depthConcatenationLayer

Depth concatenation layer

Description

A depth concatenation layer takes inputs that have the same height and width and concatenates them along the channel dimension.

Specify the number of inputs to the layer when you create it. The inputs 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.

Creation

Syntax

layer = depthConcatenationLayer(numInputs)

layer = depthConcatenationLayer(numInputs,'Name',name)

Description

layer = depthConcatenationLayer(numInputs) creates a depth concatenation layer that concatenates numInputs inputs along the channel dimension. This function also sets the NumInputs property.

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

Properties

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',...,'inN', where N is NumInputs. 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.

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.

The DepthConcatenationLayer object stores this property as a character vector.

Data Types: char | string

InputNames — Input Names

{'in1','in2',…,'inN'} (default)

Input names, specified 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, stored as 1. This layer has a single output only.

Data Types: double

OutputNames — Output names

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

This property is read-only.

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

Data Types: cell

Examples

Create and Connect Depth Concatenation Layer

Create a depth concatenation layer with two inputs and the name 'concat_1'.

concat = depthConcatenationLayer(2,'Name','concat_1')

concat = 
  DepthConcatenationLayer with properties:

          Name: 'concat_1'
     NumInputs: 2
    InputNames: {'in1'  'in2'}

Create two ReLU layers and connect them to the depth concatenation layer. The depth concatenation layer concatenates the outputs from the ReLU layers.

relu_1 = reluLayer('Name','relu_1');
relu_2 = reluLayer('Name','relu_2');

net = dlnetwork;
net = addLayers(net,relu_1);
net = addLayers(net,relu_2);
net = addLayers(net,concat);

net = connectLayers(net,'relu_1','concat_1/in1');
net = connectLayers(net,'relu_2','concat_1/in2');

plot(net)

Algorithms

Layer Input and Output Formats

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, or using the forward and predict functions with dlnetwork objects.

This table shows the supported input formats of DepthConcatenationLayer 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 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. The layer may support additional formats such as formats with additional "S" (spatial) or "U" (unspecified) dimensions.

The output format matches the input format.

Input Format	Output Format
"SSCB" (spatial, spatial, channel, batch)	"SSCB" (spatial, spatial, channel, batch)
"SSSCB" (spatial, spatial, spatial, channel, batch)	"SSSCB" (spatial, spatial, spatial, channel, batch)
"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)
"SSC" (spatial, spatial, channel)	"SSC" (spatial, spatial, channel)
"SSSC" (spatial, spatial, spatial, channel)	"SSSC" (spatial, spatial, spatial, channel)

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

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

Complex Numbers

DepthConcatenationLayer 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

DepthConcatenationLayer 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.