additionLayer

R2026b

Addition layer

Description

An addition layer adds inputs from multiple neural network layers element-wise.

Creation

Syntax

layer = additionLayer(numInputs)

layer = additionLayer(numInputs,Name=name)

Description

layer = additionLayer(numInputs) creates an addition layer and sets the NumInputs property.

layer = additionLayer(numInputs,Name=name) also sets the optional Name property. For example, additionLayer(3,Name="add") creates an addition layer with three inputs and sets the name to "add".

Input Arguments

numInputs — Number of inputs

positive integer

Number of inputs to the layer, specified as a positive integer greater than or equal to 2.

The inputs have the names 'in1', 'in2', and 'inN', where N is the number of inputs. For example, if number of inputs is 3, then the inputs have the names 'in1', 'in2', and 'in3'. Use the input names when connecting or disconnecting the layer using the connectLayers or disconnectLayers functions.

This argument sets the NumInputs property.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

name — Layer name

"" (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to unnamed layers.

This argument sets the Name property.

Data Types: char | string

Properties

NumInputs — Number of inputs

positive integer

This property is read-only after object creation. To set this property, use the corresponding positional input argument when you create the AdditionLayer object.

Number of inputs to the layer, represented as a positive integer greater than or equal to 2.

The inputs have the names 'in1', 'in2', and 'inN', where N is the number of inputs. For example, if number of inputs is 3, then the inputs have the names 'in1', 'in2', and 'in3'. Use the input names when connecting or disconnecting the layer using the connectLayers or disconnectLayers functions.

Data Types: double

Name — Layer name

'' (default) | character vector

Layer name, specified as a character vector. For Layer array input, the trainnet and dlnetwork functions automatically assign names to unnamed layers.

Data Types: char

InputNames — Input Names

Read-only: {'in1','in2',…,'inN'} (default)

This property is read-only.

Input names, represented as {'in1','in2',...,'inN'}, where N is the number of inputs of the layer.

Data Types: cell

NumOutputs — Number of outputs

Read-only: 1 (default)

This property is read-only.

Number of outputs from the layer, represented as 1. This layer has a single output only.

Data Types: double

OutputNames — Output name

Read-only: {'out'} (default)

This property is read-only.

Output name, represented as {'out'}. This layer has a single output only.

Examples

Create and Connect Addition Layer

Create an empty neural network dlnetwork object and add an addition layer with two inputs and the name 'add'.

net = dlnetwork;
layer = additionLayer(2,'Name','add');
net = addLayers(net,layer);

Add two ReLU layers to the neural network and connect them to the addition layer. The addition layer outputs the sum of the outputs from the ReLU layers.

layer = reluLayer('Name','relu1');
net = addLayers(net,layer);
net = connectLayers(net,'relu1','add/in1');

layer = reluLayer('Name','relu2');
net = addLayers(net,layer);
net = connectLayers(net,'relu2','add/in2');

Visualize the updated network in a plot.

plot(net)

Algorithms

Layer Input and Output Formats

Most layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray objects. The format of a dlarray object is a string of characters in which each character describes the corresponding dimension of the data. The format consists of one or more of these characters:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, you can describe 2-D image data that is represented as a 4-D array, where the first two dimensions correspond to the spatial dimensions of the images, the third dimension corresponds to the channels of the images, and the fourth dimension corresponds to the batch dimension, as having the format "SSCB" (spatial, spatial, channel, batch).

You can interact with these dlarray objects in automatic differentiation workflows, such as those for:

• developing a custom layer

• using a functionLayer object

• using the forward and predict functions with dlnetwork objects

This table shows the supported input formats of AdditionLayer objects and the corresponding output format. If the software passes the output of the layer to a custom layer that does not inherit from the nnet.layer.Formattable class, or to a FunctionLayer object with the Formattable property set to 0 (false), then the layer receives an unformatted dlarray object with dimensions ordered according to the formats in this table. The formats listed here are only a subset of the formats that the layer supports. The layer might support additional formats, such as formats with additional "S" (spatial) or "U" (unspecified) dimensions.

Input Format	Output Format
"CB" (channel, batch)	"CB" (channel, batch)
"SCB" (spatial, channel, batch)	"SCB" (spatial, channel, batch)
"SSCB" (spatial, spatial, channel, batch)	"SSCB" (spatial, spatial, channel, batch)
"SSSCB" (spatial, spatial, spatial, channel, batch)	"SSSCB" (spatial, spatial, spatial, channel, batch)
"CBT" (channel, batch, time)	"CBT" (channel, batch, time)
"SCBT" (spatial, channel, batch, time)	"SCBT" (spatial, channel, batch, time)
"SSCBT" (spatial, spatial, channel, batch, time)	"SSCBT" (spatial, spatial, channel, batch, time)
"SSSCBT" (spatial, spatial, spatial, channel, batch, time)	"SSSCBT" (spatial, spatial, spatial, channel, batch, time)
"CU" (channel, unspecified)	"CU" (channel, unspecified)
"SC" (spatial, channel)	"SC" (spatial, channel)
"SSC" (spatial, spatial, channel)	"SSC" (spatial, spatial, channel)
"SSSC" (spatial, spatial, spatial, channel)	"SSSC" (spatial, spatial, spatial, channel)
"BU" (batch, unspecified)	"BU" (batch, unspecified)

If several input formats contain the same label, then the corresponding data for each input must be a singleton or the same size as the nonsingleton data for the other inputs. If an input format contains multiple U or S labels, additionLayer matches each label to the labels in the other input formats by their order of appearance.

In dlnetwork objects, AdditionLayer objects also support these input and output format combinations.

Input Format	Output Format
"CT" (channel, time)	"CT" (channel, time)
"SCT" (spatial, channel, time)	"SCT" (spatial, channel, time)
"SSCT" (spatial, spatial, channel, time)	"SSCT" (spatial, spatial, channel, time)
"SSSCT" (spatial, spatial, spatial, channel, time)	"SSSCT" (spatial, spatial, spatial, channel, time)

Complex Numbers

AdditionLayer objects support complex-valued input and outputs (since R2024a). The layer applies the same underlying operation to complex-valued input as it does to real-valued input and outputs complex-valued data where applicable.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Version History

Introduced in R2017b

R2024a: Support for complex-valued inputs and outputs

AdditionLayer objects support complex-valued input and outputs. The layer applies the same underlying operation to complex-valued input as it does to real-valued input and outputs complex-valued data where applicable.