# activation function for neural network in R

While creating a neural network in R, is it important to normalize the input data based on activation function? For Example - if the activation function is tanh input data should range from -1 to 1 and for sigmoid activation 0 to 1.

The sigmoid function $$g$$ gives outputs in $$[0.5,1)$$ for $$x > 0$$. If you normalize your inputs to be positive, you're giving up half the output rage of $$g$$.
The $$\tanh$$ function gives outputs in $$[-0.76, 0.76]$$ (approximately) for $$x\in[-1,1]$$. Rescaling the inputs to $$\tanh$$ in this way gives up almost 24% of the output range.