The Scaled Exponential Linear Unit (SELU) activation function is defined as:

`scale * x`

if`x > 0`

`scale * alpha * (exp(x) - 1)`

if`x < 0`

where `alpha`

and `scale`

are pre-defined constants
(`alpha = 1.67326324`

and `scale = 1.05070098`

).

Basically, the SELU activation function multiplies `scale`

(> 1) with the
output of the `activation_elu`

function to ensure a slope larger
than one for positive inputs.

The values of `alpha`

and `scale`

are
chosen so that the mean and variance of the inputs are preserved
between two consecutive layers as long as the weights are initialized
correctly (see `initializer_lecun_normal()`

)
and the number of input units is "large enough"
(see reference paper for more information).

## Notes

To be used together with

`initializer_lecun_normal()`

.To be used together with the dropout variant

`layer_alpha_dropout()`

(legacy, depracated).

## See also

Other activations: `activation_elu()`

`activation_exponential()`

`activation_gelu()`

`activation_hard_sigmoid()`

`activation_leaky_relu()`

`activation_linear()`

`activation_log_softmax()`

`activation_mish()`

`activation_relu()`

`activation_relu6()`

`activation_sigmoid()`

`activation_silu()`

`activation_softmax()`

`activation_softplus()`

`activation_softsign()`

`activation_tanh()`