Add note about label noise in CIFAR-10 dataset documentation

[ssam18]

I was working with the CIFAR-10 dataset and noticed that the documentation doesn't mention the known issue of label noise. I kept running into images that seemed mislabeled - airplanes labeled as frogs, horses as cats, etc. At first I thought something was wrong with my code, but after researching, I learned this is a well-known characteristic of the original CIFAR-10 dataset.

I've added a brief note to the load_data() docstring to inform users about this label noise issue. This should help new users understand that some label inconsistencies are expected and not due to bugs in their code or the Keras implementation.

The note is concise and placed right after the dataset description, so users will see it before they start working with the data. It mentions that:

• The dataset has a small percentage of mislabeled samples
• This is inherent to the original dataset
• It may impact training and evaluation

Fixes #21631

[ssam18]

This is a simple documentation improvement that should help avoid confusion for users who encounter mislabeled images in CIFAR-10. The note is factual, concise, and doesn't require any code changes - just making the existing dataset behavior more transparent in the docs.

[gemini-code-assist]

Summary of Changes

Hello @ssam18, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the CIFAR-10 dataset documentation by incorporating a crucial note regarding label noise. This addition aims to proactively inform users about the inherent presence of mislabeled samples within the dataset, thereby setting appropriate expectations and preventing potential confusion or misdiagnosis of issues during model development and evaluation.

Highlights

• Documentation Update: Added a note to the load_data() docstring in keras/src/datasets/cifar10.py to inform users about the known issue of label noise in the CIFAR-10 dataset.
• User Awareness: The new note clarifies that a small percentage of mislabeled samples is inherent to the original CIFAR-10 dataset and may affect model training and evaluation, preventing user confusion.

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[gemini-code-assist]

Code Review

This pull request adds a helpful note to the cifar10.load_data() docstring, warning users about the known label noise in the CIFAR-10 dataset. This is a valuable addition that can save users from confusion and debugging time. My review includes one suggestion to adjust the placement of this note within the docstring to align with the Keras API style guide, which will improve documentation consistency.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 78.08%. Comparing base (edbf8f5) to head (461a33b).

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21855      +/-   ##
==========================================
- Coverage   82.47%   78.08%   -4.40%     
==========================================
  Files         577      577              
  Lines       59508    59508              
  Branches     9332     9332              
==========================================
- Hits        49080    46464    -2616     
- Misses       8015    10747    +2732     
+ Partials     2413     2297     -116     

Flag	Coverage Δ
keras	77.93% <ø> (-4.37%)	⬇️
keras-jax	?
keras-numpy	57.55% <ø> (ø)
keras-openvino	34.34% <ø> (ø)
keras-tensorflow	64.12% <ø> (+<0.01%)	⬆️
keras-torch	63.60% <ø> (-0.01%)	⬇️

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[fchollet]

LGTM, sure seems worth including, thanks for the PR