CapsNet | PyTorch implementation of CapsNet | Machine Learning library

by leftthomas Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | CapsNet Summary

CapsNet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. CapsNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However CapsNet build file is not available. You can download it from GitHub.

A PyTorch implementation of CapsNet based on NIPS 2017 paper Dynamic Routing Between Capsules.

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Quality

Security

License

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Support

CapsNet has a low active ecosystem.

It has 143 star(s) with 39 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 3 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CapsNet is current.

Quality

CapsNet has 0 bugs and 0 code smells.

Security

CapsNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CapsNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CapsNet is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

CapsNet releases are not available. You will need to build from source code and install.

CapsNet has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

CapsNet saves you 86 person hours of effort in developing the same functionality from scratch.

It has 221 lines of code, 15 functions and 6 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed CapsNet and discovered the below as its top functions. This is intended to give you an instant insight into CapsNet implemented functionality, and help decide if they suit your requirements.

Log training loss
Return an iterator over MNIST dataset
Reset meter accuracy
Calculate the loss and reconstruct the model
Augmenting image
Compute logits
Squash a tensor
Augment image
Get data from MNIST dataset
Reset metrics

Get all kandi verified functions for this library.

CapsNet Key Features

No Key Features are available at this moment for CapsNet.

CapsNet Examples and Code Snippets

No Code Snippets are available at this moment for CapsNet.

Community Discussions

Trending Discussions on CapsNet

Capsule network parameters

poor accuracy of Capsule Network - mistake in the implementation?

np.random.randint causes ValueError: low >= high

InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]

QUESTION

Capsule network parameters

Asked 2022-Mar-04 at 08:54

i have found the parameters used for MNIST dataset which is as below

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ANSWER

Answered 2022-Mar-04 at 08:54

The data was audio (13,9,1) so converting it to spectrogram image and then reading it with target size (28,28) helped me workaround the issue of using capsule network for the audio dataset.

This workaround can be used if you want to go with the original hyperparameters and network designs of the capsule network with dynamic routing paper.

Source https://stackoverflow.com/questions/70916323

QUESTION

poor accuracy of Capsule Network - mistake in the implementation?

Asked 2022-Feb-17 at 14:10

I am working on a Capsule Network implementation that should be customizable. I found a code that is pretty straightforward (https://towardsdatascience.com/implementing-capsule-network-in-tensorflow-11e4cca5ecae). I used the code and changed it to my needs.

However, I the code does not score the same accuracy on a test dataset (MNIST) as other implementations and the paper "Dynamic Routing between Capsules" suggest. Is there a possible mistake in the implementation of the capsule network? The code uses tf subclassing to create the CapsNet model. Heres the class of the model:

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ANSWER

Answered 2022-Feb-17 at 14:10

While not having looked at your code in detail 1% difference is really not a lot when working with deep learning. The difference might be cause by a different (random) weight initialisation or slightly different gradients that lead to a different learning trajectory. Re-training the network might thus lead to slightly different results each time.

Source https://stackoverflow.com/questions/71159560

QUESTION

np.random.randint causes ValueError: low >= high

Asked 2021-Mar-16 at 09:14

I'm working on CapsNet from here , which is implemented on the MNIST dataset with 10 digits, but I've changed the code to work with a dataset with three classes. Model training and testing work fine, but the manipulate latent function causes an error:

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ANSWER

Answered 2021-Mar-16 at 09:14

This is because you're using sum() instead of len().

Source https://stackoverflow.com/questions/66645547

QUESTION

InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]

Asked 2021-Mar-13 at 12:27

I have a dataset with more than 4000 images and 3 classes, and I'm reusing a code for capsule neural network with 10 classes but I modified it to 3 classes, when I'm running the model the following error occurs at the last point of the first epoch (44/45):

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ANSWER

Answered 2021-Mar-13 at 12:25

Try make the X set so that the batch size perfectly fits the data i think the batch size remainder is 15 after fitting to all the data

For eg : make it a multiple of 100

Source https://stackoverflow.com/questions/66612464

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CapsNet

You can download it from GitHub.
You can use CapsNet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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