AdditionLayer - Addition layer - MATLAB (original) (raw)

Description

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

Specify the number of inputs to the layer when you create it. The inputs to the layer have the names 'in1','in2',...,'inN', where N is the number of inputs. Use the input names when connecting or disconnecting the layer by using connectLayers or disconnectLayers. All inputs to an addition layer must have the same dimension.

Creation

Syntax

Description

`layer` = additionLayer(`numInputs`) creates an addition layer that adds numInputs inputs element-wise. This function also sets the NumInputs property.

example

`layer` = additionLayer(`numInputs`,'Name',name) also sets the Name property.

example

Properties

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

The inputs have the names 'in1','in2',...,'inN', where N isNumInputs. For example, if NumInputs 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: char | string

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

Data Types: cell

This property is read-only.

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

Data Types: double

This property is read-only.

Output names, stored as {'out'}. This layer has a single output only.

Data Types: cell

Examples

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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.

Algorithms

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Layers in a layer array or neural network 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 formats consists of one or more of these characters:

• "S" — Spatial
• "C" — Channel
• "B" — Batch
• "T" — Time
• "U" — Unspecified

For example, 2-D image data 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, can be described 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, or using the forward andpredict functions with dlnetwork objects.

This table shows the supported input formats ofAdditionLayer 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 a FunctionLayer object with theFormattable property set to 0 (false), then the layer receives an unformatteddlarray object with dimensions ordered according to the formats in this table. The formats listed here are only a subset. The layer may support additional formats such as formats with additional"S" (spatial) or "U" (unspecified) dimensions.

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.

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

Version History

Introduced in R2017b

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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.