Neural Networks - Algorithms, Applications,and ... - Csbdu.in

108 BackpropagationFigure 3.7This BPN will do four-to-one data compression.the input to the network and the target output. Using the backpropagationprocess, the network will develop the internal weight coding so that the imageis compressed into one-quarter of its original size at the outputs of thehidden units. If we then read out the values produced by the hidden-layerunits in our network and transmit those values to our receiving station, wecan reconstruct the original image by propagating the compressed image tothe output units in an identical network. Such a system is depicted in Figure3.8.Network Sizing. There are two problems remaining to be solved for this application:the first is the network-sizing problem, and the second is the generationof the sample data sets needed to train the network. We will address thenetwork-sizing aspect first.It is unrealistic to expect to create a network that will contain an inputunit for every pixel in a single video image. Even if we restricted ourselves tothe relatively low resolution of the 525-line scan rate specified by the NationalTelevision Standard Code (NTSC) for commercial television, our network wouldhave to have 336,000 input units (525 lines x 640 pixels). Moreover, the entirenetwork would contain roughly 750,000 processing units (336000 + 336000/4 +336000) and 50 billion connections. As we have mentioned in earlier chapters,simulating a network containing a large number of units and a vast number