By Heaton
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The signature for the "getError" method is shown below. public double getError(int len) Now that you have seen how to calculate errors you are ready to see the methods that are used to train the neural network. These methods are covered in the next section. The Training and Resetting Methods Once you have obtained the neural network output and calculated the error for an input pattern, you can train the neural network to better recognize this pattern next time. This is done very simply calling the learn method.
The only property of the teacher layer that should be set is the name. The name property of the teacher layer should be set to "Teacher Layer". The teacher layer requires access to the input data too. 1, which is the anticipated output. A second file input layer should be added to the neural network. This file input layer should be named "Teacher File Input", as it will be connected to the teacher layer. The properties of the teacher file input layer are set as follows. The input file to the teacher file input layer is the same input file as was specified for the main "File Input Layer".
LearnRate – The learning rate for the backpropagation training algorithm. This parameter will be discussed in much greater detail in Chapter 5. momentum – The momentum for the backpropagation training algorithm. This parameter will be discussed in much greater detail in Chapter 5. The input and output layers are determined by how you represent your problem as a neural network. There are many ways to determine this. Through this book you will be presented with problems that will be adapted to neural networks.