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diff --git a/Themes/styles 2.css b/Themes/styles 3.css index afc641a..afc641a 100644 --- a/Themes/styles 2.css +++ b/Themes/styles 3.css diff --git a/about/index 2.html b/about/index 3.html index bc95246..93c93b7 100644 --- a/about/index 2.html +++ b/about/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/about"/><meta name="twitter:url" content="https://navanchauhan.github.io/about"/><meta name="og:url" content="https://navanchauhan.github.io/about"/><title>About Me | Navan Chauhan</title><meta name="twitter:title" content="About Me | Navan Chauhan"/><meta name="og:title" content="About Me | Navan Chauhan"/><meta name="description" content="About Me!"/><meta name="twitter:description" content="About Me!"/><meta name="og:description" content="About Me!"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a class="selected" href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>About Me</h1><p>Hi! My name is Navan Chauhan. <a href=""./assets/résumé.pdf"">Résumé</a></p><img src="/images/me.jpeg"/><h3>What do I like?</h3><ul><li>In my free time I like restoring and colourising (YES, I follow British English) photographs. <a href="https://www.behance.net/gallery/73508827/Restorations-and-Colourisation">My Behance Profile</a></li></ul><ul><li>I also like automating the mundane stuff using Python, and I have started dabbling in Swift.</li></ul><ul><li>I love creating weird machine learning models using Tensorflow ( I personally preffer Turicreate tbh )</li></ul><ul class="item-list"></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/about"/><meta name="twitter:url" content="https://navanchauhan.github.io/about"/><meta name="og:url" content="https://navanchauhan.github.io/about"/><title>About Me | Navan Chauhan</title><meta name="twitter:title" content="About Me | Navan Chauhan"/><meta name="og:title" content="About Me | Navan Chauhan"/><meta name="description" content="About Me!"/><meta name="twitter:description" content="About Me!"/><meta name="og:description" content="About Me!"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a class="selected" href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>About Me</h1><p>Hi! My name is Navan Chauhan. <a href=""./assets/résumé.pdf"">Résumé</a></p><img src="/images/me.jpeg"/><h3>What do I like?</h3><ul><li>In my free time I like restoring and colourising (YES, I follow British English) photographs. <a href="https://www.behance.net/gallery/73508827/Restorations-and-Colourisation">My Behance Profile</a></li></ul><ul><li>I also like automating the mundane stuff using Python, and I have started dabbling in Swift.</li></ul><ul><li>I love creating weird machine learning models using Tensorflow ( I personally preffer Turicreate tbh )</li></ul><ul class="item-list"></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
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files differindex 924ddb0..924ddb0 100644 --- a/assets/résumé 2.pdf +++ b/assets/résumé 3.pdf diff --git a/feed 2.rss b/feed 2.rss deleted file mode 100644 index 0da058c..0000000 --- a/feed 2.rss +++ /dev/null @@ -1,718 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content"><channel><title>Navan Chauhan</title><description>Welcome to my personal fragment of the internet.</description><link>https://navanchauhan.github.io/</link><language>en</language><lastBuildDate>Sat, 18 Jan 2020 19:19:50 +0530</lastBuildDate><pubDate>Sat, 18 Jan 2020 19:19:50 +0530</pubDate><ttl>250</ttl><atom:link href="https://navanchauhan.github.io/feed.rss" rel="self" type="application/rss+xml"/><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy</guid><title>Converting between image and NumPy array</title><description>Short code snippet for converting between PIL image and NumPy arrays.</description><link>https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy</link><pubDate>Tue, 14 Jan 2020 00:10:00 +0530</pubDate><content:encoded><![CDATA[<h1>Converting between image and NumPy array</h1><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">numpy</span> -<span class="kn">import</span> <span class="nn">PIL</span> - -<span class="c1"># Convert PIL Image to NumPy array</span> -<span class="n">img</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">"foo.jpg"</span><span class="p">)</span> -<span class="n">arr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> - -<span class="c1"># Convert array to Image</span> -<span class="n">img</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> -</div> - -</code></pre><h2>Saving an Image</h2><pre><code><div class="highlight"><span></span><span class="k">try</span><span class="p">:</span> - <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="s2">"JPEG"</span><span class="p">,</span> <span class="n">quality</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">optimize</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">progressive</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> -<span class="k">except</span> <span class="ne">IOError</span><span class="p">:</span> - <span class="n">PIL</span><span class="o">.</span><span class="n">ImageFile</span><span class="o">.</span><span class="n">MAXBLOCK</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> - <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="s2">"JPEG"</span><span class="p">,</span> <span class="n">quality</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">optimize</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">progressive</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> -</div> - -</code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector</guid><title>Building a Fake News Detector with Turicreate</title><description>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</description><link>https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector</link><pubDate>Sun, 22 Dec 2019 11:10:00 +0530</pubDate><content:encoded><![CDATA[<h1>Building a Fake News Detector with Turicreate</h1><p><strong>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</strong></p><p>Note: These commands are written as if you are running a jupyter notebook.</p><h2>Building the Machine Learning Model</h2><h3>Data Gathering</h3><p>To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and wheter it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own</p><h3>Dependencies</h3><p>I used a Google Colab instance for training my model. If you also plan on using Google Colab then I reccomend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreat is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.</p><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> -<span class="na">!pip uninstall -y mxnet</span> -<span class="na">!pip install mxnet-cu100==1.4.0.post0</span> -</div> - -</code></pre><p>If you do not wish to train on GPU or are running it on your computer, you can ignore the last two lines</p><h3>Downloading the Dataset</h3><pre><code><div class="highlight"><span></span><span class="nt">!wget</span><span class="na"> -q "https</span><span class="p">:</span><span class="nc">//github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"</span> -<span class="nt">!unzip</span><span class="na"> fake_or_real_news.csv.zip</span> -</div> - -</code></pre><h3>Model Creation</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">turicreate</span> <span class="kn">as</span> <span class="nn">tc</span> -<span class="n">tc</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">set_num_gpus</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># If you do not wish to use GPUs, set it to 0</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">dataSFrame</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">SFrame</span><span class="p">(</span><span class="s1">'fake_or_real_news.csv'</span><span class="p">)</span> -</div> - -</code></pre><p>The dataset contains a column named "X1", which is of no use to us. Therefore, we simply drop it</p><pre><code><div class="highlight"><span></span><span class="n">dataSFrame</span><span class="o">.</span><span class="n">remove_column</span><span class="p">(</span><span class="s1">'X1'</span><span class="p">)</span> -</div> - -</code></pre><h4>Splitting Dataset</h4><pre><code><div class="highlight"><span></span><span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">dataSFrame</span><span class="o">.</span><span class="n">random_split</span><span class="p">(</span><span class="o">.</span><span class="mi">9</span><span class="p">)</span> -</div> - -</code></pre><h4>Training</h4><pre><code><div class="highlight"><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">text_classifier</span><span class="o">.</span><span class="n">create</span><span class="p">(</span> - <span class="n">dataset</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> - <span class="n">target</span><span class="o">=</span><span class="s1">'label'</span><span class="p">,</span> - <span class="n">features</span><span class="o">=</span><span class="p">[</span><span class="s1">'title'</span><span class="p">,</span><span class="s1">'text'</span><span class="p">]</span> -<span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="o">+-----------+----------+-----------+--------------+-------------------+---------------------+</span> -<span class="o">|</span> <span class="n">Iteration</span> <span class="o">|</span> <span class="n">Passes</span> <span class="o">|</span> <span class="n">Step</span> <span class="n">size</span> <span class="o">|</span> <span class="n">Elapsed</span> <span class="n">Time</span> <span class="o">|</span> <span class="n">Training</span> <span class="n">Accuracy</span> <span class="o">|</span> <span class="n">Validation</span> <span class="n">Accuracy</span> <span class="o">|</span> -<span class="o">+-----------+----------+-----------+--------------+-------------------+---------------------+</span> -<span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.156349</span> <span class="o">|</span> <span class="mf">0.889680</span> <span class="o">|</span> <span class="mf">0.790036</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">4</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.359196</span> <span class="o">|</span> <span class="mf">0.985952</span> <span class="o">|</span> <span class="mf">0.918149</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mi">6</span> <span class="o">|</span> <span class="mf">0.820091</span> <span class="o">|</span> <span class="mf">1.557205</span> <span class="o">|</span> <span class="mf">0.990260</span> <span class="o">|</span> <span class="mf">0.914591</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">3</span> <span class="o">|</span> <span class="mi">7</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.684872</span> <span class="o">|</span> <span class="mf">0.998689</span> <span class="o">|</span> <span class="mf">0.925267</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">4</span> <span class="o">|</span> <span class="mi">8</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.814194</span> <span class="o">|</span> <span class="mf">0.999063</span> <span class="o">|</span> <span class="mf">0.925267</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">9</span> <span class="o">|</span> <span class="mi">14</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">2.507072</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">0.911032</span> <span class="o">|</span> -<span class="o">+-----------+----------+-----------+--------------+-------------------+---------------------+</span> -</div> - -</code></pre><h3>Testing the Model</h3><pre><code><div class="highlight"><span></span><span class="n">est_predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> -<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="s1">'label'</span><span class="p">],</span> <span class="n">test_predictions</span><span class="p">)</span> -<span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s1">'Topic classifier model has a testing accuracy of {accuracy*100}% '</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">Topic</span> <span class="n">classifier</span> <span class="n">model</span> <span class="n">has</span> <span class="n">a</span> <span class="n">testing</span> <span class="n">accuracy</span> <span class="n">of</span> <span class="mf">92.3076923076923</span><span class="o">%</span> -</div> - -</code></pre><p>We have just created our own Fake News Detection Model which has an accuracy of 92%!</p><pre><code><div class="highlight"><span></span><span class="n">example_text</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"title"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Middling ‘Rise Of Skywalker’ Review Leaves Fan On Fence About Whether To Threaten To Kill Critic"</span><span class="p">],</span> <span class="s2">"text"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Expressing ambivalence toward the relatively balanced appraisal of the film, Star Wars fan Miles Ariely admitted Thursday that an online publication’s middling review of The Rise Of Skywalker had left him on the fence about whether he would still threaten to kill the critic who wrote it. “I’m really of two minds about this, because on the one hand, he said the new movie fails to live up to the original trilogy, which makes me at least want to throw a brick through his window with a note telling him to watch his back,” said Ariely, confirming he had already drafted an eight-page-long death threat to Stan Corimer of the website Screen-On Time, but had not yet decided whether to post it to the reviewer’s Facebook page. “On the other hand, though, he commended J.J. Abrams’ skillful pacing and faithfulness to George Lucas’ vision, which makes me wonder if I should just call the whole thing off. Now, I really don’t feel like camping outside his house for hours. Maybe I could go with a response that’s somewhere in between, like, threatening to kill his dog but not everyone in his whole family? I don’t know. This is a tough one.” At press time, sources reported that Ariely had resolved to wear his Ewok costume while he murdered the critic in his sleep."</span><span class="p">]}</span> -<span class="n">example_prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">tc</span><span class="o">.</span><span class="n">SFrame</span><span class="p">(</span><span class="n">example_text</span><span class="p">))</span> -<span class="k">print</span><span class="p">(</span><span class="n">example_prediction</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="o">+-------+--------------------+</span> -<span class="o">|</span> <span class="k">class</span> <span class="err">| </span><span class="nc">probability</span> <span class="o">|</span> -<span class="o">+-------+--------------------+</span> -<span class="o">|</span> <span class="n">FAKE</span> <span class="o">|</span> <span class="mf">0.9245648658345308</span> <span class="o">|</span> -<span class="o">+-------+--------------------+</span> -<span class="p">[</span><span class="mi">1</span> <span class="n">rows</span> <span class="n">x</span> <span class="mi">2</span> <span class="n">columns</span><span class="p">]</span> -</div> - -</code></pre><h3>Exporting the Model</h3><pre><code><div class="highlight"><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s1">'FakeNews'</span> -<span class="n">coreml_model_name</span> <span class="o">=</span> <span class="n">model_name</span> <span class="o">+</span> <span class="s1">'.mlmodel'</span> -<span class="n">exportedModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">export_coreml</span><span class="p">(</span><span class="n">coreml_model_name</span><span class="p">)</span> -</div> - -</code></pre><p><strong>Note: To download files from Google Volab, simply click on the files section in the sidebar, right click on filename and then click on downlaod</strong></p><p><a href="https://colab.research.google.com/drive/1onMXGkhA__X2aOFdsoVL-6HQBsWQhOP4">Link to Colab Notebook</a></p><h2>Building the App using SwiftUI</h2><h3>Initial Setup</h3><p>First we create a single view app (make sure you check the use SwiftUI button)</p><p>Then we copy our .mlmodel file to our project (Just drag and drop the file in the XCode Files Sidebar)</p><p>Our ML Model does not take a string directly as an input, rather it takes bag of words as an input. DescriptionThe bag-of-words model is a simplifying representation used in NLP, in this text is represented as a bag of words, without any regatd of grammar or order, but noting multiplicity</p><p>We define our bag of words function</p><pre><code><div class="highlight"><span></span><span class="kd">func</span> <span class="nf">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">String</span><span class="p">)</span> <span class="p">-></span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]</span> <span class="p">{</span> - <span class="kd">var</span> <span class="nv">bagOfWords</span> <span class="p">=</span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]()</span> - - <span class="kd">let</span> <span class="nv">tagger</span> <span class="p">=</span> <span class="bp">NSLinguisticTagger</span><span class="p">(</span><span class="n">tagSchemes</span><span class="p">:</span> <span class="p">[.</span><span class="n">tokenType</span><span class="p">],</span> <span class="n">options</span><span class="p">:</span> <span class="mi">0</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">range</span> <span class="p">=</span> <span class="n">NSRange</span><span class="p">(</span><span class="n">location</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="n">length</span><span class="p">:</span> <span class="n">text</span><span class="p">.</span><span class="n">utf16</span><span class="p">.</span><span class="bp">count</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">options</span><span class="p">:</span> <span class="bp">NSLinguisticTagger</span><span class="p">.</span><span class="n">Options</span> <span class="p">=</span> <span class="p">[.</span><span class="n">omitPunctuation</span><span class="p">,</span> <span class="p">.</span><span class="n">omitWhitespace</span><span class="p">]</span> - <span class="n">tagger</span><span class="p">.</span><span class="n">string</span> <span class="p">=</span> <span class="n">text</span> - - <span class="n">tagger</span><span class="p">.</span><span class="n">enumerateTags</span><span class="p">(</span><span class="k">in</span><span class="p">:</span> <span class="n">range</span><span class="p">,</span> <span class="n">unit</span><span class="p">:</span> <span class="p">.</span><span class="n">word</span><span class="p">,</span> <span class="n">scheme</span><span class="p">:</span> <span class="p">.</span><span class="n">tokenType</span><span class="p">,</span> <span class="n">options</span><span class="p">:</span> <span class="n">options</span><span class="p">)</span> <span class="p">{</span> <span class="kc">_</span><span class="p">,</span> <span class="n">tokenRange</span><span class="p">,</span> <span class="kc">_</span> <span class="k">in</span> - <span class="kd">let</span> <span class="nv">word</span> <span class="p">=</span> <span class="p">(</span><span class="n">text</span> <span class="k">as</span> <span class="bp">NSString</span><span class="p">).</span><span class="n">substring</span><span class="p">(</span><span class="n">with</span><span class="p">:</span> <span class="n">tokenRange</span><span class="p">)</span> - <span class="k">if</span> <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">nil</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span><span class="o">!</span> <span class="o">+=</span> <span class="mi">1</span> - <span class="p">}</span> <span class="k">else</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="p">=</span> <span class="mi">1</span> - <span class="p">}</span> - <span class="p">}</span> - - <span class="k">return</span> <span class="n">bagOfWords</span> - <span class="p">}</span> -</div> - -</code></pre><p>We also declare our variables</p><pre><code><div class="highlight"><span></span><span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">title</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">headline</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertTitle</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertText</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">showingAlert</span> <span class="p">=</span> <span class="kc">false</span> -</div> - -</code></pre><p>Finally, we implement a simple function which reads the two text fields, creates their bag of words representation and displays an alert with the appropriate result</p><p><strong>Complete Code</strong></p><pre><code><div class="highlight"><span></span><span class="kd">import</span> <span class="nc">SwiftUI</span> - -<span class="kd">struct</span> <span class="nc">ContentView</span><span class="p">:</span> <span class="n">View</span> <span class="p">{</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">title</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">headline</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> - - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertTitle</span> <span class="p">=</span> <span class="s">""</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertText</span> <span class="p">=</span> <span class="s">""</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">showingAlert</span> <span class="p">=</span> <span class="kc">false</span> - - <span class="kd">var</span> <span class="nv">body</span><span class="p">:</span> <span class="n">some</span> <span class="n">View</span> <span class="p">{</span> - <span class="n">NavigationView</span> <span class="p">{</span> - <span class="n">VStack</span><span class="p">(</span><span class="n">alignment</span><span class="p">:</span> <span class="p">.</span><span class="n">leading</span><span class="p">)</span> <span class="p">{</span> - <span class="n">Text</span><span class="p">(</span><span class="s">"Headline"</span><span class="p">).</span><span class="n">font</span><span class="p">(.</span><span class="n">headline</span><span class="p">)</span> - <span class="n">TextField</span><span class="p">(</span><span class="s">"Please Enter Headline"</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="err">$</span><span class="n">title</span><span class="p">)</span> - <span class="p">.</span><span class="n">lineLimit</span><span class="p">(</span><span class="kc">nil</span><span class="p">)</span> - <span class="n">Text</span><span class="p">(</span><span class="s">"Body"</span><span class="p">).</span><span class="n">font</span><span class="p">(.</span><span class="n">headline</span><span class="p">)</span> - <span class="n">TextField</span><span class="p">(</span><span class="s">"Please Enter the content"</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="err">$</span><span class="n">headline</span><span class="p">)</span> - <span class="p">.</span><span class="n">lineLimit</span><span class="p">(</span><span class="kc">nil</span><span class="p">)</span> - <span class="p">}</span> - <span class="p">.</span><span class="n">navigationBarTitle</span><span class="p">(</span><span class="s">"Fake News Checker"</span><span class="p">)</span> - <span class="p">.</span><span class="n">navigationBarItems</span><span class="p">(</span><span class="n">trailing</span><span class="p">:</span> - <span class="n">Button</span><span class="p">(</span><span class="n">action</span><span class="p">:</span> <span class="n">classifyFakeNews</span><span class="p">)</span> <span class="p">{</span> - <span class="n">Text</span><span class="p">(</span><span class="s">"Check"</span><span class="p">)</span> - <span class="p">})</span> - <span class="p">.</span><span class="n">padding</span><span class="p">()</span> - <span class="p">.</span><span class="n">alert</span><span class="p">(</span><span class="n">isPresented</span><span class="p">:</span> <span class="err">$</span><span class="n">showingAlert</span><span class="p">){</span> - <span class="n">Alert</span><span class="p">(</span><span class="n">title</span><span class="p">:</span> <span class="n">Text</span><span class="p">(</span><span class="n">alertTitle</span><span class="p">),</span> <span class="n">message</span><span class="p">:</span> <span class="n">Text</span><span class="p">(</span><span class="n">alertText</span><span class="p">),</span> <span class="n">dismissButton</span><span class="p">:</span> <span class="p">.</span><span class="k">default</span><span class="p">(</span><span class="n">Text</span><span class="p">(</span><span class="s">"OK"</span><span class="p">)))</span> - <span class="p">}</span> - <span class="p">}</span> - - <span class="p">}</span> - - <span class="kd">func</span> <span class="nf">classifyFakeNews</span><span class="p">(){</span> - <span class="kd">let</span> <span class="nv">model</span> <span class="p">=</span> <span class="n">FakeNews</span><span class="p">()</span> - <span class="kd">let</span> <span class="nv">myTitle</span> <span class="p">=</span> <span class="n">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="n">title</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">myText</span> <span class="p">=</span> <span class="n">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="n">headline</span><span class="p">)</span> - <span class="k">do</span> <span class="p">{</span> - <span class="kd">let</span> <span class="nv">prediction</span> <span class="p">=</span> <span class="k">try</span> <span class="n">model</span><span class="p">.</span><span class="n">prediction</span><span class="p">(</span><span class="n">title</span><span class="p">:</span> <span class="n">myTitle</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="n">myText</span><span class="p">)</span> - <span class="n">alertTitle</span> <span class="p">=</span> <span class="n">prediction</span><span class="p">.</span><span class="n">label</span> - <span class="n">alertText</span> <span class="p">=</span> <span class="s">"It is likely that this piece of news is </span><span class="si">\(</span><span class="n">prediction</span><span class="p">.</span><span class="n">label</span><span class="p">.</span><span class="n">lowercased</span><span class="si">())</span><span class="s">."</span> - <span class="bp">print</span><span class="p">(</span><span class="n">alertText</span><span class="p">)</span> - <span class="p">}</span> <span class="k">catch</span> <span class="p">{</span> - <span class="n">alertTitle</span> <span class="p">=</span> <span class="s">"Error"</span> - <span class="n">alertText</span> <span class="p">=</span> <span class="s">"Sorry, could not classify if the input news was fake or not."</span> - <span class="p">}</span> - - <span class="n">showingAlert</span> <span class="p">=</span> <span class="kc">true</span> - <span class="p">}</span> - <span class="kd">func</span> <span class="nf">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">String</span><span class="p">)</span> <span class="p">-></span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]</span> <span class="p">{</span> - <span class="kd">var</span> <span class="nv">bagOfWords</span> <span class="p">=</span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]()</span> - - <span class="kd">let</span> <span class="nv">tagger</span> <span class="p">=</span> <span class="bp">NSLinguisticTagger</span><span class="p">(</span><span class="n">tagSchemes</span><span class="p">:</span> <span class="p">[.</span><span class="n">tokenType</span><span class="p">],</span> <span class="n">options</span><span class="p">:</span> <span class="mi">0</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">range</span> <span class="p">=</span> <span class="n">NSRange</span><span class="p">(</span><span class="n">location</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="n">length</span><span class="p">:</span> <span class="n">text</span><span class="p">.</span><span class="n">utf16</span><span class="p">.</span><span class="bp">count</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">options</span><span class="p">:</span> <span class="bp">NSLinguisticTagger</span><span class="p">.</span><span class="n">Options</span> <span class="p">=</span> <span class="p">[.</span><span class="n">omitPunctuation</span><span class="p">,</span> <span class="p">.</span><span class="n">omitWhitespace</span><span class="p">]</span> - <span class="n">tagger</span><span class="p">.</span><span class="n">string</span> <span class="p">=</span> <span class="n">text</span> - - <span class="n">tagger</span><span class="p">.</span><span class="n">enumerateTags</span><span class="p">(</span><span class="k">in</span><span class="p">:</span> <span class="n">range</span><span class="p">,</span> <span class="n">unit</span><span class="p">:</span> <span class="p">.</span><span class="n">word</span><span class="p">,</span> <span class="n">scheme</span><span class="p">:</span> <span class="p">.</span><span class="n">tokenType</span><span class="p">,</span> <span class="n">options</span><span class="p">:</span> <span class="n">options</span><span class="p">)</span> <span class="p">{</span> <span class="kc">_</span><span class="p">,</span> <span class="n">tokenRange</span><span class="p">,</span> <span class="kc">_</span> <span class="k">in</span> - <span class="kd">let</span> <span class="nv">word</span> <span class="p">=</span> <span class="p">(</span><span class="n">text</span> <span class="k">as</span> <span class="bp">NSString</span><span class="p">).</span><span class="n">substring</span><span class="p">(</span><span class="n">with</span><span class="p">:</span> <span class="n">tokenRange</span><span class="p">)</span> - <span class="k">if</span> <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">nil</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span><span class="o">!</span> <span class="o">+=</span> <span class="mi">1</span> - <span class="p">}</span> <span class="k">else</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="p">=</span> <span class="mi">1</span> - <span class="p">}</span> - <span class="p">}</span> - - <span class="k">return</span> <span class="n">bagOfWords</span> - <span class="p">}</span> -<span class="p">}</span> - -<span class="kd">struct</span> <span class="nc">ContentView_Previews</span><span class="p">:</span> <span class="n">PreviewProvider</span> <span class="p">{</span> - <span class="kd">static</span> <span class="kd">var</span> <span class="nv">previews</span><span class="p">:</span> <span class="n">some</span> <span class="n">View</span> <span class="p">{</span> - <span class="n">ContentView</span><span class="p">()</span> - <span class="p">}</span> -<span class="p">}</span> -</div> - -</code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression</guid><title>Polynomial Regression Using TensorFlow</title><description>Polynomial regression using TensorFlow</description><link>https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression</link><pubDate>Mon, 16 Dec 2019 14:16:00 +0530</pubDate><content:encoded><![CDATA[<h1>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><p>In this, we will be performing polynomial regression using 5 types of equations -</p><ul><li>Linear</li><li>Quadratic</li><li>Cubic</li><li>Quartic</li><li>Quintic</li></ul><h2>Regression</h2><h3>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><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><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">tensorflow.compat.v1</span> <span class="kn">as</span> <span class="nn">tf</span> -<span class="n">tf</span><span class="o">.</span><span class="n">disable_v2_behavior</span><span class="p">()</span> -<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span> -<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> -<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span> -</div> - -</code></pre><h2>Dataset</h2><h3>Creating Random Data</h3><p>Even though in this tutorial we will use a Position Vs Salary datasset, it is important to know how to create synthetic data</p><p>To create 50 values spaced evenly between 0 and 50, we use NumPy's linspace funtion</p><p><code>linspace(lower_limit, upper_limit, no_of_observations)</code></p><pre><code><div class="highlight"><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> -<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> -</div> - -</code></pre><p>We use the following function to add noise to the data, so that our values</p><pre><code><div class="highlight"><span></span><span class="n">x</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> -<span class="n">y</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> -</div> - -</code></pre><h3>Position vs Salary Dataset</h3><p>We will be using https://drive.google.com/file/d/1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9/view (Salary vs Position Dataset)</p><pre><code><div class="highlight"><span></span><span class="nt">!wget</span><span class="na"> --no-check-certificate 'https</span><span class="p">:</span><span class="nc">//docs.google.com/uc?export</span><span class="o">=</span><span class="l">download&id=1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9' -O data.csv</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"data.csv"</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">df</span> <span class="c1"># this gives us a preview of the dataset we are working with</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="o">|</span> <span class="n">Position</span> <span class="o">|</span> <span class="n">Level</span> <span class="o">|</span> <span class="n">Salary</span> <span class="o">|</span> -<span class="o">|-------------------|-------|---------|</span> -<span class="o">|</span> <span class="n">Business</span> <span class="n">Analyst</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">45000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Junior</span> <span class="n">Consultant</span> <span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mi">50000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Senior</span> <span class="n">Consultant</span> <span class="o">|</span> <span class="mi">3</span> <span class="o">|</span> <span class="mi">60000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Manager</span> <span class="o">|</span> <span class="mi">4</span> <span class="o">|</span> <span class="mi">80000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Country</span> <span class="n">Manager</span> <span class="o">|</span> <span class="mi">5</span> <span class="o">|</span> <span class="mi">110000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Region</span> <span class="n">Manager</span> <span class="o">|</span> <span class="mi">6</span> <span class="o">|</span> <span class="mi">150000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Partner</span> <span class="o">|</span> <span class="mi">7</span> <span class="o">|</span> <span class="mi">200000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">Senior</span> <span class="n">Partner</span> <span class="o">|</span> <span class="mi">8</span> <span class="o">|</span> <span class="mi">300000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">C</span><span class="o">-</span><span class="n">level</span> <span class="o">|</span> <span class="mi">9</span> <span class="o">|</span> <span class="mi">500000</span> <span class="o">|</span> -<span class="o">|</span> <span class="n">CEO</span> <span class="o">|</span> <span class="mi">10</span> <span class="o">|</span> <span class="mi">1000000</span> <span class="o">|</span> -</div> - -</code></pre><p>We convert the salary column as the ordinate (y-cordinate) and level column as the abscissa</p><pre><code><div class="highlight"><span></span><span class="n">abscissa</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Level"</span><span class="p">]</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span> <span class="c1"># abscissa = [1,2,3,4,5,6,7,8,9,10]</span> -<span class="n">ordinate</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Salary"</span><span class="p">]</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span> <span class="c1"># ordinate = [45000,50000,60000,80000,110000,150000,200000,300000,500000,1000000]</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">abscissa</span><span class="p">)</span> <span class="c1"># no of observations</span> -<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Salary'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Position'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Salary vs Position"</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> -</div> - -</code></pre><img src="https://navanchauhan.github.io//assets/gciTales/03-regression/1.png"/><h2>Defining Stuff</h2><pre><code><div class="highlight"><span></span><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> -<span class="n">Y</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> -</div> - -</code></pre><h3>Defining Variables</h3><p>We first define all the coefficients and constant as tensorflow variables haveing a random intitial value</p><pre><code><div class="highlight"><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"a"</span><span class="p">)</span> -<span class="n">b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"b"</span><span class="p">)</span> -<span class="n">c</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"c"</span><span class="p">)</span> -<span class="n">d</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"d"</span><span class="p">)</span> -<span class="n">e</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"e"</span><span class="p">)</span> -<span class="n">f</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"f"</span><span class="p">)</span> -</div> - -</code></pre><h3>Model Configuration</h3><pre><code><div class="highlight"><span></span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.2</span> -<span class="n">no_of_epochs</span> <span class="o">=</span> <span class="mi">25000</span> -</div> - -</code></pre><h3>Equations</h3><pre><code><div class="highlight"><span></span><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> -<span class="n">deg2</span> <span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span><span class="o">*</span><span class="n">X</span> <span class="o">+</span> <span class="n">c</span> -<span class="n">deg3</span> <span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">X</span> <span class="o">+</span> <span class="n">d</span> -<span class="n">deg4</span> <span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">d</span><span class="o">*</span><span class="n">X</span> <span class="o">+</span> <span class="n">e</span> -<span class="n">deg5</span> <span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="n">c</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">d</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">e</span><span class="o">*</span><span class="n">X</span> <span class="o">+</span> <span class="n">f</span> -</div> - -</code></pre><h3>Cost Function</h3><p>We use the Mean Squared Error Function</p><pre><code><div class="highlight"><span></span><span class="n">mse1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">deg1</span><span class="o">-</span><span class="n">Y</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">)</span> -<span class="n">mse2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">deg2</span><span class="o">-</span><span class="n">Y</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">)</span> -<span class="n">mse3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">deg3</span><span class="o">-</span><span class="n">Y</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">)</span> -<span class="n">mse4</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">deg4</span><span class="o">-</span><span class="n">Y</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">)</span> -<span class="n">mse5</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">deg5</span><span class="o">-</span><span class="n">Y</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">)</span> -</div> - -</code></pre><h3>Optimizer</h3><p>We use the AdamOptimizer for the polynomial functions and GradientDescentOptimizer for the linear function</p><pre><code><div class="highlight"><span></span><span class="n">optimizer1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">GradientDescentOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">mse1</span><span class="p">)</span> -<span class="n">optimizer2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">mse2</span><span class="p">)</span> -<span class="n">optimizer3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">mse3</span><span class="p">)</span> -<span class="n">optimizer4</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">mse4</span><span class="p">)</span> -<span class="n">optimizer5</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">mse5</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">init</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">()</span> -</div> - -</code></pre><h2>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><pre><code><div class="highlight"><span></span><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> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">init</span><span class="p">)</span> - <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">no_of_epochs</span><span class="p">):</span> - <span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">):</span> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer1</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">x</span><span class="p">,</span> <span class="n">Y</span><span class="p">:</span><span class="n">y</span><span class="p">})</span> - <span class="k">if</span> <span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">1000</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span> - <span class="n">cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse1</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="k">print</span><span class="p">(</span><span class="s2">"Epoch"</span><span class="p">,(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="s2">": Training Cost:"</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span><span class="s2">" a,b:"</span><span class="p">,</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> - - <span class="n">training_cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse1</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="n">coefficient1</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> - <span class="n">constant</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> - -<span class="k">print</span><span class="p">(</span><span class="n">training_cost</span><span class="p">,</span> <span class="n">coefficient1</span><span class="p">,</span> <span class="n">constant</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="nt">Epoch</span><span class="na"> 1000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 2000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 3000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 4000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 5000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 6000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 7000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 8000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 9000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 10000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 11000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 12000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 13000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 14000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 15000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 16000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 17000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 18000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 19000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 20000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 21000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 22000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 23000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 24000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">Epoch</span><span class="na"> 25000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">88999125000.0</span><span class="err"> </span><span class="nc">a,b</span><span class="p">:</span><span class="err"> </span><span class="nc">180396.42</span><span class="err"> </span><span class="nc">-478869.12</span> -<span class="nt">88999125000.0</span><span class="na"> 180396.42 -478869.12</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span> -<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">abscissa</span><span class="p">:</span> - <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">coefficient1</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">constant</span><span class="p">))</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span> <span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Fitted line'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Linear Regression Result'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span> -<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> -</div> - -</code></pre><img src="https://navanchauhan.github.io//assets/gciTales/03-regression/2.png"/><h3>Quadratic Equation</h3><pre><code><div class="highlight"><span></span><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> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">init</span><span class="p">)</span> - <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">no_of_epochs</span><span class="p">):</span> - <span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">):</span> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer2</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">x</span><span class="p">,</span> <span class="n">Y</span><span class="p">:</span><span class="n">y</span><span class="p">})</span> - <span class="k">if</span> <span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">1000</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span> - <span class="n">cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse2</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="k">print</span><span class="p">(</span><span class="s2">"Epoch"</span><span class="p">,(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="s2">": Training Cost:"</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span><span class="s2">" a,b,c:"</span><span class="p">,</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">))</span> - - <span class="n">training_cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse2</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="n">coefficient1</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> - <span class="n">coefficient2</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> - <span class="n">constant</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> - -<span class="k">print</span><span class="p">(</span><span class="n">training_cost</span><span class="p">,</span> <span class="n">coefficient1</span><span class="p">,</span> <span class="n">coefficient2</span><span class="p">,</span> <span class="n">constant</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="nt">Epoch</span><span class="na"> 1000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">52571360000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">1002.4456</span><span class="err"> </span><span class="nc">1097.0197</span><span class="err"> </span><span class="nc">1276.6921</span> -<span class="nt">Epoch</span><span class="na"> 2000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">37798890000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">1952.4263</span><span class="err"> </span><span class="nc">2130.2825</span><span class="err"> </span><span class="nc">2469.7756</span> -<span class="nt">Epoch</span><span class="na"> 3000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">26751185000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">2839.5825</span><span class="err"> </span><span class="nc">3081.6118</span><span class="err"> </span><span class="nc">3554.351</span> -<span class="nt">Epoch</span><span class="na"> 4000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">19020106000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">3644.56</span><span class="err"> </span><span class="nc">3922.9563</span><span class="err"> </span><span class="nc">4486.3135</span> -<span class="nt">Epoch</span><span class="na"> 5000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">14060446000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">4345.042</span><span class="err"> </span><span class="nc">4621.4233</span><span class="err"> </span><span class="nc">5212.693</span> -<span class="nt">Epoch</span><span class="na"> 6000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">11201084000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">4921.1855</span><span class="err"> </span><span class="nc">5148.1504</span><span class="err"> </span><span class="nc">5689.0713</span> -<span class="nt">Epoch</span><span class="na"> 7000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">9732740000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">5364.764</span><span class="err"> </span><span class="nc">5493.0156</span><span class="err"> </span><span class="nc">5906.754</span> -<span class="nt">Epoch</span><span class="na"> 8000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">9050918000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">5685.4067</span><span class="err"> </span><span class="nc">5673.182</span><span class="err"> </span><span class="nc">5902.0728</span> -<span class="nt">Epoch</span><span class="na"> 9000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8750394000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">5906.9814</span><span class="err"> </span><span class="nc">5724.8906</span><span class="err"> </span><span class="nc">5734.746</span> -<span class="nt">Epoch</span><span class="na"> 10000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8613128000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6057.3677</span><span class="err"> </span><span class="nc">5687.3364</span><span class="err"> </span><span class="nc">5461.167</span> -<span class="nt">Epoch</span><span class="na"> 11000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8540034600.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6160.547</span><span class="err"> </span><span class="nc">5592.3022</span><span class="err"> </span><span class="nc">5122.8633</span> -<span class="nt">Epoch</span><span class="na"> 12000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8490983000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6233.9175</span><span class="err"> </span><span class="nc">5462.025</span><span class="err"> </span><span class="nc">4747.111</span> -<span class="nt">Epoch</span><span class="na"> 13000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8450816500.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6289.048</span><span class="err"> </span><span class="nc">5310.7583</span><span class="err"> </span><span class="nc">4350.6997</span> -<span class="nt">Epoch</span><span class="na"> 14000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8414082000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6333.199</span><span class="err"> </span><span class="nc">5147.394</span><span class="err"> </span><span class="nc">3943.9294</span> -<span class="nt">Epoch</span><span class="na"> 15000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8378841600.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6370.7944</span><span class="err"> </span><span class="nc">4977.1704</span><span class="err"> </span><span class="nc">3532.476</span> -<span class="nt">Epoch</span><span class="na"> 16000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8344471000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6404.468</span><span class="err"> </span><span class="nc">4803.542</span><span class="err"> </span><span class="nc">3120.2087</span> -<span class="nt">Epoch</span><span class="na"> 17000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8310785500.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6435.365</span><span class="err"> </span><span class="nc">4628.1523</span><span class="err"> </span><span class="nc">2709.1445</span> -<span class="nt">Epoch</span><span class="na"> 18000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8277482000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6465.5493</span><span class="err"> </span><span class="nc">4451.833</span><span class="err"> </span><span class="nc">2300.2783</span> -<span class="nt">Epoch</span><span class="na"> 19000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8244650000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6494.609</span><span class="err"> </span><span class="nc">4274.826</span><span class="err"> </span><span class="nc">1894.3738</span> -<span class="nt">Epoch</span><span class="na"> 20000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8212349000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6522.8247</span><span class="err"> </span><span class="nc">4098.1733</span><span class="err"> </span><span class="nc">1491.9915</span> -<span class="nt">Epoch</span><span class="na"> 21000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8180598300.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6550.6567</span><span class="err"> </span><span class="nc">3922.7405</span><span class="err"> </span><span class="nc">1093.3868</span> -<span class="nt">Epoch</span><span class="na"> 22000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8149257700.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6578.489</span><span class="err"> </span><span class="nc">3747.8362</span><span class="err"> </span><span class="nc">698.53357</span> -<span class="nt">Epoch</span><span class="na"> 23000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8118325000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6606.1973</span><span class="err"> </span><span class="nc">3573.2742</span><span class="err"> </span><span class="nc">307.3541</span> -<span class="nt">Epoch</span><span class="na"> 24000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8088001000.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6632.96</span><span class="err"> </span><span class="nc">3399.878</span><span class="err"> </span><span class="nc">-79.89219</span> -<span class="nt">Epoch</span><span class="na"> 25000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">8058094600.0</span><span class="err"> </span><span class="nc">a,b,c</span><span class="p">:</span><span class="err"> </span><span class="nc">6659.793</span><span class="err"> </span><span class="nc">3227.2517</span><span class="err"> </span><span class="nc">-463.03156</span> -<span class="nt">8058094600.0</span><span class="na"> 6659.793 3227.2517 -463.03156</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span> -<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">abscissa</span><span class="p">:</span> - <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">coefficient1</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient2</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">constant</span><span class="p">))</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span> <span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Fitted line'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Quadratic Regression Result'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span> -<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> -</div> - -</code></pre><img src="https://navanchauhan.github.io//assets/gciTales/03-regression/3.png"/><h3>Cubic</h3><pre><code><div class="highlight"><span></span><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> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">init</span><span class="p">)</span> - <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">no_of_epochs</span><span class="p">):</span> - <span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">):</span> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer3</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">x</span><span class="p">,</span> <span class="n">Y</span><span class="p">:</span><span class="n">y</span><span class="p">})</span> - <span class="k">if</span> <span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">1000</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span> - <span class="n">cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse3</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="k">print</span><span class="p">(</span><span class="s2">"Epoch"</span><span class="p">,(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="s2">": Training Cost:"</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span><span class="s2">" a,b,c,d:"</span><span class="p">,</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d</span><span class="p">))</span> - - <span class="n">training_cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse3</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="n">coefficient1</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> - <span class="n">coefficient2</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> - <span class="n">coefficient3</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> - <span class="n">constant</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> - -<span class="k">print</span><span class="p">(</span><span class="n">training_cost</span><span class="p">,</span> <span class="n">coefficient1</span><span class="p">,</span> <span class="n">coefficient2</span><span class="p">,</span> <span class="n">coefficient3</span><span class="p">,</span> <span class="n">constant</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="nt">Epoch</span><span class="na"> 1000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">4279814000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">670.1527</span><span class="err"> </span><span class="nc">694.4212</span><span class="err"> </span><span class="nc">751.4653</span><span class="err"> </span><span class="nc">903.9527</span> -<span class="nt">Epoch</span><span class="na"> 2000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3770950400.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">742.6414</span><span class="err"> </span><span class="nc">666.3489</span><span class="err"> </span><span class="nc">636.94525</span><span class="err"> </span><span class="nc">859.2088</span> -<span class="nt">Epoch</span><span class="na"> 3000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3717708300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">756.2582</span><span class="err"> </span><span class="nc">569.3339</span><span class="err"> </span><span class="nc">448.105</span><span class="err"> </span><span class="nc">748.23956</span> -<span class="nt">Epoch</span><span class="na"> 4000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3667464000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">769.4476</span><span class="err"> </span><span class="nc">474.0318</span><span class="err"> </span><span class="nc">265.5761</span><span class="err"> </span><span class="nc">654.75525</span> -<span class="nt">Epoch</span><span class="na"> 5000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3620040700.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">782.32324</span><span class="err"> </span><span class="nc">380.54272</span><span class="err"> </span><span class="nc">89.39888</span><span class="err"> </span><span class="nc">578.5136</span> -<span class="nt">Epoch</span><span class="na"> 6000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3575265800.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">794.8898</span><span class="err"> </span><span class="nc">288.83356</span><span class="err"> </span><span class="nc">-80.5215</span><span class="err"> </span><span class="nc">519.13654</span> -<span class="nt">Epoch</span><span class="na"> 7000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3532972000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">807.1608</span><span class="err"> </span><span class="nc">198.87044</span><span class="err"> </span><span class="nc">-244.31102</span><span class="err"> </span><span class="nc">476.2061</span> -<span class="nt">Epoch</span><span class="na"> 8000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3493009200.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">819.13513</span><span class="err"> </span><span class="nc">110.64169</span><span class="err"> </span><span class="nc">-402.0677</span><span class="err"> </span><span class="nc">449.3291</span> -<span class="nt">Epoch</span><span class="na"> 9000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3455228400.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">830.80255</span><span class="err"> </span><span class="nc">24.0964</span><span class="err"> </span><span class="nc">-553.92804</span><span class="err"> </span><span class="nc">438.0652</span> -<span class="nt">Epoch</span><span class="na"> 10000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3419475500.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">842.21594</span><span class="err"> </span><span class="nc">-60.797424</span><span class="err"> </span><span class="nc">-700.0123</span><span class="err"> </span><span class="nc">441.983</span> -<span class="nt">Epoch</span><span class="na"> 11000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3385625300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">853.3363</span><span class="err"> </span><span class="nc">-144.08699</span><span class="err"> </span><span class="nc">-840.467</span><span class="err"> </span><span class="nc">460.6356</span> -<span class="nt">Epoch</span><span class="na"> 12000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3353544700.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">864.19135</span><span class="err"> </span><span class="nc">-225.8125</span><span class="err"> </span><span class="nc">-975.4196</span><span class="err"> </span><span class="nc">493.57703</span> -<span class="nt">Epoch</span><span class="na"> 13000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3323125000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">874.778</span><span class="err"> </span><span class="nc">-305.98932</span><span class="err"> </span><span class="nc">-1104.9867</span><span class="err"> </span><span class="nc">540.39465</span> -<span class="nt">Epoch</span><span class="na"> 14000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3294257000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">885.1007</span><span class="err"> </span><span class="nc">-384.63474</span><span class="err"> </span><span class="nc">-1229.277</span><span class="err"> </span><span class="nc">600.65607</span> -<span class="nt">Epoch</span><span class="na"> 15000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3266820000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">895.18823</span><span class="err"> </span><span class="nc">-461.819</span><span class="err"> </span><span class="nc">-1348.4417</span><span class="err"> </span><span class="nc">673.9051</span> -<span class="nt">Epoch</span><span class="na"> 16000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3240736000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">905.0128</span><span class="err"> </span><span class="nc">-537.541</span><span class="err"> </span><span class="nc">-1462.6171</span><span class="err"> </span><span class="nc">759.7118</span> -<span class="nt">Epoch</span><span class="na"> 17000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3215895000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">914.60065</span><span class="err"> </span><span class="nc">-611.8676</span><span class="err"> </span><span class="nc">-1571.9058</span><span class="err"> </span><span class="nc">857.6638</span> -<span class="nt">Epoch</span><span class="na"> 18000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3192216800.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">923.9603</span><span class="err"> </span><span class="nc">-684.8093</span><span class="err"> </span><span class="nc">-1676.4642</span><span class="err"> </span><span class="nc">967.30475</span> -<span class="nt">Epoch</span><span class="na"> 19000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3169632300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">933.08594</span><span class="err"> </span><span class="nc">-756.3582</span><span class="err"> </span><span class="nc">-1776.4275</span><span class="err"> </span><span class="nc">1088.2198</span> -<span class="nt">Epoch</span><span class="na"> 20000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3148046300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">941.9928</span><span class="err"> </span><span class="nc">-826.6257</span><span class="err"> </span><span class="nc">-1871.9355</span><span class="err"> </span><span class="nc">1219.9702</span> -<span class="nt">Epoch</span><span class="na"> 21000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3127394800.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">950.67896</span><span class="err"> </span><span class="nc">-895.6205</span><span class="err"> </span><span class="nc">-1963.0989</span><span class="err"> </span><span class="nc">1362.1665</span> -<span class="nt">Epoch</span><span class="na"> 22000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3107608600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">959.1487</span><span class="err"> </span><span class="nc">-963.38116</span><span class="err"> </span><span class="nc">-2050.0586</span><span class="err"> </span><span class="nc">1514.4026</span> -<span class="nt">Epoch</span><span class="na"> 23000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3088618200.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">967.4355</span><span class="err"> </span><span class="nc">-1029.9625</span><span class="err"> </span><span class="nc">-2132.961</span><span class="err"> </span><span class="nc">1676.2717</span> -<span class="nt">Epoch</span><span class="na"> 24000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3070361300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">975.52875</span><span class="err"> </span><span class="nc">-1095.4292</span><span class="err"> </span><span class="nc">-2211.854</span><span class="err"> </span><span class="nc">1847.4485</span> -<span class="nt">Epoch</span><span class="na"> 25000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">3052791300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">983.4346</span><span class="err"> </span><span class="nc">-1159.7922</span><span class="err"> </span><span class="nc">-2286.9412</span><span class="err"> </span><span class="nc">2027.4857</span> -<span class="nt">3052791300.0</span><span class="na"> 983.4346 -1159.7922 -2286.9412 2027.4857</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span> -<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">abscissa</span><span class="p">:</span> - <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">coefficient1</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient2</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient3</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">constant</span><span class="p">))</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span> <span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Fitted line'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Cubic Regression Result'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span> -<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> -</div> - -</code></pre><img src="https://navanchauhan.github.io//assets/gciTales/03-regression/4.png"/><h3>Quartic</h3><pre><code><div class="highlight"><span></span><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> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">init</span><span class="p">)</span> - <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">no_of_epochs</span><span class="p">):</span> - <span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">):</span> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer4</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">x</span><span class="p">,</span> <span class="n">Y</span><span class="p">:</span><span class="n">y</span><span class="p">})</span> - <span class="k">if</span> <span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">1000</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span> - <span class="n">cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse4</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="k">print</span><span class="p">(</span><span class="s2">"Epoch"</span><span class="p">,(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="s2">": Training Cost:"</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span><span class="s2">" a,b,c,d:"</span><span class="p">,</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">e</span><span class="p">))</span> - - <span class="n">training_cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse4</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="n">coefficient1</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> - <span class="n">coefficient2</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> - <span class="n">coefficient3</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> - <span class="n">coefficient4</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> - <span class="n">constant</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> - -<span class="k">print</span><span class="p">(</span><span class="n">training_cost</span><span class="p">,</span> <span class="n">coefficient1</span><span class="p">,</span> <span class="n">coefficient2</span><span class="p">,</span> <span class="n">coefficient3</span><span class="p">,</span> <span class="n">coefficient4</span><span class="p">,</span> <span class="n">constant</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="nt">Epoch</span><span class="na"> 1000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1902632600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">84.48304</span><span class="err"> </span><span class="nc">52.210594</span><span class="err"> </span><span class="nc">54.791424</span><span class="err"> </span><span class="nc">142.51952</span><span class="err"> </span><span class="nc">512.0343</span> -<span class="nt">Epoch</span><span class="na"> 2000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1854316200.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">88.998955</span><span class="err"> </span><span class="nc">13.073557</span><span class="err"> </span><span class="nc">14.276088</span><span class="err"> </span><span class="nc">223.55667</span><span class="err"> </span><span class="nc">1056.4655</span> -<span class="nt">Epoch</span><span class="na"> 3000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1812812400.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">92.9462</span><span class="err"> </span><span class="nc">-22.331177</span><span class="err"> </span><span class="nc">-15.262934</span><span class="err"> </span><span class="nc">327.41858</span><span class="err"> </span><span class="nc">1634.9054</span> -<span class="nt">Epoch</span><span class="na"> 4000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1775716000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">96.42522</span><span class="err"> </span><span class="nc">-54.64535</span><span class="err"> </span><span class="nc">-35.829437</span><span class="err"> </span><span class="nc">449.5028</span><span class="err"> </span><span class="nc">2239.1392</span> -<span class="nt">Epoch</span><span class="na"> 5000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1741494100.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">99.524734</span><span class="err"> </span><span class="nc">-84.43976</span><span class="err"> </span><span class="nc">-49.181057</span><span class="err"> </span><span class="nc">585.85876</span><span class="err"> </span><span class="nc">2862.4915</span> -<span class="nt">Epoch</span><span class="na"> 6000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1709199600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">102.31984</span><span class="err"> </span><span class="nc">-112.19895</span><span class="err"> </span><span class="nc">-56.808075</span><span class="err"> </span><span class="nc">733.1876</span><span class="err"> </span><span class="nc">3499.6199</span> -<span class="nt">Epoch</span><span class="na"> 7000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1678261800.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">104.87324</span><span class="err"> </span><span class="nc">-138.32709</span><span class="err"> </span><span class="nc">-59.9442</span><span class="err"> </span><span class="nc">888.79626</span><span class="err"> </span><span class="nc">4146.2944</span> -<span class="nt">Epoch</span><span class="na"> 8000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1648340600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">107.23536</span><span class="err"> </span><span class="nc">-163.15173</span><span class="err"> </span><span class="nc">-59.58964</span><span class="err"> </span><span class="nc">1050.524</span><span class="err"> </span><span class="nc">4798.979</span> -<span class="nt">Epoch</span><span class="na"> 9000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1619243400.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">109.44742</span><span class="err"> </span><span class="nc">-186.9409</span><span class="err"> </span><span class="nc">-56.53944</span><span class="err"> </span><span class="nc">1216.6432</span><span class="err"> </span><span class="nc">5454.9463</span> -<span class="nt">Epoch</span><span class="na"> 10000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1590821900.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">111.54233</span><span class="err"> </span><span class="nc">-209.91287</span><span class="err"> </span><span class="nc">-51.423084</span><span class="err"> </span><span class="nc">1385.8513</span><span class="err"> </span><span class="nc">6113.5137</span> -<span class="nt">Epoch</span><span class="na"> 11000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1563042200.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">113.54405</span><span class="err"> </span><span class="nc">-232.21953</span><span class="err"> </span><span class="nc">-44.73371</span><span class="err"> </span><span class="nc">1557.1084</span><span class="err"> </span><span class="nc">6771.7046</span> -<span class="nt">Epoch</span><span class="na"> 12000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1535855600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">115.471565</span><span class="err"> </span><span class="nc">-253.9838</span><span class="err"> </span><span class="nc">-36.851135</span><span class="err"> </span><span class="nc">1729.535</span><span class="err"> </span><span class="nc">7429.069</span> -<span class="nt">Epoch</span><span class="na"> 13000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1509255300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">117.33939</span><span class="err"> </span><span class="nc">-275.29697</span><span class="err"> </span><span class="nc">-28.0714</span><span class="err"> </span><span class="nc">1902.5308</span><span class="err"> </span><span class="nc">8083.9634</span> -<span class="nt">Epoch</span><span class="na"> 14000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1483227000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">119.1605</span><span class="err"> </span><span class="nc">-296.2472</span><span class="err"> </span><span class="nc">-18.618649</span><span class="err"> </span><span class="nc">2075.6094</span><span class="err"> </span><span class="nc">8735.381</span> -<span class="nt">Epoch</span><span class="na"> 15000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1457726700.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">120.94584</span><span class="err"> </span><span class="nc">-316.915</span><span class="err"> </span><span class="nc">-8.650095</span><span class="err"> </span><span class="nc">2248.3247</span><span class="err"> </span><span class="nc">9384.197</span> -<span class="nt">Epoch</span><span class="na"> 16000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1432777300.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">122.69806</span><span class="err"> </span><span class="nc">-337.30704</span><span class="err"> </span><span class="nc">1.7027153</span><span class="err"> </span><span class="nc">2420.5771</span><span class="err"> </span><span class="nc">10028.871</span> -<span class="nt">Epoch</span><span class="na"> 17000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1408365000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">124.42179</span><span class="err"> </span><span class="nc">-357.45245</span><span class="err"> </span><span class="nc">12.33499</span><span class="err"> </span><span class="nc">2592.2983</span><span class="err"> </span><span class="nc">10669.157</span> -<span class="nt">Epoch</span><span class="na"> 18000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1384480000.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">126.12332</span><span class="err"> </span><span class="nc">-377.39734</span><span class="err"> </span><span class="nc">23.168756</span><span class="err"> </span><span class="nc">2763.0933</span><span class="err"> </span><span class="nc">11305.027</span> -<span class="nt">Epoch</span><span class="na"> 19000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1361116800.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">127.80568</span><span class="err"> </span><span class="nc">-397.16415</span><span class="err"> </span><span class="nc">34.160156</span><span class="err"> </span><span class="nc">2933.0452</span><span class="err"> </span><span class="nc">11935.669</span> -<span class="nt">Epoch</span><span class="na"> 20000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1338288100.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">129.4674</span><span class="err"> </span><span class="nc">-416.72803</span><span class="err"> </span><span class="nc">45.259155</span><span class="err"> </span><span class="nc">3101.7727</span><span class="err"> </span><span class="nc">12561.179</span> -<span class="nt">Epoch</span><span class="na"> 21000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1315959700.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">131.11403</span><span class="err"> </span><span class="nc">-436.14285</span><span class="err"> </span><span class="nc">56.4436</span><span class="err"> </span><span class="nc">3269.3142</span><span class="err"> </span><span class="nc">13182.058</span> -<span class="nt">Epoch</span><span class="na"> 22000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1294164700.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">132.74377</span><span class="err"> </span><span class="nc">-455.3779</span><span class="err"> </span><span class="nc">67.6757</span><span class="err"> </span><span class="nc">3435.3833</span><span class="err"> </span><span class="nc">13796.807</span> -<span class="nt">Epoch</span><span class="na"> 23000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1272863600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">134.35779</span><span class="err"> </span><span class="nc">-474.45316</span><span class="err"> </span><span class="nc">78.96117</span><span class="err"> </span><span class="nc">3600.264</span><span class="err"> </span><span class="nc">14406.58</span> -<span class="nt">Epoch</span><span class="na"> 24000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1252052600.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">135.9583</span><span class="err"> </span><span class="nc">-493.38254</span><span class="err"> </span><span class="nc">90.268616</span><span class="err"> </span><span class="nc">3764.0078</span><span class="err"> </span><span class="nc">15010.481</span> -<span class="nt">Epoch</span><span class="na"> 25000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1231713700.0</span><span class="err"> </span><span class="nc">a,b,c,d</span><span class="p">:</span><span class="err"> </span><span class="nc">137.54753</span><span class="err"> </span><span class="nc">-512.1876</span><span class="err"> </span><span class="nc">101.59372</span><span class="err"> </span><span class="nc">3926.4897</span><span class="err"> </span><span class="nc">15609.368</span> -<span class="nt">1231713700.0</span><span class="na"> 137.54753 -512.1876 101.59372 3926.4897 15609.368</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span> -<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">abscissa</span><span class="p">:</span> - <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">coefficient1</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient2</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient3</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient4</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">constant</span><span class="p">))</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span> <span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Fitted line'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Quartic Regression Result'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span> -<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> -</div> - -</code></pre><img src="https://navanchauhan.github.io//assets/gciTales/03-regression/5.png"/><h3>Quintic</h3><pre><code><div class="highlight"><span></span><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> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">init</span><span class="p">)</span> - <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">no_of_epochs</span><span class="p">):</span> - <span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">):</span> - <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer5</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">x</span><span class="p">,</span> <span class="n">Y</span><span class="p">:</span><span class="n">y</span><span class="p">})</span> - <span class="k">if</span> <span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">1000</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span> - <span class="n">cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse5</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="k">print</span><span class="p">(</span><span class="s2">"Epoch"</span><span class="p">,(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="s2">": Training Cost:"</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span><span class="s2">" a,b,c,d,e,f:"</span><span class="p">,</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">e</span><span class="p">),</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">f</span><span class="p">))</span> - - <span class="n">training_cost</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mse5</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">X</span><span class="p">:</span><span class="n">abscissa</span><span class="p">,</span><span class="n">Y</span><span class="p">:</span><span class="n">ordinate</span><span class="p">})</span> - <span class="n">coefficient1</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> - <span class="n">coefficient2</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> - <span class="n">coefficient3</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> - <span class="n">coefficient4</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> - <span class="n">coefficient5</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> - <span class="n">constant</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="nt">Epoch</span><span class="na"> 1000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1409200100.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">7.949472</span><span class="err"> </span><span class="nc">7.46219</span><span class="err"> </span><span class="nc">55.626034</span><span class="err"> </span><span class="nc">184.29028</span><span class="err"> </span><span class="nc">484.00223</span><span class="err"> </span><span class="nc">1024.0083</span> -<span class="nt">Epoch</span><span class="na"> 2000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1306882400.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">8.732181</span><span class="err"> </span><span class="nc">-4.0085897</span><span class="err"> </span><span class="nc">73.25298</span><span class="err"> </span><span class="nc">315.90103</span><span class="err"> </span><span class="nc">904.08887</span><span class="err"> </span><span class="nc">2004.9749</span> -<span class="nt">Epoch</span><span class="na"> 3000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1212606000.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">9.732249</span><span class="err"> </span><span class="nc">-16.90125</span><span class="err"> </span><span class="nc">86.28379</span><span class="err"> </span><span class="nc">437.06552</span><span class="err"> </span><span class="nc">1305.055</span><span class="err"> </span><span class="nc">2966.2188</span> -<span class="nt">Epoch</span><span class="na"> 4000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1123640400.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">10.74851</span><span class="err"> </span><span class="nc">-29.82692</span><span class="err"> </span><span class="nc">98.59997</span><span class="err"> </span><span class="nc">555.331</span><span class="err"> </span><span class="nc">1698.4631</span><span class="err"> </span><span class="nc">3917.9155</span> -<span class="nt">Epoch</span><span class="na"> 5000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">1039694300.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">11.75426</span><span class="err"> </span><span class="nc">-42.598194</span><span class="err"> </span><span class="nc">110.698326</span><span class="err"> </span><span class="nc">671.64355</span><span class="err"> </span><span class="nc">2085.5513</span><span class="err"> </span><span class="nc">4860.8535</span> -<span class="nt">Epoch</span><span class="na"> 6000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">960663550.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">12.745439</span><span class="err"> </span><span class="nc">-55.18337</span><span class="err"> </span><span class="nc">122.644936</span><span class="err"> </span><span class="nc">786.00214</span><span class="err"> </span><span class="nc">2466.1638</span><span class="err"> </span><span class="nc">5794.3735</span> -<span class="nt">Epoch</span><span class="na"> 7000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">886438340.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">13.721028</span><span class="err"> </span><span class="nc">-67.57168</span><span class="err"> </span><span class="nc">134.43822</span><span class="err"> </span><span class="nc">898.3691</span><span class="err"> </span><span class="nc">2839.9958</span><span class="err"> </span><span class="nc">6717.659</span> -<span class="nt">Epoch</span><span class="na"> 8000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">816913100.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">14.679965</span><span class="err"> </span><span class="nc">-79.75113</span><span class="err"> </span><span class="nc">146.07385</span><span class="err"> </span><span class="nc">1008.66895</span><span class="err"> </span><span class="nc">3206.6692</span><span class="err"> </span><span class="nc">7629.812</span> -<span class="nt">Epoch</span><span class="na"> 9000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">751971500.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">15.62181</span><span class="err"> </span><span class="nc">-91.71608</span><span class="err"> </span><span class="nc">157.55713</span><span class="err"> </span><span class="nc">1116.7715</span><span class="err"> </span><span class="nc">3565.8323</span><span class="err"> </span><span class="nc">8529.976</span> -<span class="nt">Epoch</span><span class="na"> 10000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">691508740.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">16.545347</span><span class="err"> </span><span class="nc">-103.4531</span><span class="err"> </span><span class="nc">168.88321</span><span class="err"> </span><span class="nc">1222.6348</span><span class="err"> </span><span class="nc">3916.9785</span><span class="err"> </span><span class="nc">9416.236</span> -<span class="nt">Epoch</span><span class="na"> 11000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">635382000.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">17.450052</span><span class="err"> </span><span class="nc">-114.954254</span><span class="err"> </span><span class="nc">180.03932</span><span class="err"> </span><span class="nc">1326.1565</span><span class="err"> </span><span class="nc">4259.842</span><span class="err"> </span><span class="nc">10287.99</span> -<span class="nt">Epoch</span><span class="na"> 12000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">583477250.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">18.334944</span><span class="err"> </span><span class="nc">-126.20821</span><span class="err"> </span><span class="nc">191.02948</span><span class="err"> </span><span class="nc">1427.2095</span><span class="err"> </span><span class="nc">4593.8</span><span class="err"> </span><span class="nc">11143.449</span> -<span class="nt">Epoch</span><span class="na"> 13000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">535640400.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">19.198917</span><span class="err"> </span><span class="nc">-137.20206</span><span class="err"> </span><span class="nc">201.84718</span><span class="err"> </span><span class="nc">1525.6926</span><span class="err"> </span><span class="nc">4918.5327</span><span class="err"> </span><span class="nc">11981.633</span> -<span class="nt">Epoch</span><span class="na"> 14000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">491722240.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">20.041153</span><span class="err"> </span><span class="nc">-147.92719</span><span class="err"> </span><span class="nc">212.49709</span><span class="err"> </span><span class="nc">1621.5496</span><span class="err"> </span><span class="nc">5233.627</span><span class="err"> </span><span class="nc">12800.468</span> -<span class="nt">Epoch</span><span class="na"> 15000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">451559520.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">20.860966</span><span class="err"> </span><span class="nc">-158.37456</span><span class="err"> </span><span class="nc">222.97133</span><span class="err"> </span><span class="nc">1714.7141</span><span class="err"> </span><span class="nc">5538.676</span><span class="err"> </span><span class="nc">13598.337</span> -<span class="nt">Epoch</span><span class="na"> 16000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">414988960.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">21.657421</span><span class="err"> </span><span class="nc">-168.53406</span><span class="err"> </span><span class="nc">233.27422</span><span class="err"> </span><span class="nc">1805.0874</span><span class="err"> </span><span class="nc">5833.1978</span><span class="err"> </span><span class="nc">14373.658</span> -<span class="nt">Epoch</span><span class="na"> 17000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">381837920.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">22.429693</span><span class="err"> </span><span class="nc">-178.39536</span><span class="err"> </span><span class="nc">243.39914</span><span class="err"> </span><span class="nc">1892.5883</span><span class="err"> </span><span class="nc">6116.847</span><span class="err"> </span><span class="nc">15124.394</span> -<span class="nt">Epoch</span><span class="na"> 18000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">351931300.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">23.176882</span><span class="err"> </span><span class="nc">-187.94789</span><span class="err"> </span><span class="nc">253.3445</span><span class="err"> </span><span class="nc">1977.137</span><span class="err"> </span><span class="nc">6389.117</span><span class="err"> </span><span class="nc">15848.417</span> -<span class="nt">Epoch</span><span class="na"> 19000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">325074400.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">23.898485</span><span class="err"> </span><span class="nc">-197.18741</span><span class="err"> </span><span class="nc">263.12512</span><span class="err"> </span><span class="nc">2058.6716</span><span class="err"> </span><span class="nc">6649.8037</span><span class="err"> </span><span class="nc">16543.95</span> -<span class="nt">Epoch</span><span class="na"> 20000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">301073570.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">24.593851</span><span class="err"> </span><span class="nc">-206.10497</span><span class="err"> </span><span class="nc">272.72385</span><span class="err"> </span><span class="nc">2137.1797</span><span class="err"> </span><span class="nc">6898.544</span><span class="err"> </span><span class="nc">17209.367</span> -<span class="nt">Epoch</span><span class="na"> 21000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">279727000.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">25.262104</span><span class="err"> </span><span class="nc">-214.69217</span><span class="err"> </span><span class="nc">282.14642</span><span class="err"> </span><span class="nc">2212.6372</span><span class="err"> </span><span class="nc">7135.217</span><span class="err"> </span><span class="nc">17842.854</span> -<span class="nt">Epoch</span><span class="na"> 22000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">260845550.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">25.903376</span><span class="err"> </span><span class="nc">-222.94969</span><span class="err"> </span><span class="nc">291.4003</span><span class="err"> </span><span class="nc">2284.9844</span><span class="err"> </span><span class="nc">7359.4644</span><span class="err"> </span><span class="nc">18442.408</span> -<span class="nt">Epoch</span><span class="na"> 23000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">244218030.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">26.517094</span><span class="err"> </span><span class="nc">-230.8697</span><span class="err"> </span><span class="nc">300.45532</span><span class="err"> </span><span class="nc">2354.3003</span><span class="err"> </span><span class="nc">7571.261</span><span class="err"> </span><span class="nc">19007.49</span> -<span class="nt">Epoch</span><span class="na"> 24000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">229660080.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">27.102589</span><span class="err"> </span><span class="nc">-238.44817</span><span class="err"> </span><span class="nc">309.35342</span><span class="err"> </span><span class="nc">2420.4185</span><span class="err"> </span><span class="nc">7770.5728</span><span class="err"> </span><span class="nc">19536.19</span> -<span class="nt">Epoch</span><span class="na"> 25000 </span><span class="p">:</span><span class="err"> </span><span class="nc">Training</span><span class="err"> </span><span class="nc">Cost</span><span class="p">:</span><span class="err"> </span><span class="nc">216972400.0</span><span class="err"> </span><span class="nc">a,b,c,d,e,f</span><span class="p">:</span><span class="err"> </span><span class="nc">27.660324</span><span class="err"> </span><span class="nc">-245.69016</span><span class="err"> </span><span class="nc">318.10062</span><span class="err"> </span><span class="nc">2483.3608</span><span class="err"> </span><span class="nc">7957.354</span><span class="err"> </span><span class="nc">20027.707</span> -<span class="nt">216972400.0</span><span class="na"> 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span> -<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">abscissa</span><span class="p">:</span> - <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">coefficient1</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient2</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient3</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient4</span><span class="o">*</span><span class="nb">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">coefficient5</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">constant</span><span class="p">))</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span> <span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span><span class="s1">'Fitted line'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Quintic Regression Result'</span><span class="p">)</span> -<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span> -<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> -</div> - -</code></pre><img src="https://navanchauhan.github.io//assets/gciTales/03-regression/6.png"/><h2>Results and Conclusion</h2><p>You just learnt Polynomial Regression using TensorFlow!</p><h2>Notes</h2><h3>Overfitting</h3><blockquote><p>> Overfitting refers to a model that models the training data too well.Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.</p></blockquote><blockquote><p>Source: Machine Learning Mastery</p></blockquote><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>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction</guid><title>Making Predictions using Image Classifier (TensorFlow)</title><description>Making predictions for image classification models built using TensorFlow</description><link>https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction</link><pubDate>Tue, 10 Dec 2019 11:10:00 +0530</pubDate><content:encoded><![CDATA[<h1>Making Predictions using Image Classifier (TensorFlow)</h1><p><em>This was tested on TF 2.x and works as of 2019-12-10</em></p><p>If you want to understand how to make your own custom image classifier, please refer to my previous post.</p><p>If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.</p><p>First we import the following if we have not imported these before</p><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">cv2</span> -<span class="kn">import</span> <span class="nn">os</span> -</div> - -</code></pre><p>Then we read the file using OpenCV.</p><pre><code><div class="highlight"><span></span><span class="n">image</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">imagePath</span><span class="p">)</span> -</div> - -</code></pre><p>The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.</p><pre><code><div class="highlight"><span></span><span class="n">image_from_array</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">)</span> -</div> - -</code></pre><p>Then we resize the image</p><pre><code><div class="highlight"><span></span><span class="n">size_image</span> <span class="o">=</span> <span class="n">image_from_array</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">50</span><span class="p">,</span><span class="mi">50</span><span class="p">))</span> -</div> - -</code></pre><p>After this we create a batch consisting of only one image</p><pre><code><div class="highlight"><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">size_image</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> -</div> - -</code></pre><p>We then convert this uint8 datatype to a float32 datatype</p><pre><code><div class="highlight"><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> -</div> - -</code></pre><p>Finally we make the prediction</p><pre><code><div class="highlight"><span></span><span class="k">print</span><span class="p">([</span><span class="s1">'Infected'</span><span class="p">,</span><span class="s1">'Uninfected'</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">))])</span> -</div> - -</code></pre><p><code>Infected</code></p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow</guid><title>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</title><description>Tutorial on creating an image classifier model using TensorFlow which detects malaria</description><link>https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow</link><pubDate>Sun, 8 Dec 2019 14:16:00 +0530</pubDate><content:encoded><![CDATA[<h1>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</h1><p><strong>Done during Google Code-In. Org: Tensorflow.</strong></p><h2>Imports</h2><pre><code><div class="highlight"><span></span><span class="o">%</span><span class="n">tensorflow_version</span> <span class="mf">2.</span><span class="n">x</span> <span class="c1">#This is for telling Colab that you want to use TF 2.0, ignore if running on local machine</span> - -<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="c1"># We use the PIL Library to resize images</span> -<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> -<span class="kn">import</span> <span class="nn">os</span> -<span class="kn">import</span> <span class="nn">cv2</span> -<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span> -<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">models</span> -<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span> -<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span> -<span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span> -<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="kn">import</span> <span class="n">Conv2D</span><span class="p">,</span><span class="n">MaxPooling2D</span><span class="p">,</span><span class="n">Dense</span><span class="p">,</span><span class="n">Flatten</span><span class="p">,</span><span class="n">Dropout</span> -</div> - -</code></pre><h2>Dataset</h2><h3>Fetching the Data</h3><pre><code><div class="highlight"><span></span><span class="err">!</span><span class="n">wget</span> <span class="n">ftp</span><span class="p">:</span><span class="o">//</span><span class="n">lhcftp</span><span class="o">.</span><span class="n">nlm</span><span class="o">.</span><span class="n">nih</span><span class="o">.</span><span class="n">gov</span><span class="o">/</span><span class="n">Open</span><span class="o">-</span><span class="n">Access</span><span class="o">-</span><span class="n">Datasets</span><span class="o">/</span><span class="n">Malaria</span><span class="o">/</span><span class="n">cell_images</span><span class="o">.</span><span class="n">zip</span> -<span class="err">!</span><span class="n">unzip</span> <span class="n">cell_images</span><span class="o">.</span><span class="n">zip</span> -</div> - -</code></pre><h3>Processing the Data</h3><p>We resize all the images as 50x50 and add the numpy array of that image as well as their label names (Infected or Not) to common arrays.</p><pre><code><div class="highlight"><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[]</span> -<span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span> - -<span class="n">Parasitized</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s2">"./cell_images/Parasitized/"</span><span class="p">)</span> -<span class="k">for</span> <span class="n">parasite</span> <span class="ow">in</span> <span class="n">Parasitized</span><span class="p">:</span> - <span class="k">try</span><span class="p">:</span> - <span class="n">image</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">"./cell_images/Parasitized/"</span><span class="o">+</span><span class="n">parasite</span><span class="p">)</span> - <span class="n">image_from_array</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">)</span> - <span class="n">size_image</span> <span class="o">=</span> <span class="n">image_from_array</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">))</span> - <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">size_image</span><span class="p">))</span> - <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> - <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span> - <span class="k">print</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span> - -<span class="n">Uninfected</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s2">"./cell_images/Uninfected/"</span><span class="p">)</span> -<span class="k">for</span> <span class="n">uninfect</span> <span class="ow">in</span> <span class="n">Uninfected</span><span class="p">:</span> - <span class="k">try</span><span class="p">:</span> - <span class="n">image</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">"./cell_images/Uninfected/"</span><span class="o">+</span><span class="n">uninfect</span><span class="p">)</span> - <span class="n">image_from_array</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">)</span> - <span class="n">size_image</span> <span class="o">=</span> <span class="n">image_from_array</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">))</span> - <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">size_image</span><span class="p">))</span> - <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> - <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span> - <span class="k">print</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span> -</div> - -</code></pre><h3>Splitting Data</h3><pre><code><div class="highlight"><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> -<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> -<span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">df</span><span class="p">[(</span><span class="nb">int</span><span class="p">)(</span><span class="mf">0.1</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)):],</span><span class="n">df</span><span class="p">[:(</span><span class="nb">int</span><span class="p">)(</span><span class="mf">0.1</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))]</span> -<span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[(</span><span class="nb">int</span><span class="p">)(</span><span class="mf">0.1</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)):],</span><span class="n">labels</span><span class="p">[:(</span><span class="nb">int</span><span class="p">)(</span><span class="mf">0.1</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">))]</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">s</span><span class="p">=</span><span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X_train</span><span class="p">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> -<span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> -<span class="n">X_train</span><span class="p">=</span><span class="n">X_train</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> -<span class="n">y_train</span><span class="p">=</span><span class="n">y_train</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> -<span class="n">X_train</span> <span class="p">=</span> <span class="n">X_train</span><span class="o">/</span><span class="mf">255.0</span> -</div> - -</code></pre><h2>Model</h2><h3>Creating Model</h3><p>By creating a sequential model, we create a linear stack of layers.</p><p><em>Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images</em></p><pre><code><div class="highlight"><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="mi">3</span><span class="p">)))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="s2">"same"</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">())</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span> -<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s2">"softmax"</span><span class="p">))</span><span class="c1">#2 represent output layer neurons </span> -<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> -</div> - -</code></pre><h3>Compiling Model</h3><p>We use the adam optimiser as it is an adaptive learning rate optimization algorithm that's been designed specifically for <em>training</em> deep neural networks, which means it changes its learning rate automaticaly to get the best results</p><pre><code><div class="highlight"><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">"adam"</span><span class="p">,</span> - <span class="n">loss</span><span class="o">=</span><span class="s2">"sparse_categorical_crossentropy"</span><span class="p">,</span> - <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">"accuracy"</span><span class="p">])</span> -</div> - -</code></pre><h3>Training Model</h3><p>We train the model for 10 epochs on the training data and then validate it using the testing data</p><pre><code><div class="highlight"><span></span><span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span><span class="n">y_test</span><span class="p">))</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">Train</span> <span class="n">on</span> <span class="mi">24803</span> <span class="n">samples</span><span class="p">,</span> <span class="n">validate</span> <span class="n">on</span> <span class="mi">2755</span> <span class="n">samples</span> -<span class="n">Epoch</span> <span class="mi">1</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">57</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0786</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9729</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">2</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0746</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9731</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0290</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">0.9996</span> -<span class="n">Epoch</span> <span class="mi">3</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0672</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9764</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">4</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0601</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9789</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">5</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0558</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9804</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">6</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">57</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0513</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9819</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">7</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0452</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9849</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.3190</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">0.9985</span> -<span class="n">Epoch</span> <span class="mi">8</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0404</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9858</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">9</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0352</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9878</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -<span class="n">Epoch</span> <span class="mi">10</span><span class="o">/</span><span class="mi">10</span> -<span class="mi">24803</span><span class="o">/</span><span class="mi">24803</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">58</span><span class="n">s</span> <span class="mi">2</span><span class="n">ms</span><span class="o">/</span><span class="n">sample</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0373</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">:</span> <span class="mf">0.9865</span> <span class="o">-</span> <span class="n">val_loss</span><span class="p">:</span> <span class="mf">0.0000e+00</span> <span class="o">-</span> <span class="n">val_accuracy</span><span class="p">:</span> <span class="mf">1.0000</span> -</div> - -</code></pre><h3>Results</h3><pre><code><div class="highlight"><span></span><span class="n">accuracy</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span> -<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'loss'</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span> -<span class="n">val_accuracy</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_accuracy'</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span> -<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_loss'</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span> - -<span class="k">print</span><span class="p">(</span> - <span class="s1">'Accuracy:'</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> - <span class="s1">'</span><span class="se">\n</span><span class="s1">Loss:'</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> - <span class="s1">'</span><span class="se">\n</span><span class="s1">Validation Accuracy:'</span><span class="p">,</span> <span class="n">val_accuracy</span><span class="p">,</span> - <span class="s1">'</span><span class="se">\n</span><span class="s1">Validation Loss:'</span><span class="p">,</span> <span class="n">val_loss</span> -<span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">Accuracy</span><span class="p">:</span> <span class="mf">98.64532351493835</span> -<span class="n">Loss</span><span class="p">:</span> <span class="mf">3.732407123270176</span> -<span class="n">Validation</span> <span class="n">Accuracy</span><span class="p">:</span> <span class="mf">100.0</span> -<span class="n">Validation</span> <span class="n">Loss</span><span class="p">:</span> <span class="mf">0.0</span> -</div> - -</code></pre><p>We have achieved 98% Accuracy!</p><p><a href="https://colab.research.google.com/drive/1ZswDsxLwYZEnev89MzlL5Lwt6ut7iwp- "Colab Notebook"">Link to Colab Notebook</a></p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips</guid><title>Splitting ZIPs into Multiple Parts</title><description>Short code snippet for splitting zips.</description><link>https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips</link><pubDate>Sun, 8 Dec 2019 13:27:00 +0530</pubDate><content:encoded><![CDATA[<h1>Splitting ZIPs into Multiple Parts</h1><p><strong>Tested on macOS</strong></p><p>Creating the archive:</p><pre><code><div class="highlight"><span></span><span class="nt">zip</span><span class="na"> -r -s 5 oodlesofnoodles.zip website/</span> -</div> - -</code></pre><p>5 stands for each split files' size (in mb, kb and gb can also be specified)</p><p>For encrypting the zip:</p><pre><code><div class="highlight"><span></span><span class="nt">zip</span><span class="na"> -er -s 5 oodlesofnoodles.zip website</span> -</div> - -</code></pre><p>Extracting Files</p><p>First we need to collect all parts, then</p><pre><code><div class="highlight"><span></span><span class="nt">zip</span><span class="na"> -F oodlesofnoodles.zip --out merged.zip</span> -</div> - -</code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response</guid><title>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</title><description>This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.</description><link>https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response</link><pubDate>Tue, 14 May 2019 02:42:00 +0530</pubDate><content:encoded><![CDATA[<h1>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</h1><blockquote><p>Based on the project showcased at Toyota Hackathon, IITD - 17/18th December 2018</p></blockquote><p><a href="https://www.irjet.net/archives/V6/i5/IRJET-V6I5318.pdf">Download paper here</a></p><p>Recommended citation:</p><h3>ATP</h3><pre><code><div class="highlight"><span></span><span class="n">Chauhan</span><span class="p">,</span> <span class="n">N</span><span class="p">.</span> <span class="p">(</span><span class="mi">2019</span><span class="p">).</span> <span class="p">&</span><span class="n">quot</span><span class="p">;</span><span class="n">Detecting</span> <span class="n">Driver</span> <span class="n">Fatigue</span><span class="p">,</span> <span class="n">Over</span><span class="o">-</span><span class="n">Speeding</span><span class="p">,</span> <span class="n">and</span> <span class="n">Speeding</span> <span class="n">up</span> <span class="n">Post</span><span class="o">-</span><span class="n">Accident</span> <span class="n">Response</span><span class="p">.&</span><span class="n">quot</span><span class="p">;</span> <span class="p"><</span><span class="n">i</span><span class="p">></span><span class="n">International</span> <span class="n">Research</span> <span class="n">Journal</span> <span class="n">of</span> <span class="n">Engineering</span> <span class="n">and</span> <span class="n">Technology</span> <span class="p">(</span><span class="n">IRJET</span><span class="p">),</span> <span class="mi">6</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o"></</span><span class="n">i</span><span class="p">>.</span> -</div> - -</code></pre><h3>BibTeX</h3><pre><code><div class="highlight"><span></span><span class="p">@</span><span class="n">article</span><span class="p">{</span><span class="n">chauhan_2019</span><span class="p">,</span> <span class="n">title</span><span class="p">={</span><span class="n">Detecting</span> <span class="n">Driver</span> <span class="n">Fatigue</span><span class="p">,</span> <span class="n">Over</span><span class="o">-</span><span class="n">Speeding</span><span class="p">,</span> <span class="n">and</span> <span class="n">Speeding</span> <span class="n">up</span> <span class="n">Post</span><span class="o">-</span><span class="n">Accident</span> <span class="n">Response</span><span class="p">},</span> <span class="n">volume</span><span class="p">={</span><span class="mi">6</span><span class="p">},</span> <span class="n">url</span><span class="p">={</span><span class="n">https</span><span class="p">:</span><span class="c1">//www.irjet.net/archives/V6/i5/IRJET-V6I5318.pdf}, number={5}, journal={International Research Journal of Engineering and Technology (IRJET)}, author={Chauhan, Navan}, year={2019}}</span> -</div> - -</code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/hello-world</guid><title>Hello World</title><description>My first post.</description><link>https://navanchauhan.github.io/posts/hello-world</link><pubDate>Tue, 16 Apr 2019 17:39:00 +0530</pubDate><content:encoded><![CDATA[<h1>Hello World</h1><p><strong>Why a Hello World post?</strong></p><p>Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :)</p>]]></content:encoded></item></channel></rss>
\ No newline at end of file diff --git a/images/favicon 2.png b/images/favicon 2.png Binary files differnew file mode 100644 index 0000000..ce3a263 --- /dev/null +++ b/images/favicon 2.png diff --git a/images/logo 2.png b/images/logo 3.png Binary files differindex caaf43c..caaf43c 100644 --- a/images/logo 2.png +++ b/images/logo 3.png diff --git a/images/me 2.jpeg b/images/me 2.jpeg Binary files differnew file mode 100644 index 0000000..cf70e23 --- /dev/null +++ b/images/me 2.jpeg diff --git a/index 2.html b/index 2.html deleted file mode 100644 index c48185b..0000000 --- a/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/"/><meta name="twitter:url" content="https://navanchauhan.github.io/"/><meta name="og:url" content="https://navanchauhan.github.io/"/><title>Hi! | Navan Chauhan</title><meta name="twitter:title" content="Hi! 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\ No newline at end of file diff --git a/index 3.html b/index 3.html new file mode 100644 index 0000000..4774cc3 --- /dev/null +++ b/index 3.html @@ -0,0 +1 @@ +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/"/><meta name="twitter:url" content="https://navanchauhan.github.io/"/><meta name="og:url" content="https://navanchauhan.github.io/"/><title>Hi! | Navan Chauhan</title><meta name="twitter:title" content="Hi! | Navan Chauhan"/><meta name="og:title" content="Hi! | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta 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href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Hi!</h1><p class="description">Welcome to my personal fragment of the internet.</p><h2>Latest content</h2><ul class="item-list"><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li><li><article><h1><a href="/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response">Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</a></h1><ul class="tag-list"><li><a href="/tags/publication">publication</a></li></ul><span>🕑 1 minute read.</span><p>This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.</p></article></li><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first post.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/2019-12-08-Image-Classifier-Tensorflow/index 2.html b/posts/2019-12-08-Image-Classifier-Tensorflow/index 3.html index 457cfde..fd0e4a0 100644 --- a/posts/2019-12-08-Image-Classifier-Tensorflow/index 2.html +++ b/posts/2019-12-08-Image-Classifier-Tensorflow/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow"/><title>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan</title><meta name="twitter:title" content="Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan"/><meta name="og:title" content="Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan"/><meta name="description" content="Tutorial on creating an image classifier model using TensorFlow which detects malaria"/><meta name="twitter:description" content="Tutorial on creating an image classifier model using TensorFlow which detects malaria"/><meta name="og:description" content="Tutorial on creating an image classifier model using TensorFlow which detects malaria"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 4 minute read.</span><h1>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</h1><p><strong>Done during Google Code-In. Org: Tensorflow.</strong></p><h2>Imports</h2><pre><code><div class="highlight"><span></span><span class="o">%</span><span class="n">tensorflow_version</span> <span class="mf">2.</span><span class="n">x</span> <span class="c1">#This is for telling Colab that you want to use TF 2.0, ignore if running on local machine</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-Tensorflow"/><title>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan</title><meta name="twitter:title" content="Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan"/><meta name="og:title" content="Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan"/><meta name="description" content="Tutorial on creating an image classifier model using TensorFlow which detects malaria"/><meta name="twitter:description" content="Tutorial on creating an image classifier model using TensorFlow which detects malaria"/><meta name="og:description" content="Tutorial on creating an image classifier model using TensorFlow which detects malaria"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 4 minute read.</span><h1>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</h1><p><strong>Done during Google Code-In. Org: Tensorflow.</strong></p><h2>Imports</h2><pre><code><div class="highlight"><span></span><span class="o">%</span><span class="n">tensorflow_version</span> <span class="mf">2.</span><span class="n">x</span> <span class="c1">#This is for telling Colab that you want to use TF 2.0, ignore if running on local machine</span> <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="c1"># We use the PIL Library to resize images</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> diff --git a/posts/2019-12-08-Splitting-Zips/index 2.html b/posts/2019-12-08-Splitting-Zips/index 3.html index ad7ea78..b3f2fca 100644 --- a/posts/2019-12-08-Splitting-Zips/index 2.html +++ b/posts/2019-12-08-Splitting-Zips/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips"/><title>Splitting ZIPs into Multiple Parts | Navan Chauhan</title><meta name="twitter:title" content="Splitting ZIPs into Multiple Parts | Navan Chauhan"/><meta name="og:title" content="Splitting ZIPs into Multiple Parts | Navan Chauhan"/><meta name="description" content="Short code snippet for splitting zips."/><meta name="twitter:description" content="Short code snippet for splitting zips."/><meta name="og:description" content="Short code snippet for splitting zips."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 0 minute read.</span><h1>Splitting ZIPs into Multiple Parts</h1><p><strong>Tested on macOS</strong></p><p>Creating the archive:</p><pre><code><div class="highlight"><span></span><span class="nt">zip</span><span class="na"> -r -s 5 oodlesofnoodles.zip website/</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-08-Splitting-Zips"/><title>Splitting ZIPs into Multiple Parts | Navan Chauhan</title><meta name="twitter:title" content="Splitting ZIPs into Multiple Parts | Navan Chauhan"/><meta name="og:title" content="Splitting ZIPs into Multiple Parts | Navan Chauhan"/><meta name="description" content="Short code snippet for splitting zips."/><meta name="twitter:description" content="Short code snippet for splitting zips."/><meta name="og:description" content="Short code snippet for splitting zips."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 0 minute read.</span><h1>Splitting ZIPs into Multiple Parts</h1><p><strong>Tested on macOS</strong></p><p>Creating the archive:</p><pre><code><div class="highlight"><span></span><span class="nt">zip</span><span class="na"> -r -s 5 oodlesofnoodles.zip website/</span> </div> </code></pre><p>5 stands for each split files' size (in mb, kb and gb can also be specified)</p><p>For encrypting the zip:</p><pre><code><div class="highlight"><span></span><span class="nt">zip</span><span class="na"> -er -s 5 oodlesofnoodles.zip website</span> diff --git a/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html b/posts/2019-12-10-TensorFlow-Model-Prediction/index 3.html index 5a31966..343bfb0 100644 --- a/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html +++ b/posts/2019-12-10-TensorFlow-Model-Prediction/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><title>Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan</title><meta name="twitter:title" content="Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan"/><meta name="og:title" content="Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan"/><meta name="description" content="Making predictions for image classification models built using TensorFlow"/><meta name="twitter:description" content="Making predictions for image classification models built using TensorFlow"/><meta name="og:description" content="Making predictions for image classification models built using TensorFlow"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 1 minute read.</span><h1>Making Predictions using Image Classifier (TensorFlow)</h1><p><em>This was tested on TF 2.x and works as of 2019-12-10</em></p><p>If you want to understand how to make your own custom image classifier, please refer to my previous post.</p><p>If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.</p><p>First we import the following if we have not imported these before</p><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">cv2</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><title>Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan</title><meta name="twitter:title" content="Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan"/><meta name="og:title" content="Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan"/><meta name="description" content="Making predictions for image classification models built using TensorFlow"/><meta name="twitter:description" content="Making predictions for image classification models built using TensorFlow"/><meta name="og:description" content="Making predictions for image classification models built using TensorFlow"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 1 minute read.</span><h1>Making Predictions using Image Classifier (TensorFlow)</h1><p><em>This was tested on TF 2.x and works as of 2019-12-10</em></p><p>If you want to understand how to make your own custom image classifier, please refer to my previous post.</p><p>If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.</p><p>First we import the following if we have not imported these before</p><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">cv2</span> <span class="kn">import</span> <span class="nn">os</span> </div> diff --git a/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 2.html b/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 3.html index 1904a24..f42d4a2 100644 --- a/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 2.html +++ b/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression"/><title>Polynomial Regression Using TensorFlow | Navan Chauhan</title><meta name="twitter:title" content="Polynomial Regression Using TensorFlow | Navan Chauhan"/><meta name="og:title" content="Polynomial Regression Using TensorFlow | Navan Chauhan"/><meta name="description" content="Polynomial regression using TensorFlow"/><meta name="twitter:description" content="Polynomial regression using TensorFlow"/><meta name="og:description" content="Polynomial regression using TensorFlow"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 16 minute read.</span><h1>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><p>In this, we will be performing polynomial regression using 5 types of equations -</p><ul><li>Linear</li><li>Quadratic</li><li>Cubic</li><li>Quartic</li><li>Quintic</li></ul><h2>Regression</h2><h3>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><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><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">tensorflow.compat.v1</span> <span class="kn">as</span> <span class="nn">tf</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-Regression"/><title>Polynomial Regression Using TensorFlow | Navan Chauhan</title><meta name="twitter:title" content="Polynomial Regression Using TensorFlow | Navan Chauhan"/><meta name="og:title" content="Polynomial Regression Using TensorFlow | Navan Chauhan"/><meta name="description" content="Polynomial regression using TensorFlow"/><meta name="twitter:description" content="Polynomial regression using TensorFlow"/><meta name="og:description" content="Polynomial regression using TensorFlow"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 16 minute read.</span><h1>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><p>In this, we will be performing polynomial regression using 5 types of equations -</p><ul><li>Linear</li><li>Quadratic</li><li>Cubic</li><li>Quartic</li><li>Quintic</li></ul><h2>Regression</h2><h3>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><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><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">tensorflow.compat.v1</span> <span class="kn">as</span> <span class="nn">tf</span> <span class="n">tf</span><span class="o">.</span><span class="n">disable_v2_behavior</span><span class="p">()</span> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> diff --git a/posts/2019-12-22-Fake-News-Detector/index 2.html b/posts/2019-12-22-Fake-News-Detector/index 3.html index c47ec61..a9dca16 100644 --- a/posts/2019-12-22-Fake-News-Detector/index 2.html +++ b/posts/2019-12-22-Fake-News-Detector/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><title>Building a Fake News Detector with Turicreate | Navan Chauhan</title><meta name="twitter:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="og:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="og:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 6 minute read.</span><h1>Building a Fake News Detector with Turicreate</h1><p><strong>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</strong></p><p>Note: These commands are written as if you are running a jupyter notebook.</p><h2>Building the Machine Learning Model</h2><h3>Data Gathering</h3><p>To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and wheter it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own</p><h3>Dependencies</h3><p>I used a Google Colab instance for training my model. If you also plan on using Google Colab then I reccomend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreat is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.</p><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><title>Building a Fake News Detector with Turicreate | Navan Chauhan</title><meta name="twitter:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="og:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="og:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 6 minute read.</span><h1>Building a Fake News Detector with Turicreate</h1><p><strong>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</strong></p><p>Note: These commands are written as if you are running a jupyter notebook.</p><h2>Building the Machine Learning Model</h2><h3>Data Gathering</h3><p>To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and wheter it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own</p><h3>Dependencies</h3><p>I used a Google Colab instance for training my model. If you also plan on using Google Colab then I reccomend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreat is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.</p><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> <span class="na">!pip uninstall -y mxnet</span> <span class="na">!pip install mxnet-cu100==1.4.0.post0</span> </div> diff --git a/posts/2020-01-14-Converting-between-PIL-NumPy/index 2.html b/posts/2020-01-14-Converting-between-PIL-NumPy/index 3.html index 9e21e26..77a91e7 100644 --- a/posts/2020-01-14-Converting-between-PIL-NumPy/index 2.html +++ b/posts/2020-01-14-Converting-between-PIL-NumPy/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy"/><title>Converting between image and NumPy array | Navan Chauhan</title><meta name="twitter:title" content="Converting between image and NumPy array | Navan Chauhan"/><meta name="og:title" content="Converting between image and NumPy array | Navan Chauhan"/><meta name="description" content="Short code snippet for converting between PIL image and NumPy arrays."/><meta name="twitter:description" content="Short code snippet for converting between PIL image and NumPy arrays."/><meta name="og:description" content="Short code snippet for converting between PIL image and NumPy arrays."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 0 minute read.</span><h1>Converting between image and NumPy array</h1><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">numpy</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPy"/><title>Converting between image and NumPy array | Navan Chauhan</title><meta name="twitter:title" content="Converting between image and NumPy array | Navan Chauhan"/><meta name="og:title" content="Converting between image and NumPy array | Navan Chauhan"/><meta name="description" content="Short code snippet for converting between PIL image and NumPy arrays."/><meta name="twitter:description" content="Short code snippet for converting between PIL image and NumPy arrays."/><meta name="og:description" content="Short code snippet for converting between PIL image and NumPy arrays."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 0 minute read.</span><h1>Converting between image and NumPy array</h1><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="nn">PIL</span> <span class="c1"># Convert PIL Image to NumPy array</span> diff --git a/posts/hello-world/index 2.html b/posts/hello-world/index 3.html index f581dcf..f9f8f79 100644 --- a/posts/hello-world/index 2.html +++ b/posts/hello-world/index 3.html @@ -1 +1 @@ -<!DOCTYPE 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title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 0 minute read.</span><h1>Hello World</h1><p><strong>Why a Hello World 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\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/hello-world"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/hello-world"/><meta name="og:url" content="https://navanchauhan.github.io/posts/hello-world"/><title>Hello World | Navan Chauhan</title><meta name="twitter:title" content="Hello World | Navan Chauhan"/><meta name="og:title" content="Hello World | Navan Chauhan"/><meta name="description" content="My first post."/><meta name="twitter:description" content="My first post."/><meta name="og:description" content="My first post."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 0 minute read.</span><h1>Hello World</h1><p><strong>Why a Hello World post?</strong></p><p>Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :)</p></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/index 2.html b/posts/index 2.html deleted file mode 100644 index 69bd527..0000000 --- a/posts/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts"/><meta name="og:url" content="https://navanchauhan.github.io/posts"/><title>Posts | Navan Chauhan</title><meta name="twitter:title" content="Posts | Navan Chauhan"/><meta name="og:title" content="Posts | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Posts</h1><p>Tips, tricks and tutorials which I think might be useful.</p><ul class="item-list"><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first 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\ No newline at end of file diff --git a/posts/index 3.html b/posts/index 3.html new file mode 100644 index 0000000..aaa7a8f --- /dev/null +++ b/posts/index 3.html @@ -0,0 +1 @@ +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts"/><meta name="og:url" content="https://navanchauhan.github.io/posts"/><title>Posts | Navan Chauhan</title><meta name="twitter:title" content="Posts | Navan Chauhan"/><meta name="og:title" content="Posts | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Posts</h1><p>Tips, tricks and tutorials which I think might be useful.</p><ul class="item-list"><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first 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\ No newline at end of file diff --git a/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 2.html b/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 3.html index 4e97617..6c4c0c2 100644 --- a/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 2.html +++ b/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 3.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response"/><meta name="twitter:url" content="https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response"/><meta name="og:url" content="https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response"/><title>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan</title><meta name="twitter:title" content="Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan"/><meta name="og:title" content="Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan"/><meta name="description" content="This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response."/><meta name="twitter:description" content="This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response."/><meta name="og:description" content="This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a class="selected" href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 1 minute read.</span><h1>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</h1><blockquote><p>Based on the project showcased at Toyota Hackathon, IITD - 17/18th December 2018</p></blockquote><p><a href="https://www.irjet.net/archives/V6/i5/IRJET-V6I5318.pdf">Download paper here</a></p><p>Recommended citation:</p><h3>ATP</h3><pre><code><div class="highlight"><span></span><span class="n">Chauhan</span><span class="p">,</span> <span class="n">N</span><span class="p">.</span> <span class="p">(</span><span class="mi">2019</span><span class="p">).</span> <span class="p">&</span><span class="n">quot</span><span class="p">;</span><span class="n">Detecting</span> <span class="n">Driver</span> <span class="n">Fatigue</span><span class="p">,</span> <span class="n">Over</span><span class="o">-</span><span class="n">Speeding</span><span class="p">,</span> <span class="n">and</span> <span class="n">Speeding</span> <span class="n">up</span> <span class="n">Post</span><span class="o">-</span><span class="n">Accident</span> <span class="n">Response</span><span class="p">.&</span><span class="n">quot</span><span class="p">;</span> <span class="p"><</span><span class="n">i</span><span class="p">></span><span class="n">International</span> <span class="n">Research</span> <span class="n">Journal</span> <span class="n">of</span> <span class="n">Engineering</span> <span class="n">and</span> <span class="n">Technology</span> <span class="p">(</span><span class="n">IRJET</span><span class="p">),</span> <span class="mi">6</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o"></</span><span class="n">i</span><span class="p">>.</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response"/><meta name="twitter:url" content="https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response"/><meta name="og:url" content="https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response"/><title>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan</title><meta name="twitter:title" content="Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan"/><meta name="og:title" content="Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan"/><meta name="description" content="This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response."/><meta name="twitter:description" content="This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response."/><meta name="og:description" content="This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a class="selected" href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 1 minute read.</span><h1>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</h1><blockquote><p>Based on the project showcased at Toyota Hackathon, IITD - 17/18th December 2018</p></blockquote><p><a href="https://www.irjet.net/archives/V6/i5/IRJET-V6I5318.pdf">Download paper here</a></p><p>Recommended citation:</p><h3>ATP</h3><pre><code><div class="highlight"><span></span><span class="n">Chauhan</span><span class="p">,</span> <span class="n">N</span><span class="p">.</span> <span class="p">(</span><span class="mi">2019</span><span class="p">).</span> <span class="p">&</span><span class="n">quot</span><span class="p">;</span><span class="n">Detecting</span> <span class="n">Driver</span> <span class="n">Fatigue</span><span class="p">,</span> <span class="n">Over</span><span class="o">-</span><span class="n">Speeding</span><span class="p">,</span> <span class="n">and</span> <span class="n">Speeding</span> <span class="n">up</span> <span class="n">Post</span><span class="o">-</span><span class="n">Accident</span> <span class="n">Response</span><span class="p">.&</span><span class="n">quot</span><span class="p">;</span> <span class="p"><</span><span class="n">i</span><span class="p">></span><span class="n">International</span> <span class="n">Research</span> <span class="n">Journal</span> <span class="n">of</span> <span class="n">Engineering</span> <span class="n">and</span> <span class="n">Technology</span> <span class="p">(</span><span class="n">IRJET</span><span class="p">),</span> <span class="mi">6</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o"></</span><span class="n">i</span><span class="p">>.</span> </div> </code></pre><h3>BibTeX</h3><pre><code><div class="highlight"><span></span><span class="p">@</span><span class="n">article</span><span class="p">{</span><span class="n">chauhan_2019</span><span class="p">,</span> <span class="n">title</span><span class="p">={</span><span class="n">Detecting</span> <span class="n">Driver</span> <span class="n">Fatigue</span><span class="p">,</span> <span class="n">Over</span><span class="o">-</span><span class="n">Speeding</span><span class="p">,</span> <span class="n">and</span> <span class="n">Speeding</span> <span class="n">up</span> <span class="n">Post</span><span class="o">-</span><span class="n">Accident</span> <span class="n">Response</span><span class="p">},</span> <span class="n">volume</span><span class="p">={</span><span class="mi">6</span><span class="p">},</span> <span class="n">url</span><span class="p">={</span><span class="n">https</span><span class="p">:</span><span class="c1">//www.irjet.net/archives/V6/i5/IRJET-V6I5318.pdf}, number={5}, journal={International Research Journal of Engineering and Technology (IRJET)}, author={Chauhan, Navan}, year={2019}}</span> diff --git a/publications/index 2.html b/publications/index 3.html index 413b6d0..e9d9062 100644 --- a/publications/index 2.html +++ b/publications/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/publications"/><meta name="twitter:url" content="https://navanchauhan.github.io/publications"/><meta name="og:url" content="https://navanchauhan.github.io/publications"/><title>Publications | Navan Chauhan</title><meta name="twitter:title" content="Publications | Navan Chauhan"/><meta name="og:title" content="Publications | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a class="selected" href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Publications</h1><p>Hopefully these grow with time, I already have tons of drafts ready. As I am currently studying in school, this allows me to experiment in Physics, Chemistry and Computer Science. I have started using LaTeX now ;)</p><ul class="item-list"><li><article><h1><a href="/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response">Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</a></h1><ul class="tag-list"><li><a href="/tags/publication">publication</a></li></ul><span>🕑 1 minute read.</span><p>This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/publications"/><meta name="twitter:url" content="https://navanchauhan.github.io/publications"/><meta name="og:url" content="https://navanchauhan.github.io/publications"/><title>Publications | Navan Chauhan</title><meta name="twitter:title" content="Publications | Navan Chauhan"/><meta name="og:title" content="Publications | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a class="selected" href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Publications</h1><p>Hopefully these grow with time, I already have tons of drafts ready. As I am currently studying in school, this allows me to experiment in Physics, Chemistry and Computer Science. I have started using LaTeX now ;)</p><ul class="item-list"><li><article><h1><a href="/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response">Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</a></h1><ul class="tag-list"><li><a href="/tags/publication">publication</a></li></ul><span>🕑 1 minute read.</span><p>This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
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\ No newline at end of file diff --git a/styles 2.css b/styles 3.css index afc641a..afc641a 100644 --- a/styles 2.css +++ b/styles 3.css diff --git a/tags/article/index 2.html b/tags/article/index 3.html index 3111fc1..426faa1 100644 --- a/tags/article/index 2.html +++ b/tags/article/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/article"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/article"/><meta name="og:url" content="https://navanchauhan.github.io/tags/article"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">article</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first post.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/article"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/article"/><meta name="og:url" content="https://navanchauhan.github.io/tags/article"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">article</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first post.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/codesnippet/index 2.html b/tags/codesnippet/index 3.html index ded55e9..edad8ab 100644 --- a/tags/codesnippet/index 2.html +++ b/tags/codesnippet/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/codesnippet"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/codesnippet"/><meta name="og:url" content="https://navanchauhan.github.io/tags/codesnippet"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">code-snippet</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/codesnippet"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/codesnippet"/><meta name="og:url" content="https://navanchauhan.github.io/tags/codesnippet"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">code-snippet</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/colab/index 2.html b/tags/colab/index 2.html deleted file mode 100644 index d6d83e5..0000000 --- a/tags/colab/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/colab"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/colab"/><meta name="og:url" content="https://navanchauhan.github.io/tags/colab"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">colab</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/colab/index 3.html b/tags/colab/index 3.html new file mode 100644 index 0000000..bd0cbc6 --- /dev/null +++ b/tags/colab/index 3.html @@ -0,0 +1 @@ +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/colab"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/colab"/><meta name="og:url" content="https://navanchauhan.github.io/tags/colab"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">colab</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/helloworld/index 2.html b/tags/helloworld/index 3.html index 4a96954..2f04641 100644 --- a/tags/helloworld/index 2.html +++ b/tags/helloworld/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/helloworld"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/helloworld"/><meta name="og:url" content="https://navanchauhan.github.io/tags/helloworld"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">hello-world</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first post.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/helloworld"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/helloworld"/><meta name="og:url" content="https://navanchauhan.github.io/tags/helloworld"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">hello-world</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first post.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/index 2.html b/tags/index 2.html deleted file mode 100644 index e1d7e2b..0000000 --- a/tags/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags"/><meta name="og:url" content="https://navanchauhan.github.io/tags"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Browse all tags</h1><ul class="all-tags"><li class="tag"><a href="/tags/article">article</a></li><li class="tag"><a href="/tags/codesnippet">code-snippet</a></li><li class="tag"><a href="/tags/colab">colab</a></li><li class="tag"><a href="/tags/helloworld">hello-world</a></li><li class="tag"><a href="/tags/publication">publication</a></li><li class="tag"><a href="/tags/swiftui">swiftUI</a></li><li class="tag"><a href="/tags/tensorflow">tensorflow</a></li><li class="tag"><a href="/tags/turicreate">turicreate</a></li><li class="tag"><a href="/tags/tutorial">tutorial</a></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/publication/index 2.html b/tags/publication/index 3.html index e784801..1ab5578 100644 --- a/tags/publication/index 2.html +++ b/tags/publication/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/publication"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/publication"/><meta name="og:url" content="https://navanchauhan.github.io/tags/publication"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">publication</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response">Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</a></h1><ul class="tag-list"><li><a href="/tags/publication">publication</a></li></ul><span>🕑 1 minute read.</span><p>This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/publication"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/publication"/><meta name="og:url" content="https://navanchauhan.github.io/tags/publication"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">publication</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response">Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response</a></h1><ul class="tag-list"><li><a href="/tags/publication">publication</a></li></ul><span>🕑 1 minute read.</span><p>This paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/swiftui/index 2.html b/tags/swiftui/index 3.html index dbcb5fb..6939a13 100644 --- a/tags/swiftui/index 2.html +++ b/tags/swiftui/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/swiftui"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/swiftui"/><meta name="og:url" content="https://navanchauhan.github.io/tags/swiftui"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">swiftUI</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/swiftui"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/swiftui"/><meta name="og:url" content="https://navanchauhan.github.io/tags/swiftui"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">swiftUI</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/tensorflow/index 2.html b/tags/tensorflow/index 2.html deleted file mode 100644 index 26de0c6..0000000 --- a/tags/tensorflow/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/tensorflow"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/tensorflow"/><meta name="og:url" content="https://navanchauhan.github.io/tags/tensorflow"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">tensorflow</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/tensorflow/index 3.html b/tags/tensorflow/index 3.html new file mode 100644 index 0000000..b6668dd --- /dev/null +++ b/tags/tensorflow/index 3.html @@ -0,0 +1 @@ +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/tensorflow"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/tensorflow"/><meta name="og:url" content="https://navanchauhan.github.io/tags/tensorflow"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">tensorflow</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/turicreate/index 2.html b/tags/turicreate/index 3.html index e0337b5..bb23d5f 100644 --- a/tags/turicreate/index 2.html +++ b/tags/turicreate/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/turicreate"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/turicreate"/><meta name="og:url" content="https://navanchauhan.github.io/tags/turicreate"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">turicreate</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/turicreate"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/turicreate"/><meta name="og:url" content="https://navanchauhan.github.io/tags/turicreate"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">turicreate</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/tutorial/index 2.html b/tags/tutorial/index 2.html deleted file mode 100644 index 1d5290b..0000000 --- a/tags/tutorial/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/tutorial"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/tutorial"/><meta name="og:url" content="https://navanchauhan.github.io/tags/tutorial"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">tutorial</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tags/tutorial/index 3.html b/tags/tutorial/index 3.html new file mode 100644 index 0000000..ef6d29b --- /dev/null +++ b/tags/tutorial/index 3.html @@ -0,0 +1 @@ +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tags/tutorial"/><meta name="twitter:url" content="https://navanchauhan.github.io/tags/tutorial"/><meta name="og:url" content="https://navanchauhan.github.io/tags/tutorial"/><title>Navan Chauhan</title><meta name="twitter:title" content="Navan Chauhan"/><meta name="og:title" content="Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><h1>Tagged with <span class="tag">tutorial</span></h1><a class="browse-all" href="/tags">Browse all tags</a><ul class="item-list"><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/tutorials/index/index 2.html b/tutorials/index/index 3.html index 7f7ed6e..566be2a 100644 --- a/tutorials/index/index 2.html +++ b/tutorials/index/index 3.html @@ -1 +1 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/tutorials/index"/><meta name="twitter:url" content="https://navanchauhan.github.io/tutorials/index"/><meta name="og:url" content="https://navanchauhan.github.io/tutorials/index"/><title>Tutorials | Navan Chauhan</title><meta name="twitter:title" content="Tutorials | Navan Chauhan"/><meta name="og:title" content="Tutorials | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
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