From 60fdf9896b52c59933a78eb3a38b297bd0c5f7f6 Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Sat, 1 Aug 2020 17:37:12 +0530 Subject: Publish deploy 2020-08-01 17:37 --- Themes/styles 2.css | 401 --------- about/index 3.html | 1 - assets/disqus 2.js | 5 - .../01-teachableMachines/.01-collect 2.png.icloud | Bin 168 -> 0 bytes .../01-teachableMachines/.02-train 2.png.icloud | Bin 164 -> 0 bytes .../gciTales/01-teachableMachines/03-label 2.png | Bin 134577 -> 0 bytes .../gciTales/01-teachableMachines/04-alert 2.png | Bin 45710 -> 0 bytes assets/gciTales/01-teachableMachines/05-html 2.png | Bin 15084 -> 0 bytes assets/gciTales/01-teachableMachines/06-js 2.png | Bin 117349 -> 0 bytes assets/gciTales/01-teachableMachines/07-eg 2.png | Bin 787109 -> 0 bytes assets/gciTales/01-teachableMachines/08-eg 2.png | Bin 832865 -> 0 bytes assets/gciTales/03-regression/1 2.png | Bin 39478 -> 0 bytes assets/gciTales/03-regression/2 2.png | Bin 67134 -> 0 bytes assets/gciTales/03-regression/3 2.png | 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Navan Chauhan

About Me

Hi! My name is Navan Chauhan.

What do I like?

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manUpObject,tagArray=[],linkArray=[],validMetaValues=[{name:"mobile-web-app-capable",manifestName:"display"},{name:"apple-mobile-web-app-capable",manifestName:"display"},{name:"apple-mobile-web-app-title",manifestName:"short_name"},{name:"application-name",manifestName:"short_name"},{name:"msapplication-TileColor",manifestName:"ms_TileColor"},{name:"msapplication-square70x70logo",manifestName:"icons",imageSize:"70x70"},{name:"msapplication-square150x150logo",manifestName:"icons",imageSize:"150x150"},{name:"msapplication-wide310x150logo",manifestName:"icons",imageSize:"310x150"},{name:"msapplication-square310x310logo",manifestName:"icons",imageSize:"310x310"}],validLinkValues=[{name:"apple-touch-icon",manifestName:"icons",imageSize:"152x152"},{name:"apple-touch-icon",manifestName:"icons",imageSize:"120x120"},{name:"apple-touch-icon",manifestName:"icons",imageSize:"76x76"},{name:"apple-touch-icon",manifestName:"icons",imageSize:"60x60"},{name:"apple-touch-icon",manifestName:"icons",imageSize:"57x57"},{name:"apple-touch-icon",manifestName:"icons",imageSize:"72x72"},{name:"apple-touch-icon",manifestName:"icons",imageSize:"114x114"},{name:"icon",manifestName:"icons",imageSize:"128x128"},{name:"icon",manifestName:"icons",imageSize:"192x192"}],generateFullMetaData=function(){for(var e=0;eAsset 1 \ No newline at end of file diff --git "a/assets/r\303\251sum\303\251 2.pdf" "b/assets/r\303\251sum\303\251 2.pdf" deleted file mode 100644 index 8931b18..0000000 Binary files "a/assets/r\303\251sum\303\251 2.pdf" and /dev/null differ diff --git a/feed 2.rss b/feed 2.rss deleted file mode 100644 index 56e2319..0000000 --- a/feed 2.rss +++ /dev/null @@ -1,987 +0,0 @@ -Navan ChauhanWelcome to my personal fragment of the internet. Majority of the posts should be complete.https://navanchauhan.github.io/enThu, 25 Jun 2020 18:17:35 +0530Thu, 25 Jun 2020 18:17:35 +0530250https://navanchauhan.github.io/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOSCompiling AutoDock Vina on iOSCompiling AutoDock Vina on iOShttps://navanchauhan.github.io/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOSTue, 2 Jun 2020 23:23:00 +0530Compiling AutoDock Vina on iOS

Why? Because I can.

Installing makedepend

makedepend is a Unix tool used to generate dependencies of C source files. Most modern programes do not use this anymore, but then again AutoDock Vina's source code hasn't been changed since 2011. The first hurdle came when I saw that there was no makedepend command, neither was there any package on any development repository for iOS. So, I tracked down the original source code for makedepend (https://github.com/DerellLicht/makedepend). According to the repository this is actually the source code for the makedepend utility that came with some XWindows distribution back around Y2K. I am pretty sure there is a problem with my current compiler configuration because I had to manually edit the Makefile to provide the path to the iOS SDKs using the -isysroot flag.

Editting the Makefile

Original Makefile ( I used the provided mac Makefile base )

BASE=/usr/local
-BOOST_VERSION=1_41
-BOOST_INCLUDE = $(BASE)/include
-C_PLATFORM=-arch i386 -arch ppc -isysroot /Developer/SDKs/MacOSX10.5.sdk -mmacosx-version-min=10.4
-GPP=/usr/bin/g++
-C_OPTIONS= -O3 -DNDEBUG
-BOOST_LIB_VERSION=
-
-include ../../makefile_common
-

I installed Boost 1.68.0-1 from Sam Bingner's repository. ( Otherwise I would have had to compile boost too 😫 )

Editted Makefile

BASE=/usr
-BOOST_VERSION=1_68
-BOOST_INCLUDE = $(BASE)/include
-C_PLATFORM=-arch arm64 -isysroot /var/sdks/Latest.sdk
-GPP=/usr/bin/g++
-C_OPTIONS= -O3 -DNDEBUG
-BOOST_LIB_VERSION=
-
-include ../../makefile_common
-

Updating the Source Code

Of course since Boost 1.41 many things have been added and deprecated, that is why I had to edit the source code to make it work with version 1.68

Error 1 - No Matching Constructor

../../../src/main/main.cpp:50:9: error: no matching constructor for initialization of 'path' (aka 'boost::filesystem::path')
-return path(str, boost::filesystem::native);
-

This was an easy fix, I just commented this and added a return statement to return the path

return path(str)
-

Error 2 - No Member Named 'nativefilestring'

../../../src/main/main.cpp:665:57: error: no member named 'native_file_string' in 'boost::filesystem::path'
-                std::cerr << "\n\nError: could not open \"" << e.name.native_file_string() << "\" for " << (e.in ? "reading" : "writing") << ".\n";
-                                                               ~~~~~~ ^
-../../../src/main/main.cpp:677:80: error: no member named 'native_file_string' in 'boost::filesystem::path'
-                std::cerr << "\n\nParse error on line " << e.line << " in file \"" << e.file.native_file_string() << "\": " << e.reason << '\n';
-                                                                                      ~~~~~~ ^
-2 errors generated.
-

Turns out native_file_string was deprecated in Boost 1.57 and replaced with just string

Error 3 - Library Not Found

This one still boggles me because there was no reason for it to not work, as a workaround I downloaded the DEB, extracted it and used that path for compiling.

Error 4 - No Member Named 'nativefilestring' Again.

But, this time in another file and I quickle fixed it

Moment of Truth

Obviously it was working on my iPad, but would it work on another device? I transfered the compiled binary and

The package is available on my repository and only depends on boost. ( Both, Vina and Vina-Split are part of the package)

]]>https://navanchauhan.github.io/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOLWorkflow for Lightning Fast Molecular Docking Part OneThis is my workflow for lightning fast molecular docking.https://navanchauhan.github.io/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOLMon, 1 Jun 2020 13:10:00 +0530Workflow for Lightning Fast Molecular Docking Part One

My Setup

  • macOS Catalina ( RIP 32bit app)
  • PyMOL
  • AutoDock Vina
  • Open Babel

One Command Docking

obabel -:"$(pbpaste)" --gen3d -opdbqt -Otest.pdbqt && vina --receptor lu.pdbqt --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3  --ligand test.pdbqt
-

To run this command you simple copy the SMILES structure of the ligand you want an it automatically takes it from your clipboard, generates the 3D structure in the AutoDock PDBQT format using Open Babel and then docks it with your receptor using AutoDock Vina, all with just one command.

Let me break down the commands

obabel -:"$(pbpaste)" --gen3d -opdbqt -Otest.pdbqt
-

pbpaste and pbcopy are macOS commands for pasting and copying from and to the clipboard. Linux users may install the xclip and xsel packages from their respective package managers and then insert these aliases into their bash_profile, zshrc e.t.c

alias pbcopy='xclip -selection clipboard'
-alias pbpaste='xclip -selection clipboard -o'
-
$(pbpaste)
-

This is used in bash to evaluate the results of a command. In this scenario we are using it to get the contents of the clipboard.

The rest of the command is a normal Open Babel command to generate a 3D structure in PDBQT format and then save it as test.pdbqt

&&
-

This tells the termianl to only run the next part if the previous command runs succesfuly without any errors.

vina --receptor lu.pdbqt --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3  --ligand test.pdbqt
-

This is just the docking command for AutoDock Vina. In the next part I will tell how to use PyMOL and a plugin to directly generate the coordinates in Vina format --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3 without needing to type them manually.

]]>https://navanchauhan.github.io/posts/2020-05-31-compiling-open-babel-on-iosCompiling Open Babel on iOSCompiling Open Babel on iOShttps://navanchauhan.github.io/posts/2020-05-31-compiling-open-babel-on-iosSun, 31 May 2020 23:30:00 +0530Compiling Open Babel on iOS

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.

I am running the Checkr1n jailbreak on my iPad and the Unc0ver jailbreak on my phone.

But Why?

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.

Let's Go!

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 cmake to build and the make to install.

It is 11 AM in the morning. I install clang, cmake and make from the Sam Bingner's repository, fired up ssh, downloaded the source code and ran the build command.`clang

Fail No. 1

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 SDKs to my iPad

scp -r /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk root@192.168.1.8:/var/sdks/
-

Them I told cmake that this is the location for my SDK 😠. Succesful! Now I just needed to use make.

Fail No. 2

It was giving the error that thread-local-storage was not supported on this device.

[  0%] Building CXX object src/CMakeFiles/openbabel.dir/alias.cpp.o
-[  1%] 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 for the current target
-THREAD_LOCAL OB_EXTERN OBAtomTyper      atomtyper;
-^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-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 for the current target
-THREAD_LOCAL OB_EXTERN OBAromaticTyper  aromtyper;
-^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-/var/root/obabel/ob-src/src/atom.cpp:107:10: error: thread-local storage is not supported for the current target
-  extern THREAD_LOCAL OBAromaticTyper  aromtyper;
-         ^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-/var/root/obabel/ob-src/src/atom.cpp:108:10: error: thread-local storage is not supported for the current target
-  extern THREAD_LOCAL OBAtomTyper      atomtyper;
-         ^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-/var/root/obabel/ob-src/src/atom.cpp:109:10: error: thread-local storage is not supported for the current target
-  extern THREAD_LOCAL OBPhModel        phmodel;
-         ^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-5 errors generated.
-make[2]: *** [src/CMakeFiles/openbabel.dir/build.make:76: src/CMakeFiles/openbabel.dir/atom.cpp.o] Error 1
-make[1]: *** [CMakeFiles/Makefile2:1085: src/CMakeFiles/openbabel.dir/all] Error 2
-make: *** [Makefile:129: all] Error 2
-

Strange but it is alright, there is nothing that hasn't been answered on the internet.

I did a little digging around and could not find a solution 😔

As a temporary fix, I disabled multithreading by going and commenting the lines in the source code.

Packaging as a deb

This was pretty straight forward, I tried installing it on my iPad and it was working pretty smoothly.

Moment of Truth

So I airdropped the .deb to my phone and tried installing it, the installation was succesful but when I tried obabel it just abborted.

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: install_name_tool -change /var/root/obabel/ob-build/lib/libopenbabel.7.dylib /usr/lib/libopenbabel.7.dylib for all the executables and then signed them using jtool

I then installed it and everything went smoothly, I even ran obabel and it executed perfectly, showing the version number 3.1.0 ✌️ Ahh, smooth victory.

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!

I spent 2 hours around this problem, only to see the documentation and relaise I hadn't setup the environment variable 🤦‍♂️

The Final Fix ( For Now )

export BABEL_DATADIR="/usr/share/openbabel/3.1.0"
-export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"
-

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? 🤔 Maybe.

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.

Hopefully, I add some more screenshots to this post.

Edit 1: Added Screenshots, had to replicate the errors.

]]>https://navanchauhan.github.io/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOSFixing X11 Error on macOS Catalina for AmberTools 18/19Fixing Could not find the X11 libraries; you may need to edit config.h, AmberTools macOS Catalinahttps://navanchauhan.github.io/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOSMon, 13 Apr 2020 11:41:00 +0530Fixing X11 Error on macOS Catalina for AmberTools 18/19

I was trying to install AmberTools on my macOS Catalina Installation. Running ./configure -macAccelerate clang gave me an error that it could not find X11 libraries, even though locate libXt showed that my installation was correct.

Error:

Could not find the X11 libraries; you may need to edit config.h
-   to set the XHOME and XLIBS variables.
-Error: The X11 libraries are not in the usual location !
-       To search for them try the command: locate libXt
-       On new Fedora OS's install the libXt-devel libXext-devel
-       libX11-devel libICE-devel libSM-devel packages.
-       On old Fedora OS's install the xorg-x11-devel package.
-       On RedHat OS's install the XFree86-devel package.
-       On Ubuntu OS's install the xorg-dev and xserver-xorg packages.
-
-          ...more info for various linuxes at ambermd.org/ubuntu.html
-
-       To build Amber without XLEaP, re-run configure with '-noX11:
-            ./configure -noX11 --with-python /usr/local/bin/python3 -macAccelerate clang
-Configure failed due to the errors above!
-

I searcehd on Google for a solution on their, sadly there was not even a single thread which had a solution about this error.

The Fix

Simply reinstalling XQuartz using homebrew fixed the error brew cask reinstall xquartz

If you do not have xquartz installed, you need to run brew cask install xquartz

]]>https://navanchauhan.github.io/publications/2020-03-17-Possible-Drug-Candidates-COVID-19Possible Drug Candidates for COVID-19COVID-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.https://navanchauhan.github.io/publications/2020-03-17-Possible-Drug-Candidates-COVID-19Tue, 17 Mar 2020 17:40:00 +0530Possible Drug Candidates for COVID-19

This is still a pre-print.

Download paper here

]]>https://navanchauhan.github.io/publications/2020-03-14-generating-vaporwaveIs it possible to programmatically generate Vaporwave?This paper is about programmaticaly generating Vaporwave.https://navanchauhan.github.io/publications/2020-03-14-generating-vaporwaveSat, 14 Mar 2020 22:23:00 +0530Is it possible to programmatically generate Vaporwave?

This is still a pre-print.

Download paper here

Recommended citation:

APA

Chauhan, N. (2020, March 15). Is it possible to programmatically generate Vaporwave?. https://doi.org/10.35543/osf.io/9um2r
-

MLA

Chauhan, Navan. “Is It Possible to Programmatically Generate Vaporwave?.” IndiaRxiv, 15 Mar. 2020. Web.
-

Chicago

Chauhan, Navan. 2020. “Is It Possible to Programmatically Generate Vaporwave?.” IndiaRxiv. March 15. doi:10.35543/osf.io/9um2r.
-

Bibtex

@misc{chauhan_2020,
- title={Is it possible to programmatically generate Vaporwave?},
- url={indiarxiv.org/9um2r},
- DOI={10.35543/osf.io/9um2r},
- publisher={IndiaRxiv},
- author={Chauhan, Navan},
- year={2020},
- month={Mar}
-}
-
]]>https://navanchauhan.github.io/posts/2020-03-08-Making-Vaporwave-TrackMaking My First Vaporwave Track (Remix)I made my first vaporwave remixhttps://navanchauhan.github.io/posts/2020-03-08-Making-Vaporwave-TrackSun, 8 Mar 2020 23:17:00 +0530Making My First Vaporwave Track (Remix)

I finally completed my first quick and dirty vaporwave remix of "I Want It That Way" by the Backstreet Boys

V A P O R W A V E

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

How to Vaporwave

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

  • Take a 1980s RnB song
  • Slow it down
  • Add Bass and Trebble
  • Add again
  • Add Reverb ( make sure its wet )

There you have your very own Vaporwave track.

( Now, there are some tracks being produced which are not remixes and are original )

My Remix

Where is the Programming?

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)

]]>https://navanchauhan.github.io/posts/2020-03-03-Playing-With-Android-TVTinkering with an Android TVTinkering with an Android TVhttps://navanchauhan.github.io/posts/2020-03-03-Playing-With-Android-TVTue, 3 Mar 2020 18:37:00 +0530Tinkering with an Android TV

So I have an Android TV, this posts covers everything I have tried on it

Contents

  1. Getting TV's IP Address
  2. Enable Developer Settings
  3. Enable ADB
  4. Connect ADB
  5. Manipulating Packages

IP-Address

These steps should be similar for all Android-TVs

  • Go To Settings
  • Go to Network
  • Advanced Settings
  • Network Status
  • Note Down IP-Address

The other option is to go to your router's server page and get connected devices

Developer-Settings

  • Go To Settings
  • About
  • Continously click on the "Build" option until it says "You are a Developer"

Enable-ADB

  • Go to Settings
  • Go to Developer Options
  • Scroll untill you find ADB Debugging and enable that option

Connect-ADB

  • Open Terminal (Make sure you have ADB installed)
  • Enter the following command adb connect <IP_ADDRESS>
  • To test the connection run adb logcat

Manipulating Apps / Packages

Listing Packages

  • adb shell
  • pm list packages

Installing Packages

  • adb install -r package.apk

Uninstalling Packages

  • adb uninstall com.company.yourpackagename
]]>https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-TerminalHow to setup Bluetooth on a Raspberry PiConnecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero Whttps://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-TerminalSun, 19 Jan 2020 15:27:00 +0530How to setup Bluetooth on a Raspberry Pi

This was tested on a Raspberry Pi Zero W

Enter in the Bluetooth Mode

pi@raspberrypi:~ $ bluetoothctl

[bluetooth]# agent on

[bluetooth]# default-agent

[bluetooth]# scan on

To Pair

While being in bluetooth mode

[bluetooth]# pair XX:XX:XX:XX:XX:XX

To Exit out of bluetoothctl anytime, just type exit

]]>https://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-TuricreateCreating a Custom Image Classifier using Turicreate to detect Smoke and FireTutorial on creating a custom Image Classifier using Turicreate and a dataset from Kagglehttps://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-TuricreateThu, 16 Jan 2020 10:36:00 +0530Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire

For setting up Kaggle with Google Colab, please refer to my previous post

Dataset

Mounting Google Drive

import os
-from google.colab import drive
-drive.mount('/content/drive')
-

Downloading Dataset from Kaggle

os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
-!kaggle datasets download ashutosh69/fire-and-smoke-dataset
-!unzip "fire-and-smoke-dataset.zip"
-

Pre-Processing

!mkdir default smoke fire
-


!ls data/data/img_data/train/default/*.jpg
-


img_1002.jpg   img_20.jpg     img_519.jpg     img_604.jpg       img_80.jpg
-img_1003.jpg   img_21.jpg     img_51.jpg     img_60.jpg       img_8.jpg
-img_1007.jpg   img_22.jpg     img_520.jpg     img_61.jpg       img_900.jpg
-img_100.jpg    img_23.jpg     img_521.jpg    'img_62 (2).jpg'   img_920.jpg
-img_1014.jpg   img_24.jpg    'img_52 (2).jpg'     img_62.jpg       img_921.jpg
-img_1018.jpg   img_29.jpg     img_522.jpg    'img_63 (2).jpg'   img_922.jpg
-img_101.jpg    img_3000.jpg   img_523.jpg     img_63.jpg       img_923.jpg
-img_1027.jpg   img_335.jpg    img_524.jpg     img_66.jpg       img_924.jpg
-img_102.jpg    img_336.jpg    img_52.jpg     img_67.jpg       img_925.jpg
-img_1042.jpg   img_337.jpg    img_530.jpg     img_68.jpg       img_926.jpg
-img_1043.jpg   img_338.jpg    img_531.jpg     img_700.jpg       img_927.jpg
-img_1046.jpg   img_339.jpg   'img_53 (2).jpg'     img_701.jpg       img_928.jpg
-img_1052.jpg   img_340.jpg    img_532.jpg     img_702.jpg       img_929.jpg
-img_107.jpg    img_341.jpg    img_533.jpg     img_703.jpg       img_930.jpg
-img_108.jpg    img_3.jpg      img_537.jpg     img_704.jpg       img_931.jpg
-img_109.jpg    img_400.jpg    img_538.jpg     img_705.jpg       img_932.jpg
-img_10.jpg     img_471.jpg    img_539.jpg     img_706.jpg       img_933.jpg
-img_118.jpg    img_472.jpg    img_53.jpg     img_707.jpg       img_934.jpg
-img_12.jpg     img_473.jpg    img_540.jpg     img_708.jpg       img_935.jpg
-img_14.jpg     img_488.jpg    img_541.jpg     img_709.jpg       img_938.jpg
-img_15.jpg     img_489.jpg   'img_54 (2).jpg'     img_70.jpg       img_958.jpg
-img_16.jpg     img_490.jpg    img_542.jpg     img_710.jpg       img_971.jpg
-img_17.jpg     img_491.jpg    img_543.jpg    'img_71 (2).jpg'   img_972.jpg
-img_18.jpg     img_492.jpg    img_54.jpg     img_71.jpg       img_973.jpg
-img_19.jpg     img_493.jpg   'img_55 (2).jpg'     img_72.jpg       img_974.jpg
-img_1.jpg      img_494.jpg    img_55.jpg     img_73.jpg       img_975.jpg
-img_200.jpg    img_495.jpg    img_56.jpg     img_74.jpg       img_980.jpg
-img_201.jpg    img_496.jpg    img_57.jpg     img_75.jpg       img_988.jpg
-img_202.jpg    img_497.jpg    img_58.jpg     img_76.jpg       img_9.jpg
-img_203.jpg    img_4.jpg      img_59.jpg     img_77.jpg
-img_204.jpg    img_501.jpg    img_601.jpg     img_78.jpg
-img_205.jpg    img_502.jpg    img_602.jpg     img_79.jpg
-img_206.jpg    img_50.jpg     img_603.jpg     img_7.jpg
-

The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate

from PIL import Image
-import glob
-
-
-folders = ["default","smoke","fire"]
-for folder in folders:
-  n = 1
-  for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
-    im = Image.open(file)
-    rgb_im = im.convert('RGB')
-    rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
-    n +=1 
-  for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
-    im = Image.open(file)
-    rgb_im = im.convert('RGB')
-    rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
-    n +=1
-


!mkdir train
-!mv default ./train
-!mv smoke ./train
-!mv fire ./train
-

Making the Image Classifier

Making an SFrame

!pip install turicreate
-


import turicreate as tc
-import os
-
-data = tc.image_analysis.load_images("./train", with_path=True)
-
-data["label"] = data["path"].apply(lambda path: os.path.basename(os.path.dirname(path)))
-
-print(data)
-
-data.save('fire-smoke.sframe')
-


+-------------------------+------------------------+
-|           path          |         image          |
-+-------------------------+------------------------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 |
-|  ./train/default/10.jpg | Height: 224 Width: 224 |
-| ./train/default/100.jpg | Height: 224 Width: 224 |
-| ./train/default/101.jpg | Height: 224 Width: 224 |
-| ./train/default/102.jpg | Height: 224 Width: 224 |
-| ./train/default/103.jpg | Height: 224 Width: 224 |
-| ./train/default/104.jpg | Height: 224 Width: 224 |
-| ./train/default/105.jpg | Height: 224 Width: 224 |
-| ./train/default/106.jpg | Height: 224 Width: 224 |
-| ./train/default/107.jpg | Height: 224 Width: 224 |
-+-------------------------+------------------------+
-[2028 rows x 2 columns]
-Note: Only the head of the SFrame is printed.
-You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
-+-------------------------+------------------------+---------+
-|           path          |         image          |  label  |
-+-------------------------+------------------------+---------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
-|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
-| ./train/default/100.jpg | Height: 224 Width: 224 | default |
-| ./train/default/101.jpg | Height: 224 Width: 224 | default |
-| ./train/default/102.jpg | Height: 224 Width: 224 | default |
-| ./train/default/103.jpg | Height: 224 Width: 224 | default |
-| ./train/default/104.jpg | Height: 224 Width: 224 | default |
-| ./train/default/105.jpg | Height: 224 Width: 224 | default |
-| ./train/default/106.jpg | Height: 224 Width: 224 | default |
-| ./train/default/107.jpg | Height: 224 Width: 224 | default |
-+-------------------------+------------------------+---------+
-[2028 rows x 3 columns]
-Note: Only the head of the SFrame is printed.
-You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
-

Making the Model

import turicreate as tc
-
-# Load the data
-data =  tc.SFrame('fire-smoke.sframe')
-
-# Make a train-test split
-train_data, test_data = data.random_split(0.8)
-
-# Create the model
-model = tc.image_classifier.create(train_data, target='label')
-
-# Save predictions to an SArray
-predictions = model.predict(test_data)
-
-# Evaluate the model and print the results
-metrics = model.evaluate(test_data)
-print(metrics['accuracy'])
-
-# Save the model for later use in Turi Create
-model.save('fire-smoke.model')
-
-# Export for use in Core ML
-model.export_coreml('fire-smoke.mlmodel')
-


Performing feature extraction on resized images...
-Completed   64/1633
-Completed  128/1633
-Completed  192/1633
-Completed  256/1633
-Completed  320/1633
-Completed  384/1633
-Completed  448/1633
-Completed  512/1633
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-Completed  640/1633
-Completed  704/1633
-Completed  768/1633
-Completed  832/1633
-Completed  896/1633
-Completed  960/1633
-Completed 1024/1633
-Completed 1088/1633
-Completed 1152/1633
-Completed 1216/1633
-Completed 1280/1633
-Completed 1344/1633
-Completed 1408/1633
-Completed 1472/1633
-Completed 1536/1633
-Completed 1600/1633
-Completed 1633/1633
-PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
-          You can set ``validation_set=None`` to disable validation tracking.
-
-Logistic regression:
---------------------------------------------------------
-Number of examples          : 1551
-Number of classes           : 3
-Number of feature columns   : 1
-Number of unpacked features : 2048
-Number of coefficients      : 4098
-Starting L-BFGS
---------------------------------------------------------
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
-| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
-| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
-| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
-| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
-| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-Performing feature extraction on resized images...
-Completed  64/395
-Completed 128/395
-Completed 192/395
-Completed 256/395
-Completed 320/395
-Completed 384/395
-Completed 395/395
-0.9316455696202531
-

We just got an accuracy of 94% on Training Data and 97% on Validation Data!

]]>https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-ColabSetting up Kaggle to use with Google ColabTutorial on setting up kaggle, to use with Google Colabhttps://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-ColabWed, 15 Jan 2020 23:36:00 +0530Setting up Kaggle to use with Google Colab

In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)

Grabbing Our Tokens

Go to Kaggle

Click on your User Profile and Click on My Account

Scroll Down untill you see Create New API Token

This will download your token as a JSON file

Copy the File to the root folder of your Google Drive

Setting up Colab

Mounting Google Drive

import os
-from google.colab import drive
-drive.mount('/content/drive')
-

After this click on the URL in the output section, login and then paste the Auth Code

Configuring Kaggle

os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
-

Voila! You can now download kaggel datasets

]]>https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPyConverting between image and NumPy arrayShort code snippet for converting between PIL image and NumPy arrays.https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPyTue, 14 Jan 2020 00:10:00 +0530Converting between image and NumPy array
import numpy
-import PIL
-
-# Convert PIL Image to NumPy array
-img = PIL.Image.open("foo.jpg")
-arr = numpy.array(img)
-
-# Convert array to Image
-img = PIL.Image.fromarray(arr)
-

Saving an Image

try:
-    img.save(destination, "JPEG", quality=80, optimize=True, progressive=True)
-except IOError:
-    PIL.ImageFile.MAXBLOCK = img.size[0] * img.size[1]
-    img.save(destination, "JPEG", quality=80, optimize=True, progressive=True)
-
]]>https://navanchauhan.github.io/posts/2019-12-22-Fake-News-DetectorBuilding a Fake News Detector with TuricreateIn this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the apphttps://navanchauhan.github.io/posts/2019-12-22-Fake-News-DetectorSun, 22 Dec 2019 11:10:00 +0530Building a Fake News Detector with Turicreate

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

Note: These commands are written as if you are running a jupyter notebook.

Building the Machine Learning Model

Data Gathering

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

Dependencies

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.

!pip install turicreate
-!pip uninstall -y mxnet
-!pip install mxnet-cu100==1.4.0.post0
-

If you do not wish to train on GPU or are running it on your computer, you can ignore the last two lines

Downloading the Dataset

!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
-!unzip fake_or_real_news.csv.zip
-

Model Creation

import turicreate as tc
-tc.config.set_num_gpus(-1) # If you do not wish to use GPUs, set it to 0
-
dataSFrame = tc.SFrame('fake_or_real_news.csv')
-

The dataset contains a column named "X1", which is of no use to us. Therefore, we simply drop it

dataSFrame.remove_column('X1')
-

Splitting Dataset

train, test = dataSFrame.random_split(.9)
-

Training

model = tc.text_classifier.create(
-    dataset=train,
-    target='label',
-    features=['title','text']
-)
-
+-----------+----------+-----------+--------------+-------------------+---------------------+
-| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| 0         | 2        | 1.000000  | 1.156349     | 0.889680          | 0.790036            |
-| 1         | 4        | 1.000000  | 1.359196     | 0.985952          | 0.918149            |
-| 2         | 6        | 0.820091  | 1.557205     | 0.990260          | 0.914591            |
-| 3         | 7        | 1.000000  | 1.684872     | 0.998689          | 0.925267            |
-| 4         | 8        | 1.000000  | 1.814194     | 0.999063          | 0.925267            |
-| 9         | 14       | 1.000000  | 2.507072     | 1.000000          | 0.911032            |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-

Testing the Model

est_predictions = model.predict(test)
-accuracy = tc.evaluation.accuracy(test['label'], test_predictions)
-print(f'Topic classifier model has a testing accuracy of {accuracy*100}% ', flush=True)
-
Topic classifier model has a testing accuracy of 92.3076923076923%
-

We have just created our own Fake News Detection Model which has an accuracy of 92%!

example_text = {"title": ["Middling ‘Rise Of Skywalker’ Review Leaves Fan On Fence About Whether To Threaten To Kill Critic"], "text": ["Expressing ambivalence toward the relatively balanced appraisal of the film, Star Wars fan Miles Ariely admitted Thursday that an online publication’s middling review of The Rise Of Skywalker had left him on the fence about whether he would still threaten to kill the critic who wrote it. “I’m really of two minds about this, because on the one hand, he said the new movie fails to live up to the original trilogy, which makes me at least want to throw a brick through his window with a note telling him to watch his back,” said Ariely, confirming he had already drafted an eight-page-long death threat to Stan Corimer of the website Screen-On Time, but had not yet decided whether to post it to the reviewer’s Facebook page. “On the other hand, though, he commended J.J. Abrams’ skillful pacing and faithfulness to George Lucas’ vision, which makes me wonder if I should just call the whole thing off. Now, I really don’t feel like camping outside his house for hours. Maybe I could go with a response that’s somewhere in between, like, threatening to kill his dog but not everyone in his whole family? I don’t know. This is a tough one.” At press time, sources reported that Ariely had resolved to wear his Ewok costume while he murdered the critic in his sleep."]}
-example_prediction = model.classify(tc.SFrame(example_text))
-print(example_prediction, flush=True)
-
+-------+--------------------+
-| class |    probability     |
-+-------+--------------------+
-|  FAKE | 0.9245648658345308 |
-+-------+--------------------+
-[1 rows x 2 columns]
-

Exporting the Model

model_name = 'FakeNews'
-coreml_model_name = model_name + '.mlmodel'
-exportedModel = model.export_coreml(coreml_model_name)
-

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

Link to Colab Notebook

Building the App using SwiftUI

Initial Setup

First we create a single view app (make sure you check the use SwiftUI button)

Then we copy our .mlmodel file to our project (Just drag and drop the file in the XCode Files Sidebar)

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

We define our bag of words function

func bow(text: String) -> [String: Double] {
-        var bagOfWords = [String: Double]()
-        
-        let tagger = NSLinguisticTagger(tagSchemes: [.tokenType], options: 0)
-        let range = NSRange(location: 0, length: text.utf16.count)
-        let options: NSLinguisticTagger.Options = [.omitPunctuation, .omitWhitespace]
-        tagger.string = text
-        
-        tagger.enumerateTags(in: range, unit: .word, scheme: .tokenType, options: options) { _, tokenRange, _ in
-            let word = (text as NSString).substring(with: tokenRange)
-            if bagOfWords[word] != nil {
-                bagOfWords[word]! += 1
-            } else {
-                bagOfWords[word] = 1
-            }
-        }
-        
-        return bagOfWords
-    }
-

We also declare our variables

@State private var title: String = ""
-@State private var headline: String = ""
-@State private var alertTitle = ""
-@State private var alertText = ""
-@State private var showingAlert = false
-

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

Complete Code

import SwiftUI
-
-struct ContentView: View {
-    @State private var title: String = ""
-    @State private var headline: String = ""
-    
-    @State private var alertTitle = ""
-    @State private var alertText = ""
-    @State private var showingAlert = false
-    
-    var body: some View {
-        NavigationView {
-            VStack(alignment: .leading) {
-                Text("Headline").font(.headline)
-                TextField("Please Enter Headline", text: $title)
-                    .lineLimit(nil)
-                Text("Body").font(.headline)
-                TextField("Please Enter the content", text: $headline)
-                .lineLimit(nil)
-            }
-                .navigationBarTitle("Fake News Checker")
-            .navigationBarItems(trailing:
-                Button(action: classifyFakeNews) {
-                    Text("Check")
-                })
-            .padding()
-                .alert(isPresented: $showingAlert){
-                    Alert(title: Text(alertTitle), message: Text(alertText), dismissButton: .default(Text("OK")))
-            }
-        }
-        
-    }
-    
-    func classifyFakeNews(){
-        let model = FakeNews()
-        let myTitle = bow(text: title)
-        let myText = bow(text: headline)
-        do {
-            let prediction = try model.prediction(title: myTitle, text: myText)
-            alertTitle = prediction.label
-            alertText = "It is likely that this piece of news is \(prediction.label.lowercased())."
-            print(alertText)
-        } catch {
-            alertTitle = "Error"
-            alertText = "Sorry, could not classify if the input news was fake or not."
-        }
-        
-        showingAlert = true
-    }
-    func bow(text: String) -> [String: Double] {
-        var bagOfWords = [String: Double]()
-        
-        let tagger = NSLinguisticTagger(tagSchemes: [.tokenType], options: 0)
-        let range = NSRange(location: 0, length: text.utf16.count)
-        let options: NSLinguisticTagger.Options = [.omitPunctuation, .omitWhitespace]
-        tagger.string = text
-        
-        tagger.enumerateTags(in: range, unit: .word, scheme: .tokenType, options: options) { _, tokenRange, _ in
-            let word = (text as NSString).substring(with: tokenRange)
-            if bagOfWords[word] != nil {
-                bagOfWords[word]! += 1
-            } else {
-                bagOfWords[word] = 1
-            }
-        }
-        
-        return bagOfWords
-    }
-}
-
-struct ContentView_Previews: PreviewProvider {
-    static var previews: some View {
-        ContentView()
-    }
-}
-
]]>https://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-RegressionPolynomial Regression Using TensorFlowPolynomial regression using TensorFlowhttps://navanchauhan.github.io/posts/2019-12-16-TensorFlow-Polynomial-RegressionMon, 16 Dec 2019 14:16:00 +0530Polynomial Regression Using TensorFlow

In this tutorial you will learn about polynomial regression and how you can implement it in Tensorflow.

In this, we will be performing polynomial regression using 5 types of equations -

  • Linear
  • Quadratic
  • Cubic
  • Quartic
  • Quintic

Regression

What is Regression?

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 ).

What is Polynomial Regression

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).

Imports

import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-

Dataset

Creating Random Data

Even though in this tutorial we will use a Position Vs Salary datasset, it is important to know how to create synthetic data

To create 50 values spaced evenly between 0 and 50, we use NumPy's linspace funtion

linspace(lower_limit, upper_limit, no_of_observations)

x = np.linspace(0, 50, 50)
-y = np.linspace(0, 50, 50)
-

We use the following function to add noise to the data, so that our values

x += np.random.uniform(-4, 4, 50)
-y += np.random.uniform(-4, 4, 50)
-

Position vs Salary Dataset

We will be using https://drive.google.com/file/d/1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9/view (Salary vs Position Dataset)

!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9' -O data.csv
-
df = pd.read_csv("data.csv")
-
df # this gives us a preview of the dataset we are working with
-
| Position          | Level | Salary  |
-|-------------------|-------|---------|
-| Business Analyst  | 1     | 45000   |
-| Junior Consultant | 2     | 50000   |
-| Senior Consultant | 3     | 60000   |
-| Manager           | 4     | 80000   |
-| Country Manager   | 5     | 110000  |
-| Region Manager    | 6     | 150000  |
-| Partner           | 7     | 200000  |
-| Senior Partner    | 8     | 300000  |
-| C-level           | 9     | 500000  |
-| CEO               | 10    | 1000000 |
-

We convert the salary column as the ordinate (y-cordinate) and level column as the abscissa

abscissa = df["Level"].to_list() # abscissa = [1,2,3,4,5,6,7,8,9,10]
-ordinate = df["Salary"].to_list() # ordinate = [45000,50000,60000,80000,110000,150000,200000,300000,500000,1000000]
-
n = len(abscissa) # no of observations
-plt.scatter(abscissa, ordinate)
-plt.ylabel('Salary')
-plt.xlabel('Position')
-plt.title("Salary vs Position")
-plt.show()
-

Defining Stuff

X = tf.placeholder("float")
-Y = tf.placeholder("float")
-

Defining Variables

We first define all the coefficients and constant as tensorflow variables haveing a random intitial value

a = tf.Variable(np.random.randn(), name = "a")
-b = tf.Variable(np.random.randn(), name = "b")
-c = tf.Variable(np.random.randn(), name = "c")
-d = tf.Variable(np.random.randn(), name = "d")
-e = tf.Variable(np.random.randn(), name = "e")
-f = tf.Variable(np.random.randn(), name = "f")
-

Model Configuration

learning_rate = 0.2
-no_of_epochs = 25000
-

Equations

deg1 = a*X + b
-deg2 = a*tf.pow(X,2) + b*X + c
-deg3 = a*tf.pow(X,3) + b*tf.pow(X,2) + c*X + d
-deg4 = a*tf.pow(X,4) + b*tf.pow(X,3) + c*tf.pow(X,2) + d*X + e
-deg5 = a*tf.pow(X,5) + b*tf.pow(X,4) + c*tf.pow(X,3) + d*tf.pow(X,2) + e*X + f
-

Cost Function

We use the Mean Squared Error Function

mse1 = tf.reduce_sum(tf.pow(deg1-Y,2))/(2*n)
-mse2 = tf.reduce_sum(tf.pow(deg2-Y,2))/(2*n)
-mse3 = tf.reduce_sum(tf.pow(deg3-Y,2))/(2*n)
-mse4 = tf.reduce_sum(tf.pow(deg4-Y,2))/(2*n)
-mse5 = tf.reduce_sum(tf.pow(deg5-Y,2))/(2*n)
-

Optimizer

We use the AdamOptimizer for the polynomial functions and GradientDescentOptimizer for the linear function

optimizer1 = tf.train.GradientDescentOptimizer(learning_rate).minimize(mse1)
-optimizer2 = tf.train.AdamOptimizer(learning_rate).minimize(mse2)
-optimizer3 = tf.train.AdamOptimizer(learning_rate).minimize(mse3)
-optimizer4 = tf.train.AdamOptimizer(learning_rate).minimize(mse4)
-optimizer5 = tf.train.AdamOptimizer(learning_rate).minimize(mse5)
-
init=tf.global_variables_initializer()
-

Model Predictions

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.

Linear Equation

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer1, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse1,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b:",sess.run(a),sess.run(b))
-
-        training_cost = sess.run(mse1,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        constant = sess.run(b)
-
-print(training_cost, coefficient1, constant)
-
Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-88999125000.0 180396.42 -478869.12
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Linear Regression Result')
-plt.legend()
-plt.show()
-

Quadratic Equation

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer2, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse2,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c:",sess.run(a),sess.run(b),sess.run(c))
-
-        training_cost = sess.run(mse2,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        constant = sess.run(c)
-
-print(training_cost, coefficient1, coefficient2, constant)
-
Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
-Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
-Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
-Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
-Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
-Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
-Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
-Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
-Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
-Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
-Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
-Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
-Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
-Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
-Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
-Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
-Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
-Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
-Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
-Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
-Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
-Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
-Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
-Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
-Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
-8058094600.0 6659.793 3227.2517 -463.03156
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,2) + coefficient2*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Quadratic Regression Result')
-plt.legend()
-plt.show()
-

Cubic

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer3, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse3,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c,d:",sess.run(a),sess.run(b),sess.run(c),sess.run(d))
-
-        training_cost = sess.run(mse3,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        coefficient3 = sess.run(c)
-        constant = sess.run(d)
-
-print(training_cost, coefficient1, coefficient2, coefficient3, constant)
-
Epoch 1000 : Training Cost: 4279814000.0  a,b,c,d: 670.1527 694.4212 751.4653 903.9527
-Epoch 2000 : Training Cost: 3770950400.0  a,b,c,d: 742.6414 666.3489 636.94525 859.2088
-Epoch 3000 : Training Cost: 3717708300.0  a,b,c,d: 756.2582 569.3339 448.105 748.23956
-Epoch 4000 : Training Cost: 3667464000.0  a,b,c,d: 769.4476 474.0318 265.5761 654.75525
-Epoch 5000 : Training Cost: 3620040700.0  a,b,c,d: 782.32324 380.54272 89.39888 578.5136
-Epoch 6000 : Training Cost: 3575265800.0  a,b,c,d: 794.8898 288.83356 -80.5215 519.13654
-Epoch 7000 : Training Cost: 3532972000.0  a,b,c,d: 807.1608 198.87044 -244.31102 476.2061
-Epoch 8000 : Training Cost: 3493009200.0  a,b,c,d: 819.13513 110.64169 -402.0677 449.3291
-Epoch 9000 : Training Cost: 3455228400.0  a,b,c,d: 830.80255 24.0964 -553.92804 438.0652
-Epoch 10000 : Training Cost: 3419475500.0  a,b,c,d: 842.21594 -60.797424 -700.0123 441.983
-Epoch 11000 : Training Cost: 3385625300.0  a,b,c,d: 853.3363 -144.08699 -840.467 460.6356
-Epoch 12000 : Training Cost: 3353544700.0  a,b,c,d: 864.19135 -225.8125 -975.4196 493.57703
-Epoch 13000 : Training Cost: 3323125000.0  a,b,c,d: 874.778 -305.98932 -1104.9867 540.39465
-Epoch 14000 : Training Cost: 3294257000.0  a,b,c,d: 885.1007 -384.63474 -1229.277 600.65607
-Epoch 15000 : Training Cost: 3266820000.0  a,b,c,d: 895.18823 -461.819 -1348.4417 673.9051
-Epoch 16000 : Training Cost: 3240736000.0  a,b,c,d: 905.0128 -537.541 -1462.6171 759.7118
-Epoch 17000 : Training Cost: 3215895000.0  a,b,c,d: 914.60065 -611.8676 -1571.9058 857.6638
-Epoch 18000 : Training Cost: 3192216800.0  a,b,c,d: 923.9603 -684.8093 -1676.4642 967.30475
-Epoch 19000 : Training Cost: 3169632300.0  a,b,c,d: 933.08594 -756.3582 -1776.4275 1088.2198
-Epoch 20000 : Training Cost: 3148046300.0  a,b,c,d: 941.9928 -826.6257 -1871.9355 1219.9702
-Epoch 21000 : Training Cost: 3127394800.0  a,b,c,d: 950.67896 -895.6205 -1963.0989 1362.1665
-Epoch 22000 : Training Cost: 3107608600.0  a,b,c,d: 959.1487 -963.38116 -2050.0586 1514.4026
-Epoch 23000 : Training Cost: 3088618200.0  a,b,c,d: 967.4355 -1029.9625 -2132.961 1676.2717
-Epoch 24000 : Training Cost: 3070361300.0  a,b,c,d: 975.52875 -1095.4292 -2211.854 1847.4485
-Epoch 25000 : Training Cost: 3052791300.0  a,b,c,d: 983.4346 -1159.7922 -2286.9412 2027.4857
-3052791300.0 983.4346 -1159.7922 -2286.9412 2027.4857
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,3) + coefficient2*pow(x,2) + coefficient3*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Cubic Regression Result')
-plt.legend()
-plt.show()
-

Quartic

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer4, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse4,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c,d:",sess.run(a),sess.run(b),sess.run(c),sess.run(d),sess.run(e))
-
-        training_cost = sess.run(mse4,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        coefficient3 = sess.run(c)
-        coefficient4 = sess.run(d)
-        constant = sess.run(e)
-
-print(training_cost, coefficient1, coefficient2, coefficient3, coefficient4, constant)
-
Epoch 1000 : Training Cost: 1902632600.0  a,b,c,d: 84.48304 52.210594 54.791424 142.51952 512.0343
-Epoch 2000 : Training Cost: 1854316200.0  a,b,c,d: 88.998955 13.073557 14.276088 223.55667 1056.4655
-Epoch 3000 : Training Cost: 1812812400.0  a,b,c,d: 92.9462 -22.331177 -15.262934 327.41858 1634.9054
-Epoch 4000 : Training Cost: 1775716000.0  a,b,c,d: 96.42522 -54.64535 -35.829437 449.5028 2239.1392
-Epoch 5000 : Training Cost: 1741494100.0  a,b,c,d: 99.524734 -84.43976 -49.181057 585.85876 2862.4915
-Epoch 6000 : Training Cost: 1709199600.0  a,b,c,d: 102.31984 -112.19895 -56.808075 733.1876 3499.6199
-Epoch 7000 : Training Cost: 1678261800.0  a,b,c,d: 104.87324 -138.32709 -59.9442 888.79626 4146.2944
-Epoch 8000 : Training Cost: 1648340600.0  a,b,c,d: 107.23536 -163.15173 -59.58964 1050.524 4798.979
-Epoch 9000 : Training Cost: 1619243400.0  a,b,c,d: 109.44742 -186.9409 -56.53944 1216.6432 5454.9463
-Epoch 10000 : Training Cost: 1590821900.0  a,b,c,d: 111.54233 -209.91287 -51.423084 1385.8513 6113.5137
-Epoch 11000 : Training Cost: 1563042200.0  a,b,c,d: 113.54405 -232.21953 -44.73371 1557.1084 6771.7046
-Epoch 12000 : Training Cost: 1535855600.0  a,b,c,d: 115.471565 -253.9838 -36.851135 1729.535 7429.069
-Epoch 13000 : Training Cost: 1509255300.0  a,b,c,d: 117.33939 -275.29697 -28.0714 1902.5308 8083.9634
-Epoch 14000 : Training Cost: 1483227000.0  a,b,c,d: 119.1605 -296.2472 -18.618649 2075.6094 8735.381
-Epoch 15000 : Training Cost: 1457726700.0  a,b,c,d: 120.94584 -316.915 -8.650095 2248.3247 9384.197
-Epoch 16000 : Training Cost: 1432777300.0  a,b,c,d: 122.69806 -337.30704 1.7027153 2420.5771 10028.871
-Epoch 17000 : Training Cost: 1408365000.0  a,b,c,d: 124.42179 -357.45245 12.33499 2592.2983 10669.157
-Epoch 18000 : Training Cost: 1384480000.0  a,b,c,d: 126.12332 -377.39734 23.168756 2763.0933 11305.027
-Epoch 19000 : Training Cost: 1361116800.0  a,b,c,d: 127.80568 -397.16415 34.160156 2933.0452 11935.669
-Epoch 20000 : Training Cost: 1338288100.0  a,b,c,d: 129.4674 -416.72803 45.259155 3101.7727 12561.179
-Epoch 21000 : Training Cost: 1315959700.0  a,b,c,d: 131.11403 -436.14285 56.4436 3269.3142 13182.058
-Epoch 22000 : Training Cost: 1294164700.0  a,b,c,d: 132.74377 -455.3779 67.6757 3435.3833 13796.807
-Epoch 23000 : Training Cost: 1272863600.0  a,b,c,d: 134.35779 -474.45316 78.96117 3600.264 14406.58
-Epoch 24000 : Training Cost: 1252052600.0  a,b,c,d: 135.9583 -493.38254 90.268616 3764.0078 15010.481
-Epoch 25000 : Training Cost: 1231713700.0  a,b,c,d: 137.54753 -512.1876 101.59372 3926.4897 15609.368
-1231713700.0 137.54753 -512.1876 101.59372 3926.4897 15609.368
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,4) + coefficient2*pow(x,3) + coefficient3*pow(x,2) + coefficient4*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Quartic Regression Result')
-plt.legend()
-plt.show()
-

Quintic

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer5, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse5,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c,d,e,f:",sess.run(a),sess.run(b),sess.run(c),sess.run(d),sess.run(e),sess.run(f))
-
-        training_cost = sess.run(mse5,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        coefficient3 = sess.run(c)
-        coefficient4 = sess.run(d)
-        coefficient5 = sess.run(e)
-        constant = sess.run(f)
-
Epoch 1000 : Training Cost: 1409200100.0  a,b,c,d,e,f: 7.949472 7.46219 55.626034 184.29028 484.00223 1024.0083
-Epoch 2000 : Training Cost: 1306882400.0  a,b,c,d,e,f: 8.732181 -4.0085897 73.25298 315.90103 904.08887 2004.9749
-Epoch 3000 : Training Cost: 1212606000.0  a,b,c,d,e,f: 9.732249 -16.90125 86.28379 437.06552 1305.055 2966.2188
-Epoch 4000 : Training Cost: 1123640400.0  a,b,c,d,e,f: 10.74851 -29.82692 98.59997 555.331 1698.4631 3917.9155
-Epoch 5000 : Training Cost: 1039694300.0  a,b,c,d,e,f: 11.75426 -42.598194 110.698326 671.64355 2085.5513 4860.8535
-Epoch 6000 : Training Cost: 960663550.0  a,b,c,d,e,f: 12.745439 -55.18337 122.644936 786.00214 2466.1638 5794.3735
-Epoch 7000 : Training Cost: 886438340.0  a,b,c,d,e,f: 13.721028 -67.57168 134.43822 898.3691 2839.9958 6717.659
-Epoch 8000 : Training Cost: 816913100.0  a,b,c,d,e,f: 14.679965 -79.75113 146.07385 1008.66895 3206.6692 7629.812
-Epoch 9000 : Training Cost: 751971500.0  a,b,c,d,e,f: 15.62181 -91.71608 157.55713 1116.7715 3565.8323 8529.976
-Epoch 10000 : Training Cost: 691508740.0  a,b,c,d,e,f: 16.545347 -103.4531 168.88321 1222.6348 3916.9785 9416.236
-Epoch 11000 : Training Cost: 635382000.0  a,b,c,d,e,f: 17.450052 -114.954254 180.03932 1326.1565 4259.842 10287.99
-Epoch 12000 : Training Cost: 583477250.0  a,b,c,d,e,f: 18.334944 -126.20821 191.02948 1427.2095 4593.8 11143.449
-Epoch 13000 : Training Cost: 535640400.0  a,b,c,d,e,f: 19.198917 -137.20206 201.84718 1525.6926 4918.5327 11981.633
-Epoch 14000 : Training Cost: 491722240.0  a,b,c,d,e,f: 20.041153 -147.92719 212.49709 1621.5496 5233.627 12800.468
-Epoch 15000 : Training Cost: 451559520.0  a,b,c,d,e,f: 20.860966 -158.37456 222.97133 1714.7141 5538.676 13598.337
-Epoch 16000 : Training Cost: 414988960.0  a,b,c,d,e,f: 21.657421 -168.53406 233.27422 1805.0874 5833.1978 14373.658
-Epoch 17000 : Training Cost: 381837920.0  a,b,c,d,e,f: 22.429693 -178.39536 243.39914 1892.5883 6116.847 15124.394
-Epoch 18000 : Training Cost: 351931300.0  a,b,c,d,e,f: 23.176882 -187.94789 253.3445 1977.137 6389.117 15848.417
-Epoch 19000 : Training Cost: 325074400.0  a,b,c,d,e,f: 23.898485 -197.18741 263.12512 2058.6716 6649.8037 16543.95
-Epoch 20000 : Training Cost: 301073570.0  a,b,c,d,e,f: 24.593851 -206.10497 272.72385 2137.1797 6898.544 17209.367
-Epoch 21000 : Training Cost: 279727000.0  a,b,c,d,e,f: 25.262104 -214.69217 282.14642 2212.6372 7135.217 17842.854
-Epoch 22000 : Training Cost: 260845550.0  a,b,c,d,e,f: 25.903376 -222.94969 291.4003 2284.9844 7359.4644 18442.408
-Epoch 23000 : Training Cost: 244218030.0  a,b,c,d,e,f: 26.517094 -230.8697 300.45532 2354.3003 7571.261 19007.49
-Epoch 24000 : Training Cost: 229660080.0  a,b,c,d,e,f: 27.102589 -238.44817 309.35342 2420.4185 7770.5728 19536.19
-Epoch 25000 : Training Cost: 216972400.0  a,b,c,d,e,f: 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
-216972400.0 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,5) + coefficient2*pow(x,4) + coefficient3*pow(x,3) + coefficient4*pow(x,2) + coefficient5*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Quintic Regression Result')
-plt.legend()
-plt.show()
-

Results and Conclusion

You just learnt Polynomial Regression using TensorFlow!

Notes

Overfitting

> 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.

Source: Machine Learning Mastery

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

]]>https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-PredictionMaking Predictions using Image Classifier (TensorFlow)Making predictions for image classification models built using TensorFlowhttps://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-PredictionTue, 10 Dec 2019 11:10:00 +0530Making Predictions using Image Classifier (TensorFlow)

This was tested on TF 2.x and works as of 2019-12-10

If you want to understand how to make your own custom image classifier, please refer to my previous post.

If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.

First we import the following if we have not imported these before

import cv2
-import os
-

Then we read the file using OpenCV.

image=cv2.imread(imagePath)
-

The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.

image_from_array = Image.fromarray(image, 'RGB')
-

Then we resize the image

size_image = image_from_array.resize((50,50))
-

After this we create a batch consisting of only one image

p = np.expand_dims(size_image, 0)
-

We then convert this uint8 datatype to a float32 datatype

img = tf.cast(p, tf.float32)
-

Finally we make the prediction

print(['Infected','Uninfected'][np.argmax(model.predict(img))])
-

Infected

]]>https://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-TensorflowCreating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting MalariaTutorial on creating an image classifier model using TensorFlow which detects malariahttps://navanchauhan.github.io/posts/2019-12-08-Image-Classifier-TensorflowSun, 8 Dec 2019 14:16:00 +0530Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria

Done during Google Code-In. Org: Tensorflow.

Imports

%tensorflow_version 2.x #This is for telling Colab that you want to use TF 2.0, ignore if running on local machine
-
-from PIL import Image # We use the PIL Library to resize images
-import numpy as np
-import os
-import cv2
-import tensorflow as tf
-from tensorflow.keras import datasets, layers, models
-import pandas as pd
-import matplotlib.pyplot as plt
-from keras.models import Sequential
-from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
-

Dataset

Fetching the Data

!wget ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip
-!unzip cell_images.zip
-

Processing the Data

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.

data = []
-labels = []
-
-Parasitized = os.listdir("./cell_images/Parasitized/")
-for parasite in Parasitized:
-    try:
-        image=cv2.imread("./cell_images/Parasitized/"+parasite)
-        image_from_array = Image.fromarray(image, 'RGB')
-        size_image = image_from_array.resize((50, 50))
-        data.append(np.array(size_image))
-        labels.append(0)
-    except AttributeError:
-        print("")
-
-Uninfected = os.listdir("./cell_images/Uninfected/")
-for uninfect in Uninfected:
-    try:
-        image=cv2.imread("./cell_images/Uninfected/"+uninfect)
-        image_from_array = Image.fromarray(image, 'RGB')
-        size_image = image_from_array.resize((50, 50))
-        data.append(np.array(size_image))
-        labels.append(1)
-    except AttributeError:
-        print("")
-

Splitting Data

df = np.array(data)
-labels = np.array(labels)
-(X_train, X_test) = df[(int)(0.1*len(df)):],df[:(int)(0.1*len(df))]
-(y_train, y_test) = labels[(int)(0.1*len(labels)):],labels[:(int)(0.1*len(labels))]
-
s=np.arange(X_train.shape[0])
-np.random.shuffle(s)
-X_train=X_train[s]
-y_train=y_train[s]
-X_train = X_train/255.0
-

Model

Creating Model

By creating a sequential model, we create a linear stack of layers.

Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images

model = models.Sequential()
-model.add(layers.Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(50,50,3)))
-model.add(layers.MaxPooling2D(pool_size=2))
-model.add(layers.Conv2D(filters=32,kernel_size=2,padding='same',activation='relu'))
-model.add(layers.MaxPooling2D(pool_size=2))
-model.add(layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu"))
-model.add(layers.MaxPooling2D(pool_size=2))
-model.add(layers.Dropout(0.2))
-model.add(layers.Flatten())
-model.add(layers.Dense(500,activation="relu"))
-model.add(layers.Dropout(0.2))
-model.add(layers.Dense(2,activation="softmax"))#2 represent output layer neurons 
-model.summary()
-

Compiling Model

We use the adam optimiser as it is an adaptive learning rate optimization algorithm that's been designed specifically for training deep neural networks, which means it changes its learning rate automaticaly to get the best results

model.compile(optimizer="adam",
-              loss="sparse_categorical_crossentropy", 
-             metrics=["accuracy"])
-

Training Model

We train the model for 10 epochs on the training data and then validate it using the testing data

history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))
-
Train on 24803 samples, validate on 2755 samples
-Epoch 1/10
-24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0786 - accuracy: 0.9729 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 2/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0746 - accuracy: 0.9731 - val_loss: 0.0290 - val_accuracy: 0.9996
-Epoch 3/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0672 - accuracy: 0.9764 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 4/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0601 - accuracy: 0.9789 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 5/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0558 - accuracy: 0.9804 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 6/10
-24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0513 - accuracy: 0.9819 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 7/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.3190 - val_accuracy: 0.9985
-Epoch 8/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0404 - accuracy: 0.9858 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 9/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0352 - accuracy: 0.9878 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 10/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0373 - accuracy: 0.9865 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-

Results

accuracy = history.history['accuracy'][-1]*100
-loss = history.history['loss'][-1]*100
-val_accuracy = history.history['val_accuracy'][-1]*100
-val_loss = history.history['val_loss'][-1]*100
-
-print(
-    'Accuracy:', accuracy,
-    '\nLoss:', loss,
-    '\nValidation Accuracy:', val_accuracy,
-    '\nValidation Loss:', val_loss
-)
-
Accuracy: 98.64532351493835 
-Loss: 3.732407123270176 
-Validation Accuracy: 100.0 
-Validation Loss: 0.0
-

We have achieved 98% Accuracy!

Link to Colab Notebook

]]>https://navanchauhan.github.io/posts/2019-12-08-Splitting-ZipsSplitting ZIPs into Multiple PartsShort code snippet for splitting zips.https://navanchauhan.github.io/posts/2019-12-08-Splitting-ZipsSun, 8 Dec 2019 13:27:00 +0530Splitting ZIPs into Multiple Parts

Tested on macOS

Creating the archive:

zip -r -s 5 oodlesofnoodles.zip website/
-

5 stands for each split files' size (in mb, kb and gb can also be specified)

For encrypting the zip:

zip -er -s 5 oodlesofnoodles.zip website
-

Extracting Files

First we need to collect all parts, then

zip -F oodlesofnoodles.zip --out merged.zip
-
]]>https://navanchauhan.github.io/posts/2019-12-04-Google-Teachable-MachinesImage Classifier With Teachable MachinesTutorial on creating a custom image classifier quickly with Google Teachanle Machineshttps://navanchauhan.github.io/posts/2019-12-04-Google-Teachable-MachinesWed, 4 Dec 2019 18:23:00 +0530Image Classifier With Teachable Machines

Made for Google Code-In

Task Description

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

Details

  1. Collecting Data

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

  1. Training

Training on teachable machines is as simple as clicking the train button. I did not even have to modify any configurations.

  1. Finding Labels

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.

  1. Adding a suggestions function

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%

  1. Running!

Here it is running!

Remix this project:-

https://luminous-opinion.glitch.me

]]>https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-ResponseDetecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident ResponseThis paper is about Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.https://navanchauhan.github.io/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-ResponseTue, 14 May 2019 02:42:00 +0530Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response

Based on the project showcased at Toyota Hackathon, IITD - 17/18th December 2018

Edit: It seems like I haven't mentioned Adrian Rosebrock of PyImageSearch anywhere. I apologize for this mistake.

Download paper here

Recommended citation:

ATP

Chauhan, N. (2019). &quot;Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.&quot; <i>International Research Journal of Engineering and Technology (IRJET), 6(5)</i>.
-

BibTeX

@article{chauhan_2019, title={Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response}, volume={6}, url={https://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}}
-
]]>https://navanchauhan.github.io/posts/2019-05-05-Custom-Snowboard-Anemone-ThemeCreating your own custom theme for Snowboard or AnemoneTutorial on creating your own custom theme for Snowboard or Anemonehttps://navanchauhan.github.io/posts/2019-05-05-Custom-Snowboard-Anemone-ThemeSun, 5 May 2019 12:34:00 +0530Creating your own custom theme for Snowboard or Anemone

Contents

  • Getting Started
  • Theme Configuration
  • Creating Icons
  • Exporting Icons
  • Icon Masks
  • Packaging
  • Building the DEB

Getting Started

Note: Without the proper folder structure, your theme may not show up!

  • Create a new folder called themeName.theme (Replace themeName with your desired theme name)
  • Within themeName.theme folder, create another folder called IconBundles (You cannot change this name)

Theme Configuration

  • Now, inside the themeName.theme folder, create a file called Info.plist and paste the following
<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-  <plist version="1.0">
-  <dict>
-    <key>PackageName</key>
-    <string>ThemeName</string>
-    <key>ThemeType</key>
-    <string>Icons</string>
-  </dict>
-</plist>
-
  • Replace PackageName with the name of the Pacakge and replace ThemeName with the Theme Name

Now, you might ask what is the difference between PackageName and ThemeName?

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 MyTheme and include two themes Blackie and White thus creating two entries. More about this in the end

Creating Icons

  • Open up the Image Editor of your choice and create a new file having a resolution of 512x512

Note: Due to IconBundles, we just need to create the icons in one size and they get resized automaticaly :ghost:

Want to create rounded icons? Create them squared only, we will learn how to apply masks!

Exporting Icons

Note: All icons must be saved as *.png (Tip: This means you can even create partially transparent icons!)

  • All Icons must be saved in themeName.theme>IconBundles as bundleID-large.png
Finding BundleIDs

Stock Application BundleIDs

NameBundleID
App Storecom.apple.AppStore
Apple Watchcom.apple.Bridge
Calculatorcom.apple.calculator
Calendarcom.apple.mobilecal
Cameracom.apple.camera
Classroomcom.apple.classroom
Clockcom.apple.mobiletimer
Compasscom.apple.compass
FaceTimecom.apple.facetime
Filescom.apple.DocumentsApp
Game Centercom.apple.gamecenter
Healthcom.apple.Health
Homecom.apple.Home
iBookscom.apple.iBooks
iTunes Storecom.apple.MobileStore
Mailcom.apple.mobilemail
Mapscom.apple.Maps
Measurecom.apple.measure
Messagescom.apple.MobileSMS
Musiccom.apple.Music
Newscom.apple.news
Notescom.apple.mobilenotes
Phonecom.apple.mobilephone
Photo Boothcom.apple.Photo-Booth
Photoscom.apple.mobileslideshow
Playgroundscome.apple.Playgrounds
Podcastscom.apple.podcasts
Reminderscom.apple.reminders
Safaricom.apple.mobilesafari
Settingscom.apple.Preferences
Stockscom.apple.stocks
Tipscom.apple.tips
TVcom.apple.tv
Videoscom.apple.videos
Voice Memoscom.apple.VoiceMemos
Walletcom.apple.Passbook
Weathercom.apple.weather

3rd Party Applications BundleID Click here

Icon Masks

  • Getting the Classic Rounded Rectangle Masks

In your Info.plist file add the following value between <dict> and - -``` -IB-MaskIcons - -``` - -* Custom Icon Masks - -**NOTE: This is an optional step, if you do not want Icon Masks, skip this step** - -* Inside your `themeName.theme` folder, create another folder called 'Bundles' - * Inside `Bundles` create another folder called `com.apple.mobileicons.framework` - -#### Designing Masks - -**Masking does not support IconBundles, therefore you need to save the masks for each of the following** - -| File | Resolution | -|------|------------| -| AppIconMask@2x~ipad.png | 152x512 | -| AppIconMask@2x~iphone.png | 120x120 | -| AppIconMask@3x~ipad.png | 180x180 | -| AppIconMask@3x~iphone.png | 180x180 | -| AppIconMask~ipad.png | 76x76 | -| DocumentBadgeMask-20@2x.png | 40x40 | -| DocumentBadgeMask-145@2x.png | 145x145 | -| GameAppIconMask@2x.png | 84x84 | -| NotificationAppIconMask@2x.png | 40x40 | -| NotificationAppIconMask@3x.png | 60x60 | -| SpotlightAppIconMask@2x.png | 80x80 | -| SpotlightAppIconMask@3x.png | 120x120 | -| TableIconMask@2x.png | 58x58 | -| TableIconOutline@2x.png | 58x58 | - -* While creating the mask, make sure that the background is not a solid colour and is transparent -* Whichever area you want to make visible, it should be coloured in black - -Example (Credits: Pinpal): - -![Credit: Pinpal](https://pinpal.github.io/assets/theme-guide/mask-demo.png) - -would result in - -![Credit: Pinpal](https://pinpal.github.io/assets/theme-guide/mask-result.png) - -### Packaging - -* Create a new folder outside `themeName.theme` with the name you want to be shown on Cydia, e.g `themeNameForCydia` -* Create another folder called `DEBIAN` in `themeNameForCydia` (It needs to be uppercase) -* In `DEBIAN` create an extensionless file called `control` and edit it using your favourite text editor - -Paste the following in it, replacing `yourname`, `themename`, `Theme Name`, `A theme with beautiful icons!` and `Your Name` with your details: - -``` -Package: com.yourname.themename -Name: Theme Name -Version: 1.0 -Architecture: iphoneos-arm -Description: A theme with beautiful icons! -Author: Your Name -Maintainer: Your Name -Section: Themes -``` - -* Important Notes: - * The package field **MUST** be lower case! - * The version field **MUST** be changed everytime you update your theme! - * The control file **MUST** have an extra blank line at the bottom! - -* Now, Create another folder called `Library` in `themeNameForCydia` -* In `Library` create another folder called `Themes` -* Finally, copy `themeName.theme` to the `Themes` folder (**Copy the entire folder, not just the contents**) - -### Building the DEB - -**For building the deb you need a `*nix` system, otherwise you can build it using your iPhones** - -##### Pre-Requisite for MacOS users -1) Install Homenbrew `/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"` (Run this in the terminal) -2) Install dpkg, by running `brew install dpkg` - -**There is a terrible thing called .DS_Store which if not removed, will cause a problem durin either build or installation** - -* To remove this we first need to open the folder in the terminal - -* Launch the Terminal and then drag-and-drop the 'themeNameForCydia' folder on the Terminal icon in the dock -* Now, run `find . -name "*.DS_Store" -type f -delete` - -##### Pre-Requisite for Windows Users -* SSH into your iPhone and drag and drop the `themeNameForCyia` folder on the terminal - -##### Common Instructions - -* You should be at the root of the folder in the terminal, i.e Inside `themeNameForCydia` -* running `ls` should show the following output - -``` -DEBIAN Library -``` - -* Now, in the terminal enter the following `cd .. && dpkg -b themeNameForCydia ` - -**Now you will have the `themeNameForCydia.deb` in the same directory** - -You can share this with your friends :+1: -

]]>https://navanchauhan.github.io/posts/hello-worldHello WorldMy first post.https://navanchauhan.github.io/posts/hello-worldTue, 16 Apr 2019 17:39:00 +0530Hello World

Why a Hello World post?

Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :)

]]>https://navanchauhan.github.io/posts/2010-01-24-experimentsExperimentsJust a markdown file for all experiments related to the websitehttps://navanchauhan.github.io/posts/2010-01-24-experimentsSun, 24 Jan 2010 23:43:00 +0530Experiments

https://s3-us-west-2.amazonaws.com/s.cdpn.io/148866/img-original.jpg

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differ diff --git a/images/logo 2.png b/images/logo 2.png deleted file mode 100644 index caaf43c..0000000 Binary files a/images/logo 2.png and /dev/null differ diff --git a/images/me 2.jpeg b/images/me 2.jpeg deleted file mode 100644 index cf70e23..0000000 Binary files a/images/me 2.jpeg and /dev/null differ diff --git a/index 3.html b/index 3.html deleted file mode 100644 index fe64afd..0000000 --- a/index 3.html +++ /dev/null @@ -1 +0,0 @@ -👋 Hi! | Navan Chauhan
Navan Chauhan

👋 Hi!

Welcome to my personal fragment of the internet. Majority of the posts should be complete.

Latest content

\ No newline at end of file diff --git a/index.html b/index.html index b8f001d..3c8d541 100644 --- a/index.html +++ b/index.html @@ -1 +1 @@ -👋 Hi! | Navan Chauhan
Navan Chauhan

👋 Hi!

Welcome to my personal fragment of the internet. Majority of the posts should be complete.

Latest content

\ No newline at end of file +👋 Hi! | Navan Chauhan
Navan Chauhan

👋 Hi!

Welcome to my personal fragment of the internet. Majority of the posts should be complete.

Latest content

\ No newline at end of file diff --git a/manifest 2.json b/manifest 2.json deleted file mode 100644 index bb4ec5d..0000000 --- a/manifest 2.json +++ /dev/null @@ -1,119 +0,0 @@ -{ - "dir": "ltr", - "lang": "en", - "name": "Hi! | Navan Chauhan", - "scope": "/", - "display": "fullscreen", - "start_url": "https://navanchauhan.github.io/", - "short_name": "Navan Chauhan", - "theme_color": "black", - "description": "Welcome to my personal fragment of the internet.", - "orientation": "any", - "background_color": "transparent", - "related_applications": [], - "prefer_related_applications": false, - "icons": [ - { - "src": "/images/favicon.png", - "type": "image/png", - "sizes": "32x32" - }, - { - "src": "/images/14a6e126-4866-93de-8df5-e0e6a3c65da1.webPlatform.png", - "sizes": "44x44", - "type": "image/png" - }, - { - "src": "/images/6b5f7f70-557f-0e4b-3d76-127534525db9.webPlatform.png", - "sizes": "48x48", - "type": "image/png" - }, - { - "src": "/images/c5840a63-85f5-62b0-c68f-2faa4aaea42b.webPlatform.png", - "sizes": "1240x600", - "type": "image/png" - }, - { - "src": "/images/82e24f17-2e71-90d8-67a7-587163282ebf.webPlatform.png", - "sizes": "300x300", - "type": "image/png" - }, - { - "src": "/images/f7842765-fff5-aa39-9f7f-fdd3024d4056.webPlatform.png", - "sizes": "150x150", - "type": "image/png" - }, - { - "src": "/images/9384518b-2a6c-0abc-136c-8c8faf49c71b.webPlatform.png", - "sizes": "88x88", - "type": "image/png" - }, - { - "src": "/images/15294abc-6c7c-ffb8-df8d-d2fad23f50b0.webPlatform.png", - "sizes": "24x24", - "type": "image/png" - }, - { - "src": "/images/f178697f-630b-bafd-7c7d-e1287b98a969.webPlatform.png", - "sizes": "50x50", - "type": "image/png" - }, - { - "src": "/images/f400aaaa-861c-78c0-0919-07a886e57304.webPlatform.png", - "sizes": "620x300", - "type": "image/png" - }, - { - "src": "/images/8c0ffe9e-b615-96cd-3e18-ab4307c859a0.webPlatform.png", - "sizes": "192x192", - "type": "image/png" - }, - { - "src": "/images/f1579c61-f17f-ff49-3f97-e942f202bebf.webPlatform.png", - "sizes": "144x144", - "type": "image/png" - }, - { - "src": "/images/9bf4aee8-92e3-932f-5388-7731928b5692.webPlatform.png", - "sizes": "96x96", - "type": "image/png" - }, - { - "src": "/images/9dc22996-fd1b-b2d3-3627-cef4fa224e25.webPlatform.png", - "sizes": "72x72", - "type": "image/png" - }, - { - "src": "/images/afd91c53-cfd0-b52e-ca49-1db0cc292b7d.webPlatform.png", - "sizes": "36x36", - "type": "image/png" - }, - { - "src": "/images/e429a798-7e86-1f02-565e-39dfab41fe36.webPlatform.png", - "sizes": "1024x1024", - "type": "image/png" - }, - { - "src": "/images/04d0580b-d347-476a-232d-9568839851cd.webPlatform.png", - "sizes": "180x180", - "type": "image/png" - }, - { - "src": "/images/cbac5b1d-0299-9db6-3747-c7aeaaaa37d0.webPlatform.png", - "sizes": "152x152", - "type": "image/png" - }, - { - "src": "/images/b0cac729-56cb-2a63-7e8b-ac62a038a023.webPlatform.png", - "sizes": "120x120", - "type": "image/png" - }, - { - "src": "/images/bb0aca46-4612-c284-055f-58850c0730bd.webPlatform.png", - "sizes": "76x76", - "type": "image/png" - } - ], - "url": "https://navanchauhan.github.io", - "screenshots": [] -} diff --git a/posts/2010-01-24-experiments/index 2.html b/posts/2010-01-24-experiments/index 2.html deleted file mode 100644 index a97086e..0000000 --- a/posts/2010-01-24-experiments/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Experiments | Navan Chauhan
Navan Chauhan
1 minute readCreated on January 24, 2010Last modified on June 1, 2020

Experiments

https://s3-us-west-2.amazonaws.com/s.cdpn.io/148866/img-original.jpg

Tagged with:
\ No newline at end of file diff --git a/posts/2019-05-05-Custom-Snowboard-Anemone-Theme/index 2.html b/posts/2019-05-05-Custom-Snowboard-Anemone-Theme/index 2.html deleted file mode 100644 index 3b3a930..0000000 --- a/posts/2019-05-05-Custom-Snowboard-Anemone-Theme/index 2.html +++ /dev/null @@ -1,117 +0,0 @@ -Creating your own custom theme for Snowboard or Anemone | Navan Chauhan
Navan Chauhan
5 minute readCreated on May 5, 2019Last modified on June 1, 2020

Creating your own custom theme for Snowboard or Anemone

Contents

  • Getting Started
  • Theme Configuration
  • Creating Icons
  • Exporting Icons
  • Icon Masks
  • Packaging
  • Building the DEB

Getting Started

Note: Without the proper folder structure, your theme may not show up!

  • Create a new folder called themeName.theme (Replace themeName with your desired theme name)
  • Within themeName.theme folder, create another folder called IconBundles (You cannot change this name)

Theme Configuration

  • Now, inside the themeName.theme folder, create a file called Info.plist and paste the following
<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-  <plist version="1.0">
-  <dict>
-    <key>PackageName</key>
-    <string>ThemeName</string>
-    <key>ThemeType</key>
-    <string>Icons</string>
-  </dict>
-</plist>
-
  • Replace PackageName with the name of the Pacakge and replace ThemeName with the Theme Name

Now, you might ask what is the difference between PackageName and ThemeName?

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 MyTheme and include two themes Blackie and White thus creating two entries. More about this in the end

Creating Icons

  • Open up the Image Editor of your choice and create a new file having a resolution of 512x512

Note: Due to IconBundles, we just need to create the icons in one size and they get resized automaticaly :ghost:

Want to create rounded icons? Create them squared only, we will learn how to apply masks!

Exporting Icons

Note: All icons must be saved as *.png (Tip: This means you can even create partially transparent icons!)

  • All Icons must be saved in themeName.theme>IconBundles as bundleID-large.png
Finding BundleIDs

Stock Application BundleIDs

NameBundleID
App Storecom.apple.AppStore
Apple Watchcom.apple.Bridge
Calculatorcom.apple.calculator
Calendarcom.apple.mobilecal
Cameracom.apple.camera
Classroomcom.apple.classroom
Clockcom.apple.mobiletimer
Compasscom.apple.compass
FaceTimecom.apple.facetime
Filescom.apple.DocumentsApp
Game Centercom.apple.gamecenter
Healthcom.apple.Health
Homecom.apple.Home
iBookscom.apple.iBooks
iTunes Storecom.apple.MobileStore
Mailcom.apple.mobilemail
Mapscom.apple.Maps
Measurecom.apple.measure
Messagescom.apple.MobileSMS
Musiccom.apple.Music
Newscom.apple.news
Notescom.apple.mobilenotes
Phonecom.apple.mobilephone
Photo Boothcom.apple.Photo-Booth
Photoscom.apple.mobileslideshow
Playgroundscome.apple.Playgrounds
Podcastscom.apple.podcasts
Reminderscom.apple.reminders
Safaricom.apple.mobilesafari
Settingscom.apple.Preferences
Stockscom.apple.stocks
Tipscom.apple.tips
TVcom.apple.tv
Videoscom.apple.videos
Voice Memoscom.apple.VoiceMemos
Walletcom.apple.Passbook
Weathercom.apple.weather

3rd Party Applications BundleID Click here

Icon Masks

  • Getting the Classic Rounded Rectangle Masks

In your Info.plist file add the following value between <dict> and - -``` -IB-MaskIcons - -``` - -* Custom Icon Masks - -**NOTE: This is an optional step, if you do not want Icon Masks, skip this step** - -* Inside your `themeName.theme` folder, create another folder called 'Bundles' - * Inside `Bundles` create another folder called `com.apple.mobileicons.framework` - -#### Designing Masks - -**Masking does not support IconBundles, therefore you need to save the masks for each of the following** - -| File | Resolution | -|------|------------| -| AppIconMask@2x~ipad.png | 152x512 | -| AppIconMask@2x~iphone.png | 120x120 | -| AppIconMask@3x~ipad.png | 180x180 | -| AppIconMask@3x~iphone.png | 180x180 | -| AppIconMask~ipad.png | 76x76 | -| DocumentBadgeMask-20@2x.png | 40x40 | -| DocumentBadgeMask-145@2x.png | 145x145 | -| GameAppIconMask@2x.png | 84x84 | -| NotificationAppIconMask@2x.png | 40x40 | -| NotificationAppIconMask@3x.png | 60x60 | -| SpotlightAppIconMask@2x.png | 80x80 | -| SpotlightAppIconMask@3x.png | 120x120 | -| TableIconMask@2x.png | 58x58 | -| TableIconOutline@2x.png | 58x58 | - -* While creating the mask, make sure that the background is not a solid colour and is transparent -* Whichever area you want to make visible, it should be coloured in black - -Example (Credits: Pinpal): - -![Credit: Pinpal](https://pinpal.github.io/assets/theme-guide/mask-demo.png) - -would result in - -![Credit: Pinpal](https://pinpal.github.io/assets/theme-guide/mask-result.png) - -### Packaging - -* Create a new folder outside `themeName.theme` with the name you want to be shown on Cydia, e.g `themeNameForCydia` -* Create another folder called `DEBIAN` in `themeNameForCydia` (It needs to be uppercase) -* In `DEBIAN` create an extensionless file called `control` and edit it using your favourite text editor - -Paste the following in it, replacing `yourname`, `themename`, `Theme Name`, `A theme with beautiful icons!` and `Your Name` with your details: - -``` -Package: com.yourname.themename -Name: Theme Name -Version: 1.0 -Architecture: iphoneos-arm -Description: A theme with beautiful icons! -Author: Your Name -Maintainer: Your Name -Section: Themes -``` - -* Important Notes: - * The package field **MUST** be lower case! - * The version field **MUST** be changed everytime you update your theme! - * The control file **MUST** have an extra blank line at the bottom! - -* Now, Create another folder called `Library` in `themeNameForCydia` -* In `Library` create another folder called `Themes` -* Finally, copy `themeName.theme` to the `Themes` folder (**Copy the entire folder, not just the contents**) - -### Building the DEB - -**For building the deb you need a `*nix` system, otherwise you can build it using your iPhones** - -##### Pre-Requisite for MacOS users -1) Install Homenbrew `/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"` (Run this in the terminal) -2) Install dpkg, by running `brew install dpkg` - -**There is a terrible thing called .DS_Store which if not removed, will cause a problem durin either build or installation** - -* To remove this we first need to open the folder in the terminal - -* Launch the Terminal and then drag-and-drop the 'themeNameForCydia' folder on the Terminal icon in the dock -* Now, run `find . -name "*.DS_Store" -type f -delete` - -##### Pre-Requisite for Windows Users -* SSH into your iPhone and drag and drop the `themeNameForCyia` folder on the terminal - -##### Common Instructions - -* You should be at the root of the folder in the terminal, i.e Inside `themeNameForCydia` -* running `ls` should show the following output - -``` -DEBIAN Library -``` - -* Now, in the terminal enter the following `cd .. && dpkg -b themeNameForCydia ` - -**Now you will have the `themeNameForCydia.deb` in the same directory** - -You can share this with your friends :+1: -

Tagged with:
\ No newline at end of file diff --git a/posts/2019-12-04-Google-Teachable-Machines/index 2.html b/posts/2019-12-04-Google-Teachable-Machines/index 2.html deleted file mode 100644 index 30ac155..0000000 --- a/posts/2019-12-04-Google-Teachable-Machines/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Image Classifier With Teachable Machines | Navan Chauhan
Navan Chauhan
2 minute readCreated on December 4, 2019Last modified on June 1, 2020

Image Classifier With Teachable Machines

Made for Google Code-In

Task Description

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

Details

  1. Collecting Data

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

  1. Training

Training on teachable machines is as simple as clicking the train button. I did not even have to modify any configurations.

  1. Finding Labels

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.

  1. Adding a suggestions function

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%

  1. Running!

Here it is running!

Remix this project:-

https://luminous-opinion.glitch.me

Tagged with:
\ No newline at end of file diff --git a/posts/2019-12-08-Image-Classifier-Tensorflow/index 2.html b/posts/2019-12-08-Image-Classifier-Tensorflow/index 2.html deleted file mode 100644 index 917844a..0000000 --- a/posts/2019-12-08-Image-Classifier-Tensorflow/index 2.html +++ /dev/null @@ -1,101 +0,0 @@ -Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan
Navan Chauhan
4 minute readCreated on December 8, 2019Last modified on June 1, 2020

Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria

Done during Google Code-In. Org: Tensorflow.

Imports

%tensorflow_version 2.x #This is for telling Colab that you want to use TF 2.0, ignore if running on local machine
-
-from PIL import Image # We use the PIL Library to resize images
-import numpy as np
-import os
-import cv2
-import tensorflow as tf
-from tensorflow.keras import datasets, layers, models
-import pandas as pd
-import matplotlib.pyplot as plt
-from keras.models import Sequential
-from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
-

Dataset

Fetching the Data

!wget ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip
-!unzip cell_images.zip
-

Processing the Data

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.

data = []
-labels = []
-
-Parasitized = os.listdir("./cell_images/Parasitized/")
-for parasite in Parasitized:
-    try:
-        image=cv2.imread("./cell_images/Parasitized/"+parasite)
-        image_from_array = Image.fromarray(image, 'RGB')
-        size_image = image_from_array.resize((50, 50))
-        data.append(np.array(size_image))
-        labels.append(0)
-    except AttributeError:
-        print("")
-
-Uninfected = os.listdir("./cell_images/Uninfected/")
-for uninfect in Uninfected:
-    try:
-        image=cv2.imread("./cell_images/Uninfected/"+uninfect)
-        image_from_array = Image.fromarray(image, 'RGB')
-        size_image = image_from_array.resize((50, 50))
-        data.append(np.array(size_image))
-        labels.append(1)
-    except AttributeError:
-        print("")
-

Splitting Data

df = np.array(data)
-labels = np.array(labels)
-(X_train, X_test) = df[(int)(0.1*len(df)):],df[:(int)(0.1*len(df))]
-(y_train, y_test) = labels[(int)(0.1*len(labels)):],labels[:(int)(0.1*len(labels))]
-
s=np.arange(X_train.shape[0])
-np.random.shuffle(s)
-X_train=X_train[s]
-y_train=y_train[s]
-X_train = X_train/255.0
-

Model

Creating Model

By creating a sequential model, we create a linear stack of layers.

Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images

model = models.Sequential()
-model.add(layers.Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(50,50,3)))
-model.add(layers.MaxPooling2D(pool_size=2))
-model.add(layers.Conv2D(filters=32,kernel_size=2,padding='same',activation='relu'))
-model.add(layers.MaxPooling2D(pool_size=2))
-model.add(layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu"))
-model.add(layers.MaxPooling2D(pool_size=2))
-model.add(layers.Dropout(0.2))
-model.add(layers.Flatten())
-model.add(layers.Dense(500,activation="relu"))
-model.add(layers.Dropout(0.2))
-model.add(layers.Dense(2,activation="softmax"))#2 represent output layer neurons 
-model.summary()
-

Compiling Model

We use the adam optimiser as it is an adaptive learning rate optimization algorithm that's been designed specifically for training deep neural networks, which means it changes its learning rate automaticaly to get the best results

model.compile(optimizer="adam",
-              loss="sparse_categorical_crossentropy", 
-             metrics=["accuracy"])
-

Training Model

We train the model for 10 epochs on the training data and then validate it using the testing data

history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))
-
Train on 24803 samples, validate on 2755 samples
-Epoch 1/10
-24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0786 - accuracy: 0.9729 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 2/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0746 - accuracy: 0.9731 - val_loss: 0.0290 - val_accuracy: 0.9996
-Epoch 3/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0672 - accuracy: 0.9764 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 4/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0601 - accuracy: 0.9789 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 5/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0558 - accuracy: 0.9804 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 6/10
-24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0513 - accuracy: 0.9819 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 7/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.3190 - val_accuracy: 0.9985
-Epoch 8/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0404 - accuracy: 0.9858 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 9/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0352 - accuracy: 0.9878 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-Epoch 10/10
-24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0373 - accuracy: 0.9865 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
-

Results

accuracy = history.history['accuracy'][-1]*100
-loss = history.history['loss'][-1]*100
-val_accuracy = history.history['val_accuracy'][-1]*100
-val_loss = history.history['val_loss'][-1]*100
-
-print(
-    'Accuracy:', accuracy,
-    '\nLoss:', loss,
-    '\nValidation Accuracy:', val_accuracy,
-    '\nValidation Loss:', val_loss
-)
-
Accuracy: 98.64532351493835 
-Loss: 3.732407123270176 
-Validation Accuracy: 100.0 
-Validation Loss: 0.0
-

We have achieved 98% Accuracy!

Link to Colab Notebook

Tagged with:
\ No newline at end of file diff --git a/posts/2019-12-08-Splitting-Zips/index 2.html b/posts/2019-12-08-Splitting-Zips/index 2.html deleted file mode 100644 index 8809371..0000000 --- a/posts/2019-12-08-Splitting-Zips/index 2.html +++ /dev/null @@ -1,4 +0,0 @@ -Splitting ZIPs into Multiple Parts | Navan Chauhan
Navan Chauhan
1 minute readCreated on December 8, 2019Last modified on June 1, 2020

Splitting ZIPs into Multiple Parts

Tested on macOS

Creating the archive:

zip -r -s 5 oodlesofnoodles.zip website/
-

5 stands for each split files' size (in mb, kb and gb can also be specified)

For encrypting the zip:

zip -er -s 5 oodlesofnoodles.zip website
-

Extracting Files

First we need to collect all parts, then

zip -F oodlesofnoodles.zip --out merged.zip
-
Tagged with:
\ No newline at end of file diff --git a/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html b/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html deleted file mode 100644 index bf1c102..0000000 --- a/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html +++ /dev/null @@ -1,9 +0,0 @@ -Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan
Navan Chauhan
1 minute readCreated on December 10, 2019Last modified on June 1, 2020

Making Predictions using Image Classifier (TensorFlow)

This was tested on TF 2.x and works as of 2019-12-10

If you want to understand how to make your own custom image classifier, please refer to my previous post.

If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.

First we import the following if we have not imported these before

import cv2
-import os
-

Then we read the file using OpenCV.

image=cv2.imread(imagePath)
-

The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.

image_from_array = Image.fromarray(image, 'RGB')
-

Then we resize the image

size_image = image_from_array.resize((50,50))
-

After this we create a batch consisting of only one image

p = np.expand_dims(size_image, 0)
-

We then convert this uint8 datatype to a float32 datatype

img = tf.cast(p, tf.float32)
-

Finally we make the prediction

print(['Infected','Uninfected'][np.argmax(model.predict(img))])
-

Infected

Tagged with:
\ No newline at end of file diff --git a/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 2.html b/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 2.html deleted file mode 100644 index 0d2cca6..0000000 --- a/posts/2019-12-16-TensorFlow-Polynomial-Regression/index 2.html +++ /dev/null @@ -1,307 +0,0 @@ -Polynomial Regression Using TensorFlow | Navan Chauhan
Navan Chauhan
17 minute readCreated on December 16, 2019Last modified on June 1, 2020

Polynomial Regression Using TensorFlow

In this tutorial you will learn about polynomial regression and how you can implement it in Tensorflow.

In this, we will be performing polynomial regression using 5 types of equations -

  • Linear
  • Quadratic
  • Cubic
  • Quartic
  • Quintic

Regression

What is Regression?

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 ).

What is Polynomial Regression

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).

Imports

import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-

Dataset

Creating Random Data

Even though in this tutorial we will use a Position Vs Salary datasset, it is important to know how to create synthetic data

To create 50 values spaced evenly between 0 and 50, we use NumPy's linspace funtion

linspace(lower_limit, upper_limit, no_of_observations)

x = np.linspace(0, 50, 50)
-y = np.linspace(0, 50, 50)
-

We use the following function to add noise to the data, so that our values

x += np.random.uniform(-4, 4, 50)
-y += np.random.uniform(-4, 4, 50)
-

Position vs Salary Dataset

We will be using https://drive.google.com/file/d/1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9/view (Salary vs Position Dataset)

!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9' -O data.csv
-
df = pd.read_csv("data.csv")
-
df # this gives us a preview of the dataset we are working with
-
| Position          | Level | Salary  |
-|-------------------|-------|---------|
-| Business Analyst  | 1     | 45000   |
-| Junior Consultant | 2     | 50000   |
-| Senior Consultant | 3     | 60000   |
-| Manager           | 4     | 80000   |
-| Country Manager   | 5     | 110000  |
-| Region Manager    | 6     | 150000  |
-| Partner           | 7     | 200000  |
-| Senior Partner    | 8     | 300000  |
-| C-level           | 9     | 500000  |
-| CEO               | 10    | 1000000 |
-

We convert the salary column as the ordinate (y-cordinate) and level column as the abscissa

abscissa = df["Level"].to_list() # abscissa = [1,2,3,4,5,6,7,8,9,10]
-ordinate = df["Salary"].to_list() # ordinate = [45000,50000,60000,80000,110000,150000,200000,300000,500000,1000000]
-
n = len(abscissa) # no of observations
-plt.scatter(abscissa, ordinate)
-plt.ylabel('Salary')
-plt.xlabel('Position')
-plt.title("Salary vs Position")
-plt.show()
-

Defining Stuff

X = tf.placeholder("float")
-Y = tf.placeholder("float")
-

Defining Variables

We first define all the coefficients and constant as tensorflow variables haveing a random intitial value

a = tf.Variable(np.random.randn(), name = "a")
-b = tf.Variable(np.random.randn(), name = "b")
-c = tf.Variable(np.random.randn(), name = "c")
-d = tf.Variable(np.random.randn(), name = "d")
-e = tf.Variable(np.random.randn(), name = "e")
-f = tf.Variable(np.random.randn(), name = "f")
-

Model Configuration

learning_rate = 0.2
-no_of_epochs = 25000
-

Equations

deg1 = a*X + b
-deg2 = a*tf.pow(X,2) + b*X + c
-deg3 = a*tf.pow(X,3) + b*tf.pow(X,2) + c*X + d
-deg4 = a*tf.pow(X,4) + b*tf.pow(X,3) + c*tf.pow(X,2) + d*X + e
-deg5 = a*tf.pow(X,5) + b*tf.pow(X,4) + c*tf.pow(X,3) + d*tf.pow(X,2) + e*X + f
-

Cost Function

We use the Mean Squared Error Function

mse1 = tf.reduce_sum(tf.pow(deg1-Y,2))/(2*n)
-mse2 = tf.reduce_sum(tf.pow(deg2-Y,2))/(2*n)
-mse3 = tf.reduce_sum(tf.pow(deg3-Y,2))/(2*n)
-mse4 = tf.reduce_sum(tf.pow(deg4-Y,2))/(2*n)
-mse5 = tf.reduce_sum(tf.pow(deg5-Y,2))/(2*n)
-

Optimizer

We use the AdamOptimizer for the polynomial functions and GradientDescentOptimizer for the linear function

optimizer1 = tf.train.GradientDescentOptimizer(learning_rate).minimize(mse1)
-optimizer2 = tf.train.AdamOptimizer(learning_rate).minimize(mse2)
-optimizer3 = tf.train.AdamOptimizer(learning_rate).minimize(mse3)
-optimizer4 = tf.train.AdamOptimizer(learning_rate).minimize(mse4)
-optimizer5 = tf.train.AdamOptimizer(learning_rate).minimize(mse5)
-
init=tf.global_variables_initializer()
-

Model Predictions

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.

Linear Equation

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer1, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse1,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b:",sess.run(a),sess.run(b))
-
-        training_cost = sess.run(mse1,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        constant = sess.run(b)
-
-print(training_cost, coefficient1, constant)
-
Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-88999125000.0 180396.42 -478869.12
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Linear Regression Result')
-plt.legend()
-plt.show()
-

Quadratic Equation

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer2, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse2,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c:",sess.run(a),sess.run(b),sess.run(c))
-
-        training_cost = sess.run(mse2,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        constant = sess.run(c)
-
-print(training_cost, coefficient1, coefficient2, constant)
-
Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
-Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
-Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
-Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
-Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
-Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
-Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
-Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
-Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
-Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
-Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
-Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
-Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
-Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
-Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
-Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
-Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
-Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
-Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
-Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
-Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
-Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
-Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
-Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
-Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
-8058094600.0 6659.793 3227.2517 -463.03156
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,2) + coefficient2*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Quadratic Regression Result')
-plt.legend()
-plt.show()
-

Cubic

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer3, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse3,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c,d:",sess.run(a),sess.run(b),sess.run(c),sess.run(d))
-
-        training_cost = sess.run(mse3,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        coefficient3 = sess.run(c)
-        constant = sess.run(d)
-
-print(training_cost, coefficient1, coefficient2, coefficient3, constant)
-
Epoch 1000 : Training Cost: 4279814000.0  a,b,c,d: 670.1527 694.4212 751.4653 903.9527
-Epoch 2000 : Training Cost: 3770950400.0  a,b,c,d: 742.6414 666.3489 636.94525 859.2088
-Epoch 3000 : Training Cost: 3717708300.0  a,b,c,d: 756.2582 569.3339 448.105 748.23956
-Epoch 4000 : Training Cost: 3667464000.0  a,b,c,d: 769.4476 474.0318 265.5761 654.75525
-Epoch 5000 : Training Cost: 3620040700.0  a,b,c,d: 782.32324 380.54272 89.39888 578.5136
-Epoch 6000 : Training Cost: 3575265800.0  a,b,c,d: 794.8898 288.83356 -80.5215 519.13654
-Epoch 7000 : Training Cost: 3532972000.0  a,b,c,d: 807.1608 198.87044 -244.31102 476.2061
-Epoch 8000 : Training Cost: 3493009200.0  a,b,c,d: 819.13513 110.64169 -402.0677 449.3291
-Epoch 9000 : Training Cost: 3455228400.0  a,b,c,d: 830.80255 24.0964 -553.92804 438.0652
-Epoch 10000 : Training Cost: 3419475500.0  a,b,c,d: 842.21594 -60.797424 -700.0123 441.983
-Epoch 11000 : Training Cost: 3385625300.0  a,b,c,d: 853.3363 -144.08699 -840.467 460.6356
-Epoch 12000 : Training Cost: 3353544700.0  a,b,c,d: 864.19135 -225.8125 -975.4196 493.57703
-Epoch 13000 : Training Cost: 3323125000.0  a,b,c,d: 874.778 -305.98932 -1104.9867 540.39465
-Epoch 14000 : Training Cost: 3294257000.0  a,b,c,d: 885.1007 -384.63474 -1229.277 600.65607
-Epoch 15000 : Training Cost: 3266820000.0  a,b,c,d: 895.18823 -461.819 -1348.4417 673.9051
-Epoch 16000 : Training Cost: 3240736000.0  a,b,c,d: 905.0128 -537.541 -1462.6171 759.7118
-Epoch 17000 : Training Cost: 3215895000.0  a,b,c,d: 914.60065 -611.8676 -1571.9058 857.6638
-Epoch 18000 : Training Cost: 3192216800.0  a,b,c,d: 923.9603 -684.8093 -1676.4642 967.30475
-Epoch 19000 : Training Cost: 3169632300.0  a,b,c,d: 933.08594 -756.3582 -1776.4275 1088.2198
-Epoch 20000 : Training Cost: 3148046300.0  a,b,c,d: 941.9928 -826.6257 -1871.9355 1219.9702
-Epoch 21000 : Training Cost: 3127394800.0  a,b,c,d: 950.67896 -895.6205 -1963.0989 1362.1665
-Epoch 22000 : Training Cost: 3107608600.0  a,b,c,d: 959.1487 -963.38116 -2050.0586 1514.4026
-Epoch 23000 : Training Cost: 3088618200.0  a,b,c,d: 967.4355 -1029.9625 -2132.961 1676.2717
-Epoch 24000 : Training Cost: 3070361300.0  a,b,c,d: 975.52875 -1095.4292 -2211.854 1847.4485
-Epoch 25000 : Training Cost: 3052791300.0  a,b,c,d: 983.4346 -1159.7922 -2286.9412 2027.4857
-3052791300.0 983.4346 -1159.7922 -2286.9412 2027.4857
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,3) + coefficient2*pow(x,2) + coefficient3*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Cubic Regression Result')
-plt.legend()
-plt.show()
-

Quartic

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer4, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse4,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c,d:",sess.run(a),sess.run(b),sess.run(c),sess.run(d),sess.run(e))
-
-        training_cost = sess.run(mse4,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        coefficient3 = sess.run(c)
-        coefficient4 = sess.run(d)
-        constant = sess.run(e)
-
-print(training_cost, coefficient1, coefficient2, coefficient3, coefficient4, constant)
-
Epoch 1000 : Training Cost: 1902632600.0  a,b,c,d: 84.48304 52.210594 54.791424 142.51952 512.0343
-Epoch 2000 : Training Cost: 1854316200.0  a,b,c,d: 88.998955 13.073557 14.276088 223.55667 1056.4655
-Epoch 3000 : Training Cost: 1812812400.0  a,b,c,d: 92.9462 -22.331177 -15.262934 327.41858 1634.9054
-Epoch 4000 : Training Cost: 1775716000.0  a,b,c,d: 96.42522 -54.64535 -35.829437 449.5028 2239.1392
-Epoch 5000 : Training Cost: 1741494100.0  a,b,c,d: 99.524734 -84.43976 -49.181057 585.85876 2862.4915
-Epoch 6000 : Training Cost: 1709199600.0  a,b,c,d: 102.31984 -112.19895 -56.808075 733.1876 3499.6199
-Epoch 7000 : Training Cost: 1678261800.0  a,b,c,d: 104.87324 -138.32709 -59.9442 888.79626 4146.2944
-Epoch 8000 : Training Cost: 1648340600.0  a,b,c,d: 107.23536 -163.15173 -59.58964 1050.524 4798.979
-Epoch 9000 : Training Cost: 1619243400.0  a,b,c,d: 109.44742 -186.9409 -56.53944 1216.6432 5454.9463
-Epoch 10000 : Training Cost: 1590821900.0  a,b,c,d: 111.54233 -209.91287 -51.423084 1385.8513 6113.5137
-Epoch 11000 : Training Cost: 1563042200.0  a,b,c,d: 113.54405 -232.21953 -44.73371 1557.1084 6771.7046
-Epoch 12000 : Training Cost: 1535855600.0  a,b,c,d: 115.471565 -253.9838 -36.851135 1729.535 7429.069
-Epoch 13000 : Training Cost: 1509255300.0  a,b,c,d: 117.33939 -275.29697 -28.0714 1902.5308 8083.9634
-Epoch 14000 : Training Cost: 1483227000.0  a,b,c,d: 119.1605 -296.2472 -18.618649 2075.6094 8735.381
-Epoch 15000 : Training Cost: 1457726700.0  a,b,c,d: 120.94584 -316.915 -8.650095 2248.3247 9384.197
-Epoch 16000 : Training Cost: 1432777300.0  a,b,c,d: 122.69806 -337.30704 1.7027153 2420.5771 10028.871
-Epoch 17000 : Training Cost: 1408365000.0  a,b,c,d: 124.42179 -357.45245 12.33499 2592.2983 10669.157
-Epoch 18000 : Training Cost: 1384480000.0  a,b,c,d: 126.12332 -377.39734 23.168756 2763.0933 11305.027
-Epoch 19000 : Training Cost: 1361116800.0  a,b,c,d: 127.80568 -397.16415 34.160156 2933.0452 11935.669
-Epoch 20000 : Training Cost: 1338288100.0  a,b,c,d: 129.4674 -416.72803 45.259155 3101.7727 12561.179
-Epoch 21000 : Training Cost: 1315959700.0  a,b,c,d: 131.11403 -436.14285 56.4436 3269.3142 13182.058
-Epoch 22000 : Training Cost: 1294164700.0  a,b,c,d: 132.74377 -455.3779 67.6757 3435.3833 13796.807
-Epoch 23000 : Training Cost: 1272863600.0  a,b,c,d: 134.35779 -474.45316 78.96117 3600.264 14406.58
-Epoch 24000 : Training Cost: 1252052600.0  a,b,c,d: 135.9583 -493.38254 90.268616 3764.0078 15010.481
-Epoch 25000 : Training Cost: 1231713700.0  a,b,c,d: 137.54753 -512.1876 101.59372 3926.4897 15609.368
-1231713700.0 137.54753 -512.1876 101.59372 3926.4897 15609.368
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,4) + coefficient2*pow(x,3) + coefficient3*pow(x,2) + coefficient4*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Quartic Regression Result')
-plt.legend()
-plt.show()
-

Quintic

with tf.Session() as sess:
-    sess.run(init)
-    for epoch in range(no_of_epochs):
-      for (x,y) in zip(abscissa, ordinate):
-        sess.run(optimizer5, feed_dict={X:x, Y:y})
-      if (epoch+1)%1000==0:
-        cost = sess.run(mse5,feed_dict={X:abscissa,Y:ordinate})
-        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c,d,e,f:",sess.run(a),sess.run(b),sess.run(c),sess.run(d),sess.run(e),sess.run(f))
-
-        training_cost = sess.run(mse5,feed_dict={X:abscissa,Y:ordinate})
-        coefficient1 = sess.run(a)
-        coefficient2 = sess.run(b)
-        coefficient3 = sess.run(c)
-        coefficient4 = sess.run(d)
-        coefficient5 = sess.run(e)
-        constant = sess.run(f)
-
Epoch 1000 : Training Cost: 1409200100.0  a,b,c,d,e,f: 7.949472 7.46219 55.626034 184.29028 484.00223 1024.0083
-Epoch 2000 : Training Cost: 1306882400.0  a,b,c,d,e,f: 8.732181 -4.0085897 73.25298 315.90103 904.08887 2004.9749
-Epoch 3000 : Training Cost: 1212606000.0  a,b,c,d,e,f: 9.732249 -16.90125 86.28379 437.06552 1305.055 2966.2188
-Epoch 4000 : Training Cost: 1123640400.0  a,b,c,d,e,f: 10.74851 -29.82692 98.59997 555.331 1698.4631 3917.9155
-Epoch 5000 : Training Cost: 1039694300.0  a,b,c,d,e,f: 11.75426 -42.598194 110.698326 671.64355 2085.5513 4860.8535
-Epoch 6000 : Training Cost: 960663550.0  a,b,c,d,e,f: 12.745439 -55.18337 122.644936 786.00214 2466.1638 5794.3735
-Epoch 7000 : Training Cost: 886438340.0  a,b,c,d,e,f: 13.721028 -67.57168 134.43822 898.3691 2839.9958 6717.659
-Epoch 8000 : Training Cost: 816913100.0  a,b,c,d,e,f: 14.679965 -79.75113 146.07385 1008.66895 3206.6692 7629.812
-Epoch 9000 : Training Cost: 751971500.0  a,b,c,d,e,f: 15.62181 -91.71608 157.55713 1116.7715 3565.8323 8529.976
-Epoch 10000 : Training Cost: 691508740.0  a,b,c,d,e,f: 16.545347 -103.4531 168.88321 1222.6348 3916.9785 9416.236
-Epoch 11000 : Training Cost: 635382000.0  a,b,c,d,e,f: 17.450052 -114.954254 180.03932 1326.1565 4259.842 10287.99
-Epoch 12000 : Training Cost: 583477250.0  a,b,c,d,e,f: 18.334944 -126.20821 191.02948 1427.2095 4593.8 11143.449
-Epoch 13000 : Training Cost: 535640400.0  a,b,c,d,e,f: 19.198917 -137.20206 201.84718 1525.6926 4918.5327 11981.633
-Epoch 14000 : Training Cost: 491722240.0  a,b,c,d,e,f: 20.041153 -147.92719 212.49709 1621.5496 5233.627 12800.468
-Epoch 15000 : Training Cost: 451559520.0  a,b,c,d,e,f: 20.860966 -158.37456 222.97133 1714.7141 5538.676 13598.337
-Epoch 16000 : Training Cost: 414988960.0  a,b,c,d,e,f: 21.657421 -168.53406 233.27422 1805.0874 5833.1978 14373.658
-Epoch 17000 : Training Cost: 381837920.0  a,b,c,d,e,f: 22.429693 -178.39536 243.39914 1892.5883 6116.847 15124.394
-Epoch 18000 : Training Cost: 351931300.0  a,b,c,d,e,f: 23.176882 -187.94789 253.3445 1977.137 6389.117 15848.417
-Epoch 19000 : Training Cost: 325074400.0  a,b,c,d,e,f: 23.898485 -197.18741 263.12512 2058.6716 6649.8037 16543.95
-Epoch 20000 : Training Cost: 301073570.0  a,b,c,d,e,f: 24.593851 -206.10497 272.72385 2137.1797 6898.544 17209.367
-Epoch 21000 : Training Cost: 279727000.0  a,b,c,d,e,f: 25.262104 -214.69217 282.14642 2212.6372 7135.217 17842.854
-Epoch 22000 : Training Cost: 260845550.0  a,b,c,d,e,f: 25.903376 -222.94969 291.4003 2284.9844 7359.4644 18442.408
-Epoch 23000 : Training Cost: 244218030.0  a,b,c,d,e,f: 26.517094 -230.8697 300.45532 2354.3003 7571.261 19007.49
-Epoch 24000 : Training Cost: 229660080.0  a,b,c,d,e,f: 27.102589 -238.44817 309.35342 2420.4185 7770.5728 19536.19
-Epoch 25000 : Training Cost: 216972400.0  a,b,c,d,e,f: 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
-216972400.0 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
-
predictions = []
-for x in abscissa:
-  predictions.append((coefficient1*pow(x,5) + coefficient2*pow(x,4) + coefficient3*pow(x,3) + coefficient4*pow(x,2) + coefficient5*x + constant))
-plt.plot(abscissa , ordinate, 'ro', label ='Original data')
-plt.plot(abscissa, predictions, label ='Fitted line')
-plt.title('Quintic Regression Result')
-plt.legend()
-plt.show()
-

Results and Conclusion

You just learnt Polynomial Regression using TensorFlow!

Notes

Overfitting

> 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.

Source: Machine Learning Mastery

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

Tagged with:
\ No newline at end of file diff --git a/posts/2019-12-22-Fake-News-Detector/index 2.html b/posts/2019-12-22-Fake-News-Detector/index 2.html deleted file mode 100644 index cdf70d3..0000000 --- a/posts/2019-12-22-Fake-News-Detector/index 2.html +++ /dev/null @@ -1,141 +0,0 @@ -Building a Fake News Detector with Turicreate | Navan Chauhan
Navan Chauhan
7 minute readCreated on December 22, 2019Last modified on June 1, 2020

Building a Fake News Detector with Turicreate

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

Note: These commands are written as if you are running a jupyter notebook.

Building the Machine Learning Model

Data Gathering

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

Dependencies

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.

!pip install turicreate
-!pip uninstall -y mxnet
-!pip install mxnet-cu100==1.4.0.post0
-

If you do not wish to train on GPU or are running it on your computer, you can ignore the last two lines

Downloading the Dataset

!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
-!unzip fake_or_real_news.csv.zip
-

Model Creation

import turicreate as tc
-tc.config.set_num_gpus(-1) # If you do not wish to use GPUs, set it to 0
-
dataSFrame = tc.SFrame('fake_or_real_news.csv')
-

The dataset contains a column named "X1", which is of no use to us. Therefore, we simply drop it

dataSFrame.remove_column('X1')
-

Splitting Dataset

train, test = dataSFrame.random_split(.9)
-

Training

model = tc.text_classifier.create(
-    dataset=train,
-    target='label',
-    features=['title','text']
-)
-
+-----------+----------+-----------+--------------+-------------------+---------------------+
-| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| 0         | 2        | 1.000000  | 1.156349     | 0.889680          | 0.790036            |
-| 1         | 4        | 1.000000  | 1.359196     | 0.985952          | 0.918149            |
-| 2         | 6        | 0.820091  | 1.557205     | 0.990260          | 0.914591            |
-| 3         | 7        | 1.000000  | 1.684872     | 0.998689          | 0.925267            |
-| 4         | 8        | 1.000000  | 1.814194     | 0.999063          | 0.925267            |
-| 9         | 14       | 1.000000  | 2.507072     | 1.000000          | 0.911032            |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-

Testing the Model

est_predictions = model.predict(test)
-accuracy = tc.evaluation.accuracy(test['label'], test_predictions)
-print(f'Topic classifier model has a testing accuracy of {accuracy*100}% ', flush=True)
-
Topic classifier model has a testing accuracy of 92.3076923076923%
-

We have just created our own Fake News Detection Model which has an accuracy of 92%!

example_text = {"title": ["Middling ‘Rise Of Skywalker’ Review Leaves Fan On Fence About Whether To Threaten To Kill Critic"], "text": ["Expressing ambivalence toward the relatively balanced appraisal of the film, Star Wars fan Miles Ariely admitted Thursday that an online publication’s middling review of The Rise Of Skywalker had left him on the fence about whether he would still threaten to kill the critic who wrote it. “I’m really of two minds about this, because on the one hand, he said the new movie fails to live up to the original trilogy, which makes me at least want to throw a brick through his window with a note telling him to watch his back,” said Ariely, confirming he had already drafted an eight-page-long death threat to Stan Corimer of the website Screen-On Time, but had not yet decided whether to post it to the reviewer’s Facebook page. “On the other hand, though, he commended J.J. Abrams’ skillful pacing and faithfulness to George Lucas’ vision, which makes me wonder if I should just call the whole thing off. Now, I really don’t feel like camping outside his house for hours. Maybe I could go with a response that’s somewhere in between, like, threatening to kill his dog but not everyone in his whole family? I don’t know. This is a tough one.” At press time, sources reported that Ariely had resolved to wear his Ewok costume while he murdered the critic in his sleep."]}
-example_prediction = model.classify(tc.SFrame(example_text))
-print(example_prediction, flush=True)
-
+-------+--------------------+
-| class |    probability     |
-+-------+--------------------+
-|  FAKE | 0.9245648658345308 |
-+-------+--------------------+
-[1 rows x 2 columns]
-

Exporting the Model

model_name = 'FakeNews'
-coreml_model_name = model_name + '.mlmodel'
-exportedModel = model.export_coreml(coreml_model_name)
-

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

Link to Colab Notebook

Building the App using SwiftUI

Initial Setup

First we create a single view app (make sure you check the use SwiftUI button)

Then we copy our .mlmodel file to our project (Just drag and drop the file in the XCode Files Sidebar)

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

We define our bag of words function

func bow(text: String) -> [String: Double] {
-        var bagOfWords = [String: Double]()
-        
-        let tagger = NSLinguisticTagger(tagSchemes: [.tokenType], options: 0)
-        let range = NSRange(location: 0, length: text.utf16.count)
-        let options: NSLinguisticTagger.Options = [.omitPunctuation, .omitWhitespace]
-        tagger.string = text
-        
-        tagger.enumerateTags(in: range, unit: .word, scheme: .tokenType, options: options) { _, tokenRange, _ in
-            let word = (text as NSString).substring(with: tokenRange)
-            if bagOfWords[word] != nil {
-                bagOfWords[word]! += 1
-            } else {
-                bagOfWords[word] = 1
-            }
-        }
-        
-        return bagOfWords
-    }
-

We also declare our variables

@State private var title: String = ""
-@State private var headline: String = ""
-@State private var alertTitle = ""
-@State private var alertText = ""
-@State private var showingAlert = false
-

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

Complete Code

import SwiftUI
-
-struct ContentView: View {
-    @State private var title: String = ""
-    @State private var headline: String = ""
-    
-    @State private var alertTitle = ""
-    @State private var alertText = ""
-    @State private var showingAlert = false
-    
-    var body: some View {
-        NavigationView {
-            VStack(alignment: .leading) {
-                Text("Headline").font(.headline)
-                TextField("Please Enter Headline", text: $title)
-                    .lineLimit(nil)
-                Text("Body").font(.headline)
-                TextField("Please Enter the content", text: $headline)
-                .lineLimit(nil)
-            }
-                .navigationBarTitle("Fake News Checker")
-            .navigationBarItems(trailing:
-                Button(action: classifyFakeNews) {
-                    Text("Check")
-                })
-            .padding()
-                .alert(isPresented: $showingAlert){
-                    Alert(title: Text(alertTitle), message: Text(alertText), dismissButton: .default(Text("OK")))
-            }
-        }
-        
-    }
-    
-    func classifyFakeNews(){
-        let model = FakeNews()
-        let myTitle = bow(text: title)
-        let myText = bow(text: headline)
-        do {
-            let prediction = try model.prediction(title: myTitle, text: myText)
-            alertTitle = prediction.label
-            alertText = "It is likely that this piece of news is \(prediction.label.lowercased())."
-            print(alertText)
-        } catch {
-            alertTitle = "Error"
-            alertText = "Sorry, could not classify if the input news was fake or not."
-        }
-        
-        showingAlert = true
-    }
-    func bow(text: String) -> [String: Double] {
-        var bagOfWords = [String: Double]()
-        
-        let tagger = NSLinguisticTagger(tagSchemes: [.tokenType], options: 0)
-        let range = NSRange(location: 0, length: text.utf16.count)
-        let options: NSLinguisticTagger.Options = [.omitPunctuation, .omitWhitespace]
-        tagger.string = text
-        
-        tagger.enumerateTags(in: range, unit: .word, scheme: .tokenType, options: options) { _, tokenRange, _ in
-            let word = (text as NSString).substring(with: tokenRange)
-            if bagOfWords[word] != nil {
-                bagOfWords[word]! += 1
-            } else {
-                bagOfWords[word] = 1
-            }
-        }
-        
-        return bagOfWords
-    }
-}
-
-struct ContentView_Previews: PreviewProvider {
-    static var previews: some View {
-        ContentView()
-    }
-}
-
Tagged with:
\ No newline at end of file diff --git a/posts/2020-01-14-Converting-between-PIL-NumPy/index 2.html b/posts/2020-01-14-Converting-between-PIL-NumPy/index 2.html deleted file mode 100644 index d995c53..0000000 --- a/posts/2020-01-14-Converting-between-PIL-NumPy/index 2.html +++ /dev/null @@ -1,15 +0,0 @@ -Converting between image and NumPy array | Navan Chauhan
Navan Chauhan
1 minute readCreated on January 14, 2020Last modified on June 1, 2020

Converting between image and NumPy array

import numpy
-import PIL
-
-# Convert PIL Image to NumPy array
-img = PIL.Image.open("foo.jpg")
-arr = numpy.array(img)
-
-# Convert array to Image
-img = PIL.Image.fromarray(arr)
-

Saving an Image

try:
-    img.save(destination, "JPEG", quality=80, optimize=True, progressive=True)
-except IOError:
-    PIL.ImageFile.MAXBLOCK = img.size[0] * img.size[1]
-    img.save(destination, "JPEG", quality=80, optimize=True, progressive=True)
-
Tagged with:
\ No newline at end of file diff --git a/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html b/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html deleted file mode 100644 index fc48793..0000000 --- a/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html +++ /dev/null @@ -1,5 +0,0 @@ -Setting up Kaggle to use with Google Colab | Navan Chauhan
Navan Chauhan
1 minute readCreated on January 15, 2020Last modified on June 1, 2020

Setting up Kaggle to use with Google Colab

In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)

Grabbing Our Tokens

Go to Kaggle

Click on your User Profile and Click on My Account

Scroll Down untill you see Create New API Token

This will download your token as a JSON file

Copy the File to the root folder of your Google Drive

Setting up Colab

Mounting Google Drive

import os
-from google.colab import drive
-drive.mount('/content/drive')
-

After this click on the URL in the output section, login and then paste the Auth Code

Configuring Kaggle

os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
-

Voila! You can now download kaggel datasets

Tagged with:
\ No newline at end of file diff --git a/posts/2020-01-16-Image-Classifier-Using-Turicreate/index 2.html b/posts/2020-01-16-Image-Classifier-Using-Turicreate/index 2.html deleted file mode 100644 index ad7bcb8..0000000 --- a/posts/2020-01-16-Image-Classifier-Using-Turicreate/index 2.html +++ /dev/null @@ -1,189 +0,0 @@ -Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire | Navan Chauhan
Navan Chauhan
6 minute readCreated on January 16, 2020Last modified on June 1, 2020

Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire

For setting up Kaggle with Google Colab, please refer to my previous post

Dataset

Mounting Google Drive

import os
-from google.colab import drive
-drive.mount('/content/drive')
-

Downloading Dataset from Kaggle

os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
-!kaggle datasets download ashutosh69/fire-and-smoke-dataset
-!unzip "fire-and-smoke-dataset.zip"
-

Pre-Processing

!mkdir default smoke fire
-


!ls data/data/img_data/train/default/*.jpg
-


img_1002.jpg   img_20.jpg     img_519.jpg     img_604.jpg       img_80.jpg
-img_1003.jpg   img_21.jpg     img_51.jpg     img_60.jpg       img_8.jpg
-img_1007.jpg   img_22.jpg     img_520.jpg     img_61.jpg       img_900.jpg
-img_100.jpg    img_23.jpg     img_521.jpg    'img_62 (2).jpg'   img_920.jpg
-img_1014.jpg   img_24.jpg    'img_52 (2).jpg'     img_62.jpg       img_921.jpg
-img_1018.jpg   img_29.jpg     img_522.jpg    'img_63 (2).jpg'   img_922.jpg
-img_101.jpg    img_3000.jpg   img_523.jpg     img_63.jpg       img_923.jpg
-img_1027.jpg   img_335.jpg    img_524.jpg     img_66.jpg       img_924.jpg
-img_102.jpg    img_336.jpg    img_52.jpg     img_67.jpg       img_925.jpg
-img_1042.jpg   img_337.jpg    img_530.jpg     img_68.jpg       img_926.jpg
-img_1043.jpg   img_338.jpg    img_531.jpg     img_700.jpg       img_927.jpg
-img_1046.jpg   img_339.jpg   'img_53 (2).jpg'     img_701.jpg       img_928.jpg
-img_1052.jpg   img_340.jpg    img_532.jpg     img_702.jpg       img_929.jpg
-img_107.jpg    img_341.jpg    img_533.jpg     img_703.jpg       img_930.jpg
-img_108.jpg    img_3.jpg      img_537.jpg     img_704.jpg       img_931.jpg
-img_109.jpg    img_400.jpg    img_538.jpg     img_705.jpg       img_932.jpg
-img_10.jpg     img_471.jpg    img_539.jpg     img_706.jpg       img_933.jpg
-img_118.jpg    img_472.jpg    img_53.jpg     img_707.jpg       img_934.jpg
-img_12.jpg     img_473.jpg    img_540.jpg     img_708.jpg       img_935.jpg
-img_14.jpg     img_488.jpg    img_541.jpg     img_709.jpg       img_938.jpg
-img_15.jpg     img_489.jpg   'img_54 (2).jpg'     img_70.jpg       img_958.jpg
-img_16.jpg     img_490.jpg    img_542.jpg     img_710.jpg       img_971.jpg
-img_17.jpg     img_491.jpg    img_543.jpg    'img_71 (2).jpg'   img_972.jpg
-img_18.jpg     img_492.jpg    img_54.jpg     img_71.jpg       img_973.jpg
-img_19.jpg     img_493.jpg   'img_55 (2).jpg'     img_72.jpg       img_974.jpg
-img_1.jpg      img_494.jpg    img_55.jpg     img_73.jpg       img_975.jpg
-img_200.jpg    img_495.jpg    img_56.jpg     img_74.jpg       img_980.jpg
-img_201.jpg    img_496.jpg    img_57.jpg     img_75.jpg       img_988.jpg
-img_202.jpg    img_497.jpg    img_58.jpg     img_76.jpg       img_9.jpg
-img_203.jpg    img_4.jpg      img_59.jpg     img_77.jpg
-img_204.jpg    img_501.jpg    img_601.jpg     img_78.jpg
-img_205.jpg    img_502.jpg    img_602.jpg     img_79.jpg
-img_206.jpg    img_50.jpg     img_603.jpg     img_7.jpg
-

The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate

from PIL import Image
-import glob
-
-
-folders = ["default","smoke","fire"]
-for folder in folders:
-  n = 1
-  for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
-    im = Image.open(file)
-    rgb_im = im.convert('RGB')
-    rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
-    n +=1 
-  for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
-    im = Image.open(file)
-    rgb_im = im.convert('RGB')
-    rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
-    n +=1
-


!mkdir train
-!mv default ./train
-!mv smoke ./train
-!mv fire ./train
-

Making the Image Classifier

Making an SFrame

!pip install turicreate
-


import turicreate as tc
-import os
-
-data = tc.image_analysis.load_images("./train", with_path=True)
-
-data["label"] = data["path"].apply(lambda path: os.path.basename(os.path.dirname(path)))
-
-print(data)
-
-data.save('fire-smoke.sframe')
-


+-------------------------+------------------------+
-|           path          |         image          |
-+-------------------------+------------------------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 |
-|  ./train/default/10.jpg | Height: 224 Width: 224 |
-| ./train/default/100.jpg | Height: 224 Width: 224 |
-| ./train/default/101.jpg | Height: 224 Width: 224 |
-| ./train/default/102.jpg | Height: 224 Width: 224 |
-| ./train/default/103.jpg | Height: 224 Width: 224 |
-| ./train/default/104.jpg | Height: 224 Width: 224 |
-| ./train/default/105.jpg | Height: 224 Width: 224 |
-| ./train/default/106.jpg | Height: 224 Width: 224 |
-| ./train/default/107.jpg | Height: 224 Width: 224 |
-+-------------------------+------------------------+
-[2028 rows x 2 columns]
-Note: Only the head of the SFrame is printed.
-You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
-+-------------------------+------------------------+---------+
-|           path          |         image          |  label  |
-+-------------------------+------------------------+---------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
-|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
-| ./train/default/100.jpg | Height: 224 Width: 224 | default |
-| ./train/default/101.jpg | Height: 224 Width: 224 | default |
-| ./train/default/102.jpg | Height: 224 Width: 224 | default |
-| ./train/default/103.jpg | Height: 224 Width: 224 | default |
-| ./train/default/104.jpg | Height: 224 Width: 224 | default |
-| ./train/default/105.jpg | Height: 224 Width: 224 | default |
-| ./train/default/106.jpg | Height: 224 Width: 224 | default |
-| ./train/default/107.jpg | Height: 224 Width: 224 | default |
-+-------------------------+------------------------+---------+
-[2028 rows x 3 columns]
-Note: Only the head of the SFrame is printed.
-You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
-

Making the Model

import turicreate as tc
-
-# Load the data
-data =  tc.SFrame('fire-smoke.sframe')
-
-# Make a train-test split
-train_data, test_data = data.random_split(0.8)
-
-# Create the model
-model = tc.image_classifier.create(train_data, target='label')
-
-# Save predictions to an SArray
-predictions = model.predict(test_data)
-
-# Evaluate the model and print the results
-metrics = model.evaluate(test_data)
-print(metrics['accuracy'])
-
-# Save the model for later use in Turi Create
-model.save('fire-smoke.model')
-
-# Export for use in Core ML
-model.export_coreml('fire-smoke.mlmodel')
-


Performing feature extraction on resized images...
-Completed   64/1633
-Completed  128/1633
-Completed  192/1633
-Completed  256/1633
-Completed  320/1633
-Completed  384/1633
-Completed  448/1633
-Completed  512/1633
-Completed  576/1633
-Completed  640/1633
-Completed  704/1633
-Completed  768/1633
-Completed  832/1633
-Completed  896/1633
-Completed  960/1633
-Completed 1024/1633
-Completed 1088/1633
-Completed 1152/1633
-Completed 1216/1633
-Completed 1280/1633
-Completed 1344/1633
-Completed 1408/1633
-Completed 1472/1633
-Completed 1536/1633
-Completed 1600/1633
-Completed 1633/1633
-PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
-          You can set ``validation_set=None`` to disable validation tracking.
-
-Logistic regression:
---------------------------------------------------------
-Number of examples          : 1551
-Number of classes           : 3
-Number of feature columns   : 1
-Number of unpacked features : 2048
-Number of coefficients      : 4098
-Starting L-BFGS
---------------------------------------------------------
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
-| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
-| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
-| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
-| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
-| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-Performing feature extraction on resized images...
-Completed  64/395
-Completed 128/395
-Completed 192/395
-Completed 256/395
-Completed 320/395
-Completed 384/395
-Completed 395/395
-0.9316455696202531
-

We just got an accuracy of 94% on Training Data and 97% on Validation Data!

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\ No newline at end of file diff --git a/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html b/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html deleted file mode 100644 index c926f0b..0000000 --- a/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html +++ /dev/null @@ -1 +0,0 @@ -How to setup Bluetooth on a Raspberry Pi | Navan Chauhan
Navan Chauhan
1 minute readCreated on January 19, 2020Last modified on June 1, 2020

How to setup Bluetooth on a Raspberry Pi

This was tested on a Raspberry Pi Zero W

Enter in the Bluetooth Mode

pi@raspberrypi:~ $ bluetoothctl

[bluetooth]# agent on

[bluetooth]# default-agent

[bluetooth]# scan on

To Pair

While being in bluetooth mode

[bluetooth]# pair XX:XX:XX:XX:XX:XX

To Exit out of bluetoothctl anytime, just type exit

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\ No newline at end of file diff --git a/posts/2020-03-03-Playing-With-Android-TV/index 2.html b/posts/2020-03-03-Playing-With-Android-TV/index 2.html deleted file mode 100644 index 9a6606b..0000000 --- a/posts/2020-03-03-Playing-With-Android-TV/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Tinkering with an Android TV | Navan Chauhan
Navan Chauhan
1 minute readCreated on March 3, 2020Last modified on June 1, 2020

Tinkering with an Android TV

So I have an Android TV, this posts covers everything I have tried on it

Contents

  1. Getting TV's IP Address
  2. Enable Developer Settings
  3. Enable ADB
  4. Connect ADB
  5. Manipulating Packages

IP-Address

These steps should be similar for all Android-TVs

  • Go To Settings
  • Go to Network
  • Advanced Settings
  • Network Status
  • Note Down IP-Address

The other option is to go to your router's server page and get connected devices

Developer-Settings

  • Go To Settings
  • About
  • Continously click on the "Build" option until it says "You are a Developer"

Enable-ADB

  • Go to Settings
  • Go to Developer Options
  • Scroll untill you find ADB Debugging and enable that option

Connect-ADB

  • Open Terminal (Make sure you have ADB installed)
  • Enter the following command adb connect <IP_ADDRESS>
  • To test the connection run adb logcat

Manipulating Apps / Packages

Listing Packages

  • adb shell
  • pm list packages

Installing Packages

  • adb install -r package.apk

Uninstalling Packages

  • adb uninstall com.company.yourpackagename
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\ No newline at end of file diff --git a/posts/2020-03-08-Making-Vaporwave-Track/index 2.html b/posts/2020-03-08-Making-Vaporwave-Track/index 2.html deleted file mode 100644 index 6d4da24..0000000 --- a/posts/2020-03-08-Making-Vaporwave-Track/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Making My First Vaporwave Track (Remix) | Navan Chauhan
Navan Chauhan
2 minute readCreated on March 8, 2020Last modified on June 1, 2020

Making My First Vaporwave Track (Remix)

I finally completed my first quick and dirty vaporwave remix of "I Want It That Way" by the Backstreet Boys

V A P O R W A V E

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

How to Vaporwave

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

  • Take a 1980s RnB song
  • Slow it down
  • Add Bass and Trebble
  • Add again
  • Add Reverb ( make sure its wet )

There you have your very own Vaporwave track.

( Now, there are some tracks being produced which are not remixes and are original )

My Remix

Where is the Programming?

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)

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\ No newline at end of file diff --git a/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS/index 3.html b/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS/index 3.html deleted file mode 100644 index 6aaf427..0000000 --- a/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS/index 3.html +++ /dev/null @@ -1,16 +0,0 @@ -Fixing X11 Error on macOS Catalina for AmberTools 18/19 | Navan Chauhan
Navan Chauhan
2 minute readCreated on April 13, 2020Last modified on June 1, 2020

Fixing X11 Error on macOS Catalina for AmberTools 18/19

I was trying to install AmberTools on my macOS Catalina Installation. Running ./configure -macAccelerate clang gave me an error that it could not find X11 libraries, even though locate libXt showed that my installation was correct.

Error:

Could not find the X11 libraries; you may need to edit config.h
-   to set the XHOME and XLIBS variables.
-Error: The X11 libraries are not in the usual location !
-       To search for them try the command: locate libXt
-       On new Fedora OS's install the libXt-devel libXext-devel
-       libX11-devel libICE-devel libSM-devel packages.
-       On old Fedora OS's install the xorg-x11-devel package.
-       On RedHat OS's install the XFree86-devel package.
-       On Ubuntu OS's install the xorg-dev and xserver-xorg packages.
-
-          ...more info for various linuxes at ambermd.org/ubuntu.html
-
-       To build Amber without XLEaP, re-run configure with '-noX11:
-            ./configure -noX11 --with-python /usr/local/bin/python3 -macAccelerate clang
-Configure failed due to the errors above!
-

I searcehd on Google for a solution on their, sadly there was not even a single thread which had a solution about this error.

The Fix

Simply reinstalling XQuartz using homebrew fixed the error brew cask reinstall xquartz

If you do not have xquartz installed, you need to run brew cask install xquartz

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\ No newline at end of file diff --git a/posts/2020-05-31-compiling-open-babel-on-ios/index 3.html b/posts/2020-05-31-compiling-open-babel-on-ios/index 3.html deleted file mode 100644 index cefb9d1..0000000 --- a/posts/2020-05-31-compiling-open-babel-on-ios/index 3.html +++ /dev/null @@ -1,44 +0,0 @@ -Compiling Open Babel on iOS | Navan Chauhan
Navan Chauhan
5 minute readCreated on May 31, 2020Last modified on June 25, 2020

Compiling Open Babel on iOS

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.

I am running the Checkr1n jailbreak on my iPad and the Unc0ver jailbreak on my phone.

But Why?

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.

Let's Go!

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 cmake to build and the make to install.

It is 11 AM in the morning. I install clang, cmake and make from the Sam Bingner's repository, fired up ssh, downloaded the source code and ran the build command.`clang

Fail No. 1

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 SDKs to my iPad

scp -r /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk root@192.168.1.8:/var/sdks/
-

Them I told cmake that this is the location for my SDK 😠. Succesful! Now I just needed to use make.

Fail No. 2

It was giving the error that thread-local-storage was not supported on this device.

[  0%] Building CXX object src/CMakeFiles/openbabel.dir/alias.cpp.o
-[  1%] 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 for the current target
-THREAD_LOCAL OB_EXTERN OBAtomTyper      atomtyper;
-^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-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 for the current target
-THREAD_LOCAL OB_EXTERN OBAromaticTyper  aromtyper;
-^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-/var/root/obabel/ob-src/src/atom.cpp:107:10: error: thread-local storage is not supported for the current target
-  extern THREAD_LOCAL OBAromaticTyper  aromtyper;
-         ^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-/var/root/obabel/ob-src/src/atom.cpp:108:10: error: thread-local storage is not supported for the current target
-  extern THREAD_LOCAL OBAtomTyper      atomtyper;
-         ^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-/var/root/obabel/ob-src/src/atom.cpp:109:10: error: thread-local storage is not supported for the current target
-  extern THREAD_LOCAL OBPhModel        phmodel;
-         ^
-/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
-#  define THREAD_LOCAL thread_local
-                       ^
-5 errors generated.
-make[2]: *** [src/CMakeFiles/openbabel.dir/build.make:76: src/CMakeFiles/openbabel.dir/atom.cpp.o] Error 1
-make[1]: *** [CMakeFiles/Makefile2:1085: src/CMakeFiles/openbabel.dir/all] Error 2
-make: *** [Makefile:129: all] Error 2
-

Strange but it is alright, there is nothing that hasn't been answered on the internet.

I did a little digging around and could not find a solution 😔

As a temporary fix, I disabled multithreading by going and commenting the lines in the source code.

Packaging as a deb

This was pretty straight forward, I tried installing it on my iPad and it was working pretty smoothly.

Moment of Truth

So I airdropped the .deb to my phone and tried installing it, the installation was succesful but when I tried obabel it just abborted.

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: install_name_tool -change /var/root/obabel/ob-build/lib/libopenbabel.7.dylib /usr/lib/libopenbabel.7.dylib for all the executables and then signed them using jtool

I then installed it and everything went smoothly, I even ran obabel and it executed perfectly, showing the version number 3.1.0 ✌️ Ahh, smooth victory.

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!

I spent 2 hours around this problem, only to see the documentation and relaise I hadn't setup the environment variable 🤦‍♂️

The Final Fix ( For Now )

export BABEL_DATADIR="/usr/share/openbabel/3.1.0"
-export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"
-

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? 🤔 Maybe.

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.

Hopefully, I add some more screenshots to this post.

Edit 1: Added Screenshots, had to replicate the errors.

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\ No newline at end of file diff --git a/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL/index 3.html b/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL/index 3.html deleted file mode 100644 index b5ac7cb..0000000 --- a/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL/index 3.html +++ /dev/null @@ -1,8 +0,0 @@ -Workflow for Lightning Fast Molecular Docking Part One | Navan Chauhan
Navan Chauhan
2 minute readCreated on June 1, 2020Last modified on June 2, 2020

Workflow for Lightning Fast Molecular Docking Part One

My Setup

  • macOS Catalina ( RIP 32bit app)
  • PyMOL
  • AutoDock Vina
  • Open Babel

One Command Docking

obabel -:"$(pbpaste)" --gen3d -opdbqt -Otest.pdbqt && vina --receptor lu.pdbqt --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3  --ligand test.pdbqt
-

To run this command you simple copy the SMILES structure of the ligand you want an it automatically takes it from your clipboard, generates the 3D structure in the AutoDock PDBQT format using Open Babel and then docks it with your receptor using AutoDock Vina, all with just one command.

Let me break down the commands

obabel -:"$(pbpaste)" --gen3d -opdbqt -Otest.pdbqt
-

pbpaste and pbcopy are macOS commands for pasting and copying from and to the clipboard. Linux users may install the xclip and xsel packages from their respective package managers and then insert these aliases into their bash_profile, zshrc e.t.c

alias pbcopy='xclip -selection clipboard'
-alias pbpaste='xclip -selection clipboard -o'
-
$(pbpaste)
-

This is used in bash to evaluate the results of a command. In this scenario we are using it to get the contents of the clipboard.

The rest of the command is a normal Open Babel command to generate a 3D structure in PDBQT format and then save it as test.pdbqt

&&
-

This tells the termianl to only run the next part if the previous command runs succesfuly without any errors.

vina --receptor lu.pdbqt --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3  --ligand test.pdbqt
-

This is just the docking command for AutoDock Vina. In the next part I will tell how to use PyMOL and a plugin to directly generate the coordinates in Vina format --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3 without needing to type them manually.

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\ No newline at end of file diff --git a/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS/index 2.html b/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS/index 2.html deleted file mode 100644 index e3a9e46..0000000 --- a/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS/index 2.html +++ /dev/null @@ -1,29 +0,0 @@ -Compiling AutoDock Vina on iOS | Navan Chauhan
Navan Chauhan
3 minute readCreated on June 2, 2020

Compiling AutoDock Vina on iOS

Why? Because I can.

Installing makedepend

makedepend is a Unix tool used to generate dependencies of C source files. Most modern programes do not use this anymore, but then again AutoDock Vina's source code hasn't been changed since 2011. The first hurdle came when I saw that there was no makedepend command, neither was there any package on any development repository for iOS. So, I tracked down the original source code for makedepend (https://github.com/DerellLicht/makedepend). According to the repository this is actually the source code for the makedepend utility that came with some XWindows distribution back around Y2K. I am pretty sure there is a problem with my current compiler configuration because I had to manually edit the Makefile to provide the path to the iOS SDKs using the -isysroot flag.

Editting the Makefile

Original Makefile ( I used the provided mac Makefile base )

BASE=/usr/local
-BOOST_VERSION=1_41
-BOOST_INCLUDE = $(BASE)/include
-C_PLATFORM=-arch i386 -arch ppc -isysroot /Developer/SDKs/MacOSX10.5.sdk -mmacosx-version-min=10.4
-GPP=/usr/bin/g++
-C_OPTIONS= -O3 -DNDEBUG
-BOOST_LIB_VERSION=
-
-include ../../makefile_common
-

I installed Boost 1.68.0-1 from Sam Bingner's repository. ( Otherwise I would have had to compile boost too 😫 )

Editted Makefile

BASE=/usr
-BOOST_VERSION=1_68
-BOOST_INCLUDE = $(BASE)/include
-C_PLATFORM=-arch arm64 -isysroot /var/sdks/Latest.sdk
-GPP=/usr/bin/g++
-C_OPTIONS= -O3 -DNDEBUG
-BOOST_LIB_VERSION=
-
-include ../../makefile_common
-

Updating the Source Code

Of course since Boost 1.41 many things have been added and deprecated, that is why I had to edit the source code to make it work with version 1.68

Error 1 - No Matching Constructor

../../../src/main/main.cpp:50:9: error: no matching constructor for initialization of 'path' (aka 'boost::filesystem::path')
-return path(str, boost::filesystem::native);
-

This was an easy fix, I just commented this and added a return statement to return the path

return path(str)
-

Error 2 - No Member Named 'nativefilestring'

../../../src/main/main.cpp:665:57: error: no member named 'native_file_string' in 'boost::filesystem::path'
-                std::cerr << "\n\nError: could not open \"" << e.name.native_file_string() << "\" for " << (e.in ? "reading" : "writing") << ".\n";
-                                                               ~~~~~~ ^
-../../../src/main/main.cpp:677:80: error: no member named 'native_file_string' in 'boost::filesystem::path'
-                std::cerr << "\n\nParse error on line " << e.line << " in file \"" << e.file.native_file_string() << "\": " << e.reason << '\n';
-                                                                                      ~~~~~~ ^
-2 errors generated.
-

Turns out native_file_string was deprecated in Boost 1.57 and replaced with just string

Error 3 - Library Not Found

This one still boggles me because there was no reason for it to not work, as a workaround I downloaded the DEB, extracted it and used that path for compiling.

Error 4 - No Member Named 'nativefilestring' Again.

But, this time in another file and I quickle fixed it

Moment of Truth

Obviously it was working on my iPad, but would it work on another device? I transfered the compiled binary and

The package is available on my repository and only depends on boost. ( Both, Vina and Vina-Split are part of the package)

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\ No newline at end of file diff --git a/posts/2020-07-01-Install-rdkit-colab/index.html b/posts/2020-07-01-Install-rdkit-colab/index.html index 4421a1f..7317c33 100644 --- a/posts/2020-07-01-Install-rdkit-colab/index.html +++ b/posts/2020-07-01-Install-rdkit-colab/index.html @@ -1,4 +1,4 @@ -Navan Chauhan
Navan Chauhan
2 minute readCreated on July 1, 2020Last modified on August 1, 2020

RDKit is one of the most integral part of any Cheminfomatic specialist's toolkit but it is notoriously difficult to install unless you already have conda installed. I originally found this in a GitHub Gist but I have not been able to found that gist again :/

Just copy and paste this in a Colab cell and it will install it 👍

import sys
+Installing RDKit on Google Colab | Navan Chauhan
Navan Chauhan
2 minute readCreated on July 1, 2020Last modified on August 1, 2020
Installing RDKit on Google Colab
RDKit is one of the most integral part of any Cheminfomatic specialist's toolkit but it is notoriously difficult to install unless you already have conda installed. I originally found this in a GitHub Gist but I have not been able to found that gist again :/
Just copy and paste this in a Colab cell and it will install it 👍
import sys
 import os
 import requests
 import subprocess
diff --git a/posts/2020-08-01-Natural-Feature-Tracking-ARJS/index.html b/posts/2020-08-01-Natural-Feature-Tracking-ARJS/index.html
index 8a62169..9623ffc 100644
--- a/posts/2020-08-01-Natural-Feature-Tracking-ARJS/index.html
+++ b/posts/2020-08-01-Natural-Feature-Tracking-ARJS/index.html
@@ -425,6 +425,6 @@ Serving HTTP on 0.0.0.0 port 8000
         <a-torus-knot radius='0.26' radius-tubular='0.05' ></a-torus-knot>
     </ a-box>
 </ a-nft>
-
Where are the GIFs?
Now that we know how to place a box in the scene and add a torus knot in it, what do we do next? We bring the classic internet back!
AFrame GIF Shader is a gif shader for A-Frame created by mayognaise.
First things first
Add <script src="https://rawgit.com/mayognaise/aframe-gif-shader/master/dist/aframe-gif-shader.min.js"></script>  to <head>
🎦
Change the box's material to add the GIF shader
...
+
Where are the GIFs?
Now that we know how to place a box in the scene and add a torus knot in it, what do we do next? We bring the classic internet back!
AFrame GIF Shader is a gif shader for A-Frame created by mayognaise.
First things first
Add <script src="https://rawgit.com/mayognaise/aframe-gif-shader/master/dist/aframe-gif-shader.min.js"></script>  to <head>
Change the box's material to add the GIF shader
...
 <a-box position='100 0.5 -180' material="shader:gif;src:url(https://media.tenor.com/images/412b1aa9149d98d561df62db221e0789/tenor.gif);opacity:.5" .....>
-
Bonus Idea: Integrate it with GitHub's new profile Readme Feature!
1) Host the code using GitHub Pages
2) Create a new repository ( the name should be your GitHub username )
3) Add QR Code to the page and tell the users to scan your profile picture
Profit 💸
Here is a demo of me scanning a rounded version of my profile picture ( It still works! Even though the image is cropped and I haven't changed any line of code )
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\ No newline at end of file
+

Bonus Idea: Integrate it with GitHub's new profile Readme Feature!

1) Host the code using GitHub Pages

2) Create a new repository ( the name should be your GitHub username )

3) Add QR Code to the page and tell the users to scan your profile picture

??) Profit 💸

Here is a screenshot of me scanning a rounded version of my profile picture ( It still works! Even though the image is cropped and I haven't changed any line of code )

Tagged with:
\ No newline at end of file diff --git a/posts/hello-world/index 2.html b/posts/hello-world/index 2.html deleted file mode 100644 index e8f2b32..0000000 --- a/posts/hello-world/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Hello World | Navan Chauhan
Navan Chauhan
1 minute readCreated on April 16, 2019Last modified on June 1, 2020

Hello World

Why a Hello World post?

Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :)

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\ No newline at end of file diff --git a/posts/index 2.html b/posts/index 2.html deleted file mode 100644 index 52f4112..0000000 --- a/posts/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Posts | Navan Chauhan
Navan Chauhan

Posts

Tips, tricks and tutorials which I think might be useful.

\ No newline at end of file diff --git a/posts/index.html b/posts/index.html index 5d14ccd..559a02f 100644 --- a/posts/index.html +++ b/posts/index.html @@ -1 +1 @@ -Posts | Navan Chauhan
Navan Chauhan

Posts

Tips, tricks and tutorials which I think might be useful.

\ No newline at end of file +Posts | Navan Chauhan
Navan Chauhan

Posts

Tips, tricks and tutorials which I think might be useful.

\ No newline at end of file diff --git a/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 2.html b/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 2.html deleted file mode 100644 index dd9d10b..0000000 --- a/publications/2019-05-14-Detecting-Driver-Fatigue-Over-Speeding-and-Speeding-up-Post-Accident-Response/index 2.html +++ /dev/null @@ -1,3 +0,0 @@ -Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response | Navan Chauhan
Navan Chauhan
1 minute readCreated on May 14, 2019Last modified on March 14, 2020

Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response

Based on the project showcased at Toyota Hackathon, IITD - 17/18th December 2018

Edit: It seems like I haven't mentioned Adrian Rosebrock of PyImageSearch anywhere. I apologize for this mistake.

Download paper here

Recommended citation:

ATP

Chauhan, N. (2019). &quot;Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response.&quot; <i>International Research Journal of Engineering and Technology (IRJET), 6(5)</i>.
-

BibTeX

@article{chauhan_2019, title={Detecting Driver Fatigue, Over-Speeding, and Speeding up Post-Accident Response}, volume={6}, url={https://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}}
-
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\ No newline at end of file diff --git a/publications/2020-03-14-generating-vaporwave/index 2.html b/publications/2020-03-14-generating-vaporwave/index 2.html deleted file mode 100644 index db34610..0000000 --- a/publications/2020-03-14-generating-vaporwave/index 2.html +++ /dev/null @@ -1,13 +0,0 @@ -Is it possible to programmatically generate Vaporwave? | Navan Chauhan
Navan Chauhan
1 minute readCreated on March 14, 2020Last modified on March 15, 2020

Is it possible to programmatically generate Vaporwave?

This is still a pre-print.

Download paper here

Recommended citation:

APA

Chauhan, N. (2020, March 15). Is it possible to programmatically generate Vaporwave?. https://doi.org/10.35543/osf.io/9um2r
-

MLA

Chauhan, Navan. “Is It Possible to Programmatically Generate Vaporwave?.” IndiaRxiv, 15 Mar. 2020. Web.
-

Chicago

Chauhan, Navan. 2020. “Is It Possible to Programmatically Generate Vaporwave?.” IndiaRxiv. March 15. doi:10.35543/osf.io/9um2r.
-

Bibtex

@misc{chauhan_2020,
- title={Is it possible to programmatically generate Vaporwave?},
- url={indiarxiv.org/9um2r},
- DOI={10.35543/osf.io/9um2r},
- publisher={IndiaRxiv},
- author={Chauhan, Navan},
- year={2020},
- month={Mar}
-}
-
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\ No newline at end of file diff --git a/publications/2020-03-17-Possible-Drug-Candidates-COVID-19/index 2.html b/publications/2020-03-17-Possible-Drug-Candidates-COVID-19/index 2.html deleted file mode 100644 index 4a8c779..0000000 --- a/publications/2020-03-17-Possible-Drug-Candidates-COVID-19/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Possible Drug Candidates for COVID-19 | Navan Chauhan
Navan Chauhan
\ No newline at end of file diff --git a/pwabuilder-sw 2.js b/pwabuilder-sw 2.js deleted file mode 100644 index 0684da5..0000000 --- a/pwabuilder-sw 2.js +++ /dev/null @@ -1,83 +0,0 @@ -// This is the service worker with the Cache-first network - -const CACHE = "pwabuilder-precache"; -const precacheFiles = [ - /* Add an array of files to precache for your app */ -]; - -self.addEventListener("install", function (event) { - console.log("[PWA Builder] Install Event processing"); - - console.log("[PWA Builder] Skip waiting on install"); - self.skipWaiting(); - - event.waitUntil( - caches.open(CACHE).then(function (cache) { - console.log("[PWA Builder] Caching pages during install"); - return cache.addAll(precacheFiles); - }) - ); -}); - -// Allow sw to control of current page -self.addEventListener("activate", function (event) { - console.log("[PWA Builder] Claiming clients for current page"); - event.waitUntil(self.clients.claim()); -}); - -// If any fetch fails, it will look for the request in the cache and serve it from there first -self.addEventListener("fetch", function (event) { - if (event.request.method !== "GET") return; - - event.respondWith( - fromCache(event.request).then( - function (response) { - // The response was found in the cache so we responde with it and update the entry - - // This is where we call the server to get the newest version of the - // file to use the next time we show view - event.waitUntil( - fetch(event.request).then(function (response) { - return updateCache(event.request, response); - }) - ); - - return response; - }, - function () { - // The response was not found in the cache so we look for it on the server - return fetch(event.request) - .then(function (response) { - // If request was success, add or update it in the cache - event.waitUntil(updateCache(event.request, response.clone())); - - return response; - }) - .catch(function (error) { - console.log("[PWA Builder] Network request failed and no cache." + error); - }); - } - ) - ); -}); - -function fromCache(request) { - // Check to see if you have it in the cache - // Return response - // If not in the cache, then return - return caches.open(CACHE).then(function (cache) { - return cache.match(request).then(function (matching) { - if (!matching || matching.status === 404) { - return Promise.reject("no-match"); - } - - return matching; - }); - }); -} - -function updateCache(request, response) { - return caches.open(CACHE).then(function (cache) { - return cache.put(request, response); - }); -} diff --git a/pwabuilder-sw-register 2.js b/pwabuilder-sw-register 2.js deleted file mode 100644 index 8850330..0000000 --- a/pwabuilder-sw-register 2.js +++ /dev/null @@ -1,19 +0,0 @@ -// This is the service worker with the Cache-first network - -// Add this below content to your HTML page, or add the js file to your page at the very top to register service worker - -// Check compatibility for the browser we're running this in -if ("serviceWorker" in navigator) { - if (navigator.serviceWorker.controller) { - console.log("[PWA Builder] active service worker found, no need to register"); - } else { - // Register the service worker - navigator.serviceWorker - .register("/pwabuilder-sw.js", { - scope: "./" - }) - .then(function (reg) { - console.log("[PWA Builder] Service worker has been registered for scope: " + reg.scope); - }); - } -} diff --git a/styles 2.css b/styles 2.css deleted file mode 100644 index 4e658d7..0000000 --- a/styles 2.css +++ /dev/null @@ -1,401 +0,0 @@ -* { - margin: 0; - padding: 0; - box-sizing: border-box; - font-size: 16px; - -webkit-text-size-adjust: 100%; -} - -body { - background: #eee; - color: #000; - font-family: -apple-system, BlinkMacSystemFont, Helvetica, Arial; - text-align: center; -} - -.wrapper { - max-width: 900px; - margin-left: auto; - margin-right: auto; - padding: 40px; - text-align: left; -} - -header { - - background: #fff; - position: relative; - color: #ededed; - line-height: 1.5em; - padding: 0 20px; -} - -/* - -header { - background: #ededed; - margin-bottom: 0em; - padding-bottom: 2em; - left: 0px; - top: 0px; - height: 8em; - width: 100%; -} -.header-background { - background-image: url(images/logo.png); - background-size: 100% 100%; - background-repeat: no-repeat; - background-size: cover; - background-position: center; - height: 200px; -} -*/ - -header .wrapper { - padding-top: 20px; - padding-bottom: 20px; - text-align: left; -} - -header a { - text-decoration: none; -} - -header .site-name { - color: #000; - margin: 0; - cursor: pointer; - font-weight: 50; - font-size: 3.5em; /*#2.3em;*/ - line-height: 1em; - letter-spacing: 1px; -} - -nav { - /*margin-top: 0.5em;*/ - text-align: left; /* right */ -} - -nav li { - margin-top: 0.5em; - display: inline-block; - background-color: #000; - color: #ddd; - padding: 4px 6px; - border-radius: 5px; - margin-right: 5px; - -} - -nav li:hover { - color: #000; - background-color: #ddd; -} -h1 { - margin-bottom: 20px; - font-size: 2em; -} - -h2 { - margin: 20px 0; - font-size: 1.5em; -} - -p { - margin-bottom: 10px; - margin-top: 5px; -} - -a { - color: inherit; - -} - -.description { - margin-bottom: 20px; -} - -.item-list > li { - display: block; - padding: 20px; - border-radius: 20px; - background-color: #d3d3d3; - margin-bottom: 20px -} - -.item-list > li:last-child { - margin-bottom: 0; -} - -.item-list h1 { - margin-bottom: 0px; /*15px*/ - font-size: 1.3em; -} -.item-list a { - text-decoration: none; -} - -.item-list p { - margin-bottom: 0; -} - -.reading-time { - display: inline-block; - border-radius: 5px; - background-color: #ddd; - color: #000; - padding: 4px 4px; - margin-bottom: 5px; - margin-right: 5px; - -} - -.tag-list { - margin-bottom: 5px; /* 15px */ -} - -.tag-list li, -.tag { - display: inline-block; - background-color: #000; - color: #ddd; - padding: 4px 6px; - border-radius: 5px; - margin-right: 5px; - margin-top: 0.5em; -} - -.tag-list a, -.tag a { - text-decoration: none; -} - -.item-page .tag-list { - display: inline-block; -} - -.content { - margin-bottom: 40px; -} - -.browse-all { - display: block; - margin-bottom: 30px; -} - -.all-tags li { - font-size: 1.4em; - margin-right: 10px; - padding: 6px 10px; - margin-top: 1em; -} - -img { - max-width: 100%; - margin-bottom: 1em; - margin-top: 1em; - width: auto\9; - height: auto; - vertical-align: middle; - border: 0; - -ms-interpolation-mode: bicubic; -} - -footer { - color: #000; -} - - - -pre { - overflow-x: auto; - font-family: Monaco,Consolas,"Lucida Console",monospace; - display: block; - background-color: #fdf6e3; - color: #586e75; - margin-bottom: 1em; - margin-top: 1em; - border-radius: 4px; -} - -.highlight { background-color: #fdf6e3; color: #586e75; } -.highlight .c { color: #627272; } -.highlight .err { color: #586e75; } -.highlight .g { color: #586e75; } -.highlight .k { color: #677600; } -.highlight .l { color: #586e75; } -.highlight .n { color: #586e75; } -.highlight .o { color: #677600; } -.highlight .x { color: #c14715; } -.highlight .p { color: #586e75; } -.highlight .cm { color: #627272; } -.highlight .cp { color: #677600; } -.highlight .c1 { color: #627272; } -.highlight .cs { color: #677600; } -.highlight .gd { color: #217d74; } -.highlight .ge { color: #586e75; font-style: italic; } -.highlight .gr { color: #d72825; } -.highlight .gh { color: #c14715; } -.highlight .gi { color: #677600; } -.highlight .go { color: #586e75; } -.highlight .gp { color: #586e75; } -.highlight .gs { color: #586e75; font-weight: bold; } -.highlight .gu { color: #c14715; } -.highlight .gt { color: #586e75; } -.highlight .kc { color: #c14715; } -.highlight .kd { color: #1f76b6; } -.highlight .kn { color: #677600; } -.highlight .kp { color: #677600; } -.highlight .kr { color: #1f76b6; } -.highlight .kt { color: #d72825; } -.highlight .ld { color: #586e75; } -.highlight .m { color: #217d74; } -.highlight .s { color: #217d74; } -.highlight .na { color: #586e75; } -.highlight .nb { color: #8d6900; } -.highlight .nc { color: #1f76b6; } -.highlight .no { color: #c14715; } -.highlight .nd { color: #1f76b6; } -.highlight .ni { color: #c14715; } -.highlight .ne { color: #c14715; } -.highlight .nf { color: #1f76b6; } -.highlight .nl { color: #586e75; } -.highlight .nn { color: #586e75; } -.highlight .nx { color: #586e75; } -.highlight .py { color: #586e75; } -.highlight .nt { color: #1f76b6; } -.highlight .nv { color: #1f76b6; } -.highlight .ow { color: #677600; } -.highlight .w { color: #586e75; } -.highlight .mf { color: #217d74; } -.highlight .mh { color: #217d74; } -.highlight .mi { color: #217d74; } -.highlight .mo { color: #217d74; } -.highlight .sb { color: #627272; } -.highlight .sc { color: #217d74; } -.highlight .sd { color: #586e75; } -.highlight .s2 { color: #217d74; } -.highlight .se { color: #c14715; } -.highlight .sh { color: #586e75; } -.highlight .si { color: #217d74; } -.highlight .sx { color: #217d74; } -.highlight .sr { color: #d72825; } -.highlight .s1 { color: #217d74; } -.highlight .ss { color: #217d74; } -.highlight .bp { color: #1f76b6; } -.highlight .vc { color: #1f76b6; } -.highlight .vg { color: #1f76b6; } -.highlight .vi { color: #1f76b6; } -.highlight .il { color: #217d74; } - - -@media (prefers-color-scheme: dark) { - .reading-time { - background-color: #000; - color: #ddd; - } - body { - background-color: #222; - } - - body, - header .site-name { - color: #ddd; - } - nav li { - background-color: #ddd; - color: #000; - - } - nav li:hover { - color: #ddd; - background-color: #000; - } - - .item-list > li { - background-color: #333; - } - - header { - background-color: #000; - } - footer { - color: #ddd; - } - - pre { - background-color: #002b36; - color: #93a1a1; - } - - .highlight { background-color: #002b36; color: #93a1a1; } - .highlight .c { color: #759299; } - .highlight .err { color: #93a1a1; } - .highlight .g { color: #93a1a1; } - .highlight .k { color: #859900; } - .highlight .l { color: #93a1a1; } - .highlight .n { color: #93a1a1; } - .highlight .o { color: #859900; } - .highlight .x { color: #e9662f; } - .highlight .p { color: #93a1a1; } - .highlight .cm { color: #759299; } - .highlight .cp { color: #859900; } - .highlight .c1 { color: #759299; } - .highlight .cs { color: #859900; } - .highlight .gd { color: #2aa198; } - .highlight .ge { color: #93a1a1; font-style: italic; } - .highlight .gr { color: #e8625f; } - .highlight .gh { color: #e9662f; } - .highlight .gi { color: #859900; } - .highlight .go { color: #93a1a1; } - .highlight .gp { color: #93a1a1; } - .highlight .gs { color: #93a1a1; font-weight: bold; } - .highlight .gu { color: #e9662f; } - .highlight .gt { color: #93a1a1; } - .highlight .kc { color: #e9662f; } - .highlight .kd { color: #3294da; } - .highlight .kn { color: #859900; } - .highlight .kp { color: #859900; } - .highlight .kr { color: #3294da; } - .highlight .kt { color: #e8625f; } - .highlight .ld { color: #93a1a1; } - .highlight .m { color: #2aa198; } - .highlight .s { color: #2aa198; } - .highlight .na { color: #93a1a1; } - .highlight .nb { color: #B58900; } - .highlight .nc { color: #3294da; } - .highlight .no { color: #e9662f; } - .highlight .nd { color: #3294da; } - .highlight .ni { color: #e9662f; } - .highlight .ne { color: #e9662f; } - .highlight .nf { color: #3294da; } - .highlight .nl { color: #93a1a1; } - .highlight .nn { color: #93a1a1; } - .highlight .nx { color: #93a1a1; } - .highlight .py { color: #93a1a1; } - .highlight .nt { color: #3294da; } - .highlight .nv { color: #3294da; } - .highlight .ow { color: #859900; } - .highlight .w { color: #93a1a1; } - .highlight .mf { color: #2aa198; } - .highlight .mh { color: #2aa198; } - .highlight .mi { color: #2aa198; } - .highlight .mo { color: #2aa198; } - .highlight .sb { color: #759299; } - .highlight .sc { color: #2aa198; } - .highlight .sd { color: #93a1a1; } - .highlight .s2 { color: #2aa198; } - .highlight .se { color: #e9662f; } - .highlight .sh { color: #93a1a1; } - .highlight .si { color: #2aa198; } - .highlight .sx { color: #2aa198; } - .highlight .sr { color: #e8625f; } - .highlight .s1 { color: #2aa198; } - .highlight .ss { color: #2aa198; } - .highlight .bp { color: #3294da; } - .highlight .vc { color: #3294da; } - .highlight .vg { color: #3294da; } - .highlight .vi { color: #3294da; } - .highlight .il { color: #2aa198; } -} - diff --git a/tags/android/index 2.html b/tags/android/index 2.html deleted file mode 100644 index cc393ad..0000000 --- a/tags/android/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
Navan Chauhan

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\ No newline at end of file diff --git a/tags/anemone/index 2.html b/tags/anemone/index 2.html deleted file mode 100644 index a7a8337..0000000 --- a/tags/anemone/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
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\ No newline at end of file diff --git a/tags/autodock-vina/index 2.html b/tags/autodock-vina/index 2.html deleted file mode 100644 index 299a457..0000000 --- a/tags/autodock-vina/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
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\ No newline at end of file diff --git a/tags/cheminformatics/index 3.html b/tags/cheminformatics/index 3.html deleted file mode 100644 index 638e81f..0000000 --- a/tags/cheminformatics/index 3.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
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\ No newline at end of file diff --git a/tags/codesnippet/index 2.html b/tags/codesnippet/index 2.html deleted file mode 100644 index 7633b4f..0000000 --- a/tags/codesnippet/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
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\ No newline at end of file diff --git a/tags/colab/index 2.html b/tags/colab/index 2.html deleted file mode 100644 index c6d9464..0000000 --- a/tags/colab/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
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\ No newline at end of file diff --git a/tags/colab/index.html b/tags/colab/index.html index 4dd800f..2f0591b 100644 --- a/tags/colab/index.html +++ b/tags/colab/index.html @@ -1 +1 @@ -Navan Chauhan
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\ No newline at end of file diff --git a/tags/designing/index 2.html b/tags/designing/index 2.html deleted file mode 100644 index 6a9a5de..0000000 --- a/tags/designing/index 2.html +++ /dev/null @@ -1 +0,0 @@ -Navan Chauhan
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  • Experiments

    🕑 1 minute read. January 24, 2010

    Just a markdown file for all experiments related to the website

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