focal-loss-keras | Focal Loss implementation in Keras | Machine Learning library

 by   mkocabas Python Version: Current License: MIT

kandi X-RAY | focal-loss-keras Summary

kandi X-RAY | focal-loss-keras Summary

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

Focal Loss implementation in Keras
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              focal-loss-keras has a low active ecosystem.
              It has 293 star(s) with 73 fork(s). There are 13 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 5 open issues and 13 have been closed. On average issues are closed in 111 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of focal-loss-keras is current.

            kandi-Quality Quality

              focal-loss-keras has 0 bugs and 0 code smells.

            kandi-Security Security

              focal-loss-keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              focal-loss-keras code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              focal-loss-keras is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              focal-loss-keras releases are not available. You will need to build from source code and install.
              focal-loss-keras 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.
              focal-loss-keras saves you 2 person hours of effort in developing the same functionality from scratch.
              It has 8 lines of code, 2 functions and 1 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed focal-loss-keras and discovered the below as its top functions. This is intended to give you an instant insight into focal-loss-keras implemented functionality, and help decide if they suit your requirements.
            • A fixed focal loss function .
            Get all kandi verified functions for this library.

            focal-loss-keras Key Features

            No Key Features are available at this moment for focal-loss-keras.

            focal-loss-keras Examples and Code Snippets

            No Code Snippets are available at this moment for focal-loss-keras.

            Community Discussions

            Trending Discussions on focal-loss-keras

            QUESTION

            Use of 1-a weight in categorical focal loss
            Asked 2019-Sep-12 at 13:25

            I am trying to use focal loss in keras/tensorflow with multiple classes which leads to use Categorical focal loss I guess. I have found some implementation here and there or there.

            As far as I get it the parameter a in focal loss is mainly used in the Binary focal loss case where 2 classes exist and the one get a as a weight and the other gets 1-a as weight. In the case of the Categorical focal loss all implementations I found use only weight a in front of each class loss like:

            ...

            ANSWER

            Answered 2019-Sep-07 at 18:13

            I'm as puzzled as you are as to why they multiply the loss by a constant. The code you supplied has alpha as a parameter with the default value set to 0.25, but perhaps when you call the function you're supposed to instead supply a tensor (with the same shape as y_pred/y_true) with the weights to this parameter? That's the only explanation I can think of.

            However, I do think you might be able to simply omit those weights in the code altogether, and instead supply your weights to tf.fit()'s class_weight parameter, and then that'll do the weighting for you. Could you keep me updated on whether that works?

            Source https://stackoverflow.com/questions/57807175

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

            Vulnerabilities

            No vulnerabilities reported

            Install focal-loss-keras

            You can download it from GitHub.
            You can use focal-loss-keras 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 .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/mkocabas/focal-loss-keras.git

          • CLI

            gh repo clone mkocabas/focal-loss-keras

          • sshUrl

            git@github.com:mkocabas/focal-loss-keras.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link