chainer.functions.softplus¶

chainer.functions.softplus(x, beta=1.0)[source]¶

Element-wise softplus function.

The softplus function is the smooth approximation of ReLU.

\[f(x)=\frac{1}{\beta}\log(1 + \exp(\beta x)),\]

where \(\beta\) is a parameter. The function becomes curved and akin to ReLU as the \(\beta\) is increasing.

Parameters:	x (`Variable` or `numpy.ndarray` or `cupy.ndarray`) – Input variable. A \((s_1, s_2, ..., s_N)\)-shaped float array. beta (float) – Parameter \(\beta\).
Returns:	Output variable. A \((s_1, s_2, ..., s_N)\)-shaped float array.
Return type:	Variable

Example

>>> x = np.arange(-2, 3, 2).astype('f')
>>> x
array([-2.,  0.,  2.], dtype=float32)
>>> F.softplus(x, beta=1.0).data
array([ 0.126928  ,  0.69314718,  2.12692809], dtype=float32)