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
[net,tr] = train(net,...) trains the network with
Training occurs according to
trainscg training parameters, shown here
with their default values:
Maximum number of epochs to train
Epochs between displays (NaN for no displays)
Generate command-line output
Show training GUI
Maximum time to train in seconds
Minimum performance gradient
Maximum validation failures
Determine change in weight for second derivative approximation
Parameter for regulating the indefiniteness of the Hessian
You can create a standard network that uses
cascadeforwardnet. To prepare a custom
network to be trained with
net.trainParam properties to desired
In either case, calling
train with the resulting network trains the
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)
help feedforwardnet and
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
perf with respect to the weight and bias variables
The scaled conjugate gradient algorithm is based on conjugate directions, as in
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
The performance gradient falls below
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