keras-cam | Keras implementation of class activation mapping | Machine Learning library

by jacobgil Python Version: Current License: No License

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kandi X-RAY | keras-cam Summary

keras-cam is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-cam has no bugs, it has no vulnerabilities and it has low support. However keras-cam build file is not available. You can download it from GitHub.

Keras implementation of class activation mapping

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keras-cam has a low active ecosystem.

It has 323 star(s) with 103 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 5 have been closed. On average issues are closed in 17 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-cam is current.

Quality

keras-cam has 0 bugs and 0 code smells.

Security

keras-cam has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-cam code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-cam 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.

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keras-cam releases are not available. You will need to build from source code and install.

keras-cam 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.

It has 136 lines of code, 10 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Creates VGG16 convolution .
Generate the class activation map .
Loads weights from a file .
Load ini - person .
Parse arguments .
Get VGG16 model .
Train the model .
Get the output layer of the given layer .
global average pooling
Return global average pooling shape .

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keras-cam Key Features

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keras-cam Examples and Code Snippets

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Community Discussions

Trending Discussions on keras-cam

CNN attention/activation maps

QUESTION

CNN attention/activation maps

Asked 2019-Feb-25 at 19:30

What are common techniques for finding which parts of images contribute most to image classification via convolutional neural nets?

In general, suppose we have 2d matrices with float values between 0 and 1 as entires. Each matrix is associated with a label (single-label, multi-class) and the goal is to perform classification via (Keras) 2D CNN's.

I'm trying to find methods to extract relevant subsequences of rows/columns that contribute most to classification.

Two examples:

https://github.com/jacobgil/keras-cam

https://github.com/tdeboissiere/VGG16CAM-keras

Other examples/resources with an eye toward Keras would be much appreciated.

Note my datasets are not actual images, so using methods with ImageDataGenerator might not directly apply in this case.

...

ANSWER

Answered 2019-Feb-25 at 19:30

There are many visualization methods. Each of these methods has its strengths and weaknesses.

However, you have to keep in mind that the methods partly visualize different things. Here is a short overview based on this paper. You can distinguish between three main visualization groups:

Functions (gradients, saliency map): These methods visualize how a change in input space affects the prediction
Signal (deconvolution, Guided BackProp, PatternNet): the signal (reason for a neuron's activation) is visualized. So this visualizes what pattern caused the activation of a particular neuron.
Attribution (LRP, Deep Taylor Decomposition, PatternAttribution): these methods visualize how much a single pixel contributed to the prediction. As a result you get a heatmap highlighting which pixels of the input image most strongly contributed to the classification.

Since you are asking how much a pixel has contributed to the classification, you should use methods of attribution. Nevertheless, the other methods also have their right to exist.

One nice toolbox for visualizing heatmaps is iNNvestigate. This toolbox contains the following methods:

Source https://stackoverflow.com/questions/44731990

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

Vulnerabilities

No vulnerabilities reported

Install keras-cam

You can download it from GitHub.
You can use keras-cam 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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