Figure: Example network of the letter classifier
This paragraph describes a simple example network, a neural network classifier for capital letters in a 5x7 matrix, which is ready for use with the SNNS simulator. Note that this is a toy example which is not suitable for real character recognition.
The network in figure is a feed-forward net with three layers of units (two layers of weights) which can recognize capital letters. The input is a 5x7 matrix, where one unit is assigned to each pixel of the matrix. An activation of corresponds to ``pixel set'', while an activation value of corresponds to ``pixel not set''. The output of the network consists of exactly one unit for each capital letter of the alphabet.
The following activation function and output function are used by default:
The net has one input layer (5x7 units), one hidden layer (10 units) and one output layer (26 units named 'A' ... 'Z'). The total of connections form the distributed memory of the classifier.
On presentation of a pattern that resembles the uppercase letter ``A'', the net produces as output a rating of which letters are probable.