Is regularization included in loss history Keras returns?
the regularisation values are by default computed in the loss and so you cannot see the regularisaiton values separately (as far as I know). For a brief explanation of an example using the cross-entropy loss with regularisation, have a look at the example in Michael Nielsen's book.

You can see what is returned and available by saving the results from calling model.fit().

You can train a model then also check out:

print(history.history.keys())


which will show you all available metrics for analysis after training the model.

You could perhaps define a custom function that compute the regularisation terms you are using, and execute that as either your own metric or as a callback function in a Keras model. If you use your own metric, those are by default recorded separately from the training/validation loss value.