CapsLayer | CapsLayer : An advanced library for capsule theory | Machine Learning library

by naturomics Python Version: Current License: Apache-2.0

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

kandi X-RAY | CapsLayer Summary

CapsLayer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. CapsLayer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Capsule theory is a potential research proposed by Geoffrey E. Hinton et al, where he describes the shortcomings of the Convolutional Neural Networks and how Capsules could potentially circumvent these problems such as "pixel attack" and create more robust Neural Network Architecture based on Capsules Layer. We expect that this theory will definitely contribute to Deep Learning Industry and we are excited about it. For the same reason we are proud to introduce CapsLayer, an advanced library for the Capsule Theory, integrating capsule-relevant technologies, providing relevant analysis tools, developing related application examples, and probably most important thing: promoting the development of capsule theory. This library is based on Tensorflow and has a similar API with it but designed for capsule layers/models.

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CapsLayer has a low active ecosystem.

It has 352 star(s) with 116 fork(s). There are 44 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 22 have been closed. On average issues are closed in 155 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CapsLayer is current.

Quality

CapsLayer has 0 bugs and 0 code smells.

Security

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

CapsLayer code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CapsLayer is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

Build file is available. You can build the component from source.

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

CapsLayer saves you 893 person hours of effort in developing the same functionality from scratch.

It has 2041 lines of code, 88 functions and 44 files.

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

Top functions reviewed by kandi - BETA

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

Load train and test set
Load a cifar10 dataset
Encodes the given dataset into examples
Load cifar100 - 100 images
Create int64 feature
3d convolutional layer
EMR routing
Define routing
Evaluate a single step
Train the model
Saves the results to file
Plot the probability of an entity presence
Download and extract data
Download and unzip a URL to a zip file
1d convolutional layer
3d convolution layer
Evaluate a trained model
Start download
Load cifar - 100 images
Matrix multiplication op
A dense layer
Train the optimizer

Get all kandi verified functions for this library.

CapsLayer Key Features

No Key Features are available at this moment for CapsLayer.

CapsLayer Examples and Code Snippets

No Code Snippets are available at this moment for CapsLayer.

Community Discussions

Trending Discussions on CapsLayer

ValueError: Inconsistent shapes: saw (1152, 10, 1, 10, 16) but expected (1152, 10, 1, 16)

QUESTION

ValueError: Inconsistent shapes: saw (1152, 10, 1, 10, 16) but expected (1152, 10, 1, 16)

Asked 2020-Mar-17 at 12:49

I am learning capsnet now, and trying to transfer the code from local computer to colab. The code runs well on my local computer, but raise an error when I try it on colab. ValueError: Inconsistent shapes: saw (1152, 10, 1, 10, 16) but expected (1152, 10, 1, 16).

When I try other matching like [3,1], I will get the following error. In this case, x's dimension backs to 4 and x[3] == y[2]. ValueError: Can not do batch_dot on inputs with shapes (1152, 10, 1, 8) and (1152, 10, 8, 16) with axes=[3, 1]. x.shape[3] != y.shape[1] (8 != 10).

I locate the reason of this error on the function tf.scan. And I installed tensorflow 1.13 on my computer. But I don't know how to fix it. Please help me.

Here is the code.

...

ANSWER

Answered 2020-Jan-07 at 16:36

Finally, I solved it. Function tf.scan() here does nothing wrong, but does not accord to my environment. The purpose of tf.scan() here is similar to the fully connected layer.

According to the definition of fully connected layer, we just need to alter the function, but don't use tf.map_fn(), since that we'll get the same error.

And try this one. This function helps a lot to solve this problem.

Source https://stackoverflow.com/questions/58980836

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

Vulnerabilities

No vulnerabilities reported

Install CapsLayer

You can download it from GitHub.
You can use CapsLayer 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

InstallationTutorials for running CapsNet on supported dataset (MNIST/CIFAR10 etc.) or your own dataset, or building your network with Capsule LayerTheoretical Analysis

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