After we've defined the network structure, we need to define the loss function for the optimization algorithm. The ErrorFunction class is used for this purpose. Its constructor takes four parameters:

• An object representing the training dataset
• A pointer to the object that represents the network's structure
• A pointer to the object that implements the loss function—SquaredLoss, in our case
• A Boolean flag telling us to use mini-batches or not

The ErrorFunction type object can be configured with a regularizer. For example, we can instantiate the object of the TwoNormRegularizer class and call the setRegularizer method in the ErrorFunction type object with two parameters: the weight decay factor value and the pointer to the regularizer object. To finish the configuration of the ErrorFunction type object, we need to call the init method.