Softplus

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Softplus#

class brainstate.nn.Softplus(name=None)#

Applies the Softplus function element-wise.

\[\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))\]

SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.

For numerical stability the implementation reverts to the linear function when \(input \times \beta > threshold\).

Shape#

  • Input: \((*)\), where \(*\) means any number of dimensions.

  • Output: \((*)\), same shape as the input.

Examples

>>> import brainstate.nn as nn
>>> import brainstate
>>> m = nn.Softplus()
>>> x = brainstate.random.randn(2)
>>> output = m(x)
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