Configure Shallow Neural Network Inputs and Outputs

This topic is part of the design workflow described in Workflow for Neural Network Design.

After a neural network has been created, it must be configured. The configuration step consists of examining input and target data, setting the network's input and output sizes to match the data, and choosing settings for processing inputs and outputs that will enable best network performance. The configuration step is normally done automatically, when the training function is called. However, it can be done manually, by using the configuration function. For example, to configure the network you created previously to approximate a sine function, issue the following commands:

p = -2:.1:2;
t = sin(pi*p/2);
net1 = configure(net,p,t);

You have provided the network with an example set of inputs and targets (desired network outputs). With this information, the configure function can set the network input and output sizes to match the data.

After the configuration, if you look again at the weight between layer 1 and layer 2, you can see that the dimension of the weight is 1 by 10. This is because the target for this network is a scalar.

net1.layerWeights{2,1}

    Neural Network Weight

            delays: 0
           initFcn: (none)
        initConfig: .inputSize
             learn: true
          learnFcn: 'learngdm'
        learnParam: .lr, .mc
              size: [1 10]
         weightFcn: 'dotprod'
       weightParam: (none)
          userdata: (your custom info)

In addition to setting the appropriate dimensions for the weights, the configuration step also defines the settings for the processing of inputs and outputs. The input processing can be located in the inputs subobject:

net1.inputs{1}

    Neural Network Input

              name: 'Input'
    feedbackOutput: []
       processFcns: {'removeconstantrows', mapminmax}
     processParams: {1x2 cell array of 2 params}
   processSettings: {1x2 cell array of 2 settings}
    processedRange: [1x2 double]
     processedSize: 1
             range: [1x2 double]
              size: 1
          userdata: (your custom info)

Before the input is applied to the network, it will be processed by two functions: removeconstantrows and mapminmax. These are discussed fully in Multilayer Shallow Neural Networks and Backpropagation Training so we won't address the particulars here. These processing functions may have some processing parameters, which are contained in the subobject net1.inputs{1}.processParam. These have default values that you can override. The processing functions can also have configuration settings that are dependent on the sample data. These are contained in net1.inputs{1}.processSettings and are set during the configuration process. For example, the mapminmax processing function normalizes the data so that all inputs fall in the range [−1, 1]. Its configuration settings include the minimum and maximum values in the sample data, which it needs to perform the correct normalization. This will be discussed in much more depth in Multilayer Shallow Neural Networks and Backpropagation Training.

As a general rule, we use the term “parameter,” as in process parameters, training parameters, etc., to denote constants that have default values that are assigned by the software when the network is created (and which you can override). We use the term “configuration setting,” as in process configuration setting, to denote constants that are assigned by the software from an analysis of sample data. These settings do not have default values, and should not generally be overridden.

For more information, see also Understanding Shallow Network Data Structures.