Summary

    In this chapter you saw how to use the Encog Workbench. The Encog Workbench provides a way to edit the .EG files produced by the Encog Framework. There are also templates available to help you quickly create common neural network patterns. There is also a GUI network editor that allows networks to be designed using drag and drop functionality.

    The workbench allows training data to be created as well. Training data can be manually entered or imported from a CSV file. Training data includes the input to the neural network, as well as the expected output. Training data that only includes input data will be used in unsupervised training. Training data that includes both input and expected output will be used in supervised training.

    The neural network can be trained using many different training algorithms. For a feedforward neural network, one of the best choices is the resilient propagation algorithm. The Encog Workbench allows you to enter parameters for the training, and then watch the progress of the training.

    The Encog Workbench will generate the code necessary to produce a neural network that was designed with it. The workbench can generate code in Java, C# or VB.Net. This code shows how to construct the neural network with the necessary layers, synapses, properties and layer tags.

    The code generated in this chapter was capable of creating the neural network that was designed in the workbench. However, you needed to add your own training code to make the program functional. The next chapter will introduce some of the ways to train a neural network.