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Celu

Description

Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula : max(0,x) + min(0,alpha*(exp(x/alpha)–1))

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
X (heterogeneous) – T : object, input tensor.

 Parameters : cluster,

alpha : float, the Alpha value in Celu formula which control the shape of the unit.
Default value “0”.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

 name (optional) : string, name of the node.

Output parameters

 

 Y (heterogeneous) – T : object, output tensor.

Type Constraints

T in (tensor(float)) : Constrain input and output types to float32 tensors.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
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