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9798 8 From which we see that neuron 1 has won. 6. 714 Now let’s calculate the length of the new weight vector (the training formula doesn’t preserve length). 79 57 Worked example (continued). Finally then let’s plot a graph showing what’s happened: Old weight vector New vector We can see that the weight vector has moved towards the input. Obviously then, this network requires a little more thought to set up compared to some of the others. The weights and inputs need to be processed so that they are all vectors of length one.
5 An alternative view This leads to an alternative view of the network’s operation which considers that information from many inputs is “squeezed” into few hidden layer units. The hidden layer is then forced to extract the most important distinguishing characteristics of the data (because it hasn’t enough weights to store everything). 3). So having few hidden layer neurons is good because it forces the network to extract as few features as possible, leading again to good generalisation. 3: All the graphs so far have shown a threshold function neuron.
3: All the graphs so far have shown a threshold function neuron. How would a sigmoid function be represented on such a graph? 6 What neural nets are bad at The arguments above demonstrate the type of problems that networks are good at and those that they are bad at. If the inputs corresponding to output “1s” and “0s” are mixed and distributed randomly through the graph, then the network would have a hard time picking all of these up (and generalising them). 7 (and that would be a lot of neurons).