Possibility to use dropout in vgg16/19

[zaccharieramzi]

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System information.

TensorFlow version (you are using): 2.8 Are you willing to contribute it (Yes/No) : Yes

Describe the feature and the current behavior/state.

I would like to be able to use dropout on the first two fully connected layers of the VGG implementations in keras applications, as specified in the original paper (and done in PyTorch).

dropout regularisation for the first two fully-connected layers (dropout ratio set to 0.5)

Will this change the current api? How?

Yes: this will mean adding a dropout kwarg to the VGG implementations, that will default to 0.0 to remain a non-breaking change.

Who will benefit from this feature?

Users willing to reproduce the original results of the VGG paper.

Contributing

• Do you want to contribute a PR? (yes/no): yes
• If yes, please read this page for instructions
• Briefly describe your candidate solution(if contributing): just adding dropout layers.

[qlzh727]

Ack, just as reference, the original paper stated that

"The training was regularised by weight decay (the L2 penalty multiplier set to 5 · 10−4) and dropout regularisation for the first two fully-connected layers (dropout ratio set to 0.5)."

https://arxiv.org/pdf/1409.1556.pdf

[qlzh727]

Feel free to send any PR if you would like to contribute. Thanks

[zaccharieramzi]

Closing as per this.

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