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BiasGelu

Description

It’s an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation.

 

Input parameters

 

specified_outputs_namearray, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

A – T : object, the normal input data.
B – T : object, the bias input data that is a 1D tensor.

 Parameters : cluster,

 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

 

 C – T : object, the output.

Type Constraints

T in (tensor(bfloat16)tensor(double)tensor(float)tensor(float16)) : Constrain input and output types to float 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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