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| author | Navan Chauhan <navanchauhan@gmail.com> | 2020-06-01 00:41:47 +0530 |
|---|---|---|
| committer | Navan Chauhan <navanchauhan@gmail.com> | 2020-06-01 00:41:47 +0530 |
| commit | daeb59f856e5660e4d333316c75114cb433fc15f (patch) | |
| tree | 3c9d1087735f8e30b9ecf04cc4f87462a12eafd4 /feed.rss | |
| parent | 429c1862546a2cbda044f459865e6cee7d9aa314 (diff) | |
Publish deploy 2020-06-01 00:41
Diffstat (limited to 'feed.rss')
| -rw-r--r-- | feed.rss | 542 |
1 files changed, 200 insertions, 342 deletions
@@ -1,57 +1,81 @@ -<?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. Majority of the posts should be complete.</description><link>https://navanchauhan.github.io/</link><language>en</language><lastBuildDate>Sun, 24 May 2020 18:26:31 +0530</lastBuildDate><pubDate>Sun, 24 May 2020 18:26:31 +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/2020-04-13-Fixing-X11-Error-AmberTools-macOS</guid><title>Fixing X11 Error on macOS Catalina for AmberTools 18/19</title><description>Fixing Could not find the X11 libraries; you may need to edit config.h, AmberTools macOS Catalina</description><link>https://navanchauhan.github.io/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS</link><pubDate>Mon, 13 Apr 2020 11:41:00 +0530</pubDate><content:encoded><![CDATA[<h1>Fixing X11 Error on macOS Catalina for AmberTools 18/19</h1><p>I was trying to install AmberTools on my macOS Catalina Installation. Running <code>./configure -macAccelerate clang</code> gave me an error that it could not find X11 libraries, even though <code>locate libXt</code> showed that my installation was correct.</p><p>Error:</p><pre><code><div class="highlight"><span></span><span class="n">Could</span> <span class="n">not</span> <span class="bp">find</span> <span class="n">the</span> <span class="n">X11</span> <span class="n">libraries</span><span class="p">;</span> <span class="n">you</span> <span class="n">may</span> <span class="n">need</span> <span class="n">to</span> <span class="n">edit</span> <span class="n">config</span><span class="p">.</span><span class="n">h</span> - <span class="n">to</span> <span class="kr">set</span> <span class="n">the</span> <span class="n">XHOME</span> <span class="n">and</span> <span class="n">XLIBS</span> <span class="n">variables</span><span class="p">.</span> -<span class="n">Error</span><span class="p">:</span> <span class="n">The</span> <span class="n">X11</span> <span class="n">libraries</span> <span class="n">are</span> <span class="n">not</span> <span class="k">in</span> <span class="n">the</span> <span class="n">usual</span> <span class="n">location</span> <span class="o">!</span> - <span class="n">To</span> <span class="n">search</span> <span class="k">for</span> <span class="n">them</span> <span class="k">try</span> <span class="n">the</span> <span class="n">command</span><span class="p">:</span> <span class="n">locate</span> <span class="n">libXt</span> - <span class="n">On</span> <span class="n">new</span> <span class="n">Fedora</span> <span class="n">OS</span><span class="err">'</span><span class="n">s</span> <span class="n">install</span> <span class="n">the</span> <span class="n">libXt</span><span class="o">-</span><span class="n">devel</span> <span class="n">libXext</span><span class="o">-</span><span class="n">devel</span> - <span class="n">libX11</span><span class="o">-</span><span class="n">devel</span> <span class="n">libICE</span><span class="o">-</span><span class="n">devel</span> <span class="n">libSM</span><span class="o">-</span><span class="n">devel</span> <span class="n">packages</span><span class="p">.</span> - <span class="n">On</span> <span class="n">old</span> <span class="n">Fedora</span> <span class="n">OS</span><span class="err">'</span><span class="n">s</span> <span class="n">install</span> <span class="n">the</span> <span class="n">xorg</span><span class="o">-</span><span class="n">x11</span><span class="o">-</span><span class="n">devel</span> <span class="n">package</span><span class="p">.</span> - <span class="n">On</span> <span class="n">RedHat</span> <span class="n">OS</span><span class="err">'</span><span class="n">s</span> <span class="n">install</span> <span class="n">the</span> <span class="n">XFree86</span><span class="o">-</span><span class="n">devel</span> <span class="n">package</span><span class="p">.</span> - <span class="n">On</span> <span class="n">Ubuntu</span> <span class="n">OS</span><span class="err">'</span><span class="n">s</span> <span class="n">install</span> <span class="n">the</span> <span class="n">xorg</span><span class="o">-</span><span class="n">dev</span> <span class="n">and</span> <span class="n">xserver</span><span class="o">-</span><span class="n">xorg</span> <span class="n">packages</span><span class="p">.</span> - - <span class="p">...</span><span class="n">more</span> <span class="n">info</span> <span class="k">for</span> <span class="n">various</span> <span class="n">linuxes</span> <span class="n">at</span> <span class="n">ambermd</span><span class="p">.</span><span class="n">org</span><span class="o">/</span><span class="n">ubuntu</span><span class="p">.</span><span class="n">html</span> - - <span class="n">To</span> <span class="n">build</span> <span class="n">Amber</span> <span class="n">without</span> <span class="n">XLEaP</span><span class="p">,</span> <span class="n">re</span><span class="o">-</span><span class="n">run</span> <span class="n">configure</span> <span class="n">with</span> <span class="err">'</span><span class="o">-</span><span class="n">noX11</span><span class="p">:</span> - <span class="p">.</span><span class="o">/</span><span class="n">configure</span> <span class="o">-</span><span class="n">noX11</span> <span class="o">--</span><span class="n">with</span><span class="o">-</span><span class="n">python</span> <span class="o">/</span><span class="n">usr</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">bin</span><span class="o">/</span><span class="n">python3</span> <span class="o">-</span><span class="n">macAccelerate</span> <span class="n">clang</span> -<span class="n">Configure</span> <span class="n">failed</span> <span class="n">due</span> <span class="n">to</span> <span class="n">the</span> <span class="n">errors</span> <span class="n">above</span><span class="p">!</span> -</div> - -</code></pre><p>I searcehd on Google for a solution on their, sadly there was not even a single thread which had a solution about this error.</p><h2>The Fix</h2><p>Simply reinstalling XQuartz using homebrew fixed the error <code>brew cask reinstall xquartz</code></p><p>If you do not have xquartz installed, you need to run <code>brew cask install xquartz</code></p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/publications/2020-03-17-Possible-Drug-Candidates-COVID-19</guid><title>Possible Drug Candidates for COVID-19</title><description>COVID-19, has been officially labeled as a pandemic by the World Health Organisation. This paper presents cloperastine and vigabatrin as two possible drug candidates for combatting the disease along with the process by which they were discovered.</description><link>https://navanchauhan.github.io/publications/2020-03-17-Possible-Drug-Candidates-COVID-19</link><pubDate>Tue, 17 Mar 2020 17:40:00 +0530</pubDate><content:encoded><![CDATA[<h1>Possible Drug Candidates for COVID-19</h1><p>This is still a pre-print.</p><p><a href="https://chemrxiv.org/articles/Possible_Drug_Candidates_for_COVID-19/11985231">Download paper here</a></p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/publications/2020-03-14-generating-vaporwave</guid><title>Is it possible to programmatically generate Vaporwave?</title><description>This paper is about programmaticaly generating Vaporwave.</description><link>https://navanchauhan.github.io/publications/2020-03-14-generating-vaporwave</link><pubDate>Sat, 14 Mar 2020 22:23:00 +0530</pubDate><content:encoded><![CDATA[<h1>Is it possible to programmatically generate Vaporwave?</h1><p>This is still a pre-print.</p><p><a href="https://indiarxiv.org/9um2r/">Download paper here</a></p><p>Recommended citation:</p><h3>APA</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">2020</span><span class="p">,</span> <span class="n">March</span> <span class="mi">15</span><span class="p">).</span> <span class="n">Is</span> <span class="n">it</span> <span class="n">possible</span> <span class="n">to</span> <span class="n">programmatically</span> <span class="n">generate</span> <span class="n">Vaporwave</span><span class="p">?.</span> <span class="n">https</span><span class="p">:</span><span class="c1">//doi.org/10.35543/osf.io/9um2r</span> -</div> - -</code></pre><h3>MLA</h3><pre><code><div class="highlight"><span></span><span class="n">Chauhan</span><span class="p">,</span> <span class="n">Navan</span><span class="p">.</span> <span class="err">β</span><span class="n">Is</span> <span class="n">It</span> <span class="n">Possible</span> <span class="n">to</span> <span class="n">Programmatically</span> <span class="n">Generate</span> <span class="n">Vaporwave</span><span class="p">?.</span><span class="err">β</span> <span class="n">IndiaRxiv</span><span class="p">,</span> <span class="mi">15</span> <span class="n">Mar</span><span class="p">.</span> <span class="mf">2020.</span> <span class="n">Web</span><span class="p">.</span> -</div> - -</code></pre><h3>Chicago</h3><pre><code><div class="highlight"><span></span><span class="n">Chauhan</span><span class="p">,</span> <span class="n">Navan</span><span class="p">.</span> <span class="mf">2020.</span> <span class="err">β</span><span class="n">Is</span> <span class="n">It</span> <span class="n">Possible</span> <span class="n">to</span> <span class="n">Programmatically</span> <span class="n">Generate</span> <span class="n">Vaporwave</span><span class="p">?.</span><span class="err">β</span> <span class="n">IndiaRxiv</span><span class="p">.</span> <span class="n">March</span> <span class="mf">15.</span> <span class="n">doi</span><span class="p">:</span><span class="mf">10.35543</span><span class="o">/</span><span class="n">osf</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="mi">9</span><span class="n">um2r</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">misc</span><span class="p">{</span><span class="n">chauhan_2020</span><span class="p">,</span> - <span class="n">title</span><span class="p">={</span><span class="n">Is</span> <span class="n">it</span> <span class="n">possible</span> <span class="n">to</span> <span class="n">programmatically</span> <span class="n">generate</span> <span class="n">Vaporwave</span><span class="p">?},</span> - <span class="n">url</span><span class="p">={</span><span class="n">indiarxiv</span><span class="p">.</span><span class="n">org</span><span class="o">/</span><span class="mi">9</span><span class="n">um2r</span><span class="p">},</span> - <span class="n">DOI</span><span class="p">={</span><span class="mf">10.35543</span><span class="o">/</span><span class="n">osf</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="mi">9</span><span class="n">um2r</span><span class="p">},</span> - <span class="n">publisher</span><span class="p">={</span><span class="n">IndiaRxiv</span><span class="p">},</span> - <span class="n">author</span><span class="p">={</span><span class="n">Chauhan</span><span class="p">,</span> <span class="n">Navan</span><span class="p">},</span> - <span class="n">year</span><span class="p">={</span><span class="mi">2020</span><span class="p">},</span> - <span class="n">month</span><span class="p">={</span><span class="n">Mar</span><span class="p">}</span> -<span class="p">}</span> -</div> - -</code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-03-08-Making-Vaporwave-Track</guid><title>Making My First Vaporwave Track (Remix)</title><description>I made my first vaporwave remix</description><link>https://navanchauhan.github.io/posts/2020-03-08-Making-Vaporwave-Track</link><pubDate>Sun, 8 Mar 2020 23:17:00 +0530</pubDate><content:encoded><![CDATA[<h1>Making My First Vaporwave Track (Remix)</h1><p>I finally completed my first quick and dirty vaporwave remix of "I Want It That Way" by the Backstreet Boys</p><h1>V A P O R W A V E</h1><p>Vaporwave is all about A E S T H E T I C S. Vaporwave is a type of music genre that emmerged as a parody of Chillwave, shared more as a meme rather than a proper musical genre. Of course this changed as the genre become mature</p><h1>How to Vaporwave</h1><p>The first track which is considered to be actual Vaporwave is Ramona Xavier's Macintosh Plus, this unspokenly set the the guidelines for making Vaporwave</p><ul><li>Take a 1980s RnB song</li><li>Slow it down</li><li>Add Bass and Trebble</li><li>Add again</li><li>Add Reverb ( make sure its wet )</li></ul><p>There you have your very own Vaporwave track.</p><p>( Now, there are some tracks being produced which are not remixes and are original )</p><h1>My Remix</h1><iframe width="300" height="202" src="https://www.bandlab.com/embed/?id=aa91e786-6361-ea11-a94c-0003ffd1cad8&blur=false" frameborder="0" allowfullscreen></iframe><h1>Where is the Programming?</h1><p>The fact that there are steps on producing Vaporwave, this gave me the idea that Vaporwave can actually be made using programming, stay tuned for when I publish the program which I am working on ( Generating A E S T H E T I C artwork and remixes)</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-03-03-Playing-With-Android-TV</guid><title>Tinkering with an Android TV</title><description>Tinkering with an Android TV</description><link>https://navanchauhan.github.io/posts/2020-03-03-Playing-With-Android-TV</link><pubDate>Tue, 3 Mar 2020 18:37:00 +0530</pubDate><content:encoded><![CDATA[<h1>Tinkering with an Android TV</h1><p>So I have an Android TV, this posts covers everything I have tried on it</p><h2>Contents</h2><ol><li><a href="#IP-Address">Getting TV's IP Address</a></li><li><a href="#Developer-Settings">Enable Developer Settings</a></li><li><a href="#Enable-ADB">Enable ADB</a></li><li><a href="#Connect-ADB">Connect ADB</a></li><li><a href="#">Manipulating Packages</a></li></ol><h2>IP-Address</h2><p><em>These steps should be similar for all Android-TVs</em></p><ul><li>Go To Settings</li><li>Go to Network</li><li>Advanced Settings</li><li>Network Status</li><li>Note Down IP-Address</li></ul><p>The other option is to go to your router's server page and get connected devices</p><h2>Developer-Settings</h2><ul><li>Go To Settings</li><li>About</li><li>Continously click on the "Build" option until it says "You are a Developer"</li></ul><h2>Enable-ADB</h2><ul><li>Go to Settings</li><li>Go to Developer Options</li><li>Scroll untill you find ADB Debugging and enable that option</li></ul><h2>Connect-ADB</h2><ul><li>Open Terminal (Make sure you have ADB installed)</li><li>Enter the following command <code>adb connect <IP_ADDRESS></code></li><li>To test the connection run <code>adb logcat</code></li></ul><h2>Manipulating Apps / Packages</h2><h3>Listing Packages</h3><ul><li><code>adb shell</code></li><li><code>pm list packages</code></li></ul><h3>Installing Packages</h3><ul><li><code>adb install -r package.apk</code></li></ul><h3>Uninstalling Packages</h3><ul><li><code>adb uninstall com.company.yourpackagename</code></li></ul>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal</guid><title>How to setup Bluetooth on a Raspberry Pi</title><description>Connecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero W</description><link>https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal</link><pubDate>Sun, 19 Jan 2020 15:27:00 +0530</pubDate><content:encoded><![CDATA[<h1>How to setup Bluetooth on a Raspberry Pi</h1><p><em>This was tested on a Raspberry Pi Zero W</em></p><h2>Enter in the Bluetooth Mode</h2><p><code>pi@raspberrypi:~ $ bluetoothctl</code></p><p><code>[bluetooth]# agent on</code></p><p><code>[bluetooth]# default-agent</code></p><p><code>[bluetooth]# scan on</code></p><h2>To Pair</h2><p>While being in bluetooth mode</p><p><code>[bluetooth]# pair XX:XX:XX:XX:XX:XX</code></p><p>To Exit out of bluetoothctl anytime, just type exit</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-Turicreate</guid><title>Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire</title><description>Tutorial on creating a custom Image Classifier using Turicreate and a dataset from Kaggle</description><link>https://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-Turicreate</link><pubDate>Thu, 16 Jan 2020 10:36:00 +0530</pubDate><content:encoded><![CDATA[<h1>Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire</h1><p><em>For setting up Kaggle with Google Colab, please refer to <a href="https://navanchauhan.github.io//posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/"> my previous post</a></em></p><h2>Dataset</h2><h3>Mounting Google Drive</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">os</span> +<?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. Majority of the posts should be complete.</description><link>https://navanchauhan.github.io/</link><language>en</language><lastBuildDate>Mon, 1 Jun 2020 00:41:37 +0530</lastBuildDate><pubDate>Mon, 1 Jun 2020 00:41:37 +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/2020-05-31-compiling-open-babel-on-ios</guid><title>Compiling Open Babel on iOS</title><description>Compiling Open Babel on iOS</description><link>https://navanchauhan.github.io/posts/2020-05-31-compiling-open-babel-on-ios</link><pubDate>Sun, 31 May 2020 11:30:00 +0530</pubDate><content:encoded><![CDATA[<h1>Compiling Open Babel on iOS</h1><p>Due to the fact that my summer vacations started today, I had the brilliant idea of trying to run open babel on my iPad. To give a little background, I had tried to compile AutoDock Vina using a cross-compiler but I had miserably failed.</p><p>I am running the Checkr1n jailbreak on my iPad and the Unc0ver jailbreak on my phone.</p><h2>But Why?</h2><p>Well, just because I can. This is literally the only reason I tried compiling it and also partially because in the long run I want to compile AutoDock Vina so I can do Molecular Docking on the go.</p><h2>Let's Go!</h2><p>How hard can it be to compile open babel right? It is just a simple software with clear and concise build instructions. I just need to use <code>cmake</code> to build and the <code>make</code> to install.</p><p>It is 11 AM in the morning. I install <code>clang, cmake and make</code> from the Sam Bingner's repository, fired up ssh, downloaded the source code and ran the build command.`clang</p><p><em>Fail No. 1</em></p><p>I couldn't even get cmake to run, I did a little digging arond StackOverflow and founf that I needed the iOS SDK, sure no problem. I waited for Xcode to update and transfered the <code>/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk</code> to <code>/var/sdks/</code> on my iPad</p><p>Them I told cmake that this is the location for my SDK π . Succesful! Now I just needed to use make.</p><p><em>Fail No. 2</em></p><p>It was giving the error that thread-local-storage was not supported on this device.</p><pre><code><div class="highlight"><span></span><span class="o">[</span> <span class="m">0</span>%<span class="o">]</span> Building CXX object src/CMakeFiles/openbabel.dir/alias.cpp.o +<span class="o">[</span> <span class="m">1</span>%<span class="o">]</span> Building CXX object src/CMakeFiles/openbabel.dir/atom.cpp.o +In file included from /var/root/obabel/ob-src/src/atom.cpp:28: +In file included from /var/root/obabel/ob-src/include/openbabel/ring.h:29: +/var/root/obabel/ob-src/include/openbabel/typer.h:70:1: error: thread-local storage is not supported <span class="k">for</span> the current target +THREAD_LOCAL OB_EXTERN OBAtomTyper atomtyper<span class="p">;</span> +^ +/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro <span class="s1">'THREAD_LOCAL'</span> +<span class="c1"># define THREAD_LOCAL thread_local</span> + ^ +In file included from /var/root/obabel/ob-src/src/atom.cpp:28: +In file included from /var/root/obabel/ob-src/include/openbabel/ring.h:29: +/var/root/obabel/ob-src/include/openbabel/typer.h:84:1: error: thread-local storage is not supported <span class="k">for</span> the current target +THREAD_LOCAL OB_EXTERN OBAromaticTyper aromtyper<span class="p">;</span> +^ +/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro <span class="s1">'THREAD_LOCAL'</span> +<span class="c1"># define THREAD_LOCAL thread_local</span> + ^ +/var/root/obabel/ob-src/src/atom.cpp:107:10: error: thread-local storage is not supported <span class="k">for</span> the current target + extern THREAD_LOCAL OBAromaticTyper aromtyper<span class="p">;</span> + ^ +/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro <span class="s1">'THREAD_LOCAL'</span> +<span class="c1"># define THREAD_LOCAL thread_local</span> + ^ +/var/root/obabel/ob-src/src/atom.cpp:108:10: error: thread-local storage is not supported <span class="k">for</span> the current target + extern THREAD_LOCAL OBAtomTyper atomtyper<span class="p">;</span> + ^ +/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro <span class="s1">'THREAD_LOCAL'</span> +<span class="c1"># define THREAD_LOCAL thread_local</span> + ^ +/var/root/obabel/ob-src/src/atom.cpp:109:10: error: thread-local storage is not supported <span class="k">for</span> the current target + extern THREAD_LOCAL OBPhModel phmodel<span class="p">;</span> + ^ +/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro <span class="s1">'THREAD_LOCAL'</span> +<span class="c1"># define THREAD_LOCAL thread_local</span> + ^ +<span class="m">5</span> errors generated. +make<span class="o">[</span><span class="m">2</span><span class="o">]</span>: *** <span class="o">[</span>src/CMakeFiles/openbabel.dir/build.make:76: src/CMakeFiles/openbabel.dir/atom.cpp.o<span class="o">]</span> Error <span class="m">1</span> +make<span class="o">[</span><span class="m">1</span><span class="o">]</span>: *** <span class="o">[</span>CMakeFiles/Makefile2:1085: src/CMakeFiles/openbabel.dir/all<span class="o">]</span> Error <span class="m">2</span> +make: *** <span class="o">[</span>Makefile:129: all<span class="o">]</span> Error <span class="m">2</span> +</div></code></pre><p>Strange but it is alright, there is nothing that hasn't been answered on the internet.</p><p>I did a little digging around and could not find a solution π</p><p>As a temporary fix, I disabled multithreading by going and commenting the lines in the source code.</p><img src="https://navanchauhan.github.io//assets/posts/open-babel/s1.png" alt=""Open-Babel running on my iPad""/><h2>Packaging as a deb</h2><p>This was pretty straight forward, I tried installing it on my iPad and it was working pretty smoothly.</p><h2>Moment of Truth</h2><p>So I airdropped the .deb to my phone and tried installing it, the installation was succesful but when I tried <code>obabel</code> it just abborted with syscall 9</p><p>Turns out because I had created an install target of a seprate folder while compiling, the binaries were refferencing a non-existing dylib rather than those in the /usr/lib folder. As a quick workaround I transferred the deb folder to my laptop and used otool and install_name tool: <code>install_name_tool -change /var/root/obabel/ob-build/lib/libopenbabel.7.dylib /usr/lib/libopenbabel.7.dylib</code> for all the executables and then signed them using jtool</p><p>I then installed it and everything went smoothly, I even ran <code>obabel</code> and it executed perfectly, showing the version number 3.1.0 βοΈ Ahh, smooth victory.</p><p>Nope. When I tried converting from SMILES to pdbqt, it gave an error saying plugin not found. This was weird. So I just copied the entire build folder from my iPad to my phone and tried runnig it. Oops, Apple Sandbox Error, Oh no!</p><p>I spent 2 hours around this problem, only to see the documentation and relaise I hadn't setup the environment variable π€¦ββοΈ</p><h2>The Final Fix ( For Now )</h2><pre><code><div class="highlight"><span></span><span class="nb">export</span> <span class="nv">BABEL_DATADIR</span><span class="o">=</span><span class="s2">"/usr/share/openbabel/3.1.0"</span> +<span class="nb">export</span> <span class="nv">BABEL_LIBDIR</span><span class="o">=</span><span class="s2">"/usr/lib/openbabel/3.1.0"</span> +</div></code></pre><p>This was the tragedy of trying to compile something without knowing enough about compiling. It is 11:30 as of writing this. Something as trivial as this should not have taken me so long. Am I going to try to compile AutoDock Vina next? π€ Maybe.</p><p>Also, if you want to try Open Babel on you jailbroken iDevice, install the package from my repository ( You, need to run the above mentioned final fix :p ). This was tested on iOS 13.5, I cannot tell if it will work on others or not.</p><p>Hopefully, I add some more screenshots to this post.</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS</guid><title>Fixing X11 Error on macOS Catalina for AmberTools 18/19</title><description>Fixing Could not find the X11 libraries; you may need to edit config.h, AmberTools macOS Catalina</description><link>https://navanchauhan.github.io/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS</link><pubDate>Mon, 13 Apr 2020 11:41:00 +0530</pubDate><content:encoded><![CDATA[<h1>Fixing X11 Error on macOS Catalina for AmberTools 18/19</h1><p>I was trying to install AmberTools on my macOS Catalina Installation. Running <code>./configure -macAccelerate clang</code> gave me an error that it could not find X11 libraries, even though <code>locate libXt</code> showed that my installation was correct.</p><p>Error:</p><pre><code><div class="highlight"><span></span>Could not find the X11 libraries<span class="p">;</span> you may need to edit config.h + to <span class="nb">set</span> the XHOME and XLIBS variables. +Error: The X11 libraries are not in the usual location ! + To search <span class="k">for</span> them try the command: locate libXt + On new Fedora OS<span class="s1">'s install the libXt-devel libXext-devel</span> +<span class="s1"> libX11-devel libICE-devel libSM-devel packages.</span> +<span class="s1"> On old Fedora OS'</span>s install the xorg-x11-devel package. + On RedHat OS<span class="s1">'s install the XFree86-devel package.</span> +<span class="s1"> On Ubuntu OS'</span>s install the xorg-dev and xserver-xorg packages. + + ...more info <span class="k">for</span> various linuxes at ambermd.org/ubuntu.html + + To build Amber without XLEaP, re-run configure with <span class="err">'</span>-noX11: + ./configure -noX11 --with-python /usr/local/bin/python3 -macAccelerate clang +Configure failed due to the errors above! +</div></code></pre><p>I searcehd on Google for a solution on their, sadly there was not even a single thread which had a solution about this error.</p><h2>The Fix</h2><p>Simply reinstalling XQuartz using homebrew fixed the error <code>brew cask reinstall xquartz</code></p><p>If you do not have xquartz installed, you need to run <code>brew cask install xquartz</code></p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/publications/2020-03-17-Possible-Drug-Candidates-COVID-19</guid><title>Possible Drug Candidates for COVID-19</title><description>COVID-19, has been officially labeled as a pandemic by the World Health Organisation. This paper presents cloperastine and vigabatrin as two possible drug candidates for combatting the disease along with the process by which they were discovered.</description><link>https://navanchauhan.github.io/publications/2020-03-17-Possible-Drug-Candidates-COVID-19</link><pubDate>Tue, 17 Mar 2020 17:40:00 +0530</pubDate><content:encoded><![CDATA[<h1>Possible Drug Candidates for COVID-19</h1><p>This is still a pre-print.</p><p><a href="https://chemrxiv.org/articles/Possible_Drug_Candidates_for_COVID-19/11985231">Download paper here</a></p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/publications/2020-03-14-generating-vaporwave</guid><title>Is it possible to programmatically generate Vaporwave?</title><description>This paper is about programmaticaly generating Vaporwave.</description><link>https://navanchauhan.github.io/publications/2020-03-14-generating-vaporwave</link><pubDate>Sat, 14 Mar 2020 22:23:00 +0530</pubDate><content:encoded><![CDATA[<h1>Is it possible to programmatically generate Vaporwave?</h1><p>This is still a pre-print.</p><p><a href="https://indiarxiv.org/9um2r/">Download paper here</a></p><p>Recommended citation:</p><h3>APA</h3><pre><code><div class="highlight"><span></span>Chauhan, N. <span class="o">(</span><span class="m">2020</span>, March <span class="m">15</span><span class="o">)</span>. Is it possible to programmatically generate Vaporwave?. https://doi.org/10.35543/osf.io/9um2r +</div></code></pre><h3>MLA</h3><pre><code><div class="highlight"><span></span>Chauhan, Navan. βIs It Possible to Programmatically Generate Vaporwave?.β IndiaRxiv, <span class="m">15</span> Mar. <span class="m">2020</span>. Web. +</div></code></pre><h3>Chicago</h3><pre><code><div class="highlight"><span></span>Chauhan, Navan. <span class="m">2020</span>. βIs It Possible to Programmatically Generate Vaporwave?.β IndiaRxiv. March <span class="m">15</span>. doi:10.35543/osf.io/9um2r. +</div></code></pre><h3>Bibtex</h3><pre><code><div class="highlight"><span></span>@misc<span class="o">{</span>chauhan_2020, + <span class="nv">title</span><span class="o">={</span>Is it possible to programmatically generate Vaporwave?<span class="o">}</span>, + <span class="nv">url</span><span class="o">={</span>indiarxiv.org/9um2r<span class="o">}</span>, + <span class="nv">DOI</span><span class="o">={</span><span class="m">10</span>.35543/osf.io/9um2r<span class="o">}</span>, + <span class="nv">publisher</span><span class="o">={</span>IndiaRxiv<span class="o">}</span>, + <span class="nv">author</span><span class="o">={</span>Chauhan, Navan<span class="o">}</span>, + <span class="nv">year</span><span class="o">={</span><span class="m">2020</span><span class="o">}</span>, + <span class="nv">month</span><span class="o">={</span>Mar<span class="o">}</span> +<span class="o">}</span> +</div></code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-03-08-Making-Vaporwave-Track</guid><title>Making My First Vaporwave Track (Remix)</title><description>I made my first vaporwave remix</description><link>https://navanchauhan.github.io/posts/2020-03-08-Making-Vaporwave-Track</link><pubDate>Sun, 8 Mar 2020 23:17:00 +0530</pubDate><content:encoded><![CDATA[<h1>Making My First Vaporwave Track (Remix)</h1><p>I finally completed my first quick and dirty vaporwave remix of "I Want It That Way" by the Backstreet Boys</p><h1>V A P O R W A V E</h1><p>Vaporwave is all about A E S T H E T I C S. Vaporwave is a type of music genre that emmerged as a parody of Chillwave, shared more as a meme rather than a proper musical genre. Of course this changed as the genre become mature</p><h1>How to Vaporwave</h1><p>The first track which is considered to be actual Vaporwave is Ramona Xavier's Macintosh Plus, this unspokenly set the the guidelines for making Vaporwave</p><ul><li>Take a 1980s RnB song</li><li>Slow it down</li><li>Add Bass and Trebble</li><li>Add again</li><li>Add Reverb ( make sure its wet )</li></ul><p>There you have your very own Vaporwave track.</p><p>( Now, there are some tracks being produced which are not remixes and are original )</p><h1>My Remix</h1><iframe width="300" height="202" src="https://www.bandlab.com/embed/?id=aa91e786-6361-ea11-a94c-0003ffd1cad8&blur=false" frameborder="0" allowfullscreen></iframe><h1>Where is the Programming?</h1><p>The fact that there are steps on producing Vaporwave, this gave me the idea that Vaporwave can actually be made using programming, stay tuned for when I publish the program which I am working on ( Generating A E S T H E T I C artwork and remixes)</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-03-03-Playing-With-Android-TV</guid><title>Tinkering with an Android TV</title><description>Tinkering with an Android TV</description><link>https://navanchauhan.github.io/posts/2020-03-03-Playing-With-Android-TV</link><pubDate>Tue, 3 Mar 2020 18:37:00 +0530</pubDate><content:encoded><![CDATA[<h1>Tinkering with an Android TV</h1><p>So I have an Android TV, this posts covers everything I have tried on it</p><h2>Contents</h2><ol><li><a href="#IP-Address">Getting TV's IP Address</a></li><li><a href="#Developer-Settings">Enable Developer Settings</a></li><li><a href="#Enable-ADB">Enable ADB</a></li><li><a href="#Connect-ADB">Connect ADB</a></li><li><a href="#">Manipulating Packages</a></li></ol><h2>IP-Address</h2><p><em>These steps should be similar for all Android-TVs</em></p><ul><li>Go To Settings</li><li>Go to Network</li><li>Advanced Settings</li><li>Network Status</li><li>Note Down IP-Address</li></ul><p>The other option is to go to your router's server page and get connected devices</p><h2>Developer-Settings</h2><ul><li>Go To Settings</li><li>About</li><li>Continously click on the "Build" option until it says "You are a Developer"</li></ul><h2>Enable-ADB</h2><ul><li>Go to Settings</li><li>Go to Developer Options</li><li>Scroll untill you find ADB Debugging and enable that option</li></ul><h2>Connect-ADB</h2><ul><li>Open Terminal (Make sure you have ADB installed)</li><li>Enter the following command <code>adb connect <IP_ADDRESS></code></li><li>To test the connection run <code>adb logcat</code></li></ul><h2>Manipulating Apps / Packages</h2><h3>Listing Packages</h3><ul><li><code>adb shell</code></li><li><code>pm list packages</code></li></ul><h3>Installing Packages</h3><ul><li><code>adb install -r package.apk</code></li></ul><h3>Uninstalling Packages</h3><ul><li><code>adb uninstall com.company.yourpackagename</code></li></ul>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal</guid><title>How to setup Bluetooth on a Raspberry Pi</title><description>Connecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero W</description><link>https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal</link><pubDate>Sun, 19 Jan 2020 15:27:00 +0530</pubDate><content:encoded><![CDATA[<h1>How to setup Bluetooth on a Raspberry Pi</h1><p><em>This was tested on a Raspberry Pi Zero W</em></p><h2>Enter in the Bluetooth Mode</h2><p><code>pi@raspberrypi:~ $ bluetoothctl</code></p><p><code>[bluetooth]# agent on</code></p><p><code>[bluetooth]# default-agent</code></p><p><code>[bluetooth]# scan on</code></p><h2>To Pair</h2><p>While being in bluetooth mode</p><p><code>[bluetooth]# pair XX:XX:XX:XX:XX:XX</code></p><p>To Exit out of bluetoothctl anytime, just type exit</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-Turicreate</guid><title>Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire</title><description>Tutorial on creating a custom Image Classifier using Turicreate and a dataset from Kaggle</description><link>https://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-Turicreate</link><pubDate>Thu, 16 Jan 2020 10:36:00 +0530</pubDate><content:encoded><![CDATA[<h1>Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire</h1><p><em>For setting up Kaggle with Google Colab, please refer to <a href="https://navanchauhan.github.io//posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/"> my previous post</a></em></p><h2>Dataset</h2><h3>Mounting Google Drive</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">os</span> <span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span> <span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">'/content/drive'</span><span class="p">)</span> -</div> - -</code></pre><h3>Downloading Dataset from Kaggle</h3><pre><code><div class="highlight"><span></span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'KAGGLE_CONFIG_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"/content/drive/My Drive/"</span> +</div></code></pre><h3>Downloading Dataset from Kaggle</h3><pre><code><div class="highlight"><span></span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'KAGGLE_CONFIG_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"/content/drive/My Drive/"</span> <span class="err">!</span><span class="n">kaggle</span> <span class="n">datasets</span> <span class="n">download</span> <span class="n">ashutosh69</span><span class="o">/</span><span class="n">fire</span><span class="o">-</span><span class="ow">and</span><span class="o">-</span><span class="n">smoke</span><span class="o">-</span><span class="n">dataset</span> <span class="err">!</span><span class="n">unzip</span> <span class="s2">"fire-and-smoke-dataset.zip"</span> -</div> - -</code></pre><h2>Pre-Processing</h2><pre><code><div class="highlight"><span></span><span class="nt">!mkdir</span><span class="na"> default smoke fire</span> -</div> - -</code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">!ls</span><span class="na"> data/data/img_data/train/default/*.jpg</span> -</div> - -</code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">img_1002.jpg</span><span class="na"> img_20.jpg img_519.jpg img_604.jpg img_80.jpg</span> +</div></code></pre><h2>Pre-Processing</h2><pre><code><div class="highlight"><span></span><span class="nt">!mkdir</span><span class="na"> default smoke fire</span> +</div></code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">!ls</span><span class="na"> data/data/img_data/train/default/*.jpg</span> +</div></code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">img_1002.jpg</span><span class="na"> img_20.jpg img_519.jpg img_604.jpg img_80.jpg</span> <span class="na">img_1003.jpg img_21.jpg img_51.jpg img_60.jpg img_8.jpg</span> <span class="na">img_1007.jpg img_22.jpg img_520.jpg img_61.jpg img_900.jpg</span> <span class="na">img_100.jpg img_23.jpg img_521.jpg 'img_62 (2).jpg' img_920.jpg</span> @@ -84,49 +108,39 @@ <span class="na">img_204.jpg img_501.jpg img_601.jpg img_78.jpg</span> <span class="na">img_205.jpg img_502.jpg img_602.jpg img_79.jpg</span> <span class="na">img_206.jpg img_50.jpg img_603.jpg img_7.jpg</span> -</div> - -</code></pre><p>The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate</p><pre><code><div class="highlight"><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> +</div></code></pre><p>The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate</p><pre><code><div class="highlight"><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="kn">import</span> <span class="nn">glob</span> <span class="n">folders</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"default"</span><span class="p">,</span><span class="s2">"smoke"</span><span class="p">,</span><span class="s2">"fire"</span><span class="p">]</span> <span class="k">for</span> <span class="n">folder</span> <span class="ow">in</span> <span class="n">folders</span><span class="p">:</span> <span class="n">n</span> <span class="o">=</span> <span class="mi">1</span> - <span class="k">for</span> <span class="nb">file</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./data/data/img_data/train/"</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s2">"/*.jpg"</span><span class="p">):</span> - <span class="n">im</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="nb">file</span><span class="p">)</span> + <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./data/data/img_data/train/"</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s2">"/*.jpg"</span><span class="p">):</span> + <span class="n">im</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="n">file</span><span class="p">)</span> <span class="n">rgb_im</span> <span class="o">=</span> <span class="n">im</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">)</span> <span class="n">rgb_im</span><span class="o">.</span><span class="n">save</span><span class="p">((</span><span class="n">folder</span> <span class="o">+</span> <span class="s2">"/"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="s2">".jpg"</span><span class="p">),</span> <span class="n">quality</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="n">n</span> <span class="o">+=</span><span class="mi">1</span> - <span class="k">for</span> <span class="nb">file</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./data/data/img_data/train/"</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s2">"/*.jpg"</span><span class="p">):</span> - <span class="n">im</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="nb">file</span><span class="p">)</span> + <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./data/data/img_data/train/"</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s2">"/*.jpg"</span><span class="p">):</span> + <span class="n">im</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="n">file</span><span class="p">)</span> <span class="n">rgb_im</span> <span class="o">=</span> <span class="n">im</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">)</span> <span class="n">rgb_im</span><span class="o">.</span><span class="n">save</span><span class="p">((</span><span class="n">folder</span> <span class="o">+</span> <span class="s2">"/"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="s2">".jpg"</span><span class="p">),</span> <span class="n">quality</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="n">n</span> <span class="o">+=</span><span class="mi">1</span> -</div> - -</code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">!mkdir</span><span class="na"> train</span> +</div></code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">!mkdir</span><span class="na"> train</span> <span class="na">!mv default ./train</span> <span class="na">!mv smoke ./train</span> <span class="na">!mv fire ./train</span> -</div> - -</code></pre><h2>Making the Image Classifier</h2><h3>Making an SFrame</h3><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> -</div> - -</code></pre><p><br></p><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> +</div></code></pre><h2>Making the Image Classifier</h2><h3>Making an SFrame</h3><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> +</div></code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">turicreate</span> <span class="k">as</span> <span class="nn">tc</span> <span class="kn">import</span> <span class="nn">os</span> -<span class="n">data</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">image_analysis</span><span class="o">.</span><span class="n">load_images</span><span class="p">(</span><span class="s2">"./train"</span><span class="p">,</span> <span class="n">with_path</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">image_analysis</span><span class="o">.</span><span class="n">load_images</span><span class="p">(</span><span class="s2">"./train"</span><span class="p">,</span> <span class="n">with_path</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">data</span><span class="p">[</span><span class="s2">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">"path"</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">path</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">path</span><span class="p">)))</span> -<span class="k">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="n">data</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'fire-smoke.sframe'</span><span class="p">)</span> -</div> - -</code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">+-------------------------+------------------------+</span> +</div></code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">+-------------------------+------------------------+</span> <span class="err">| path | image |</span> <span class="nt">+-------------------------+------------------------+</span> <span class="err">| ./train/default/1.jpg | Height: 224 Width: 224 |</span> @@ -160,9 +174,7 @@ <span class="nt">[2028</span><span class="na"> rows x 3 columns]</span> <span class="na">Note</span><span class="p">:</span><span class="err"> </span><span class="nc">Only</span><span class="err"> </span><span class="nc">the</span><span class="err"> </span><span class="nc">head</span><span class="err"> </span><span class="nc">of</span><span class="err"> </span><span class="nc">the</span><span class="err"> </span><span class="nc">SFrame</span><span class="err"> </span><span class="nc">is</span><span class="err"> </span><span class="nc">printed.</span> <span class="nt">You</span><span class="na"> can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.</span> -</div> - -</code></pre><h3>Making the Model</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> +</div></code></pre><h3>Making the Model</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">turicreate</span> <span class="k">as</span> <span class="nn">tc</span> <span class="c1"># Load the data</span> <span class="n">data</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">'fire-smoke.sframe'</span><span class="p">)</span> @@ -178,16 +190,14 @@ <span class="c1"># Evaluate the model and print the results</span> <span class="n">metrics</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span> -<span class="k">print</span><span class="p">(</span><span class="n">metrics</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span> +<span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span> <span class="c1"># Save the model for later use in Turi Create</span> <span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'fire-smoke.model'</span><span class="p">)</span> <span class="c1"># Export for use in Core ML</span> <span class="n">model</span><span class="o">.</span><span class="n">export_coreml</span><span class="p">(</span><span class="s1">'fire-smoke.mlmodel'</span><span class="p">)</span> -</div> - -</code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">Performing</span><span class="na"> feature extraction on resized images...</span> +</div></code></pre><p><br></p><pre><code><div class="highlight"><span></span><span class="nt">Performing</span><span class="na"> feature extraction on resized images...</span> <span class="na">Completed 64/1633</span> <span class="na">Completed 128/1633</span> <span class="na">Completed 192/1633</span> @@ -245,17 +255,11 @@ <span class="na">Completed 384/395</span> <span class="na">Completed 395/395</span> <span class="na">0.9316455696202531</span> -</div> - -</code></pre><p>We just got an accuracy of 94% on Training Data and 97% on Validation Data!</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab</guid><title>Setting up Kaggle to use with Google Colab</title><description>Tutorial on setting up kaggle, to use with Google Colab</description><link>https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab</link><pubDate>Wed, 15 Jan 2020 23:36:00 +0530</pubDate><content:encoded><![CDATA[<h1>Setting up Kaggle to use with Google Colab</h1><p><em>In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)</em></p><h2>Grabbing Our Tokens</h2><h3>Go to Kaggle</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss1.png" alt=""Homepage""/><h3>Click on your User Profile and Click on My Account</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss2.png" alt=""Account""/><h3>Scroll Down untill you see Create New API Token</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss3.png"/><h3>This will download your token as a JSON file</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss4.png"/><p>Copy the File to the root folder of your Google Drive</p><h2>Setting up Colab</h2><h3>Mounting Google Drive</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">os</span> +</div></code></pre><p>We just got an accuracy of 94% on Training Data and 97% on Validation Data!</p>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab</guid><title>Setting up Kaggle to use with Google Colab</title><description>Tutorial on setting up kaggle, to use with Google Colab</description><link>https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab</link><pubDate>Wed, 15 Jan 2020 23:36:00 +0530</pubDate><content:encoded><![CDATA[<h1>Setting up Kaggle to use with Google Colab</h1><p><em>In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)</em></p><h2>Grabbing Our Tokens</h2><h3>Go to Kaggle</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss1.png" alt=""Homepage""/><h3>Click on your User Profile and Click on My Account</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss2.png" alt=""Account""/><h3>Scroll Down untill you see Create New API Token</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss3.png"/><h3>This will download your token as a JSON file</h3><img src="https://navanchauhan.github.io//assets/posts/kaggle-colab/ss4.png"/><p>Copy the File to the root folder of your Google Drive</p><h2>Setting up Colab</h2><h3>Mounting Google Drive</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">os</span> <span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span> <span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">'/content/drive'</span><span class="p">)</span> -</div> - -</code></pre><p>After this click on the URL in the output section, login and then paste the Auth Code</p><h3>Configuring Kaggle</h3><pre><code><div class="highlight"><span></span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'KAGGLE_CONFIG_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"/content/drive/My Drive/"</span> -</div> - -</code></pre><p>Voila! You can now download kaggel datasets</p>]]></content:encoded></item><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> +</div></code></pre><p>After this click on the URL in the output section, login and then paste the Auth Code</p><h3>Configuring Kaggle</h3><pre><code><div class="highlight"><span></span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'KAGGLE_CONFIG_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"/content/drive/My Drive/"</span> +</div></code></pre><p>Voila! You can now download kaggel datasets</p>]]></content:encoded></item><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> @@ -264,45 +268,27 @@ <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> +</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="kc">True</span><span class="p">,</span> <span class="n">progressive</span><span class="o">=</span><span class="kc">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="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="kc">True</span><span class="p">,</span> <span class="n">progressive</span><span class="o">=</span><span class="kc">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> +</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> +</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="k">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> +</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> +</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> @@ -312,35 +298,23 @@ <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> +</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 βRise Of Skywalkerβ 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 publicationβs 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. βIβm 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,β 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 reviewerβs Facebook page. βOn the other hand, though, he commended J.J. Abramsβ skillful pacing and faithfulness to George Lucasβ vision, which makes me wonder if I should just call the whole thing off. Now, I really donβt feel like camping outside his house for hours. Maybe I could go with a response thatβs somewhere in between, like, threatening to kill his dog but not everyone in his whole family? I donβt know. This is a tough one.β 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="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Topic classifier model has a testing accuracy of </span><span class="si">{</span><span class="n">accuracy</span><span class="o">*</span><span class="mi">100</span><span class="si">}</span><span class="s1">% '</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="kc">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 βRise Of Skywalkerβ 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 publicationβs 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. βIβm 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,β 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 reviewerβs Facebook page. βOn the other hand, though, he commended J.J. Abramsβ skillful pacing and faithfulness to George Lucasβ vision, which makes me wonder if I should just call the whole thing off. Now, I really donβt feel like camping outside his house for hours. Maybe I could go with a response thatβs somewhere in between, like, threatening to kill his dog but not everyone in his whole family? I donβt know. This is a tough one.β 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="nb">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="kc">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> +</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> +</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> @@ -359,16 +333,12 @@ <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> +</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> +</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> @@ -443,33 +413,19 @@ <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> +</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="k">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="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">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> +</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> +</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> @@ -481,77 +437,55 @@ <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> +</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> +</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> +</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> +</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> +</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> +</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> +</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> +</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> +</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="nb">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="nb">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> @@ -577,9 +511,7 @@ <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> +</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> @@ -587,26 +519,22 @@ <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> +</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="nb">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="nb">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> @@ -632,9 +560,7 @@ <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> +</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> @@ -642,16 +568,14 @@ <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> +</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="nb">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> @@ -659,10 +583,8 @@ <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="nb">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> @@ -688,9 +610,7 @@ <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> +</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> @@ -698,16 +618,14 @@ <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> +</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="nb">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> @@ -716,10 +634,8 @@ <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="nb">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> @@ -745,9 +661,7 @@ <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> +</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> @@ -755,16 +669,14 @@ <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> +</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="nb">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> @@ -773,9 +685,7 @@ <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> +</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> @@ -801,9 +711,7 @@ <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> +</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> @@ -811,49 +719,29 @@ <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> +</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> +</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="nb">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">numpy</span> <span class="k">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">import</span> <span class="nn">tensorflow</span> <span class="k">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">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> +<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">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> +</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> +</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> @@ -865,7 +753,7 @@ <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="nb">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> @@ -876,23 +764,17 @@ <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="nb">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> +</div></code></pre><pre><code><div class="highlight"><span></span><span class="nv">s</span><span class="o">=</span>np.arange<span class="o">(</span>X_train.shape<span class="o">[</span><span class="m">0</span><span class="o">])</span> +np.random.shuffle<span class="o">(</span>s<span class="o">)</span> +<span class="nv">X_train</span><span class="o">=</span>X_train<span class="o">[</span>s<span class="o">]</span> +<span class="nv">y_train</span><span class="o">=</span>y_train<span class="o">[</span>s<span class="o">]</span> +<span class="nv">X_train</span> <span class="o">=</span> X_train/255.0 +</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> @@ -905,17 +787,11 @@ <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> +</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> +</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> @@ -936,55 +812,37 @@ <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> +</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="nb">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> +</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/posts/2019-12-04-Google-Teachable-Machines</guid><title>Image Classifier With Teachable Machines</title><description>Tutorial on creating a custom image classifier quickly with Google Teachanle Machines</description><link>https://navanchauhan.github.io/posts/2019-12-04-Google-Teachable-Machines</link><pubDate>Wed, 4 Dec 2019 18:23:00 +0530</pubDate><content:encoded><![CDATA[<h1>Image Classifier With Teachable Machines</h1><p>Made for Google Code-In</p><p><strong>Task Description</strong></p><p>Using Glitch and the Teachable Machines, build a Book Detector with Tensorflow.js. When a book is recognized, the code would randomly suggest a book/tell a famous quote from a book. Here is an example Project to get you started: https://glitch.com/~voltaic-acorn</p><h3>Details</h3><ol><li>Collecting Data</li></ol><p>Teachable Machine allows you to create your dataset just by using your webcam! I created a database consisting of three classes ( Three Books ) and approximately grabbed 100 pictures for each book/class</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/01-collect.png"/><ol start="2"><li>Training</li></ol><p>Training on teachable machines is as simple as clicking the train button. I did not even have to modify any configurations.</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/02-train.png"/><ol start="3"><li>Finding Labels</li></ol><p>Because I originally entered the entire name of the book and it's author's name as the label, the class name got truncated (Note to self, use shorter class names :p ). I then modified the code to print the modified label names in an alert box.</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/03-label.png"/><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/04-alert.png"/><ol start="4"><li>Adding a suggestions function</li></ol><p>I first added a text field on the main page and then modified the JavaScript file to suggest a similar book whenever the model predicted with an accuracy >= 98%</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/05-html.png"/><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/06-js.png"/><ol start="5"><li>Running!</li></ol><p>Here it is running!</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/07-eg.png"/><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/08-eg.png"/><p>Remix this project:-</p><p>https://luminous-opinion.glitch.me</p>]]></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>Edit: It seems like I haven't mentioned Adrian Rosebrock of PyImageSearch anywhere. I apologize for this mistake.</p><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/2019-05-05-Custom-Snowboard-Anemone-Theme</guid><title>Creating your own custom theme for Snowboard or Anemone</title><description>Tutorial on creating your own custom theme for Snowboard or Anemone</description><link>https://navanchauhan.github.io/posts/2019-05-05-Custom-Snowboard-Anemone-Theme</link><pubDate>Sun, 5 May 2019 12:34:00 +0530</pubDate><content:encoded><![CDATA[<h1>Creating your own custom theme for Snowboard or Anemone</h1><h3>Contents</h3><ul><li>Getting Started</li><li>Theme Configuration</li><li>Creating Icons</li><li>Exporting Icons</li><li>Icon Masks</li><li>Packaging</li><li>Building the DEB</li></ul><h2>Getting Started</h2><p><strong>Note: Without the proper folder structure, your theme may not show up!</strong></p><ul><li>Create a new folder called <code>themeName.theme</code> (Replace themeName with your desired theme name)</li><li>Within <code>themeName.theme</code> folder, create another folder called <code>IconBundles</code> (<strong>You cannot change this name</strong>)</li></ul><h2>Theme Configuration</h2><ul><li>Now, inside the <code>themeName.theme</code> folder, create a file called <code>Info.plist</code> and paste the following</li></ul><pre><code><div class="highlight"><span></span><span class="o"><?</span><span class="n">xml</span> <span class="n">version</span><span class="p">=</span><span class="s">"1.0"</span> <span class="n">encoding</span><span class="p">=</span><span class="s">"UTF-8"</span><span class="p">?</span><span class="o">></span> -<span class="o"><!</span><span class="n">DOCTYPE</span> <span class="n">plist</span> <span class="n">PUBLIC</span> <span class="s">"-//Apple//DTD PLIST 1.0//EN"</span> <span class="s">"http://www.apple.com/DTDs/PropertyList-1.0.dtd"</span><span class="o">></span> - <span class="p"><</span><span class="n">plist</span> <span class="n">version</span><span class="p">=</span><span class="s">"1.0"</span><span class="o">></span> - <span class="p"><</span><span class="n">dict</span><span class="p">></span> - <span class="p"><</span><span class="n">key</span><span class="p">></span><span class="n">PackageName</span><span class="o"></</span><span class="n">key</span><span class="p">></span> - <span class="p"><</span><span class="n">string</span><span class="p">></span><span class="n">ThemeName</span><span class="o"></</span><span class="n">string</span><span class="p">></span> - <span class="p"><</span><span class="n">key</span><span class="p">></span><span class="n">ThemeType</span><span class="o"></</span><span class="n">key</span><span class="p">></span> - <span class="p"><</span><span class="n">string</span><span class="p">></span><span class="n">Icons</span><span class="o"></</span><span class="n">string</span><span class="p">></span> - <span class="o"></</span><span class="n">dict</span><span class="p">></span> -<span class="o"></</span><span class="n">plist</span><span class="p">></span> -</div> - -</code></pre><ul><li>Replace <code>PackageName</code> with the name of the Pacakge and replace <code>ThemeName</code> with the Theme Name</li></ul><p>Now, you might ask what is the difference between <code>PackageName</code> and <code>ThemeName</code>?</p><p>Well, if for example you want to publish two variants of your icons, one dark and one white but you do not want the user to seperately install them. Then, you would name the package <code>MyTheme</code> and include two themes <code>Blackie</code> and <code>White</code> thus creating two entries. More about this in the end</p><h2>Creating Icons</h2><ul><li>Open up the Image Editor of your choice and create a new file having a resolution of 512x512</li></ul><p><strong>Note: Due to IconBundles, we just need to create the icons in one size and they get resized automaticaly</strong> :ghost:</p><p><strong>Want to create rounded icons?</strong> Create them squared only, we will learn how to apply masks!</p><h2>Exporting Icons</h2><p><strong>Note: All icons must be saved as <code>*.png</code> (Tip: This means you can even create partially transparent icons!)</strong></p><ul><li>All Icons must be saved in <code>themeName.theme>IconBundles</code> as <code>bundleID-large.png</code></li></ul><h5>Finding BundleIDs</h5><p><strong>Stock Application BundleIDs</strong></p><p>| Name | BundleID | |-------------|----------------------| | App Store | com.apple.AppStore | | Apple Watch | com.apple.Bridge | | Calculator | com.apple.calculator | | Calendar | com.apple.mobilecal | | Camera | com.apple.camera | | Classroom | com.apple.classroom | | Clock | com.apple.mobiletimer | | Compass | com.apple.compass | | FaceTime | com.apple.facetime | | Files | com.apple.DocumentsApp | | Game Center | com.apple.gamecenter | | Health | com.apple.Health | | Home | com.apple.Home | | iBooks | com.apple.iBooks | | iTunes Store | com.apple.MobileStore | | Mail | com.apple.mobilemail | | Maps | com.apple.Maps | | Measure | com.apple.measure | | Messages | com.apple.MobileSMS | | Music | com.apple.Music | | News | com.apple.news | | Notes | com.apple.mobilenotes | | Phone | com.apple.mobilephone | | Photo Booth | com.apple.Photo-Booth | | Photos | com.apple.mobileslideshow | | Playgrounds | come.apple.Playgrounds | | Podcasts | com.apple.podcasts | | Reminders | com.apple.reminders | | Safari | com.apple.mobilesafari | | Settings | com.apple.Preferences | | Stocks | com.apple.stocks | | Tips | com.apple.tips | | TV | com.apple.tv | | Videos | com.apple.videos | | Voice Memos | com.apple.VoiceMemos | | Wallet | com.apple.Passbook | | Weather | com.apple.weather |</p><p><strong>3rd Party Applications BundleID</strong> Click <a href="http://offcornerdev.com/bundleid.html">here</a></p><h3>Icon Masks</h3><ul><li>Getting the Classic Rounded Rectangle Masks</li></ul><p>In your <code>Info.plist</code> file add the following value between <code><dict></code> and </dict> +</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/posts/2019-12-04-Google-Teachable-Machines</guid><title>Image Classifier With Teachable Machines</title><description>Tutorial on creating a custom image classifier quickly with Google Teachanle Machines</description><link>https://navanchauhan.github.io/posts/2019-12-04-Google-Teachable-Machines</link><pubDate>Wed, 4 Dec 2019 18:23:00 +0530</pubDate><content:encoded><![CDATA[<h1>Image Classifier With Teachable Machines</h1><p>Made for Google Code-In</p><p><strong>Task Description</strong></p><p>Using Glitch and the Teachable Machines, build a Book Detector with Tensorflow.js. When a book is recognized, the code would randomly suggest a book/tell a famous quote from a book. Here is an example Project to get you started: https://glitch.com/~voltaic-acorn</p><h3>Details</h3><ol><li>Collecting Data</li></ol><p>Teachable Machine allows you to create your dataset just by using your webcam! I created a database consisting of three classes ( Three Books ) and approximately grabbed 100 pictures for each book/class</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/01-collect.png"/><ol start="2"><li>Training</li></ol><p>Training on teachable machines is as simple as clicking the train button. I did not even have to modify any configurations.</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/02-train.png"/><ol start="3"><li>Finding Labels</li></ol><p>Because I originally entered the entire name of the book and it's author's name as the label, the class name got truncated (Note to self, use shorter class names :p ). I then modified the code to print the modified label names in an alert box.</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/03-label.png"/><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/04-alert.png"/><ol start="4"><li>Adding a suggestions function</li></ol><p>I first added a text field on the main page and then modified the JavaScript file to suggest a similar book whenever the model predicted with an accuracy >= 98%</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/05-html.png"/><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/06-js.png"/><ol start="5"><li>Running!</li></ol><p>Here it is running!</p><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/07-eg.png"/><img src="https://navanchauhan.github.io//assets/gciTales/01-teachableMachines/08-eg.png"/><p>Remix this project:-</p><p>https://luminous-opinion.glitch.me</p>]]></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>Edit: It seems like I haven't mentioned Adrian Rosebrock of PyImageSearch anywhere. I apologize for this mistake.</p><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>Chauhan, N. <span class="o">(</span><span class="m">2019</span><span class="o">)</span>. <span class="p">&</span>quot<span class="p">;</span>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.<span class="p">&</span>quot<span class="p">;</span> <i>International Research Journal of Engineering and Technology <span class="o">(</span>IRJET<span class="o">)</span>, <span class="m">6</span><span class="o">(</span><span class="m">5</span><span class="o">)</span></i>. +</div></code></pre><h3>BibTeX</h3><pre><code><div class="highlight"><span></span>@article<span class="o">{</span>chauhan_2019, <span class="nv">title</span><span class="o">={</span>Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response<span class="o">}</span>, <span class="nv">volume</span><span class="o">={</span><span class="m">6</span><span class="o">}</span>, <span class="nv">url</span><span class="o">={</span>https://www.irjet.net/archives/V6/i5/IRJET-V6I5318.pdf<span class="o">}</span>, <span class="nv">number</span><span class="o">={</span><span class="m">5</span><span class="o">}</span>, <span class="nv">journal</span><span class="o">={</span>International Research Journal of Engineering and Technology <span class="o">(</span>IRJET<span class="o">)}</span>, <span class="nv">author</span><span class="o">={</span>Chauhan, Navan<span class="o">}</span>, <span class="nv">year</span><span class="o">={</span><span class="m">2019</span><span class="o">}}</span> +</div></code></pre>]]></content:encoded></item><item><guid isPermaLink="true">https://navanchauhan.github.io/posts/2019-05-05-Custom-Snowboard-Anemone-Theme</guid><title>Creating your own custom theme for Snowboard or Anemone</title><description>Tutorial on creating your own custom theme for Snowboard or Anemone</description><link>https://navanchauhan.github.io/posts/2019-05-05-Custom-Snowboard-Anemone-Theme</link><pubDate>Sun, 5 May 2019 12:34:00 +0530</pubDate><content:encoded><![CDATA[<h1>Creating your own custom theme for Snowboard or Anemone</h1><h3>Contents</h3><ul><li>Getting Started</li><li>Theme Configuration</li><li>Creating Icons</li><li>Exporting Icons</li><li>Icon Masks</li><li>Packaging</li><li>Building the DEB</li></ul><h2>Getting Started</h2><p><strong>Note: Without the proper folder structure, your theme may not show up!</strong></p><ul><li>Create a new folder called <code>themeName.theme</code> (Replace themeName with your desired theme name)</li><li>Within <code>themeName.theme</code> folder, create another folder called <code>IconBundles</code> (<strong>You cannot change this name</strong>)</li></ul><h2>Theme Configuration</h2><ul><li>Now, inside the <code>themeName.theme</code> folder, create a file called <code>Info.plist</code> and paste the following</li></ul><pre><code><div class="highlight"><span></span><?xml <span class="nv">version</span><span class="o">=</span><span class="s2">"1.0"</span> <span class="nv">encoding</span><span class="o">=</span><span class="s2">"UTF-8"</span>?> +<!DOCTYPE plist PUBLIC <span class="s2">"-//Apple//DTD PLIST 1.0//EN"</span> <span class="s2">"http://www.apple.com/DTDs/PropertyList-1.0.dtd"</span>> + <plist <span class="nv">version</span><span class="o">=</span><span class="s2">"1.0"</span>> + <dict> + <key>PackageName</key> + <string>ThemeName</string> + <key>ThemeType</key> + <string>Icons</string> + </dict> +</plist> +</div></code></pre><ul><li>Replace <code>PackageName</code> with the name of the Pacakge and replace <code>ThemeName</code> with the Theme Name</li></ul><p>Now, you might ask what is the difference between <code>PackageName</code> and <code>ThemeName</code>?</p><p>Well, if for example you want to publish two variants of your icons, one dark and one white but you do not want the user to seperately install them. Then, you would name the package <code>MyTheme</code> and include two themes <code>Blackie</code> and <code>White</code> thus creating two entries. More about this in the end</p><h2>Creating Icons</h2><ul><li>Open up the Image Editor of your choice and create a new file having a resolution of 512x512</li></ul><p><strong>Note: Due to IconBundles, we just need to create the icons in one size and they get resized automaticaly</strong> :ghost:</p><p><strong>Want to create rounded icons?</strong> Create them squared only, we will learn how to apply masks!</p><h2>Exporting Icons</h2><p><strong>Note: All icons must be saved as <code>*.png</code> (Tip: This means you can even create partially transparent icons!)</strong></p><ul><li>All Icons must be saved in <code>themeName.theme>IconBundles</code> as <code>bundleID-large.png</code></li></ul><h5>Finding BundleIDs</h5><p><strong>Stock Application BundleIDs</strong></p><table><thead><tr><th>Name</th><th>BundleID</th></tr></thead><tbody><tr><td>App Store</td><td>com.apple.AppStore</td></tr><tr><td>Apple Watch</td><td>com.apple.Bridge</td></tr><tr><td>Calculator</td><td>com.apple.calculator</td></tr><tr><td>Calendar</td><td>com.apple.mobilecal</td></tr><tr><td>Camera</td><td>com.apple.camera</td></tr><tr><td>Classroom</td><td>com.apple.classroom</td></tr><tr><td>Clock</td><td>com.apple.mobiletimer</td></tr><tr><td>Compass</td><td>com.apple.compass</td></tr><tr><td>FaceTime</td><td>com.apple.facetime</td></tr><tr><td>Files</td><td>com.apple.DocumentsApp</td></tr><tr><td>Game Center</td><td>com.apple.gamecenter</td></tr><tr><td>Health</td><td>com.apple.Health</td></tr><tr><td>Home</td><td>com.apple.Home</td></tr><tr><td>iBooks</td><td>com.apple.iBooks</td></tr><tr><td>iTunes Store</td><td>com.apple.MobileStore</td></tr><tr><td>Mail</td><td>com.apple.mobilemail</td></tr><tr><td>Maps</td><td>com.apple.Maps</td></tr><tr><td>Measure</td><td>com.apple.measure</td></tr><tr><td>Messages</td><td>com.apple.MobileSMS</td></tr><tr><td>Music</td><td>com.apple.Music</td></tr><tr><td>News</td><td>com.apple.news</td></tr><tr><td>Notes</td><td>com.apple.mobilenotes</td></tr><tr><td>Phone</td><td>com.apple.mobilephone</td></tr><tr><td>Photo Booth</td><td>com.apple.Photo-Booth</td></tr><tr><td>Photos</td><td>com.apple.mobileslideshow</td></tr><tr><td>Playgrounds</td><td>come.apple.Playgrounds</td></tr><tr><td>Podcasts</td><td>com.apple.podcasts</td></tr><tr><td>Reminders</td><td>com.apple.reminders</td></tr><tr><td>Safari</td><td>com.apple.mobilesafari</td></tr><tr><td>Settings</td><td>com.apple.Preferences</td></tr><tr><td>Stocks</td><td>com.apple.stocks</td></tr><tr><td>Tips</td><td>com.apple.tips</td></tr><tr><td>TV</td><td>com.apple.tv</td></tr><tr><td>Videos</td><td>com.apple.videos</td></tr><tr><td>Voice Memos</td><td>com.apple.VoiceMemos</td></tr><tr><td>Wallet</td><td>com.apple.Passbook</td></tr><tr><td>Weather</td><td>com.apple.weather</td></tr></tbody></table><p><strong>3rd Party Applications BundleID</strong> Click <a href="http://offcornerdev.com/bundleid.html">here</a></p><h3>Icon Masks</h3><ul><li>Getting the Classic Rounded Rectangle Masks</li></ul><p>In your <code>Info.plist</code> file add the following value between <code><dict></code> and </dict> ``` <key>IB-MaskIcons</key> |