Activation function between LSTM layers

In the above link, the answer to the question whether activation function are required for LSTM layers was answered as follows: as an LSTM unit already consists of multiple non-linear activation functions, it is not necessary to use a (recurrent) activation function.

My question: Is there a specific reason why Keras by default uses a "tanh" activation and "sigmoid" recurrent_activation if those activations are not necessary? I mean, for a Dense layer the default activation is "none". Keras could just have used none as default for LSTM units as well, right? Could it be that Keras uses these activations for a reason? Also, a lot of tutorials or blogs use ReLu (without clarifying why), and I have not come across a one specifying "none" as (recurrent) activation. Why is ReLu used so much (while the outputs from the LSTM unit are already activated)?



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