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Feed-Forward Network

Introduction

Feed-forward network boxes provide COGENT with a general two-layer feed-forward network capability. They provide an object which consists of a set of input nodes and a set of output nodes, together with a set of weighted connections between the nodes. Properties can be used to set the number of nodes in the input and output sets can be set to any arbitrary integer. Networks are able to map input vectors (of the correct width) to output vectors, and to learn input/output correspondences (subject to the usual perceptron learning limitations).

Networks can be sent a variety of messages. If a network is sent a raw vector (represented by a list of numbers), and the width of that vector is equal to the width of the network's input layer, the network will transform the input vector and produce an output vector, which will be sent off along any send arrows leaving the network. If, on the other hand, a network receives a signal of the form train(InputVector, OutputVector), where InputVector is a vector whose width is equal to that of the network's input layer and where OutputVector is a vector whose width is equal to that of the network's output layer, then the network will adjust its weight matrix so when triggered by InputVector, its output more nearly approximates OutputVector. Any message received by a network which is not of one of the above two forms will result in a warning.

If a network receives several input vectors at once, then all vectors are processed (in pseudo-parallel) and a set of output vectors is generated. If a network receives several training vector pairs at once, then all pairs are used to calculate a set of weight modifications, and the average weight modifications are applied to the network. Thus, a network can be trained sequentially (by feeding it a different training pair on each cycle), or it can be trained in a parallel burst (by feeding a whole set of training pairs at once). If a network is sent both raw vectors and training data on the same cycle, the raw vectors are processed before the training takes effect.

Properties

Networks are highly configurable. Fourteen properties govern their behaviour:


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