Training a Neural Network

    Training a neural network is a process where the neural network's weights and thresholds are modified so that the neural network will produce output according to the training data. There are many different ways to train a neural network. The choice of training method will be partially determined by the neural network type you are creating. Not all neural network types work with all training methods.

    To train the neural network open it as you did for Figure 4.7. Click the “Train” button at the top of the window. This will display a dialog box that allows you to choose a training method, as seen in Figure 4.10.

Figure 4.10: Choosing a Training Method

    Choose the resilient training method, under propagation. This is usually the best training method available for a supervised feedforward neural network. There are several parameters you can set for the resilient training method. For resilient training it is very unlikely that you should ever change any of these options, other than perhaps the desired maximum error, which defaults to 1%. You can see this dialog box in Figure 4.11.

Figure 4.11: Resilient Propagation Training

    Selecting okay will open a window that will allow you to monitor the training progress, as seen in Figure 4.12.

Figure 4.12: About to Begin Training

    To begin training, click the “Start” button on the training dialog box. The network will begin training. For complex networks, this process can go on for days. This is a very simple network that will finish in several hundred iterations. You will not likely even see the graph begin as the training will complete in a matter of seconds. Once the training is complete, you will see the following screen.

Figure 4.13: Training Complete

    The training is complete because the current error fell below the maximum error allowed that was entered in Figure 4.11, which is 1%. Now that the network has been trained it can produce meaningful output when queried.