Neural network

This article shows how the Multi Propagation (MPROP) algorithm was implemented for Encog for Java. Though this article focuses on the Java implementation the C# version would be very similar. MPROP is based on resilient propagation, but is designed to work well with multicore computers and gain maximum performance.

    1. What is the best general purpose training algorithm, provided by Encog, for a feedforward neural network?

    2. What is the difference in the training data used by supervised and unsupervised training?

    3. Can both neural networks and training data be stored in an .EG file?

    4. Why should training a neural network occur before querying it?

    5. How else can you load training into the workbench, other than manually entering it.

    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 Encog workbench provides two ways that you can make use of your neural network in Java code. First, you can save the neural network and training data to an .EG file. Java applications can then load data from this .EG file. Using .EG files will be covered in much greater detail in Chapter 9, “Encog Persistence”.

    Another way to generate code is to use the Encog Workbench. The Encog workbench can generate code in the following languages.

    Querying the neural network allows you to specify values for the inputs to the neural network and observe the outputs. To query the neural network, click “Query” at the top of the network editor seen in Figure 4.7. This will open the query window as seen in Figure 4.14.

Figure 4.14: Query the 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.

    A training set is a collection of data to be used to train the neural network. There are two types of training sets commonly used with Encog.

    We will begin by creating a neural network. The Encog Workbench starts with an empty file. Once things have been added to this empty file, it can be saved to an .EG file. This .EG file can then be loaded by the workbench again or loaded by Java or C# Encog applications. The C# and Java versions of Encog read exactly the same type of .EG files.