From 4ad86e23b4f96f4ecec7d30f7b4bced251df82bb Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Mon, 28 Jan 2019 23:36:07 +0530 Subject: [PATCH 01/12] Finished pandas --- intro_to_pandas.ipynb | 1873 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1873 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..a571ea6 --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,1873 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ] + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# Intro to pandas" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QvJBqX8_Bctk" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", + " * Access and manipulate data within a `DataFrame` and `Series`\n", + " * Import CSV data into a *pandas* `DataFrame`\n", + " * Reindex a `DataFrame` to shuffle data" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TIFJ83ZTBctl" + }, + "cell_type": "markdown", + "source": [ + "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", + "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "s_JOISVgmn9v" + }, + "cell_type": "markdown", + "source": [ + "## Basic Concepts\n", + "\n", + "The following line imports the *pandas* API and prints the API version:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aSRYu62xUi3g", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "12fa141d-377a-4c4c-8599-df6c427b69eb" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "daQreKXIUslr" + }, + "cell_type": "markdown", + "source": [ + "The primary data structures in *pandas* are implemented as two classes:\n", + "\n", + " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", + " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", + "\n", + "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fjnAk1xcU0yc" + }, + "cell_type": "markdown", + "source": [ + "One way to create a `Series` is to construct a `Series` object. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DFZ42Uq7UFDj", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "fc694254-6130-4545-eb41-a7f1bb724814" + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "ecbfd4b2-15d1-4c1d-bd1d-3a24eab939fc" + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785\n", + "2 Sacramento 485199" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "09934820-52fd-41b2-da70-2e59043a4003" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -114.31 34.19 15.0 5612.0 1283.0 \n", + "1 -114.47 34.40 19.0 7650.0 1901.0 \n", + "2 -114.56 33.69 17.0 720.0 174.0 \n", + "3 -114.57 33.64 14.0 1501.0 337.0 \n", + "4 -114.57 33.57 20.0 1454.0 326.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 1015.0 472.0 1.4936 66900.0 \n", + "1 1129.0 463.0 1.8200 80100.0 \n", + "2 333.0 117.0 1.6509 85700.0 \n", + "3 515.0 226.0 3.1917 73400.0 \n", + "4 624.0 262.0 1.9250 65500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396 + }, + "outputId": "4982fe2f-4881-4394-b667-017c5a3ce5ef" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "XtYZ7114n3b-" + }, + "cell_type": "markdown", + "source": [ + "## Accessing Data\n", + "\n", + "You can access `DataFrame` data using familiar Python dict/list operations:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_TFm7-looBFF", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "54b6d60a-3cea-40cf-f56f-66ea7ee89863" + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "b5ecdb68-7a22-48f2-9458-ae7c442060f5" + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 128 + }, + "outputId": "8b776e1e-8edb-4924-e9c6-2d1cce50671b" + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "eef474bb-fe8a-4496-d7fb-0b1defe8e2a6" + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "6550d4f7-5808-440d-c7c8-328528e6031b" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "03d2d117-2c6b-4554-9663-0d1182bd5685" + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "bbacb491-728d-4e72-e926-b20ba1330398" + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
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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": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "660d0ce8-fb0c-40d5-8e0f-991780456ade" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities['Is big and named after a saint'] = (cities['City name'].apply(lambda val: val.startswith(\"San\"))) & (cities['Area square miles'] > 50)\n", + "cities\n" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs big and named after a saint
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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", + " Is big and named after a saint \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "17bb2c95-6d33-47fd-de42-7ef816babe65" + }, + "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": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs big and named after a saintIs 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", + " Is big and named after a saint Is wide and has saint name \n", + "0 False False \n", + "1 True True \n", + "2 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "a1c17777-3455-440e-f49d-80058024d9b7" + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "0ba268c9-28e0-49a6-8665-83733a1ab9aa" + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "3036b316-3151-4856-8bf5-7b35afbcea40" + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs big and named after a saintIs wide and has saint name
2Sacramento48519997.924955.055147FalseFalse
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", + " Is big and named after a saint Is wide and has saint name \n", + "2 False False \n", + "0 False False \n", + "1 True True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "7f65884a-40d3-4d7c-d70a-1f67bf7e6108" + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs big and named after a saintIs wide and has saint name
1San Jose1015785176.535754.177760TrueTrue
2Sacramento48519997.924955.055147FalseFalse
0San Francisco85246946.8718187.945381FalseFalse
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "\n", + " Is big and named after a saint Is wide and has saint name \n", + "1 True True \n", + "2 False False \n", + "0 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "834a7c1a-b383-4e01-8209-6e69e95ab39a" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([3,5,0])" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs big and named after a saintIs wide and has saint name
3NaNNaNNaNNaNNaNNaN
5NaNNaNNaNNaNNaNNaN
0San Francisco852469.046.8718187.945381FalseFalse
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "3 NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN \n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "\n", + " Is big and named after a saint Is wide and has saint name \n", + "3 NaN NaN \n", + "5 NaN NaN \n", + "0 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "outputId": "437f422e-be4c-48d4-8094-65bfa9fb2a72" + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs big and named after a saintIs wide and has saint name
0San Francisco852469.046.8718187.945381FalseFalse
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5NaNNaNNaNNaNNaNNaN
2Sacramento485199.097.924955.055147FalseFalse
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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", + " Is big and named after a saint 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": 28 + } + ] + }, + { + "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 f1678661a39d96bc28e7ae4b2890ddd8db8ed1c9 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Wed, 30 Jan 2019 23:33:14 +0530 Subject: [PATCH 02/12] Finished part 1 (pandas) --- Copy_of_intro_to_pandas.ipynb | 1917 +++++++++++++++++++++++++++++++++ 1 file changed, 1917 insertions(+) create mode 100644 Copy_of_intro_to_pandas.ipynb diff --git a/Copy_of_intro_to_pandas.ipynb b/Copy_of_intro_to_pandas.ipynb new file mode 100644 index 0000000..bb89272 --- /dev/null +++ b/Copy_of_intro_to_pandas.ipynb @@ -0,0 +1,1917 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# Intro to pandas" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QvJBqX8_Bctk" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", + " * Access and manipulate data within a `DataFrame` and `Series`\n", + " * Import CSV data into a *pandas* `DataFrame`\n", + " * Reindex a `DataFrame` to shuffle data" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TIFJ83ZTBctl" + }, + "cell_type": "markdown", + "source": [ + "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", + "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "s_JOISVgmn9v" + }, + "cell_type": "markdown", + "source": [ + "## Basic Concepts\n", + "\n", + "The following line imports the *pandas* API and prints the API version:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aSRYu62xUi3g", + "outputId": "069886f0-798d-49f3-dc87-84ac6ebfe1f2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "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": "1ad854fa-0e5f-412a-94b7-f88a52922c38", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 8, + "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": 8 + } + ] + }, + { + "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": "18209db4-d839-4856-b329-f7bc7ecb3c45", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 9, + "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": 9 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "5d9b8d8c-64d0-4e76-842e-0b866ddfcee5" + }, + "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": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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mean-119.56210835.62522528.5893532643.664412539.4108241429.573941501.2219413.883578207300.912353
std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
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75%-118.00000037.72000037.0000003151.250000648.2500001721.000000605.2500004.767000265000.000000
max-114.31000041.95000052.00000037937.0000006445.00000035682.0000006082.00000015.000100500001.000000
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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": 10 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WrkBjfz5kEQu" + }, + "cell_type": "markdown", + "source": [ + "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "s3ND3bgOkB5k", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "8132848d-9797-48d2-aca4-381e267b37b6" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
0-114.3134.1915.05612.01283.01015.0472.01.493666900.0
1-114.4734.4019.07650.01901.01129.0463.01.820080100.0
2-114.5633.6917.0720.0174.0333.0117.01.650985700.0
3-114.5733.6414.01501.0337.0515.0226.03.191773400.0
4-114.5733.5720.01454.0326.0624.0262.01.925065500.0
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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": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396 + }, + "outputId": "69bd1d7c-071c-4846-bc1a-25d870967aee" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "XtYZ7114n3b-" + }, + "cell_type": "markdown", + "source": [ + "## Accessing Data\n", + "\n", + "You can access `DataFrame` data using familiar Python dict/list operations:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_TFm7-looBFF", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "eb1b070f-edce-4125-fba7-15e23964f625" + }, + "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": 14, + "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": 14 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "639fe042-eef5-4e54-ddfa-7e3863aa2280" + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 128 + }, + "outputId": "9bc8a9d6-6efe-40bf-ede1-8e72792fc14b" + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 16, + "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": 16 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "b823c9b3-9217-4761-88b4-eee115437dda" + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 17, + "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": 17 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "d76c8e0b-323b-4bcb-a420-6971132fc921" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 18, + "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": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "2909aaac-13d3-4f0c-caae-4d1e6b3ba62e" + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "0671d7d3-d578-4760-e2d7-6da504853be7" + }, + "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": 20, + "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": 20 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "49c9c91f-4719-4a66-f084-a0a0e432a8c6" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities['Is named after a saint and is big'] = (cities['City name'].apply(lambda name: name.startswith('San'))) & (cities['Area square miles']>50)\n", + "cities" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs named after a saint and is big
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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", + " Is named after a saint and is big \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "304284ad-0c08-42d9-d4bb-f207be78d334" + }, + "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": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs named after a saint and is bigIs wide and has saint name
0San Francisco85246946.8718187.945381FalseFalse
1San Jose1015785176.535754.177760TrueTrue
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "\n", + " Is named after a saint and is big Is wide and has saint name \n", + "0 False False \n", + "1 True True \n", + "2 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ff9bcf77-3669-44f2-d445-661d9f468f48" + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "d876004a-aee1-4dbb-d5d8-e0cfcf2b1370" + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "a79713fc-6efe-46f0-c855-cf848cf343cb" + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs named after a saint and is bigIs wide and has saint name
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Is named after a saint and is big Is wide and has saint name \n", + "2 False False \n", + "0 False False \n", + "1 True True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "2d95f18f-31b0-4207-af1b-a72a294c6b9d" + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs named after a saint and is bigIs wide and has saint name
1San Jose1015785176.535754.177760TrueTrue
2Sacramento48519997.924955.055147FalseFalse
0San Francisco85246946.8718187.945381FalseFalse
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "\n", + " Is named after a saint and is big Is wide and has saint name \n", + "1 True True \n", + "2 False False \n", + "0 False False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "a459b41f-d827-4db2-8315-58832e09697c" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([3,0,8,11,1])" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1San Jose1015785.0176.535754.177760TrueTrue
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "3 NaN NaN NaN NaN \n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "8 NaN NaN NaN NaN \n", + "11 NaN NaN NaN NaN \n", + "1 San Jose 1015785.0 176.53 5754.177760 \n", + "\n", + " Is named after a saint and is big Is wide and has saint name \n", + "3 NaN NaN \n", + "0 False False \n", + "8 NaN NaN \n", + "11 NaN NaN \n", + "1 True True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "outputId": "387a1bf4-976a-4ae3-c091-7745dc511bf3" + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs named after a saint and is bigIs wide and has saint name
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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", + " Is named after a saint and is 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": 30 + } + ] + }, + { + "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 c83e5c931c9f595c71d9e35167e190083ac50dcd Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Wed, 30 Jan 2019 23:47:32 +0530 Subject: [PATCH 03/12] Finished part 2 (tensorflow) --- first_steps_with_tensor_flow.ipynb | 2019 ++++++++++++++++++++++++++++ 1 file changed, 2019 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..fdf41af --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,2019 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "first_steps_with_tensor_flow.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ajVM7rkoYXeL", + "ci1ISxxrZ7v0" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# First Steps with TensorFlow" + ] + }, + { + "metadata": { + "id": "Bd2Zkk1LE2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Learn fundamental TensorFlow concepts\n", + " * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n", + " * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n", + " * Improve the accuracy of a model by tuning its hyperparameters" + ] + }, + { + "metadata": { + "id": "MxiIKhP4E2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California." + ] + }, + { + "metadata": { + "id": "6TjLjL9IU80G", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "In this first cell, we'll load the necessary libraries." + ] + }, + { + "metadata": { + "id": "rVFf5asKE2Zt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ipRyUHjhU80Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll load our data set." + ] + }, + { + "metadata": { + "id": "9ivCDWnwE2Zx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vVk_qlG6U80j", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use." + ] + }, + { + "metadata": { + "id": "r0eVyguIU80m", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "57e80813-021c-4e1b-d4a3-0e836092f9fc" + }, + "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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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "15407 -122.3 37.9 37.0 2125.0 489.0 \n", + "2752 -117.7 34.1 25.0 2054.0 609.0 \n", + "11064 -121.0 37.7 18.0 4197.0 1006.0 \n", + "6063 -118.2 33.9 35.0 1649.0 424.0 \n", + "160 -116.2 33.7 38.0 1695.0 352.0 \n", + "... ... ... ... ... ... \n", + "12 -114.6 34.8 48.0 1291.0 248.0 \n", + "9118 -119.0 35.4 39.0 598.0 149.0 \n", + "12111 -121.4 39.0 20.0 755.0 147.0 \n", + "8663 -118.5 34.2 35.0 2592.0 490.0 \n", + "14724 -122.2 37.8 52.0 2198.0 397.0 \n", + "\n", + " population households median_income median_house_value \n", + "15407 912.0 462.0 2.9 217.2 \n", + "2752 2271.0 564.0 2.3 150.0 \n", + "11064 2203.0 874.0 2.2 118.6 \n", + "6063 1786.0 388.0 1.4 105.6 \n", + "160 1279.0 305.0 2.1 68.5 \n", + "... ... ... ... ... \n", + "12 580.0 211.0 2.2 48.6 \n", + "9118 366.0 132.0 1.9 57.9 \n", + "12111 457.0 157.0 2.4 67.0 \n", + "8663 1427.0 434.0 5.1 246.4 \n", + "14724 984.0 369.0 3.2 156.5 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "HzzlSs3PtTmt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Examine the Data\n", + "\n", + "It's a good idea to get to know your data a little bit before you work with it.\n", + "\n", + "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles." + ] + }, + { + "metadata": { + "id": "gzb10yoVrydW", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "9a749922-dd09-40a9-a605-c7d7f2757784" + }, + "cell_type": "code", + "source": [ + "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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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": [ + "\n", + "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument." + ] + }, + { + "metadata": { + "id": "UMl3qrU5MGV6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 2: Define the Target" + ] + }, + { + "metadata": { + "id": "cw4nrfcB7kyk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:" + ] + }, + { + "metadata": { + "id": "l1NvvNkH8Kbt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the label.\n", + "targets = california_housing_dataframe[\"median_house_value\"]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4M-rTFHL2UkA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 3: Configure the LinearRegressor" + ] + }, + { + "metadata": { + "id": "fUfGQUNp7jdL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n", + "\n", + "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. " + ] + }, + { + "metadata": { + "id": "ubhtW-NGU802", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Use gradient descent as the optimizer for training the model.\n", + "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n", + "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + "\n", + "# Configure the linear regression model with our feature columns and optimizer.\n", + "# Set a learning rate of 0.0000001 for Gradient Descent.\n", + "linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-0IztwdK2f3F", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 4: Define the Input Function" + ] + }, + { + "metadata": { + "id": "S5M5j6xSCHxx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n", + "the data, as well as how to batch, shuffle, and repeat it during model training.\n", + "\n", + "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n", + "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n", + "\n", + "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n", + "\n", + "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n", + "the size of the dataset from which `shuffle` will randomly sample.\n", + "\n", + "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor." + ] + }, + { + "metadata": { + "id": "RKZ9zNcHJtwc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wwa6UeA1V5F_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** We'll continue to use this same input function in later exercises. For more\n", + "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)." + ] + }, + { + "metadata": { + "id": "4YS50CQb2ooO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 5: Train the Model" + ] + }, + { + "metadata": { + "id": "yP92XkzhU803", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n", + "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n", + "train for 100 steps." + ] + }, + { + "metadata": { + "id": "5M-Kt6w8U803", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = linear_regressor.train(\n", + " input_fn = lambda:my_input_fn(my_feature, targets),\n", + " steps=100\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7Nwxqxlx2sOv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 6: Evaluate the Model" + ] + }, + { + "metadata": { + "id": "KoDaF2dlJQG5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's make predictions on that training data, to see how well our model fit it during training.\n", + "\n", + "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n" + ] + }, + { + "metadata": { + "id": "pDIxp6vcU809", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "c159f59f-add2-4e85-85af-fe377078e0cb" + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56367.025\n", + "Root Mean Squared Error (on training data): 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "77c3988e-9543-476a-804c-1e4a212302e2" + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "0e92e629-4fc9-4e27-9ca2-cdb851037865" + }, + "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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predictionstargets
count17000.017000.0
mean0.1207.3
std0.1116.0
min0.015.0
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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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "75607ce0-9e4d-4196-f84f-a4575127945c" + }, + "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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D+cqVK7F06VI4HA4sXLgQTqcTTz31lJZly2rRrd8cmxkujx/+gADT141aJZnk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\nM6qb3W41QRAEBIJBxRnZSgKvkulmA3FHMbkpWqH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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "t0lRt4USU81L", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This initial line looks way off. See if you can look back at the summary stats and see the same information encoded there.\n", + "\n", + "Together, these initial sanity checks suggest we may be able to find a much better line." + ] + }, + { + "metadata": { + "id": "AZWF67uv0HTG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Tweak the Model Hyperparameters\n", + "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n", + "\n", + "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n", + "\n", + "For each period, we'll compute and graph training loss. This may help you judge when a model is converged, or if it needs more iterations.\n", + "\n", + "We'll also plot the feature weight and bias term values learned by the model over time. This is another way to see how things converge." + ] + }, + { + "metadata": { + "id": "wgSMeD5UU81N", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]]\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label]\n", + "\n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Output a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kg8A4ArBU81Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Achieve an RMSE of 180 or Below\n", + "\n", + "Tweak the model hyperparameters to improve loss and better match the target distribution.\n", + "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination." + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "f0a060f3-a6e6-4f9b-e314-573aa9b2124b" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00001,\n", + " steps=1000,\n", + " batch_size=5\n", + ")" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.63\n", + " period 01 : 214.42\n", + " period 02 : 204.04\n", + " period 03 : 194.97\n", + " period 04 : 187.23\n", + " period 05 : 180.94\n", + " period 06 : 175.66\n", + " period 07 : 171.99\n", + " period 08 : 169.21\n", + " period 09 : 167.45\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 115.8 207.3\n", + "std 95.5 116.0\n", + "min 0.1 15.0\n", + "25% 64.0 119.4\n", + "50% 93.2 180.4\n", + "75% 138.0 265.0\n", + "max 1661.7 500.0" + ], + "text/html": [ + "
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std95.5116.0
min0.115.0
25%64.0119.4
50%93.2180.4
75%138.0265.0
max1661.7500.0
\n", + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 167.45\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Ndt5YfIr4g0Zat/Bhzqwo/P3kK0rUPiqVijuGteVsah6bfzlLVJMAerSp3+VY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\nIEpR4hNiEzd+CiqwPbiG/zqURnN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predictionstargets
count17000.017000.0
mean116.8207.3
std96.4116.0
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25%64.6119.4
50%94.0180.4
75%139.3265.0
max1676.8500.0
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p4YfsDgcrthwmIzOfojIzkaEGEuJjmDW2Kxr1udNEJXEhhGdU7NrHkTsfQh0Y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\nyiqJ3qnBLHg8mcT49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yDL9Pj3GwQ0FP1pt6LZuhhlWSHOO0YgDZjQ8EEcFii9el2QiIPA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\ncc7jW1Pcl+OzrLnHbRiNx17fnDlzjJ07dxqZmZnGpEm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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "M8H0_D4vYa49", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality." + ] + }, + { + "metadata": { + "id": "QU5sLyYTqzqL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Is There a Standard Heuristic for Model Tuning?\n", + "\n", + "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n", + "\n", + "That said, here are a few rules of thumb that may help guide you:\n", + "\n", + " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n", + " * If the training has not converged, try running it for longer.\n", + " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n", + " * But sometimes the exact opposite may happen if the learning rate is too high.\n", + " * If the training error varies wildly, try decreasing the learning rate.\n", + " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n", + " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n", + "\n", + "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify." + ] + }, + { + "metadata": { + "id": "GpV-uF_cBCBU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Feature\n", + "\n", + "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n", + "\n", + "Don't take more than 5 minutes on this portion." + ] + }, + { + "metadata": { + "id": "YMyOxzb0ZlAH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "e8dbe4cd-d6f8-470e-8c84-71c05ef27d5f" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "train_model(\n", + "learning_rate=0.00005,\n", + "steps=500,\n", + "batch_size=5,\n", + "input_feature=\"population\")" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 222.79\n", + " period 01 : 209.51\n", + " period 02 : 198.00\n", + " period 03 : 189.94\n", + " period 04 : 184.35\n", + " period 05 : 180.51\n", + " period 06 : 178.07\n", + " period 07 : 176.46\n", + " period 08 : 175.92\n", + " period 09 : 175.97\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 127.9 207.3\n", + "std 102.7 116.0\n", + "min 0.3 15.0\n", + "25% 70.7 119.4\n", + "50% 104.4 180.4\n", + "75% 154.0 265.0\n", + "max 3193.5 500.0" + ], + "text/html": [ + "
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dLpZsOezvcERERGoVDd+QSqmrs4yIiEjdsn79ehYvXsy7774LgMPhYO7cuQwa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4R0HV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GQg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\nDsfMD8y58N7GjxM6eB6jzUTuM5/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predictionstargets
count17000.017000.0
mean118.7207.3
std95.3116.0
min0.215.0
25%65.6119.4
50%96.9180.4
75%142.8265.0
max2961.6500.0
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2Ii8eT/eV7ygh0YiEh/hz6aizKSp28cZylXGIiEjjoqREI2Z3uNiclFbmfZuT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\nzZa2htUoaWlxmEw1Ptl2qBItS9x2dWfeXbaPZ9/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+5Blc5ioSrxlG7ktPYEhOdH8D1eUY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\n97BixQpZq3Fs376d++67j5kzZwI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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "Lte03ba28YoD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From a7600ed607c331115f17265fb9449d132344e7f5 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Thu, 31 Jan 2019 00:07:48 +0530 Subject: [PATCH 04/12] Finished part 3 (features and outliers) --- synthetic_features_and_outliers.ipynb | 1323 +++++++++++++++++++++++++ 1 file changed, 1323 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..95dd2cb --- /dev/null +++ b/synthetic_features_and_outliers.ipynb @@ -0,0 +1,1323 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "synthetic_features_and_outliers.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "i5Ul3zf5QYvW", + "jByCP8hDRZmM", + "WvgxW0bUSC-c" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Synthetic Features and Outliers" + ] + }, + { + "metadata": { + "id": "jnKgkN5fHbGy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Create a synthetic feature that is the ratio of two other features\n", + " * Use this new feature as an input to a linear regression model\n", + " * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data" + ] + }, + { + "metadata": { + "id": "VOpLo5dcHbG0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n", + "\n", + "First, we'll import the California housing data into a *pandas* `DataFrame`:" + ] + }, + { + "metadata": { + "id": "S8gm6BpqRRuh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "9D8GgUovHbG0", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "0db0aebf-1673-4c09-9305-64dd609934b8" + }, + "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": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
10789-120.735.635.03451.0713.01550.0653.02.9161.7
10588-120.537.617.0315.089.0130.058.01.479.2
15633-122.437.941.02591.0585.01638.0462.01.879.7
14893-122.239.816.02026.0396.01031.0382.01.973.1
6141-118.233.930.01147.0260.01219.0210.02.193.2
..............................
13285-121.940.513.04581.0881.01799.0734.02.399.5
4357-118.034.033.02464.0627.02932.0568.03.1165.8
4316-118.033.831.01960.0380.01356.0356.04.1225.9
6447-118.334.215.05036.01299.03164.01175.02.9238.7
14715-122.237.852.01813.0271.0637.0277.04.0263.4
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17000 rows × 9 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "10789 -120.7 35.6 35.0 3451.0 713.0 \n", + "10588 -120.5 37.6 17.0 315.0 89.0 \n", + "15633 -122.4 37.9 41.0 2591.0 585.0 \n", + "14893 -122.2 39.8 16.0 2026.0 396.0 \n", + "6141 -118.2 33.9 30.0 1147.0 260.0 \n", + "... ... ... ... ... ... \n", + "13285 -121.9 40.5 13.0 4581.0 881.0 \n", + "4357 -118.0 34.0 33.0 2464.0 627.0 \n", + "4316 -118.0 33.8 31.0 1960.0 380.0 \n", + "6447 -118.3 34.2 15.0 5036.0 1299.0 \n", + "14715 -122.2 37.8 52.0 1813.0 271.0 \n", + "\n", + " population households median_income median_house_value \n", + "10789 1550.0 653.0 2.9 161.7 \n", + "10588 130.0 58.0 1.4 79.2 \n", + "15633 1638.0 462.0 1.8 79.7 \n", + "14893 1031.0 382.0 1.9 73.1 \n", + "6141 1219.0 210.0 2.1 93.2 \n", + "... ... ... ... ... \n", + "13285 1799.0 734.0 2.3 99.5 \n", + "4357 2932.0 568.0 3.1 165.8 \n", + "4316 1356.0 356.0 4.1 225.9 \n", + "6447 3164.0 1175.0 2.9 238.7 \n", + "14715 637.0 277.0 4.0 263.4 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "I6kNgrwCO_ms", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll set up our input function, and define the function for model training:" + ] + }, + { + "metadata": { + "id": "5RpTJER9XDub", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VgQPftrpHbG3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \n", + " Returns:\n", + " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", + " after training the model.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label].astype('float32')\n", + "\n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + " \n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Create a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", + " \n", + " return calibration_data" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FJ6xUNVRm-do", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Try a Synthetic Feature\n", + "\n", + "Both the `total_rooms` and `population` features count totals for a given city block.\n", + "\n", + "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n", + "\n", + "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n", + "\n", + "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n", + "the final RMSE should be.)" + ] + }, + { + "metadata": { + "id": "isONN2XK32Wo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**." + ] + }, + { + "metadata": { + "id": "5ihcVutnnu1D", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "d6aaf4c9-1efb-4a27-bf34-5102cdc50138" + }, + "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=0.005,\n", + " steps=5000,\n", + " batch_size=10,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.72\n", + " period 01 : 189.83\n", + " period 02 : 169.53\n", + " period 03 : 152.98\n", + " period 04 : 140.70\n", + " period 05 : 133.33\n", + " period 06 : 130.96\n", + " period 07 : 131.07\n", + " period 08 : 131.70\n", + " period 09 : 132.69\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 201.5 207.3\n", + "std 92.6 116.0\n", + "min 46.1 15.0\n", + "25% 165.2 119.4\n", + "50% 198.4 180.4\n", + "75% 226.6 265.0\n", + "max 4416.2 500.0" + ], + "text/html": [ + "
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6vY77JjYmpn9wnS+qqrX58+eze/dunE4n9913H0OGDGHFihXMmzePn3/+GW/v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LyZiqsnTNTtY3dNDdH0qEHIfuc7z2NQZ4fEUzH23oA2DSBBc3z8mhuPDUFdIUx66zO0zdVk+i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\nTdNY/WYXDz1xgFhM4+Y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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i5Ul3zf5QYvW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Leaz2oYMQcBf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "817ffc91-eb44-406d-f4d6-dc25a274db76" + }, + "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": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.74\n", + " period 01 : 189.66\n", + " period 02 : 168.97\n", + " period 03 : 153.29\n", + " period 04 : 141.26\n", + " period 05 : 134.07\n", + " period 06 : 131.39\n", + " period 07 : 130.62\n", + " period 08 : 130.90\n", + " period 09 : 132.33\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 199.4 207.3\n", + "std 91.6 116.0\n", + "min 45.6 15.0\n", + "25% 163.4 119.4\n", + "50% 196.3 180.4\n", + "75% 224.2 265.0\n", + "max 4372.0 500.0" + ], + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean199.4207.3
std91.6116.0
min45.615.0
25%163.4119.4
50%196.3180.4
75%224.2265.0
max4372.0500.0
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1ZRRGQ/0bQSWEEEKcb/XvbFrUiRMFNk9ONJxwaoFNu1Nha2rly7T6W4w8Mb4L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b7cdWsovj4dZyeMIAjChW4bG8fBUwZW/5rB0J6BuOk1bR2SIAiC0MacTkrcddddjB49GtXFZ6iB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qpIUSer0evT7x8sFgEKMxXsbvdrvp6urC6/Xico3+EHS5XHR1Tdy0cVhKihW9/ugbwdTUjz6D\n+Ws3LyQUidHTHybFacJsHP1cuirfov03f8FanE/JbWUou9ajpOZA0Xn837peWnsVFpWWkJWRRlNr\nO9t3vz/0kTJ20wz0spVQtINQNHGCRTiqEo6G0TRorpEI90aw22V+e/8CXnm3mhc31o5b55J5WeRk\nJfPI83to6vQDoGkw6DXj65aRZYWv3l7Ia7v209U7/vP0JFsoKnCPfH6hSIzdNb5jPjahiILdZuby\nhUXUv3+YmtZedDKYU0KYXaFxwUfPQAid0UCqxzbytkee3zPhtBCrxcjtq+cc8/5PhXBYYf1mLy+9\n1sbOfX0AOB16bvxENh+/PJPCfNsHXEGAyflePFcc67HSNI3W9hAHqgc4VOPnYE38//sHEgOIzDQz\nC+YmM7PYwYwiOzOK7Gfd0alh4mvrxIjHSxCEI5VOT+XOT83h18/t4efP7OZfr5/L+SKYEARhCkzZ\nwXdN007o7WP19ASO+r7UVAddXQMfel1H0gMDfUGGrxj19bD3n7+FpNcx7T++SGzXBrDYiZUsYt3B\nMO81hJlZVMCs4ml09/axcct24p+RhN1UjF7nIBzzEow2THh/mgaBDiuRfiOyUcFTGALCXLM4j0Aw\nMq5645rFeTS39vLWrnjAocYkBtusxIIGZL2CIztIQYFEyYArYaznsItLMhM+v86eAF09wWM+JpoC\nz/+9g7/+sRtFhXnnO/inG7L4zd934usff/sUhxklEh15XsJRZWS9R3prVysfW5Q7ZUc56psCVFb5\nWP92N4OBeGXKnNkOysvcXH15Lv19g4A6qV9jZ6vJ/l48m419rFRVo70rPFIBUdsQpLYhMPL1OCwj\nzUTJTDtFBdaRKgiHPfFHeiwSpqvr+PrBnEnE19aJmezHSwQcgnD2mF/s4SufmsOvnt3LL5/ZzVc/\nNYeSQvdUL0sQhHPMKQ0lrFYroVAIs9lMR0cHaWlppKWl4fWOjpzs7Oxk/vz5p3JZx03TNGq/8QOi\nnT5yv3U7Kd3bQZaIzrsEbC46IiGy0g1cWFpCMBTizU1biQ0dubAZCzHokokovci6xomvr8Jgm43o\noAGdOYY9e5C+kEZ3f4hMt+2oIzN9fQG6+8NEAzoG22xoiozBFsWaEUDW5HBqWAAAIABJREFUafzo\n0fdwOYzkptkJhKL0DIRHQo3PX3M+3d2DI2s4VqNPTYPIgIFglwVNkbFYJb76uXwuXpCCJEnH1YsD\nTnzCybCT1X8iGFTYtLWHig1eDtfFA6+UJD1XXp3OZUvdZKabAY6rt4YgnAhV1WjtCLNjX4Cde7qp\naQhQ1xggEEw8MpWZbqK0xBnvAZFvpSjfIpqpCoIgCJNibpGHf71uDv/77B5++bc9fOVTc5hbJIIJ\nQRBOnVP6V+0ll1zCa6+9xic/+Ulef/11li1bxrx587j33nvp7+9Hp9Oxfft2vvvd757KZR23jj88\nSV/lJpzLFpE7EyRfgOjsC4glpRGzZnD1UhPpTWY0VWX95q0EQ/GKA6shH6PeTVQZYDBcjcthYMWC\ndHZXx8dvJttN9PRHGGiJVzjorVHsWYNIQ3vg17Y2cuuVs4GJR2Y6rEYYtOFviT+dFk8QU0qYsU3z\nuwcidA9EWFGaxRWL8kY29jpd4kb7aI0+YyEdgU4LSkgPkobZHb+Puh4bi6V4qd/x9OKAE5twAien\n/4SmaRyuC1BR5WXTlh5CYRVZgoVznZSXeVg4Nwm9XkwdECaPomq0toVG+z80xisgQuHRAEKSICvD\nxAXzRkdwTsu1YrOKviWCIAjCyVNS6OZfr5/LL5/Zza+e3c0d185hfrFnqpclCMI54qSFEnv37uW+\n++6jpaUFvV7Pa6+9xgMPPMB3vvMdnnzySbKysli9ejUGg4G77rqL2267DUmSuPPOO0eaXp5OAu8f\npulHv0TvSmb6bZei69hDND2fcFYR//1CBxH6WLHsEvQGHdPdAbLKC7j/iZ2YDdmYDOnE1EEGw4cA\nlV5/mCsuzOXGFcX0+cMEAgrf+q8DxEI6DPYItozASCABULWzjUhE4ZYrZo4bGdrvj/HL39fT3WJA\n0qvYMwfHTegYa1e1jxtXTj9mpcHYcKGrJ0zQaybSZwQkDPYIltQgOoM2cpvhJpY6WT5qNcdYxzvh\nZNiRjTeH+08AR53qcTT+wRgb3u6mospLQ3N8zGqq28jqj7m5bKkbj+vsPH8vnFqKotE8FEDUDh3D\nqGsMEh7TNFaWIDvTTFG+lbklKaS7dUzLtWCxiABCEARBOPXOL3Dx9evn8otndvPQs3u449oSSqen\nTvWyBEE4B5y0UKKkpIS//OUv497+xz/+cdzbrrzySq688sqTtZSPTAmEqP7yd9HCEYp+dCfWjj3E\nrE7Ukgt5dHM/TT0qly+fh95gor+njewiJx5bEi57NpqajaKG8IcOohEPC4arAfQ6iRc3NvL6K35i\nYR1GZxhrejChwmHYO+93srPax9K5mSMVAgdrBnngN7V4u6OUzLLTFGlB1h+7J0f3QJi/vnaQW6+a\nddQqA50sc9OK6djVJJ58oY1IUEU2KljTghisiY31JjpuMVE1x5GOt6oiHFXYcWjixqcTTfWYiKZp\n7Dvkp7LKx9vbeohENXQ6WLwwmfLlHuae50Ani6oI4cOJxTSaWoMJFRD1TQEikdHvRVmG3CzzyPSL\nogIrBbmWkcktokeCIAiCcDqYXeDiGzfO42dP7+LXz+3lXz5ZwsKZIpgQBOHkEoeSj0PjDx4kdLiO\n9Fs+SaquDk3So5YuZd3hCJurQyxZVEqax0VdYzPv79/PFfMuYne1iqZmo6oR/OEDaIxu5oerAX77\n7AFef9WPGpMxpYSweEITBhLDQhGFym3NaJqGU0vh0aeb0VRYc20m112dwQ/+1DsygeNY3trbjsWs\nP2qVwb6DA/z+sWbqm4NYLTKfvSGLzTU1dB8xahAmPm5xPI63quLD9p8A6O2P8uZb3VRWeWntiF8j\nM91EeZmHFZe4xFhE4YRFYyqNLSFqhwKImoYADU1BorHRAEKng9wsy0j4UJhvpSDHgskkepIIgiAI\np7+ZeSl888b5/OypXTz8wl6+9InzuWBW2lQvSxCEs5gIJT5A9ytv0vWXZ7HMKqJwsQPJ7yM6ZzGH\nBy08vqWbObOmU5SfQ6e3m7e27gJNZdv+EM9v0LCaoCCnlwMNMj0DJFQDHKz1U/HqIGpMxuwOYnaF\njxlIDNMUeOWVfgZ7B0ly6vnml6Yxd3b8uMu//dMC/uvP22np8qN+wBCT4SqDsbzdEf78dAsbt/QA\nsHKpm89el0VykoFQZf9xH7c4ER9UVXHi/Sc0dr8/QMUGL+/u7EVRwKCXWL7YxaoyN+fPsCMdzwMt\nnPOiUZWG5jEVEA1BGlqCxMYEEHqdRF5OYgVEfo4Fo0EEEIIgCMKZa0ZuMt+8aR4PPrWLh1/Yxxc1\njUWz06d6WYIgnKVEKHEMkdYO6u7+EZLZxMwvXYbe34SSW4ySXcTjL/nIycqkdM4s/IMB1m/eiqqq\nuOxuXtyoodfD7Z+0kJdRSDian1AN8P4hPz/8eTVKDKxpAUzJkeNaTyykY7DNihrVobfESCkIsrel\njeJCC/5AhCS7iR98fhEDgQgNHQNs3N3K1v0TH30YrjLIIb75evH1Tp75ezuhsErxNCu3r8llRpFt\n5PbHe9xish1v/wlvd4Q3Nvl4Y6OPLl/88czPMVNe5mH5Yhd2m/hSF44uHFFpaApS2zhaAdHYEkQZ\n055Fr5coyLFQWGClKC8eQORlmzGIAEIQBEE4C03PSeaum+bz4JM7+e2L+1A1jYvPy5jqZQmCcBYS\nO7Wj0BSFmq/+O0pPH9PuWoMj0oTqdBGbtQApKYdZxTZSMoqJRKOs2/QuoXAEnWxDYhqaBp+72kxe\nRnzDbDLoSLKb6POHqa0L87Pf1qMoGumFUSL68YGEUS8RGfNqrKZBpM9IoMsCmoTZFcLsDtEXgspt\nzWza3Uo4oiZMpSiZ5mZ6TjI1Le9MePwhxWEiyW5i81YfP3v4MG2dYZwOPbfdnMPKpW7kI3osHO9x\ni5PhaIHIdWVFbNnRS8UGLzv29KNqYDbJrCpzU17mYfo0q6iKEMYJh1XqmgIjRzBqG4I0tgZRx0zh\nNBokivKtCRUQOVlmDHoRQAiCIAjnjuLsJO769HwefHIXj7z0PpoKi0tEMCEIwuQSocRRtP3qTwy8\nvZ2UlReTldGPJhuJzlsCzmxCsp2sPA+RmMT2nbvpHxjA5UhCRzGKKvPZj5mZkRd/aMeOs2xrVhns\nsKKTJb79lUIOezup3BYYd99L52UhSxKbdrcRDCkEOqxEBoxIsootaxCDLbG3Q2ioo/+RUylMBh02\ns2HCUEKnGbj/oTre292PLMPVq1K5eXUmNuv4L4lwVEkIIj6oieVkOzIQCQZh49s9fPnb++jpiz8W\n06dZWVXmYdmiFDG9QBgRDCnUDY3erBn6X0trKOF4k9EoMX2aLd7/Ic9KUYGFnEyLGAkrCIIgCEBR\nVhJ3f3o+P31iJ7//+/uomsaSOZlTvSxBEM4iIpSYwMC23TQ/8DsMGalMvzIHWR2MBxKubGKmFPa2\nmogqMtM9YS65toimjmzWvgZ9gxo3rDQxt3j0YR0eZxnqNRLstCLJYM0a4LC385hHInSyzLz8dL73\n00MoER06cwx75iCy4QOaRZDYL2IwmFiJoakQ6jZz8LAOtH5K5yTxzzdkkZ9jGXedsYFKd384oRLj\naJM7TpZoVOXd7X1UVPnYsz8+pcBm1XHVZamUl7kpyD21QYlw+gkGlfjxizEVEC3tIbQx3zJmk8zM\nYltCBUR2hhmdTgQQgiAIgnA00zKdfOvmUh54Ygd/+Md+VFVj2bysqV6WIAhnCRFKHCHW76fmzntB\nVZlx+wpM2gCxabNRs4rQ7Jns7zDjj+jIckbJTooxGJR55s14IHH1EiMXl4xOdAhHFbYf7CLoMxHy\nWZB0KvbsQfRmZSQ4ONqRiPWbffzmz40oER2m5BCW1GNP5hhruF9E/L/joYSmQXTAQMBrQYvJyHqV\n227O5p9uKMbrnXhix3CgMuzISoxToaklSEWVjzc3+/APxg/4nzfDTvlyN4sXpmAyinL6c9FgQKG2\nITBaAVEfoK0znBBAWMwys6fbhyZgxKdhZGWYxfhXQRAEQfgQ8jMcQ8HETv74ygE0oEwEE4IgTAIR\nSoyhaRoN/+9/iDS1kr3mclz2AdSUNJTp8yEph9oeE76AnhSLQrEnQjii8cgLQbp6NFYsNLByoTHh\nej39IZprJMK9ZmS9ij3Hj84YP2oxdpzl2CMR4YjK79c2UVnlw2KWySgKEdaFTujzGDuVwuU00dEV\nJdhpJRbUg6RhdoXIyofLlqYetedCOKqw49DETTKHA5WT1VMiFFbYvLWXiiovB6oHAXA69Ky+Mo1V\nyzxkZ5pPyv0Kpyf/YGwkfKhtCI4EEGNZLTLnz7THqx/yrRQWWMlMM43rjSIIgiAIwoeXlx4PJu5/\nfAd/euUAqqpxaWn2VC9LEIQznAglxvA98w98z72Kbc508ufo0EwWovMugeQ82gatNPUasRpUzksP\noSgaf/h7iOYulYvO13P1JYmBhKJoPPFsZzyQMCo4sv0JRy8mGmfZ2hHi/ofqqG8OUphn4e47Cnlz\nd8OEkycATAaZcFQd9/ZZeckARMIa9DsZaIgAEgZbFEtqEJ1RZeHsnGOGCn3+8IS9KCAxUJlMNQ0B\nKjZ42bilm0BQRZKgtMTJqjI3F85PEk0GzwH9/hi19aP9H2rrA3R4E48g2aw65s52JFRApKeKAEIQ\nBEEQToXcNDv3rIkHE39+7SCKqnHZwpypXpYgCGcwEUoMCdU1Uf/dnyDbrcz+1AxkWSM69xJw5dGj\nOjnUZUQva8zJDCFLGo++HKKmRWFusY7rV5gSKg4iUZUHH65jy44+Ulwyakofsi6xF8TYcZYAb23t\n4aE/NhAMqVxxqYfP35yD0SBP2HdiTpGLSEThQFMv4WgYWQJVi4cUkgSb9rSz5b1++tqNRMJgtIDJ\n7UdvjSFLkJ1q5/pLC4/5eCTZTbicJnwTTu4YH6h8WIMBhY1buqnY4KW2MQiAO8XA1avSWLXMTZpn\ncu5HOP309UdH+z80xisghse5DrPbdMw73zHS/6Ewz0p6qlFMVREEQRCEKZSTaueeNQu4//EdPFZx\nCE3TWHVB7lQvSxCEM5QIJYZ4n/4H6mCAGf9SjsWqEps+Dy2jiIA+lX2t8eMCJRkhTHqVJyvD7KtT\nmJGr4zOXmxNeoQ0GFX78q1r27B9gzmwH99xZwIub6yZsZgnxBo5/eqqFl9/owmyS+cYXCyi72DVy\nvSMnT1hMep5aV83mfR0jtxmeJBCOqsSCOgKddpRw/KhGRoFKyDAw0o9C1aCp088z62uP2RfCZNBR\nOiN1wiqNIwOVE6VpGgeqB6mo8vLW1h4iEQ1ZhkWlSZSXeSid4xTn/s8yPX3RkRGcw0GEryeacBun\nQ09piTOhAiLVLQIIQRAEQTgdZXtsfHtNKT9Zu4O1lYdRVY3PXH3+VC9LEIQzkAglhqR/4dOkZOlx\naQ0oqVkoxfOI2rLY02YhpkrMTA2TZFZ4cWOEbftj5KXL3Hq1OWFsYL8/xg9/Vk11XYCLSpP45r9M\nw2iQj9rMstMb5v7f1FFdFyAny8ztt2Qys8g54fr0OonK95rZfrCT7oHIuPerMYmg10KkP36MxOiI\nYEkNEjVoSBMM7Bg7oeNojjUd5MPoH4jx5mYflVU+mtvifTLSU42Ul3lYcYkLV4rxA64gnAm6eyLU\nNARo6/Kxd38vNfUBunsTA4hkp56Fc53xKRgF8T4Q7hSDCCAEQRAE4QyS6bZxz5pSfvL4Dp5YV40/\nonDlBblYzWKLIQjC8RM/MYYYI93YtAY0i43YnEtQk/LY12UjGJXJTY6Q6YxRuTVK1c4o6S6ZL3zC\ngsk4uoHy9UT4wU+raWoNsXKJiztuzU8YMzi2mSXA1p29/PL/GvAPKuRP06NL7uEXz7cfdezm2opD\nvLmjddy6NQ3CPSaC3WZQJXSmGNa0IHpLfFKFepQJosN9IY51AvDIKo2xgcrxUlWNvQcGqKjy8c72\nXmIxDb1eYumiFMrL3JTMcoheAGcoTdPw9Yw5gjE0DaOnL5ZwO1eygQvnJ8UbUA5VQIgAShAEQRDO\nDpluG99Zs4CfPb2Lv2+qo2p7M9dfWswlczKQxYsNgiAcBxFKDJG9zaDTE523BM09jcO9DnqDOjy2\nGIWuKJt3R3nl7Qgup8SXVpuxWUZ/yLZ2hPj+A9V0+SJcc3kat96YfdSNdiymsfa5Vp57pQODXmLB\nIiO1PZ1I8SET48ZuKqrK2srDbNg5PpCIDuoJdFlQIzokWcWSFsSYFEkYHTrcb+JIJ9IX4shA5Xh0\n90ZZt8lH5UYvHV3xyo6cTDPly91cutiN0yG+9M4kmqbR5YuMCSCC1DQE6B9IDCA8LgOLSuMBxIJ5\nbtzJEilJhqNcVRAEQRCEs0G6y8oPb1vExn0dPFVxiD+8vJ8NO1tYUz6DaZkTVwELgiAMEzvDIUr+\nTBR3MjjSaQ65aOs3YDcqzE4Ls+NQlGfXh7FbJL642kKSfbSCoa4xwA8erKavP8aaazO5/uMZSJJE\nOKqMqy7w9UR44OE6DhwexGBSMWf4aehTmShEHj5e8bcNNby5vSVxrVGZYJeZqN8IaJiSwpg9oXHN\nNAGyPDaauwbHvf2j9oWYiKJobN/TT0WVl/d296GqYDRKrFziony5h5lFNlGefwbQNI2Orgi1jaM9\nIGobAgz4lYTbpbqNXLwwmcI8y1AfCCvJztEAIjXVQVfXwKleviAIZ4lDhw5xxx13cOutt3LLLbeM\nvH3jxo184Qtf4ODBgwC8+OKLPProo8iyzI033sgNN9wwVUsWhHOaQa/jplUzmVfg4sk3q9l2oJMf\nPbqNZfMy+dTyIpxWUSUpCMLERCgxzJIMOgNe1UONz4hRpzInM8yhxhiPV4QxGeGLq82kJo8GEu8f\n8vNfv6ghGFL44i25fGxlKoqq8uQbh9lxqIvu/vDIcYxZ6en84pEG+v0xDPYItvQAku7Yxyu6eoPs\nONQ18jZNhVC3mVCPCTQJvSWGJTWI3qxMfBHgi584n6pdrZPWF2Iind4wlRt9rNvkG2leWJhvobzM\nw7KLXNiskxt+CJNH0zTaO8MJFRC1jQH8g4lfU+mpRkpmJU7BENUugiCcLIFAgB/+8IcsXrw44e3h\ncJjf/e53pKamjtzuoYce4plnnsFgMHD99ddTXl5OcnLyVCxbEATAnWTmjtUl7K/vZm3lYap2tbHt\nQBfXlhVyaWlWwvFkQRAEEKHEKL0Jv2Jmf7sZWYKSjDCtnTEefTmELMFt11jITh3dXL+3u4+f/LoW\nRdH4xu0FLBuamPHkuuqEiRXevjAvverl6W4/Op2EJy9CzBSYsDpirBSHGUVR8fWH0TSI+g0Euyyo\nMRlJp2JJDWB0RI95HbfTTGqy5SP3hZhINKaydWcfFRu87Hp/AE0Dq0XmyhUeVpV5KMo/seMewsmn\nqhptHeH4FIyG4QqIIIFgYgCRmWZi/vnOkf4PhflW7Dbxo0IQhFPHaDTyyCOP8MgjjyS8/eGHH2bN\nmjXcf//9AOzatYs5c+bgcDgAWLBgAdu3b2flypWnfM2CICSaXeDie5+7kDe3t/D8pjoeqzjEhp0t\nfKZ8BjPzUqZ6eYIgnEbETmNIOCaxp92Mokmcnx7C74/yfy8FUVT43NVmCrNHN/Ibt3Tzi9/Xo5Ml\nvvOVIi6YlxS/RlRJqGxQYxKD7VZiAQN6o8rXb8/nDxV7OJ4DDKUzPFTtbkMJywS6LMQCBkDDlBLC\n4g4h6+JNLj/oGsMBxIfpCzGRlrYQFRu9vPlW90g/gVnFNsrLPFxyYTJmk6iKOB0oqkZreygePNTH\n+z/UNQYIhtSR20gSZKWbWDjXOVIBMS3Pgs0qfiwIgjC19Ho9en3iz6K6ujoOHDjA1772tZFQwuv1\n4nKNjtF2uVx0dXVxLCkpVvT6k/O7KjXVcVKuKxw/8RxMvSOfgzVXJXHVsiL+/PL7VLzbyH1rd1A2\nP5vPXXM+nmTLFK3y7Ca+D6aeeA5OjNh9DGnt1xOOyUxzRZBiUX73fIhQGNZcYeK8aaMP06tvdvG7\nvzZhMcv829eKOW+GfeR9ff4w3f1hAKIBHYNtNjRFxmCLYs8MkJllJMlupNc/fqSnLIEGuIaOV1x+\nQR53/89u+jsdgITeGsWaFkRnjG8ql8zJYH99D76h+zvyWsvnZ03aEY1wROXtbT1UVPl4/5AfAIdd\nxzWXp1G+zE1utviFMpUURaO5LTRaAVEfoL4pSCicGEBkZ5iHej/EKyCm5VmxWkSIJAjCmeHHP/4x\n99577zFvo31QWg/09AQma0kJRB+dqSeeg6l3rOfg5pXFLJqVytqKQ1TtbGHLvnY+fkk+l1+Yh0Ev\njnRMFvF9MPXEczCxYwU1IpQYkuWMYTepGNQoD/0tyEBA49rlRhbMjDfu0zSNv/2jg8eebcXp0PO9\nbxZTeMQRhSS7iRSHidYGCHrNAFg8QUwpYcwmHb95bu+EgQTEQ4QrFuXhsBrZ/G4f3/yPgwz49cgG\nBUtqEIMtlnBU42MX5WM26hOOioxcqzSbz14+8yM/JnWNASo3+tjwdjeDgXiJ/9zZDsqXu7moNBmD\nQfwCOdUURaOpNUhNfXCkEWVdU4BIZPQPcVmCnCwzhfnWkQqIglwLFrMIIARBODN1dHRQW1vL3Xff\nDUBnZye33HILX/3qV/F6vSO36+zsZP78+VO1TEEQPkBRVhL/9k8XsGl3G3/bUMPfNtSycXcba1ZN\nZ26RZ6qXJwjCFBGhxBCTXsMWi/HQ8yG6+zWuuMjI0nnxLsGapvHoUy288FonqW4j37urmOwM87hr\nhMMagTY7Qa+CpFexZw6it8Q386GIQigyviGl22liVl4K111aTEtrmJ8+VM2h2gAmo4wrK4pqHUSS\nj/wYMy6neaQSYjKbWAaDChvf7aGiykt1XfzVpJQkPVdenc5lyzxkph3fGFHho4vGVJpaEisgGpqD\nRKJjAggZ8rIsFBZYKcq3UJhvZVquFZNJBEaCIJw90tPTqaysHPn3ypUr+etf/0ooFOLee++lv78f\nnU7H9u3b+e53vzuFKxUE4YPIkkTZvCwumJnK8xvrWLe9hZ8/vZt5RW4+vWo66ZNw3FgQhDOLCCWG\nhKMav38xSLtPZdk8A+WL4hUSiqLxm0cbeWOTj+xME9+/azoe1/iRRodqBrn/N7V4uxWM9hiWtEFk\nvYbJICNJEIqo4z7GZJBRVZVNOzuoqvLT740/HUsuTObWm3J4fXs9lduOPc5zMppYaprG4doAFVVe\nNr3bQyisIkuwcK6T8uUeLpibhE4nRnmeTNGoSmNLKD6CszFAbX2A+uYgsdhoAKHTQV62ZXQCRr6V\n/BwLJqMIIARBOLvs3buX++67j5aWFvR6Pa+99hr/+7//O26qhtls5q677uK2225DkiTuvPPOkaaX\ngiCc3qxmA2vKZ1A2L4u1lYfYVeNjX303VyzK4+OLCzAZRYWnIJwrJO14DmCeZo51RufDnuGpeDfC\nq+9EWDhTz6cvNyFLEtGoyoO/q+ed93opLrDy798oHjcGUdM0/lHZxaNPtRBTNMzuIGZX+AOna8Q/\nFsK9RkI+C5oqIRsVli2z8fXPnAcQHy+6rnrCSojJGKc04I/x3p5Bnn+lhYbmEACpbiOrlrlZudQ9\nYfhyLpus82GRqEp9U3CkAqK2PkBjS4iYMvqtqNdL5GdbEnpA5OdYzqgjM+I83fETj9WJEY/XiZns\nx+tMb951sr52xNfl1BPPwdT7sM+BpmlsPdDJk+uq6RkIk+IwcdPKYi6clYZ0PH9UCyPE98HUE8/B\nxERPieMwt1iP0QBL5xqQJYlgUOF/flXL7v0DlMyy8/++WjSuKeBgQOGhPzbw9nu9JDn02DIHCTJx\n40n1iOgnGtAT6LSgRnRIsoolNYgpOUJLX5RwVMFk0KGT5Ukf56lpGvsO+qmo8vL2tl6iMQ2dDhZf\nkMzlZR7mnudAlsUP/8kSjsQDiJr6wEgI0dQaRBlzkseglyjIS6yAyMs2i6ZPgiAIgiCcEyRJYtHs\ndOYVefj72/W89m4jD7+wj/U7WlhTPoOcVPsHXkMQhDOXCCWGpLtk0ocqA/r9MX70s2oO1wW4cH4S\nd395GsYjXqGubQhw/2/qaO8Mc94MO7fenMH/PL5twmuPDSTUqESgy0LUbwQ0jM4wFk8IWR+/Uc9A\niD5/OGF852SM8+zti/LmZh8VVT7aOuLBSVa6idVXZXPhPBvJTsNHuv5HFY4qkxa8TJVQWBkJIIZ7\nQDS3hVDHnNwxGiSKCmwU5Y9WQORmWdDrRRAkCIIgCMK5zWTUcd3yIpbOzeSJysPsqvHx/T9sZeWC\nbFYvm4bVPLV/rwqCcHKIUOIIvp4IP/hpNU2tIVYscXHnrfkJ/RQ0TaNig4/fr20iGtO47up0bl6d\nRUxVcTlNE47odDlMlBS62fhWH31tetAkDJYY5tQgenNi88sUh5kk+8TNJE90466oGrv29VNR5WPr\nzl4UJb4pXr7YRXmZm/Nm2ElLc05pedHoEZUuuvvDuJwmSmekTtoRlZMlGFSoO6ICoqUtlBBAmYwy\nMwqHAoiC+CSMnEyz6M8hCIIgCIJwDOkpVr52wzx213hZW3mYyvea2bK/g+uXF7FkbiayONIhCGcV\nEUqM0dYR4vs/rabTG+Hjq1L53KdzEo4yBEMKv/1LExve7sZu0/HtrxSwcG4SADqdjtIZqeNGdGoa\nZNhSeGd9jO4uAw67jps/lcnbNfU0d42fxjG2ieWwE924e7sjvLHRxxubfHT54iNIC3IslC93U3ax\nC7vt9Hnan1xXnfCY+frDI/9es2rGVC0rQSCoUNsYoOOtXnbv66GmPkBrR5ix3VjMJplZ0+0JFRBZ\nmWZ04iiMIAiCIAjChzK3yMPsfBevb23kpc31/PGVA6zf2cpnymdQmOWc6uUJgjBJTp/d6RRrbgvx\n7/cdorc/xs2rM7nhmoyExjpNLUF+8us6mttCzCi0cveXC0l1Jzb/xPM2AAAgAElEQVSCPHJEp81g\nIeKz8lZVCJ0Orrk8jZs+kckLm2to7vKPW0Numn3CcZ7Hs3GPxTTe291HRZWXHXv6UbX4Rrm8zE35\ncg/FBdbTrlFQOKqw41DXhO/bccjLdcuLTvlRjsFAjJqGxAqI4eMuwyxmmfNm2Ed6QBTlW8lMN4le\nHIIgCIIgCJPMoJe5enEBi8/P4Kk3q3l3fyc/+vM2ls7N5PrlRThtojG7IJzpRCgxZNOWbnr7Y9z+\nmRyuuiwt4X3rN/t4+M9NhCMq15Sn8dkbsiZsQjjcmPKqiwp4/PlW3qjqQVEU5s528IU1OeRmW465\nEQ+E/n979x4eZX3nffw958kkmZwnIQkEEgQ0yBkPHItAXetWrVIJaHq5T5enro/PrnupXUpr066W\n68Lt1sOlLVbbLouCURa79llPgYpgQbRiA0YRTUIgB8iRhJBMkjk8f0wyZDBBQoF7CJ/XP2aS+775\nzh2Iv/uT7+/38+HzB7H0u/RXPbjPviKb7TtbePtPTbS0+gAYl+ti0bxU5sxMIiYmetdnaG3vonmA\n6S4w8Noa59rxdl84eOhbB+JoQ3fEMbEuC1deHk9eTgxTrkwhLdlERpoCCBEREZELKdnt5O6bJ7Jg\nagsvlBzg3b11fPhZA7fMGcN107OietqviJyeQolet/1tBgtmp5CednI9h67uAL/ZcJiS7U24Ysx8\n//+M4drpSYNeIxgMsv29Fta9VENLaw9pKXb+riCLa6YlYjKZ6OrxU1HTOqQH8YEe3IMB6Gm3cbDa\nwn1/3g+EHp5vXJjGonkpjB55/h7kz6WEOMeg63Ccbm2Ns9F23BcOH/qCiPrGyAAiLtbC5Cviye23\nC0ZGmj3cYaLtfURERESMNX5UEkV/N5O399Tw+x2VbNz6OdtLa1m+eByX5ww+TheR6KVQopfNao4I\nJGqPevm3X1Zy8HAnuaNieOCeXEZ4Bn9Irqjq4NkXDrP/ixPYbSaW3pTBt27IwOEwh9aE2Po5Hx1o\noKmtC7OJiPUI+gz0IN7/wd3fZaar1UF3m41gIJQGXz4uluvnp3HN9EQc9osrIXbYBl6HAwZeW+NM\nHWvtobyqN3zo7YBobO6JOMYdZ2XqRHd4/Ye80S7SUuxRN8VFRERERCJZzGYWzRjJVVeks/mdcnaU\n1vFvGz9i5gQPS68bS7LbaXSJIjIECiUGsPPPLTz12yo6vQG+/rVUvrss+0tbgvZpa/exYXMtJe80\nEgjC1dMS+F8F2XhST4YLp64JERggkICBH8SDAUi0JFJ56AR+b+jbZbIEcCSFdgf53q3j/8p3a6xT\n1+FIincydVzqgGtrDKT5WE9E90NFVQdNLZEBRILbyrQr3eHwITfHRWqyTQGEiIiIyEXM7bJz1w2X\nM39KFi+UHOCD/fWUljdy47Wj+ZurRmKzRu80ZhE5SaFEr64eP03HvPzhzSbe+GMjToeZ+1aMZv61\nyQMe7w8EKXmnkRc219J+wk/WCAd/v3wkU/LdX7ruYGtCmE0QBJIHeBAvP9jBW9sb2fFeM53eAGDF\n5fZjjvOSnmFh2oQzf3CPZn3rcNw2P++0250Gg0GaWnoi1oCoqOoIr6PRJynBxozJ7tAUjN4QIjlR\nAYSIiIjIcDVmhJtVhdPZue8Im7Z9wSvbK3h3by3LFo1jythUo8sTka9wyYcSfdttfvBxA4cP2PB7\nrbgTzPzr/ePIyR54bYZPDrTz3IbDVB7qJMZp5q6lWdy40IPV+uUH39Mt5hgMwgMFU8jNSsBhs3Ci\nw8/29xrYsr2RikOdAKQk2fjm1z0snJNCQoL1tA/uFzOHzRJeSyMYDNLY3BOeetEXRLS2RQYQKUk2\nZk5JCO+AkZsTCiBERERE5NJiNpmYM2kE08al8eqfKtny52qe3LSXSXkpLFt4GenJF8eaayKXoks+\nlOibWtHZ6MTvtWJ3d2NO6+BP+6vJyR4XcWxzSzfrXq5h+3stACyYnUzhkiySEgZ/ED7dYo7Jbidj\nMt2UV3ayZUcjf/qghe7uIGYzXD01gcXzU5ky0Y2l304P53M3CiMEg0HqG7tP6YDopK09MoBIS7Fz\n9bSEcPiQl+Mi8TT3XUREREQuPS6nlYKFlzF30gg2bPmcveVNfHKwma/PHMXfzsrBab/kH39Eos4l\n/a+y/9QKZ5IXW2wPFqcfkym0xsFt8/Nw2Cz0+AL8v5J6Xnr1CN6uAHk5Lv7+jmwmjI37yj9jsMUc\nA34TsX43D/70ANV1XgAyPA4WzU1hweyUYfkb/2AwyJGGbipO6YBoP+GPOM6Taid/fGK4A2LMqBgS\n3MPvfoiIiIjI+ZGVFscDBVP48LMGXvzj57z2XhW7yo7w7QV5XH15uqb2ikSRSzqU6D+1wmQBa8zJ\nh+O+7TkPH+7htxurqT3ahTvOyt8VZLNwbkpE98JX6Vv7Yc9njdQf9RE8EcOJVgulgW6sVhNzrkpi\n8fxUJo6PwzyE60azQCBIXX3XlzogOjojA4gMj+PkNpy9XRDxcZf0X0sREREROQdMJhMzJni4Mi+F\n13ZV8fruQ/z61U/Y9lEtdywex0jPV/+CUUTOv0v66e90UyvibDH8el0tH+5tw2yCbyxMY9ktI4iL\nHfota231YeuM53hVN8cbuwEYmelk8bxU5s9Kxn2RP4QHAkFqj3ZF7IJReaiDjs5AxHEj0h2hXTDC\na0DEEOu6uN+7iIiIiEQ3h83Ct+blMnvSCIq3fs5Hnzfyk9+9z3VTs7ll3hhinerIFTHSJf1EONDU\nimAAvM1OWo85qAq0ccW4OFbckc3okUNby8HvD7JnXysl25v4cG8rgQA47Gaum5PC4nkpjM+LvSjb\nxvyBILV13nD3QyiA6MTbdTKAMJkgM8PBjMm96z+MdpE7yoUrZngtzikiIiIiFw9PYgz/97ZJ7Kto\nYsOWz9m6p5rdnx7ltvm5zJ2UOWw6lkUuNpd0KAGRUyuO1vrxNrnwdZtITrJx1+1ZzLkqaUjhQX1j\nF1u2N7H13Saaj/UAkJfjYvH8FOZenXxRPZj7/UGq67yndEB00tV9MoAwmyBrhDPU+dC3BsTIGGIu\novcpIiIiIpeOK3NTePi7SZR8cJhXdx5k3Rufse0vtSxdMJbLRiZgMZuNLlHkknLJhxIWs5nli8bh\n8rtZ92EtVquJ2270sORvM3A6zuzBuscX4P2PWinZ3sjeT44TDIIrxszfLEhl8bxUcnOif8cMny/I\n4drOfus/dHDwcCfdPcHwMWZzaNpJeAeM0S5Gj4w54/skIiIiIhINrBYzN1yTwzX5Gby87QveKzvK\noxs/wm4zMybDTV5WAnmZbnKzEkiItRtdrsiwdsmHEn0m5MXxNwtSuenrHkakO8/onOo6L1u2N/L2\nn5rDW1hOGBvL4vmpzJ6RhMMRnSlrjy/AoZpQB0RdfR0f72+l6nAnPb6TAYTFAiMzY8jrm36REwog\nHPbofE8iIiIiIkOVFO/gf38zn69NyWLnx3WU17Zx4PAxPjt8LHxMaoKTvKwEcjPd5GUmMCo9DqtF\nY2KRc0WhRK8JY+POaIvPrq4AO//cwpYdTXxyoB2A+DgLN33dw6J5KYzMjDnfpQ5JT0+AqurOiDUg\nDlV78flPBhBWi4lR2ZEdEDnZMdht+mErIiIiIsPfuJGJjBuZCEBnl4+KujYqalopr22joraN3Z8c\nZfcnR4FQl8XojPhQSNHbUZHsPrNfaorIlymUOEOVhzoo2d7EO7uaw9taTr4inkXzUrh6aiK2KHiA\n7+oOUHU4FED0rQFxqKYTf79dOK1WE6NHxoTXf5g+JRW3KxAV9YuIiIiIGC3GYSV/dDL5o5MBCAaD\n1Ld0Ul4bCinKa1qpqG3ji5pW+OAwEOq46OukyMtyk5Mej92mKc4iZ0KhxGl0dvrZsbuFku2NfHGw\nA4CkBBs3XJfKormpZHgchtXW1RWg8nBv+NDbAXG41kug3y6cdpvpZPdDbwfEyMwYrNaTC3empcXT\n0HDcgHcgIiIiIhL9TCYT6cku0pNdzJo4AoCubj8Hj7SFw4ny2jY+/KyBDz9rAMBiNjHSExexNkVa\ngvOi3H1P5HxTKHGKYDDIgYoOSt5p5E8ftODtCmA2wYzJbhbPS2X6pAQslgv7w6TT66fyUGQHRE2t\nl8DJGRjY7SYuGxNLXm8HRG5ODNkjIgMIERERERH56znsFsaPSmL8qCQg9AzR1OalvKaN8tpQJ0XV\nkeMcPHKcrR+GznG7bOT2dlLkZiYwZkQ8Trsex0T0r6BXV3eAkncaKdneyKEaLwCeVDu3fiOF6+ak\nkJJ0YVbd7ej0U3moo98uGJ3UHPES7BdAOB1mxo+NjeiAyMpwXvCwREREREREQt0UqQkxpCbEcPUV\n6QD0+PxUHW0Pr01RXtvKX75o5C9fNPaeA9lpcaFOit6wIj3ZhVndFGIAb7ePprYufL4Ao9LjLmhX\nj0KJXptfO8JLrx7BajExa0Yii+enMunyeMzm8/fNONHhp6Jf90NoN4yuiAAixmnm8sviIjogMjOc\nWM5jXSIiIiIi8texWS2MzUpgbFZC+HMtx7uo6Lc2xcEjxzlc3862v9QCEOu0MibTzdjMBHKz3OSO\ncONy2ox6CzJM+AMBWtu7aWrz0tzWRXObN/xx6L9eTnh94eOL7ppJTkb8BatPoUSvxfNSSUuxM2Ny\nAonuc/8Pv/2ELyJ8qKjqpK6+K+IYV4yF/PFxoe6HHBe5o12M8DjOazAiIiIiIiIXRlK8g+njPUwf\n7wHA5w9Q3dB+ctpHTRsfVzTzcUVz+JwRKa7w2hR5mQlkpsbq+UDCgsEgnV2hLoemNi8tbV6aIoIH\nLy3Huwn0/813Pw6bhZQEJ2My3aS4nYxIiSUzNfaCvgeFEr1Sk+0smpt6Tq7V1u6jonfxyfKqDioO\ndnC0sTvimLhYC5Muj4/ogEhPUwAhIiIiInKpCG0v6mZ0hpuF07MBaOvopqLfLh8VdW3U7a3j3b11\nADjtFsaMcIfXpsjLdBPvujBTzeXC8/kDtBwPhQz9Oxv6Bw/ebv+A55pMvTvDZIUCh+R4B8luZ+hj\nt4OUBCcuh9XwBVgVSvyVWtt6wt0PoYUoO2loigwg4uMsTMmPD60B0RtCeFLthn/zRUREREQkurhd\ndqaMTWXK2NAvTAOBIDWNJ8KdFOW1rXxa1cKnVS3hczxJMeRluhmVmQD+AC6nFZfDRqzT2vuxFZfT\nhtNh0ZoVUSQYDHLC66Op1TvglIqmNi+t7d0M3OMALoeV1IQYUtyhsCHZ7egNHELBQ2K8HYvZfEHf\n09lQKDEELa09vVMvTk7DaGrpiTjGHW9l6kR3RAdEWooCCBERERERGTpz7/aiIz1xfG1KFgAnvD1U\n1raF16aoqG1jV9lRdpUdPe21TECMozeocFqJddpwOazEOK2hAKM3vOgLMmKdtoiv2axmPdcMQY/P\nT/PxLppbvzylou91ty8w4LkWs4mkeAfjRiaS7O7f4eAMhxAxjuHxOD883sU5FgwGaTn25Q6I5mOR\nAURSgpXpk9wRHRApSTb9QxURERERkfMm1mljYm4KE3NTAAgEg9S3dILVQu2RNjq8Pjq6fHR4e+jw\n+jjh9dHZ+/pEl48Or4+jLZ10dbcP6c+1Wkyh0KJfsNEXXpzsyIgMNvrCjxiHxdDf2geDQfyBIH5/\nEF8ggM8fxO8P4POHPvb5A/gDwfBrvz9Ajz8QOr7vmED/14GIa/Ud0+ULUNfQTnObl7aOnkHriYux\nMSIl9pTA4WSnQ0Ks/ZKZ2q9QolfzsR7e3NYQCiEOdnCszRfx9eREGzOnJIS7H/JyXCRfoG1CRURE\nREREBmM2mchIdpGWFk+G23HG5/n8gd6woi/E8HHC2xP++NRwo6MrFHB0eHtoONaJPzDYxIKBOe2W\niOkkoUAj1Knhclhx2q34+z3khwOAQN/rAQIEX/+vDxYghEKGoVV79qwWMyluB1lpcV+aUtEXQjhs\nlgtUTfRTKNHrzW0NvPTqEQBSk21cPTUh3AGRm+MiKUFb8YiIiIiIyPBhtZiJd9nPaqHMYDBIty/Q\nG170hAOLznB40RMRdvQPNprbuqhpOHHOQgKrxYzVYsJqMWOxmLCazThtFqzO3tcWM1azCYvFHHGs\n1dLvc+Z+5/d9zXzKdU89/5RrWswmRo9MpruzS93zQ6BQotct16cz6fJ4skY4z8uWoCIiIiIiIsOF\nyWTCYbPgsFlIij/z7ow+gUAQb3df50UorOjq9vcLCnrDALPplNCh92NzKCiwmE1RFQAkxjto8HZ/\n9YESplCiV0yMhfzx8UaXISIiIiIiMuyZzb3rUzj1C+FLXfTvDyIiIiIiIiIiw1LUdEqsXr2a0tJS\nTCYTq1atYtKkSUaXJCIiIiIiIiLnUVSEEu+//z5VVVUUFxdTXl7OqlWrKC4uNrosERERERERETmP\nomL6xq5du1i0aBEAeXl5tLa20t4+tD1zRUREREREROTiEhWdEo2NjeTn54dfJycn09DQQFxc3IDH\nJyW5sFoH39c1LU0LVg6F7teZ070aGt2vM6d7NTS6X0Oj+yUiIiLRKipCiVMFg6ffsbalpWPQr6Wl\nxdPQcPxclzRs6X6dOd2rodH9OnO6V0Oj+zU05/p+KeAQERGRcykqpm94PB4aGxvDr+vr60lLSzOw\nIhERERERERE536IilJg9ezZvvvkmAGVlZXg8nkGnboiIiIiIiIjI8BAV0zemTZtGfn4+BQUFmEwm\nioqKjC5JRERERERERM6zqAglAB544AGjSxARERERERGRCygqpm+IiIiIiIiIyKVHoYSIiIiIiIiI\nGEKhhIiIiIiIiIgYQqGEiIiIiIiIiBjCFAwGg0YXISIiIiIiIiKXHnVKiIiIiIiIiIghFEqIiIiI\niIiIiCEUSoiIiIiIiIiIIRRKiIiIiIiIiIghFEqIiIiIiIiIiCEUSoiIiIiIiIiIIYZVKLF69WqW\nLl1KQUEBe/fuNbqcqPboo4+ydOlSbrvtNt566y2jy7koeL1eFi1axObNm40uJeq9+uqr3HTTTdx6\n661s27bN6HKi1okTJ7j33nspLCykoKCAHTt2GF1SVDpw4ACLFi3i+eefB6Curo7CwkKWL1/OP/3T\nP9Hd3W1whdFloPt11113ceedd3LXXXfR0NBgcIXDi8YextOYJjponGQsjb2Mp3Hd2Rs2ocT7779P\nVVUVxcXF/OxnP+NnP/uZ0SVFrffee4/PP/+c4uJinnvuOVavXm10SReFX/3qVyQkJBhdRtRraWnh\n6aefZsOGDaxdu5atW7caXVLUeuWVVxgzZgzr16/niSee0M+tAXR0dPDwww9z7bXXhj/35JNPsnz5\ncjZs2EBOTg6bNm0ysMLoMtD9evzxx7n99tt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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZjQrZ8mcHFiU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Identify Outliers\n", + "\n", + "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values. Ideally, these would lie on a perfectly correlated diagonal line.\n", + "\n", + "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n", + "\n", + "Do you see any oddities? Trace these back to the source data by looking at the distribution of values in `rooms_per_person`." + ] + }, + { + "metadata": { + "id": "P0BDOec4HbG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "7b36b693-3b14-4a6f-c0ad-01f4084bc172" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "plt.figure(figsize=(25,6))\n", + "plt.subplot(1,2,1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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G0D8Yxu53T477fIvmTEPTZ+rgqXSw8IiIiMgATIkMa+XhcBiPPPIIenp6EA6H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\nat9TTZhM34+Zi88jt9XAYjHM71pUoia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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jByCP8hDRZmM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "s0tiX2gdRe-S", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "efd07e32-1428-4c52-e6b1-1b3025ae59b6" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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n0SeUaJBq/MSDjh4Jet1gQVWHzYL51ROwcfl0NOz7NOt9jvJdwpFkngfhRFmS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\ncknCtgvZEA0yQ9eV5s2owMrLq+AstY4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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kMQD0Uq3RqTX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n", + "\n", + "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:" + ] + }, + { + "metadata": { + "id": "POTM8C_ER1Oc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "58125ec6-10b5-40e8-a479-e4d47ca7d335" + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "9l0KYpBQu8ed", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Clip Outliers\n", + "\n", + "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n", + "\n", + "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n", + "\n", + " clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n", + "\n", + "The above `clipped_feature` will have no values less than `0`." + ] + }, + { + "metadata": { + "id": "rGxjRoYlHbHC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "7be4b6ae-d090-4b2d-89be-3287cd630701" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 10))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "WvgxW0bUSC-c", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "8YGNjXPaSMPV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results." + ] + }, + { + "metadata": { + "id": "9YyARz6gSR7Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "bc67832a-6433-4432-be69-97f2898acbff" + }, + "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": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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8gMSEWGYk9cJkVB5VRFqXi8f0YHNmDsu/2ceQhDi6dQjzd0giIiJNSlf4ItJs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DcZSBIsSjexOsvMpNjxvaC8cNHN1t117NxfT1u7hiRB1vwoNuTGs3Bu1Lj3w7jeGIbBuUp/+84+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\nhBBCCCGEEEIIIaJCQgk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "f0885db7-b8d3-42b8-e2db-3b67cfdd82fb" + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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VlAiOUmXN2RmMZlVZN9TZsU5l8lehSud8Y1tO7zIlyti6vg33b1xkujWLQDAL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\nVjeCjXHY19adcPGIFcMrTSAgEAjCHD3pxtGTblhoCotnVyn+3ZFP3bB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qSKZkv0+6T+kE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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From ca3435df8ccf24bc438f5bb1a7a2fd031f444398 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Thu, 31 Jan 2019 00:38:07 +0530 Subject: [PATCH 05/12] Finished part 4 (validation set) --- validation.ipynb | 1575 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1575 insertions(+) create mode 100644 validation.ipynb diff --git a/validation.ipynb b/validation.ipynb new file mode 100644 index 0000000..2d95fd5 --- /dev/null +++ b/validation.ipynb @@ -0,0 +1,1575 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "validation.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "4Xp9NhOCYSuz", + "pECTKgw5ZvFK", + "dER2_43pWj1T", + "I-La4N9ObC1x", + "yTghc_5HkJDW" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zbIgBK-oXHO7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Validation" + ] + }, + { + "metadata": { + "id": "WNX0VyBpHpCX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Use multiple features, instead of a single feature, to further improve the effectiveness of a model\n", + " * Debug issues in model input data\n", + " * Use a test data set to check if a model is overfitting the validation data" + ] + }, + { + "metadata": { + "id": "za0m1T8CHpCY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), to try and predict `median_house_value` at the city block level from 1990 census data." + ] + }, + { + "metadata": { + "id": "r2zgMfWDWF12", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "8jErhkLzWI1B", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First off, let's load up and prepare our data. This time, we're going to work with multiple features, so we'll modularize the logic for preprocessing the features a bit:" + ] + }, + { + "metadata": { + "id": "PwS5Bhm6HpCZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "J2ZyTzX0HpCc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "sZSIaDiaHpCf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **training set**, we'll choose the first 12000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "P9wejvw7HpCf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "5065d4f8-1706-4b03-9144-c8b2a3f8fda2" + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2638.9 538.6 \n", + "std 2.1 2.0 12.6 2133.0 414.2 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1462.0 297.0 \n", + "50% 34.2 -118.5 29.0 2139.0 433.0 \n", + "75% 37.7 -118.0 37.0 3143.2 649.0 \n", + "max 42.0 -114.3 52.0 30405.0 4957.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1430.6 500.4 3.9 2.0 \n", + "std 1125.2 377.6 1.9 1.3 \n", + "min 6.0 1.0 0.5 0.1 \n", + "25% 793.0 282.0 2.6 1.5 \n", + "50% 1168.0 409.0 3.5 1.9 \n", + "75% 1726.0 603.0 4.8 2.3 \n", + "max 35682.0 4769.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "JlkgPR-SHpCh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "eeb91aa8-7abf-4e8b-cc8b-b8b571a115b3" + }, + "cell_type": "code", + "source": [ + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "training_targets.describe()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 12000.0\n", + "mean 207.0\n", + "std 115.8\n", + "min 15.0\n", + "25% 119.4\n", + "50% 179.9\n", + "75% 265.0\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "5l1aA2xOHpCj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "fLYXLWAiHpCk", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "2de33ec6-7861-4bec-8747-dc5ade61b6b7" + }, + "cell_type": "code", + "source": [ + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_examples.describe()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 5000.0 5000.0 5000.0 5000.0 5000.0 \n", + "mean 35.6 -119.6 28.5 2655.2 541.4 \n", + "std 2.2 2.0 12.6 2288.9 438.5 \n", + "min 32.5 -124.3 1.0 11.0 3.0 \n", + "25% 33.9 -121.8 18.0 1462.8 295.0 \n", + "50% 34.2 -118.5 28.0 2108.0 435.0 \n", + "75% 37.7 -118.0 37.0 3165.2 647.2 \n", + "max 42.0 -114.6 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 5000.0 5000.0 5000.0 5000.0 \n", + "mean 1427.2 503.3 3.9 2.0 \n", + "std 1200.6 400.6 1.9 0.9 \n", + "min 3.0 3.0 0.5 0.0 \n", + "25% 781.0 280.0 2.6 1.5 \n", + "50% 1164.0 408.0 3.5 2.0 \n", + "75% 1710.2 609.0 4.7 2.3 \n", + "max 28566.0 6082.0 15.0 29.4 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "id": "oVPcIT3BHpCm", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "e1367686-21f3-4c5d-823d-507a6c2421cb" + }, + "cell_type": "code", + "source": [ + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "validation_targets.describe()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 208.1\n", + "std 116.3\n", + "min 22.5\n", + "25% 119.8\n", + "50% 181.2\n", + "75% 265.2\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "z3TZV1pgfZ1n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Examine the Data\n", + "Okay, let's look at the data above. We have `9` input features that we can use.\n", + "\n", + "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense will get you far.\n", + "\n", + "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data." + ] + }, + { + "metadata": { + "id": "4Xp9NhOCYSuz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "gqeRmK57YWpy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's check our data against some baseline expectations:\n", + "\n", + "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n", + "\n", + "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n", + "\n", + "If you look closely, you may see some oddities:\n", + "\n", + "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n", + "\n", + "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n", + "\n", + "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n", + "\n", + "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source." + ] + }, + { + "metadata": { + "id": "fXliy7FYZZRm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Plot Latitude/Longitude vs. Median House Value" + ] + }, + { + "metadata": { + "id": "aJIWKBdfsDjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n", + "\n", + "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`." + ] + }, + { + "metadata": { + "id": "5_LD23bJ06TW", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 498 + }, + "outputId": "bc8c8513-a1e2-4045-e7cb-f4dde3a3841f" + }, + "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": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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yZAt7jmvUtWgUBBUThir0PthW7bqKdZubsz62uTLKR3saKC48Y+zWprQ075L92UvZLwwp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\nVEy2+QwLg6RcEzR8jZTxmpcr2GQynReKMxOxMfBa2LxKsnqofbPv2HDLx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RsAACAASURB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\neB03tJnUUT3ZQqehy4MCjdJ6pCnJZGwsy8QwA8bG69+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jxqZCjg00dlIdGwjxAoVlxGlB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\nK6WBc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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\"], num_epochs=1, shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value\"], num_epochs=1, shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # 2. Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " validation_predictions =np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zFFRmvUGh8wd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "b041e7b9-96e0-4035-9c0a-be9d9c23b430" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n", + " learning_rate=0.00003,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 217.08\n", + " period 01 : 199.27\n", + " period 02 : 184.95\n", + " period 03 : 176.70\n", + " period 04 : 169.08\n", + " period 05 : 166.00\n", + " period 06 : 164.85\n", + " period 07 : 164.84\n", + " period 08 : 165.93\n", + " period 09 : 167.08\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IiFRyFVZgdu7cyfHjx5k/fz4pKSmMHDmSzp07M27cOAYPHswXX3zB559/zqRJk5g2bRoL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\nePXjIry3tQRud2CuIpJKpLgx/Vr8dtpCmCKi8UX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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "I-La4N9ObC1x", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Xyz6n1YHbGef", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i1imhjFzbWwt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "d45a0c9a-9ff5-47e5-cf9b-53bb66279a6e" + }, + "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": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 217.14\n", + " period 01 : 199.30\n", + " period 02 : 184.47\n", + " period 03 : 174.47\n", + " period 04 : 169.23\n", + " period 05 : 166.06\n", + " period 06 : 164.70\n", + " period 07 : 164.79\n", + " period 08 : 165.34\n", + " period 09 : 166.61\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + 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iYmKqqqxqM7x7CG51nPjq20QKz5fSxCuEyIB2JOac5EDaIXuXJyIiUqNVWYCx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\nPsK2bduuuH0ic4odDtfH7nO9OGYsXazN1MszavD4lmU4fLY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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "65sin-E5NmHN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 5: Evaluate on Test Data\n", + "\n", + "**In the cell below, load in the test data set and evaluate your model on it.**\n", + "\n", + "We've done a lot of iteration on our validation data. Let's make sure we haven't overfit to the pecularities of that particular sample.\n", + "\n", + "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n", + "\n", + "How does your test performance compare to the validation performance? What does this say about the generalization performance of your model?" + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1939a624-47ad-4e84-aacb-b6d2c42e4f0a" + }, + "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", + "predict_test_input_fn = lambda: my_input_fn(test_examples, test_targets[\"median_house_value\"], num_epochs=1, shuffle=False)\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "root_mean_squared_error = math.sqrt(metrics.mean_squared_error(test_predictions, test_targets))\n", + "print(\"Final RMSE (on test data) is %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data) is 162.36\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "yTghc_5HkJDW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "_xSYTarykO8U", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c2b36716-3706-4764-d85b-99990c65c94c" + }, + "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": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 162.36\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Q7UYbGosHaHM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 994b58597b03fcce6d6b3213bbb8d12f925707e6 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Thu, 31 Jan 2019 22:59:18 +0530 Subject: [PATCH 06/12] Finished part 5 (feature sets) --- feature_sets.ipynb | 1582 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1582 insertions(+) create mode 100644 feature_sets.ipynb diff --git a/feature_sets.ipynb b/feature_sets.ipynb new file mode 100644 index 0000000..0344908 --- /dev/null +++ b/feature_sets.ipynb @@ -0,0 +1,1582 @@ +{ + "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": 1205 + }, + "outputId": "032b459e-3487-493b-85dd-8c32f50b25fd" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2653.7 542.2 \n", + "std 2.1 2.0 12.7 2201.4 422.1 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1467.0 300.0 \n", + "50% 34.2 -118.5 29.0 2135.5 435.0 \n", + "75% 37.7 -118.0 37.0 3155.0 652.0 \n", + "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1432.9 503.4 3.9 2.0 \n", + "std 1112.4 385.0 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 792.0 282.0 2.6 1.5 \n", + "50% 1173.0 410.0 3.5 1.9 \n", + "75% 1720.0 607.0 4.7 2.3 \n", + "max 16122.0 5189.0 15.0 52.0 " + ], + "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": 359 + }, + "outputId": "b30ee539-6fe4-4a04-b57c-7b991a76a1b0" + }, + "cell_type": "code", + "source": [ + "correlation_dataframe = training_examples.copy()\n", + "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n", + "\n", + "correlation_dataframe.corr()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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latitudelongitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomerooms_per_persontarget
latitude1.0-0.90.0-0.0-0.1-0.1-0.1-0.10.1-0.1
longitude-0.91.0-0.10.10.10.10.1-0.0-0.1-0.0
housing_median_age0.0-0.11.0-0.4-0.3-0.3-0.3-0.1-0.10.1
total_rooms-0.00.1-0.41.00.90.90.90.20.10.1
total_bedrooms-0.10.1-0.30.91.00.91.0-0.00.10.0
population-0.10.1-0.30.90.91.00.90.0-0.1-0.0
households-0.10.1-0.30.91.00.91.00.0-0.00.1
median_income-0.1-0.0-0.10.2-0.00.00.01.00.20.7
rooms_per_person0.1-0.1-0.10.10.1-0.1-0.00.21.00.2
target-0.1-0.00.10.10.0-0.00.10.70.21.0
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms \\\n", + "latitude 1.0 -0.9 0.0 -0.0 \n", + "longitude -0.9 1.0 -0.1 0.1 \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": 4 + } + ] + }, + { + "metadata": { + "id": "RQpktkNpia2P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n", + "\n", + "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n", + "\n", + "Use this information to try removing features. You can also try developing additional synthetic features, such as ratios of two raw features.\n", + "\n", + "For convenience, we've included the training code from the previous exercise." + ] + }, + { + "metadata": { + "id": "bjR5jWpFr2xs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jsvKHzRciH9T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g3kjQV9WH3pb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "varLu7RNH3pf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters." + ] + }, + { + "metadata": { + "id": "DSgUxRIlH3pg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "ca40f2c9-6227-4d0f-caea-81d8483a2acb" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = [\"longitude\", \"rooms_per_person\"\n", + "]\n", + "\n", + "assert minimal_features, \"You must select at least one feature!\"\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "#\n", + "# Don't forget to adjust these parameters.\n", + "#\n", + "_ = train_model(\n", + " learning_rate=0.01,\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": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 116.25\n", + " period 01 : 119.87\n", + " period 02 : 116.18\n", + " period 03 : 116.00\n", + " period 04 : 116.17\n", + " period 05 : 115.99\n", + " period 06 : 115.99\n", + " period 07 : 115.92\n", + " period 08 : 116.47\n", + " period 09 : 115.85\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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B5FGS0mItXIEWlJlskCTpsJ+vzGSL7+RrU8HVHoQ3EB6uUomIiJKKASaD7N+B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\nc689NchjLxzHMm7juuUp3Hl9zozcPW/y6xwYfzJmG6N3tJ/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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": 622 + }, + "outputId": "db8f0ed5-9069-4d1a-d129-6af1d3e34af9" + }, + "cell_type": "code", + "source": [ + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 165.52\n", + " period 01 : 124.75\n", + " period 02 : 117.25\n", + " period 03 : 116.72\n", + " period 04 : 115.93\n", + " period 05 : 115.37\n", + " period 06 : 114.73\n", + " period 07 : 114.21\n", + " period 08 : 114.23\n", + " period 09 : 113.30\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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miXndDnyRHDBM1peoF0ZERCQRFGASoGtKVwCWbfvG4kpERERaJwWYBOjbvg8A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\nJ3PnzqWgoICuri7KysqsjnZJ++GHH1i5ciUACxY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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": 364 + }, + "outputId": "f799fcad-2ca5-4749-9913-5084ad27b432" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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sw6LG6px22PYaC1wOC0a9IdnX1dda0La4Hu1L5yIciaG3fxR/+9Ihxd+jJjV2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\nc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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": 622 + }, + "outputId": "a74e16c2-7674-4a8c-b064-4832322e6c0e" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#\n", + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 227.14\n", + " period 01 : 216.98\n", + " period 02 : 206.91\n", + " period 03 : 196.92\n", + " period 04 : 187.07\n", + " period 05 : 177.37\n", + " period 06 : 167.80\n", + " period 07 : 158.44\n", + " period 08 : 149.30\n", + " period 09 : 140.39\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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JrE/qgcaQ5pZ1h+sjMcetHVFEROSWVGAKucplPHn70QA61qhD8u5A0i9U4HJS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\nVXi//avUcmqOYcpk25VfeXX1B+w7p/s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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": 622 + }, + "outputId": "32ebbbeb-9a7b-4b9c-d36b-f7c5d322d6a7" + }, + "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": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 227.15\n", + " period 01 : 217.00\n", + " period 02 : 206.93\n", + " period 03 : 196.96\n", + " period 04 : 187.12\n", + " period 05 : 177.38\n", + " period 06 : 167.83\n", + " period 07 : 158.45\n", + " period 08 : 149.31\n", + " period 09 : 140.56\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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FGBERkSLk/PlzrF27Kl/PfeGF4ZQpU/amj7/77tS7VdZdVygvJSAiIiI3NnXqRA4c2Eeb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\nXce8nA1M+CCbr/aU0dDQYHRMh6UdGBEREYM4WZy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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 99a89e5c58a99fa8650c8c687074d25c24454691 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Thu, 31 Jan 2019 23:21:09 +0530 Subject: [PATCH 07/12] Finished part 6 (feature crosses) --- feature_crosses.ipynb | 1619 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1619 insertions(+) create mode 100644 feature_crosses.ipynb diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb new file mode 100644 index 0000000..60481fe --- /dev/null +++ b/feature_crosses.ipynb @@ -0,0 +1,1619 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_crosses.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ZTDHHM61NPTw", + "0i7vGo9PTaZl" + ] + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Feature Crosses" + ] + }, + { + "metadata": { + "id": "F7dke6skIK-k", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Improve a linear regression model with the addition of additional synthetic features (this is a continuation of the previous exercise)\n", + " * Use an input function to convert pandas `DataFrame` objects to `Tensors` and invoke the input function in `fit()` and `predict()` operations\n", + " * Use the FTRL optimization algorithm for model training\n", + " * Create new synthetic features through one-hot encoding, binning, and feature crosses" + ] + }, + { + "metadata": { + "id": "NS_fcQRd8B97", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "4IdzD8IdIK-l", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First, as we've done in previous exercises, let's define the input and create the data-loading code." + ] + }, + { + "metadata": { + "id": "CsfdiLiDIK-n", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "10rhoflKIK-s", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ufplEkjN8KUp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "9672b072-51b9-4216-801d-0744d0ae0b59" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2642.3 540.7 \n", + "std 2.1 2.0 12.6 2146.6 420.5 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1465.0 296.0 \n", + "50% 34.2 -118.5 29.0 2126.0 434.0 \n", + "75% 37.7 -118.0 37.0 3161.2 650.0 \n", + "max 42.0 -114.5 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 1427.0 502.1 3.9 2.0 \n", + "std 1092.7 382.6 1.9 1.2 \n", + "min 6.0 1.0 0.5 0.1 \n", + "25% 793.0 282.0 2.6 1.5 \n", + "50% 1166.0 410.0 3.5 1.9 \n", + "75% 1718.0 607.0 4.8 2.3 \n", + "max 16122.0 5189.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "oJlrB4rJ_2Ma", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "NBxoAfp2AcB6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hweDyy31LBsV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## FTRL Optimization Algorithm\n", + "\n", + "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)." + ] + }, + { + "metadata": { + "id": "S0SBf1X1IK_O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1Cdr02tLIK_Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "b9d5cb93-96c1-4307-b48c-9524021545e7" + }, + "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 : 228.77\n", + " period 01 : 116.06\n", + " period 02 : 127.27\n", + " period 03 : 129.75\n", + " period 04 : 128.55\n", + " period 05 : 144.80\n", + " period 06 : 116.14\n", + " period 07 : 120.75\n", + " period 08 : 108.96\n", + " period 09 : 124.07\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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yioiINBWXjoEBWLp0KfPnz2fOnDkMHz7c+b5hGMdc/3jvHy48PBCr1dJoNf5e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\n94CwdrcZcCpOTpSd4vOLOykzVaBRaUiJGMF98fcSZGh/Xkx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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(latitude, boundaries=get_quantile_based_boundaries(training_examples[\"latitude\"], 10))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(housing_median_age, boundaries=get_quantile_based_boundaries(training_examples[\"housing_median_age\"], 7))\n", + " bucketized_median_income =tf.feature_column.bucketized_column(median_income, boundaries=get_quantile_based_boundaries(training_examples[\"median_income\"], 7))\n", + " bucketized_rooms_per_person =tf.feature_column.bucketized_column(rooms_per_person, boundaries=get_quantile_based_boundaries(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": 622 + }, + "outputId": "a66aad90-5661-4d73-e124-71194aef630f" + }, + "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 : 171.40\n", + " period 01 : 145.22\n", + " period 02 : 128.77\n", + " period 03 : 117.59\n", + " period 04 : 109.60\n", + " period 05 : 103.67\n", + " period 06 : 99.09\n", + " period 07 : 95.42\n", + " period 08 : 92.42\n", + " period 09 : 89.89\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ZbTn2Jh3S2V67rBrAhsM3uayaiIhITyxgWthItyDYmdji+O0zyCrXvau2h5Ma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\nQiI5X2iAKo+N9VeuFlxVhN1izO2Al5Das1ELKTBZkEmlXpK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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": 622 + }, + "outputId": "f4632790-514d-4bbb-ef1c-5b6c206039d7" + }, + "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 : 171.36\n", + " period 01 : 145.13\n", + " period 02 : 128.60\n", + " period 03 : 117.41\n", + " period 04 : 109.41\n", + " period 05 : 103.50\n", + " period 06 : 98.94\n", + " period 07 : 95.20\n", + " period 08 : 92.20\n", + " period 09 : 89.71\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ERNTmsIAhIiJqZ06cOKpTu08//QiZmRmP3P7qq8v1FZLesYAhIiJqR7KyMnHkyCGd2i5d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\nj4gRo9JZwQPV91HnWn3TRWYuc4nFYjR7g7xzooO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MiCyoSCTCm2++yZYtW/jz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\naUcFRkRERNLO/weXRmeAnrcg5QAAAABJRU5ErkJggg==\n", + "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(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": "xZuZMp3EShkM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "fd99e1b1-da7a-4783-ba13-088d3cdd85dc" + }, + "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 : 165.15\n", + " period 01 : 137.07\n", + " period 02 : 120.01\n", + " period 03 : 108.65\n", + " period 04 : 100.61\n", + " period 05 : 94.62\n", + " period 06 : 90.09\n", + " period 07 : 86.53\n", + " period 08 : 83.63\n", + " period 09 : 81.22\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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NYQFDREREbQ4LGCIionbm4MF9Bh338ccfIjs7q9HXX3vtZWOFZHQsYIiIiNqRnJxs7N27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\n0yn//J49e7jhFxEl4UZ2RJRxa9euxd69e9HY2Ijdu3cjFAohGAzi4Yc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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": 622 + }, + "outputId": "26cbc803-6dfd-4902-977b-b8568872d0b4" + }, + "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 : 165.65\n", + " period 01 : 137.58\n", + " period 02 : 120.52\n", + " period 03 : 109.05\n", + " period 04 : 101.10\n", + " period 05 : 95.15\n", + " period 06 : 90.55\n", + " period 07 : 87.00\n", + " period 08 : 84.15\n", + " period 09 : 81.71\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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50eO+Bjd69HSxQN+uDohPL8alGzl6ipCIiKh1YAGjRS5mTnjCqQ/SyzJxITOi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\nBblZXdRkSzaZJgVMCqRTqUuyUZPkkh7DUyM0D7dGl5uGWvD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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 b54521c190d08c076cb718b6a43172d250c0cf8a Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Thu, 31 Jan 2019 23:41:41 +0530 Subject: [PATCH 08/12] Finished part 7 (logistic regression) --- logistic_regression.ipynb | 1712 +++++++++++++++++++++++++++++++++++++ 1 file changed, 1712 insertions(+) create mode 100644 logistic_regression.ipynb diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb new file mode 100644 index 0000000..b82e197 --- /dev/null +++ b/logistic_regression.ipynb @@ -0,0 +1,1712 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "logistic_regression.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "dPpJUV862FYI", + "i2e3TlyL57Qs", + "wCugvl0JdWYL" + ] + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Logistic Regression" + ] + }, + { + "metadata": { + "id": "LEAHZv4rIYHX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n", + " * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem" + ] + }, + { + "metadata": { + "id": "CnkCZqdIIYHY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now." + ] + }, + { + "metadata": { + "id": "9pltCyy2K3dd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Frame the Problem as Binary Classification\n", + "\n", + "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n", + "\n", + "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`." + ] + }, + { + "metadata": { + "id": "67IJwZX1Vvjt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and prepare the input features and targets." + ] + }, + { + "metadata": { + "id": "fOlbcJ4EIYHd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "lTB73MNeIYHf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`." + ] + }, + { + "metadata": { + "id": "kPSqspaqIYHg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FwOYWmXqWA6D", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "7b6e3845-13db-4486-ce2b-976241830b2b" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2643.6 538.8 \n", + "std 2.1 2.0 12.6 2185.3 422.8 \n", + "min 32.5 -124.3 1.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1454.0 295.0 \n", + "50% 34.2 -118.5 29.0 2126.0 432.0 \n", + "75% 37.7 -118.0 37.0 3150.0 650.0 \n", + "max 42.0 -114.3 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1428.3 500.8 3.9 2.0 \n", + "std 1169.2 386.4 1.9 1.2 \n", + "min 6.0 2.0 0.5 0.0 \n", + "25% 785.0 281.0 2.6 1.5 \n", + "50% 1165.0 406.0 3.6 1.9 \n", + "75% 1715.2 606.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "uon1LB3A31VN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## How Would Linear Regression Fare?\n", + "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n", + "\n", + "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)." + ] + }, + { + "metadata": { + "id": "smmUYRDtWOV_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "B5OwSrr1yIKD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SE2-hq8PIYHz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_regressor_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TDBD8xeeIYH2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "2d30c83c-5f7b-408d-92b9-3b09cf44a2fa" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 0.45\n", + " period 01 : 0.45\n", + " period 02 : 0.45\n", + " period 03 : 0.45\n", + " period 04 : 0.46\n", + " period 05 : 0.45\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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f78+y+9quWldmR86g2lzD3osH7G4vhOhfJMERQnTY6eIzKCiM1rc+e6qmzkxq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\nIPm1B4n2HsfiyPlMC5gqW0GIqyYBRwghrgMzpgbxz8+Ps7OwktsyxqF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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": "QBuKtXzmBB3_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "eb98858c-eb04-4b56-8ac9-f43c247e2492" + }, + "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": "fec0a01e-7e8e-4e6b-a59d-49e14463c72a" + }, + "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(feature_columns=construct_feature_columns(training_examples),optimizer=my_optimizer)\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VM0wmnFUIYH9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "e0ac7fd4-b0f6-447b-a74d-9e34ddd7e999" + }, + "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 : 0.60\n", + " period 01 : 0.58\n", + " period 02 : 0.56\n", + " period 03 : 0.55\n", + " period 04 : 0.54\n", + " period 05 : 0.54\n", + " period 06 : 0.53\n", + " period 07 : 0.53\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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fOe7s0JgOrX1snKmIiEjtstnIzLZt2xg0aBAAwcHB5OTkkJ+fX2WMt7c32dnZ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\nkczMzHANT0RE5HOZTCZKcu2U5NpZe9NVvH/0LG8fcLH/RCv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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": 622 + }, + "outputId": "c8199c08-50e7-4728-f100-cf94898dfdbf" + }, + "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 : 0.58\n", + " period 02 : 0.56\n", + " period 03 : 0.55\n", + " period 04 : 0.54\n", + " period 05 : 0.54\n", + " period 06 : 0.53\n", + " period 07 : 0.53\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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eTuFAYloFZCgiImI/VMzYIXcHNwYF9yOvKJ9v4n+47vWGYTCqZwgA0avjKdZG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\n+cncubCQaeOd172JSlEUpqZNpiR1Eic7TrPz/LtUtp2ksu0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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": 51 + }, + "outputId": "37434202-17c3-46af-b293-ed610f5e7c37" + }, + "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: 0.72\n", + "Accuracy on the validation set: 0.75\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "47xGS2uNIYIE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n", + "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n", + "obtain the true positive and false positive rates needed to plot a ROC curve." + ] + }, + { + "metadata": { + "id": "xaU7ttj8IYIF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "6c0bf775-2994-4045-92c0-2f0d3267bac3" + }, + "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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kr3tpbrtwHee7ZqeonEj0laq2GtblZnGupQQ/d1+Wpy1ieFjG1X9QDDhSwkL0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\nWEII4VSkhK/Do3/d67g9OEpK+FJsdhs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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "PIdhwfgzIYII", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n", + "\n", + "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n", + "\n", + "**Verify if all metrics improve at the same time.**" + ] + }, + { + "metadata": { + "id": "XKIqjsqcCaxO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "1620844a-23af-41e7-fcf7-0e43dd8b8d0b" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.59\n", + " period 01 : 0.57\n", + " period 02 : 0.56\n", + " period 03 : 0.55\n", + " period 04 : 0.55\n", + " period 05 : 0.54\n", + " period 06 : 0.53\n", + " period 07 : 0.56\n", + " period 08 : 0.53\n", + " period 09 : 0.53\n", + "Model training finished.\n", + "AUC on the validation set: 0.75\n", + "Accuracy on the validation set: 0.77\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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QWAghhDBVUswYGbVKzZyeM1AralYlr6WqvkrrtrZWZky/ozu19Y2s2pZqwCiF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\nWbhHKBGe4eSfLSKn8ZSNIhx9pJgRQohRLHa8iVefmcWz34y79oPFLaE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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": 656 + }, + "outputId": "21cf52ad-2adf-418d-b671-30cb86deaff4" + }, + "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 : 0.47\n", + " period 09 : 0.47\n", + "Model training finished.\n", + "AUC on the validation set: 0.81\n", + "Accuracy on the validation set: 0.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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hc9DLL/+dXbt2sGjRO7z33lv8+99LAXjjjVd48MF7uP/+O1m58lsAHnnkAT788D1mzHiQ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\nTIxvO/qXooDOLDv0FR/uX0JhaVE9RS0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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 50a82f090220978c9e2f4b8632a301336b54a9bf Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Thu, 31 Jan 2019 23:43:19 +0530 Subject: [PATCH 09/12] Finished part 8 (sparsity and l1 regularisation) --- sparsity_and_l1_regularization.ipynb | 1164 ++++++++++++++++++++++++++ 1 file changed, 1164 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..e2370bc --- /dev/null +++ b/sparsity_and_l1_regularization.ipynb @@ -0,0 +1,1164 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "sparsity_and_l1_regularization.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "yjUCX5LAkxAX" + ] + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Sparsity and L1 Regularization" + ] + }, + { + "metadata": { + "id": "g8ue2FyFIjnQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Calculate the size of a model\n", + " * Apply L1 regularization to reduce the size of a model by increasing sparsity" + ] + }, + { + "metadata": { + "id": "ME_WXE7cIjnS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n", + "\n", + "L1 regularization is a good way to increase sparsity.\n", + "\n" + ] + }, + { + "metadata": { + "id": "fHRzeWkRLrHF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and create feature definitions." + ] + }, + { + "metadata": { + "id": "pb7rSrLKIjnS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "3V7q8jk0IjnW", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pAG3tmgwIjnY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "d4c4aa8a-3d8c-4df2-97a6-e9c12535aa27" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2653.4 541.6 \n", + "std 2.1 2.0 12.6 2215.0 429.0 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1454.8 295.0 \n", + "50% 34.2 -118.5 29.0 2127.0 433.0 \n", + "75% 37.7 -118.0 37.0 3153.5 649.0 \n", + "max 41.9 -114.3 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1432.7 503.6 3.9 2.0 \n", + "std 1147.7 392.4 1.9 1.2 \n", + "min 6.0 1.0 0.5 0.0 \n", + "25% 785.0 279.0 2.6 1.5 \n", + "50% 1165.0 408.0 3.6 1.9 \n", + "75% 1721.0 605.0 4.7 2.3 \n", + "max 28566.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gHkniRI1Ijna", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bLzK72jkNJPf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_buckets(feature_values, num_buckets):\n", + " quantiles = feature_values.quantile(\n", + " [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n", + " return [quantiles[q] for q in quantiles.keys()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "al2YQpKyIjnd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + "\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"households\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"longitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"latitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"housing_median_age\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"housing_median_age\"], 10))\n", + " bucketized_total_rooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_rooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n", + " bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_bedrooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n", + " bucketized_population = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"population\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"median_income\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"rooms_per_person\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"rooms_per_person\"], 10))\n", + "\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n", + "\n", + " feature_columns = set([\n", + " long_x_lat,\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_total_rooms,\n", + " bucketized_total_bedrooms,\n", + " bucketized_population,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hSBwMrsrE21n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Calculate the Model Size\n", + "\n", + "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works." + ] + }, + { + "metadata": { + "id": "e6GfTI0CFhB8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def model_size(estimator):\n", + " variables = estimator.get_variable_names()\n", + " size = 0\n", + " for variable in variables:\n", + " if not any(x in variable \n", + " for x in ['global_step',\n", + " 'centered_bias_weight',\n", + " 'bias_weight',\n", + " 'Ftrl']\n", + " ):\n", + " size += np.count_nonzero(estimator.get_variable_value(variable))\n", + " return size" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XabdAaj67GfF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Reduce the Model Size\n", + "\n", + "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n", + "\n", + "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n", + "\n", + "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?" + ] + }, + { + "metadata": { + "id": "G79hGRe7qqej", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Task 1: Find a good regularization coefficient.\n", + "\n", + "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n", + "\n", + "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n", + "\n", + "Again, the model will train on the entire data set, so expect it to run slower than normal." + ] + }, + { + "metadata": { + "id": "1Fcdm0hpIjnl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " regularization_strength,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " regularization_strength: A `float` that indicates the strength of the L1\n", + " regularization. A value of `0.0` means no regularization.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 7\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on validation data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " # Compute training and validation loss.\n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9H1CKHSzIjno", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "outputId": "f07c190c-58cf-476e-9605-8540cd46530a" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=0.05,\n", + " steps=1000,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.26\n", + " period 01 : 0.24\n", + " period 02 : 0.24\n", + " period 03 : 0.23\n", + " period 04 : 0.23\n", + " period 05 : 0.23\n", + " period 06 : 0.23\n", + "Model training finished.\n", + "Model size: 791\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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FJMw0o+hRXfDzdOSHQ6nEJ9tGcGjvEsjkrvdRXlPOsrjPqDaqv8ifEEIIYQkJ\nM83IzqDlqbvC0GoUlq1PoKS8Wu2SABgc2I+BAbdz/uIFvju9Tu1yhBBCCItImGlmIQGu3BMVQmFJ\nFSs2J9rMlayndL2XIOcAdl3Yx8HMI2qXI4QQQjSahBkVTBjYgS7t3Ig5mcOe2Ey1ywHAoDXwZMR0\n7LV2fJX4HRmlWWqXJIQQQjSKhBkVaDQKsyaF4WCn5Yutp8i24iUZLOHr6MP0HpOpMlazJPYzKmoq\n1C5JCCGEuCEJMyrxdndg+phuVFbVsmR9ArVG9U/XBujj25NR7YeSVZbNl4nf2cxuMCGEEKIhEmZU\nNDDcj/49fEm6UMyGvSlql2N2b+gEOrl14HD2MX6+sE/tcoQQQojrkjCjIkVReGRsNzxd7Vi7J5mk\nC0VqlwSAVqPliYjpOOud+O70OpKLz6tdkhBCCNEgCTMqc7TX8+TEMEwmE0vWJVBeaRvrvLjbufFY\n+DSMJiNLYz+npLpU7ZKEEEKIa5IwYwO6d/Bg3IBgsgvL+WrbabXLMevu2YWJIWMoqCzk04SvMZps\n47geIYQQ4koSZmzEfcM6EeznzO7jGRw+ma12OWZjO46ih2dXEvJO8kPKdrXLEUIIIa4iYcZG6LQa\nnrorHL1OwyebEim4WKl2SQBoFA2Phk3Fw86d9Wd/IDHfdiZHQgghBEiYsSmB3k5MGdWZ0ooaPt6Q\ngNFGTot2NjjxRMTDaBQNy+O/pLDSNg5UFkIIIUDCjM0Z2SeIXqFexCcXsDUmTe1yzELcOnB/50mU\nVJeyLO4Lao21apckhBBCABJmbI6iKDw2oQcujnpW7UgiLbtE7ZLMhrcbzG2+vTlblMx/kzapXY4Q\nQggBSJixSW5OBh6b0IOaWiP/WRdPdY1tTEEURWFa9wfwc/RhW+rPHM2OVbskIYQQQsKMrYrs7M2I\nPkFcyCnlu51n1S7HzF5nz5MRMzBo9Hx24luyy3LVLkkIIUQbJ2HGhk0Z1Rl/T0d+OJRK/Ll8tcsx\nC3T2J7rb/VTUVrA07jOqaqvVLkkIIUQbJmHGhtnptTx1dxhajcKyDQmUlNtOaBgQcBtRgQO4UJLB\nN6fWqF2OEEKINkzCjI3r6O/KvUNDKCyp4tNNiTZ1FesHu9xNe5cg9mUcYm/6IbXLEUII0UZJmGkB\nxg/oQNf27hw+lcPu4xlql2Om1+p5MmIGDjoHvjn1PWkX09UuSQghRBskYaYF0GgUnpzUAwc7HV9u\nPU12QZnaJZl5O3gyM2wK1cYalsZ9RllVudolCSGEaGMkzLQQ3m4OzLizK5XVtSxZl0Ct0XYu+tjT\nO4w7O4wkpzyPN3e8R3yebe1RA0JuAAAgAElEQVQOE0II0bpJmGlBBob7MzDMj6T0YtbtSVa7nHom\nhdzJ7X6RnC04z7+Pfcxbh97nSPZxudK2EEIIq5Mw08JMv7MrXq52rNubzJkLtnONJK1Gy2Ph0/jb\nna9xm29vLpRksCzucxYeeIe96YeoMdaoXaIQQohWSsJMC+Nor+fJSWFggiXr4imvtK2Q0NGjHY9H\nPMy8gX9gcEB/8soL+CLxW+bve5vtqbupqq1Su0QhhBCtjHbBggUL1C7iVpSVWe+Ho5OTnVW3f7O8\n3RyorjFy7EwexaVV9Onqo3ZJZpd75qR3opdPGAMDbgcgqfAcsXkn2JN+gFqTkUAnf/RavcrV2gZb\n/ZzZMumZ5aRnlpOeWc6aPXNysmvwMZnMtFD3Dg2hg58Lu2MziEnMVrucBnnYu/NAl7tYOPhVxne8\ng1qTkXVnNzN371/5b9Imiqsuql2iEEKIFk7CTAul02p46u4wDDoNn25OpOBipdolXZezwYlJncay\ncPAc7g2dgF6r44eU7czb+1dWnlxDXnmB2iUKIYRooSTMtGABXk5MGdWZ0ooalq5PwNgCTod20Nkz\npsMIFg6aw5Su9+FqcOHnC3tZsP9tViSsJLM0S+0ShRBCtDA6tQsQt2ZEnyCOJ+VxLCmPHw+lMrZ/\nsNolNYpeq2dYu0EMCezP4exjbEnZzoHMwxzMPEJvnwjGdhhJsGs7tcsUQgjRAkiYaeEUReGxCT2Y\nt+wA3+1MIqyjJ+19ndUuq9G0Gi39/ftyu18ksbkJbEneztGcWI7mxNLDsytjO4yks3snFEVRu1Qh\nhBA2SnYztQKuTgYen9iDmloTi9fFU11Tq3ZJFtMoGnr7RPDy7c/xfOQsunp05kT+Kd775T+8e+Tf\nxOWekFWFhRBCXJNMZlqJXqHejOwbxPYjF/h2RxLTRndVu6SboigK3T270N2zC+eKUtiSsp3Y3AQ+\nOr6cIOcA7uwwkr6+vdAoksOFEELUkZ8IrcjkkZ0J8HJka0wacefy1C7nloW4deDpXo/yav/fcbtf\nJOklmSyP/5I39/+dPRcOUC2rCgshhEDCTKtip9fy1F3haDUKy9af4GIrWewpyDmAx8KnMX/gH4kK\nHEBBRSFfnvyO+Xvf4qfzP1MpqwoLIUSbJmGmleng78J9wzpRVFrFp5tPtqrjTHwcvZja/QHeGPwK\ndwQPo7y2gu/OrGfu3kVsOreVsuoytUsUQgihAgkzrdC4/sF0a+/OkVM57DqeoXY5Tc7dzo37O09i\n4eA5TAgZg8lkYv25H3h97yK+P7OBospitUsUQgjRjCTMtEIajcKTk8JwsNPx1dbTZOW3zomFs96J\niSFjWDh4Dvd1noi91o6t53cyb99bfH3ye3LL89UuUQghRDOQMNNKebnZM2NsVyqra1m8LoGaWqPa\nJVmNvc6e0cHDeWPQK0ztdj/uBld2XdjHG/v/xqcJX5MhqwoLIUSrZtUws2jRIqZMmUJ0dDTHjx+v\n99j+/fuZPHky0dHRzJkzB6Px1x+2FRUVjB49mtWrV1uzvFZvYJg/A8P9OJdRzPq9yWqXY3V6rZ6o\noIHMG/gyj4ZNxd/Rl4OZR/jzgX+w+PinJBefV7tEIYQQVmC1dWYOHjxISkoKK1euJCkpiVdffZWV\nK1eaH583bx4rVqzA39+fF154gV27djF8+HAAPvroI9zc3KxVWpsyfUw3TqcWsW5vMhEhXnRu1/r7\nqtVo6effh9v8ehOXe4ItKds5lhvPsdx4unl0ZmyHUXT1CJVVhYUQopWw2mRm3759jB49GoDQ0FCK\nioooKSkxP7569Wr8/f0B8PT0pKCg7qrJSUlJnDlzhhEjRlirtDbF0V7HrLvCAFi8Lp7yyrazNotG\n0dDLJ5w/3PYss/s8RXePLpwsOMMHRxfzzuF/cTwnHqOp9e5+E0KItsJqk5nc3FzCw8PNtz09PcnJ\nycHZue66QZe/Zmdns2fPHmbPng3A22+/zdy5c1mzZk2j3sfDwxGdTtvE1f/Kx8fFattuLj4+LjyY\neZFvt53mu13n+N3UvlZ/P1vj69uHIV37cCYvmTUntnDwwlH+E/sp7d0Cubf7WAYH34ZWY73P0Y3Y\nYs9snfTMctIzy0nPLKdGz5rtcgbXWu8kLy+Pp59+mvnz5+Ph4cGaNWuIjIykffv2jd5uQYH1ztTx\n8XEhJ+ei1bbfnMb0DeJgfCY/xaTSrZ0b/br7WuV9bL1nbngxs9s0xgSN4sfzO4jJOsqHB5bz1bH/\nMrrDCAb634Zeq2/Wmmy9Z7ZIemY56ZnlpGeWs2bPrheSrBZmfH19yc3NNd/Ozs7Gx8fHfLukpIRZ\ns2bx4osvEhUVBcCOHTtITU1lx44dZGZmYjAY8Pf3Z/DgwdYqs83QaTU8dVcYb3xyiBWbEwkNdMXT\n1V7tslQT6OzPzLBoJobcybbzO9mbcYivT65m07kfGRU8jKjAgdjr7NQuUwghRCNY7ZiZIUOGsGXL\nFgDi4+Px9fU171oCeOutt5g5cybDhg0z3/fee+/x3Xff8c033/DQQw/xzDPPSJBpQgFeTkSP6kJp\nRQ3LNpzA2IpWB75Z3g6eTOl2H28OmsOY4BFU1Fby/ZkNzN27iA1nf6CkulTtEoUQQtyA1SYzffv2\nJTw8nOjoaBRFYf78+axevRoXFxeioqJYs2YNKSkprFq1CoBJkyYxZcoUa5UjLhkeGcjxpDyOnsnl\nh4OpjBsQrHZJNsHNzoV7O0/gzg4j2Jm2l+1pu9mYvJWtqT8zNHAgo4KH4m7X+s8EE0KIlkgxtfCL\n91hzf2Zr3V9aXFbFvGUHKauo5vVHbifYr+kO1motPausrWJP+gG2nf+ZwsoidIqWAQG3MyZ4BD6O\nXk36Xq2lZ81JemY56ZnlpGeWU+uYGVkBuA1ydTTw+ITu1NSaWLwugarqWrVLsjl2WgOj2g9lwaA/\nMa37A7jbu7Mn/QBv7P8by+O/5EJJ67vmlRBCtFTNdjaTsC29Qr0Z1TeIn45cYNWOJKaN6ap2STZJ\nr9ExJHAAA/1v55ecWH5I2U5M1lFiso7S07sHYzuMIsStg9plCiFEm9boMFNSUoKzszO5ubkkJyfT\nt29fNBoZ7LRkk0d25kRKAVsPp9Er1IuITk27+6Q10Wq03O4XyW2+vYnPS2RLyk/E5p4gNvcEXd1D\nGdtxFN08OsuqwkIIoQLtggULFtzoSQsXLqSwsJCgoCAmT55MRkYG+/fvZ+TIkc1Q4vWVlVVZbdtO\nTnZW3b7atFoNXdq5set4BvHn8hnc0x87/a0tHNfae6YoCr6OPgwK6EdXj84UVRVzsuAMBzOPEJeX\niLPBCV9Hb4tCTWvvmTVIzywnPbOc9Mxy1uyZk1PDy2U0arSSkJDAQw89xKZNm7jvvvt4//33SUlJ\nabIChXqC/Vy4f3gnikqr+HRT4jUXNxRXUxSFLh6deC7ySf50+wtE+vQk9eIFlsSu4C8H3uVAxmFq\njXIskhBCNIdGhZnLP+B27NjBqFGjAKiqkrTaWoztF0z3YHd+OZ3Lz8fS1S6nxQl2bcesnjN4fcDv\nGeh/O9nluaw4sZIF+//Gz2l7qaqtVrtEIYRo1RoVZkJCQpgwYQKlpaX06NGDNWvWyFWtWxGNRuHJ\nSWE42un4attpsvKtd4mI1szfyZcZYZNZMPBPDG83mItVF1l5ag3z9v2VH1N2UF5ToXaJQgjRKjVq\nnZna2lpOnTpFaGgoBoOB+Ph42rdvj6ura3PUeF2yzkzTOXgii//7bzwhAS7MmX4bOq3lB3i3tZ5d\nT3HVRban7ubntL1U1FbioHNgRLvBjGgXhbPByfw86ZnlpGeWk55ZTnpmOZteZ+bEiRPmayX985//\n5G9/+xunTp1qsgKFbejfw49B4f6cy7jI2j3JapfT4rkaXLgndDwLB7/KXZ3GoVU0bErexty9i1h1\nei0FFYVqlyiEEK1Co8LMn//8Z0JCQoiJiSE2Npa5c+fywQcfWLs2oYLpd3bF282eDfuSOZUqP2yb\ngqPegXEdR7Fw8Bwe7HI3TnontqfuZv6+t/nixLekFKbJgddCCHELGhVm7Ozs6NixI9u2bWPy5Ml0\n7txZ1phppRzsdDw5KQyApesTKKuoUbmi1sOgNTCyfRQLBv2R6d0fwsvBg70Zh3h5y194Y//fWHNm\nI+eKzmM0GdUuVQghWpRGLZpXXl7Opk2b2Lp1K88++yyFhYUUFxdbuzahkq7t3Zk4qAPr96bw5dZT\n5nAjmoZOo2NQYD8GBNzG8Zx44ooSOJwey4/nd/Dj+R2427nR2yeCSJ8IQt06otXc2to/QgjR2jUq\nzLz00kusWLGCl156CWdnZz788EMeffRRK5cm1HT3kBDiz+WzNy6TXqFe9O/hp3ZJrY5G0RDp25Mx\n4YO5kJlPYv4pjubEEZubwM60PexM24Oz3ole3uFE+kbQ1aMzeo1cgUQIIX6r0VfNLisr49y5cyiK\nQkhICA4ODtaurVHkbCbrycwvY8Hyg+g0Gt58oj+ervY3fE1b79nN+G3Pao21nCpM4mhOHMdy4rhY\nVQKAvdaent49iPSJoIdXN+y0BrVKVp18ziwnPbOc9Mxyap3N1Kj/5m3dupUFCxbg7++P0WgkNzeX\nhQsXMnz48CYrUtgef09Hpt7RhU83n2Tp+gT+MLUPGrn2kNVpNVp6eHalh2dXpnS9l3NF5zmaE8vR\nnDgOZf3Coaxf0Gv0hHl1I9InggivHjjqbeM/F0IIoYZGhZmlS5eydu1aPD09AcjKymL27NkSZtqA\nYb0DOZ6Uxy+nc9ly8DzjB8gVopuTRtEQ6t6RUPeO3N95EqklFziWHccvl6Y2x3Li0Cpaunl0JtIn\ngl4+4bgYnNUuWwghmlWjwoxerzcHGQA/Pz/0er3VihK2Q1EUZo7vztn0g6zeeZbwjp4E+zU86hPW\noygKwS7tCHZpx12h48gszeJoThxHs2NJyD9JQv5Jvjq5ms7uIeYDiD3s3dUuWwghrK5RYcbJyYmP\nP/6YwYMHA7B7926cnJxu8CrRWrg6Gnh8Yg/++c0x/rM2nvmP9sNwi1fXFrfO38mPcU5+jOt4B7nl\n+RzLieNoThxnCs9xuvAsq06vpYNreyIvBRtfRx+1SxZCCKto1AHAeXl5vP/++xw/fhxFUYiMjOT5\n55+vN61RixwA3Hy++PEU2w6ncUffdjx8Z9drPkd6Zrmm7llRZTHHcuI5lhPHqcIk87o1gU7+dcHG\ntyeBTv4oLfj4J/mcWU56ZjnpmeXUOgC40Wcz/VZSUhKhoaE3XVRTkTDTfKqqa3nz0xjSc0t58aHe\n9Ar1uuo50jPLWbNnpdVlHM9N4FhOLCfyT1NjrFsE0cfBi0ifnvT2iaCDazs0SstaBFM+Z5aTnllO\nema5FhdmHnnkEVasWHHTRTUVCTPN63zWRRZ+GoOTg543n+iPq2P904OlZ5Zrrp5V1FQQn3eSozmx\nxOUlUlVbBXBpkb7wS4v0hbSIRfrkc2Y56ZnlpGeWs+lTs69FriXTNgX7ufDA8FC+2X6GTzYm8vwD\nPVv07oq2xF5nz21+vbnNrzfVtdUkFpzmaHYcx3Pj2Zm2l51pey8t0hdGb58Iunl2kUX6hBAtwk3/\nSyU/wNquO/u3J/ZsHkfP5LLzWDojIoPULklYSK/V09M7jJ7eYdQaazldeNa8SN/ejEPszTiEvdae\nCO/uRPr0JKyNL9InhLBt1w0zq1atavCxnJycJi9GtAwaReGJiT2Y//FBvt52mu7BHvh7OqpdlrhJ\nWo2W7p5d6O7Zhcld7zEv0ncsJ46YrKPEZB2VRfqEEDbtumHm8OHDDT4WGRnZ5MWIlsPT1Z5HxnXn\nozVxLF4bz6szbkOnbVkHkYqr/XaRvrSSdI5mx5qnNsdy4tAoGrp5dKaPT09ZpE8IYRNu+gBgWyEH\nAKtr6foE9sZlMmlwB+4fFio9uwktpWd1i/TFczQnltSLFwBQUAh170ikT89mXaSvpfTMlkjPLCc9\ns5xNHwA8bdq0q46R0Wq1hISE8Mwzz+DnJ1dUbqseHtOVU6mFbNiXQkSI13U/bKJl+3WRvlHkXbFI\nX1JhMmcKz9Ut0udSt0hfb98I/GSRPiFEM9EuWLBgwY2elJGRQU1NDQ888AB9+/YlLy+Prl274u/v\nz8cff8w999zTDKVeW1lZldW27eRkZ9XttwZ6nYaOAS7sjs3gRHIBYwZ0oLqqRu2yWpSW+Dlz1DsQ\n4taBQYH9GBI4AB9HL2qMtZwrTiGx4DQ70/ZyNDuW4qqLOOoccDE4N+lJAy2xZ2qTnllOemY5a/bM\nycmuwccaNZk5fPgwy5cvN98ePXo0Tz31FIsXL2bbtm23XqFo0bq0c2fSoI6s25vM2ysO8cjYbrg5\nyZkvbYWbnStDgwYxNGgQpdVlxOYmcDQnjhP5p9iUvJVNyVvxdvAyX1ahg2v7FrdInxDCtjUqzOTl\n5ZGfn2++fMHFixdJT0+nuLiYixdlf6KAu4Z05GxGMb+cyuF0aiEPj+lK/x6+cgp/G+Okd2RgwO0M\nDLidippKEvJPcjQ7lri8E2w9v5Ot53e2yEX6hBC2rVEHAK9atYq///3vBAUFoSgKaWlp/L//9//w\n8vKirKyMqVOnNket1yQHANsOo8nEwZO5fLIhnqpqI327+jBDpjQ31BY+Z+ZF+nLiiM1JoLSmDKgL\nP72864KNJYv0tYWeNTXpmeWkZ5az+csZlJSUkJycjNFoJDg4GHf35jlr4UYkzNgWHx8X4k9l8fHG\nRE6lFuJkr+PhO7syoIefTGka0NY+Z5cX6bt8qndRVd33bq+1I8K7R6MW6WtrPWsK0jPLSc8sZ9Nh\nprS0lE8++YTY2FjzVbNnzpyJvb19kxZ6MyTM2JbLPTOaTPx0OI1VO5OoqjbSp4t33bE0zg0fwNVW\nteXPmdFkJLn4PEez4ziaE0teRQEAeo2OMM9u9PaJoKd3Dxz19RdlbMs9u1nSM8tJzyxn02HmpZde\nws/PjwEDBmAymdi7dy8FBQW88847TVrozZAwY1t+27PsgjKZ0tyAfM7qmEwm0koyOJpTt0hfZmkW\ngHmRvkifCHr5hONqcJGe3QTpmeWkZ5az6TBzrStkz5gxg88+++zWq7tFEmZsy7V6JlOa65PP2bVl\nlmZfWnk4lvO/WaQv3L8LhloH3O3dcLdzxd3ODVeDi5wldR3yObOc9MxyNr1oXnl5OeXl5Tg41F2P\npaysjMrKyqapTrR6GkVh9O3t6RXqxfKNifxyOpdTl854GhAmUxpxbf5OvoxzGnVpkb4CjuXGcTQ7\n1rxI329pFA2uBhc87Nxws3O79NX119v2brgZXNFr9Sp8N0IIa2pUmJkyZQrjx48nIiICgPj4eGbP\nnm3VwkTr4+vhyMvT+rD9yAW+3XGGxesSOJSYLVMacUNeDh6Maj+UUe2HcrGqhCpDGclZGRRWFv3m\nVzEpF9MwFp9vcFvOeifc7X6d6NT7dWnSY6+1l5AtRAvSqDDz4IMPMmTIEOLj41EUhblz59rELibR\n8mgUhTtua0fPUC+WbzhhntJMG9OVgTKlEY3gYnDGxycAL3yv+bjRZKSkupTCivohp7CyiILKIooq\ni8guzyWtJL3B97DTGq4OOpcDkH3d7531TrJbSwgb0bhFHYCAgAACAgLMt48fP26VgkTb4OvuUG9K\ns2RdAjGJ2cwY2w13mdKIW3B5d5OrwYVg2l3zOSaTiYraCgoqiiiqLDaHnILfTHqyynIafB+tosXt\n0nTn2ru03HCzc0HXyLVzhBA376b/lrXwi20LG3DllOaTjTKlEc1HURQcdA44ODsQ6Ozf4POqaqsp\nujTV+e3urMu/P1eUwlmu/e+hgoKzwanecTy/nfS42blhr5MAL8StuOkwIz9oRFPxdXfgD1PrpjSr\ndiSxZF0Ch05k88g4mdIIdRm0enwcvfBx9GrwObXGWi5Wl9SFm4r6Qefyr4zSLPMZWdfioLO/enfW\nb47lcdI5yr+7QjTgumFm+PDh1/zLYzKZKCgosFpRou357ZTm6JlcTi8tZNrorgwMlymNsF1ajdYc\nOnC99nNMJhOlNWV1u7QqCq/atXX5dsaltXWuRa/R4dbQgcuXdm256J3lWleiTbpumPnyyy+bqw4h\ngF+nNDt+ucC325NYsv7SGU8ypREtmKIoOOudcNY7EeQc0ODzKmurrpjwXL1Lq7CyiKTCZEzX2a3l\nZuda//idS8fzdNN0wK7GWXZpiVbpumEmKCjolja+aNEijh07hqIovPrqq/Tq1cv82P79+3n33XfR\naDSEhITwl7/8hcrKSl555RXy8vKorKzkmWeeYeTIkbdUg2h5NIrCqL7t6NnJi+UypRFtiJ3WgJ+j\nD36OPg0+p9ZYS1FV8a9Bp6LwqsCTdjGdlOLU+i9MqPvibe9JgLM/gU7+BF766uvoLQcqixbNap/e\ngwcPkpKSwsqVK0lKSuLVV19l5cqV5sfnzZvHihUr8Pf354UXXmDXrl2UlpYSERHBrFmzuHDhAo8/\n/riEmTbM59KUZucvF/hGpjRCAHW7tTztPfC092jwOUaTkdLqMnO4KagopNhURFJuKuklGcTmJhCb\nm/DrNhUtfo4+BDj5mQNOoLM/nvYecvq5aBGsFmb27dvH6NGjAQgNDaWoqIiSkhKcnZ0BWL16tfn3\nnp6eFBQUcO+995pfn5GRgZ+fn7XKEy2ERlEY2bcdEVdMaU4tKWTamC4MCveXKY0Q16BRNLgYnHEx\nONPepW7CfuUy8xerSkgvySS9NJP0kkwySi/9vjSTw9nHzNsxaA11AcfJn0AnPwKdAwhw8sfV4Cx/\n94RNsVqYyc3NJTw83Hzb09OTnJwcc4C5/DU7O5s9e/bUW1E4OjqazMxM/u///u+G7+Ph4YhOZ70D\n3q53LQhxbdbomY+PC28/78Pm/cksXxfP0vUnOHY2n2cf7I2Xm0OTv19zk8+Z5aRnlrvcMx9c6ET9\nY3eMJiO5ZQWkFqVzvvACqUXppBalX3OXlYvBiWD3INq7BtLeLZBg90DauwbiaGj5fxd/Sz5nllOj\nZ822k/Ra69Lk5eXx9NNPM3/+fDw8fh2Zfv3115w4cYKXX36ZtWvXXvd/AAUFZVapF+QiYzfD2j3r\n18WbkMf7s3xTIocSsnjm7Z+YOroLgyNa7pRGPmeWk55ZrjE9UzAQrO9IsE9HuHTYTq2xluzyXPMk\nJ+PS14Ts08Rnn6r3eg87d/NuqoBLkxx/R58Wez0s+ZxZzqYvNHkzfH19yc3NNd/Ozs7Gx+fXg9pK\nSkqYNWsWL774IlFRUQDExcXh5eVFQEAAPXr0oLa2lvz8fLy8Gl7jQbQ93u4O/CE6kh1H0/lm+xmW\nbThBTGI2j4zrjoeLHEsjRFPSarQEOPkR4OTHbfQ2319ZW0VmaRbppVmkl2SQcelrfF4i8XmJ5ucp\nKPg6etcFnCuOx/Fx8JLjcUSTsVqYGTJkCB9++CHR0dHEx8fj6+tr3rUE8NZbbzFz5kyGDRtmvi8m\nJoYLFy7w2muvkZubS1lZWb2JjRCXKYrCyD5B9AzxZPmmRI4l5TF36YEWP6URoqWw0xro4NqeDq7t\n691fUl1KRkkWGaWZXLhikpNVlsMvObHm5+k1OvwdfS8dh/Prgcfudm7y91dYTDFZ8boE77zzDjEx\nMSiKwvz580lISMDFxYWoqCj69etHnz59zM+dNGkS99xzD6+99hoZGRlUVFTw3HPPMWrUqOu+hzVH\ngDJitJwaPTOZTOw8ms7K7WeorKqlV6gXM1vQlEY+Z5aTnllOzZ6ZTCYKK4tIL60LOZd3WWWWZlFt\nrKn3XAedPQFXnDZ++cBjJ71js9ctnzPLqbWbyaphpjlImLEtavYst7Cc5ZsSOZFSgKOdrsVMaeRz\nZjnpmeVssWdGk5Gc8jzz9KYu5GSRXZZz1cKAbgaX+iHH2R9/Jz/stAar1WeLPbN1re6YGSGa2+Vj\naS5PaZZtOMGhxOwWNaURoi3RKBrzIoGR9DTfX11bTWZZTr0pTnpJJokFp0ksOG1+noKCl4PnFQsA\n+hHg5I+fo49c1qGNkTAjWhVFURjRJ4iITp58simR40l5vL70ANNayJRGCAF6rZ72LoG0dwmsd395\nTfmlA42vnORkcjw3nuO58ebnXV4E8MopToCTP5727nLQcSslYUa0St5uDvx+SiQ7j6Wz8ieZ0gjR\nGjjoHOjk1pFObh3N95lMJoqrSn5d+O/yKeSlWaSXZtZ7vZ3WULeryrwAoB9BzgG4GJwRLZuEGdFq\nKYrCiMggIkLqT2mm3tGFIT1lSiNEa6AoCm52LrjZudDds4v5fqPJSH5Fgfk4nMunj5+/mEZy8fl6\n23DWO9W7VlWAc906OSAL5rUUcgDwdcjBX5az1Z6ZTCZ+vjSlqbCxM55stWe2THpmOelZnRpjDdll\nueYFAC+fPp5bkX/Vc13tnHHVu+J+6UrkbnZuuNu54m7nhvulq5E76RzlP0ZXkAOAhbAiRVEYHhlE\nRIgXn2w6YZ7SRN/RmaieAfKPkRBthE6jq5vAOPvDFZf/q6ipJLMsi/SSX08fL6opIrssl7SS9Aa3\np9focDPUhZ3LAcf9Uuhxuxx6DC4tdhXklkLCjGhTvNzseWlKpHlKs3xjIodP5tjMlEYIoQ57nR0d\nXYPp6Bpsvs/Hx4Xs7GIqaisorCy+dBXyYooqiym69PvCyiKKKos4W5Ry1enkV3LSO/4adgy/Tniu\nnPg4653kP1Y3ScKMaHNkSiOEaCxFUXDQOeCgc7h0HM211RprKa66SFFV8RUhp34AyivP50JJRoPb\n0CnaeuHGPOUxXDHlsXPFIFOeq0iYEW3W5SnNruMZfL3tNMs3JhKTmMPMcd3wdLVXuzwhRAui1Wjx\nsHfHw979us+rqKm4Kuz8NgAlF5/HaDI2uA0nneOl0PPbXVq/Tnuc9U5t6jR0CTOiTVMUhWG9Awnv\n6MknmxOJPZvH3GUHiJ6XydgAAB8tSURBVB7VhaheMqURQjQte509/jp7/J18G3yO0WSsm/JUFl+a\n6hRdFYDyKwqvOvX8SlpFi6vBxRx2rjyep+5r3e8NVlxBuTlJmBGCS1Oayb3ZdTyDlT+dZvmmRA6d\nzObRcd1lSiOEaFYaRWM+Y6rDdZ5XUVNJUVX9sHM5/FwOQikXUzlX3PCUx0HnUD/sGH475XHDxWD7\nUx4JM0JccnlKc3ldmriz+TKlEULYLHudHfa6ustBNMRoMnKxqvRS4Ll6l9blAJRRmtXgNjSK5ooz\ntn57inrdbTeDK/Y69U6ikDAjxG94utrzO5nSCCFaAY2iMS8qGEy7Bp9XVVvV4C6twspiiqqKSb14\n4aoFB6/koLNnRuT99HaNtMa3cl0SZoS4hoamNFNGdWGoTGmEEK2MQWvA19EbX0fvBp9jNBkprS67\nFHCu3qV1seoiOo06sULCjBDX8dspzSebEolJzObR8TKlEUK0LRpFg4vBGReDM+1dgq75HLVWmrbt\nI3qEsAGXpzQLnxhARCdP4s7VTWl+PpZOC78aiBBCtAoSZoRoJE9Xe373UG8eG98dgE82JfLuN8fI\nK6pQuTIhhGjbJMwIYQFFURh6xZQmXqY0QgihOgkzQtwE85RmQncURaY0QgihJgkzQtwkRVEY2qtu\nStOzk5d5SrPz6AWZ0gghRDOSMCPELfJ0tefFh3pdmtIofLr5JO+uPCpTGiGEaCYSZoRoAr9OafrT\nK9SL+OQCmdIIIUQzkTAjRBPydLVn9oO9eHxCj3pTmtyicrVLE0KIVkvCjBBNTFEUonoF8OcnB5in\nNPOWHWSHTGmEEMIqJMwIYSUeLnbMfrAXT0ysm9Ks2HySf8iURgghmpyEGSGsSFEUhvT8dUqTIFMa\nIYRochJmhGgGMqURQgjrkTAjRDO51pRm7rKDrN5+mrKKmv/f3r0HR13f/x5/fnc39+wmm2R3c7+i\nAgmBCKECAbRSqJbqT60FqWin5zBlnI7aKZ5xsEJ7rI4w1umIVi3W31H89ZAW+XnorypBBX8UiQlq\nIQQQSCA3cmfJhRAgl/PHhgW8oFE2uxtejxmG7De7m/f3M9ndVz7f9/f78Xd5IiJBS2FGZIRdOEtj\nNgz+/b/2seyPO1j/7iHaTmimRkRkuCz+LkDkSnRulmbimAR2HWpj039XUVJex5ZddUy+xsm8wjRy\nUmL8XaaISFBQmBHxo+iIEO688WqKcl2U729hc3ktuw60sOtACzkpNuYVplNwdQJmkyZRRUS+jMKM\nSACwmE1My0vkulwXn9aeoKS8jn8dbuOPDXtJiAlnzpQ0ZuYnERGml6yIyGfpnVEkgBiGwdgMO2Mz\n7DQd72FLeR07KhpZ/+4h/t8/q5k1MZkbJ6eSEBPh71JFRAKGwoxIgEqMi2TxvGu4bVY22z5p4N2P\n69lcVseW8nqmjHUwtzCd7GSbv8sUEfE7hRmRABcdEcL86Zl8/zvplO1vZnNZHWX7Wyjb38KY1Bjm\nFaZRcJUDk8nwd6kiIn6hMCMSJCxmE9PzkpiWm8iB2hOUlNWyu6qdw/UdJMSE873CNIomqK9GRK48\netcTCTKGYTAuw864DDuN7SfZsqueHRWN/N93DvHG9iPMHuqriY8J93epIiIjQmFGJIglxUdxz7xr\nuG1mFtv+dYz3Pqrn7bJaSsrrmDLWwbyp6WQlqa9GREY3hRmRUcAaGcoPp2fy/amf76u5KjWGuYXp\nFFyVoL4aERmVFGZERpEQi4kZE5KYnpfI/ho3m8vqqKhu51B9BY7YcL43JY2i/CTCQ/XSF5HRQ+9o\nIqOQYRiMz4xjfGYcDW0n2VJexwd7m/jLub6aSZ6+mjib+mpEJPgpzIiMcikJUfz0prHcPjubbR83\n8N7H9bz1oaevpnCsk7lT08hMVF+NiAQvn4aZJ554gt27d2MYBsuXLyc/P9/7vdLSUp5++mlMJhNZ\nWVk8/vjjmEwmVq9ezUcffURfXx8///nPmTt3ri9LFLli2CJDuaUoi5uuS6d0XzMl5XWU7mumdF8z\nV6fFMq8wjYlj1FcjIsHHZ2GmrKyMmpoaiouLqaqqYvny5RQXF3u/v2LFCl599VUSExO5//772b59\nO2FhYRw6dIji4mLcbje33XabwozIZRZiMTMzP5miCUnsO+pmc3kte6uPc7DuBE57hKevZkISYaFm\nf5cqIvK1+CzM7Ny5kzlz5gCQk5NDR0cH3d3dREdHA7Bx40bv13Fxcbjdbn74wx96Z29sNhunTp2i\nv78fs1lvqiKXm2EY5GbFkZsVR0NrN1t21fHB3mb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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "yjUCX5LAkxAX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "hgGhy-okmkWL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n", + "stronger regularization gives us smaller models, but can affect the classification loss." + ] + }, + { + "metadata": { + "id": "_rV8YQWZIjns", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "outputId": "ee1364be-e24c-44c8-f057-b5ab376c5201" + }, + "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": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.32\n", + " period 01 : 0.28\n", + " period 02 : 0.27\n", + " period 03 : 0.26\n", + " period 04 : 0.25\n", + " period 05 : 0.25\n", + " period 06 : 0.25\n", + "Model training finished.\n", + "Model size: 757\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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WI81MO2dno2Pm2J7UFLvhfLUHl8oy2ZOx39xlCSGEEC1GmpkOYFgfL3p3dSU7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\n2/v8c2yqXFy40s6FK+289YHCgqx4ihc4WDbPTmyMBBshpkL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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From ceb7dd69ab5314319f62678605654039bad6b154 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Fri, 1 Feb 2019 00:00:23 +0530 Subject: [PATCH 10/12] neural networks --- intro_to_neural_nets.ipynb | 1183 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1183 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..2a1dd53 --- /dev/null +++ b/intro_to_neural_nets.ipynb @@ -0,0 +1,1183 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_neural_nets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "O2q5RRCKqYaU", + "vvT2jDWjrKew" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eV16J6oUY-HN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Intro to Neural Networks" + ] + }, + { + "metadata": { + "id": "_wIcUFLSKNdx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n", + " * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model" + ] + }, + { + "metadata": { + "id": "_ZZ7f7prKNdy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n", + "\n", + "One important set of nonlinearities was around latitude and longitude, but there may be others.\n", + "\n", + "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly." + ] + }, + { + "metadata": { + "id": "J2kqX6VZTHUy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's load and prepare the data." + ] + }, + { + "metadata": { + "id": "AGOM1TUiKNdz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2I8E2qhyKNd4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pQzcj2B1T5dA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "0418e621-6195-4231-cfb0-70e08476898c" + }, + "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": 39, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.7 2646.9 539.2 \n", + "std 2.1 2.0 12.6 2173.7 418.0 \n", + "min 32.5 -124.3 1.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1460.0 296.0 \n", + "50% 34.2 -118.5 29.0 2129.0 434.0 \n", + "75% 37.7 -118.0 37.0 3148.0 649.2 \n", + "max 42.0 -114.6 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1433.4 502.0 3.9 2.0 \n", + "std 1162.0 383.0 1.9 1.2 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 786.0 281.0 2.6 1.5 \n", + "50% 1167.0 409.0 3.5 1.9 \n", + "75% 1725.0 607.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RWq0xecNKNeG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Building a Neural Network\n", + "\n", + "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n", + "\n", + "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n", + "\n", + "`hidden_units=[3,10]`\n", + "\n", + "The preceding assignment specifies a neural net with two hidden layers:\n", + "\n", + "* The first hidden layer contains 3 nodes.\n", + "* The second hidden layer contains 10 nodes.\n", + "\n", + "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n", + "\n", + "By default, all hidden layers will use ReLu activation and will be fully connected." + ] + }, + { + "metadata": { + "id": "ni0S6zHcTb04", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zvCqgNdzpaFg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural net regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U52Ychv9KNeH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `DNNRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2QhdcCy-Y8QR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train a NN Model\n", + "\n", + "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n", + "\n", + "Run the following block to train a NN model. \n", + "\n", + "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n", + "\n", + "Your task here is to modify various learning settings to improve accuracy on validation data.\n", + "\n", + "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n", + "\n", + "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n", + "\n", + "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n" + ] + }, + { + "metadata": { + "id": "rXmtSW1yKNeK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 911 + }, + "outputId": "293b6336-3258-47cd-8e22-d53a87598589" + }, + "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)\n" + ], + "execution_count": 43, + "outputs": [ + { + "output_type": "error", + "ename": "ImportError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mImportError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtraining_targets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_targets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mvalidation_examples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_examples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m validation_targets=validation_targets)\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_nn_regression_model\u001b[0;34m(learning_rate, steps, batch_size, hidden_units, training_examples, training_targets, validation_examples, validation_targets)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;31m# Create a DNNRegressor object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mmy_optimizer\u001b[0m \u001b[0;34m=\u001b[0m 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tf.contrib.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mautograph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbatching\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbayesflow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name autograph", + "", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" + ] + } + ] + }, + { + "metadata": { + "id": "O2q5RRCKqYaU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution" + ] + }, + { + "metadata": { + "id": "j2Yd5VfrqcC3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search." + ] + }, + { + "metadata": { + "id": "IjkpSqmxqnSM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 911 + }, + "outputId": "9a48945a-9f4a-4085-ea0a-86d9c80d48dd" + }, + "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": 34, + "outputs": [ + { + "output_type": "error", + "ename": "ImportError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mImportError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtraining_targets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_targets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mvalidation_examples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_examples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m validation_targets=validation_targets)\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_nn_regression_model\u001b[0;34m(learning_rate, steps, batch_size, hidden_units, training_examples, training_targets, validation_examples, validation_targets)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;31m# Create a DNNRegressor object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mmy_optimizer\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mfeature_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconstruct_feature_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/lazy_loader.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 54\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/lazy_loader.pyc\u001b[0m in \u001b[0;36m_load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;31m# Import the target module and insert it into the parent's namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 43\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_module_globals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_local_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python2.7/importlib/__init__.pyc\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m 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tf.contrib.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mautograph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbatching\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbayesflow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name autograph", + "", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" + ] + } + ] + }, + { + "metadata": { + "id": "c6diezCSeH4Y", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Evaluate on Test Data\n", + "\n", + "**Confirm that your validation performance results hold up on test data.**\n", + "\n", + "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n", + "\n", + "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)." + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 231 + }, + "outputId": "03682db9-03b2-477d-a6e4-1f2928298ee0" + }, + "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", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, test_targets[\"median_house_value\"], num_epochs=1, shuffle=False)\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", + "root_mean_squared_error = math.sqrt(metrics.mean_squared_error(test_predictions, test_targets))\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocess_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalifornia_housing_test_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mpredict_testing_input_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmy_input_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_examples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_targets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"median_house_value\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdnn_regressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredict_testing_input_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predictions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_predictions\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mroot_mean_squared_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_targets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'dnn_regressor' is not defined" + ] + } + ] + }, + { + "metadata": { + "id": "vvT2jDWjrKew", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "FyDh7Qy6rQb0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n", + "\n", + "Note that we don't have to randomize the test data, since we will use all records." + ] + }, + { + "metadata": { + "id": "vhb0CtdvrWZx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From cf1b6a5414c84c3d4a29e4076450310f188e44e4 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Fri, 1 Feb 2019 00:03:35 +0530 Subject: [PATCH 11/12] Finished improving neural network performance --- improving_neural_net_performance.ipynb | 1719 ++++++++++++++++++++++++ 1 file changed, 1719 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..31e9a38 --- /dev/null +++ b/improving_neural_net_performance.ipynb @@ -0,0 +1,1719 @@ +{ + "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" + ] + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "cellView": "both", + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "eV16J6oUY-HN" + }, + "cell_type": "markdown", + "source": [ + "# Improving Neural Net Performance" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "0Rwl1iXIKxkm" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n", + "\n", + "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lBPTONWzKxkn" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, we'll load the data." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "VtYVuONUKxko", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "B8qC-jTIKxkr", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ah6LjMIJ2spZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "f1f81004-7845-4161-a4f4-4a3dcaa2b884" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2640.2 539.4 \n", + "std 2.1 2.0 12.6 2156.9 420.8 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1464.8 295.0 \n", + "50% 34.3 -118.5 29.0 2129.5 434.0 \n", + "75% 37.7 -118.0 37.0 3149.2 648.0 \n", + "max 41.9 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1428.9 501.4 3.9 2.0 \n", + "std 1118.9 383.8 1.9 1.0 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "NqIbXxx222ea" + }, + "cell_type": "markdown", + "source": [ + "## Train the Neural Network\n", + "\n", + "Next, we'll train the neural network." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6k3xYlSg27VB", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "De9jwyy4wTUT", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural network model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "W-51R3yIKxk4", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " my_optimizer,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A tuple `(estimator, training_losses, validation_losses)`:\n", + " estimator: the trained `DNNRegressor` object.\n", + " training_losses: a `list` containing the training loss values taken during training.\n", + " validation_losses: a `list` containing the validation loss values taken during training.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor, training_rmse, validation_rmse" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KueReMZ9Kxk7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "c5e0c8ac-379b-4c24-eaa5-eeb7c33c52a3" + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 158.27\n", + " period 01 : 144.47\n", + " period 02 : 128.85\n", + " period 03 : 119.55\n", + " period 04 : 113.21\n", + " period 05 : 107.72\n", + " period 06 : 107.94\n", + " period 07 : 108.44\n", + " period 08 : 107.37\n", + " period 09 : 104.68\n", + "Model training finished.\n", + "Final RMSE (on training data): 104.68\n", + "Final RMSE (on validation data): 105.48\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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4/cGk5qbx5cFvyC/IL8ekIiIilYsKTBlzcbTjwUFNyC8wmLniMDm5JReTngFd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\npMCEQVYqdUk25zjiTMydnEGi3UzZMRdfVDRQc6q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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "flxmFt0KKxk9" + }, + "cell_type": "markdown", + "source": [ + "## Linear Scaling\n", + "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Dws5rIQjKxk-", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def linear_scale(series):\n", + " min_val = series.min()\n", + " max_val = series.max()\n", + " scale = (max_val - min_val) / 2.0\n", + " return series.apply(lambda x:((x - min_val) / scale) - 1.0)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "MVmuHI76N2Sz" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Normalize the Features Using Linear Scaling\n", + "\n", + "**Normalize the inputs to the scale -1, 1.**\n", + "\n", + "**Spend about 5 minutes training and evaluating on the newly normalized data. How well can you do?**\n", + "\n", + "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n", + "\n", + "Sanity check your normalized data. (What would happen if you forgot to normalize one feature?)\n" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yD948ZgAM6Cx", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "c16a2b47-96af-425c-ca17-557d512dd12e" + }, + "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", + " #\n", + " # Your code here: normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 234.69\n", + " period 01 : 220.86\n", + " period 02 : 185.73\n", + " period 03 : 132.01\n", + " period 04 : 115.75\n", + " period 05 : 112.18\n", + " period 06 : 108.04\n", + " period 07 : 103.04\n", + " period 08 : 97.31\n", + " period 09 : 90.81\n", + "Model training finished.\n", + "Final RMSE (on training data): 90.81\n", + "Final RMSE (on validation data): 91.34\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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TPEWVPJcdQjp16hSTJk3io48+cp6QO3fuXLy8vHj00Uedz2vRogVbtmwhIyOD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\nAUZEREQyzv8HmH4ZylN8YtEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "jFfc3saSxg6t" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ax_IIQVRx4gr" + }, + "cell_type": "markdown", + "source": [ + "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n", + "\n", + "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "D-bJBXrJx-U_", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n", + " steps=2000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "MrwtdStNJ6ZQ" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Optimizer\n", + "\n", + "** Use the Adagrad and Adam optimizers and compare performance.**\n", + "\n", + "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n", + "\n", + "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "61GSlDvF7-7q", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 911 + }, + "outputId": "be702a05-e015-4ce4-ce73-a4c42eb4109f" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 83.67\n", + " period 01 : 74.85\n", + " period 02 : 73.70\n", + " period 03 : 69.89\n", + " period 04 : 69.22\n", + " period 05 : 69.32\n", + " period 06 : 67.85\n", + " period 07 : 69.93\n", + " period 08 : 67.11\n", + " period 09 : 67.06\n", + "Model training finished.\n", + "Final RMSE (on training data): 67.06\n", + "Final RMSE (on validation data): 67.20\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 201.31\n", + " period 01 : 119.28\n", + " period 02 : 111.29\n", + " period 03 : 103.81\n", + " period 04 : 92.28\n", + " period 05 : 79.38\n", + " period 06 : 72.77\n", + " period 07 : 71.25\n", + " period 08 : 70.96\n", + " period 09 : 69.82\n", + "Model training finished.\n", + "Final RMSE (on training data): 69.82\n", + "Final RMSE (on validation data): 70.15\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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q1IVjx47yv/+tZsaMj/NtFxb2M7Vr1+GNN97m6NH9bN68BYD09HQ+/XQBWq2W\nsWNf5eLFC7z4YhCbNm3g5Zdf5YsvlgHw229RXLp0kSVLVpKens7w4YPp1KkLAOXLl2fevCUsWbKA\nfft2Exg4pFTPiSUy6eWk8+fP0717d7766isAjh49yosvvkhQUBD//ve/SUrKnaJ8xYoVDBw4kEGD\nBhEREWHKSKWqU50WANywkVmthRDiaZJbxOwH4MCBCDp06ExExC5Gjx7JkiULjL9vf3flyiWaNPEE\noHXrv3piHBwcmDLlbYKDR3H16mWSknQFbn/27Bm8vHJ/e+zs7KhZszbXr18HwNOzOQBubm6kpKQU\nuP2TxmQ9MWlpaUyfPh0fHx/je7NmzeKTTz6hdu3aLF26lPXr1+Pv78+2bdv45ptvSElJYciQIXTo\n0CHPVOSWysfTi3U7vierYgLJOj3aSg7mjiSEEE+ddl3rFNlrYgq1a9fh7t14YmNvk5yczP79e3Fx\ncSMkZDpnz55h4cLPCtxOUUCtVgEY76fMyspi7tw5rFq1DmdnFyZOfLPQ46pUKh6c8dBgyDLu78Hf\nzTI4LeIjMVlPjI2NDcuXL8fNzc34nqOjIzpdbnWZlJSEo6MjR44coWPHjtjY2ODk5ETVqlW5cOGC\nqWKVKrVaTYVkZ1TWmWyN2G/uOEIIIR4jH58OfP75Yjp27ExSko6qVasBEBGxB4PBUOA21avX4OzZ\naACOHDkCQFpaKlZWVjg7uxAbe5uzZ6MxGAyo1Wqys7PzbN+wYWOOHz/253ZpxMTcoFq16qb6iBbP\nZD0xGo0GjSbv7t955x2GDRuGg4MDFStW5O2332bFihU4OTkZ13FyciI+Pp4GDRoUum9HR3s0GtP1\n1BQ2W2ZBvBzrcoCbnEi5xusl2E48mpK0jXi8pG0sl7SNafTr15vBgwezefNm0tLSmDRpEgcP7mXo\n0KHs2bOTfft2YGWlxsWlAra21lSsaEfXri8wduxYJkwIpmXLllhZqalb9xk6duzAa6+9RMOGDRk1\n6lUWL/6MtWvXMmPGeZYvz71XpkIFW7p378jJk5G8+eZrGAwGJk78D9Wru2Fjo8HRsTyurrnrZWWV\neyraXaWYuM9pwYIFODo6MmzYMF566SVef/11WrZsSWhoKFWqVCEtLQ07OzuGDx8OwIQJEwgICKBD\nhw6F7tOU08qXdNr6u0lJhBydCWlaFvZ5R2a1NqGSto14fKRtLJe0jeWStim+wgqyx/qLe+7cOVq2\nbAlAu3btOHXqFG5ubty5c8e4TmxsbJ5LUJbOuWJFrFMqQXk9x4+dNHccIYQQ4qnxWIsYFxcX4/0u\nJ0+epEaNGrRt25a9e/eSmZmDdWkJAAAgAElEQVRJbGwscXFx1K1b93HG+seqGCqjUsGu8zKrtRBC\nCPG4mOyemFOnThEaGkpMTAwajYawsDCmTZvG1KlTsba2pmLFisycORMHBwcCAwMZNmwYKpWKDz74\noMxdkunaoBWr489yw+bOw1cWQgghRKkw+T0xpmBJ98RA7jC518M+QLEyMKPVRBydKpko3dNNrh9b\nLmkbyyVtY7mkbYrPIu6JeVKp1Wq0yS6oNAZ+2rfP3HGEEEKIp4IUMaWkqbYWAKfSrps5iRBCCPF0\nkCKmlPTp4IuSoyJZK7NaCyHE02Lnzu107tzG+CDXv/vuu/XGeY9M5dKlCwQHj8r3/p494cXex9q1\nqzh16vdCl7///hTu3ct4pHymJEVMKamorYB1siOq8skcjTxh7jhCCCEeg507w6hatRp79xa/YHgc\nsrKyWL9+XbHXDwp6iSZNmhW6fNq0WZQrZ1sa0UqVzGJdiqoZKnOFBPZcOEGb1s3NHUcIIYQJ6fVJ\nREefZsqU91i3bg0BAQMBiIz8lfnzP8XJyRlnZxc8PKpiMBiYMeMD4uPjSE9PZ8SIUQQE9OLo0SN/\nrutC9eo1qFSpEs2bt+Sbb74iLS2N4OC3OH78GHv37iInJwcfn/aMGDGKuLhYQkImY21tTd269fNl\nmz9/LhcvXuCTT2bTqFFjDh8+xJ078UybNpNvvvmKM2dOk5mZSUDAAPr2DWDGjA/o0qUbSUk6fv/9\nN3S6RK5du8qQIUH06RPAwIF9WbNmPf/97xxcXFw5dy6a2NjbvPfeRzRo0JDPPvuYkyd/p1at2ly7\ndpVp02ZSpYqHydtAiphS1K1Ra76IjeamzGothBCPTWLMTtJ0Z0p1n/aVGuFY9bki19m9O5x27TrQ\npo0PoaEfER8fh6urG8uWLSQkZDr16tVnwoQ38PCoSnKyntat2+Lv34eYmBuEhEwmIKAXS5YsICTk\nQ+rUqcfYsa/i7d0GgIsXL/D115uwsbHh+PFjLF68ArVaTWBgP154YQgbN35Dt249CAx8ka++WsWF\nC+fzZBsyJIgzZ04xYcJktm3bQmzsbZYuXUlmZibu7h68/vp47t3LIDAwgL59A/Jse/HiBZYuXcmN\nG9d5//136NMn7/LMzEzmzl3IDz9sZPv2rWg0Gn7//TdWrFjL5cuXGDFiaCm0QPFIEVOKvJ59Fq7a\nYaiYwN07CTi7OD18IyGEEGVSeHgYw4ePxMrKCl/fbuzatYPBg4dx69Yt6tXL7R3x8mrBvXv30God\niI4+zebNm1Cp1Oj1SQDExt6ifv2GALRt28444WPduvWwsbEBwNbWluDgUVhZWaHT6dDr9Vy5chlf\n3+4ANG/eisOHDxWZ9dlnG6FSqShXrhx6fRKvvTYCjUaDTpeYb90mTZphZWWFq6sbqakp+ZZ7euZe\naXB1rcyZM6e5cuUyjRo1Ra1WU6dOXdzdqzzK6XwkUsSUIrVaTcUUF5JcrvPT/v0M79/P3JGEEOKJ\n51j1uYf2mpS2uLhYzpw5xcKFn6FSqcjIyECrrcDgwcPyPLD1/qPYdu7cjl6vZ9GiFej1el55JSjf\nPlUqlfFva2trAG7fvsX69f9j5cr/YW9vT1BQoHG/KpX6z78fPphEo8nd3/Hjx4iKimThws/RaDQ8\n91zHfOtaWf01wXJBj5LLv1xBrf4r+4Ofw9Tkxt5S1syhNgCnM2SotRBCPKnCw8Po338Qq1d/zapV\n6/j66+/Q6/XExNzAxcWVa9euoCgKx48fA0Cn01GligdqtZqIiN1kZWUB4OTkzNWrV8jOzubo0SP5\njqPT6XB0dMTe3p5z585y+/ZtsrKyqF69BmfP5l5Ci4qKzLedSqU29uo8KClJh5tbZTQaDQcORJCd\nnWPM8qiqVq3GuXNnURSFK1cuc/v2rX+0v5KQIqaU9e7QBSVbTYr2rgy1FkKIJ1R4eBi9e/c1vlap\nVPj79yE8PIxRo8YwdeokJk16Cze3ygB06dKVQ4f2M27caOzs7HBzc2PhwoW8+uoY3n33P0yePJ4a\nNWrm6eUAqFevPnZ29owePYJdu3bQr9/zfPppKIMGvcjWrZsZPz6Y5OT8T/11cXHBYMhi6tRJed5v\n1aoNN25cIzh4FDExN2jXrgOffDLrH52Lhg0b8cwz1Rk1ajgbNqyjZs3aj236IJl24G9K4zHQb26a\nQ1alOwyzC8THp1UpJRPyiG7LJW1juaRtLJerq5atW3fyzDPVqVLFgzlzZuDl1ZIePfzMHa1EMjMz\n2bVrB/7+fUhPT2fo0IFs2PAjGk3p3bFS2LQDck+MCVTLrsxl7rDn8u9SxAghhCiUoii8884E7O3L\n4+johK9vN3NHKjEbGxvOnj3Dxo3rUatVvPLKa6VawBRFihgT6NG0DctunuZ2OZnVWgghROHatPGh\nTRsfc8f4x956a6JZjiv3xJhAs4YNIb08BodE4uPvmjuOEEII8USSIsZEKqW4oLLKZsv+CHNHEUII\nIZ5IUsSYiJdj7lDr6Hs3zJxECCGEeDJJEWMivTt2Qcm2ItVBZrUWQgghTEGKGBOxt7PDRu+Eyi6N\ng4d/NXccIYQQJrBz53Y6d26DTqcrcPl3363niy+WmTTDpUsXCA4e9cjbBweP4tKlC2zbtoWIiD35\nlvfuXfSIqT17cmfwPnz4EN9/v/GRczwKKWJMqEaOOwARV06bOYkQQghT2LkzjKpVq7F3b7i5o/xj\nvXr1pXNn3xJtk5WVxfr164DcuZ/69x9oimiFkiHWJuTv1ZYF108SaytDrYUQ4kmj1ycRHX2aKVPe\nY926NQQE5P6AR0b+yvz5n+Lk5IyzswseHlUxGAzMmPEB8fFxpKenM2LEKAICenH06JE/13WhevUa\nVKpUiebNW/LNN1+RlpZGcPBbHD9+jL17d5GTk4OPT3tGjBhFXFwsISGTsba2pm7d+vmyTZkygRde\nGPLnBJQZDB06iHXrvmPWrA/zZGjf/q+5k774YhmVKlWiX78BTJs2lbi4WJ59tpFx+dGjR1ixYinW\n1tZotVo+/HA28+fP5eLFC3zyyWwaNWrMpUsXCQ5+kw0bvmbXrh0AdOzYmWHDXmLGjA9wcXHl3Llo\nYmNv8957H9GgQcN/1AZSxJhQw3r14GwFsh0SiY2Lp7Kbq7kjCSHEE+fn6/GcTMg/2/I/0dSpAv7P\nFP3f7N27w2nXrgNt2vgQGvoR8fFxuLq6sWzZQkJCplOvXn0mTHgDD4+qJCfrad26Lf7+fYiJuUFI\nyGQCAnqxZMkCQkI+pE6deowd+yre3m0AuHjxAl9/vQkbGxuOHz/G4sUrUKvVBAb244UXhrBx4zd0\n69aDwMAX+eqrVVy4cD5Pts6dfTl4cD9eXi04evQI3t5tSU1NyZfhwSLmvqNHD2MwGFi27EtOnz7F\nxo3rAUhOTub99z/Cw6Mq06e/x5EjvzBkSBBnzpxiwoTJbNu2BYCbN2P4+ectLF++BoBRo4YbZ9zO\nzMxk7tyF/PDDRrZv3ypFjKV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JDw9j+PCRWFlZ4evbjV27djB48DBu3bpFvXr1AfDyasG9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "FSPZIiYgyh93" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "X1QcIeiKyni4" + }, + "cell_type": "markdown", + "source": [ + "First, let's try Adagrad." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ntn4jJxnypGZ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "5JUsCdRRyso3" + }, + "cell_type": "markdown", + "source": [ + "Now let's try Adam." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "lZB8k0upyuY8", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "twYgC8FGyxm6" + }, + "cell_type": "markdown", + "source": [ + "Let's print a graph of loss metrics side by side." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8RHIUEfqyzW0", + "colab": {} + }, + "cell_type": "code", + "source": [ + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "UySPl7CAQ28C" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Explore Alternate Normalization Methods\n", + "\n", + "**Try alternate normalizations for various features to further improve performance.**\n", + "\n", + "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n", + "\n", + "For example, many features have a median of `-0.8` or so, rather than `0.0`." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "QWmm_6CGKxlH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 + }, + "outputId": "b641f643-2efd-4afd-87e8-8f6f33c0e5c6" + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Xx9jgEMHKxlJ" + }, + "cell_type": "markdown", + "source": [ + "We might be able to do better by choosing additional ways to transform these features.\n", + "\n", + "For example, a log scaling might help some features. Or clipping extreme values may make the remainder of the scale more informative." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "baKZa6MEKxlK", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def log_normalize(series):\n", + " return series.apply(lambda x:math.log(x+1.0))\n", + "\n", + "def clip(series, clip_to_min, clip_to_max):\n", + " return series.apply(lambda x:(\n", + " min(max(x, clip_to_min), clip_to_max)))\n", + "\n", + "def z_score_normalize(series):\n", + " mean = series.mean()\n", + " std_dv = series.std()\n", + " return series.apply(lambda x:(x - mean) / std_dv)\n", + "\n", + "def binary_threshold(series, threshold):\n", + " return series.apply(lambda x:(1 if x > threshold else 0))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "-wCCq_ClKxlO" + }, + "cell_type": "markdown", + "source": [ + "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n", + "\n", + "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8ToG-mLfMO9P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "cb4332f0-6f85-41bf-c97c-122542ddcef6" + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " #\n", + " # YOUR CODE HERE: Normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 215.03\n", + " period 01 : 139.58\n", + " period 02 : 114.97\n", + " period 03 : 113.43\n", + " period 04 : 111.58\n", + " period 05 : 109.48\n", + " period 06 : 106.79\n", + " period 07 : 103.30\n", + " period 08 : 98.84\n", + " period 09 : 93.85\n", + "Model training finished.\n", + "Final RMSE (on training data): 93.85\n", + "Final RMSE (on validation data): 93.82\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ZyZQpUxg3bhxz5swJL9ebPXs2iYmJBz13bu6Bl00eC0lJHgoLyyJ2fjlyapvopHaJXmqb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\nzPz8Km6tqKG0MPc979LI+7umAWbVqlXs2bOHdev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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", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "b7atJTbzU9Ca" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Use only Latitude and Longitude Features\n", + "\n", + "**Train a NN model that uses only latitude and longitude as features.**\n", + "\n", + "Real estate people are fond of saying that location is the only important feature in housing price.\n", + "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n", + "\n", + "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n", + "\n", + "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5McjahpamOc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "P8BLQ7T71JWd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1hwaFCE71OPZ" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "djKtt4mz1ZEc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Dw2Mr9JZ1cRi" + }, + "cell_type": "markdown", + "source": [ + "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances." + ] + } + ] +} \ No newline at end of file From c7266b9b1511ba61876e97bf38d0b85e3a28c009 Mon Sep 17 00:00:00 2001 From: Adrija Acharyya <43536715+adrijaacharyya@users.noreply.github.com> Date: Fri, 1 Feb 2019 00:27:07 +0530 Subject: [PATCH 12/12] Completed --- ...classification_of_handwritten_digits.ipynb | 2415 +++++++++++++++++ 1 file changed, 2415 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..23264e6 --- /dev/null +++ b/multi_class_classification_of_handwritten_digits.ipynb @@ -0,0 +1,2415 @@ +{ + "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" + ] + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mPa95uXvcpcn", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Classifying Handwritten Digits with Neural Networks" + ] + }, + { + "metadata": { + "id": "Fdpn8b90u8Tp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)" + ] + }, + { + "metadata": { + "id": "c7HLCm66Cs2p", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n", + " * Compare the performance of the linear and neural network classification models\n", + " * Visualize the weights of a neural-network hidden layer" + ] + }, + { + "metadata": { + "id": "HSEh-gNdu8T0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class." + ] + }, + { + "metadata": { + "id": "2NMdE1b-7UIH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random." + ] + }, + { + "metadata": { + "id": "4LJ4SD8BWHeh", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "outputId": "986d6511-6084-479a-dd03-8b58b0362fe9" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import glob\n", + "import math\n", + "import os\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "mnist_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "# Use just the first 10,000 records for training/validation.\n", + "mnist_dataframe = mnist_dataframe.head(10000)\n", + "\n", + "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n", + "mnist_dataframe.head()" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ScmYX7xdZMXE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Build a Linear Model for MNIST\n", + "\n", + "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n", + "\n", + "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix). The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n", + "\n", + "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training." + ] + }, + { + "metadata": { + "id": "cpoVC4TSdw5Z", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " \n", + " # There are 784 pixels in each image.\n", + " return set([tf.feature_column.numeric_column('pixels', shape=784)])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kMmL89yGeTfz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls." + ] + }, + { + "metadata": { + "id": "OeS47Bmn5Ms2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n", + " \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n", + "\n", + " Args:\n", + " features: The training features.\n", + " labels: The training labels.\n", + " batch_size: Batch size to use during training.\n", + "\n", + " Returns:\n", + " A function that returns batches of training features and labels during\n", + " training.\n", + " \"\"\"\n", + " def _input_fn(num_epochs=None, shuffle=True):\n", + " # Input pipelines are reset with each call to .train(). To ensure model\n", + " # gets a good sampling of data, even when number of steps is small, we \n", + " # shuffle all the data before creating the Dataset object\n", + " idx = np.random.permutation(features.index)\n", + " raw_features = {\"pixels\":features.reindex(idx)}\n", + " raw_targets = np.array(labels[idx])\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8zoGWAoohrwS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_predict_input_fn(features, labels, batch_size):\n", + " \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n", + "\n", + " Args:\n", + " features: The features to base predictions on.\n", + " labels: The labels of the prediction examples.\n", + "\n", + " Returns:\n", + " A function that returns features and labels for predictions.\n", + " \"\"\"\n", + " def _input_fn():\n", + " raw_features = {\"pixels\": features.values}\n", + " raw_targets = np.array(labels)\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size)\n", + " \n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "G6DjSLZMu8Um", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, and a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `LinearClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + "\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create a LinearClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(),\n", + " n_classes=10,\n", + " optimizer=my_optimizer,\n", + " config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ItHIUyv2u8Ur", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n", + "\n", + "Stop if you get anything above about 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "yaiIhIQqu8Uv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "041257cb-c2fe-4eed-c10b-d4e03fcc7946" + }, + "cell_type": "code", + "source": [ + "classifier = 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": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.52\n", + " period 01 : 3.91\n", + " period 02 : 3.76\n", + " period 03 : 3.58\n", + " period 04 : 3.51\n", + " period 05 : 3.52\n", + " period 06 : 3.29\n", + " period 07 : 3.29\n", + " period 08 : 3.37\n", + " period 09 : 3.51\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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XMYQQQnQuJruOu2rVKlxcXJg0aRJLly7lqaeeAmDatGmEhYURFhZGZGQkc+bM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\negT6xo0bqampYfHixdTU1FBXV/e1ffEiRhQRZmV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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "266KQvZoMxMv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "lRWcn24DM3qa", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here is a set of parameters that should attain roughly 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "TGlBMrUoM1K_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=30,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mk095OfpPdOx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Replace the Linear Classifier with a Neural Network\n", + "\n", + "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n", + "\n", + "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class." + ] + }, + { + "metadata": { + "id": "rm8P_Ttwu8U4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "157d84cf-6fd9-427f-82d8-cb28f493fb74" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n", + "#\n", + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " periods = 10\n", + " steps_per_period = steps / periods \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", + " 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", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\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", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \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", + " 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", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \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", + " 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", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier\n", + "\n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.78\n", + " period 01 : 3.22\n", + " period 02 : 2.42\n", + " period 03 : 2.29\n", + " period 04 : 2.43\n", + " period 05 : 2.00\n", + " period 06 : 1.96\n", + " period 07 : 2.06\n", + " period 08 : 1.93\n", + " period 09 : 1.71\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Vz5K/7T0risLtgxYxNGAQaaUn+PjMF06sUgghhKNJODvZ2MFBRAXr2X+8kJyi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\nCWdhN85q5+q6Ji4UVnGhsNr2OXZbI8U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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": 326 + }, + "outputId": "b022cc93-c550-4dab-ed0a-a4c0d3d0d868" + }, + "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": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZfzsTYGPPU8I", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "qXvrOgtUR-zD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we verify the accuracy on the test set." + ] + }, + { + "metadata": { + "id": "scQNpDePSFjt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "EVaWpWKvSHmu", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "predict_test_input_fn = create_predict_input_fn(\n", + " test_examples, test_targets, batch_size=100)\n", + "\n", + "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n", + " \n", + "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n", + "print(\"Accuracy on test data: %0.2f\" % accuracy)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WX2mQBAEcisO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Visualize the weights of the first hidden layer.\n", + "\n", + "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n", + "\n", + "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n", + "\n", + "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained." + ] + }, + { + "metadata": { + "id": "eUC0Z8nbafgG", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1172 + }, + "outputId": "215c93b6-6659-433e-8484-21b73f19d72e" + }, + "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": 17, + "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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Ul+FhjJno9EyVx71rz/4d3yNEOfUlepKu7S6MOWMwGAwGg8FgMBgMBoPBMIaw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\n9FH50HKvuGa+ymulWp6YS9aYy3JUXtU66Ion6DZFHxtB4ef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L1PVSKvOoBslJXL+CdF3P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62Nh3ooE+M038KdOimOM1soae1EXI++oF1NAuRSNEbObhFxH3487MjhK9LxmevvtLTB/aN7ULvk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fti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DzC0pMrZAh6DTz07A0mjhsBzS79Xo65EVdDQ+8TBjvRijXSRvfTu2GJPu1+9EsYXyftTUel\nYS6/bCzm+M92bhB5ra3o15F6GSyj37vrPZH3yt/uo7PFiY/Q2yL9Eml9mv8F1rvMi9FPpb1M9j3g\nNr+RzELa2ueCH5dCNj4OFBaKvLsvwvXTxnqaHW/A9VZbXC+eM4jNKVv/jetjxBRp+21LxXjjc2FQ\nquxpwnvQnGb9gLIvHyny6ph1M+8vUfq57IWUukSuk66GnwM+honkes3Xoaipct/SuA992hp2YG/h\nHSB7KbQcw3j38sS2a1hWqsj78XP0vfhg1SoTcyvtCxfJdadsN45nQCr2ZTETpPVr1Tb0LuBrrrNT\navWrf4GVOu+V1G/ppcD3fT3t2C+4W+x7Q7LkvsiVOKpxDkNHyT1G+HCcq+DB6O9QzbT+RETn3Hue\niTe/ss7E/j4WO+A+9PlIZX0R2wpkD8HQseivEXUe7FM9WG89ay+HYH+Mvwa2Lr7z8MMir51Zm0cP\nwXFNuihH5NXuxvzA+zkGBmaJvEonemZV/Ii9e8rlcl5z9zy7f8dtYf07fCPltRhqw/VXtw29RyIs\n9sreIWz+WYF14kSl7AtzzmysXXxMj756rMir34U+Fdymne+hHQ2yZ1RHFXqPlNbjO/Wv2ivyhqTh\nHqezFvcaPj5yDEcOxdpsL8LaumfZLpGXOR7Wy+2nMHclLZbnu2Efu4+bSC6F937xT5b9OuyHsHbx\nvmwebXKeDGW9adpZb8uwjAiRx3vu8PHC1yciorLtxSZOzMV44f0reV9FItk3M2fpEnyHOnnMU6Zh\nP9PRgXEZGSnvPwsPfGJiNzfMKb29cuyEJrL+a+weJjhTfnd+LM8GvJdTT4u0tK4qwOdKn46+MEEZ\nci/A7aV5nxprX5hRc7EXvfl3z5u4plnul9xZE1RucT3qXIzv2GmyJ1A364E34ZEHTNzSIvf8kVk4\n7vwaC4mW+2S3aIxVDw+MOWuPrN4u/Lv6Z8zD0efLtb7vt9v9EpFWziiKoiiKoiiKoiiKogwo+uOM\noiiKoiiKoiiKoijKAHJGWVPSgiEm5ragRETlX6NENn4aSiBtFmvl8dEoIXRzR2nS5i92irwJF6L0\n2ZaM0tyE7AtFnocHytaqylGyHZeMsn2nU0ptmprwXm0VKPmLGiZtBQu/gX1gA5PDTL1lqsjzZSXQ\nHXUoY3R2SgvJYxtxjKY+gBI4D29ZyselNyTd1VxCE7MsTpw/RDzm6YfPUvotSlzjzpcSIE9Wpu3J\nbGt5OTORlDl5+EMO5qiQ0oyIkbAcrN8vJXP/oeWELMOPYmXUp5dBLpN5o/yNkdug97Qh7mV260RE\n/hEo1++yQ7aVOE8eo15Wlld7DGV9rcWynNlaGu9K9ryPMXzuH6QN/XnZuP76+3E+Es85R+S1t6Mc\nNzgY0iNuhUxEdOLT70zMy+lv+vsNIm/TM5AOffVHyArDbLAmPPfBmeI53Dr2kjm4rk5vkHKYfUy2\n8fD8uXigT1pIdrDPfuBV2C6HT4gXeSNTUVL4wpInTHzx1Aki750PId26c9kycjWzHoO07Ms/SYvh\nG56FRWn+B7Asbjklx9m6FZCgLP/7kyZ+/NJ7RN7Hn/3FxM8+/C8Tj79JyiL4NVK0GnMAL70ff+U4\n8RxPf8wHb933gYkXLjhX5C1+9SkTXz4BMqtZI6XU5dIXrzPx5vmYh/N/+kTkBWVCZnH5hb838TPX\nXCPyMqdeTmeL1Ish+znyyT7xWEgASvCLv8f1NmTpKJF38iPMX437UKZ8rExahi56EGP/8Md4r6su\nkqXTGVejRr21ElIbXjLPJbhERLVMvjnqfMgi3L3kfMpL/7uZvDXIIpuxl0KWFDYG87tV5rJ1H/LO\nY1KE9BvkmNj/d4zzxJcuJVfDbW9LLdbBfO3yYta7tkRpWRyUjvFYzyQDEWPk/FPHrGDTUmGJvm+1\ntH9etRPz/ENLIVcdHIfPymVmRETcfD1+Ksq8IyKmiTxHJqRVTUxGYj9QJfLampmkuwdzuZuHm8jr\nYDa9znbsfXyjpByZW5WmSLWMS6ncUCT+zWURnswGvLdD7tO43Cg6BOc3eqK0te/rwT7AUYZ9jleI\nlD8lTsGa4nSiPN9+Enugkl3F4jljbsOcHMPs0N0scqLMc1PwPVj5fECA3K9Fsem6LQHnumTXDyLP\nn80JidOwH3Y0SXnl7lc3m3j+y/PJ1QSm477B+p0DMtg+jc1n7aVS+nDiW7Qp4K0IMmKkVCh1FvZF\nbm4493n/+lbkHTmO8TSNSUBPn/r1/SoRkRfby/N9Wfi4OJnHxiO3Rq49KlstdLDH+P3TsJlSelq3\nG58p5VLINJry5HkMHy3nJVfizq4jOoNig8+79XvlsXSwvXYT2/9HjpXHrzUfe6LkyzDnFX4oJStZ\nS7Du8nXMzRNrEj8XRHK9Kty42sQhQ+XNmY8P5DXu7ngN3mKDiMjTX7bFwHPk//M2Hd3d2BNUbpAS\n5qhzpLTW1fT1YI/NrdeJiMIbIBWq2lps4n6nvLfyDkNLi+IfsA/i+38iop3fYE8zMxfzz7AEKVl0\nMDnnkOHYyyfPRnuL9jq5jtmiIV3jvwEUrZRr7rjbHsL7NGE8Fm9aJ/Ii2ZpevRP7Bes8FJKFcZF1\n1ywT5735o8jrsNx3WdHKGUVRFEVRFEVRFEVRlAFEf5xRFEVRFEVRFEVRFEUZQM4oa/LwwcOlq06I\nx9KuQwlSEetEnjArU+SV/wi5gpN1fp44W5Z5J58/3sRubigrqz4lS4uC4lDS5OGNUrKWFpQDdjiK\nxXNCw1DyXfT5uyb2DpKdvX2Z3CgjDiVRflE2kRcYgi7VvV14355W2dn6vMcgyTr5TxyjxEtkSSJ3\nNTobtBxDeaBfrCxt941AqWTSApRDkqWTdJcd5WhuHvhNj5daEhH5RuNYtZxAGXVItsWxgT2v+Sjy\n/OLwfFuaLN/m8DLRqo2y7C95LkrdvL1RLtvb2y7y7MVw5/KNQCl204lakce70KfNheSpYa90AUi5\nVHbGdyXnPgJJiJ9/sngsgLlKPH3l4ybu6pHl23c/hc7z99yPvNe+fUrk/fwEXEK25zNpxs1jRF7m\nZJRShx5ESWEdc5u4d/Gz4jm3XXCBieNn4fkr794s8g4WoaTYzQ3jrblASt0W34hrbMW7KBu86ZJh\nIm/qn+81cW4LJENfPfKFyJtzn5RRuprfX/GCiT/Y8pN4bOkUvPeba/5m4ovHXCnyfjm228TVpzE/\nDt0kS5aPrUSJ76Mv3mziBkspce5SOFR9yaSi89h88MUr34nnXPvcFSZOY93z0xdMFnkXZmPu/WL7\nP018+lPpfNDtQJnyXUshUf39I38XeQ8x16m1R3828UUj54q8yyvwHW//97/JlRR8jTl/1M3S8oI7\nDuXci7Hu5SXLg3PvR5l8+XpInKbPlB39y1bh+lu+BU4+T5x/s8irP4I5MJC7K7E1rmSllO74xmKu\nTZwO+XFLpZSglq2E1C33fpx37lhARBSdAUmb0wlJyaqH3xB5Vz55iYm5PK63S85XY+6VTo2uxlEN\nWU6SZb6oXIu1gX/GPqeUVXLJK5f9WMv1uWNR4wGsG+kJsSLvpVshHd1zDHun4BQm7WDSMiKiwfMg\n4XM48JyODlnmzSXIfmyd7qxsFXnu7phvgwZBYuPhI+VpXOruw8rYG/ZYvnt2FJ0t+GdIuEBKe7wC\nmRSbnUMvm5QxbHwO88jYJdiHFq6S10samw/zNxaYeNrj80ReXx/2Ss5uHNtm5gJzrLxcPCfsM8wb\nhwqx9p0zZ7TI84vEeav4CZ+hr1s6LnYzmTbf8wUkShedZrZHK/4Bbn8RY+VaMnShdINyNa2nMf+H\n5si9oh+bp4KHwLmm6sfTIo9LH4ZdCqeVht1yPOZ/stbEfIzkn5SSUt7aYNNHkFiWMKfBK26cJZ7T\ndAAyop4eyM5a8qUjUNQUSFO4m82xr6QsJyYR37e2AsfIKokIZXJa7obnnxgk8go+xN4n+bnLyJVw\nByV+j0BEFMgcYzvqsA/vrJZ78k7msOPpgfnG13IP1tWIPO7+FDU9ReT5R2PdbTiKcdDTjOd7Bcv7\nwPrtGAchzAGu/IcCkdd9Dl6Dj1//eHnMuxpwLXLJkNX9LiQbbmFBQZD4+sfLcdnTemY5zH9LAHPM\n7XXINZm7FfJWF91NnSKP399F5eJeLTlmkEirWV9s4hl3QardfFy6efL7Si718/DAuA+Jk/fVbXa4\nz9nZ3Ltv13GRFzQErqnBzBWMy5mJiDrZuhvK7me5SxwRUewIrCE9PZA8jbr/OpH3y5/kvsiKVs4o\niqIoiqIoiqIoiqIMIPrjjKIoiqIoiqIoiqIoygCiP84oiqIoiqIoiqIoiqIMIGfsOdNWBr2ULU1q\n5mt3QNcYMQ761Mp1UgfK9Wv1W6GdCxoq9VxtDXisvRzva+3jcuqTwyaOYu/rEwGtp7+lr0rhFvTQ\n4PrEoEhpBWpLLzaxnVlNevrInjDe3tBQR6dOM7HTKbXbDeWwCYuYAlvG8tVSu2hLx7GNPQsOsEnM\nas4rQNp4txRBK9nDdINBgyJEXuQgaCB5/4TC4u9FXhCzRAwZBOu5mu0lIq+O6Tq57Zo/Gy9VzOqV\niKi8AbrdxAh8vgCLRWx/P8aM04m+Av7+KSKvKxo6RH8bbOzcs+Rvlo4anNe6Hfjc/glSW1q9BZ83\nVrYJ+a9pK0MPhw7fNvEY7/GSnQRN6OWvPiryjq1YaeKLRkPLHhYmrZVnPo7jl/4ZrjHvgDCR5xOB\n87FqN/qg3PkH9KVIiJbjKHEhdKG/uxRWzx9u+ljkrXkc/Uk2Pvm2iSc/eoXIczRjHpo3G9+j6NMj\nIs99Cc5pZyN6AnCLWiKi5JxL6Gzy9Jt3mTjv6w/FYx9u/cXEVcXoR7Psc9kTqLsbetxn7viHie95\nSA463o/hy9dwnRbWSHvNwC/wvpdNh81oSwHO75WPLRTPiUqEPvjSl9Dr4ZtH3hJ5b7xyv4mbTqPP\nwp/e/kjkrViyDJ/v6HoTP33/9SIv+ypYZre0wBLxb3+9T+QFW+YvV9LP+nHxtYqIKJr1jCn4AD1i\nht04R+T19UHLHZINXXtnnby28yvRn+SmGTOQVyvzMhbAsr50E/oj9PdC4+6fIOdJTu0h6LBDh8me\nD6N/jz4o3t6s50OhtOVtr8Q8VPYTep/Ehsi9g7MT373xEPqiFO8sFnkTHpJ24a6GWypXrDkpHos+\nj/W2Y9aqfL0kIqrbxPZBk2H/ae2R0N0EvbqT6fjDJ0nL0NqNWCdHpeIzBA3GcU+dKK2M+/txjkNC\nYONcfGiFyAtORX+bXS9ifkkcJ61ZbWwNdzowh9Rtlj1sIs9F77N2tlfsqpF2qW2B7Bo5h1wKt8H2\ni7RYeOdhfe/vw/USMkRa4np74jX463l5yB47p75Cr6m00SkmLl8ne9MMv+xWEzf1o4/L8X3oC+Xj\nJW101x3CvnZ8Jvo2elr642x+EXN1UgrrY2Tpf8et4MvZ3iv75nEiL34S/l2ydquJO2plL5DKn9C/\nYZCLzyGRtJ9tYxboRLJPh5OtaYkLh4g8Yg7Gp7/BOYkeLtd4D2/sBfjcO7hWjtv0Zhxf3lsxxB+9\nto78JK2vD5fgWN/0J2zmrT05uurxXlGTcP3l7zol8jbsRA+aKVnoeRQ4RO7FTm7HfZdH5W//zZ33\nk3I1vOdM22k5Tworezb/eYfLe6tI1ounYSf2C/W7ZI8mfs/gqMK6E5Qmj0t/P/r+hA7BGON7QN5T\nzUrtFszvPpYepUc/xP1dtxPvk5Qt5/TmQhyLRNaTtbNGruGn2pbjH6wfjS1Rrp812+S9lKs5+R2u\nnayrRorHqtejHxbvydJkWRdDUnEe2stwfpLOl6+XOHaaiY9/9pWJBy+WPQSrDqFHIbc6d3PDfM3v\ny4mIij7DhJBxHXrczhske4TZj8K2vCkfa8bpH/NFXu7tsr/gf4jMkr1uWptwf8/nDd6H738+u+zX\nakUrZxRFURRFURRFURRFUQYQ/XFGURRFURRFURRFURRlADmjrKlkNeyz068YLh7zskEe46iG7CN0\neIzIazmJ0vj4i2Gj1Wkpm/T0QxkmL8W22lgX1aLsyLkDFnTZt8K+qrXYLp4TPhylch01eN/SLVvp\ntxhyE2yD+/uldVnx7tUm5jbYvmHSWtQ3FJ+9rVSWNHGiJiX/5mOuwH4EZVvWcn9+fB3lKD8ji81b\nTw/K1risImHSeJHX24tSvbojKO+yH5ZSivDRKLFuL0HZ86FvUMaZmintHCtOovSclwUPGhwu8uwn\nUeLLv197o7RU5GXtIWEodXYPkiXHJV+jPM4rGKV87cXynHqHyrJHVxI+CNeOu7scZ5PuwbVYux1l\nmJue/IfIy70bNscjrrnRxHb7bpHXXonzkXML7Bbb26UcLyIHZfcPvgZr36qfUZo76v5rxXO6uzGO\nnnnmNhN//6iUw3AbxZhsjJUTH0j76RVrYCH60vewga4uk5KLwmWwK+ZSggmP3SbyDi5/E599iZTK\nuAJelv3MX6WsaeZP+IzX/QOW29dfc5HIs/linMWFoXzUas2YOReloffNW2Tiu2ZdJfLOGYLycJ8Y\nSAM6KzCvX/+0HEvDkt41cXk9JKAr98o5lZducjnQ0MTlIq+jldkL52I+vOaeJ0Tel8xSsbeLWZXm\nSevFxn2QYCQ/vZhcyaD52SY+sUrK58Y/MA2fiUljHa3SnrroY2mZ+h/8LNKjpnasVzYfzD0h7aEi\nr6UG1yaXEh/9GNapCSMTxXOiWTl9eCwkgRVH18q8Mcg7tQtytJgsOffX7Vlj4iBWih2aK/cE9cza\nNmU+yo25xIDof1t1u5r2Usxzseeni8eamPWmJ7Pb7aqT0oekS1HS7OzEeOQyJiKi0CGYw/iatP3v\nm0Uel9JEheMY+sf9tiStuRnSmfqDKP/3tEiYG49BkpswBmOBW70SScmAuze2iNEzpM170xGs6dzq\nNPp8mecf/duf/b+l/Qz7qpNrsX+NHwZpy+EfDos8f3ZddbdA0sv3dkREnm04FoEZmHft+y2W5cw+\n1WbD+IgIxHFIS5RjvYdJPWxpuLZ9I+Q1cMGTkHV+cDesWLmsgoho4mDsF6KYTLHWIonoSMccz8dL\ncIbcUzVa7NFdDbdpDx8p7eW7me2xm6pTSYAAACAASURBVCf+nlyyIk/kFVThPExciP27u6+8zWk7\nhT3IyY8gjc26Q85n/L4mMAn75vhy3NM0F0iL7Ak3Y4/F1/ro0VKC1d2Je5SdL20w8bBzZd7Rj3HN\nrmD3Kwu6pDxt/O3QmtVsxTmOPkfeW3RZ5iVX4sdk/n7R8r6Nyzt4awl+H0kkJWx8r83/n4gochz2\ncH7hGKtdLfLezyMQ10/lZtw/hAzFfj98krzP8GH3cVxOdWi/lL6eYuNt/lTISX0sUq2ek7hPrfkF\nsiCb5b6lpxlzD5eF9ctt3a/8h2vhcwlf04iIIiZh3eCW42mXZos833Acw5KVkEm111eLvJpSzNFc\ninnwFSnJbWnHuuvN5ab9G/HaJVJiHjcbErI9LyHPKleNGAo5FLe1H33vFJGX/zbW2ciJGH/uI+Tr\ndTDJuf0gxkig5d575G2/LpMyr3vGRxVFURRFURRFURRFUZSziv44oyiKoiiKoiiKoiiKMoCcUdaU\nefUIE1etKxSPRU9D6Sovbw3MlKVa3Y0oo7Mz56WI8bLEuvBDlPTHnA/nnLYiWaZ24cOzTNzKumB3\nt6D0MTw7RTyn8RjK/IIG4fPxEiYioqix+E6+vih1q80/IPICk1Fu3HQC5fR9PfL1QpJRUujhg0Od\ncoUsAcv7x04Txz0nnRhcQdR4lKXbT9SKx4LTcTxCWcf8pnwpE/AZjzJFf3+UgPNu2UREfn74zo5E\njIvwMbJjvoOVlJcVotStlEkkHF3SqYs7NP18CLKA9BzpNmE/iNcLmI3y3i67LEkPT88xcfGO70wc\nliXLapPmozS5aAVkDLw8k4iot1Oef1dy+FW47ew9LR3RcphDU9YtKHfd9stBkTeSlZbW10AO9O69\nUl7z8CfvmPjHR581cXSadLl473PIGKbn4FiOuhxOUGV7NornbF2+w8QLnoO8JuVEvcjrZPMGdw2y\nFnTOHonu7wc+hCSJl8QSEY1/9F4Tf/cw3I+2fr5L5LV1Yh4ZtYRczup/QjKSEC7nyhHD4XrU1IQ5\n4eEHrxF5mXPQbT7vky9MHDsxS+Q9ffndJr7yOsyb10ydKvLKmAvav5ZBsvnoW7eb+Id71ovnHHwL\nY2bjLlyLc0fI0vA333vExA7mbHTVxdKJh883MWxt+fCvfxR5LWycOBog+fnlsJQqLLhgMp0tuFwp\neXyKeIy7qsWdj/NpC5aymfh5GGddzDmCrxNEROMK8Bo1zTh+ocOlo1J7BSSpXAIz4SE4PAUFSacE\nb29IM2prIBdsOSWdF463vGfitkKsx40HVom8qpOY7xOZhCpiSIbIix+BcuHCdXjfSoujX+ZizCkR\nZ8F8K3wU5nm+jhMRhY/EesX3FlY3kDpW9s7nnPARFmlGK3MH+Ral3CV18n0DmMQmdTyuA+5g0+dc\nJ57TWY/rgDu08XNFRNRYhOvcm8mC4y+U56eOOZQEDcOBb7e8XnA2c9hhj7XkS6lH4jzpiulKirdB\nJpCdLMf3hAfPM/Gp9yHva+2UzjmjrxhrYi6pb6ltEXnRuRgTLcdx3lIuk/s5pxPXqb0a85Inc8px\ns+wdvD3wWCyb/6xy+N5ejKMxQ1G2HzFROsR4h0D62noK5yNpppTDNJyCVKNuB/YVbUXyfWNmptHZ\npPJHvHd9vXzvEUtxfrhbpqdFEsNlY2s+3kS/xSWPXGzi3g7INhrzpPQ+dQrWzL4+jJmQbBxDNw8p\ns/aNgCzYJwnXh6enlPn0dOM7Hi3DdwoOkI5jo9Nw3Ht6sb9MXTRM5B18F/sYLl89+ZHcA2awezpX\nE8BkTY0HpXyFO1LV74VEjrcWICKKnpJiYhuba/uccm/t7oXrxdsb+6j+QHnPYD+Bz+HN3IUa2Gfg\nchorjSXMacmyXwtk8vLyCswH8b1yl8rdMQP9IHkaEi/HRAe7JwoZgfXdYXGE5M5XZ4O4JOzzrZLN\nYHaP2MUcyCq/k5Kv4BHI4y6RvH0JEVHECIxVHx/m+hwtWy2EtGNd42uwF3PQSj5fzoEl67GHThyP\n+9Jl734n8obU4X0rGnG+bxwmnWG5LJ87LjYXyHuX0p9xLNIWYU/uaZFXln0Fl7DEh+h/oZUziqIo\niqIoiqIoiqIoA4j+OKMoiqIoiqIoiqIoijKAnFHWdOoTlGRm3SU7C5f/kG/ipPkosWvYL7u6h49F\nyVDcMJSyF3z3pciLPBdlR92sXCpynJQ/efmj7M9vFMrM/PxSTNzZWcafQnGj4ETh78/z5GdtbUb3\n9w1Pvm/i3BtkKSgvPedlxHHjxog8NzeUrnZUo4NzyOAokZc8T3ZodzVuHpCz9HXJ8sAWJg3rYbIz\nzwBZftbXh/JPeyVkDHHp0kmmru4XE7t74fuXbJWyuH2F+PcFk+DYwQsC2y3lx59tRbf6K85Bd3oP\nPzmMI1hJOZcyJeZYPmsluuTzsklnpyyNbDiE0j4uzwoZIs+jtfTSleQ+cImJV176oHhszo24rra9\niu80db4ct4de32ZiLpG4/e3fibzvH37axCNvRBf6A+/tFHkX5uaauI+V/FX8CLcm7tZDRDTyHEjE\nXrnhVRNz9y0ioivuhXSnhzlo2JikkIgoIBpzQNmPkJzt/H6/yPOLwrzRzuRy1/3jRZHX0iLlMa5m\nwjCU+PvGybLWP7/2gYln7Yf0Yd6Ds0Ued7w6ugcllLwkn4joqa8gPWpvx/XWcFDKUYbPROnlsPGQ\nOHz4+Ocmvu0tKWkLSEIJ87ULLsfz35SlpdytL246SrTnT5RjbnIWPgN3T1lyn5R5fvcO5peWDkjf\nnv9OlqpWlsjv6Er841n59t5K8Vj8efgedQdRqt/Ub5GJMkecj1+By1hXjyzzvuZmOG4FtWGs97RL\nB0Hu9GBLwjXi7Y3y6P4zuDwEh2AOrvMvF4/xcvC4GZBn1WyXDlQTH55pYkcN5heHXZa4t/fJUun/\nEJoiJUM9rd2/mucqmpk8zcfiimM/VmNN/5/P1CI/E7/mAhLhkmV1RanZWGxivi4mR8rrKpNJq7ls\n1lEJ55jI3EHiOV2NkPZwOUubRV4Ulobx092AtZW7yhARZdyAsWDPw7mLm5Up8vwiUK7O5+jAQdKt\npPjToyZO/POl5EoScjHf1O2U4zYwE+Mp8RLsUYPy5TH3YMc5NAPj2yoxzP8K60vSpBQT7/rrRpGX\ndQUkK9XrcG64ZNvTLiViw8Zg3vVg5e817PlERBVrMafEsD1zk8UNM/UKSAJt8ZCmeXoGibw+5niX\nvgSSl7rd8ljy/StJ41aXYEvHnBWcLc9PL3OM4dI8d3f5t2W+B2lsw+e96By5D1r/BmSBjm6M/clh\nuSLPwwN7BnvtPhNzRy9rK4P6fbinCEjEZ0gbKR0SC9dAJtzJ5nwPy3dadwRjLisR90L7P9oj8lJy\n8BiX/gZYXJOcFhmRK6lZX2xiW6Z0E6zegHEcw/YBdTvkGlLLnKY8mHsYdyAkInL3xDV7+lscy/gL\n5ByVMBrSxtYWHMtmG45Rq0XG23IMj0VlwVWt4bhsCcHlaNzx0nqMM2NxP+LL9rmtpVKuFDUBcxl3\nJWs/LWV+Vtc8V9PrwPUWNETqifm9ljc7P1weSEQUORbfZe1f0P4g2+K6FcTaoASn4bU9LLLPpoM4\nR9ztsJW1Pal1yLEUmoNz58Wka3Uvy+N+2XTcS44KwXnc+c9tIi+ISdK4hNna8oVzfAVkhbHDpNQ5\ndFSMNV2glTOKoiiKoiiKoiiKoigDiP44oyiKoiiKoiiKoiiKMoDojzOKoiiKoiiKoiiKoigDyBl7\nzqQynW7DIamtbz4NDVjvV+jVEjlJ9oipZ1aTvV0/m9iLaaOJiIJSoQ/mtqo1Fk2iTzh0X27MGrjs\nF1jajX9E6jvri9B/wisAn9Vqn8mttFOnQHvc3Sp7kHAtOLctrdgldaBezPJsyMWLTdzZKfW8PiHt\ndDapYRalUROTxWN1u6Gb5HpC/1ipTeZWgG1N0Pl1dUmtc0gIbA9PfIueFeGxUoMaWovX++IX9JIp\nZdaiI9OkfeNRpr9NvwbH05YuexV0VEOf7xeD96mv2SjyuprRF6D1JMYz15wSEXnaoFfkesfSb46J\nvIQ5sheAK+nrg47VelxSJs5h8YUmbm09IvL2rIH+ceHzS3/zvbaeQL+TBVkvm9jrHmld2VYG7Wb4\n0BQTL7sbVtwZMVJX+dpr6A3y/rq/47O9uFrk9TuhTXXzxHVes1Fq8FMXY1x11qK/0IX3ni/yNr25\n0cTb2Peb1Sx705x4G2Nxyh+nkat57vOVJn77qz+Lxz6c/w8TV+/G2MpfLu0wfb3x2Csr8XrLZz8l\n8u6dfZmJH//3XSae9PhdIu+Gaejr4uuNc/zoSzebeM2fPhfPyZmG3kHLHvnMxFOGDhV5gWk4PzfN\ngS327xctEnnurDfRgpefN/Hu118Webe984SJH7sE36OyVPaY8Q9OorMFt/EcdMvY38zz8MZ8Gpkj\n+4pV70UfDt5nZvIQmdfTjN4gvPdC6txJIq9s014Td9Si10HNNljKRk2Qa7MtEseoYvsBE1dZ1twq\n1h9j5sOYX9IvOk/k7f8rxkjKZei9U79P7h1Cs9EHxzsY+wC/2ECRF5wi7cJdTRebL7rtsr9ZHzvW\nYaxvXstRuWcIHApNPu+R42npg+Ydjp42/Dz0r5Z9gPhrBGYwPX46+nA4ndLiuY3p7nn/mNAxUuO+\n/2ucY96fY+y4OJGX98YOfAbWR6e7UfbRqbVjX8F7sbUWyx4J4RPj6WzB+zGk3yCttHm/uYrV6JFo\n7Ydx+mvsCXPuxL4nbJBcZ4ddiX3fwY9wvcmuakT2I+hNkXk9+hAGbmeWv07Ze8E3Ev1NSlbi8wRk\nyM9ax3oAtbKeQi3VckwUf4nXSJqPOWX9C++KvIZW7JWSmF99a4c819ziedgF5HL6utG7xTdK9klx\nVOK7pbH+ln4R0na64AOM79mj0TfJK0RaDxezPebsKZi/92w8KvLiZmw3ceMRHPfYiTie5evkHiti\nDHpt1GzD9dGakS/y+ljvjfNYv7XqJnntLByHfjnhw9DjMCRHzo2tzMrei/UI4/dIRERHP8F+J330\n1eRK+Dnk/beIiJrZvMn79AQP++3+QsEZGI/W7+Fg492X7fE76uS9VEQEjpO9Gn2D+DHyCpT72spj\n6InmUYG1vr5FXmML5081cd5u9P4Lsdih8+eF2vBZg/1lnzPet6WH9109V96zVf+EvlOZshWsS2iw\n4/P275bzVH0zHuNrSNp42QfHwe7Bpv8ee/HqTcUij/fec3bh3HXZ5fwTMRFrJu+NGjIU10TpSnk/\n5mXDefVh6++07GyR9/NOXBMzRqKh1js//STy/vbaAybeugLW9SmWvnHZd6BPZ8VPGBcR42Q/xu5m\nueewopUziqIoiqIoiqIoiqIoA4j+OKMoiqIoiqIoiqIoijKAnFHWFBAPe7s+Sxlm9CSURLccR8la\nZPookddVj9JSXmaUkDtN5LW1HTexLytB8vSTFrthmcyG7WiBibkVb1O5LG/yDcPrBQRBrtRZL0vg\nOu2QafByK16uRyTtsyMGo8Sx+McdIs8nBK/R0QH5UO1RWT5ZyyzokqXSwSU421E2X/SZtAqOnw3r\nuRpWchY6TJZNdrWibJJLnhyOEpFns+F4ZM5ldsg90taz7QWU3o/Lhb1wVRkkRYU1UjL1yHXXmdg7\nAsc2MEmW/jo7cH6cDnx3q5OsVwBKG61lohxHBUr5+LjgNvFERA2HWOmrrGD7r/HywrWYESfL1e31\nu038xWOQuUy/VNY85oyEXWdjAbNW3i0t5R9b/icTF+7/1MStp6XlYNbCa028+5W/mfiOdyFL2ffi\ne+I5M4ejbDDvjbUmjhkjD5h3KOQOj97ymon/uVa+Xksd5oCAZJTSxg+ZK/LGLkKZ5RXnwip8+V2P\nibwFz19PZ5MP1kPKdf/8P4jHHnvuJhPffS/kPHPHSunM9W/ca+LR62Ej2V4o7VmXLkL9uacvjuf+\nN2VpO5fVxIdBIsjHxYw/XCie4x+IMtMlTKay22I/uPPvP5o4OgRjOH2sLIO1peCxon1fmLi5RpYS\nn1wNW8bXWdmp0+kQeU9eCmnr06tca6tt34/r3Fq+3d+LNYVb4h7/1y8iL242rsUlt11k4pdfWC7y\nJjZhbpwwG7KN7x9bJvImLMW1XvwN1tLc+2eZuPSnA+I5fblY1/yiUYq99tAhkRcRhPm+9GusrT6R\nxSIveZGUtP2HZosUqPYAZE5Drx9t4s5GeQ77++W662ps6Vg3eHk0EVEZO4Y+oZjzo6aliDxuM85t\nPfudslS+swrzT2cDxkjYSDmX83L79lJIHILTIQ91tsvjxK8dbmldtPakyONSlahgjFuvYCkxd3Oz\nCnX+h0Bme0pEVLuZrf1MdtB6Qq71yZcNo7NF8HCUlO99fYt4LHM29iIRkzFfhQ6SMi5Pti/l5fg+\naVIu3casb5sdOAd+3vJc8/PRcBRjPWocPkPhJ/Iai54E6UJgCsZl8Qq5V+S2yyePQX445XfTRN7x\nDyDh8GD221bGzIZ9NpeUNO+S8uFhd4z/zddwBZFccmnZp/W0oPy/9STGFrc2J5LnwTcW89nhPQUi\nr60Tr7f7ICTOoRY5yvj4i0382ct/MfFz/3jRxKlRct44Pw5zZRSTYhT9Iud//wTkcTvz6CgpPW06\niD3w3k2Qqk0Kk5IYPg9xq++gDHnNRle20tkieibW9P4+eRLdmTV0fy/uJYvYPGuFC5Mb90ppbMwM\n3Ad21mA+5RbbRESHDrxt4kELsSesqt1p4p42aX09ZCFs6Au/xXo3NFfKHD0DcM+ZzKQtVhvosBDI\ndW2ZmFOs7RP43M1lj10Ncr63Sh1dTcII7MX53EhEVLcF+7GETKxd9jy5xtv4HPYJrtPQ0XK9K16O\nx4KG41qKmyL3Eq3l2HO1s3YK/X2YD6LOSxHPCWD3qUWf4L7X2mrB2Yt9BpcbxkdIG/EP/v6Nifl9\nTKxFLl69EfdWXALf3yd/Q7FKW61o5YyiKIqiKIqiKIqiKMoAoj/OKIqiKIqiKIqiKIqiDCBnlDUV\nvA33ofBJUsLRzbpJ8/LgqkM7RV74cBSnubnh7SoObRJ5XDrkFfzrpcJERO4+KKXl3by5y0EvKxMk\nIqrbA3ekXVvwvlwKRUTUwsp+Y1gJvpXseyAX6O5GOVfKLCkj6WhFKV7ZJkhP+iyfzzdedqd3Nbz8\nrktW0gnpFXfQKlou5U+pV6GMy8cfJXxhYfI72+3oYt3eCFlETPIskTfy/ukm7m6DxMl3F+RfQ8Jk\nOXRrAUrYeLmYf6DsZt7Wg7IynxCMucAQ6aZUuhVl0MGDUP6Z/7508AlhjzUxCV9AgsXRKkCOJ1fy\n2CV3mvjhd24Xj/nZUKaXHg2JSZelc729BLIk3v1+9Xp5zT50Hco/IzJQ4kn9ckx8ce+jJh67BGXP\nNf8fe+8ZXVd1dYFu9d5777Ll3gvuNsaFYoPpJfSShISEhBBIQuoXQkIKIYEQWiDg0MGmGHABG/fe\nZMuSZfXe61XX+/FGzpxrB/zGeFwN/Vnz19K469577jl7r73P0ZpzFoHa8swnn4r35CSipTxlHVoX\n818+JPLObsR8YSpTd6dst779MjgAvbzlMSeuLZVK6407Ma4yF6Ll9nSldE5b3Sdb8t0NPz9cnykZ\nktozPIjjevT+25147HWrRV57A5wfrroArj1NNdLpYd7D1zhxTzdqke1OEESUJ6Y0jL9lnRN/7+I7\nxHt+8uQ9TnzgOdA5lz4iXZjmdeOYgsNBobxyzlUi79XtTzjxC9/B9b5ojawvTCPK/wSuYNvWSzqV\n7YTgTqTSuLXbqAfJiYLpCV1ZbTKvF3nsgnPXZbJOlpWjndfTD9+VkWZRG4lS2djBrevUHm3VA3bV\nOfQ+KE9dvdKdcPsunNvVNyxy4sKt0oFkwQqcl7q9mKcFVZI2ya4wTUfhjHFuzzmRlzEr3Yljr5Pn\nxR3oaUC7eG29rCvxF4L+3FGCuhmWK1ude6jlvGEvaglToYwxfBmEs0r8/HSR5qrHtWNK+GAfxkh0\n0gLxnvydrzhx/SnQIA6fs84nUTB479OwS7pzZZDT1oALLf82/dWXKL5cN9KvGS/yzFfQpNyB0l24\nbn7ecjvbXYkW/P42jOn+Tjm+g4kWPdiL31uzJ1/kndyMv6cuxW+0nWRiJ4OK6OWFPcLgIPY5yZeM\nEe9pPon5Gz0FbfLFpZLOMf9uXPvjL8MximlMxhiTeRn2TkyPG3fVZPNV4GsYerxWvLbr91udeN1f\nLjXuRgONLZ9guY8KH481s/ID1Jxx104ReW0F2Nyyo+rstTNE3pgjoG3sOw3KU2u3pI9kZIN6ynR7\ndjScOUvuUQPJOaiTznvEeEmbr/kMczP1UtTNs9beM2wi5qxPJWpl7S4pJ8CURV7P2a3UGGO8Q6Rz\nlTvBdB6+HzPGGG+6p2OpiqwrpXNO0wGMA3ZG9QqUY4IlKfheYGhAUmG96LuKN2FP2HkWNThumdyH\ntRzF2E9eCCpTp0Xr723EOU++DPO5cU+FyDt27KwTDxSXOvHkbPm9oUQb5XtgphsaY0xXmdznuRsJ\ni3BcTME1xpgcL+zh2FVtaJ8876UbQVcbexuoy7ZECF+7sBysra6mr96H99SgjpbtK3Xi3FWSCrXz\nFTy/CKf9YESOXMNTaL/D7nW/fvY+kVe7DXM2fBLmc8XHkj6csCjdiTuLMc56rP0X09i+DNo5o1Ao\nFAqFQqFQKBQKhUIxitCHMwqFQqFQKBQKhUKhUCgUowh9OKNQKBQKhUKhUCgUCoVCMYo4r+ZMxHRo\nWfhZ1m3+0bCdY2vlhn2SvxwePsuJWZ/FZEsbqfKPyOpq7YVO7KqW9tTRmeDMDgyQxXEUuF0hKZLf\nydz6ujZw/yfmSc5fpD+4xxHTwOl3WfZzrtZ6J+6qxjE07ZFWpSlrYeWYvBAW4+fek/oIaZdNMiOJ\n6OnQC+oqkXxFtg5jLRzvUKlLwRbc0WQROJgreXTBweD9dXmAP9pYv1XkNR6RXOr/opNsOF0W9zhk\nDDiZbPXdVCK54f4RGKseXnj+2N0tLSXZrtmbbLWTlmeJvKY90BKIX4gx09vqEnmumpGzKbz/z9Ag\ncTV0itfaimG3OeuBZU586kk5zraegG3dd++4zYnXWN+1/nt/duK7nnvKiT3HyDEx62ZopDz0bVhp\n/+PjPznxI3+4W7wncco8Jx4fAg2gH918s8hbeccSJ/7r7b904stvXS7y7lyOv/c+/pkTz7h3vsib\ndP/lTlyyc6MTP/bBRpF34h1YL8ZfadyOY0+85sTfeu6P4rWX74Wt9+Sp4PaKummMefgbOL/fuwcH\n6RcrrUDPvQ9Npedf+sCJv7HmQpH31w+fdOIz62Fvfno9LKhvWrRIvKdyA7j/M2+HLkydZVuavwlj\nM+9CcOYnp6eLvOL/QD+hivjGt//gMZH33GMPOLEH2XPe+ndpid5SK21W3Yl60hZhDSpjjElYku7E\nHp44vo5CyVdvIl57/wDqbkG1rIspUah5xzaR7aSlqRPaCY7/xb+914mHh/HZOTfKOdFwHFzpebdh\nXrb/Xda1ex+81onZHnzBw1IHpvozXPvuMqyLw8PSVvUU6TzF5EK/LNCyJGadkJFA7AVYx5pJ+8YY\nY/raoKnHa03tdqlN01wELYoosuMuOyL3QS2kqzYhEOsLc+6NMcaXNOBSLsH+wdsbGnhFn74t3sNr\nXC9ZLV+8Vl7vgATSnyB9kbaT9SKv6n1cx+BcWL8GWrbxrIvQdBjnL26B1IDrayEtD+lG+7WRPh8f\n2F0udZ2Ger/cij04ReoJDvVjjjTsgl5ExFRpuTrhQuiLnNwCi92okBCRF0L2xQdfhJ7b3HsXOrE9\njlhLq/A56K1NWDhW5HWRjk7mQoyjQfu3kgxOZyHpNlr6P5GTsc/toX1FSILU04tNlJqT7gbr95V8\nVCBeCyB76oyroYHXcrpO5PmSZiLXnHpLA+Q5srXmOtptaW398rrrnHjLcex/V83EXj56TrJ4T+1n\nuK7B2Zg7tg5mENmtd5POVH2T3J/7N3+5HqVtd89/J1wAnc+6rXKc2dot7oSrFuOH66cxxkRMxlyq\nIe0Of2vPErcw3YlZGypmrrQrNrymUNhRJNdZF+mThFItCx0H3ZH6HVK/h7U3vUjnLW5xusjj39tK\nGk0D7VJvZ/p86FMNdqM+h9DxGGNMbwvWXdYnsfV7oqYnmpFEB2klHX9L3tPmzocOU9n7mKdxlp10\nAmm2hcXhnrC5/KTIYy3IATo3tj5eYCrqY0v1l9/Px1l23tlzsDawbXXsBXJ9Yu0X3t/s+rvUxc3M\nxVzvpGcK0ZPlOhGagetavg33nGHjYkReX4ucIza0c0ahUCgUCoVCoVAoFAqFYhShD2cUCoVCoVAo\nFAqFQqFQKEYR56U1+YSSnRdZKhojba+8vdFylL50qcjr60PLbEcb2qA8veVzIW5BdXWhzcxuU6sJ\nQssnt4EFJqK1tK1U2gCe2gZbr7wktGe2N0gaSncf2seGh9Ar11HTLvLa89HKPDyIdqmefnmOSl+n\nlv5v4nxFz5KtkK1n0RJMbpduQ0s+rkHKGqtNthptYdEzcVztZ6WVGf/OwR78zpZCaa/ZQlSmkFS0\nbgaFZIs8g85QU/tFqRN7BmBI9llt7dwGxjaFIRkRIq+lAL83cjxazrrrJB2Ix1z5BrQph0+QtLjg\nHLSpFZHVYc7t00Se3aLpTlR9hFbz9Kuk/eCxV2GjuWz6TCf+5+bNIu87V8MCc8+TO5w4d568Nsvv\nwRx+/IZbnfjOp74n8k6+hpbHnzzwDSeu3oVxP9AlWzI/eB30p4JutDS2tcr2yQ0/BaVmzQ2gOP3z\nL2+JvO89dosT73x2pxP3d8ix408W3jmLYXv4/VUXi7w5OaATTR4BWlPa1WhxLf5sg3jtRWq3Xv9d\n0NMiI+eJvN+99iP8Qe29z9//3GfIoAAAIABJREFUisjz9kJL7h82PufEXl6SEvPhjx914k1HcB3+\n/AGsqjvbZKt5BVma7vknzvvkiyVFMzkZBc2L5va6ldIOmFs+b/AG7Wr1NDnHju3Eccy6AhaNfn7S\nWnrdEpy//RXrjDtRfxzUo4QZspYzlYlb9QcmycIeRG26bK2cnSzn9sGXsd4F+WE9jsuQLbL9VBu9\nvNCWXXFgmxOHWRaShR+i5jW2Y43bfPSoyFv5TZxLticuOXFM5PE1/PQ90JETImR9HhzCWhJJLdpN\nRBEyxpi4RZJ27G6wRWl3pdwLBCbj+jAFJcZq3w5KQ17jTtC1shfKmsp04oAk7FXq9kj6k18U5mYX\n0XRCMukcWjSxBrJhPlyCYz1bK/dBnjQ2mfZx0TWS/tR5FnsupgpFW9QCtpD2iwIdq7O0ReQFJkja\njzvhEwwqnIe1p0xbCwpM+ftYk+y1IZJqfth1GHP7H3tX5mWArjTjBtD1bSvtht1Y12bcNseJP3j0\nQyde+5vLxXu2/w5rNbfq1+2VNJdrbgKNN3Iq5k7NVknZbiDaOO9La/fIa5PrhWMv34d9d/+gpEmN\nIUr5SIDHY0SKrBfthdiL+tI9iXeQpEEyXW2QLHttCtCMUszNrURX+tkd14k8pgvOyQUF25/mb4dl\nrxy/mGjvdH+y8+kdIm880dWqPseczV0mLdZPfIxxO3E2jsGuAbExWIc6zuB8hY6V1y1yilwn3QlX\nHagoAYkWHYuOt68J5yXY2rv3tWMd4/15UKKkVA50Y1/JtJ+OSkltjJqIvXxwOr6r4QvUXd8of/Ee\npv4FxOF32NS0DhqXTPEMt+iQVV/g+g7Q2hcxReb1EE0qfDz2C1UbJVW8lihjGSOgiOFHtK6pN84U\nr5W9h3vpnJumOvHx5/eJvCyiB4UmoobZdDf/ZFobaJq6quW9Gtf2yFTcj6WQ1Xl3hbz2TFfKuALH\nWvSSPNbtB0AXDyEb+tX3XSTyeMp50+/r75T3OHxvGpmGY20vkPub6Fnnp4pq54xCoVAoFAqFQqFQ\nKBQKxShCH84oFAqFQqFQKBQKhUKhUIwizktrKt+EdqrIsbKNOiAW7V4e4fiYlmrZShueAKXm3mao\n9ve1y9ZSVjY/tx7t0jsPSSeewd1ouc6MQ8taajrikmLpeFHaABVnbp9c/ou1Iu/MM7udmBW749Nk\nSx2rx3sHQP3cN0y2xw31oQ2ucivaJ2Pnpoq8nkapTO1uRFJrn33eu6vQpu4biuPntlBjjPGPRrs1\njwv/QD+R196BaxwWjjHiHXxK5LHyefQMtHf1NuL9tpp5KLXl87kdcMl2wxBqX2RHpdI3pFJ4xCSc\nl8EutP6ySrwxxnSRMnfqOoxnIztLRZ67we3znVWyfW/JI3BTefm7cACakCrH2fR7v+XEUftBqQnP\ntdzNKtCqe9PvrzVfhfQ5aOHd9DaoLRPpe2fcv1C859A2XIOqk5878W++/w+Rl0s0pOf/AUelqxZK\nik/LCTg25IxB231fq2yfPPIUaD3vbIGL1Q1XLBN5wZmyzdbdeOknr+O7/GW9eHMbXJjKiBLp6SPp\nTx3FaKf90+/XO3FNs2yxXjwBFJln7v6ZE1/yTel4tewXN+Ez7kPbe/luqNUnzZ4u3tNdBXcldhgq\nel7W/xt+jPb9tlOgG06640aR5+uL9utzAW86cfyidJE3OxF/n34WVACfFZI68fzfHjIjhcnfucCJ\nm09IxxCmpYTFoXX96DP/EnnxNOeiZqL+2XTfzAkY00wBGrBaaYeIdpq//k3zZfCPkbTLvHXoie5k\nalWCbH3PfwNrLs95+xiYJnTNI1c48YkX9ou8ZQ+iXXjvXzDGZn9X1oqaz+DqkZZn3A5e7xIulDZC\nfB2iaH1q2CudX/i1yDm4Pq2H5TyIvwjuFY3k9hWSI9e4yLGonb2NaCFnmrVNwTpdBSpxegz2aekW\nRzqA1vCBVqxx7JJhjDEDHfjbOwT7m+qPz4q8hOU4Zzy2bAeqnmbp/uVONB8CJbyhTlJ2kjqwr2K3\nGHaoNMaY3u66L31twl2zRF7B86APBxCN3qZVR83AOKjdgjG89BaM784KuVfwIspZaxc5tQzIvU0j\nubx1nsNn2JQuL6K0VhK97ZKfXCLyzryA3+RD7+mynIuiJo0cHcYYWUtCciUVp4fo6EwZYMqJMcZ4\nkxMR7/Ob98v7gYkpqKlzpqKwJCyXVESm8nvsAueipwrzL+uWKeI9x/8GOmf6alCUcsZLhxhPopOl\nkLPNrtck5aKmBWN6Shq+q71Ayg4Ep4HSFUpuYS0nZB068BT2Pil/di9vO5xore2FksLhnQ4KGlOe\nPMkNyRhjajdjvvBcHOiRNcrLF/ecw0QVSrD2C6GZqK/dRBtKuRxrM8s+GGNM9R7Q+/qodkVaLkns\nHsv3LZ6+ci4mk8OrN9Ew2UHIGGMCksj1mNYI3xhJQ+d7lZHA0RexXucRNdQYYzpcOB+uOsyDkIAA\nkcf3i40FWDdarf1S6hrMvyG652w9LPPC6PnD2XewN46aiWvC1FpjjBmk+8L1P/i3E09Ok3NxWgau\nTzw5GzcekJId3sG4101eDophd52sL2c+wr3ulNtmO3HVh5Kexs57RrL3jTHaOaNQKBQKhUKhUCgU\nCoVCMarQhzMKhUKhUCgUCoVCoVAoFKMIfTijUCgUCoVCoVAoFAqFQjGKOK/mTMoKWAxGjpOWoS1n\nwLOq20V2kJbFW8x1sGkcIE568lRpuX36TfDkWV/D64h8fjR3LHicpXXgChacwTFEhUj9AbYSzJkK\nflnZBydEXtyFeI3t+/K3nBZ5YxeAb+ZFnElPX3k6E2aDr+cd3PiVeba1o7vhIk0b3xCpc8F2Y01H\nwd/uLpO6JsyrDooAv7y3XWp7hAaC9xdO9rGBxKc0xphewUMH/9bTF+fT3+IU+5B1Yg9xUCOTJS+y\nvhA86soNsPxNuVTaFNZtg8VdYAY4uxHjpAYLawn5EO+w6pMikTeS1q8hxCNOGict3h697i4nXjhu\nnBPPfugOkfeXm6E5w7z27z5zp8hjHaUfX/cHJ/7n1ndEXtRU8GKnHcZvH0sWe7MTLxPv2VMOi2z/\nIIyPP7z7c5HXWY35su1vsANua5f6TF6NONZjJ8FtvePb3xZ5oVnQtvjmEhxrl2W9aF97d+Pq74Pz\nX7JR1pWENNh6e16Pule+QeZVFINH/uBvYHXeY1nFZ67EOGkuh25I5cYzIu/wa9CP+fAQ5k5YEOb5\nkQ3SXnne3bDCvop4vy88JfVxuLZlrYEl+qnX3hB5XaW4Dp+dBKf45l9cJfLuu+R+J/7H5teceMvD\nD4u8uNlyvXInWs9Aw6zX0gs7uwXnNjQAv2Puj6Vl+9AQzouvL/jUgYGyhgQnHKe/MCY6ayTP2Yus\nHeOnoh72usDdbjpWI97z+j83OfFL72FeTp4k/Tn/8OT3cNz94IXv/PCgyEuqhebRzHsxPjKJn22M\nMX00JlLGg+NdvVlqmmRcMcOMJFiPrO6zEvGafwLWHrY1tdHTgDnHOnzBa6VITsW7mMOJK6Ex0XJS\n6h3U7sX4CSRtO9aF8Q6RFsJLLoM2yt6PMU+7euTa3FqGsRpNeyRXldSwCZ+KGsjW6VGzpOYC64Sw\n1hJb6hpjTB9pDZoFxq1orIcGyYxvS0tw1r5pOoT50nxO6nWkLIJ2jge9p/WI1D2ImYbfz9boQalS\nk9ArAHMxiDTM6ndAy8KL9FGMMSYhBtoYnXTdpiwZL/KSl0FH7NybB3DcXtIuesw67AMy2vEZ3dXt\nIi/5IuisCF2j96U+H9tCmxGQn+F1ouO0vD48Hn1CsP/ivZgxxoRFQrihtuELJ866barIK3kFNTWC\nbI99Q+TnFf4T9S1uaTryZmM/2FrQwG8xrj7MCbb6PndaalVleUFbivVjxiRLe93YMIwt1qOZMEFq\nZO19FloyE1dijNQelroZ8QkjZ4nOukesyWSMrAFsfT1g3ft0dmKchSeQzmKx1JPqb8Yc8Ymg/Xmo\nrI11VNvY5pw1a8Jyo8V7Bl2oteUHyXL73Fdbbov7liipEeNqQD3sKsc+x9Y39KD510+aYP7xUisu\nbLzUf3U3PEn/6vR78h45ZQr0mgZpTfKx6ll3DdZFthIPDpPn5vUH6b7fD/NvfF66yNv9L+jBsmZd\nQCjWZi8veY9ZuRf6TwuWUw3wkLWy+hjmSMIiPPPoaZbrYuMB3O+ceAIamwGR8jdFBGMfUPgq1uPA\nEKmJU/w57h8nytskY4x2zigUCoVCoVAoFAqFQqFQjCr04YxCoVAoFAqFQqFQKBQKxSjivLSmiDy0\nDFVulq31vmQ1HZSKdiJu7TXGmKNPv+DEiSvQQtnRcUzkhY5Ba9npt9B2OG/WBJEXlI5Wt/GdaAtr\nO432wj2F0rJq5VzYwJ47jja1Q8XFIu+Ki9AWy61yCx+QdrvnqC1ymOy/cu6ZKfJO/YOsXsPQssWt\n4cYYkzh/BHxCCdzC12fRkOLJ5q3sHViAcbunMZL+lH4d2uYbD1aKvMjJ6HkteA5todwqZ4wxERMw\ntphGE5iCseSqlTSNIG7zJju58i+2i7x+ardm276mg5IKELNIWqr9FxUbC8TficvRhs7Hyu3Lxhjj\n7X/e6fS18NzPQeFo7XpevDYmCa2w8Qvxm353w/dEHlvGjb8KtoxR0YtE3mXLYRU8jmwn60t3iLyA\nKMy/Rb/Ad/3nPlCU9te+L97DVIKhAIzF009+IfIGyB5x2jKMtwiLYhCSiN9e/DO0Jw4NSZvfoFC0\nAcclwkaxqP3fIu+9n77rxN988Ut6Db8m0mdc6sRp01eL14r34hr/9ec4rt2npA39j9atc+JX/gQa\nUVigbK/87hrYVcdk4Jp2TJI2rrf/FtS1ky2gTJ39ArbfEePkef/wZ7A3Hz8F8+Mnr0h60emnQUmr\nJRqhTTHc/AnsG1csBJ3l+EuSOvOHd2CRXXMa43Hy95eIvO7GkbO1j56EsVRSJI8vfU66E/tFox25\ntUS2lwfGo+17aBB1qadb1ii/ANRTb298nk9quMjb+BCs6LMyQb/wCbdasQmrFuA8Z8WBOjBuWpbI\n8wnF2tVKNJyMOEkBzFkD+sT+v2E+27a8S35woRP3kC10/4BcF2t2ow5HXyavrzvg4YU1aaBd1ovI\nFVj/OqkV3aZtc7s920l7WRaxHW1obf/8H1ivpi+XlNwmsv0Nn4g5F5yO682WtcZImvQF/rim9l6M\nqSMtZMnMdqTGSLpS3Tnsq1Itq1JDa2HNJlDSktbIuR2WM3JUirRZWO9aTkrb4HDavwYmY99YdkrO\nRc+voKaPuX2xyOvvw7X28ERrvK+v3Ct11KHO+UXgnI29B/vL/X/4VLzndCX2UX4+OK+eFl2pcivo\nRikX4zzXfH5O5J15DvTU2DmgeIbnybEz2Id1tovsvUP8Zd3oqqI5MM64HT5kax+9IEW8FpSMsV+7\nDb8zMFlSZ6o2rXfisPPYOgfnYN/CVssVH8h935hvgS7YQ/TVmk8x1rmGGCMthVtPghaXPSVd5DEt\np6QaeeNmyNob6INx63kCY6G7QVIHp6zFfq6HXvPxlnvS2hpJGXMn6reXOvHwgKyTgXTfFkd71CHL\nTjo0EjWrh+ypmzvlvQDvdXxjcM49/aw9eTCuTy0fH92D2RTD/N24f5x3F8lydMo1gq2Q209hjIVP\nkHulHrqPGerF93paY8fVhLUwlu5NvIPk8Rl5at2Oid/A/XLTIUmFjl+Y7sQDZFW98+39Im8c1ZWY\nCaiPfhblK68d9wNxRBv1CpC/OaAM+w6fQKyzzWdRD2q3SGpyQwPq9ezv4R5n71/k/WIozdniV1A3\nOxvkmMu+DnTvtjOYRyzDYowxIVmoKa56uvZ9cn+TkjXWnA/aOaNQKBQKhUKhUCgUCoVCMYrQhzMK\nhUKhUCgUCoVCoVAoFKOI8/IwPD3RzhyULJWQQ6l1x9sP7YW97bKdPIEoIQEx5MLkJRWozTCcHjKX\nQDG5emepzKMuz6EetAkV16E1cGySVDznVr4Jq0CTitwhjyFqFt534o0jThyaK9tys25GC+FhUm0e\n6JJtb36xaOHqIfVqW2279H18V/St7m/frvoIbXp8PWz4RqK9y1UjlapdVTj+5iNodUu0Pq+rAu2v\nHS60rMXESGVyF7UEdlBLILdEd7Q1i/ewmxS3FdtOUA170J7q5csty7LV3C+cWlDzMX5sxXduXWVH\ng95Gl8gbGpQtmu4Et8teMEa2jYeHoRX0wDtoy7v5oXUiLygJ8+/VH4FCMzlNtvM+8xZoSfkvgrbx\n6s/fEnnf+udPnHjzTx934l0F+LwNK2W74/ULYNcx+5uIC2tk++TKH69y4n/+EBSfn74uKV3FO0FD\nWvc4KC/VZ7aIvLrPSvFdBX934nm3zxN5iZGRZiRReoBoXrJj3cRPRDvpg0+jNv3hW8+IvN+//bYT\nv/UBKEnHqSXTGGM2PPALJ/7ZC6CXPv/TH4m8z4v+48RXTAf96c7ly514nOUQM+cqUDi5VffAHz4U\nebWtX04vmjNJumnd+gTqQ3c1asimXx8QeYv8rnfi5lbQGH730z+JPKb63fWcdHz6umg8hlbaqBmS\nEsI1pq+1h2JZK5ii2V6M9SkqT9bToCBQqGrPbXXikDjp6jR2Cv4uzQdFYmwOakVbvmzv//PboMQ9\neOc1Trzri+MiL/ZYqRPPuAGt/umRkuby/lOgaly4DuMoZqZ0zhqmOpl9B8a8t5+k4Zx4ApQ4436G\noaj5yVfIFuPqzaA8s6tGmEXvY5eciFR8RumnO0Ve3ETQ09Ljsb8p+DBf5GUvgbNVIDmeNO4HFSd0\nrHQX8Y/GmOP2etvNZpDmKVPVmD5gjDE+NNcDfRGHj5c0tobdoIgHj0Hd5D2AMcb48TiRxl1fG0NE\nnwiMkfu5k8+jdoz7Bpx8pl4vXcBOvwmKffYqXMPmIkkVCs/COB4awnzu7ZFrV+pY1Jv9n//eiRu2\nw60pYaK0PBoYxLWJTcF+c8t7e0XejCzUB6ZmDFv0kDZyYwyuor1WoaS1dFDr/hBRiXOvkY5t7BY2\nEvCPZrcvSSfg2hkzD5Qne3yzC1A9rffswGWMMenX4B6gl1yEAhJk/WGq2CDt7eursC+NjpaUhmDa\ni/qQy2eI5cxT8zHqS+5YODd1lsq54000nbSFWAuaD8gxx+6yDSWoAROIomLM/55bd8KTaP1R0+W6\n2HIMa3XDHqLwxUiaiy/JSZw+i/qSl50q8pgiwjSSQXIENsaYUKJUVn5AchdET7VdkyYswJoZnIjP\nrtwsXS65PnONs+UTvINRQ4PJ9dZ26e2lOsy/I2KSJTExwu6+TGWyqYPFL+BeNW45xuNF910o8g48\nB6ektChc08Pb5Xo379rZTuxB0hdd5XLfOO9+3BefeAK0pPZunLPZ35fyDCEHsWbW7kTtzVkiF6HC\nbbiu0UnyXDMGicbFzm72mGsjBzemOlfslDIqhVvxvWl/ufp/vk87ZxQKhUKhUCgUCoVCoVAoRhH6\ncEahUCgUCoVCoVAoFAqFYhShD2cUCoVCoVAoFAqFQqFQKEYR59WcaTwOnpZvuOSXexEX+cifYBk9\n9f6lIm9oANyxur2wuqrZUy7yMi+HP197ATiTIXGS89ZZS3Zjs8ABvmAq+NDHNkib7gkXweIzOB2c\nv7Qwm7MKLl/uheAeB1rH0HwMnLyYLPC/bU2T+EXpTlyxEfwy21IrZrbk5LsbYWTJadtrVrM1YRJ+\np21llrQCPL3+Tpwn5uwaIzmtc36w2IlbiYdnjORoshZK1adFTsz6LsZI7R8vPxxfe5HUUki4CLxs\nT+IbF78r+Y5sOehLlpdhuZLT39OE38g23ZnXS162h+fIPeu8/2XopOz+9V/Ea9m3g0+/4fannTjl\nffk7xn0b/M7vvPikEz9wybUiLyyNLOqJ01nTLDWATj0P/ZRLH4fmzGGyev7tmw+J95x9GRo2YQnZ\nTrzgRsmFDo+DxWwr2SgeffE5kbfp031OfCVxPwd7JQ80i3QGPF6HTkR8ntScWV/2nhOvMe6HF/Gy\ntz79mXhteBjW0Jc/Cg2Qxg6p//TJsZfpPWQ5ftdckffyz95w4ucffsCJ45dJvRJvb/Dk/7MbGj4b\nH4SOS8UGybd+7F3kvXuQtEE83hV57Z9gzrFd7JWzV4i8/gFcr7Ag8HQLSqQ94szfQB8ne3WeE+8+\nfVrkXX3pYjNS6GtH/bO1uaqPg28+4/sLnbjoeakH1HYC1pDjboe9emBgusirK4d2UtMRfLb/Mrkm\nsc3nlGuhM3D89cNOXNYo62QQ2eWyBemcmdIrl21f28hK2y9WanyMSYTOgC9pmuz/s7SuDKbvnfQ9\nnKPBQbmW9PWPrM4Fa5UNdMnvYi017yDsdcrfkbb2Q/2YfzwWushi2xhjAjNgB1yxFdzzcZfLNYT1\nX1qOQxNnsBPHx1pG/++x49qH5qHmN+6qMF+F0PHI6y5vF6/FrMW88iWLY1edHOvRZNHsQ+eoq0p+\nXkC8HKvuRNKFpMEyKPcLuVdhDTn9MuZB2gqpOZAwHuO2uwq/0WWdF2+2Dv8E1zBqjtQ49Pb53Ikz\nr4Q2V90B7G06iqT2SwxpW7AGRuRpuV+rb4MmiW8Trk1rjdQqYf2FI3/b7cQpc9NEXtwSrAWsJdh0\nWGqaJK9ys1iQBQ9v7J08feQ+uuU49EoiyW530CXnbMJiaGDUkz3ykLUXqPkMe96qAvzOiGB5rr1I\na9AzAPUxdxXmR2ep1MaoyIfORSqNF9arMMaY7FtQo6u2YVyEjJX6lmxJXbEDx93ZI2vAjCtxTLHz\noc9Ss1nqXIRaepfuBN/vuCyr78hp0FhiC+qGA9LWPjgBtWLKXNyDhY6Re1mutd1Ub0Ky5flr3I89\nB+uncE2KyJM6I7W7cJ5720hrkzTKjDFCt4Zh3wcGp6H2N9O8si28eQ4EkJ6NbQVv21G7G8G0VvXU\ny+uYeCnqQPsZHFfh0UqRN+PWOU68+1nor82/Qe5ReS3roXvHoNRwkecbhPoYRPeweVdAAy8qVu7l\nPWaj7rWcwrWzbd6TMvHsoLMY8znQssjua8Ox8t7HVSXXxYY6rP1Zi3CPEztBjrP/Lx0v7ZxRKBQK\nhUKhUCgUCoVCoRhF6MMZhUKhUCgUCoVCoVAoFIpRxHlpTbVkRxc+QVpIhqaiFSia2nUG+qRlKLe7\nelMbV9a68SIvLB2tof7UttVmWf9FTkV7XMxEtPJV7YTF14LvSztqpqJEJOE9lRV7RJ4/tSqxHWT7\nOct+8CzoHelXwZav8Glp+5q8FpZsbNM91C9pTZ0V1BopO8rdgtAMtIS56iV9ZHgAx8ItvVHTpNVj\n62m0s3cU4/enrJa2ztzq5uWL1na7FZvtqYf60QYWTLZ4kbnSPs/fn60sQS1oL9wh86JxHas/Qcto\n5hp5chvJ0o+pHiWvSivZ1CvpfWR/3F0r29m4hTLu7ouNO/Hc3T904oXrZovXomMw3n+yHuNs40PP\niryD96934mt+ivb5n62X1KPm06AzLroEraUrg28VeXUncJ7uWrzYiW9etcyJ287V81vM1r1HnXjn\nIVBernpIeuX6+aG+XLsCLdo8bowx5o4/3ujE8Skrnbhw+3qRd/Bx0EOCA0Fhe/iKe0Tezd9YbUYS\nbO1+2W/WitcaD2L83Hohrvd18+eLvLAw0Nhe/Q6u3Zkq2SL8242gaBXvx/koe09apy/6JaxfDz4D\nyhzbtwfEy5bvpCi0DzeUY/6xxb0xxvxrGyhPv/3JnU78s6vl9dn8MOzbX9u1y4lvIjtvY4wZd91U\nJ2ba42cF0rLdx2fkqBRxc1CXBnpka6of1Z7tvwPd199HtjBHDuH4hoawPvX3SzqMXyjyApPRZuvt\nLVtuo4gaW/juSSeOjUJ7cP+gXHeWXoqW4IajRNWdKm1Qd+/HPF1zKSzuy96VVLLkGbC55ZbtWZbF\n5b4/fe7EXbVEe/5MUthiJ8njcDd8yA7TXpOrPj7rxJHTsRYmrswReXU7Sp04NAet90Fpsi371KvY\nn4SHYS7ZVJx+WifZMrvtFGjBPdVy3eF5EJAMqlbSZZKKwpafXv64PiFZkgrQdBT0uX6iU7nKJXUm\nOBv0G27lZ7qYMcb0Ei3YZBu3glvNm45KKk5AHLW/3wJaK9PSjZHW7pFTMOY2vb9b5OXUYx/INMwj\np86KvIQIrIt5l2J/OEi1wra+TrkM+6ja7aVOPHudtP3m/UfrMbTqR6ZFirSGfaC0jbsBNdO24eW9\nHFNHbDt0PrcJksXlFrSdxvgOTpdzJyQbv63hC+xN2uok7YxnUvZa3F8wtdMYY5JzMJ9zV+J+oHGX\npGbELAIFjKklpz9EPYxPkHNn+ynQHleS9INtSdxRgfPeWwfqiE3BaitCXtalOFa/CCkzUbMFVByu\nV43Vcj2JmS/31O6EqJnW/cMgW18TbY+tho2R+weWT7DpY40nQXXLXIv9+bBFNWLrara4zlwF+Y2i\ndz8V77ElHf6LuLkpMo+owExJ6rHsyllawdMPdXKgXc7F4CwcK9cH/g3G/O9a5W4076/+yteKd4Mm\nNzCEY4wIsq4j7dPn3HqBE/e1yOcDZz4DXZ5r6hK67zDGmNNPYx/JVGKm9NYUbRXvaSYaeHcF1szs\nW6aKvFMfYz6PW4GxFJRi0ZqodrIUyUB3n8gLLkH94s/wj5F76DZLjsOGds4oFAqFQqFQKBQKhUKh\nUIwi9OGMQqFQKBQKhUKhUCgUCsUo4ry0pgn3ofXrxBPbxGvcdtVLDgM9jVLdmdvtPLzQspa6Jk/k\nFb8G15Wk1WjHjZ0h+2C9vNCKPDiI9rFAUrf2C5XtSNFJUO2vOrnZfBXY8cInGN/DLZfGGJN+NT6v\nYiNau6PmSdelsnfw2hC120VPlS1/xqICuBsNRLfptdS3w6eAksZOTnZbK7cPJ12Ea+LpLYdQfzvO\nYfMptJX5Rso2zBByzWICQWfYAAAgAElEQVQ6R+xctF32dctjaCsFbYNpUrFzpANBewnaj/3jqN3O\nElcPSKTfW4a2yYAU2ZbdfAwtlJ3UZho9T7Y5DnSOnLvIgjVwfTj5iXSdclU/5cTT77jPidu7pftJ\nM7n+RKfj85giZowxvmFol45NgKtOS4ukAWbOucKJH34SzhF1O9B6bKu9z84BLWDRL+534oaqL0Te\nr676hhNfvBQ0rpqCWpE3/bbvOvF7P3jQie02y+k/APWrbg9q0q+//0eR98FDcLuadoNxO9b/+h0n\nvu/F34nXuhIw3v/8jx84cWSuNb7b4UZ3wY1Qv187QdL2enrQin7p8m878RfnpKPS4ef/6sRHDqLN\ndD7R53IuvFK859dUv0PC8b0+IZLqMjsXtTxmNuZLf79sSY/MQHv4M7/a4MQn1r8k8nY+i3Hi6kOt\nWf5tWUPz/wMayWXkJOYOnHoKa9W4b0mKYRDRSiasRGt96wlJ7wsnB736k6AhNWwvE3mJq1Fro/Iw\nDvp6ZUts5BjQMifeBlpwUAzqu/+7+8R7gjNBFzhHToqhjbJuTMvAZ/P6Hj5GtvS35+OYag+jVk//\noXTmYipBPdGCwifHibywHOnQ4W407IJjZF+jbLfOuAkuSkwbqvqoSOSFkftJ4wGsY+y2YYwxGUtR\n92q+KKXPlvSW7lLUABe1YieshCtRk+Vw4k30LHYU8rba89n9iVvlW45Img+7VEQQnb27TK7HQ7QH\njJ6JvU9vqzyX3VWSkuBO7PkbKJVRIfKc89zkccuuPsYY01WJ46v5FG37N//2GpF3+gU4rgX445xX\nNsi5mDEb84VpGq4a7FcrSqXzS+IA6iS7efY0yLnYU4vP8Ish+n+RdFKsKsI6OYUcbFqOyvUzhep4\n9WbQUngvbIwxSUslzcDdCB+HeWRLGdQewXgfR/S0KMsptHEv5h+f97hoSQsZJGe2gQ6iRbRIClBK\nxJe7BQ3k43t8Iv3Fe65dBQpn3ELUa3s/XbgF1LfYcah7Q72SspK0AvWfHYrY9dEY6fJU8gFoy8kz\nJI0p/3XQyrNn3WTciaiZoAR2WRTInmqM25iFOKZ2y8U1fCLOBV9D25kreQnmcDNR7vj9xkgn2Jjx\noBjWnQLVzTdcXkN2bmXnsP42uU8OGoe13sMbeS6LduqiORtGVFW/SOm61EAOxnwvEWFRxJiGORLg\n62PDrxD76hhySz7wj10ij+mTdduwt7B/y8S1k53YRTIRBU9LmvrUH8IB1tcX57CnB7Wh5oCUo4ia\nDv5l1AyMpZI3Tog8P6Kcx8zAnD38R+mmyhhz/RQn7m2W6139Ydz3Ms19oEdSFju4zkmTa2OMds4o\nFAqFQqFQKBQKhUKhUIwq9OGMQqFQKBQKhUKhUCgUCsUoQh/OKBQKhUKhUCgUCoVCoVCMIjyGbe8x\nQmUx9BH62qUV8iBZvlV9CB52cKbkd7rKoS2Qew90LlgXxBhjuiuR10IWgT7BviIv+xbYyLLFNXPc\n2gskBzj3xsVOnP/Xj5049Wpp5136Grj/8cvBaQyy+OPdZJXmFwa+YuUHhSJvmCy/ImaCa+fpLZ+J\n+ZF1eOaU6427sfeJR504fmmGeK2drBTZ6jxivORunnsFOhdsOz1kWUIGsDWeB3h+fG6NMSZqFvip\nbLvaSrz4xIuk3hBzLbuqMF5sW8FG4uSz9bptcecXjfPuE4Lr6BMoufrV26BRwhbjdWR5aYwxvnQd\np91wn3En8j96xombD0mNgAn3wv65/gT0aJr2S22CuMXpTlzyDiwf5zx8i8hrrcO16qrGef7H/70m\n8q66EBbPqZeDu95yEtfw3I5i8Z7Vv4Nl8uAgOON2GfKgsdPRgWM99eTnIi/rZnA/w+OgE3H2o00i\njzUWUidfguPb/Y7Ii5kI7n9UlLSwdgdqq9934vZSqROw/nFordzxOCzCTz4jtUI2HjzoxLfdgGsf\nPVN6nO57FlawhdXgwf7kDWljXVeDmrjncfBsPzoMXvZ9P7hWvCeULO/ZUj5jnrRE3/0b6Nkwfz7A\nstBMyIIN+vJxqPF3XXSRyEuJhTaBqwc1v7JJridZ8dBaWfSrXxl3oqEB+muFL0iuNet18Np17nNp\ntzvnR7Cb76zEOGi29D8GSB+BLWXPbJZ26Ek5+L1c8/LfhMZARLC0cgybgHMZSFo5PPeMkVbIQamw\niQyMk5/XnI957xsKTY4+yzI0hGymWR8u/6/bRR5rpsx/+BHjbhx66U84pizLivgL8P9Dx4Hj7mut\nNUFkG129GbUucblcu1rI+pXXO3vtqt6EcZJG+5Paz8HbT7pI2nm76rGudZVDP6WjSGpoBCTherHe\nUFCi1FhroevIejt9LXIPyJbH/bQ/9A2Xv4ntmmfc9gPjTlSeQ/22x21PE3RmfEOxvreekToXrCOX\nfCksrRv2WdbKZJ/KFugd52QdF8fQgGMIJp29IUs3ongT5vOYddA07CiSdc2DNDDYUj12UbrIY32h\ncNJF4npijLQAPvw61hVfS0uQ/77UzRpexhhTUfiWE7eckvpcbC9deRAW4f4+cp8WS+tf6S7SuQiW\na00IzWfW97G1R3yohvFr3oGo6wXPHxTviZ/75Xod3ZYGS1Uxrk/aVLyn6rjcs8UkYsx4BeH3Rk5L\nFHk1dA/mG435x+PUGGOi52IMu1tz5tSnzzrxQLccZ2wp3HQQv9HP2ge052NuRtI9QnCKtFev/QzX\nNzgb54g1Zowxpn4XNNx4/eR7rpZjUocpegbGUUcJaqit9eJD935sEe1vrYusVRMQj9ca9lSIPD4+\n3g/7WfWUNcdm3/tj424ce/NJJ+48K9cQzwDUgfiluEfma2qMMUG0NlRtwbo4aGmsTfzmHLxGFuFN\nh6WddyCts01Ul0PzUNuqdpSI90TlQS+t9QyeCfgFyGcKsUvTnZg1/+KWyXtltmIPJe2gzrOy/rO+\nKmtDsU6qMfKeJHfezcaGds4oFAqFQqFQKBQKhUKhUIwi9OGMQqFQKBQKhUKhUCgUCsUo4ry0JoVC\noVAoFAqFQqFQKBQKxchCO2cUCoVCoVAoFAqFQqFQKEYR+nBGoVAoFAqFQqFQKBQKhWIUoQ9nFAqF\nQqFQKBQKhUKhUChGEfpwRqFQKBQKhUKhUCgUCoViFKEPZxQKhUKhUCgUCoVCoVAoRhH6cEahUCgU\nCoVCoVAoFAqFYhShD2cUCoVCoVAoFAqFQqFQKEYR+nBGoVAoFAqFQqFQKBQKhWIUoQ9nFAqFQqFQ\nKBQKhUKhUChGEfpwRqFQKBQKhUKhUCgUCoViFKEPZxQKhUKhUCgUCoVCoVAoRhH6cEahUCgUCoVC\noVAoFAqFYhShD2cUCoVCoVAoFAqFQqFQKEYR+nBGoVAoFAqFQqFQKBQKhWIUoQ9nFAqFQqFQKBQK\nhUKhUChGEfpwRqFQKBQKhUKhUCgUCoViFKEPZxQKhUKhUCgUCoVCoVAoRhH6cEahUCgUCoVCoVAo\nFAqFYhThfb4XT336rBOHZkWJ1/rae5y4amOhE6dePV7k1X52zomjZyc7cXBymMgbGhhy4uKXjjpx\n5k2TRV5nRasTu2o7nThmJj67rahRvCc8N9qJh4eGnbjpSLXIi5qW5MQlrxxzYu8QX5GXculYJ+5t\nw3noKm8VeS2Hap3YNybAiSOnJoi8+s9LnXjeQ48Yd+PAc487cdplk8Rr5R+ccOLklblOPNDTL/La\nzuCcDtJrrppOkRc7L9WJe5q6ndjDw0PktZ6oc2LvIB8n9o0KdOLexm7xnv5mnGtPfy/zVQjJwVgN\nSAh2Yv/IQJE32DfoxH6heK1qW5HIc5W3O3H0BRhn7WfkOPP0x++YdsN9X3l8/3+w50//58TxF2aK\n1wJiQ5y4YuNpJx7oltcwKD3ciUOzcY68/OS57KHz7h2EsV+9SZ6X8ClxTjzoGsD3pGBu120tEe9x\ntbmceOydM5y4ZP0JkZe4OtuJS9865cQhqbJu+NP1Dc2MxAue8rlzZ3mLEw/RdR+0zpGXP0rixDXf\nMu7GvRde6MQ1LS3itSffw9w//Y/9OI77Foi8/i6cw6CIdCf+5bUPibwfPY/jP/vcYSfOvn2qyGs8\njDrI5yZuPj67YV+5eE/V7jIn9vXBuD9eViby5i1C/a4vrHfiDfv3i7xr5s1z4sxrJjrxsX/JvLGX\nTXDi9gLMv+yr5TlytaH2JmVcbtyJgy/+yYnTL50mXqs7hLUwYSZqbUetPH+e3hifXGs9rHFb/RHm\nHK+RrS0dIq++rc2JL/2/a524p7UZx/aFvDZ9jRhHgWmh+J7+IZGXcfEFTuzqwFjpKJPrnT/V7q5K\nHE/7aVkny4prnHh4GOtx3pKxIq90F/YOa/74R+NuHH3jSSeOnBQvXuO9AZ+P7rI2kcfXMXwy6mHL\n4VqRF5iOuuXpg/d0V7SLPK4/UbOwH3HV4Hp3V8lrHz4h1on5GvQ0u0ReP+1VOotRe+IWp4u87lp8\nvou+q6+lR+TFLUpzYg9PrO+8rhpjTEAManRy1hXGnTjynyecuOG4dc5DsOfyCaU9nKfci7TRnjIy\nN8aJA+KDRV7nOZyz4MwIJ67dKedV3NwUfPYJ1LzQ8fjswZ4B8Z7aQ5VOnDADe4zSvaUiz5P2Ua6+\nPie291dRIdgT9PajvvTQe4wxZmAIYzvngiwcX6+8hpXHcHwjMRcPvoDPdFnjm/ffvJ+LmCjnLO9L\ne2ns97X3iryOgiYn9ovFfIm9IFXktZ6ia5eL/VI/fV7Np+fEe/zp8wKSUVNDsyJF3gBdf59ArJ+d\n5bK+hGRgnLWfxXEb63oP9eN6DXTg+CImy3uNsjfznXjxr39t3Ildj/7KiWMWponXeI/fsKvCiROs\nvey5V447cfzSDCduPiDv1ZIuHePE3dU4Z92Vsp6GjUNt5PrnF4na0N8p50T1p8VOnHUj9i+NBypF\nXvgE1PvWk7ifiZmdIvLK3sA5T7kiz4nF9TTGdBRhrY6chuvG64UxxvhHBzlx6tirjLvx6UPYR3a4\n5BqSmIoa5huFcxiYFCryGnfhXMUuwVjoqe8Seb7h/k588n3cA8y88wKR11WFa8zzl+8DfcL8xXtq\nj1Q5cWQa5l/sfDnPO0tQ10PoHqJo/TGRF0zrWNLFuFfmOmGMMcU7zjrxtLvwO5oOyvHj6Ytjn3LN\nd40N7ZxRKBQKhUKhUCgUCoVCoRhFnLdzJjAx9KtfpAe3oRPxNK16U6FIy7gW/z3srsVTza5q+YST\nO1qyb8F/dqu3nhV54ePxJJSfxpa9jaeTyatzxXvOvngEx3M9/isbNjZG5HHHDXfs1O+R//Vspqek\n/ATWK0CezpiFeELnQ0///awOjtR148xIwifUz4lL3joiXgvJwZPCwT48ze9rlU9Mo6YmOjF3IfjH\nBIm8oAT8h6GF/pPF/xE0xpgI6h5q+ALnNygN3R32Zw/20n8bgnE++cmnMdZ/q+g/FlUfy86PpBU5\nTnzmmX1OHDJGdomlX4P/1vMx8JNPY4xp3COfjLoTnnT+AuNC5GueuL78xD0kNdbKw3xpPImOlo6z\nzSKP/4PE1y18ovw8v3D6z2Qqnlr3NKCbKufW2eI9baWYO53UadbXLf974eWDcxtJ3+sTLp+O+wTx\nf0TxrDkw2ur0o44d7tprOS2fentY/1V1N265bqUT//uNzeK1jhJchxk/utqJH7n6RyLvxnXovtm8\n5T9OfPNtF4u8Nx56y4kjg/HUP8dLPpMv2YH/FC34Cf4TU70f/zk4ufmUeM/UdegYCac6OsFrnsgL\nCMB/v7bd+QsnvuPa1SIvdAy6G/k/Smkz5X/gnvo//N5H/vOwEw8Py//0vvfLDU787X+5t3MmbgGO\n6cRftojXxtyFbrAjf3zfidPW5om8njrMES/6z2mX1ZkROQt1t68V//nr2CP/A3X5Y3c5ccMprIUh\nVE8DU+R6HjaO1u2PsM7aHV0pK9AN21aINdL+jyP/ZzJ+Dta0+i/k+jnhYtRTvwjUkOhc2XXLXSAj\ngbCxGHOuOtkB2lWM2pRyOTp6hqclirz+TvyXuvYT/Bc9YVWWyONOiY4ijO+gNNkJ6Ev7CQ+ap+F5\nqIF2R2R3FfZSA114bXhQdkBxtyR3YdVtLxV53OHsG+pPsZ/I86JOUWOwf6vfK9fB5sPolEp2czNi\nxSH8Fz5ttqwVDUfwvT5h8tgZYUm4Bn3UcTHYJcf3YDftj2ish1EXjTHG9FPnQmsLxlW4N/7TPmz9\nNzx+Os55Tz06V+NSo0VeB+2hoxLwvT5WdzePI+6qaMlvEHkx1J3V24Tfzl1CxhgT4Cs/393gjmu7\nC4g7O/l3tZ2Rv4X/c95yFHtPrnPGGOPhhc8fpnnAeztjjHFVo4OH99B+VJeiZieJ9/hRN4En7WGo\nQdAYI7uuuROnp1bWdX4jz3u7445re+QU2lvvqRB5mTfKznl3Io46Xeq3y26yhJXohB6i81z2n5Mi\nL+1KrBt8Pbjz0BhZ27gbo6da1vGAROyVe+qQx92QwbRGGmNMOHW4NeynWmZdxEZ6ra8J9aCsLF/k\nxS3DeeHfFDZGzu2uUqw5HcXYC0ZNk91PfK88EkichzrKHZbGyPvF4AzEXzz5mcjLzMQ6eZTuOWdb\nHTGVG8448ZjFuG8/9NwekRcdivHdR52A/oGYl/b9fFYy9lx9bZhjQ1Znpy/dx/B8iciW9xDtJbg+\nR/6514ldVjdiWCDqw+l/HXTipHnpIo/ry5dBO2cUCoVCoVAoFAqFQqFQKEYR+nBGoVAoFAqFQqFQ\nKBQKhWIUoQ9nFAqFQqFQKBQKhUKhUChGEefVnGEXl0HLvcc3BFwv1mmItVS6KzeBUxa/GMrc7cVS\nqZrdoBpIuT6aXJiMMaZhHzhhzDGLX4rPHrCU8FlDpPR18AHH3DlX5FV/DN595Hgowfdbau9xC8Ah\n7CwjpecMqcjOyug+4cRZtbRUuojfm/JD43bEk+tK80npaBBBXHZ2aQhMkPoElR8WOHHyKiils3uW\nMcb0thL/nTiyySuk5kJbMbQ+cm6f7sQNpGg9YHG+w8eDs828QdZiMMaYhBXg+wdFg6/pFyv1Repp\nLAURp7XHcqBiNwYXqcGzWrkxxiRfMsaMFFIvw/mr+EjqOrHWUecZcFUTpKyHcBZh7muI5SQQFIt5\n1d+DMcH8XWOMaSX+um/Yl9eDxuOSe9y4E9fXKxiaBeO+JbVpyt6FxkniRV/tItF4AIrsEaSPU1t0\nRuTFzgan/dST4LPm0tgzxphKPrfLjNtRcxrz76oFkn/LzljlO3Z/ZZ4/uYisuR36M8GpUvtg4SB+\nW91RcjuwKMvLfvltJz7291edmDVFlv5slXiPjw9qatFr25046+pZIu8BcryqJS2Tqx9eK/JCEsFR\nfuIOuCHd9IDUi/n2T6934u4m8PY7LeegNY9cZkYKXPO9LHelT/5vkxOzDkzd8eMiz5PWVq7BcVOk\n/lhvN2rWoSd2OvHcH8vf9/mvcN2iw0lTKQo6DD4Rsl6xvk3u3dDKCbR044aGsP55UA1ptRyJUkgv\nYMND/3bi6csth8Bt0Dgacw1eay4pEHldFVJ/x93gMdN+UupXeJAmiKsBWgVtJ+UaEkwc/KQ14MzX\n75B1j/XJgrMwT1uPy8+LmYM6Vbez1Il5vbO12NhVyCsANcR2F2FXMF4LImdIHR1eT9iFr71YapOx\nhk0T6buwo44xxvTUnJ9b/3UQn4O5Y68NoaQlMTyAolddJMdt2jSc84FKHGt7o9wHJJAWAzvF1VbJ\nvWx8Es6Ljxeu+5nNGN95F0t9Jdapaa/EuAyJl/uwiiZ8VyprszRJrZJw0lVpIQ0vLy+pk1e/D2Mk\nkly/QnPlnqDyWJUZSbDmU6/lMsZumX2kixNqaQOy/mPCcuwZbD0p1s7jNY5dZ40xJmI67gFYNyuV\nNKg6rTkx2I2xz7owtuYRu5L6Ul0OTpf6J14B2COxzlEEOWUaY0wXnSN22/TwsZw4m6QDqjtRuxma\nW6zTZYwxDTTO+FzwemKM1DgsfxvOo7ELU78yj8dEsqXtxlMkLAcaL6x5OmDpHbJWEDvU2eONtRrZ\nXbRht9T5qd2C8yJ0Ki3tmEjSLxqiWmbfzw645N/uxtltmAeJudIRjR2lWLds1g1y/y7uL+i89XXI\ne+nQCbjXYJc2doUyxpgg0vViXTB25uXxZ4zUl935MvbTOYlSw8eXHNZaSU810F/OWXaWTMzDZ/Ac\nNcaYjjOotzzWT285LfKyZmSY80E7ZxQKhUKhUCgUCoVCoVAoRhH6cEahUCgUCoVCoVAoFAqFYhRx\nXlpT81G0qtoto2znxdSjwGirLY/sFrtrpP0bo+UU2VNHsB2dfH6UuhqtSn1daFcUdnv/Y/mInxk2\nHq1tzQWyVdOb3lezHS1ScQvSRZ6PD9rG/SLR9lb6mrSFY8u+mLkpTtxyTLbVZt00xYwkqjbDQjp+\noWyl4va+dmpZs+06AxJAaan4CJQRX6tdk6kZwRloRXM1WtZ/9HmdFWglExS5udniPaGhaIHv6Djh\nxEEp0u6t+iP8Xv+b0LIWf0GmyDvzDOzQsr5B18ByU2a75V5qcU9emSPyGqiNPFk6qX5tnHoaVt8Z\nV8mWaL8wzJegFGk5yGD78cFe0B1arVb9ttNo8R/oQMtn5AzZDhiajdbnsEy0P/a0go5Qt1Pa6DJd\nIJ4sBquJ6mCMMUmrcG4T0sDP8vKSVtpefrCL5tZ/2x6x5DXQSqKojb+3TbZQZ14taU7uxpwHYaX9\nwneeE6/Np3Mz1IPfsv2ktGac0IxaMv0eUJ5sa3emuLV0Ydwef3K3yPs8/1knHpeMWr72999z4n2P\nviTeU96Itv6VP8P18fCQS8pP/gFaU0AktRXX1Ym8XY/C+vpEGSghkWNkO3NvJ1r+meJauOusyFv5\nm7vMSKHlCOr34JC0K+bW18LXtjqx3dZ+8gvU0Nwi0CUyrpQUIB9/zOeMJaiHR/64SeQt+fk3nNjV\nhfNS8irGfUCybL+NoPb+T3/9kRMvuGuByONW5AZq7w+bFCvyWvNxTdc8CvrZvt/LYw2PQOt/3eel\nThxiWYva9Bh3IzgVNYL3CMbI9u2mfdgneFuWxaFEZa78ANfUw7KrT74YlFdPes2mQrNFbOQk1NSA\nGKylw8OWxTO1vbcWonYHWjRUtrRmy1+2ojVGUmzaTmBt8E+Qn8fWstFUU+uttv7QsfK6uhMDndin\n+J3Hep1rY0C5vIa8brS34lzEjpPUEW7jZ2vltFhZo1xEjYoiymIw2SSz/bkx0gI9gugXg9Y+LC4c\nY9aTKJVMGTXGmOBOXKu6NqzHbHFrjDGDXRg73oGoD/Z+PzpBUmbdDf79/rGSthdG1AWmxwxadA9h\nUU9ze9iyQA5MBlWsjehpvjGS9tlRiM+LuQBrbgNZxYdYVJduoooGJOEaRM+QltstpzCv+ppBNbKp\n4zxOmOYfnC6vB1M9PIgCwrbQxliUG8mW/trw9CfrcIuy01WK8xJ/IfZ99p6lYS+OL/MmrIWdFsXV\nNwT7wNwbljhx3TG5V0qbDTp2dzf2mAFjUVt7uyWlNWPa1U7c3o57uvY6uUf1j8H6xHMnfom8z3CR\nXERfK65T2zG5727ajXHF1uGBSZLaODwg56a7kbMClLQ+i2LoQ3ImXA/teyYfug+s2iLPG6PDhc+P\nS8Bcam6Q1zuM9ir8fMA3DOPgaGmpeI/rRaxjrd2YY6Hj5XpUsR97mjiiyUbPlpIqvFcJJwqoX7is\nG65yHHvkdNwz2c8ybMkWG9o5o1AoFAqFQqFQKBQKhUIxitCHMwqFQqFQKBQKhUKhUCgUo4jz0pri\nF6I9i9tljTGmhdT5W06gzTtgsaRVBJCzANOauD3KGEmj4XapPsspydMX7Uk120A98o1Ae5NvuKQ+\nsKp2ENEdXJaLgF/0l7dLdZZLJxBW8x4iF4DQPNni2HkO7+trw+9IXC7pMJWfgIYT9w3jd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I/h3IxrP2N8qxyNX0j7+M/l2+plzEcT+OJheJoUaZrh1fgvsUQ1Xo247CZSVrprRZ6diL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\nmmBy4B+/dtox+QniWFQy1ppuuq/DvkERl1iR7rT7m3vRbvGJuPTFBU7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Q5MPS5PcFxeNv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\npOe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wDMMwxhF7OGMYhmEYhmEYhmEYhjGOXLbmDNtUhadrvdSF52G9GZEFTXpMka5N0F8L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\ngHa99Jn35VLMuBdaaVdrnbQMWk2uP1H9ltZuF1/CNnK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DMIxx5LI1Z9ixqLOu85LtMq4r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VaprGtdaaACu1wYo75WST9wk+Oih9xsmCJkjBH8GncMtRu/JF2igsMmlmJo0wUZTKnK3Yra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\n2q51tbkhaMrYVjs2qfux1XDb3h95bVePvvJzsKLtfhF1OHqfbVP98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7UVe7Ae14u18KNH\nEJeqbtCa+cuPYQy2fAAFQVwL62gUOu2xbrRDtdrSMzYMna6/EPUr1nz+RtVv4CDGN9cmW36/Lkri\nd+JSOildj5oaXHtCRM8R1m6PHNPWp1x3jGtVlW7SmnmeV7llqDUyFdXfy1aRfEzBGlxbrn8hIhI+\nD610b+cRr+0r0P34s6eo5kzSsZ7NJ9tNrr3glFVRdUy4NtS0owXnNWc+YDvMymua1GulZNd57Jv7\nvHbRSj1nj/wv1KBpvgf1kH79pe+rfps/stVrt34c9V52f/0F1W84hhpB7/4Y6hhkZyEedj95Xr1n\ny+evRz+q7bb/b/Rn16zAb+I9W8NtG1S/th/D1nnx+3Hcjbv0Olt1A/YIp76JGip1tzjWorP6uqYT\nnmOZmXrOc50KrpuUV633YuNdiIdcm4zrNYloO3OuVTLa0ab65VENi2yqV5SKY17y+0VEJoexpwo1\nY33KLdHxlOt2TU/j+MYHdU2wbKo1Eu/HWphXqWtxDR7E+wqpDuBUWNe5SI5ibSq/TdJO4XJY53Ld\nLhGR9sewbuT4UIMlx6lDUl2PPQTvTZbcqGvYFLai39kfIu6Vr9I1tHwXMC54/EzPIG6ET+r7GOae\ne1HHa2pEn0+uW5ZH9Q6nx/VcyS3nmI/YOzl5WfXjmB0+imOquk3v42fd4n5pJLQWNTmmnbpifD3q\n78M+PtepZTdO913uvofhOne81rh1Qi7vRt29omWI3dHzuA/qfv6Ces9jB1GjaEMLzt+6pTquPfPm\nm177SAcspu+5fqvqN34J1/7aLbjPiDj1pBKXqc4pjYlEZ1j14/opMg/lZ3yO1TTD9Yy4hl3hIl3L\ntPtXqD/EVtpF+bpeGtek5etYtkHvg6IXqVbPtdu8djyM815UoescTUzg3oVr53Ac/49/Y49VuOTK\nNuU5BVhfUpO4BtFz+p6ardm5DlPFTl13MODUjnOxzBnDMAzDMAzDMAzDMIwFxB7OGIZhGIZhGIZh\nGIZhLCBXlTVx6lhohU7nZavMMUrHn+yLq35sE8ppands26j6PX8AKWI3rIRFXk6RTrEqXofUw9pV\nt3jtaBTW3H7HRneiD+mfLFc6f+Si6vf9l1/22rs24vg4fV5EZG0j0pNmXoHEKVCrU0YLmiGp4bTI\n3ue1ZV9otT636SbZj2tScWOTei3YgPS5yEmkZ7kSnXgXUu8zKUUsPqHT68fi+K5VDbDedG0tOZ39\njYeResiSths3rHaOAdeOLX+793Sqfi3vQNokW7++hVmMzZ5XMX4Sl7XVZg/JgVZ+CqndA7u1LK75\nvTqtLp1MjuO8cOq1iEj/QciwApU4r5Mjei6y/eCEk7LNzNGcZTtbf6m2gM8kyQWn1Q4dQMrtUKhT\nvadkJVJfffmY23ll2jadmZ5G+mRWlo4HocVIAR88gvTU4QNawlZzO1JSIxeQ5l3ipDJHunFNS0q2\nSbrheVVEqdwiWkaa6MX1KWjVKaNC0pmCVqRhJp3U6YPf3uu1CwNIH62/QafnjhyEvOXYeaSJsv32\nW46V5KV9r3TiuLt0qm7hcozb5ltu9tquBCGHbA/790MmwBb3ItrOdtX7IcdwJTbD+zAGG7Xj6m8N\nSwYynRRZllKwDCneq2MKW8KGluHcRtq0HK94JcYnp/b6iwOqX04+S4/wGZELkBXHL+n0aLZt7TuG\n+VK9Wlshc0p6HsmkgrX62oRPI1awFHiiR8eayhshkeMU6qRjGTrfsiZOP452jajXXvk/sK7e/p5r\nvHaoVZ+ba76IMXhp92te+6YvaRnv7v/5rNduWgaJamOlXvu3fBDflZGJ8768Fu9x15ltjfd57V/W\nj3SJAAAgAElEQVT+8O+8dusdy1S/jucghyprQEzx+3UMZLnl6DlYHPP5EtHyyHGyix3Zp2Nv5BT2\nFVs/l96YyunqQ0f1viqX5Hj+IsSbt8iV6HdpGaFe74KluAbRXpyXgnptFT50HOe5dBWOYbwbsZFj\nvYjeH4bbEbsynX7BIshe4v1s2a26qfPC0i+2YxbR++vJIZIZL9YyY16r54MkybrcEgrNtJ9j2U/4\nhJYUlZAkdOQQ1rSGe5erfnO07wuQ7OzSkUuqX1kJ4hvHs3KKycWONDl8huI3fY8rYyvbhDETPoU1\nsnefPoaCEoyfxgewkHU/r6WNldc1ee2xEorDzr67emu9zBfZFMtZRi2ipWoc52MdWmJ9aRjnb/l6\naHay87S1O5enyPRh7pSv03ubjAy8L9qDedW9F/u8Jw4fVu9hKdNffPvbXvuhr/y56vfl3/uQ1450\n4ne8eVrLpDasgawuQSUharY2qH681xknKVPd3VoCPvBqJ/5xg6QdLn1RsFjvPXMojsbasGb6CnV8\nGBrB8devwZibcWTvw/sha8trxO9ne3QRkapVuB8fbse9GpdG6B/V92P59HmTgzjv7m/yBbFP63sZ\n+193b1dNMl5fLu6lChbrmBo+gfPX+ABKMlz+hS5HUb1rsVwNy5wxDMMwDMMwDMMwDMNYQOzhjGEY\nhmEYhmEYhmEYxgJyVVlTAVVgjjgViTlVuWApUusT3TplNDMb6ZWxNqR+XejT7hVbW+E4s/SDcCng\n6t0iIjmUOsfVmEtKrvXagZt1uti5nz/ltd/1yT/y2s2LdVrR9auQLrxlNVLJTpzT8qcQpUGNkLvC\nolKd4hhtR9rXeDt+u+s4lRXQKXvpJqcMKfBuFf6LryGNq3oJpCUjTnp9YQVkB+XX4vy6Kah1bZAk\nFFOq/cyETq/ktNumFqSKVxQhFa1wlZZS7H0M6Yfb34k0twC5I4iIjBxGSmv4Ms57w61OGhllzSfJ\nvSJQreVpPR1IOz3yL3DuqFtXp/qpdOk0Symy/BgjrpsKpxqyJIl/k4hIMUkTByh9Nu6klmZRCmly\nCNe3O6odAsqbkPr8pX/+gdeeITeDP3/Pe9R72PmqpB6ytYwMPSeSSThhjY8jHXBmxpGvnMDcZNlk\n5Y1Nqh9LMMZIfhFzpB6zThpwumEJY9RxlJqOIWWbY0Tpel25vvdFpO/HziHGRHq1pOi6L0D2OXoc\n8TbHcTJil4XrllMsJ3nfgR/tV+/hdPDpCxhz1/7+TrkSySSlW89omQ+7PPF4LmnSKb3DbXArcWMZ\nU7Gz6Yqv/bYk+q4sCRTB+GSnJDctm9cxll7mluvYc/FhpPBmkHYhK6g/r3g10shZusDzN6dES6Ei\nx3A96jZTursjm5ym4+P0/L6XOuRKcEpwvpNGzLBTjq9Qj8vK7Y1u97Tip/Nx6Jt71Gs1JTjmInJ3\n6X5OO/n5yzu9NstGL3xXO0bWVeMzCpehnblKn+sXv73bayemEA/u+jTm8rmHjqr3/OVnPuO1SyhW\nJBwp3c4/+6TXnpxEfL34knZ1Yjeb7scRe9lNQ0SkiNbnJXdAOjKyRzuwLP/IXTJfhFowRlzJ6/BZ\ndgzBmuQL6vUzeuHt0/Ndt0MRyLVYChXp0un0PNff+DrkbDPklMMyMBGRrb+D/StLQAoqHNfMOZLR\nk9tHrEuv4aqcAMXWDMdxMZOcSlgeknKc08Yv0zqp1XJpIY/cTKNn9LoYprIJvPccd1xs4pew/tXe\nifuJgrJW1Y/j6KrPY+85dEq7bn3va4947Zvj2Ksseg/a+TXF6j2hpiavPXwG8pYJZy6yjG38In5H\nyz0rVL/CFsQh3l/W36Y3mINHEIt5n3fpV2dUP3auTTeDL2IvVrRW3wvlU2xkN8HyrXoPXZzAXkSV\nXDivZafszJmVxY6EOp6yxJClaR1UPuGpl15S7+kawr3uh++5x2vnNWiXN473MRp7XNpBREsYK+j3\nujJRlg6maC/I90oiIrmO41q64ftRdvoV0U5Hldc3oR9JJ0VE8vz4LUW03rn3JOxQpfayU1qyOEaO\nxvyMIbQaa9BbHJFpnsfO4rPzanW/2CV8F8fHim36OcIASeF8RVg/3XPE44TdLQtX6/vZWDs5xTqG\nwyKWOWMYhmEYhmEYhmEYhrGg2MMZwzAMwzAMwzAMwzCMBcQezhiGYRiGYRiGYRiGYSwgV605k+iB\nTjIrV2vcq2+BZVmStP8ZWdpWamoU+kLWKAdGtIYwLxcatekJaGSrrtE2eLFe1BOJj0EDPEd63mRi\nQL3n9Zeh0f7pP3zVa184p2toFObBviunHO1trRtUP663U1eLeidcV0ZEpHA5tHZ+0geXb9HWi25d\nnXSTUwptPdtLiois/vAmr935s5Nee3+b1t/eUrzOa4+S/i+3Sn/ekmqqH9OA+hUZ2Vo3OfQ6zn3N\nHagF0/7jA167qVpf+yDpGFmX/fg/Pav6bWiGVWtegOqxONasbMvYtgf64LM92gr03o/AArjn9U6v\nPXpa6yIbmudPzxs+Dx2sW2uDtavl65q8duSs1huHSbt6aQ/0wRmOD2ec9PB/9E//dMVjuv/22702\n2w+yHXp2kWN9XQvBejKJfjMzWqebnQ3d5lgHrg1bFYvocRAly9aKnbpeBZ+jfNIys92xyFv1o+km\nvxFj5NLz2nKx4RbMg5kkjmPAsddkLezoCWhfSzZom1+2Xu5+Fdc7K1M/k295ALW2nvnGc1772l2o\n67Rh+Xr1nm99HXr8hjKqUfGktvjkejZ9L+MY1n74E6pfwg/NPM/TkXZtP5iKYmxybZqckK6nwnVc\n0g1bUs+mtNY6l6zsox1Y4woXlap+PM64loAbo3jcsg3lSy9o+8/t3ajNk6L6Gn6qr5Ht1Klp/uAa\nfDbVWnLrUrD1aTbr5J1j5d/ENSTYNlxEZIJs4qvIVjt6YVT1i5zBfK75sKSdeDf2N02bmvRrVIeL\nr0mgxqlvth9rxQzVawrU6X5+ijMlKzEnnv3yU6rfO764y2t/87+jjte5J055bXf+Njt23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LJLFWl5J8iPf3IiKdD+P46r/0bkk3YSqzkeW4abE0J78O13HCcVGtWnKD1w4E8J5USsfo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XIpLqxzxmAwGAwGg8FgMBgMBoNhGmF/nDEYDAaDwWAwGAwGg8FgmEbYH2cMBoPBYDAYDAaDwWAw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\n0fev+RzWweSrWIPJl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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