Neural Network Toolbox™

Create pattern recognition network

Syntax

• net = newfit(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF)
  

Description

newfit(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) takes the following:

P	R x Q1 matrix of Q1 representative R-element input vectors.
T	SN x Q2 matrix of Q2 representative SN-element target vectors.
Si	Sizes of N-1 hidden layers, S1 to S(N-1), default = []. (Output layer size SN is determined from T.)
TFi	Transfer function of ith layer. Default is 'tansig' for hidden layers, and 'linear' for output layer.
BTF	Backpropagation network training function. Default = 'trainlm'.
BLF	Backpropagation weight/bias learning function. Default = 'learngdm'.
PF	Performance function, default = 'mse'.
IPF	Row cell array of input processing functions. Default is {'fixunknowns','remconstantrows','mapminmax'}.
OPF	Row cell array of output processing functions. Default is {'remconstantrows','mapminmax'}.
DDF	Data division function. Default = 'dividerand'.

It returns an N-layer feed-forward backpropagation network.

The transfer functions TF{i} can be any differentiable transfer function such as tansig, logsig, or purelin.

The training function BTF can be any of the backpropagation training functions such as trainlm, trainbfg, trainrp, traingd, etc.

Memory Requirements

trainlm is the default training function because it is very fast, but it requires a lot of memory to run. If you get an "out-of-memory" error when training, try doing one of these approaches:

1. Slow trainlm training, but reduce memory requirements, by setting NET.trainParam.mem_reduc to 2 or more. (See HELP TRAINLM.)
2. Use trainbfg, which is slower but more memory efficient than trainlm.
3. Use trainrp, which is slower but more memory efficient than trainbfg.

The learning function BLF can be either of the backpropagation learning functions such as learngd or learngdm.

The performance function can be any of the differentiable performance functions such as mse or msereg.

Examples

• load simpleclass_dataset
  net = newpr(simpleclassInputs,simpleclassTargets,20);
  net = train(net,simpleclassInputs,simpleclassTargets);
  simpleclassOutputs = sim(net,simpleclassInputs);
  

Algorithm

newpr returns a network exactly as newff does, but with an output layer transfer function of 'tansig' and additional plotting functions included in the network's net.plotFcn property.

See Also

newff, newcf, newelm, sim, init, adapt, train, trains

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