CapsNet-Keras | Keras implementation of CapsNet in NIPS2017 paper | Machine Learning library

by XifengGuo Python Version: v0.1 License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | CapsNet-Keras Summary

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

A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error ＝ 0.34%.

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Quality

Security

License

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Support

CapsNet-Keras has a medium active ecosystem.

It has 2462 star(s) with 667 fork(s). There are 98 watchers for this library.

It had no major release in the last 12 months.

There are 27 open issues and 95 have been closed. On average issues are closed in 80 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CapsNet-Keras is v0.1

Quality

CapsNet-Keras has 0 bugs and 0 code smells.

Security

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

CapsNet-Keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CapsNet-Keras 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-Keras releases are available to install and integrate.

CapsNet-Keras 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-Keras saves you 104 person hours of effort in developing the same functionality from scratch.

It has 265 lines of code, 18 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Plot training and validation loss
Construct a CapsNet
Reshape inputs
Evaluate the tensor
Squash vectors
This function manipulates the model with the given parameters
Combine generated images
Test the model
Load the MNIST dataset

Get all kandi verified functions for this library.

CapsNet-Keras Key Features

No Key Features are available at this moment for CapsNet-Keras.

CapsNet-Keras Examples and Code Snippets

CapsNet-Keras,Usage,Training

Python

Lines of Code : 3

License : Permissive (MIT)

Copy

$ git clone https://github.com/streamride/CapsNet-keras-imdb.git
$ cd CapsNet-Keras

$ python capsulenet.py

CapsNet-Keras,Usage,Testing

Python

Lines of Code : 1

License : Permissive (MIT)

Copy

$ python capsulenet.py --is_training 0 --weights result/trained_model.h5

Community Discussions

Trending Discussions on CapsNet-Keras

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

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:

...

ANSWER

Answered 2021-Mar-16 at 09:14

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

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

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

Vulnerabilities

No vulnerabilities reported

Install CapsNet-Keras

You can download it from GitHub.
You can use CapsNet-Keras 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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