focal-loss | Tensorflow version implementation of focal loss | Machine Learning library

by fudannlp16 Python Version: Current License: No License

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kandi X-RAY | focal-loss Summary

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

Tensorflow version implementation of focal loss for binary and multi classification

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focal-loss has a low active ecosystem.

It has 97 star(s) with 25 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 0 have been closed. On average issues are closed in 640 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 is current.

Quality

focal-loss has no bugs reported.

Security

focal-loss has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

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

focal-loss has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Calculate the focal loss of the given labels .
Calculate the focal loss .

Get all kandi verified functions for this library.

focal-loss Key Features

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

focal-loss Examples and Code Snippets

Calculate the loss of the loss function .

python

Lines of Code : 26

License : Permissive (MIT License)

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def _neg_loss(pred, gt):
  ''' Modified focal loss. Exactly the same as CornerNet.
      Runs faster and costs a little bit more memory
    Arguments:
      pred (batch x c x h x w)
      gt_regr (batch x c x h x w)
  '''
  pos_inds = gt.eq(1).float(

Compute negative loss .

python

Lines of Code : 23

License : Permissive (MIT License)

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def _slow_neg_loss(pred, gt):
  '''focal loss from CornerNet'''
  pos_inds = gt.eq(1)
  neg_inds = gt.lt(1)

  neg_weights = torch.pow(1 - gt[neg_inds], 4)

  loss = 0
  pos_pred = pred[pos_inds]
  neg_pred = pred[neg_inds]

  pos_loss = torch.log(po

Community Discussions

Trending Discussions on focal-loss

Use of 1-a weight in categorical focal loss

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

You can download it from GitHub.
You can use focal-loss 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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