def Selu(alpha=1.6732632423543772848170429916717,
lmbda=1.0507009873554804934193349852946):
r"""Returns an `Elu`-like layer with an additional scaling/slope parameter.
.. math::
f(x) = \left\{ \begin{array}{cl}
\lambda \cdot \alpha \cdot (e^x - 1) & \text{if}\ x \leq 0, \\
\lambda \cdot x & \text{otherwise}.
\end{array} \right.
Args:
alpha: Coefficient multiplying the exponential, for negative inputs.
lmbda: Coefficient scaling the whole function.
"""
return Fn('Selu', lambda x: lmbda * np.where(x > 0, x, alpha * np.expm1(x)))
def Elu(a=1.):
r"""Returns a ReLU-like layer with exponential outputs for negative inputs.
.. math::
f(x) = \left\{ \begin{array}{cl}
a \cdot (e^x - 1) & \text{if}\ x \leq 0, \\
x & \text{otherwise}.
\end{array} \right.
(Asymptotically, :math:`f(x)\rightarrow -a` as :math:`x\rightarrow - \infty`.)
Args:
a: Coefficient multiplying the exponential, for negative inputs.
"""
return Fn('Elu', lambda x: np.where(x > 0, x, a * np.expm1(x)))
def Selu(x,
alpha=1.6732632423543772848170429916717,
lmbda=1.0507009873554804934193349852946):
return lmbda * np.where(x > 0, x, alpha * np.expm1(x))
def Elu(x, a=1., **unused_kwargs):
return np.where(x > 0, x, a * np.expm1(x))
def Elu(a=1.):
return Fn('Elu', lambda x: np.where(x > 0, x, a * np.expm1(x)))
def Elu(x, a=1.):
return np.where(x > 0, x, a * np.expm1(x))
