rnn_lm | various simple RNNs trained on synthetic grammars | Code Quality library

by matpalm Python Version: Current License: No License

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

kandi X-RAY | rnn_lm Summary

rnn_lm is a Python library typically used in Code Quality, Neural Network applications. rnn_lm has no bugs, it has no vulnerabilities and it has low support. However rnn_lm build file is not available. You can download it from GitHub.

various simple RNNs trained on synthetic grammars

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Quality

Security

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rnn_lm has a low active ecosystem.

It has 29 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

rnn_lm has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of rnn_lm is current.

Quality

rnn_lm has 0 bugs and 0 code smells.

Security

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

rnn_lm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

rnn_lm does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

rnn_lm 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.

rnn_lm saves you 165 person hours of effort in developing the same functionality from scratch.

It has 410 lines of code, 34 functions and 12 files.

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

Top functions reviewed by kandi - BETA

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

Calculates the 3 - item stats for each prob_seq
Return a human - readable stats
Calculates the perplexity of a sequence of sequences
Computes the perplexity of a sequence of probabilities

Get all kandi verified functions for this library.

rnn_lm Key Features

No Key Features are available at this moment for rnn_lm.

rnn_lm Examples and Code Snippets

No Code Snippets are available at this moment for rnn_lm.

Community Discussions

Trending Discussions on rnn_lm

PyTorch - applying attention efficiently

QUESTION

PyTorch - applying attention efficiently

Asked 2018-Dec-11 at 10:25

I have build a RNN language model with attention and I am creating context vector for every element of the input by attending all the previous hidden states (only one direction).

The most straight forward solution in my opinion is using a for-loop over the RNN output, such that each context vector is computed one after another.

...

ANSWER

Answered 2018-Dec-10 at 22:32

Ok, for clarity: I assume we only really care about vectorizing the for loop. What is the shape of x? Assuming x is 2-dimensional, I have the following code, where v1 executes your loop and v2 is a vectorized version:

Source https://stackoverflow.com/questions/53706462

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

Vulnerabilities

No vulnerabilities reported

Install rnn_lm

You can download it from GitHub.
You can use rnn_lm 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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