By Christopher MacLeod
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Additional info for An Introduction to Practical Neural Networks and Genetic Algorithms For Engineers and Scientists
So can we use this scanning action to help a neural network find the pattern it’s looking for? 34 The answer to this question is yes. Exactly the same thing can be done with an imagerecognising network. 3. During this process, the pattern to be recognised will eventually end up in the centre of the grid. This is not dissimilar to what our eyes do when we study a scene. One important point about training a network for this sort of task is that we have to train it to recognise “noise” or irrelevant data as well as the wanted pattern or it will give false positives.
First we must make it one unit in length. 51 1 2 3 Output 1 Output 2 Output 3 Note that, since we’re only interested in the neuron with the largest output, there’s no need to apply a sigmoid or threshold squashing function to the neuron. 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.
Image information is reduced to correct size for network inputs by pixel averaging or similar method. Network input starts as whole image size When one size has scanned the image, network input area reduces in size Input area keeps reducing and scanning until it is smallest practical size. Since the neural net inputs are of a fixed size, inputting different sizes of image means manipulating the original image size until it fits onto the network inputs. This is easily done by averaging adjacent pixels together until the image is the correct size for the network.