Pattern recognition network
Pattern recognition networks are feedforward networks that can
be trained to classify inputs according to target classes. The target
data for pattern recognition networks should consist of vectors of
all zero values except for a 1 in element
the class they are to represent.
Row vector of one or more hidden layer sizes (default = 10)
Training function (default =
|Performance function (default = |
and returns a pattern recognition neural network.
This example shows how to design a pattern recognition network to classify iris flowers.
[x,t] = iris_dataset; net = patternnet(10); net = train(net,x,t); view(net) y = net(x); perf = perform(net,t,y); classes = vec2ind(y);