patternnet

Pattern recognition network

Syntax

patternnet(hiddenSizes,trainFcn,performFcn)

Description

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 i, where i is the class they are to represent.

patternnet(hiddenSizes,trainFcn,performFcn) takes these arguments,

hiddenSizes

Row vector of one or more hidden layer sizes (default = 10)

trainFcn

Training function (default = 'trainscg')

performFcnPerformance function (default = 'crossentropy')

and returns a pattern recognition neural network.

Examples

Pattern Recognition

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);

See Also

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Topics

Introduced in R2010b

Deep Learning Toolbox Documentation
Support