Can both Linear Activation Function and non-linear Activation Function be used simultaneously in a single neural network?
my point of view, two different activation functions can affect network stability
Thanks @Senthilkumar
Only non-linear activation functions are useful between different layers. If you dont' apply a non-linear activation between two layers, you will basically be transforming both layers into one: w2(w1x + b1) + b2 = w3(x) + b3 which is still linear.
So, from what I understand from your statement, when two activation functions are involved, it will always be a linear activation function @Amer
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