Machine Learning & AI
Basic

Tanh Activation

Squashes input to the range (-1, 1); zero-centred activation.

Formula

tanh⁡(x)=ex−e−xex+e−x\tanh(x) = \dfrac{e^{x}-e^{-x}}{e^{x}+e^{-x}}

Variables

xInput value

Example

tanh(0)=0, tanh(2)=0.964

Did You Know?

Because tanh is zero-centred it often trained early networks better than the sigmoid.

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