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diff --git a/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html b/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html index 1f3cfbc..86a1954 100644 --- a/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html +++ b/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html @@ -2,14 +2,27 @@ <html lang="en"> <head> - <link rel="stylesheet" href="https://unpkg.com/latex.css/style.min.css" /> + <meta http-equiv="X-UA-Compatible" content="IE=edge"> + <meta http-equiv="content-type" content="text/html; charset=utf-8"> + <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1"> + <meta name="theme-color" content="#6a9fb5"> + + <title>Polynomial Regression Using TensorFlow</title> + + <!-- + <link rel="stylesheet" href="https://unpkg.com/latex.css/style.min.css" /> + --> + + <link rel="stylesheet" href="/assets/c-hyde.css" /> + + <link rel="stylesheet" href="http://fonts.googleapis.com/css?family=PT+Sans:400,400italic,700|Abril+Fatface"> + <link rel="stylesheet" href="/assets/main.css" /> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> - <title>Polynomial Regression Using TensorFlow</title> <meta name="og:site_name" content="Navan Chauhan" /> <link rel="canonical" href="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html" /> - <meta name="twitter:url" content="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html /> + <meta name="twitter:url" content="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html" /> <meta name="og:url" content="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html" /> <meta name="twitter:title" content="Polynomial Regression Using TensorFlow" /> <meta name="og:title" content="Polynomial Regression Using TensorFlow" /> @@ -26,24 +39,43 @@ <script data-goatcounter="https://navanchauhan.goatcounter.com/count" async src="//gc.zgo.at/count.js"></script> <script defer data-domain="web.navan.dev" src="https://plausible.io/js/plausible.js"></script> - <link rel="manifest" href="manifest.json" /> + <link rel="manifest" href="/manifest.json" /> </head> -<body> - <center><nav style="display: block;"> -| -<a href="/">home</a> | -<a href="/about/">about/links</a> | -<a href="/posts/">posts</a> | -<!--<a href="/publications/">publications</a> |--> -<!--<a href="/repo/">iOS repo</a> |--> -<a href="/feed.rss">RSS Feed</a> | -</nav> -</center> - -<main> +<body class="theme-base-0d"> + <div class="sidebar"> + <div class="container sidebar-sticky"> + <div class="sidebar-about"> + <h1><a href="/">Navan</a></h1> + <p class="lead" id="random-lead">Alea iacta est.</p> + </div> + + <ul class="sidebar-nav"> + <li><a class="sidebar-nav-item" href="/about/">about/links</a></li> + <li><a class="sidebar-nav-item" href="/posts/">posts</a></li> + <li><a class="sidebar-nav-item" href="/3D-Designs/">3D designs</a></li> + <li><a class="sidebar-nav-item" href="/feed.rss">RSS Feed</a></li> + <li><a class="sidebar-nav-item" href="/colophon/">colophon</a></li> + </ul> + <div class="copyright"><p>© 2019-2024. Navan Chauhan <br> <a href="/feed.rss">RSS</a></p></div> + </div> +</div> - <h1>Polynomial Regression Using TensorFlow</h1> +<script> +let phrases = [ + "Something Funny", "Veni, vidi, vici", "Alea iacta est", "In vino veritas", "Acta, non verba", "Castigat ridendo mores", + "Cui bono?", "Memento vivere", "अहम् ब्रह्मास्मि", "अनुगच्छतु प्रवाहं", "चरन्मार्गान्विजानाति", "coq de cheval", "我愛啤酒" + ]; + +let new_phrase = phrases[Math.floor(Math.random()*phrases.length)]; + +let lead = document.getElementById("random-lead"); +lead.innerText = new_phrase; +</script> + <div class="content container"> + + <div class="post"> + <h1 id="polynomial-regression-using-tensorflow">Polynomial Regression Using TensorFlow</h1> <p><strong>In this tutorial you will learn about polynomial regression and how you can implement it in Tensorflow.</strong></p> @@ -57,19 +89,19 @@ <li>Quintic</li> </ul> -<h2>Regression</h2> +<h2 id="regression">Regression</h2> -<h3>What is Regression?</h3> +<h3 id="what-is-regression">What is Regression?</h3> <p>Regression is a statistical measurement that is used to try to determine the relationship between a dependent variable (often denoted by Y), and series of varying variables (called independent variables, often denoted by X ).</p> -<h3>What is Polynomial Regression</h3> +<h3 id="what-is-polynomial-regression">What is Polynomial Regression</h3> <p>This is a form of Regression Analysis where the relationship between Y and X is denoted as the nth degree/power of X. Polynomial regression even fits a non-linear relationship (e.g when the points don't form a straight line).</p> -<h2>Imports</h2> +<h2 id="imports">Imports</h2> <div class="codehilite"> <pre><span></span><code><span class="kn">import</span> <span class="nn">tensorflow.compat.v1</span> <span class="k">as</span> <span class="nn">tf</span> @@ -80,9 +112,9 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h2>Dataset</h2> +<h2 id="dataset">Dataset</h2> -<h3>Creating Random Data</h3> +<h3 id="creating-random-data">Creating Random Data</h3> <p>Even though in this tutorial we will use a Position Vs Salary dataset, it is important to know how to create synthetic data</p> @@ -104,7 +136,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h3>Position vs Salary Dataset</h3> +<h3 id="position-vs-salary-dataset">Position vs Salary Dataset</h3> <p>We will be using https://drive.google.com/file/d/1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9/view (Salary vs Position Dataset)</p> @@ -159,7 +191,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d <p><img src="/assets/gciTales/03-regression/1.png" alt="" /></p> -<h2>Defining Stuff</h2> +<h2 id="defining-stuff">Defining Stuff</h2> <div class="codehilite"> <pre><span></span><code><span class="n">X</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="s2">"float"</span><span class="p">)</span> @@ -167,7 +199,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h3>Defining Variables</h3> +<h3 id="defining-variables">Defining Variables</h3> <p>We first define all the coefficients and constant as tensorflow variables having a random initial value</p> @@ -181,7 +213,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h3>Model Configuration</h3> +<h3 id="model-configuration">Model Configuration</h3> <div class="codehilite"> <pre><span></span><code><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.2</span> @@ -189,7 +221,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h3>Equations</h3> +<h3 id="equations">Equations</h3> <div class="codehilite"> <pre><span></span><code><span class="n">deg1</span> <span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="n">X</span> <span class="o">+</span> <span class="n">b</span> @@ -200,7 +232,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h3>Cost Function</h3> +<h3 id="cost-function">Cost Function</h3> <p>We use the Mean Squared Error Function</p> @@ -213,7 +245,7 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h3>Optimizer</h3> +<h3 id="optimizer">Optimizer</h3> <p>We use the AdamOptimizer for the polynomial functions and GradientDescentOptimizer for the linear function</p> @@ -231,12 +263,12 @@ Polynomial regression even fits a non-linear relationship (e.g when the points d </code></pre> </div> -<h2>Model Predictions</h2> +<h2 id="model-predictions">Model Predictions</h2> <p>For each type of equation first we make the model predict the values of the coefficient(s) and constant, once we get these values we use it to predict the Y values using the X values. We then plot it to compare the actual data and predicted line.</p> -<h3>Linear Equation</h3> +<h3 id="linear-equation">Linear Equation</h3> <div class="codehilite"> <pre><span></span><code><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span> @@ -300,7 +332,7 @@ values using the X values. We then plot it to compare the actual data and predic <p><img src="/assets/gciTales/03-regression/2.png" alt="" /></p> -<h3>Quadratic Equation</h3> +<h3 id="quadratic-equation">Quadratic Equation</h3> <div class="codehilite"> <pre><span></span><code><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span> @@ -365,7 +397,7 @@ values using the X values. We then plot it to compare the actual data and predic <p><img src="/assets/gciTales/03-regression/3.png" alt="" /></p> -<h3>Cubic</h3> +<h3 id="cubic">Cubic</h3> <div class="codehilite"> <pre><span></span><code><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span> @@ -431,7 +463,7 @@ values using the X values. We then plot it to compare the actual data and predic <p><img src="/assets/gciTales/03-regression/4.png" alt="" /></p> -<h3>Quartic</h3> +<h3 id="quartic">Quartic</h3> <div class="codehilite"> <pre><span></span><code><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span> @@ -498,7 +530,7 @@ values using the X values. We then plot it to compare the actual data and predic <p><img src="/assets/gciTales/03-regression/5.png" alt="" /></p> -<h3>Quintic</h3> +<h3 id="quintic">Quintic</h3> <div class="codehilite"> <pre><span></span><code><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span> @@ -564,13 +596,13 @@ values using the X values. We then plot it to compare the actual data and predic <p><img src="/assets/gciTales/03-regression/6.png" alt="" /></p> -<h2>Results and Conclusion</h2> +<h2 id="results-and-conclusion">Results and Conclusion</h2> <p>You just learnt Polynomial Regression using TensorFlow!</p> -<h2>Notes</h2> +<h2 id="notes">Notes</h2> -<h3>Overfitting</h3> +<h3 id="overfitting">Overfitting</h3> <blockquote> <blockquote> @@ -585,14 +617,15 @@ values using the X values. We then plot it to compare the actual data and predic <p>Basically if you train your machine learning model on a small dataset for a really large number of epochs, the model will learn all the deformities/noise in the data and will actually think that it is a normal part. Therefore when it will see some new data, it will discard that new data as noise and will impact the accuracy of the model in a negative manner</p> + </div> <blockquote>If you have scrolled this far, consider subscribing to my mailing list <a href="https://listmonk.navan.dev/subscription/form">here.</a> You can subscribe to either a specific type of post you are interested in, or subscribe to everything with the "Everything" list.</blockquote> <script data-isso="https://comments.navan.dev/" src="https://comments.navan.dev/js/embed.min.js"></script> <section id="isso-thread"> <noscript>Javascript needs to be activated to view comments.</noscript> </section> -</main> + </div> <script src="assets/manup.min.js"></script> <script src="/pwabuilder-sw-register.js"></script> </body> |