UpdatedAugust 11, 2025

BiasAdd

Description

Add input with bias, then add residual inputs.

Input parameters

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

Graphs in : cluster, ONNX model architecture.

X – T : object, input tensor. Dimensions are (N, S, C), where N is the batch size, S is image size H*W, and C is number of channels.
bias – T : object, bias tensor. Dimensions are (C).
skip – T : object, residual tensor. Dimensions are (N, S, C).

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

Y – T : object, the output tensor with dimensions (N, S, C).

Type Constraints

T in (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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