libdnn | convolutional neural network with GPU acceleration | Machine Learning library

by poweic C++ Version: Current License: Apache-2.0

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

kandi X-RAY | libdnn Summary

libdnn is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Neural Network applications. libdnn has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

A lightweight and user friendly C++ library for deep and convolutional neural network with GPU acceleration

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Quality

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libdnn has a low active ecosystem.

It has 338 star(s) with 59 fork(s). There are 37 watchers for this library.

It had no major release in the last 6 months.

There are 12 open issues and 9 have been closed. On average issues are closed in 75 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of libdnn is current.

Quality

libdnn has 0 bugs and 0 code smells.

Security

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

libdnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

libdnn 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.

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

Installation instructions, examples and code snippets are available.

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libdnn Key Features

No Key Features are available at this moment for libdnn.

libdnn Examples and Code Snippets

No Code Snippets are available at this moment for libdnn.

Community Discussions

Trending Discussions on libdnn

Optimization Target must be a link

QUESTION

Optimization Target must be a link

Asked 2019-Apr-30 at 23:29

I have an autoencoder model of 4 linear layers written using chainer.Chain. Running the optimizer.setup line in Trainer section give me the following error:

...

ANSWER

Answered 2019-Apr-30 at 23:29

StackedAutoencoder inherits NNBase class, which inherits object class, so they are not chainer.Chain class.

You can refer official example for how to define your own network. For example, MNIST example defines MLP as follows:

Source https://stackoverflow.com/questions/55918873

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

Vulnerabilities

No vulnerabilities reported

Install libdnn

Before you install, you should be able to run g++, nvcc and have your environment variable PATH and LD_LIBRARY_PATH set. If you feel that you have little doubt about what I’m talking about, I refer you to go through [FAQ](https://github.com/botonchou/libdnn/wiki/Frequently-Asked-Questions).
git clone https://github.com/botonchou/libdnn.git
cd libdnn/
./install-sh

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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