Create a layer graph from an array of layers. Connect the'relu_1'layer to the'add'layer.

layers = [ imageInputLayer([32 32 3],'Name','input') convolution2dLayer(3,16,'Padding','same','Name','conv_1') batchNormalizationLayer('Name','BN_1') reluLayer('Name','relu_1') convolution2dLayer(3,16,'Padding','same','Stride',2,'Name','conv_2') batchNormalizationLayer('Name','BN_2') reluLayer('Name','relu_2') additionLayer(2,'Name','add')]; lgraph = layerGraph(layers); lgraph = connectLayers(lgraph,'relu_1','add/in2');

Plot the layer graph.

figure plot(lgraph);

Load a pretrained GoogLeNet convolutional neural network as aDAGNetworkobject. If the Deep Learning Toolbox™ Modelfor GoogLeNet Networksupport package is not installed, then the software provides a download link.

net = googlenet

net = DAGNetwork with properties: Layers: [144×1 nnet.cnn.layer.Layer] Connections: [170×2 table]

Plot the network.

figure('Units','normalized','Position',[0.1 0.1 0.8 0.8]); plot(net)

Load a pretrained AlexNet convolutional neural network as aSeriesNetworkobject. If the Deep Learning Toolbox™ Modelfor AlexNet Networksupport package is not installed, then the software provides a download link.

net = alexnet

net = SeriesNetwork with properties: Layers: [25x1 nnet.cnn.layer.Layer] InputNames: {'data'} OutputNames: {'output'}

Plot the network.

plot(net)