Add connectionist temporal classification (CTC) loss algorithm by ZJUGuoShuai · Pull Request #11240 · TheAlgorithms/Python · GitHub
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Add connectionist temporal classification (CTC) loss algorithm - #11240

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ZJUGuoShuai:add-ctc-loss
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Add connectionist temporal classification (CTC) loss algorithm#11240
ZJUGuoShuai wants to merge 2 commits into
TheAlgorithms:masterfrom
ZJUGuoShuai:add-ctc-loss

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@ZJUGuoShuai

@ZJUGuoShuai ZJUGuoShuai commented Jan 13, 2024

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Describe your change:

  • Connectionist Temporal Classification (CTC) loss is used in speech recognition, handwriting recognition and other sequence problems. It's used to get around not knowing the alignment between the input and the output.
  • The implementation has been verified to align with PyTorch's CTCLoss.
  • 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".

@ZJUGuoShuai

ZJUGuoShuai commented Jan 16, 2024

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@algorithms-keeper algorithms-keeper Bot mentioned this pull request Jan 17, 2024
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tianyizheng02
tianyizheng02 requested changes Jun 3, 2024
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Comment thread machine_learning/loss_functions.py
Comment on lines +478 to +487

@tianyizheng02 tianyizheng02 Jun 3, 2024 •
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A couple suggestions to clarify the documentation:

  1. PyTorch cites Graves et al for its implementation. Since your implementation is also based on this paper, please add it as a reference as well.

  2. Please add a short paragraph explaining how the loss is actually calculated. This should explain some general questions about your variables (What is blank? What is alpha, and why is it calculated using DP?) so that the reader understands what is being calculated. I ask for this because this repository is meant for educational purposes, so we want readers to understand how and why the implementation works.

    Also, in your implementation you use np.logaddexp for log-probabilities when calculating alpha rather than calculating probabilities directly. Since this differs from the definitions in the Graves et al paper, please be sure to note this implementation detail in your explanation.

@cclauss cclauss mentioned this pull request Sep 13, 2026
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@tianyizheng02 tianyizheng02 tianyizheng02 requested changes
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@imSanko imSanko imSanko approved these changes
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@ZJUGuoShuai @tianyizheng02 @imSanko