ShuffleNet | ShuffleNet Implementation in TensorFlow | Machine Learning library

by MG2033 Python Version: Current License: Apache-2.0

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

kandi X-RAY | ShuffleNet Summary

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

An implementation of ShuffleNet introduced in TensorFlow. According to the authors, ShuffleNet is a computationally efficient CNN architecture designed specifically for mobile devices with very limited computing power. It outperforms Google MobileNet by small error percentage at much lower FLOPs. Link to the original paper: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices.

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Quality

Security

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Support

ShuffleNet has a low active ecosystem.

It has 381 star(s) with 130 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 7 have been closed. On average issues are closed in 13 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ShuffleNet is current.

Quality

ShuffleNet has 0 bugs and 0 code smells.

Security

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

ShuffleNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ShuffleNet 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

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

ShuffleNet 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.

ShuffleNet saves you 275 person hours of effort in developing the same functionality from scratch.

It has 665 lines of code, 45 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Generator for batch selection
Run the test
Add summary information to summaries
Save the current checkpoint
Creates a dense layer
Embed dense p
Add summaries for a variable
Creates a variable with weight decay
Parse arguments
Test the model
Creates the directories for the experiment
Loads the training data
Saves a checkpoint
Show the default parameters

Get all kandi verified functions for this library.

ShuffleNet Key Features

No Key Features are available at this moment for ShuffleNet.

ShuffleNet Examples and Code Snippets

No Code Snippets are available at this moment for ShuffleNet.

Community Discussions

Trending Discussions on ShuffleNet

Compile only one layer files in caffe

QUESTION

Compile only one layer files in caffe

Asked 2018-Jan-16 at 14:01

When we developed a new layer in caffe, which is new_layer.cpp, new_layer.cu, and new.layer.hpp, we have to recompile caffe, is it possible to compile only this new layer files, like an update to the original library, or we have to recompile the whole library again? Can someone give me a hint?

ADD: actually I downloaded the layer files from someone's implementation on github: https://github.com/farmingyard/ShuffleNet there are .cpp .cu .hpp files, I put .cpp and .cu in caffe/src/caffe/layers, put .hpp in caffe/include/caffe/layers/, then add in caffe.proto as:

...

ANSWER

Answered 2018-Jan-16 at 09:50

When compiling using make, make knows what source it already compiled and what changed since the last build. If you only make local changes, make will only compile/link the source that changed and what is affected by it (A change to a header file might require compilation of other sources that #include this header).
That is, you do not need to do anything special, just keep using make.
If you make clean you delete all compiled objects and forces make to re-compile the entire project from scratch.

Source https://stackoverflow.com/questions/48275980

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

Vulnerabilities

No vulnerabilities reported

Install ShuffleNet

You can download it from GitHub.
You can use ShuffleNet 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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