ab3ekOEmovuGMM3yh3HAUjEyCgVlBTUsOksgZm1E6g\nITy2tt7lsyM3ZJwlyE9JFpLcqawM0H7gMKn9+4ju2UvHng4Odybptbz0e6pI6yVD86rYlHkdqusC\n1J1fR92ESrz+k5+5KY4ayWXacRzaewd4v30fu7pbaIu30291kjH6UTTr2JkzbtxWmDKjigZ/LedV\nNjK9bhyl3uLc2yKfHbkh4yxBfkqykOTOycbaHIiQ2LOHnp0HaD/QQ2efRZ8aYsAdhmFb7V41TWVI\np6apjPrpjVTWhVBV2Wr/oFws0xnLZEdHG1s79rG//xCdqQ6idOMYx7bkdWwVNRXMnlznqaGptJ5p\n1U1MqC7D4yrs823lsyM3ZJwlyE9JFpLcOd2xdhwHs6eHgV27adveSkd7lO64Rp9Rhqkd3brTHIuw\nO01VZQl1k6upnzEBb6A4t/7ORD6X6b7EAO+37WNHVwut0UP0mp2k1H5Qj21UYydLMDIhQnoFdb5a\nanwVhL0BKvxBQj4vAa+B32OgqqN3V718duSGjLME+SnJQpI7H2WsHdsm3d5G59a9HNp9mMPdKXoy\nHmJGiOEHcH1Oggq/TXV9kPoLxlE5ZRzaGDtDfrQt05Ztsb+vjeb2fezpO0jH4Ml1tnri66w7loaT\ncYHpQrXdGHhwqyWUqF58hpeA20+p20+4JEC5t5Ryv5+g10XA68JlqDk7Tj/axrlYyThLkJ+SLCS5\nM9Jj7ZgmA/taaG3eT3tLD10Rh14CWOrRM6o1O0NYi1MVNqgZX079zIn4aiqK+qSsQlimHcehPx1h\nZ1cL2zsP0JPoI5qJkbDiJK0EaZKYShKUU7ecdWwFTAPHdIHlQnc8uBQPHtWLV/fiN3wE3X5CngBl\nw7f6Swx8JTraWR6eKYRxLgYyzhLkpyQLSe7kYqytZJKO5j0c2t5KR0eM7rhOTPMfM4/PjFDuyVBV\nVULdlFqqZ0xGD5z8D6XQFMsy7TgOKStFNBMjkorRHe+nOxahNzFAJBVlIBUlZsZJWHHSTpIMCWw1\nc+o35jS2+l1+Sj1+wiVByn1Byn1+Al4XAa+B29BQFKVoxnm0k3GWID8lWUhyJ19jHevq5+Dm3bTv\n7eJwd5pe04OlHD3RSrdShOx+KgJQ01BK3dRxBCZPRPUU5vH2sbxMW7ZFNBMnlonRm4jQHYvQEx+g\nL5kN/2gmRtyKk7ITpJ0EppI6g61+F45pgOlGx41L8aDjwqVmb27dg0dz49U9eF0efC4vAZcHv9tL\nwF2C12PgcWl43Doel4ZLz91hgEI2lpfnIyTIT0EWktwZLWNt2zade9s52HyA9oP9dEUc4gwLbcfG\nn+4jrMYIehV8fhf+kJdAuZ9AZRhXWRg9FEb1+0flB/FoGedC4DgOSStFLBMjkorSHc+Gf19igP5k\nlIFMlFjmyFZ/ggzJ097qP/bfASwdx9LB0sHWwNJRHQMNF7riwlBcuBQXbs2NW3dTopXgdbnxGiX4\njRL87pLBFQJ3doXApVEyuFJwZC9BMZLl+cODvLC/+yHEWVJVlepJdVRPqhuaFoumOLTtIId2tHH4\ncJweJUSUMsgAvYO3vYDTjds6iNuM47HilGgWXjf4vDr+YAmBMj/+yiCucBg9HEYPhVA9JSepROSb\noiiU6B5KdA8VJeVMDJ36Ndmt/hieoMqhwz1EUwkiqTjRVJxYKkkskyCeSZIwkyTNJCk7RdpKk9FS\nZNQ0lpHBUuJDewKswdsJT/078sMkMJhljq0es1Jw5F5zDDSMoysFqhu35saju/HqJZQYHjyagaEb\nGJqOW9NxaQYuQ8etGbh0HUNX0TV16F7XFAxNRT8yXVPRdeWszysQI0+2yJG1vVwqpLG2LJuezij9\nhyMMdPYz0BMlGkkSi2WIpyBhqTic5MPMsXFbCdxmHLcZo8RJUeJy8JVo+AIuAiEf/orBsA+F0EMh\ntNIQqmGMSO2FNM6F7KOOc8Y2SZpJkmaKpJUkkcmuBGRXChLEUtkVgnjm6DwpK0XaSZGx05ikschg\nK+aI/D6OAzgq2Co4anaFwVGOneYoQ49VNBRUFLKPVTRURUNTNDQ0VEVFU3X0wWm6qqOrR+8NzcBQ\ndQxVw9B0XEdWLHQdl27g0gzcuk51RSmpmIXb0DD07EqGy9AwNBXDUFGLdE/EcLJFLsRZ0DSVypog\nlTVBoOG4nzuOQyKWJjqQItqfJNLVz0DXANH+BLFomljSw0DGS4TKoy+ygf7B2z4bt3UYt7kPtxnD\nY8YoUS28nsFd+aUe/GWB7G780tDQ1r0WCKLI1lBRMFQdw+Un4PKfeuYPYTv2UNAPv49lEgwkE9k9\nBZkksXSClJXGtC1M28S0LSzbxHQsrGE3WzWxsLEdCxsLBwubNA42DhZ8IDcdju44OLPCB2+ncmBw\nJcPWwFZxbG3wsYZjayh2diVCcXRUdDR0NEVHV3R0xUBXDAw1e3NpRvacBs3Arbtway48uguP7sZj\nuHDrOm5DxzCyex9choqhZ1cgXEdWIgb3ToyWQxkS5EKcJUVR8PrdeP1uqmqDQNVx8ziOQyKeyW7J\nD6QY6I0z0B1hoDdObCBFLO5lIP2BsAdIA53AYRu3Fcdtdg2GfRy3Fcfryu7K9wXd+MN+XOHQ4JZ9\nNuyTVphMbzzbFU8d/MBRFFAVFEUdfHyK6aJgqIqK1yjBa+TmEI5lZwPftC1Mx8SyLTK2eXTa4OO0\nmSZlmqQtk5SZIWOapKwMGcskY5ukzey9aZtkrOyKxZHnR97LciwcxSJtpTFVE0vJYGkmNiY2KRzF\nHFqxOOMVCnPwNuyYxtAeB0v7wAqDeszKA7aG6mjZFQclu/KgKwaakt3L0BSq5bMLrhjJYT+pnAb5\nr3/9a377298OPX///ff55S9/ycMPPwzA+eefzyOPPJLLkoQ4pxRFwetz4fW5oPbE83ww7KMDKQZ6\nYkR7owz0J4nHVKIpLxGn8vgXx4CojXtfArfZjtvcjduMo9tpNMdCcWxUx0KzLVTHQnEsNCf7+IQ3\n20IdfI2iOCjHF4ZXogAAD3tJREFUhP2wx4o6GP7Z6SjDVwQGH6vKsdMVJbsn4bj3yk5XS0rQgqXo\npaVowSB6sBSttBQ9GEQLlqKWlMgKxmlIJ1JEDnXTf7iXSHeUgb4E0WiGWMImllFQFQj7FSoqvVQ1\nVVBzXj2+0jPbI6Cp2V3nrhz1WfqwQxiO42A6FhkrTdrOkLbSpK3M0OOkmSKRSZPIpEiZaRJmmmQm\nRcoaNq+VIW2nydgZTDtDxjGx9Ex2JcXJYJPAVk6+enBk5SE9bFqPrWNal6Jr536Q8naM/K233uK5\n555j165dfPWrX2XWrFncf//93HrrrcybN+9DXyvHyAuXjPXZORL2sYEU0Ugyuzs/kiLaEyPaHyc6\nkCaetLFH9K/ZyYY6NhrZexX76DTnaOhnp59opeDIY3NommKbR59bmex8dgbdSuOykqgn2deq6Pox\nQa8Fg4OPj4a9Hsz+LFehn6vl2U6lsCIRzEg/yZ5+Il0DDPTGiETSxOIm8bRCzNJJ4CatnWSr3LFx\nmwksVcfUjr34kNtOENLTlAVUKqp9VI2vIjyhHj0UGhUrT6Phc8N2bEzbHAr9tJUhY2eOfT64MpGy\n0lR4yphZOW3E/v1ReYz8scce47vf/S7Lli1j1qxZAMyfP59169adMsiFGGuGb9lX1pz4D3p42Pu8\nbrq7o5imjW3ZmKaNNXgzLRt78P7INMtysj8ben70/oPTMoPTbOvcbAMYuoLbALdq4SKDy05hZBLo\nqShGoh+tqxfj0AFcVhLDSqE5x28pKYZxkqAPfmBloBTV48l5WDmOg5NKYvZHhgLaikTI9PeT7I8S\n7U8yEMsQS0LcVEkoJSR1H0nDT2boWgO+wdvg76zaeJwk5UoEn8vGV6IRCLgIhEoIVgQIVoUwQiHs\nVIrefYc4vL+T7s44PVHodzx02KV0HDl/Y0cXunWQYKaPkCtDWalGRU2Q8sZK3DV1GNXVqK6xdT16\nVVFxaS5cmovh4z4a5CXI33vvPWpra9E0jWAwODS9vLyczs7OU74+HPaij3Dv7A9b2xEjS8Y6N5om\nlZ/T93dsZyjozYx19N6yMTM2pmlhZuyhFYQjz03TOjpt2OuSCZNYNEU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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", + "outputId": "1eac4061-7b8c-48b2-f763-1f9d32d2cae8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 79.60\n", + " period 01 : 79.81\n", + " period 02 : 74.49\n", + " period 03 : 71.09\n", + " period 04 : 74.06\n", + " period 05 : 72.67\n", + " period 06 : 71.30\n", + " period 07 : 69.61\n", + " period 08 : 70.76\n", + " period 09 : 69.20\n", + "Model training finished.\n", + "Final RMSE (on training data): 69.20\n", + "Final RMSE (on validation data): 69.15\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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4vYjqusZLh3/kpFohhBCdhASVLkylUjE61Jcmg8K+tAL6uV9qqd9oaLR0eUII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\nRAW5cqiwgac3ZtPdN2jFasV3CltKKGgosXUZY540KkIIMQb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FCCGEEKr1/wF3\n2MbPnGlfLQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "5JUsCdRRyso3" + }, + "cell_type": "markdown", + "source": [ + "Now let's try Adam." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "lZB8k0upyuY8", + "outputId": "d61c2f21-c073-4a18-dca3-22bf64c8dabc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 213.99\n", + " period 01 : 135.95\n", + " period 02 : 116.44\n", + " period 03 : 109.26\n", + " period 04 : 98.95\n", + " period 05 : 87.29\n", + " period 06 : 76.63\n", + " period 07 : 71.92\n", + " period 08 : 70.96\n", + " period 09 : 70.49\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.49\n", + "Final RMSE (on validation data): 70.31\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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GTQGmDnRsFUpoE18Kj/06jaRrwoiIiLiTAkwdMJtM9I22UZxedTq17o0kIiLi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\nYWA6DBwDH1WcBkcFRkREbCfSHc680bnMG5172ccFrAC+gI8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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "twYgC8FGyxm6" + }, + "cell_type": "markdown", + "source": [ + "Let's print a graph of loss metrics side by side." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8RHIUEfqyzW0", + "outputId": "a2d5448a-533b-4fd7-9df7-bba1af980358", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "cell_type": "code", + "source": [ + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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wYfzvf//jww8/ZPbs2axevZrQ0FDjMoSDBg2icePGpgpNCCGEqDdMVsiDg4ONLR2dnZ3J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\ncxiZ6XmWDlUIIcQ1pJALoxuNm2d9tZ2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nf302m8U0Sze6qKuro7u7m56eHmprawc/U1tb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\nRhpaGgmFJdw/jvRnCfKzkk7y+WRyFodOJDnYluBAe5wDvW3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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "UySPl7CAQ28C" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Explore Alternate Normalization Methods\n", + "\n", + "**Try alternate normalizations for various features to further improve performance.**\n", + "\n", + "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n", + "\n", + "For example, many features have a median of `-0.8` or so, rather than `0.0`." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "QWmm_6CGKxlH", + "outputId": "17775e76-8b5a-4994-f3d1-8a7a43dba901", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 735 + } + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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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", + "outputId": "648292e8-b686-461e-f898-2ad414ff37ba", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " normalized_dataframe = pd.DataFrame()\n", + " normalized_dataframe[\"latitude\"] = log_normalize(examples_dataframe[\"latitude\"])\n", + " normalized_dataframe[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " normalized_dataframe[\"housing_median_age\"] = log_normalize(examples_dataframe[\"housing_median_age\"])\n", + " normalized_dataframe[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 5000))\n", + " normalized_dataframe[\"total_bedrooms\"] = linear_scale(clip(examples_dataframe[\"total_bedrooms\"], 0, 5000))\n", + " normalized_dataframe[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " normalized_dataframe[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " normalized_dataframe[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " normalized_dataframe[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 10000))\n", + " return normalized_dataframe\n", + "\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=0.15),\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": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 115.83\n", + " period 01 : 111.61\n", + " period 02 : 107.78\n", + " period 03 : 104.52\n", + " period 04 : 103.84\n", + " period 05 : 104.24\n", + " period 06 : 104.32\n", + " period 07 : 103.53\n", + " period 08 : 92.50\n", + " period 09 : 96.28\n", + "Model training finished.\n", + "Final RMSE (on training data): 96.28\n", + "Final RMSE (on validation data): 97.08\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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3rC06nYYFqw/w78Fki9usY+/G5LAn8HTwYO2p31gfu8kKlQohhBDVgwSY26RZ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\n1tZWvPDCC0hISLj82ttvv43Fixfjo48+gr+/P3b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+ "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", + "outputId": "19ec82a5-c387-4883-8d54-a36d1b81a0f2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "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": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 90.68\n", + " period 01 : 76.41\n", + " period 02 : 72.75\n", + " period 03 : 72.43\n", + " period 04 : 70.39\n", + " period 05 : 69.78\n", + " period 06 : 69.38\n", + " period 07 : 68.96\n", + " period 08 : 69.11\n", + " period 09 : 68.62\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.62\n", + "Final RMSE (on validation data): 68.08\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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REZ+loCIiIiI+S0FFREREfJaCioiISCe2evXHbj3v6aef5NCh3BM+/utf39ZeJbUrBRUR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\nYYHMHp3M2KEJ+Fl9J7DoO+Ob1Bffpd6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "b7atJTbzU9Ca" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Use only Latitude and Longitude Features\n", + "\n", + "**Train a NN model that uses only latitude and longitude as features.**\n", + "\n", + "Real estate people are fond of saying that location is the only important feature in housing price.\n", + "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n", + "\n", + "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n", + "\n", + "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5McjahpamOc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "34b4cbb7-d1d6-4325-e3ae-bf8f23587b95" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#\n", + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " normalized_dataframe = pd.DataFrame()\n", + " normalized_dataframe[\"latitude\"] = log_normalize(examples_dataframe[\"latitude\"])\n", + " normalized_dataframe[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return normalized_dataframe\n", + "\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", + "_, adagrad_training_rmse, adagrad_validation_rmse = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=50000,\n", + " batch_size=50,\n", + " hidden_units=[10, 8, 6, 4],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 115.38\n", + " period 01 : 118.76\n", + " period 02 : 107.07\n", + " period 03 : 106.09\n", + " period 04 : 102.44\n", + " period 05 : 96.69\n", + " period 06 : 99.03\n", + " period 07 : 98.58\n", + " period 08 : 97.43\n", + " period 09 : 97.98\n", + "Model training finished.\n", + "Final RMSE (on training data): 97.98\n", + "Final RMSE (on validation data): 98.11\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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9Pbhz8WhGDg5xdlmt5uvhw9DAIWSV5zAsrHmScYJWI4mISBehAHMS6hvsPLcm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\ntOEUpY+KdWkiEgdMRgJutjCQw24a1otf6k18Gsi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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "P8BLQ7T71JWd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1hwaFCE71OPZ" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "djKtt4mz1ZEc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "334cd4cc-1bce-4f1e-9f07-57bbca2e58c0" + }, + "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": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 132.14\n", + " period 01 : 107.24\n", + " period 02 : 105.38\n", + " period 03 : 104.33\n", + " period 04 : 102.80\n", + " period 05 : 102.09\n", + " period 06 : 101.33\n", + " period 07 : 101.16\n", + " period 08 : 100.69\n", + " period 09 : 100.33\n", + "Model training finished.\n", + "Final RMSE (on training data): 100.33\n", + "Final RMSE (on validation data): 100.82\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Ns8msibwiIiJ+oADTS6n2SMJCrOSXdPTABFuCSYscSXF9Ka3uNj9XJyIiMrwo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\neganEmdxNHocbxTMaHJJLkSUEMJKCGEliLA7hJA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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Dw2Mr9JZ1cRi" + }, + "cell_type": "markdown", + "source": [ + "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances." + ] + } + ] +} \ No newline at end of file From 77ae0407f55168cf2294845210d9c7f1a1cf5d2e Mon Sep 17 00:00:00 2001 From: Ayan Dutta Date: Sun, 17 Feb 2019 21:57:02 +0530 Subject: [PATCH 11/11] MNIST Digit Classification programming exercise solved!!! --- ...classification_of_handwritten_digits.ipynb | 2922 +++++++++++++++++ 1 file changed, 2922 insertions(+) create mode 100644 multi_class_classification_of_handwritten_digits.ipynb diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb new file mode 100644 index 0000000..094738e --- /dev/null +++ b/multi_class_classification_of_handwritten_digits.ipynb @@ -0,0 +1,2922 @@ +{ + "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": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mPa95uXvcpcn", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Classifying Handwritten Digits with Neural Networks" + ] + }, + { + "metadata": { + "id": "Fdpn8b90u8Tp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)" + ] + }, + { + "metadata": { + "id": "c7HLCm66Cs2p", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n", + " * Compare the performance of the linear and neural network classification models\n", + " * Visualize the weights of a neural-network hidden layer" + ] + }, + { + "metadata": { + "id": "HSEh-gNdu8T0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class." + ] + }, + { + "metadata": { + "id": "2NMdE1b-7UIH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random." + ] + }, + { + "metadata": { + "id": "4LJ4SD8BWHeh", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236 + }, + "outputId": "62b6edd7-52d2-4ded-f731-b8821cbf9c0b" + }, + "cell_type": "code", + "source": [ + "import glob\n", + "import math\n", + "import os\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "mnist_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "# Use just the first 10,000 records for training/validation.\n", + "mnist_dataframe = mnist_dataframe.head(10000)\n", + "\n", + "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n", + "mnist_dataframe.head()" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 72\n", + "7191 0\n", + "2808 0\n", + "2085 0\n", + "9903 0\n", + "3535 0\n", + "... ..\n", + "108 0\n", + "6154 0\n", + "4526 0\n", + "8941 0\n", + "676 0\n", + "\n", + "[10000 rows x 1 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "vLNg2VxqhUZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set." + ] + }, + { + "metadata": { + "id": "JfFWWvMWDFrR", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def parse_labels_and_features(dataset):\n", + " \"\"\"Extracts labels and features.\n", + " \n", + " This is a good place to scale or transform the features if needed.\n", + " \n", + " Args:\n", + " dataset: A Pandas `Dataframe`, containing the label on the first column and\n", + " monochrome pixel values on the remaining columns, in row major order.\n", + " Returns:\n", + " A `tuple` `(labels, features)`:\n", + " labels: A Pandas `Series`.\n", + " features: A Pandas `DataFrame`.\n", + " \"\"\"\n", + " labels = dataset[0]\n", + "\n", + " # DataFrame.loc index ranges are inclusive at both ends.\n", + " features = dataset.loc[:,1:784]\n", + " # Scale the data to [0, 1] by dividing out the max value, 255.\n", + " features = features / 255\n", + "\n", + " return labels, features" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mFY_-7vZu8UU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "outputId": "4e03ca30-0c02-42fc-f108-679dee15bd3a" + }, + "cell_type": "code", + "source": [ + "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n", + "training_examples.describe()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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0.0 0.0 \n", + "\n", + " 784 \n", + "count 2500.0 \n", + "mean 0.0 \n", + "std 0.0 \n", + "min 0.0 \n", + "25% 0.0 \n", + "50% 0.0 \n", + "75% 0.0 \n", + "max 0.0 \n", + "\n", + "[8 rows x 784 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "wrnAI1v6u8Uh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Show a random example and its corresponding label." + ] + }, + { + "metadata": { + "id": "s-euVJVtu8Ui", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 360 + }, + "outputId": "2f87c10c-a4a3-4c15-fa4d-9bd40c69c8cb" + }, + "cell_type": "code", + "source": [ + "rand_example = np.random.choice(training_examples.index)\n", + "_, ax = plt.subplots()\n", + "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n", + "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n", + "ax.grid(False)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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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": 1121 + }, + "outputId": "50543241-3bbe-48f5-9406-74c379fdc6ce" + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.89\n", + " period 01 : 4.35\n", + " period 02 : 4.03\n", + " period 03 : 3.87\n", + " period 04 : 3.67\n", + " period 05 : 3.70\n", + " period 06 : 3.65\n", + " period 07 : 3.74\n", + " period 08 : 3.65\n", + " period 09 : 3.54\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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K4zqP4q5eN4OiMG//lyzPWN3hbpkTQoi2TsK7FUL9XOneyYuUYyXkFFXZuxyb\n6ePfkzn97sXbyYtF6cv4MuV76hs7zoxzQgjR1kl4t9KYfmEArHWQ0fcp4e4hPDjgPjp7hLM1bwdv\n7/qIirr2v2CLEEK0BxLerRQf44enm46N+3OprTPauxyb8nRy5599/07/gD6kl2XwStI75FR2rGlj\nhRCiLZLwbiWNWkVCnxCqa41sSXa84NKptdzW80YmRYyluKaE13a8x4Hig/YuSwghOjQJbwtIiA9F\npSis3pntkBdvKYrC5Kjx3NbzRhpMRj7Y8xlrMjc4ZC+EEMIWJLwtwNvdib5d/cgsqCQtu9ze5djN\ngMB4/tn377jpXPnxyCK+Pfw/jI2OdSpBCCFsQcLbQk5duLZ6l2NPXhLp2YmHBtxHqFswG7K38N6e\nTyitcdwPNEIIYQ0S3hbSvZMXwb4uJB0soLzKsef/9nH2Zk6/e+nl14NDJanc+8tjfJnyPVkVHXsq\nWSGEsBX13Llz51pr5zU1NSQmJuLm5kZsbKx5+6ZNm7j//vv56aefKCgoYNCgQRfcl8Fg2UB0dXWy\n6D4VRcFkMrEnrRhXvYau4V4W23d7pFFp6BfQGw+dG4U1hRw8kcqGnC2klqTjqnXBX++Loij2LrND\nsfR7Wpyb9Nk2pM9NXF2dzrldY80X/eCDD/D09Dxr+3PPPccnn3xCYGAgN910ExMmTCA6OtqapdjE\n0LhgflyXxtpdOUy8rDMqlWOHk0pRMTJsKNfEj2Ptwe2sztzA4ZJUDpem4a/3ZVTYcAYH98dZ42zv\nUoUQol2xWninpaWRmprKqFGjztiemZmJp6cnwcHBACQkJLB58+YOEd4uzhqG9Axi3e4c9qYVEx/j\nZ++S2gSVoqKXXw96+fUguzKXNZkb2J6/ix+O/Mzio8sZGjyIhLCh+Op97F2qEEK0C1Y75/3SSy/x\n8MMPn7W9sLAQH58/fkj7+PhQWFhorTJsbnTfpvnOHf3CteaEugVzU+z1PDf0Ua6IHI9GpWFV5u88\ntfkl5u37ktTSo3KLmRBCXIBVRt4LFy4kPj6e8PBwi+3T29sFjUZtsf0B+Pu7W3R/p/YZG+HD/vQT\nNCgqgv1cLf4a7dGfe+2PO1Gh13CD8Qo2Hd/Br4dXsbtwH7sL99HFuzOTuo5hSHg/NGqrntnpcKzx\nnhZnkz7bhvS5eVb5ybh27VoyMzNZu3YteXl56HQ6goKCGDp0KAEBARQV/bGEZn5+PgEBARfcZ0mJ\nwaI1+vu7U1hYYdF9njKiVxApGSdYsOowU8e0/9MBrXWhXvdw60ls3x6klh5lTeZ69hYl887Wz/hi\n14+MDBvK8JDBuOnkQ9CFWPMzFBMpAAAgAElEQVQ9Lf4gfbYN6XOT5j7AWCW833zzTfOf33nnHUJD\nQxk6dCgAYWFhVFZWkpWVRVBQEGvWrOHVV1+1Rhl2079bAO6rjrB+bw5Xj4hEp7XsEYOOSFEUYryj\niPGOoqi6mLVZG9mcs51f0pezLGMVg4L6MSpsOCFuQfYuVQgh7M5mxyQXLFiAu7s748aNY+7cuTzw\nwAMATJo0icjISFuVYRNajYqRfUL4dfMxth8sYFivYHuX1K746X2ZEvMXJkeOZ0tuEmsyN7AxZxsb\nc7bR3TuG0eHD6eHbDZUi0xQIIRyTYmonVwdZ+vCJtQ/JFJVV8+8PNxMR5M4Ttwy02uu0B63tdaOp\nkX1FyazJ3MCR0nQAAl38GRU2nMuC++Ok1lmq1HZNDjPahvTZNqTPTWx62FyAn6eePl382J1axNHc\nciKDPexdUrulUlT08Y+jj38cmRXZrMncQFL+br47/D8WpS9jeMhlJIQNxdvZsSfGEUI4DjnuaEVj\n+jXdNrZmZ7adK+k4wt1DubnHNJ4d+igTI8aiVlSsOL6WJze/yKf7v+Jo2TF7lyiEEFYnI28r6hHp\nQ4CXnq0p+UwdE42bXmvvkjoMTyd3rogaz4TOo0nK383qzPXsKNjDjoI9RHh0Ykz4cOL9e6FWycWC\nQoiOx6pzm1tSW5/b/FwURaHBaGJfejEerjqiQ8+eKtYRWLPXapWacPdQRoQOJtorCkODgSMl6ewq\n3Mfm3CSMJiNBroHo1B3/g5PMBW0b0mfbkD43scvc5gKG9w7mf+vTWbMzm3EDw1HJYhxWoSgK3Xyi\n6eYTTYGhkLVZm9icu52f05ay5OhKLgvuz+iw4QS5XnhOASGEaOskvK3MTa9lUGwAG/flkXz0BHFR\nvvYuqcMLcPFnateruCJyPJtyt7EuaxMbsrewIXsLPXy7MSZsBN19YmRVMyFEu9Xi8K6srMTNzY2i\noiIyMjLo168fKpVc79YSY/qFsXFfHqt3Zkt425CLVs/YTgmMDhvO3qJkVmeuJ7n4EMnFhwhyDWRM\n2HAGBvVziEPqQoiOpUXnvJ999llKS0sJDQ1l6tSp5ObmsmXLFkaPHm2DEpu0x3Pep3i7O7E3rYhD\nmaUM6xWEi7NjhYW9z12pFBXBroEMDRlInG936oz1pJams7comQ05W6huqCHQ1b/dL01q7z47Cumz\nbUifmzR3zrtFQ+fk5GSuv/56li5dyjXXXMNbb73FsWNyS87FGN03DJMJ1u3OsXcpDq2zRzi39ryB\nZ4c+QmLnMQAsP7aaJza9wGcHvuZYeaadKxRCiAtrUXifmoRt7dq1jBnT9AOvrk4+EV2MQbEBuDpr\nWL8nh/qGRnuX4/C8nDy5sksizw19lBu7XUeAiz9J+bt5OekdXtvxPvuKkmVpUiFEm9Wi8I6MjGTS\npElUVVURGxvLwoUL8fR0zNueLpVOq2Z472DKDfXsOFRg73LESTq1jmGhl/H4oDnMir+THr7dSC/L\n4MO983kl6V0OFB+SEBdCtDktmtvcaDRy+PBhunTpgk6n48CBA4SHh+PhYbspP9vb3Obnkl9i4JH/\nbCE6zJNHb+pv09e2p/Y2R3FOZR5Ljq5gV+E+AKI8I7gicjzdfNr28q7trc/tlfTZNqTPTZqb27xF\nI++UlBTzutxvvPEGL7/8MocPH7ZogY4g0NuFuCgfUrPKOJ4vb8q2KsQtiDt7zeDhgf+kl18P0ssy\neHv3R7y580NSS4/auzwhhGhZeD/33HNERkaSlJTEvn37eOKJJ3j77betXVuHNKZvGABrd8l8521d\nuHsIf+99Kw8NuI8ePt04UprOGzs/4N3dH5NRftze5QkhHFiL7vN2cnIiIiKC7777jqlTpxIdHS33\neF+i3l188fVwYvOBfKaMisbFWebJaes6e4QzM/4O0ssyWJz+GyknDpNy4jBxvrFcETWecPdQe5co\nhHAwLUrg6upqli5dysqVKxk+fDilpaWUl5dbu7YOSaVSGNU3lNp6I5sP5Nm7HHERojwj+Effu5nd\n92908Yxgf3EKL25/i3n7viCnUv4thRC206LwnjNnDr/88gtz5szBzc2NL7/8kltvvdXKpXVcI3qH\noFYprN6ZJVcyt0Ndvbtwf797mBV/JxEendhduJ/nt73Bp/u/Ir9K7iQQQlhfi642BzAYDBw9ehRF\nUYiMjESv11u7tjN0hKvNT/fRogNsSc7nwRv6EtvZ22512IK9e21NJpOJA8UHWZy+nMzKHBQUBgX1\nY2LEWPxdbDsVbkfuc1sifbYN6XOT5q42b9EJ15UrVzJ37lyCgoJobGykqKiIZ599loSEBIsW6UjG\n9AtjS3I+a3Zmdfjw7sgURSHOL5aevt3ZU3SAX9N/Y2veDrbn72JwUH8SI8biq5d/XyGEZbUovD/+\n+GMWLVqEj48PAPn5+cyePVvCuxW6hHoQHuDGzsNFlFTU4u1+7vlrRfugKArx/nH09uvBroK9/Hp0\nJZtyt7M1bydDQwaRGDEGLyeZ2EgIYRktOuet1WrNwQ0QGBiIVutYi2tYmqIojO4XSqPJxO97ZL7z\njkKlqOgfGM/jl83h5thpeDt7sT57M09tfokfDy+irFYOAwohWq9FI29XV1c+/fRThg4dCsCGDRtw\ndXW1amGOYHCPQH5Yk8q63dlMHtIZjVpuv+soVIqKy4L7MyAwnq15O1iasYo1WRvYkLOVhLChjOs0\nCjed/B8SQlyaFi0JOmTIEJYvX85XX33FqlWrcHV15dFHH7XpRWvteUnQ5mjUKkor60g5VkJ4gBsh\nfh3zh3lb6LW9qBQV4e6hjAwdgpeTB8crskg+cYj12ZupM9YR7h6C1kLriTtyn21J+mwb0ucmzS0J\n2uKrzf8sLS2NLl26tKqoi9HRrjY/Jaeoisc/3kr3Tl48dGM/e5djFW2l121BvbGeDTlbWX5sNRV1\nleg1zowJH8Ho8BHoW7meuPTZNqTPtiF9btKqq83P5emnn+aLL7645IJEkxA/V2I7e5NyrIScoqoO\nO/oWTbRqLaPDhzMsZBC/Z29mxbG1/Hp0BWszNzK2UwIjw4birJGLF0XrldaWcbgkjcMladQYa4n2\niqSbdzRBLgEoimLv8kQrXXJ4y+QiljO6bygpx0pYsyubv47rau9yhA3o1DrGdkpgeMhlrM3axKrj\n6/g5fSmrMn9nfOfRjAgdgs5Ch9OFY6ioq2wK69I0DpekUmAoOuPxXQV7AXDXudHVqwtdvZt++ev9\nJMzboUsOb/nHtpz4GD+83HRs2p/LdQlROOtkvnNH4axxJjFiDAlhQ1h9fD2rMzewIHUxq46vY3zE\nGIaFXIZWJe8HcTZDvYEjpenm0XVO1R9T9DqpdfT07W4OaL1az5HStJNfm8qOgj3sKNgDgJeT58mv\ni6arVxeZl6CdOO9PhR9//LHZxwoLCy1ejKPSqFUkxIfy84ajbEnOZ1S8LHThaPQaPZOjxjMqfDgr\nj69jbdZGfjj8MyuPrSMxYgxDggeiVqntXaawo5qGGlJLj54cWaeRVZGDiaYjoFqVlu7eMeaw7uQe\ndtb7xd/Fl6EhgzCZTBQYCjl0cpR+pCSNbXk72Za3EwA/Z58/wty7C55OHjb/XsWFnfeCtUceeeS8\nT37hhRcsXlBzOuoFa6eUVNTy4PubCPFz5enbB3aoIxttrdftQUVdJSuOreX37E3UNzbg6+zDxMix\nDArs22yIS59tw1Z9rjPWkV52zDxaPlaRRaOpEQCNoibCs5N5tBzh2emSj9A0mhrJrco3j+CPlKZT\n3VBtfjzQJcD8oaCrVxeb3eIo7+cmzV2wdslXm9taRw9vgPf/t4+kQ4U8clM/YsK87F2OxbTFXrcX\nZbXlLD+2ho3ZW2gwGQnQ+zExciwDAuNRKWfOC9AW+mwymahrrMdQb6Cy3kBVfRWGhmqq6quoqjec\n8cvQYECn0hHg4oe/ix8Bej8CXPzwdfZp00cZrNXn+sYGMsqOc7gklcOlaWSUHafBZASabjns7B5u\nDtEoz87o1DqL1wBNYZ5VkcPh0jQOlaSSVnqUWuMft2yFugXT1asLMd5diPGKwkVrnVuG28L7uS1o\nVXjfeOONZ40E1Wo1kZGR3HvvvQQGBlqmyvNwhPA+eKyEl7/ZxeAegdz9l572Lsdi2mKv25uSmlKW\nHVvN5pztGE1GglwDmRw5jnj/OHOIW7rPxkYjVQ2nB24VVfV/BLGhwXBWIFc1GGhobGjR/lWKyjyS\n/PN2P2efpkA/GepN4e6Pt7PnWR9abM1SfTY2GjlekcWhkqZD12llGdQ31gOgoBDuHkKMdxe6eUfT\nxTMC51beStiaOo9VZJmPAKSXZVB/8t/4VJ2nDrF38Yy02N0S8nOjSavC+9133+Xo0aNMmDABlUrF\nypUrCQ4OxtPTk99//51PP/3U4gX/mSOEt8lk4vGPt1JQUs1rM4fh4WqdT9a21hZ73V4VV59gacYq\ntubtoNHUSKhbMJMjx9PbrwcBAR7n7HOjqZHqhpozQrhpNHx2IFc1GDCc/LoaY22L69Jr9Lhq9Lhq\nXXHVuuCi/ePPrhqXpt//9MtZ7UytsY7C6mIKDIUUVhdRYGj6VVhdRGV91Vmvo1Fp8Nf7/hHo5hG7\nPx46d5ucbrrU93OjqZGsyhzz4enU0vQzRrQhrkHmc80xXpG4aF0sWbbFNB0haDqcf6gkjYzy4xj/\ndISgm3fTyDzKM+KS75qQnxtNWhXet912G5999tkZ2+6++24++ugjZsyYwZdffmmZKs/DEcIbYNWO\nLL5acZjrEqKYPCTC3uVYRFvtdXtWYChkydFVJOXvwoSJTu5hxAV3pbi87Iwgrqo3YKivNl/YdCE6\nlRZXreufwvf0UHbB7WT4umhO/a63ymFuQ72Bgj8FeoGhkAJDMTXGmrO+3kmtw19/ZqCfOhzvqnWx\nWLC39P1sMpnIrcrnUEkqR0rSOHzWuWR/88g6xisKd52bReqztTpjHWllGeYPJcf/dG4+0rOz+UNJ\nhEc4mhaem5efG01aNUlLcXExJ06cMC9OUlFRQU5ODuXl5VRUSHMtaUjPIH5cm8baXdlMvKwzKlXH\nuXBNWE6Aiz+39pxOYsRofj26gp0FezlekWV+XKWocNW64K5zJ8gl4IwRsZvmjyA+YzSscbHYVK2W\n4KJ1IULbiQiPTmdsN5lMVNZXkW8opNBQZA74wuoi8g2FZFWevdCPi0ZvPvQe4OJ7Rri3dma70+sq\nMBSePFfcdCj89KMHvs4+xPvHmc9bd5RV5nRqHbE+XYn1aZqjorqhhrTSo+Z7zlNLj3KkNJ1fj65A\np9IS5RnR9IHFuwud3EPb9PUNbVmLRt4//vgjr7zyCqGhoSiKQlZWFn/729/w9fXFYDBwww03WL1Q\nRxl5A3yx7CBrd+dw33W96Bvjb+9yWq0t97qjKKouxslNoa6yKfSc1U4d6o6Flmo0NVJWW940Wj85\nUm8asRdTVF1sPrx7Onedm/kwfKDe33w43l/vd85Dvqe/n4uqT5jPBR8uSaOsrtz8deb7p09OiOKr\n9zlrX46gqt5Aamm6+QPN6fejO6udiPaKNB+BCHULtto1HO1Vq682r6ysJCMjg8bGRjp16oSXl22v\nhnak8M4sqOSpT7cRF+nDnGnx9i6n1dpyrzsS6fP5GRuNlNSWkm8oOm3E3jR6L64pOeepBS8nTwJc\n/AnQ+zb97uKHRg9Jx/dzpCSN4poS89e6aV3NI8puMnNZs8wzwZ28qv70meBcNHpiTn7gGdKlD7pa\nV4fvYavCu6qqivnz57Nv3z4URSE+Pp5bbrkFZ2fbXf3oSOEN8MJ/d3Akq4wX/jaYQO+2eeFKS7X1\nXncU0udL19DYQFH1CfOh96Zwb7qQrrS27JzPOT1ounp3Idg10OGD5lKcmoP91LUBp38g8nH2Js63\n+8nZ4qIdcsrgVoX3nDlzCAwM5LLLLsNkMrFp0yZKSkp49dVXLV5ocxwtvLck5/HRomQmDApn2pgY\ne5fTKm291x2F9Nk66sxXxDeN2D09XAjRhp1xiFdYzqlTEUer0tmVm2y+yE+r0tDVO/pkmMc6zDSu\nrbpgraioiNdff93899GjRzNjxgzLVCbOqX/XADxcjrBhby7XjIhCp5WLOoSwB51aR6hbMKFuwYB8\nSLI2P70PfnofrvIfQ15+KellxzhQfJD9xSkcKD7IgeKDwEKCXQPp6dudON/uRHlGONyFby0K7+rq\naqqrq9Hrm2bSMRgM1Na2/B5QcfG0GhUj+oTw6+ZjbE3JZ0TvEHuXJIQQNqVWqYnxjiLGO4qroydR\nXF1yMsBTOFSSysrj61h5fB16jTOxPl2J842lh2+3dnvb3cVoUXhPmzaNiRMnEhcXB8CBAweYPXu2\nVQsTkBAfwpItx1izM1vCWwjh8Hz13owMG8LIsCHUGes5XJJ6clR+kJ0Fe9lZsBcFhU4eYcT5difO\nN5Yw95AOeXqjReE9ZcoUhg0bxoEDB1AUhSeeeMImE7M4Oj9PPX26+LE7tYijueVEBsvqPkIIAaBT\na4nziyXOL5apJhN5hgL2FzUdWk8ry+BYeSa/Hl2Bh87dfHi9m0+Mxe7rt7cWL0MTHBxMcHCw+e97\n9+61SkHiTGP6h7I7tYjVO7O4Y3IPe5cjhBBtjqIoBLsGEuwayLjOozDUV5Ny4rD5HPnm3O1szt2O\nWlHTxSvy5Ki8OwEu/u32DoFLW0OOptmEhPX1iPAhwFvPtpQCpo2JwU3veLdKCCHExXDR6ukf2If+\ngX1oNDVyvCKL/UVN58qbJtRJZUHqYvz0vuZReYxXVJuaYfBCLjm82+unlfZGpSiM7hvKd6tT2bA3\nl8TLOl34SUIIIYCmqYIjPJqm2b0iajxlteUcKD7EgeKDHDxxmHVZG1mXtRGdSks3nxjzfeXezm17\nWebzhndCQsI5Q9pkMlFSUnKOZwhrGNYrmAW/p7N2VzbjB4Wjkg9OQghxSTydPBgaMpChIQNpaGwg\nrTTDfBvavqJk9hUlA03rlvc8edFbpGenNnfR23nD++uvv7ZVHeI83PRaLosNZMO+XDbvz2NYr+AL\nP0kIIcR5aVQauvlE080nmutirqTQUGy+p/xIaTrZlbn8dmwNrhoXYn2bbkWL9e2Km9bV3qWfP7xD\nQ0NtVYe4gPEDw9maks+nv6ZworyGyUMjZAQuhBAW5O/iyyiXYYwKH0atsY5DJ46w/+RFb0n5u0nK\n342CQqRnJ3r6xhLn251Qt2C7nEZu8cIk9uZo06Oey7G8Ct5dsJfi8lr6d/Xn9smx6J0u+bIFm2mP\nvW6PpM+2IX22jbbUZ5PJRE5VHvuLUthffJCjZcfMC9l4OXnS8+R58m7e0ThrnCz62q1eVczeJLyb\nlBvq+HDhfg4eLyXEz5X7ruvV5hcuaa+9bm+kz7YhfbaNttznyvoqUoqbbkVLLj5EVYMBAFetC3MH\nP4SL1nI/k1s1t7loOzxcdMyZFs/3q1NZuSOLZ+cn8beretIrytfepQkhhENw07oyMKgvA4P60mhq\nJKP8OPuLDlJVX4VWrbNJDVYL7+rqah5++GGKi4upra3l3nvvZfTo0ebHx4wZQ1BQEGp102Tyr776\nKoGBgdYqp0PRqFXcOK4rnQLd+WL5Id78fg/XJkQxaXBnuYVPCCFsSKWoiPKMIMozwqava7XwXrNm\nDXFxcdx1111kZ2dz++23nxHeAPPmzcPV1f5X7bVXw3sHE+rvyrsL9vHTunSO51dy+6RYnHSOtbqO\nEEI4GquF96RJk8x/zs3NlVG1lUQGe/DkrQN5/3/72H6wgNziKmZd15sAL729SxNCCGElVr9gbfr0\n6eTl5fHhhx/SvXt38/YxY8bQr18/srOz6d+/Pw888MB5D/k2NBjRaGRE2Zz6hkbm/byPpZsycHfR\n8tCMAcR3DbB3WUIIIazAJlebp6Sk8NBDD7Fo0SJzQC9cuJARI0bg6enJzJkzueaaa0hMTGx2H3K1\necv8vieHL5cfotFk4vpR0UwYFG738+AdtddtjfTZNqTPtiF9btLc1eZWm+9t//795ObmAhAbG4vR\naOTEiRPmx6+++mp8fX3RaDSMHDmSw4cPW6sUhzKyTwj//ms/PFx1fL8mlXm/JFNbb7R3WUIIISzI\nauGdlJTEp59+CkBRUREGgwFvb28AKioquOOOO6irqwNg+/btxMTEWKsUhxMd6smTtwykS4gHW5Lz\neeG/Oygqq7Z3WUIIISzEaofNa2pqeOyxx8jNzaWmpoZZs2ZRWlqKu7s748aN4/PPP2fhwoU4OTnR\no0cPnnjiifMe3pXD5hevvqGRr1Yc4vc9ubjptdxzdRyxnb1tXocj9LotkD7bhvTZNqTPTWSGtT9x\nlDeGyWRi7e4cvl5xGJMJpl0ezdj+YTY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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": { + "base_uri": "https://localhost:8080/", + "height": 992 + }, + "outputId": "e3576443-4371-4b9b-c1c5-2eb364aa03e7" + }, + "cell_type": "code", + "source": [ + "_ = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=30,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.85\n", + " period 01 : 4.32\n", + " period 02 : 4.03\n", + " period 03 : 3.91\n", + " period 04 : 3.91\n", + " period 05 : 3.52\n", + " period 06 : 3.76\n", + " period 07 : 3.56\n", + " period 08 : 3.56\n", + " period 09 : 3.61\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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mJOFtAg62VtwyNJDKmgbW7e8YS4tGeITzxwGP423nxbb03bx74hMq6trXPe9C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\n1GfvuLnPbrebssqr7D5RxYEztbR3dAEwPiWG3Cw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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "mk095OfpPdOx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Replace the Linear Classifier with a Neural Network\n", + "\n", + "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n", + "\n", + "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class." + ] + }, + { + "metadata": { + "id": "rm8P_Ttwu8U4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n", + "#\n", + "def train_dnn_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 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 feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\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.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": "8EMDRViaCNCb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 992 + }, + "outputId": "4c44d15e-4e38-4988-92f0-72ee1e3b920e" + }, + "cell_type": "code", + "source": [ + "classifier_self = train_dnn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=20,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 5.65\n", + " period 01 : 4.13\n", + " period 02 : 3.76\n", + " period 03 : 2.97\n", + " period 04 : 2.49\n", + " period 05 : 2.17\n", + " period 06 : 2.33\n", + " period 07 : 1.96\n", + " period 08 : 2.25\n", + " period 09 : 2.16\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.94\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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2tpDOLN0qIS2EEDYg4awgnVbN9DFRtLQZWWOlBTGUJiEthBC2J+GssJT4cFz1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\nNxC4qPr+KORjh34/MuQH15kv5BXOIiJyQRx2O45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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "TOfmiSvqu8U9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n" + ] + }, + { + "metadata": { + "id": "evlB5ubzu8VJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "outputId": "c8c68253-3fe4-41f2-d32e-1cf3bb262b18" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": { + "base_uri": "https://localhost:8080/", + "height": 992 + }, + "outputId": "e66b2d7e-0cf6-498c-c649-d83e36ce80c8" + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 5.71\n", + " period 01 : 3.38\n", + " period 02 : 3.32\n", + " period 03 : 2.90\n", + " period 04 : 2.31\n", + " period 05 : 2.25\n", + " period 06 : 2.29\n", + " period 07 : 2.29\n", + " period 08 : 2.27\n", + " period 09 : 1.88\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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cJmJvdiUs/VyVHaONQqQmHDl1edAb9W6okIiIPNGQwvmZZ55BRkYGmpqasHbt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\nvCYFgiBgwxe5aOFVvkQeTSlVYknsAjw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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "qXvrOgtUR-zD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we verify the accuracy on the test set." + ] + }, + { + "metadata": { + "id": "scQNpDePSFjt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "outputId": "d0a4d28b-940d-4db7-c68a-5069b627799b" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 1175 + }, + "outputId": "4e808a53-736a-4571-f531-b293f68c2a21" + }, + "cell_type": "code", + "source": [ + "print(classifier.get_variable_names())\n", + "\n", + "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n", + "\n", + "print(\"weights0 shape:\", weights0.shape)\n", + "\n", + "num_nodes = weights0.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights0.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n", + "weights0 shape: (784, 100)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ItjA5C7StcKumOheSBffMOGjep5tbVLvKAGj842SVxZ9bcbOmDw0QBS7/Rtgc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\n95aWomZW8W5dE2jgKHJl11uoeTE1qG2dp6ewFs/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3xmH5l+/HtU1cVu3m5jDP/H5YeDef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\num5QfBn61mANxkubs0fl8+o5Q3WiyvTcM0L211lUP6Bpm7Y1zkvF+7h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3ph32zuM0XgrW\nT1fv4c/gWpQ8N/+fY8fzROq5uG9OeSFVw7KFavrFFOjnh7bt6Ae5V2F/4Fpnu/N1sMk4F3tqtx4l\n2z8nlOJeHfjVNtUufSb6ausuPDOFxTvPga04t+lfRE2WkBC9V0m+ArVqeO3qrMQ44H2oiIif9piJ\nU7D3jIrS32WkL8E+YO+vt3pxuFPTimvAjZLF+O6/blHtildg7uU6Y6ef2KPaZawqlLNhmTOGYRiG\nYRiGYRiGYRiTiH05YxiGYRiGYRiGYRiGMYmcVdbEaVfxBY5d8WtILUqidPXoTJ1yy2n8IyRlSZ1R\nrNr1NiC9jaUF9W+fUu3Y0nlkBNZmQ2T1nTJfp9+WnkHaqb8dqU7tZ3Q6auEqpCNFJny0pe7QAM6j\n+whkVnGOpbOf0tGiKWXUtcI88w9IMEoWSNDh1Pu0JTqli1PkWF7lXsPOo0j7jqO+MDai0ybDopG2\n5p4nw5aOnC4dHw/rtu52LTnhNO8yki7FxOtUc78fxzrqx71Kmq5tkzllkYkv0hbZI32Uok8pi24a\n9UjXR9unflyaSVYSk6vtis88dtCLOc120/Pvq3btvZA+fPpnn/Titt11ql3rFqRXltyKFPWF37hU\ntXvze0958ZLbkHbYdQKp+pmljoUkpdkmluN+VD5+ULVLvwH9YMYypHi279PStGObkNaYk465oapR\nyxRSFkMadHwr5q78LN0nQsImLu1XRMsDw6N0/xsbRf8+9Rf0/cSZ+hg7aN7KHkAfTCrT8qeTz8Oa\nO3k+0sarnjis2vE551yOa81poUnTtRSqfh9J3OiSuSm3i78Mu3VO33ZTf9Pyce9Y7pY8W1tt8r9b\nqd9GpelU+Izzzp4y+nGoexF9LiJJW8yynMVP9rZ+Z25oJMlEoAf3sKRE38P+dqRzxyWT1OZMl2oX\nReuusu4km+A3H92s3lOUjn7lm4LrH+qkb9e/hPP1kc1tXKFe7/obkULOchPXujhA+wBOFe90pF/8\nvpJ5EnT6KiHRcm2TeUyEJ+B6hsfo+x0ejXPpOoa9gJsCzynRLPlyrY15foxMQJ9uPYA5K6FE236P\njaH/jI6in3H6t4hIWBjmnu4zkLdEp+k9m78Hn8HHGhjS9sqBIfTBnBWQBvgH9b7qfw32YEL3LaFc\nX5fG12GRWngj5EH1r+rrknIOxhzLzwb8en1v3lLlxaMkpfCVa4k+p7xHpeGaN9P856vR45cl0mN+\nvN9djzJXYd/cfRLrbIRPryVVT2OOjyWJCcusRESSpmEOqH3puBdnna/35607tKQ52PAeteYNfX/m\nX4dNce8ZzDFNG7XUNpfs5vc+Dyl0fqruFyy9TZoN+cU9//5J1a5zD55Jmkn6ln0x9lhhUXqct9B1\nYkvwkFD9O/gA2XvXvI/PLrzwA9WujySVvM6W36onxKZ38Rm8L3VlxvGlem8bTNKopIErZe0gm+P2\n7Vi3U5bo9Y7LH8TQM1NEn+7f0WSHHkW28e7YHqOx2NONsT3lk3h+OPTADvWekqtnePHpJ7AvTZun\n9yIp5+B8+VkvcZa2Oa9/tYJew3hjea+ISPpK7FkCdNxhTkmIgCPTCzZcWqK/RtuHN1P/zqPHgaR8\n3a+KroB0cGQEfTgiQo/F5n149m3dgzXpf8mC81FG5eAfd3nxjFtxH7sPN6n3hNNnNFE5j94TeozF\nl2J+ZAnkuZ/VevhRkiZyXLTMmSv3oK+zrLBgnZbPVT2FOfrD9jeWOWMYhmEYhmEYhmEYhjGJ2Jcz\nhmEYhmEYhmEYhmEYk8hZZU2RyZD2cLV2EZHcK1CF+PhfkCYUV6vTy8Mp3ZJTfHpPaUecorVIXax8\nAVXtXRnOKUozi6W035wrSPqwW0sfZn0GVaA5dbNgvq60HhKOvEGuUp11gU5bYpcnTv3vOqTTstPJ\nSWaYZFejTuX2FEduE2xYeuRWMx8n5xpOZ+51HIsGapFW5puK1LSIOJ1uOOi4PP2TtKnT1L+7apBy\nnJy83IsDAbx/fFTnmrPULCIi+UPfIyLStA2fnbYQ/adlW41qNz5G95hcQ0YHdTrzIKWgJpKTGL9f\nRCT7ghKZKHxluObdlD4vIlJ6O3Li+ihdeu3dWoa05y87vfgwpQbGRelU/f1VVXhtK2QRKfOGVbuV\nX1njxTw/zLz1E17c1bVbvWeU0qqrnjrkxdU1OiWxSCBrSpyBNNHDj+uUxGWfh2xmsBn9oPopLWuK\nzYJbQKoPcbiTDs4uOhPBmb/BRaL0Dp3LONgKCUsZvcYpriIiOeTE0X2aJJtdWj6Sv7zIi+teQ2rt\njM+fo9pV/BHzLY8xfy/mgx5nvh6j+ZHHQWSKnl/qXoEkhh1KXCc2npfTl2LedM+pnhwcSj45F+0c\n96e+ai0bCCZRWUi3HnPcMHw0P0SSvIhlPiIigQGMA3ZRciVAGQuQ9t1bgfTg7i79eTlFGKecUs7O\nJyvOn6/ew85L3K7y1eOqXdo0pP6zI5E4ahVe11h25a6f3Jf6KnFO7NwgIpI0P1MmkvhiyFES84rU\naw07ME5ZhtV9Ws+93PcTSSISGHDWEFoX+d4nluk0b17zOo9hP8Gp7fEpet/S14H1LsqHz2vedUy1\nK1lzpReHl5PT0m4tKQ2hexxKfcl1XGSZcf27SE9318XM5RMnMYyg/Qy7EomIZF5I/Y4OKSbXp9qx\nrCuMHOoKz9HHzfeXadulZcGZq4q8OCqR5EoX67mC6SO5Du+7hxynFp7n+mgcuTIS5YxH0rQkR3Ix\n3Il9aVwJ0vs7D+u97OjgxDluiYiM0N6z/JN6nmp4A2tXXyvG0bRPLdTt3qJxQA6r7NQooq9bw8sf\n7Qg043qsLyxT3PsI9lHREXpMZJVjzmKnS14LRER6KnC+7HB74vG3VLuUBZD8s9NP3dYq1c4fwP2Z\nvQwSSl+J44rbrs8xmDRsQAmK7Iv0HJVBa/ogzXlhUVrWOUCug4MtONa0c/RzYMv7GHOFa/FskeA4\nUQ404PO6T6BP15Pb1VRHItZNUrAwkqPxM5CISCo9m7KDr0saOTn1nsBnJzsyqZ6TeI3dLHOcaznR\n0nt28M2Zop9pspbCqq1xO/YJsY7z1EmSUPO80rF7l2qXS8/tPcfx3J6+XJeqGGrHPBUYxTzafRzS\nzgx63hbRzwP8/J19sT6nAI3TxZ9YjOM5pSWB7H7KbmRF63QtkvTFuN+uyynjunW5WOaMYRiGYRiG\nYRiGYRjGJGJfzhiGYRiGYRiGYRiGYUwi9uWMYRiGYRiGYRiGYRjGJHLWmjPxZJUZ79hmNm2CliqT\n6nq4NU0klCwpqc5A88Yq1az9CP7dVwW9euZKrfuNT4eFWupSaLsqqBZN2Sdmq/ew3ttHVtx9Tl0V\nttlmi0u2ABfRmmy2MIxwzp3tDPOuRI2e4Vat+4zJ1hroYNNT0fGhsYhIDFnSJZIVZc8Z3S40EteD\ntees1xPR9tSsxXZt41jDWzP0qhd3HUGtELbIE9H3gS1iExxdLffVXqpp4FptRibh89kCODpVW4u2\nk9YwNifhQ98jItK8FZrgPC0T/dgkT4c+dcvft6vXQsnOMWUuNM8nntK1BHJzoZlnq7qRDl2vY/Va\n2OC1fgAdqKvVj8/D5yXOQPz2d3/txb5oPSZGSC8aL+gr0ZG69kt/I9U4KvhwqzsRXU+ph7TCSz+1\nXLUbH8W94vlq5xvarn3/AejbP/vwdRJsctdCs1v55CH1GttDth2BPnr2F5apdvVvQdvtK0Xf76/T\nmuimnbA9LLwM889Ac69qx5rotj0Ys0MtuNZurYK5n0If4VoPfDwiIn1hmMt5LKbM1vVE2L65ZQdq\nQ/H8JCIST5bPx36PekalN89R7Wrfg1X1zMslqCSSrn2gXl/zIZrbu2kui87Uc0oo1bYYaqD1aaq+\nfrG5mG+iuW7SsK4BEZGIccZ1xLimQkyeU2uD1quWTZi7cpbpNZfXOK4J4CvW9pm1z6LGCdfl4Vpm\nIiJD9BkZq/G33PVzoFrbeAabfmVnrLXhXEMgbSVql4z06nOJ8OGY2w9grkwo1vdxjO4J17/qPKJr\nYwX6sMbxWBqi9XNwUNdO4zU4LAb9MXWBrtNQf3CjF3PdvLR5eapdSAjqY4yPo3ZO10ldFyyS+lzG\nMuj9uf+JiHRXoC5Atv5TH5t0su/tcfZzgw24FlEpOF8+dxFdnyok9MMt1EXcGgaY8yKdzzvzKNbd\npDmYN3uO4TrkXlWu3sN7k6Y3MXe582k/WSsnzMSaGx6ta5/wOfFnRDvH2rab9mit6GO5l+vj66+f\n2LFYswd9OjJF7/t471lyHWrR1Tx7VLVra8MxLrwdddXaduqaQPE0b6UuQk2vPMfyne3DuY5Q0WzU\nw0hbojs0PzdUPAo7b19eomrH+9+cS8u8uNOpW3nyOTxDlF6G2iqjZO0tIlJ4AT7j0GOoy+fa/PYe\nxx6pfIUElVhaX8bH9BzAz0JRabi/o07NtnHaU7Ode1K5riXTcxRjiZ8Z2M5bRKSHapJMvxG1ZaJp\nPnDXJ+4GofSPmDxdI4SfR3jdT5ql9zZNb6IWUuIcvMY1TEREkmZjrkii/XQ91QsUEUmYrq9F0KF6\ngoGA3t90HscawH29fYceYzk0v7XT+EtZlK3a1b6M2j8ZK7GG+PJ0fa/W/VVevPLb13txfwfst7mO\npoi+d7HRmMtL79Q1Ykb6sMZxTSa/88xaeC0s1pvewRxd/fI+1Y73SwGyte+r03Non/Ms7mKZM4Zh\nGIZhGIZhGIZhGJOIfTljGIZhGIZhGIZhGIYxiYSMs2e0YRiGYRiGYRiGYRiG8X8Vy5wxDMMwDMMw\nDMMwDMOYROzLGcMwDMMwDMMwDMMwjEnEvpwxDMMwDMMwDMMwDMOYROzLGcMwDMMwDMMwDMM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6HhFdRuSZyjayo1bcBakboM+3jXBZjdyXhPGeY4aXFtmRGag3Ou0M9SUQmYk3n/wfN75uoi9R6u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x3W9+6sQtTZakPg4Spah7sPd1dEiHvvbLqD+P//I+J06fO1PkHXsI56dwciWNTZot8tzTsA8d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xI7cYQcB8Gf/dsOr909iDFzf+9a1Y8dIhbdj/g4QVKhoUtamjzeg3ExMQxHkuyV2mlj7nbE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I1LmYqUgblCnyxhgTKqRbe7fiGc6/mFOnEyOlrBCpoK35DdRu2EVIKfS2iNTLjbjpF9/1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\ng8q5aB0sk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p5mN5mgYoDGXHiMrFFjfrLbJX150nxpadqyt9p8EOKnS4vdvnLMKy9pmYcs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Xwxau56qWHpUvebfX7uuDlGyU0kTH23XKaMseSAlZljLRr+fE7DRS5jv3Qt6QmaHTZdneu5ss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\n9/d07aCGO9GP6wO1PHVe9cukuo2To4gpvAcQEamsxzU6exL7vs0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JTtWPPTCDQay/sQHy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PhU8JwpeQRRkVFRWvfc3w+NZ0wSriE8ytF4duIa+i5iXmUu0brV1Mfh29DbAs1kzh3aO4h1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QWnNtKRFtjZy3DPV2Tr15XvVbuQvXs+sU6gqwFWHiYm2JF54K3eZYL+aEa3dZ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\nw16cfXXW3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5fnHUs99DUgV0fmyz070IFr5xwaECXd7mx3OG/j/Is4xxuqpWtS2kyirtGz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\noVAoFAqFQqFQKBQKhWIUoT/OK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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "5Rxe67G7IobJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1175 + }, + "outputId": "0ef5c8a9-dad9-4870-aba4-d8c9de9d9463" + }, + "cell_type": "code", + "source": [ + "print(classifier_self.get_variable_names())\n", + "\n", + "weights0 = classifier_self.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n", + "\n", + "print(\"weights0 shape:\", weights0.shape)\n", + "\n", + "num_nodes = weights0.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights0.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['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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oFAqFQjGP0JczCoVCoVAoFAqFQqFQKBTziFvKmlxEb0rzS5q4n6Q4uQtB95mZ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\nMVEurAFsPZyWLmuLcK2uuADbTsv94STV2ClYgntEnkfGGJNWjddGG6Rt9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kJc7idwipk9jpQl7zOdZ+yboeGh11x4qsuKWE1/DwIdgDFwuLe3st9xV6HVIDIySTa3TFOtbP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T+DpS7BHuTdP0M6ueYL2vVTaCnbdN+rTnze3fqZi58LOyqwV8yfidIXyxYdUO8Ji6V7QntK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\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Sg/3N5Eo0CgonP6Wu2s4Msz+J8z/9AlLEcifKqto9lKaYSg5c7OZjjDEBSlNnF6XOozIN\nc2gAqaVzvwIJSyiA5z1sSUyO1yAlM/kC+o+dVhY9Hecel46q2pyyZowx7UdRbZwrbNtuGDlz0fff\n+T+v4b+nyn6RlGa7nISP0puRAunKkM4MBelIAeQ+/f37fiPajcvEffrsL7/ixF/+8p2i3ZO/ed2J\nB3+L57T99GnRjl0P2qjifctBpE6fefdVcczm43AJuffum5y41JI57v/e0048+fOQZnztG78V7f7p\nhhuc+C+/fteJNzx4nWg3fvFtTlxzFOf08E8+LdoleGQ6ZbipfxPSklySjBljTP3r+CyG5jbvwUbR\nrmIDJgl2s2MnAGOMCQQw1oNBOe6Zoz96x4mzKOWfJRIna6WE7zqSWk2g+YClRsYY8+VPfcqJW0lC\nuXi+dNTb9AGczjY+hn7R8LZMw0+fi/Rwdnrw10ppxqQ7ZSp7OGH3u6hoOea7qiBzzZuG59Ret1+0\nGyIHoHHL4R7Q2yVT61MKIS9giaAtd+jvx9yYlYvS/4EA5KkREVIu0HwC7k8Fs3EORXPk2OnthSQn\nORnp5MPDch1LSKIU9RFKf46S2wxOX+ZUX5YVG2PMyBDkwxlXzgD+/yJ5Isa6PeezM0PbfjzT9m55\njqkuPP8QOTK176wT7abdQJIncvPot/rtQAhp5B29SKPv7MAaXmClmqfOwHrH88H0BXIvxtSeavjI\nz26+EbnybReQkr5y6lTRLoH2cFnktJE6Ua7HrZYcNpwMkswuOlHOfxnzMJd1ncd1RETJ/yfpmY71\n1EXXJPZlxpi0CcVOHOzHfkjIDIwxeddCLsjrcctpjLegdU88MyBDYoen3l655qYXox91t2GuYIm2\nMVKG008OJLZDKTtpZUzDPqf6teOiXZQlrRhNLjx3TPy7dAPWirmP0FxZL98NeP/dcBjytEm3Sbkh\nOxIODWDOioyT81T2TLxT7P0v7Ptc8Szdl/NG4xbIYNil5vzvD4l20x/FHO17HP0sfZrcd3NfZUfM\nzUePina3rYX898HvQq+05/e7RLvV//IhtjBhgu9/+pyPdktkGVK0tWdJK8A4cI+HbPbM0/L+8X1n\nmVpi0Uc738aTmyWXtygovVW06yDZ99DQh7/PGGNMZxNkk5HRGB8dlrSK4feoqKXSEa31KMo7pJWj\nH7DbmjF/X44j3IT6sIbEuuU7E+9ZWZ7msc7RXQI55zCtd7ZSt+FNfMflOtw32xU3dRI2AK2N2GM2\nduJ+LkyTcxv/9uB2YyznLmoT7c78aqsT59+ANXPh55eIdod+gbE08zOQfrXuks5S559Eu2RyWOtt\nlLJb9/iPdv41RjNnFEVRFEVRFEVRFEVRxhT9cUZRFEVRFEVRFEVRFGUM0R9nFEVRFEVRFEVRFEVR\nxpAr1pwZGYZWrHWH1FVNWjoB7YZQG6TP0o2374b2eph09j2VsgZC4jhoBU+9BRuxzl5pX3b7WmhO\n2SK6dVu1E5d+Zpb87hzURemmvztI2jpjLIs90p41vndJtPMsgJ5y18uoJTBr3iTRbuF42Md1V0Eb\nZ1suX96CugoVoyAJrd6FOj2xUVI73PocdNCF06DRZitxY4zxnUTtgsgYdJuiRatEu+4OPLtkD9VJ\nyZe1FNgKtqsOfSTQAS2297S08+4lW89+qhGQ2COf4wBZaefPQn8JhaRGuVDY4KKvx8RI3Wp/P+pt\nzLkRfSsyVv622Xnko7Wm/yjN76IP/vHxv4jP/u052BV3NkIr/sXvSK/EvX/Y48T/csujTvzZO6Xd\n4nf/glo1+7/3pBOvsazir/o2rLR/ds8XnTiR6hAVV0hryEKynn/+pc1O/K0//0y0GxnBnNLZeNKJ\nn9zyI9HO7aYx9m/fc+K3fr9VtHtkGWqfcF9uOiufWekajOHMNVeZcMNzVscxWasgPgf1K6J7octO\ntXToXeekdvVvFKyVNSbOv/OiE/PcZtcPmHg3+nT9a6h7423FeMlOkWPi0nZZC+Zv5BTLehMZZMGd\n58Gc8u72g6Ldook4d3891hAe88YYU5CN+mG+VjzHcbfLGjbN2y87cUmY63jlLBnvxN7T1eIzzxQ8\n35ERaMPtehjZxdAzDwxgTHRbFqSpEzHX+qm8QVxclmjncqHOxeAg6k309WHeqNt0QhzD63toGmp3\n9HTLOhdc8tKsKwAAIABJREFUM6a9Cdbew4OyJlhSerETJ2TiOTVslXM/1xIruBb7iM6zsmabbeMZ\ndkj/3nlEjkWuEeemOgbxPqnB72jDfetrp9oeMbIGCNfOa7xA2vrx8jlevlztxL96+WUnvuNabAyi\n/keuO9Pune/E7YdRn6q/TtZMeX4ntPCfvBG2oJHR8vvcZdDJx1CdNq6TZIwxR99AbZBpq8qdeHhI\n9ov4rCQzWkRFYy9Vv0X225TJmIsCXuwrYqz6Zv2teG5DNE+GumVNpeb9sCzn/lJ6j9xvth/GePbR\nniA6BX83baac0+veQF2nwBIMdGHfbYzp9OIcqp/HuphUKuvf8TPkOjrth+pFu94q9EuuOcN1Qf43\n8JBtdUq5XEM6j+Metu7CXqy6UtZiY+v5mbfimaSUSOv5mBjUemg+hv1SxgJZJ+WZR1DfbuECrC8J\neejP/ib5vlN+G6y0my/sdOLCmyeLds0H8Bxn3If6FZ2n5J6X7Yozk/Ecf/vTfxbtXnxqkxNzv13y\nhWWiXe3LsAcu+GdZa+Ufhd+nuivlHiVlAmqGpM3Cs46y6vywTXLXOawHUz87T7ajWmU+qleauUDu\nN6Ni8f1dF3BOyWTV3Na2RRzTtAt7oNo91TjGem/Lvhr14LjWXkK+rMOZTLVz+P2mbstJ0S5I7zS8\nX+hvlH1sKCjn13DD+9L4bDl3t+3E+OM5bMSa8+No/R8epPf+GvkOlrMGNWySaqg+i1Ub1X8Z6yzX\nP7zu+sVO3LztsjhmwcP/6sS15/7sxENBWbMnoQDjin+XOPSe3C+t+BzeJXmcxlp1vHi/3kHrMdfe\nMcaY3ovoM2Xzzd+hmTOKoiiKoiiKoiiKoihjiP44oyiKoiiKoiiKoiiKMoZcUdaUxin4h2UKYWQM\nftdhe+aQT6aCps3Bd3go3efyCzKl69AOpKQuXg/r3Lh0aTfWT7bQ8Rn4rOAWpA3mFa0Xx1SffMGJ\nY8luiy09jTGmYTNSwFlO1d0vbQ9nUlrjohtmO7FttXbpA1ijcRrepHVTRDt/nUxbCzdsOR5heZlN\nuBoyDr63dmpyqJvt1ZByd/GNtz7y77ZGQa4U8kl5QkoF0rnjUpG6y1bcl1pkiufKCqSWplLqa5T1\nHNl2LSIC1x4bK/1YuzqQlp2ZgzTvqCiZpibs9Oj2nX1LplGz/XG4eWkPJEk/fvsl8VlfHyQm2cXX\nOHFb4zbRrnwBpA9sa5y/uky0e/BayKGumgYbylWPXSPaXd77hhNvIYvsjS7YVudcNV4cU3AYY+Ke\n737ciQ/+1/+IdskTkAr62qtID/74/VKC5V4OWVNoCOmTZTnS2m/zV//diWd9aYUTV9z5cdGus01a\nNoYbVwEkErGW9Z+X5thoN1LgeZ4zRloOs1xp0C/lfbE0rtp3YSz29Ej7T/4OTtWN2otjes/J8ZsQ\nizmg6Br0n24rHXXCrGInZonEgsAE0S5rJiyyeR7Km5Yn2gXIQjM+l6Qzb0vpDFvEhptgD9YD25bX\n34pnE5mD9Nv+Fikx6XNhHDTtR/qsfd7R0egvPA/V7pdjm9OK06djzX3za7CAnbVSWiF7z2J+LbkB\nzzfJXS7a9fUhzdvQGhfqlWu9txMp87y2Jk+Q9vS9tUhRHqFUX059N8aYwChaMNt/m+2ojTHGuw/S\nlOFWjI/jlTJ1ev5irEl7dmFPY8uH55diHu0PYpw+8sPfiHafWLnSiVcsgNyh3otx5XbL+aCJ0rl5\nzFc2ScnmutnYq/ia8AxSs5NFO+8hzEOuInwW7ZJSl/l34fzYttRnydPYrjjc+FvpvlhS7KR8/LuT\npKxmRKaXJ+bgGjtO457FpklJ0UAr5IIFN2Hf1HO5Q7TrOQv5RDztFTur0a71LTlP9pB8c+qnNjrx\n4KBltZ6AfukPkHX70VrRbhrJmlp2VeOYGvl97knynv2NpBIpk8qaMvVD24WLA0/DvrggW84DfK97\nayGLmGtJWFi+xdbVHktaPTiIudjfgJjHjjHGjMvAefzkj6848aOfghwoc0mRkWDcc7+qfe2MaJW5\nGMf1kYw3MV+OxY5j6I+r78G+xXugQbSrKMA18rx54EUpH15y71IzWqTPwVptSyDZXpr3G/ac4irE\nesdSULbVNsaYyhewZuYtx54lo3CBaHd557v4bCZkaz01GIsjQ1Jq00FSxIxC/N1Qh9wDte/B/ijv\neuxnav4sn3U/Pd9QF8ZsDtlSG2NMTxXOiaVpfTVdol3uanlcuOG1O9GSNUWSJD5nAd5jLz4nLduL\nb8d6x/LpzqNSPhyXiXVj8+uYA669dbFol0DjomkH3gd8FzHX5iwZJ45pa4PULzkH7yGdtVKSP9iD\ne52Qg+sty5VyyGAXnn82/a2Tv9wr2rnpfT6KZInct40xJs7a/9to5oyiKIqiKIqiKIqiKMoYoj/O\nKIqiKIqiKIqiKIqijCFX1GFw+rxd4Z4r8F/agRRtr+WuNH4Ajguc1pM+X1ZGXzIZKYS7/4pUvNJs\nmW6cu6LYiZPzECcmIm0pGJRpaqmFcAKpegspUUOWW1MGSbCe/QmcTj57/WrR7sAWpNT1UWppgUem\n3sVT6v+km5EWastwIoqk1CjcFF6FNLg4q7J0ZAzOpeskpSOXytRfXyfSP0PWfWNqDsLVyxWHPpM5\nS8oTYqk/sYMWpxw3dkrnkukVuI4+cr+q7ZSpumlJ6JttJUj/zF4m094KyzY4cSDQ5sQ+3wHRrq8V\nn518B25Ubqt6e/4cmT4bTn7wxh+d+PE77xWfpbrg8lPXjjS/rz73bdGu8zzGVTVJxrILpEXYfzxN\njhWU8lfzkpRxDVO6+q0LFzrxpseRSlo+vlAcc9sP4RLFjl2bjj0t2k1sRX/5wk/vduJf/5Ns9wmq\n9r/6W59w4osvbRftfvXM605cRxKBJR+X6cHjFq0xowoNdVvqkjYdcx3PEbZcMmMu5s6O40gTDVmy\nprbtGIueRTim6tWjol30EcgYImg+SCJp2bRimeaeNA7/FvJQayqLJSlTkKSNtpSCHWOiE5HqPNAo\n1xNOE+05hzRgz3w5v/RckLKBcMLn6sqTzgw91ZiXYlyY/0OWo5yvBg56KRPJpaBWpjB3kowmMRd/\ny3dMSlaSyvCs9v9guxN/+4knnPgHCQ+LY1iuW3gUYztrpnQdjIoiyUo1+lScJQPg/QLvD+IzXaJd\nLKVs99TgfrFU2v4+MwqqCpYYDrT1ic9yrsF+ooNSsacWSRlD5QncjzkTaZ3NktKjdpL68Lr2o4c/\nL9q1NaJPz50HeVnTJczXRR+Tsuj+FowRH63hc66TNmWcvs2OWZxaboxh40IhJ7DdLTktm12O/HWy\nD6fNlOnh4cR3Hutd0SopaWg5hn1aBkkufGek7GrAi2cfJDcVvyUniCV5aR9dY6hX3peaejyr0/vg\njvTs61iDomOlfPFXj2JdZPe2/m4pA4iMxlybPR3XZMtDIqIwEeeuYhmE3NfxfNVOfZn/jjHG9Pkg\nm0pNnWPCzawNkNzZkp1dv8WefWIF9nC8thgjZZYTPoPvq3xGSnvOncd1DtN+MxCS7nA8Tjcug+tR\nBJd0sNz1+pvwjDPmYM2d9ZkHRLtgEP02hqRlVfteEe1YZrH9GUhHVnxiiWjX3YE5gPut7bI42Dd6\nDnjspDVsyZpyyZWHnY16LkhJINcNiKH1peu8dH+a+Ck83+QcyJp8Xrm3ceXj+v203+LyCV1n2sQx\nKRUombD/PZQ+sMtbzCouduLm97FOBweljNM9Ee+2LZux7l/40zHRLqMcpR6az+GcPGWWLJjGrAmz\nE6Ux8h0x4JPXnD4Pcw47jrksGWTVs7g2/u0gw5IitpF0ftEU7Du6z8n9G0u7Dl2AhH3jMsj0fEfl\nnojddHPnkjytxyq9Qk5xZ/6KNaPWK88hk/4d/w7m7zSX3N+wFJj3xinFstTCmZ/D+XKyND3+f8f+\n/X9SFEVRFEVRFEVRFEVR/rfQH2cURVEURVEURVEURVHGkCvKmrpOU2rVHJmaGkVVm9kBaOldi0S7\nPX9CBebmXyONp3zpRNluE9LRVm5EpebMWTKNuI2q0rvdqAgdCCClqbf3rDhmOIQUu+5zSI9Lsxwa\nanYjNe3Bz8LxqdNKqbvmfuQgVb58ynwUqYVpTtxOKX/RlkQsdWqWGU3YZcCWHXh3I+02iuQE7zy1\nXbS76WFIX/h5Nx6VKajVbegzc0qRGm6nrNe9es6JT1yqduK4GJzD5DwpVWA5QUc3rikzO020O1tJ\nfcQHiZt9DoP+5504pQCpkf0+mWrZTRXBiyfgnE6frBLtynKly0k4OfMSZHZLJk8Wny37xlecuL1l\nO30iH3Z7D9I6P3vvTU78zzfcKto98vPPOfHZ55Ge+NtNm0S7DEqZnUJuAZ/8yV1OPD/nBnHMT1uR\nkrnrHPrAJz61VrSbcuudTvzMQ191YpasGWPMsz+BG83H70PK7ox7PivafY2d50im0L6zTrSbsEI6\nRYSblp3VTsyOTMYYk0CV8blyv+1UEJ+BtFPPDFzX4MBHpyxvegZzb0lmpvgstRRps+wMtfmJ7U68\n/Jb54pggpYaOkESi87BMLZ3waUgNdj33thN3WPLX9HpIdngOyJ0t5a9NRzDfJJJs0k7rL7hBSnPC\nCV+77fySMR1p93XvYm0ouFaO2fbjmHf7W3EvYqw+wdKRxs2QDw8NyLTxx78Ht7NYun/J6Xi2rx6Q\ncs0Hb7neieMz0feGh6UrRfM+/F12guq05CFp05C2y7KPQb/slz0XkR48bj0kOtatNAmZcqyHm5oX\n8Hxy10nHum6SxSVPwj2McUs5SkE11pTUWbj+/gYptfWUYV5ZkIRn2tEsJdg547EX4L+bRZJcW3Li\nLsb6l0xzRX+7lGp1ncXa3F+F55OQL6V5gyTB85LkMdNKSW8/jLGYMhlzSmKRlFIER9F1K4WcwOp3\nSflKFEleecz21cpnw84onILffknu+0omYk5p3oq94t4L0imOnQLXLYbz6PgsPNtMS27iSsa86/FA\nItwdLfeXfd1wFGWpVtNWuRdheRZL2GwZplhzSC7BfcoYY9oOYJ3MLzZhJ5mctnqr5Zgoz8LYDJJb\nX9ByAG3egmeSsxp7z952udZkJEN2UDgHsmu/1S8K20geVIL+XXIrnmlMjHyOp34F91J2qeuJle8k\nF57b4sTj74BMrOOg3E+zXDWfyyZYkyXLhNvqMHfNfkjKn+rfor4qP/qH4feCghvk+12I1gDh+EPu\nkMZYsl76Ps+MHKsd+kh3JcZ9+gz5ntpxHPsR/o7s+dirBzrk/HRqB/alLGVavnC6aMcSsRfe+cCJ\n71i9TLQLdqDPJhajvwxflv2c3zPH34J1kd3zjJFz7WiQOhHzyuCAdPas+QucqHidyFsn3TcHWrD2\ntJyHzLPlotwzsOtnEsm2W6rl3Ft5Hs/xN997zIlrj5CUzhoTHoPruPzmbieO9UgZr3c/xty0jZDL\nhZ6V60kxub6lTMFc3t8kyxNwaYCe87SPL5UyqexVxeZKaOaMoiiKoiiKoiiKoijKGKI/ziiKoiiK\noiiKoiiKoowh+uOMoiiKoiiKoiiKoijKGHLFmjNuslIdGZZ6rnMvwXIqPRlasbpNF0W7hg5orqKj\noMV6+j+2iXYzS6A9XLf2Pif++WOPiXbxpKf31/3KiUNd0A2nlMu6ET2VOIeYJGjcTr5/RrSbdfNM\nJw60Q2uY4JaWfQlkEzrzUVh5sS2oMVLL3EdayhP7zot2y+fK2irhpu8StI0xKbKmQWcvtIEZibBD\ny0yWVrddZO2WVAI9cmapvNcx9IzHbYT/af1r8poTyW5sRhT0wVsOocbJjHHS+jrWg+eQ0gnd4LBV\nf8HXh2tKJE1j93mp+XORHXDAAy0k17YxxphYqvvANTX6g9JCs4U0z5OWm7DyxmuwUbzrK+vFZz//\n9INOfOf373DixESp581wY5wWr1nhxA9bGtakdOiw5zyK+/fNaVLPy3NCLtm1//GRZ514b+2r4ph9\nP97uxAsnQpfM+mxjjPngmz924rX/ep0Tcx0BY4z5lzsed+KXn0BNnI8nyhokR18+4sRz7pznxKWf\nv1O06+mBpXBKSvh9CtNnY6zzmDLGmCayWSxaD0105dPSHjLnKrKOPAE9b3yurNHB9tLXLcMzbSTb\nR2OMGWiCJp/t5hcsR02vqAR5PzvJtrDs4xCvt++RNXy6a3B+k2/EfGDX5GCr6VAHagn0VUs721mP\nYL6tewPacLtWy2gSS3+rdV+t+CyS9MZsYdtxRlri8tjpptpu6QtlXY+OE7jPB3ehb5YXyHY796G2\n2+MPPeTEn1l7jRNHWPXGXKR/HyQbdu9p+QwDbdCd95DWf7BXWlK2H6J6QPmY3wes2icF12Hce0/g\nvgxalsQRUZiH8+RSEBbSF+Ee2vbPbqqBwdbuzduqRbuyu7Fn6KtHnx63fppo1/A+aj30BfC3Mgpk\nPamoeLLenIR5OSYec3dysvzuUAj7jrYqzHPDQbku9l5Au+Rp+G7bMjqlAnp6tm9v/qBatMu7Gus2\nz8tJ+fKaumtk/YBw4srBupGcLztJwI9xFZuAPYtdy8hLdaxiyUaWa1oZIy132W53XIesUTc0jBov\n+09i33P1etSS4fFhjDFR8bh/PT2Y17jGjDGyzs/QACx7S++QNcEGuvCsm3dUOzFbbBsj+7aYgy3b\ndHv+DzcXfn/YiTMXF4rP3OPx7FppfTn13mnRjmuenHgC7xcVhfL7ShahjuXxd0868fhiuQ8vXY15\nqmUn5nl/O/aRA2014pjpD21w4q4W1JlJSZkl2kUlbHfi4WHc65FB+Z719K9hzX3btahl0n1WjqnW\nBvTB0qtw3rx+GGNMfLa0/Q0ncfTd9a/L/X6A+lMh1aNpJ6t5Y+Sz7zyJvUPHMbl+ppNN+VA/ar+0\n7pNrV/spXH9MEvpwO9X24f9ujDEleahFWpSB95u+Vlm7yEO1Qj9GBXz2HTsn2i2OqXDiuiqcT16e\nfHfKpbV/iKzIY6wapcEuWWsp3EREYN31Hpf33TUOe4bsxcVOPEJznjGyvllyAuZUV6m03I6IxJgd\n6sd8Zm1VzM2P4h3g2DOoBTP/AewHG96Vvz0kFeFvdZ/BeNm79bhox7Vq0tvQry61tIh2+bl4XgHa\n02TMkXURa1/FuI+IxpVUvXBStCu06jLZaOaMoiiKoiiKoiiKoijKGKI/ziiKoiiKoiiKoiiKoowh\nV5Q1pVHaVrOVCl8wD+lnsemQmJx7Q1r/cWooS0y+eY+UExw4KdPg/sbrB6Wd1eYdsITl9O2CbKQc\nDbTJNOogSZRyrkWquWeeTGNs244UxYJbYX1q27QOkJ2fj1Jdk8tkOm9kLFKU3STbcB9PEO0a30I6\nVtkCE3ZyrkX6sb9eygkKZiAla/smpJYK2z5jTGIh0tnO/BWStqgoaesZF40uxWnV6Qtk6hfbqwY7\nkaYXS8dXtUrbtfEuWCpWt+K+17XLFE9Ob2UrvPIKKd+5+DpJWJKQkpk6W1qsR7vQbwdCOO8JuVLm\nwzZ54ebRp77hxG999Xfis0W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900489fr7RLvWVthnezxLnLggXfa3W38Ee/DKD/7sxGxt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\nW5/40PMwRj7HjiNNThxN85cxxgQ7+p0496rxThzo7BftOo7hOzwzc50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FW5rB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H6rVtMlu7++twD3LC+pxGIjimoWb9LMQoqcs8yJvzS/R9627W4ymWVD2KeX7+m/vUa4kJWP/8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ddPllVfwmNR5yMisO8Pdsr+J23U0yBtNfoFcf8ZY2TfN+6RMGD1nBkYlvMpmOhto/rwsey5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/lrq71IJrq+eNz0WNjdr3pW3m+Aegg2bdZMCq7ZFSjHoWyWehg978kaxXMs3jcdqDZG32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qpXvZidEINjteseu6mkJaBGLfrici+u2axponk3T/figADU96aT2qUmcyFc83oqN+Pz9ut7GJqM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\n3bdO5Z19AnuklGy47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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?" + ] + } + ] +} \ No newline at end of file