Scaled conjugate gradient backpropagation
net.trainFcn = 'trainscg'
[net,tr] = train(net,...)
trainscg is a network training function that updates weight and bias
values according to the scaled conjugate gradient method.
net.trainFcn = 'trainscg' sets the network trainFcn
property.
[net,tr] = train(net,...) trains the network with
trainscg.
Training occurs according to trainscg training parameters, shown here
with their default values:
net.trainParam.epochs | 1000 | Maximum number of epochs to train |
net.trainParam.show | 25 | Epochs between displays (NaN for no displays) |
net.trainParam.showCommandLine | false | Generate command-line output |
net.trainParam.showWindow | true | Show training GUI |
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 | 6 | Maximum validation failures |
net.trainParam.sigma | 5.0e-5 | Determine change in weight for second derivative approximation |
net.trainParam.lambda | 5.0e-7 | Parameter for regulating the indefiniteness of the Hessian |
You can create a standard network that uses trainscg with
feedforwardnet or cascadeforwardnet. To prepare a custom
network to be trained with trainscg,
Set net.trainFcn to 'trainscg'.
This sets net.trainParam to trainscg’s default
parameters.
Set net.trainParam properties to desired
values.
In either case, calling train with the resulting network trains the
network with trainscg.
Here is a problem consisting of inputs p and targets
t to be solved with a network.
p = [0 1 2 3 4 5]; t = [0 0 0 1 1 1];
A two-layer feed-forward network with two hidden neurons and this training function is created.
net = feedforwardnet(2,'trainscg');
Here the network is trained and retested.
net = train(net,p,t); a = net(p)
See help feedforwardnet and help cascadeforwardnet
for other examples.
trainscg 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.
The scaled conjugate gradient algorithm is based on conjugate directions, as in
traincgp, traincgf, and traincgb, but
this algorithm does not perform a line search at each iteration. See Moller (Neural
Networks, Vol. 6, 1993, pp. 525–533) for a more detailed discussion of the scaled
conjugate gradient algorithm.
Training stops when any of these conditions occurs:
The maximum number of epochs (repetitions) is reached.
The maximum amount of time is exceeded.
Performance is minimized to the goal.
The performance gradient falls below min_grad.
Validation performance has increased more than max_fail times since
the last time it decreased (when using validation).
Moller, Neural Networks, Vol. 6, 1993, pp. 525–533
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