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Adds Categorical Focal Cross Entropy Loss - #10696

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Describe your change:

This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by
focusing on hard examples.

  • Add an algorithm?
  • Fix a bug or typo in an existing algorithm?
  • Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  • Documentation change?

Checklist:

  • I have read CONTRIBUTING.md.
  • This pull request is all my own work -- I have not plagiarized.
  • I know that pull requests will not be merged if they fail the automated tests.
  • This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  • All new Python files are placed inside an existing directory.
  • All filenames are in all lowercase characters with no spaces or dashes.
  • All functions and variable names follow Python naming conventions.
  • All function parameters and return values are annotated with Python type hints.
  • All functions have doctests that pass the automated testing.
  • All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  • If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

@algorithms-keeperalgorithms-keeperBot added awaiting reviews This PR is ready to be reviewed tests are failing Do not merge until tests pass labels Oct 19, 2023
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

@ank426ank426 closed this by deleting the head repository Oct 7, 2024
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Adds Categorical Focal Cross Entropy Loss by ank426 · Pull Request #10696 · TheAlgorithms/Python · GitHub
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Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
Oct 7, 2024
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Adds Categorical Focal Cross Entropy Loss by ank426 · Pull Request #10696 · TheAlgorithms/Python · GitHub
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Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
Oct 7, 2024
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, 'i');
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try {
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Adds Categorical Focal Cross Entropy Loss by ank426 · Pull Request #10696 · TheAlgorithms/Python · GitHub
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Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
Oct 7, 2024
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, 'i');
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Adds Categorical Focal Cross Entropy Loss by ank426 · Pull Request #10696 · TheAlgorithms/Python · GitHub
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Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
Oct 7, 2024
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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var __re = new RegExp('^' + ".*" + '
Adds Categorical Focal Cross Entropy Loss by ank426 · Pull Request #10696 · TheAlgorithms/Python · GitHub
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Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
Oct 7, 2024
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(function(){
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Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
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Adds Categorical Focal Cross Entropy Loss by ank426 · Pull Request #10696 · TheAlgorithms/Python · GitHub
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Adds Categorical Focal Cross Entropy Loss - #10696
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Contributor
Describe your change:
This adds categorical focal cross entropy loss. It's a variation of categorical cross-entropy that addresses class imbalance by

focusing on hard examples.
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
  •  Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  •  Documentation change?
Checklist:
  •  I have read CONTRIBUTING.md.
  •  This pull request is all my own work -- I have not plagiarized.
  •  I know that pull requests will not be merged if they fail the automated tests.
  •  This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  •  All new Python files are placed inside an existing directory.
  •  All filenames are in all lowercase characters with no spaces or dashes.
  •  All functions and variable names follow Python naming conventions.
  •  All function parameters and return values are annotated with Python type hints.
  •  All functions have doctests that pass the automated testing.
  •  All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  •  If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
@algorithms-keeperalgorithms-keeperBot
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tests are failing
Do not merge until tests pass
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
@ank426ank426
closed this by deleting the head repository
Oct 7, 2024
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