beam_search | Beam search for neural network sequence | Translation library

by ottokart Python Version: Current License: MIT

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

kandi X-RAY | beam_search Summary

beam_search is a Python library typically used in Utilities, Translation, Pytorch, Tensorflow, Neural Network applications. beam_search 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.

Beam search for neural network sequence to sequence (encoder-decoder) models.

Support

Quality

Security

License

Reuse

Support

beam_search has a low active ecosystem.

It has 33 star(s) with 17 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of beam_search is current.

Quality

beam_search has 0 bugs and 0 code smells.

Security

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

beam_search code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

beam_search 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

beam_search 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.

beam_search saves you 23 person hours of effort in developing the same functionality from scratch.

It has 65 lines of code, 5 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Return a sequence of values
Return a sequence of nodes
Return a sequence of extras

Get all kandi verified functions for this library.

beam_search Key Features

No Key Features are available at this moment for beam_search.

beam_search Examples and Code Snippets

No Code Snippets are available at this moment for beam_search.

Community Discussions

Trending Discussions on beam_search

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

im2txt: Load input images from memory (instead of read from disk)

QUESTION

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

Asked 2019-Sep-08 at 16:22

In a pytorch model training process I get this error:

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.LongTensor [128, 1]] is at version 8; expected version 7 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!

with stack trace

...

ANSWER

Answered 2019-Sep-08 at 16:22

A tensor matching this description torch.cuda.LongTensor [128, 1], should narrow down your search.

A quick google search revealed that, LongTensors are most commonly returned by min , max, sort. so the lines

Source https://stackoverflow.com/questions/57631705

QUESTION

im2txt: Load input images from memory (instead of read from disk)

Asked 2017-Apr-29 at 15:55

I'm interested in modifying the tensorflow implementation of Show and Tell, in particular this v0.12 snapshot, in order to accept an image in numpy form instead of read it from disk.

Loading a filename using the upstream code results in a python string after

...

ANSWER

Answered 2017-Apr-29 at 15:55

The original code:

Source https://stackoverflow.com/questions/43662094

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

Vulnerabilities

No vulnerabilities reported

Install beam_search

You can download it from GitHub.
You can use beam_search 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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