Fix the Doc of alpha in mobilenet_v3

[ILCSFNO]

Fixes #21562

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[gemini-code-assist]

Summary of Changes

Hello @ILCSFNO, 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!

I've updated the documentation for the "alpha" parameter within the "mobilenet_v3" application in Keras. This change clarifies the acceptable "alpha" values when using the default 'imagenet' weights, addressing issue #21562.

Highlights

• Documentation Update: Clarified the valid "alpha" values for the "mobilenet_v3" model when using default "imagenet" weights, specifying "0.75" and "1.0" for non-minimalistic models and "1.0" for minimalistic models.

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[gemini-code-assist]

Code Review

This pull request updates the docstring for the alpha parameter in MobileNetV3 to clarify its valid values when using pretrained ImageNet weights. My review focuses on improving the clarity and formatting of this new documentation to make it more readable.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 62.14%. Comparing base (4e1b250) to head (d2254f0).

❗ There is a different number of reports uploaded between BASE (4e1b250) and HEAD (d2254f0). Click for more details.

HEAD has 6 uploads less than BASE

Flag	BASE (4e1b250)	HEAD (d2254f0)
keras	5	2
keras-torch	1	0
keras-tensorflow	1	0
keras-jax	1	0

Additional details and impacted files

@@             Coverage Diff             @@
##           master   #21563       +/-   ##
===========================================
- Coverage   82.73%   62.14%   -20.60%     
===========================================
  Files         567      567               
  Lines       56464    56464               
  Branches     8825     8825               
===========================================
- Hits        46718    35087    -11631     
- Misses       7582    19200    +11618     
- Partials     2164     2177       +13     

Flag	Coverage Δ
keras	62.13% <ø> (-20.42%)	⬇️
keras-jax	?
keras-numpy	58.41% <ø> (+0.11%)	⬆️
keras-openvino	34.76% <ø> (+0.12%)	⬆️
keras-tensorflow	?
keras-torch	?
keras.applications	22.81% <ø> (?)
keras.applications-numpy	22.81% <ø> (?)
keras.applications-openvino	22.81% <ø> (?)

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[ILCSFNO]

Only fix the doc, not known why the performance is changed, could anyone point it out? Thanks a lot!

[fchollet]

LGTM, thanks

[ILCSFNO]

Thanks a lot!