see-rnn | general weights , gradients , & activations visualization | Machine Learning library

by OverLordGoldDragon Python Version: 1.15.1 License: MIT

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

kandi X-RAY | see-rnn Summary

see-rnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras, Neural Network applications. see-rnn has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install see-rnn' or download it from GitHub, PyPI.

RNN weights, gradients, & activations visualization in Keras & TensorFlow (LSTM, GRU, SimpleRNN, CuDNN, & all others).

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Quality

Security

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see-rnn has a low active ecosystem.

It has 160 star(s) with 18 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 4 open issues and 11 have been closed. On average issues are closed in 14 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of see-rnn is 1.15.1

Quality

see-rnn has 0 bugs and 0 code smells.

Security

see-rnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

see-rnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

see-rnn 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

see-rnn releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

see-rnn saves you 1080 person hours of effort in developing the same functionality from scratch.

It has 2459 lines of code, 160 functions and 13 files.

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

Top functions reviewed by kandi - BETA

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

Visualize a model weights
Detect nanans
Get the weights of an RNN layer
Validate arguments for rnn
Construct an RNN heatmap
Get the weights for the given model
Get cell weights
Validate input arguments
Get a layer by id
Visualize out the gradients of the outputs
Get model parameters
Gets gradients from training data
Get gradients
Train a model on the given batch
Make data
Visualize out the output of a model
Get the outputs of the given model
Compute the gradients of the last output
Plot features for a 2D image
Create a model
Find the version number
Visualize weights

Get all kandi verified functions for this library.

see-rnn Key Features

No Key Features are available at this moment for see-rnn.

see-rnn Examples and Code Snippets

No Code Snippets are available at this moment for see-rnn.

Community Discussions

Trending Discussions on see-rnn

"Either the username or the repository does not exist"

QUESTION

"Either the username or the repository does not exist"

Asked 2020-Apr-24 at 15:25

travis-encrypt OverLordGoldDragon see-rnn; repository; travis-encrypt version: 1.3.1, Win OS. Seems cli.py looks here, which shows build passing - but an error is thrown anyway; full trace below.

Any resolution?

...

ANSWER

Answered 2020-Apr-24 at 15:25

An alternative is to use below via the travis client, taken from Travis PyPi deployment:

Source https://stackoverflow.com/questions/61334195

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

Vulnerabilities

No vulnerabilities reported

Install see-rnn

pip install see-rnn. Or, for latest version (most likely stable):.

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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