tf-explain | Interpretability Methods for tf.keras models | Machine Learning library

by sicara Python Version: 0.3.1 License: MIT

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

kandi X-RAY | tf-explain Summary

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

Interpretability Methods for tf.keras models with Tensorflow 2.x

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Support

tf-explain has a medium active ecosystem.

It has 978 star(s) with 108 fork(s). There are 50 watchers for this library.

It had no major release in the last 12 months.

There are 38 open issues and 51 have been closed. On average issues are closed in 116 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tf-explain is 0.3.1

Quality

tf-explain has 0 bugs and 0 code smells.

Security

tf-explain has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-explain code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-explain 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

tf-explain 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.

Top functions reviewed by kandi - BETA

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

Generate an explanation of validation_data
Displays an array
Displays a heatmap
Convert an image to an image
Provide explanation of the sensitivity map
Get the sensitivity map for the given image
Apply grey patch
Return an explanation of the validation data
Generates interpolations for images
Convert tensor to normalized grayscale
Generate a linear path between baseline and target
Generates an explanation of the gradients
Generate random images
Return the explanation of the model
Displays the gradient of the model
Generate an explanation of validation data
Print smooth gradient
Return an explanation of the score model
Generate image summary
Visualize activations
Log integrated gradients
Return an explanation of validation_data
Creates a confusion matrix
Displays the sensitivity map
Save grid to file
Saves the grayscale
Save the grays
Save the grid
Saves a grayscale object

Get all kandi verified functions for this library.

tf-explain Key Features

No Key Features are available at this moment for tf-explain.

tf-explain Examples and Code Snippets

No Code Snippets are available at this moment for tf-explain.

Community Discussions

Trending Discussions on tf-explain

ModuleNotFoundError: No module named 'tf_explain'

QUESTION

ModuleNotFoundError: No module named 'tf_explain'

Asked 2021-Apr-20 at 12:23

I am trying to install tf-explain .So after installing anaconda and tensorflow now I am trying to install tf-explain So in order I have used :

...

ANSWER

Answered 2021-Apr-20 at 10:06

I tested on Windows and Linux (colab) os and it works.

Source https://stackoverflow.com/questions/67176320

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

Vulnerabilities

No vulnerabilities reported

Install tf-explain

tf-explain is available on PyPi as an alpha release. To install it:.
tf-explain offers 2 ways to apply interpretability methods. The full list of methods is the Available Methods section.