trainrp (Neural Network Toolbox)

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trainrp

Examples See Also

RPROP backpropagation

Syntax

[net,tr] = trainrp(net,Pd,Tl,Ai,Q,TS,VV)

info = trainrp(code)

Description

trainrp is a network training function that updates weight and bias values according to the resilient backpropagation algorithm (RPROP).

trainrp(net,Pd,Tl,Ai,Q,TS,VV,TV) takes these inputs,

net - Neural network.

Pd - Delayed input vectors.

Tl - Layer target vectors.

Ai - Initial input delay conditions.

Q - Batch size.

TS - Time steps.

VV - Either empty matrix [] or structure of validation vectors.

TV - Either empty matrix [] or structure of test vectors.

and returns,

net - Trained network.

TR - Training record of various values over each epoch:

TR.epoch - Epoch number.

TR.perf - Training performance.

TR.vperf - Validation performance.

TR.tperf - Test performance.

Training occurs according to the trainrp's training parameters shown here with their default values:

net.trainParam.epochs 100 Maximum number of epochs to train

net.trainParam.show 25 Epochs between showing progress

net.trainParam.goal 0 Performance goal

net.trainParam.time inf Maximum time to train in seconds

net.trainParam.min_grad 1e-6 Minimum performance gradient

net.trainParam.max_fail 5 Maximum validation failures

net.trainParam.lr 0.01 Learning rate

net.trainParam.delt_inc 1.2 Increment to weight change

net.trainParam.delt_dec 0.5 Decrement to weight change

net.trainParam.delta0 0.07 Initial weight change

net.trainParam.deltamax 50.0 Maximum weight change

Dimensions for these variables are:

Pd - No x Ni x TS cell array, each element P{i,j,ts} is a Dij x Q matrix.

Tl - Nl x TS cell array, each element P{i,ts} is a Vi x Q matrix.

Ai - Nl x LD cell array, each element Ai{i,k} is an Si x Q matrix.

where

Ni = net.numInputs

Nl = net.numLayers

LD = net.numLayerDelays

Ri = net.inputs{i}.size

Si = net.layers{i}.size

Vi = net.targets{i}.size

Dij = Ri * length(net.inputWeights{i,j}.delays)

If VV is not [], it must be a structure of validation vectors,

VV.PD - Validation delayed inputs.

VV.Tl - Validation layer targets.

VV.Ai - Validation initial input conditions.

VV.Q - Validation batch size.

VV.TS - Validation time steps.

which is used to stop training early if the network performance on the validation vectors fails to improve or remains the same for max_fail epochs in a row.

If TV is not [], it must be a structure of validation vectors,

TV.PD - Validation delayed inputs.

TV.Tl - Validation layer targets.

TV.Ai - Validation initial input conditions.

TV.Q - Validation batch size.

TV.TS - Validation time steps.

which is used to test the generalization capability of the trained network.

trainrp(code) returns useful information for each code string:

'pnames' - Names of training parameters.

'pdefaults' - Default training parameters.

Examples

Here is a problem consisting of inputs P and targets T that we would like to solve with a network.

P = [0 1 2 3 4 5];
T = [0 0 0 1 1 1];

Here a two-layer feed-forward network is created. The network's input ranges from [0 to 10]. The first layer has two tansig neurons, and the second layer has one logsig neuron. The trainrp network training function is to be used.

Create and Test a Network

net = newff([0 5],[2 1],{'tansig','logsig'},'trainrp');
a = sim(net,p)

Train and Retest the Network

net.trainParam.epochs = 50;
net.trainParam.show = 10;
net.trainParam.goal = 0.1;
net = train(net,p,t);
a = sim(net,p)

See newff, newcf, and newelm for other examples.

Network Use

You can create a standard network that uses trainrp with newff, newcf, or newelm.

To prepare a custom network to be trained with trainrp:

1.: Set net.trainFcn to 'trainrp'. This will set net.trainParam to trainrp's default parameters.

2.: Set net.trainParam properties to desired values.

In either case, calling train with the resulting network will train the network with trainrp.

Algorithm

trainrp can train any network as long as its weight, net input, and transfer functions have derivative functions.

Backpropagation is used to calculate derivatives of performance perf with respect to the weight and bias variables X. Each variable is adjusted according to the following:

dX = deltaX.*sign(gX);

where the elements of deltaX are all initialized to delta0 and gX is the gradient. At each iteration the elements of deltaX are modified. If an element of gX changes sign from one iteration to the next, then the corresponding element of deltaX is decreased by delta_dec. If an element of gX maintains the same sign from one iteration to the next, then the corresponding element of deltaX is increased by delta_inc. See Reidmiller and Braun, Proceedings of the IEEE International Conference on Neural Networks,, 1993, pp. 586-591.

Training stops when any of these conditions occur:

1.: The maximum number of epochs (repetitions) is reached.

2.: The maximum amount of time has been exceeded.

3.: Performance has been minimized to the goal.

4.: The performance gradient falls below mingrad.

5.: Validation performance has increased more than max_fail times since the last time it decreased (when using validation).