Load a pretrained GoogLeNet convolutional neural network.

net = googlenet

net = 
  DAGNetwork with properties:

         Layers: [144×1 nnet.cnn.layer.Layer]
    Connections: [170×2 table]

Analyze the network. analyzeNetwork displays an interactive plot of the network architecture and a table containing information about the network layers.

Investigate the network architecture using the plot to the left. Select a layer in the plot. The selected layer is highlighted in the plot and in the layer table.

In the table, view layer information such as layer properties, layer type, and sizes of the layer activations and learnable parameters. The activations of a layer are the outputs of that layer.

Select a deeper layer in the network. Notice that activations in deeper layers are smaller in the spatial dimensions (the first two dimensions) and larger in the channel dimension (the last dimension). Using this structure enables convolutional neural networks to gradually increase the number of extracted image features while decreasing the spatial resolution.

Show the total number of learnable parameters in each layer by clicking the arrow in the top-right corner of the layer table and select Total Learnables. To sort the layer table by column value, hover the mouse over the column heading and click the arrow that appears. For example, you can determine which layer contains the most parameters by sorting the layers by the total number of learnable parameters.

analyzeNetwork(net)

Create a simple convolutional network with shortcut connections. Create the main branch of the network as an array of layers and create a layer graph from the layer array. layerGraph connects all the layers in layers sequentially.

layers = [
    imageInputLayer([32 32 3],'Name','input')
    
    convolution2dLayer(5,16,'Padding','same','Name','conv_1')
    reluLayer('Name','relu_1')
    
    convolution2dLayer(3,16,'Padding','same','Stride',2,'Name','conv_2')
    reluLayer('Name','relu_2') 
    additionLayer(2,'Name','add1')
    
    convolution2dLayer(3,16,'Padding','same','Stride',2,'Name','conv_3')
    reluLayer('Name','relu_3') 
    additionLayer(3,'Name','add2')
    
    fullyConnectedLayer(10,'Name','fc')
    classificationLayer('Name','output')];

lgraph = layerGraph(layers);

Create the shortcut connections. One of the shortcut connections contains a single 1-by-1 convolutional layer skipConv.

skipConv = convolution2dLayer(1,16,'Stride',2,'Name','skipConv');
lgraph = addLayers(lgraph,skipConv);
lgraph = connectLayers(lgraph,'relu_1','add1/in2');
lgraph = connectLayers(lgraph,'add1','add2/in2');

Analyze the network architecture. analyzeNetwork finds four errors in the network.

analyzeNetwork(lgraph)

Investigate and fix the errors in the network. In this example, the following issues cause the errors:

Apply these modifications to the layer graph construction from the beginning of this example and create a new layer graph.

layers = [
    imageInputLayer([32 32 3],'Name','input')
    
    convolution2dLayer(5,16,'Padding','same','Name','conv_1')
    reluLayer('Name','relu_1')
    
    convolution2dLayer(3,16,'Padding','same','Stride',1,'Name','conv_2')
    reluLayer('Name','relu_2') 
    additionLayer(2,'Name','add1')
    
    convolution2dLayer(3,16,'Padding','same','Stride',2,'Name','conv_3')
    reluLayer('Name','relu_3') 
    additionLayer(2,'Name','add2')
    
    fullyConnectedLayer(10,'Name','fc')
    softmaxLayer('Name','softmax');
    classificationLayer('Name','output')];

lgraph = layerGraph(layers);

skipConv = convolution2dLayer(1,16,'Stride',2,'Name','skipConv');
lgraph = addLayers(lgraph,skipConv);
lgraph = connectLayers(lgraph,'relu_1','add1/in2');
lgraph = connectLayers(lgraph,'add1','skipConv');
lgraph = connectLayers(lgraph,'skipConv','add2/in2');

Analyze the new architecture. The new network does not contain any errors and is ready to be trained.

analyzeNetwork(lgraph)

Create a layer graph for a custom training loop. For custom training loop workflows, the layer graph must not have an output layer.

layers = [
    imageInputLayer([28 28 1],'Normalization','none','Name','input')
    convolution2dLayer(5, 20,'Name','conv1')
    batchNormalizationLayer('Name','bn1')
    reluLayer('Name','relu1')
    convolution2dLayer(3,20,'Padding',1,'Name','conv2')
    batchNormalizationLayer('Name','bn2')
    reluLayer('Name','relu2')
    convolution2dLayer(3, 20,'Padding', 1,'Name','conv3')
    batchNormalizationLayer('Name','bn3')
    reluLayer('Name','relu3')
    fullyConnectedLayer(10,'Name','fc')
    softmaxLayer('Name','softmax')];

lgraph = layerGraph(layers);

Analyze the layer graph using the analyzeNetwork function and set the 'TargetUsage' option to 'dlnetwork'.

analyzeNetwork(lgraph,'TargetUsage','dlnetwork')

Here, the function does not report any issues with the layer graph.