1<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/NeuralNet/ActivationFunctions/HyperbolicTangent.php">[source]</a></span> 2 3# Hyperbolic Tangent 4An 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. 5 6$$ 7{\displaystyle \tanh(x)={\frac {e^{x}-e^{-x}}{e^{x}+e^{-x}}}} 8$$ 9 10## Parameters 11This activation function does not have any parameters. 12 13## Example 14```php 15use Rubix\ML\NeuralNet\ActivationFunctions\HyperbolicTangent; 16 17$activationFunction = new HyperbolicTangent(); 18``` 19