mygrad.nnet.activations.selu

mygrad.nnet.activations.selu(x: ArrayLike, *, constant: bool | None = None) → Tensor

Returns the scaled exponential linear activation (SELU) elementwise along x.

The SELU is given by λɑ(exp(x) - 1) for x < 0 and λx for x ≥ 0.

Parameters:

xArrayLike

Input data.

constantOptional[bool]

If True, the returned tensor is a constant (it does not back-propagate a gradient)

Returns:

mygrad.Tensor

The SELU function applied to x elementwise.

References

[1]

Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter Self-Normalizing Neural Networks https://arxiv.org/abs/1706.02515

Examples

>>> import mygrad as mg
>>> from mygrad.nnet.activations import selu
>>> x = mg.arange(-5, 6)
>>> x
Tensor([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])
>>> y = selu(x, alpha=0.1); y
Tensor([-1.74625336, -1.72589863, -1.67056873, -1.52016647, -1.11133074,
     0.        ,  1.05070099,  2.10140197,  3.15210296,  4.20280395,
     5.25350494])

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