How does keras model fit differ when called multiple times with different datasets each time and when called once with all datasets?
In first scenario, the weights will be optimized first with first dataset and then will be changed(updated) for the second dataset and so on. In second scenario, you are simultaneously asking the model to learn patterns from all the datasets. This means the weights will be adjusted according to all the datasets all at once. I will prefer the second approach because NN have the tendency to forget/break when trained on new datasets. They are more likely to focus on the data they have seen recently.
I am working with time series datasets where I have two different cases. One where my sequences are of same size and the other one where the sequences are of different lengths. When I have same length sequences I can merge all the datasets and then fit the model once.
But for different length sequences, I was wondering how differently should the keras model.fit will behave

if the models are fitted with each different length sequences one by one with  batch size=length of sequence
if the models are fitted once with all the sequences merged together having a fixed batch size

And based on the given scenario what should be the correct or better course of action?