analyzeNetwork

Analyze deep learning network architecture

Syntax

analyzeNetwork(layers)

Description

analyzeNetwork(layers) analyzes the deep learning network architecture specified by layers. The analyzeNetwork function displays an interactive visualization of the network architecture, detects errors and issues with the network, and provides detailed information about the network layers. The layer information includes the sizes of layer activations and learnable parameters, the total number of learnable parameters, and the sizes of state parameters of recurrent layers. Use the network analyzer to visualize and understand the network architecture, check that you have defined the architecture correctly, and detect problems before training. Problems that analyzeNetwork detects include missing or disconnected layers, mismatching or incorrect sizes of layer inputs, incorrect number of layer inputs, and invalid graph structures.

Examples

Analyze Trained Network

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)

Fix Errors in Network Architecture

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:

• A softmax layer, which outputs class probabilities, must precede the classification layer. To fix the error in the output classification layer, add a softmax layer before the classification layer.

• The skipConv layer is not connected to the rest of the network. It should be a part of the shortcut connection between the add1 and add2 layers. To fix this error, connect add1 to skipConv and skipConv to add2.

• The add2 layer is specified to have three inputs, but the layers only has two inputs. To fix the error, specify the number of inputs as 2.

• All the inputs to an addition layer must have the same size, but the add1 layer has two inputs with different sizes. Because the conv_2 layer has a 'Stride' value of 2, this layer downsamples the activations by a factor of two in the first two dimensions (the spatial dimensions). To resize the input from the relu2 layer so that it has the same size as the input from relu1, remove the downsampling by setting the 'Stride' value of the conv_2 layer to 1.

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)

Input Arguments

layers — Network architecture

Layer array | LayerGraph object | SeriesNetwork object | DAGNetwork object

Network architecture, specified as a Layer array, LayerGraph object, SeriesNetwork object, or DAGNetwork object.