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System information

• TensorFlow version (you are using): 2.x
• Are you willing to contribute it (Yes/No): Yes

Describe the feature and the current behavior/state.

TensorFlow Addons are developing a progress bar (TQDM Progress bar) and we receive and issue where when user specify initialEpoch > 0 during model.fit and when using tfa.callbacks.TQDMProgressBar the progress bar will never reach 100% because in TQDMProgressBar code we never consider the case when user set initialEpoch

model.fit(x=X, y=y, class_weight=None, batch_size=batchSize, verbose=0, callbacks=tfa.callbacks.TQDMProgressBar(), validation_split=0.2, shuffle=True, epochs=epochCount, initial_epoch=initialEpoch)

tensorflow/addons#1748

While searching for a solution, I noticed that initial_epoch is never passed to Callback's self.params dictionary and thus making it hard to implement the feature where users set an initial epoch, thus I am asking if it would make sense for TensorFlow to pass initial_epoch to Callback's self.params for us to implement that feature. The other way around would be to ask user to specify initial_epoch again in the progress bar but that would not be ideal. Thank you so much for your time!

Will this change the current api? How?

No, but this will add another key into Callbacks' self.params dictionary.

Who will benefit with this feature?

Users of TensorFlow Addons TQDM Progress Bar and thus that may need initial_epoch in their custom callbacks.

Any Other info.

TensorFlow Addons TQDM Progress Bar Source code:
https://github.com/tensorflow/addons/blob/master/tensorflow_addons/callbacks/tqdm_progress_bar.py