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connectLayers

Connect layers in neural network

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Syntax

netUpdated = connectLayers(net,s,d)

Description

netUpdated = connectLayers(net,s,d) connects the source layer s to the destination layer d in the dlnetwork object net. The updated network, netUpdated, contains the same layers as net and includes the new connection.

example

Examples

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Open Live Script

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)

Figure contains an axes object. The axes object contains an object of type graphplot.

Open Live Script

Define a two-output neural network that predicts both categorical labels and numeric values given 2-D images as input.

Specify the number of classes and responses.

numClasses = 10;
numResponses = 1;

Create an empty neural network.

net = dlnetwork;

Define the layers of the main branch of the network and the softmax output.

layers = [
    imageInputLayer([28 28 1],Normalization="none")

    convolution2dLayer(5,16,Padding="same")
    batchNormalizationLayer
    reluLayer(Name="relu_1")

    convolution2dLayer(3,32,Padding="same",Stride=2)
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3,32,Padding="same")
    batchNormalizationLayer
    reluLayer

    additionLayer(2,Name="add")

    fullyConnectedLayer(numClasses)
    softmaxLayer(Name="softmax")];

net = addLayers(net,layers);

Add the skip connection.

layers = [
    convolution2dLayer(1,32,Stride=2,Name="conv_skip")
    batchNormalizationLayer
    reluLayer(Name="relu_skip")];

net = addLayers(net,layers);
net = connectLayers(net,"relu_1","conv_skip");
net = connectLayers(net,"relu_skip","add/in2");

Add the fully connected layer for the regression output.

layers = fullyConnectedLayer(numResponses,Name="fc_2");
net = addLayers(net,layers);
net = connectLayers(net,"add","fc_2");

View the neural network in a plot.

figure
plot(net)

Figure contains an axes object. The axes object contains an object of type graphplot.

Input Arguments

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Neural network, specified as a dlnetwork object.

Connection source, specified as a character vector or a string scalar.

  • If the source layer has a single output, then s is the name of the layer.

  • If the source layer has multiple outputs, then s is the layer name followed by the "/" character and the name of the layer output: "layerName/outputName".

Example: "conv"

Example: "mpool/indices"

Connection destination, specified as a string scalar or a character vector.

  • If the destination layer has a single input, then d is the name of the layer.

  • If the destination layer has multiple inputs, then d is the layer name followed by the "/" character and the name of the layer input: "layerName/inputName".

Example: "fc"

Example: "add/in1"

Output Arguments

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Updated network, returned as an uninitialized dlnetwork object.

To initialize the learnable parameters of a dlnetwork object, use the initialize function.

The connectLayers function does not preserve quantization information. If the input network is a quantized network, then the output network does not contain quantization information.

Version History

Introduced in R2017b

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See Also

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