def softsign_activation(x, **kwargs):
# See X. Glorot and Y.
# Bengio. Understanding the difficulty of training deep feedforward neural
# networks. In Proceedings of the 13th International Workshop on
# Artificial Intelligence and Statistics, 2010.
# - http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
# This returns the element-wise value of:
# x / ( 1 + |x| )
# This has a slope = 1 / ( 1 + |x| )^2
# Thus softsign also asymptotically approches slope of 0 for both large positive and negative values, but the drop-
# off is quadratic rather than exponential .
return(T_softsign(x))
def softsign(self, x):
return T_softsign(x)
def softsign(x):
return T_softsign(x)
