[source] # Hyperbolic Tangent An S-shaped function that squeezes the input value into an output space between -1 and 1. Hyperbolic Tangent (or *tanh*) has the advantage of being zero centered, however is known to *saturate* with highly positive or negative input values which can slow down training if the activations become too intense. $$ {\displaystyle \tanh(x)={\frac {e^{x}-e^{-x}}{e^{x}+e^{-x}}}} $$ ## Parameters This activation function does not have any parameters. ## Example ```php use Rubix\ML\NeuralNet\ActivationFunctions\HyperbolicTangent; $activationFunction = new HyperbolicTangent(); ```