generalized_dice_loss

by gravitino Python Version: Current License: No License

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kandi X-RAY | generalized_dice_loss Summary

generalized_dice_loss is a Python library. generalized_dice_loss has no bugs, it has no vulnerabilities and it has low support. However generalized_dice_loss build file is not available. You can download it from GitHub.

generalized_dice_loss

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generalized_dice_loss has a low active ecosystem.

It has 7 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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It has a neutral sentiment in the developer community.

The latest version of generalized_dice_loss is current.

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generalized_dice_loss has no bugs reported.

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generalized_dice_loss has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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generalized_dice_loss Key Features

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Community Discussions

Trending Discussions on generalized_dice_loss

How to implement multi class dice loss function without using argmax function(argmax is not differentiable)?

QUESTION

How to implement multi class dice loss function without using argmax function(argmax is not differentiable)?

Asked 2019-May-14 at 00:44

I am trying to implement a multi class dice loss function in tensorflow. Since it is multi class dice, I need to convert the probabilities of each class into its one-hot form. For example, if my network outputs these probabilities:
[0.2, 0.6, 0.1, 0.1] (assuming 4 classes)
I need to convert this into:
[0 1 0 0]
This can be done by using tf.argmax followed by tf.one_hot

...

ANSWER

Answered 2019-May-14 at 00:44

Take a look at How is the smooth dice loss differentiable?. You won't need to do the conversion (convert [0.2, 0.6, 0.1, 0.1] to [0 1 0 0]). Just leave them as the continuous value between 0 and 1.

If I understand correctly, the loss function is just a surrogate to achieve your expected objective. Even though it is not the same, as long as it is a good proxy, it is fine (otherwise, it is not differentiable).

In the evaluation time, feel free to use the tf.argmax to get the real metric.

Source https://stackoverflow.com/questions/56116065

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Vulnerabilities

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Install generalized_dice_loss

You can download it from GitHub.
You can use generalized_dice_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.

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