Selu#
Versioned name: Selu-1
Category: Activation function
Short description: Selu is a scaled exponential linear unit element-wise activation function.
Detailed Description
Selu operation is introduced in this article, as activation function for self-normalizing neural networks (SNNs).
Selu performs element-wise activation function on a given input tensor data
, based on the following mathematical formula:
where α and λ correspond to inputs alpha
and lambda
respectively.
Another mathematical representation that may be found in other references:
Attributes: Selu operation has no attributes.
Inputs
1:
data
. A tensor of type T and arbitrary shape. Required.2:
alpha
. 1D tensor with one element of type T. Required.3:
lambda
. 1D tensor with one element of type T. Required.
Outputs
1: The result of element-wise Selu function applied to
data
input tensor. A tensor of type T and the same shape asdata
input tensor.
Types
T: arbitrary supported floating-point type.
Example
<layer ... type="Selu">
<input>
<port id="0">
<dim>256</dim>
<dim>56</dim>
</port>
<port id="1">
<dim>1</dim>
</port>
<port id="2">
<dim>1</dim>
</port>
</input>
<output>
<port id="3">
<dim>256</dim>
<dim>56</dim>
</port>
</output>
</layer>