CapsNet Tweak Application icon

CapsNet Tweak 1.0

24.4 MB / 1+ Downloads / Rating 5.0 - 1 reviews


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CapsNet Tweak, developed and published by First Hit Counts Dev, has released its latest version, 1.0, on 2017-12-20. This app falls under the Education category on the Google Play Store and has achieved over 50 installs. It currently holds an overall rating of 5.0, based on 1 reviews.

CapsNet Tweak APK available on this page is compatible with all Android devices that meet the required specifications (Android 5.0+). It can also be installed on PC and Mac using an Android emulator such as Bluestacks, LDPlayer, and others.

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App Screenshot

App Screenshot

App Details

Package name: fhc.tfsandbox.capsnettweak

Updated: 7 years ago

Developer Name: First Hit Counts Dev

Category: Education

App Permissions: Show more

Installation Instructions

This article outlines two straightforward methods for installing CapsNet Tweak on PC Windows and Mac.

Using BlueStacks

  1. Download the APK/XAPK file from this page.
  2. Install BlueStacks by visiting http://bluestacks.com.
  3. Open the APK/XAPK file by double-clicking it. This action will launch BlueStacks and begin the application's installation. If the APK file does not automatically open with BlueStacks, right-click on it and select 'Open with...', then navigate to BlueStacks. Alternatively, you can drag-and-drop the APK file onto the BlueStacks home screen.
  4. Wait a few seconds for the installation to complete. Once done, the installed app will appear on the BlueStacks home screen. Click its icon to start using the application.

Using LDPlayer

  1. Download and install LDPlayer from https://www.ldplayer.net.
  2. Drag the APK/XAPK file directly into LDPlayer.

If you have any questions, please don't hesitate to contact us.

Previous Versions

CapsNet Tweak 1.0
2017-12-20 / 24.4 MB / Android 5.0+

About this app

Allows you to run a part of a Capsule Network model on the device and modify the output capsule parameters to produce different images.

Could provide some insights into what was learned for each parameter.

Capsule Networks are based on Geoffrey Hinton's paper Dynamic Routing Between Capsules

I extracted the decoder after training and saved some of the digit capsule layer output to be able to run inference on it on the device (offline).

A decoder is found at the end of a Capsule Network and is used to reconstruct the source image given the capsule outputs from the digit caps layer. During training, its loss contributes to the final loss of the model and this ends up , hopefully, giving each parameter something meaningful and a lot of times an easily recognizable feature.

Implemented in Tensorflow - source: https://github.com/JsFlo/CapsNet

App Permissions

Allows an application to read from external storage.
Allows an application to write to external storage.