dynet | DyNet : The Dynamic Neural Network Toolkit | Machine Learning library

by clab C++ Version: 2.1.2 License: Apache-2.0

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

kandi X-RAY | dynet Summary

dynet is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. dynet has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

The Dynamic Neural Network Toolkit.

Support

Quality

Security

License

Reuse

Support

dynet has a medium active ecosystem.

It has 3365 star(s) with 714 fork(s). There are 185 watchers for this library.

It had no major release in the last 12 months.

There are 262 open issues and 669 have been closed. On average issues are closed in 239 days. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dynet is 2.1.2

Quality

dynet has 0 bugs and 0 code smells.

Security

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

dynet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

dynet 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

dynet releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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dynet Key Features

No Key Features are available at this moment for dynet.

dynet Examples and Code Snippets

No Code Snippets are available at this moment for dynet.

Community Discussions

Trending Discussions on dynet

why does g++ add includes differently within makefile?

Getting multiple variables in TensorFlow and other frameworks (overheads)

QUESTION

why does g++ add includes differently within makefile?

Asked 2022-Jan-29 at 17:37

I am new to make files and having an issue where the g++ command works when run on the command line but not within a make file. The following line works:

...

ANSWER

Answered 2022-Jan-29 at 17:37

GNU Make in it's infinite wisdom has chosen to use it's built-in rules. You can run without them by make --no-builtin-rules. It's not nice to have that as a prerequisite for building your files, but it might be useful when debugging "strange behavior" as it proves whether it's the built-in rules interfering with your mental model or not.

As for the wisdom itself, you only say how to build ai from main.o - not how to build main.o. That is what you're seeing - the built-in rule for building main.o.

What I think you want to do instead is have the rule like this:

Source https://stackoverflow.com/questions/70889091

QUESTION

Getting multiple variables in TensorFlow and other frameworks (overheads)

Asked 2021-Oct-06 at 08:10

If I have the following graph and want to get the values of tensors T1 and T2 in TF without eager execution, how would I do this? I only know of eval() or session.run() (running that twice could be an option) or tf.print(), but printing is not desired (for performance reasons).

Especially, how is this functionality implemented in TensorFlow? Does this impose a big overhead towards just getting T2? I would be happy to be pointed to relevant resources as well.

I'm generally looking for discussions on this -- if people want to add comparisons to how other frameworks do this (Caffe, Torch, CNTK, Theano, Chainer, DyNet, etc.), that's great! In the end, I am trying to understand how these frameworks could be expanded by operators that return operator-specfic metrics that a user can use to monitor training.

Thanks!

...

ANSWER

Answered 2021-Oct-06 at 08:10

you can pass multiple parameters to session.run, and it will run the network once and return each of those parameters.

For example (from the docs):

Source https://stackoverflow.com/questions/69445120

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

Vulnerabilities

No vulnerabilities reported

Install dynet

You can find tutorials about using DyNet here (C++) and here (python), and here (EMNLP 2016 tutorial). One aspect that sets DyNet apart from other tookits is the auto-batching feature. See the documentation about batching. The example folder contains a variety of examples in C++ and python.
DyNet relies on a number of external programs/libraries including CMake and Eigen. CMake can be installed from standard repositories.
You can install dynet for C++ with the following commands. For more details refer to the documentation.
You can install DyNet for python by using the following command. For more details refer to the documentation.