From daeb59f856e5660e4d333316c75114cb433fc15f Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Mon, 1 Jun 2020 00:41:47 +0530 Subject: Publish deploy 2020-06-01 00:41 --- assets/posts/open-babel/s1.png | Bin 0 -> 604137 bytes feed.rss | 542 ++++++++------------- index.html | 2 +- .../index.html | 43 ++ posts/index.html | 2 +- sitemap.xml | 2 +- tags/cheminformatic/index.html | 1 + tags/index.html | 2 +- tags/ios/index.html | 1 + tags/jailbreak/index.html | 2 +- 10 files changed, 250 insertions(+), 347 deletions(-) create mode 100644 assets/posts/open-babel/s1.png create mode 100644 posts/2020-05-31-compiling-open-babel-on-ios/index.html create mode 100644 tags/cheminformatic/index.html create mode 100644 tags/ios/index.html diff --git a/assets/posts/open-babel/s1.png b/assets/posts/open-babel/s1.png new file mode 100644 index 0000000..ec988a4 Binary files /dev/null and b/assets/posts/open-babel/s1.png differ diff --git a/feed.rss b/feed.rss index f808cbb..4e5aa1e 100644 --- a/feed.rss +++ b/feed.rss @@ -1,57 +1,81 @@ -Navan ChauhanWelcome to my personal fragment of the internet. Majority of the posts should be complete.https://navanchauhan.github.io/enSun, 24 May 2020 18:26:31 +0530Sun, 24 May 2020 18:26:31 +0530250https://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. β€œIs It Possible to Programmatically Generate Vaporwave?.” IndiaRxiv, 15 Mar. 2020. Web.
-

-
-

Chicago

Chauhan, Navan. 2020. β€œIs 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
+Navan ChauhanWelcome to my personal fragment of the internet. Majority of the posts should be complete.https://navanchauhan.github.io/enMon, 1 Jun 2020 00:41:37 +0530Mon, 1 Jun 2020 00:41:37 +0530250https://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 11:30:00 +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 /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk to /var/sdks/ on my iPad
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 πŸ˜”
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 with syscall 9
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 πŸ€¦β€β™‚οΈ
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? πŸ€” 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.
]]>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. β€œIs It Possible to Programmatically Generate Vaporwave?.” IndiaRxiv, 15 Mar. 2020. Web.
+
Chicago
Chauhan, Navan. 2020. β€œIs 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/"
+
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
+
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
@@ -84,49 +108,39 @@
 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
+
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)
+  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)
+  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
+

!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
+
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 = 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)
+print(data)
 
 data.save('fire-smoke.sframe')
-

-
-

+-------------------------+------------------------+
+

+-------------------------+------------------------+
 |           path          |         image          |
 +-------------------------+------------------------+
 |  ./train/default/1.jpg  | Height: 224 Width: 224 |
@@ -160,9 +174,7 @@
 [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
+
Making the Model
import turicreate as tc
 
 # Load the data
 data =  tc.SFrame('fire-smoke.sframe')
@@ -178,16 +190,14 @@
 
 # Evaluate the model and print the results
 metrics = model.evaluate(test_data)
-print(metrics['accuracy'])
+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...
+

Performing feature extraction on resized images...
 Completed   64/1633
 Completed  128/1633
 Completed  192/1633
@@ -245,17 +255,11 @@
 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
+
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
+
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
@@ -264,45 +268,27 @@
 
 # Convert array to Image
 img = PIL.Image.fromarray(arr)
-

-
-
Saving an Image
try:
-    img.save(destination, "JPEG", quality=80, optimize=True, progressive=True)
+
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
+    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"
+
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
+
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(
+
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            |
@@ -312,35 +298,23 @@
 | 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)
+
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 β€˜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. β€œI’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. β€œOn 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."]}
+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 β€˜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. β€œI’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. β€œOn 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)
-

-
-
+-------+--------------------+
+print(example_prediction, flush=True)
+
+-------+--------------------+
 | class |    probability     |
 +-------+--------------------+
 |  FAKE | 0.9245648658345308 |
 +-------+--------------------+
 [1 rows x 2 columns]
-

-
-
Exporting the Model
model_name = 'FakeNews'
+
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] {
+
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)
@@ -359,16 +333,12 @@
         
         return bagOfWords
     }
-

-
-
We also declare our variables
@State private var title: String = ""
+
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
+
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 = ""
@@ -443,33 +413,19 @@
         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
+
]]>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)
+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)
+
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  |
+
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   |
@@ -481,77 +437,55 @@
 | 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]
+
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
+
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")
+
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")
+
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
+
Model Configuration
learning_rate = 0.2
 no_of_epochs = 25000
-

-
-
Equations
deg1 = a*X + b
+
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)
+
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)
+
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:
+
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))
+        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
+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
@@ -577,9 +511,7 @@
 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 = []
+
predictions = []
 for x in abscissa:
   predictions.append((coefficient1*x + constant))
 plt.plot(abscissa , ordinate, 'ro', label ='Original data')
@@ -587,26 +519,22 @@
 plt.title('Linear Regression Result')
 plt.legend()
 plt.show()
-

-
-
Quadratic Equation
with tf.Session() as sess:
+
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))
+        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
+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
@@ -632,9 +560,7 @@
 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 = []
+
predictions = []
 for x in abscissa:
   predictions.append((coefficient1*pow(x,2) + coefficient2*x + constant))
 plt.plot(abscissa , ordinate, 'ro', label ='Original data')
@@ -642,16 +568,14 @@
 plt.title('Quadratic Regression Result')
 plt.legend()
 plt.show()
-

-
-
Cubic
with tf.Session() as sess:
+
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))
+        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)
@@ -659,10 +583,8 @@
         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
+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
@@ -688,9 +610,7 @@
 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 = []
+
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')
@@ -698,16 +618,14 @@
 plt.title('Cubic Regression Result')
 plt.legend()
 plt.show()
-

-
-
Quartic
with tf.Session() as sess:
+
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))
+        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)
@@ -716,10 +634,8 @@
         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
+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
@@ -745,9 +661,7 @@
 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 = []
+
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')
@@ -755,16 +669,14 @@
 plt.title('Quartic Regression Result')
 plt.legend()
 plt.show()
-

-
-
Quintic
with tf.Session() as sess:
+
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))
+        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)
@@ -773,9 +685,7 @@
         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 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
@@ -801,9 +711,7 @@
 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 = []
+
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')
@@ -811,49 +719,29 @@
 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
+
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
+
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 numpy as np
 import os
 import cv2
-import tensorflow as tf
+import tensorflow as tf
 from tensorflow.keras import datasets, layers, models
-import pandas as pd
-import matplotlib.pyplot as plt
+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
+
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 = []
+
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/")
@@ -865,7 +753,7 @@
         data.append(np.array(size_image))
         labels.append(0)
     except AttributeError:
-        print("")
+        print("")
 
 Uninfected = os.listdir("./cell_images/Uninfected/")
 for uninfect in Uninfected:
@@ -876,23 +764,17 @@
         data.append(np.array(size_image))
         labels.append(1)
     except AttributeError:
-        print("")
-

-
-
Splitting Data
df = np.array(data)
+        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()
+
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'))
@@ -905,17 +787,11 @@
 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",
+
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
+
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
@@ -936,55 +812,37 @@
 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
+
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(
+print(
     'Accuracy:', accuracy,
     '\nLoss:', loss,
     '\nValidation Accuracy:', val_accuracy,
     '\nValidation Loss:', val_loss
 )
-

-
-
Accuracy: 98.64532351493835 
+
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
| Name | BundleID | |-------------|----------------------| | App Store | com.apple.AppStore | | Apple Watch | com.apple.Bridge | | Calculator | com.apple.calculator | | Calendar | com.apple.mobilecal | | Camera | com.apple.camera | | Classroom | com.apple.classroom | | Clock | com.apple.mobiletimer | | Compass | com.apple.compass | | FaceTime | com.apple.facetime | | Files | com.apple.DocumentsApp | | Game Center | com.apple.gamecenter | | Health | com.apple.Health | | Home | com.apple.Home | | iBooks | com.apple.iBooks | | iTunes Store | com.apple.MobileStore | | Mail | com.apple.mobilemail | | Maps | com.apple.Maps | | Measure | com.apple.measure | | Messages | com.apple.MobileSMS | | Music | com.apple.Music | | News | com.apple.news | | Notes | com.apple.mobilenotes | | Phone | com.apple.mobilephone | | Photo Booth | com.apple.Photo-Booth | | Photos | com.apple.mobileslideshow | | Playgrounds | come.apple.Playgrounds | | Podcasts | com.apple.podcasts | | Reminders | com.apple.reminders | | Safari | com.apple.mobilesafari | | Settings | com.apple.Preferences | | Stocks | com.apple.stocks | | Tips | com.apple.tips | | TV | com.apple.tv | | Videos | com.apple.videos | | Voice Memos | com.apple.VoiceMemos | | Wallet | com.apple.Passbook | | Weather | com.apple.weather |
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 
+

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 diff --git a/index.html b/index.html index 47bb964..9da4764 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/posts/2020-05-31-compiling-open-babel-on-ios/index.html b/posts/2020-05-31-compiling-open-babel-on-ios/index.html new file mode 100644 index 0000000..cdc60f4 --- /dev/null +++ b/posts/2020-05-31-compiling-open-babel-on-ios/index.html @@ -0,0 +1,43 @@ +Compiling Open Babel on iOS | Navan Chauhan
Navan Chauhan
5 minute readCreated on May 31, 2020Last modified on June 1, 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 /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk to /var/sdks/ on my iPad

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 πŸ˜”

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 with syscall 9

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 πŸ€¦β€β™‚οΈ

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? πŸ€” 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.

Tagged with:
\ No newline at end of file diff --git a/posts/index.html b/posts/index.html index 8ff429a..49a0c6e 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.

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