# How to make a custom activation function in keras with a learnable parameter?

The answer to this question is generally to implement it as a new layer and do

layer = Dense(num_neurones)(previous_layer)
out = TheActivationFunction()(layer)


as described here.

However, I want to use it for an LSTM cell, like this

LSTM(units = num_units, activation = TheActivationFunction(), return_sequences = True)


Specifically, I would like to implement the function x -> tanh(beta * x) with beta a learnable parameter.