Conjugate gradient backpropagation with PolakRibiére updates
net.trainFcn = 'traincgp'
[net,tr] = train(net,...)
traincgp
is a network training function that updates weight and bias
values according to conjugate gradient backpropagation with PolakRibiére updates.
net.trainFcn = 'traincgp'
sets the network trainFcn
property.
[net,tr] = train(net,...)
trains the network with
traincgp
.
Training occurs according to traincgp
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 ( 
net.trainParam.showCommandLine  false  Generate commandline 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  1e10  Minimum performance gradient 
net.trainParam.max_fail  6  Maximum validation failures 
net.trainParam.searchFcn  'srchcha'  Name of line search routine to use 
Parameters related to line search methods (not all used for all methods):
net.trainParam.scal_tol  20  Divide into 
net.trainParam.alpha  0.001  Scale factor that determines sufficient reduction in

net.trainParam.beta  0.1  Scale factor that determines sufficiently large step size 
net.trainParam.delta  0.01  Initial step size in interval location step 
net.trainParam.gama  0.1  Parameter to avoid small reductions in performance, usually set to

net.trainParam.low_lim  0.1  Lower limit on change in step size 
net.trainParam.up_lim  0.5  Upper limit on change in step size 
net.trainParam.maxstep  100  Maximum step length 
net.trainParam.minstep  1.0e6  Minimum step length 
net.trainParam.bmax  26  Maximum step size 
You can create a standard network that uses traincgp
with
feedforwardnet
or cascadeforwardnet
. To prepare a custom
network to be trained with traincgp
,
Set net.trainFcn
to 'traincgp'
.
This sets net.trainParam
to traincgp
’s default
parameters.
Set net.trainParam
properties to desired
values.
In either case, calling train
with the resulting network trains the
network with traincgp
.
traincgp
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:
X = X + a*dX;
where dX
is the search direction. The parameter a
is
selected to minimize the performance along the search direction. The line search function
searchFcn
is used to locate the minimum point. The first search direction is
the negative of the gradient of performance. In succeeding iterations the search direction is
computed from the new gradient and the previous search direction according to the formula
dX = gX + dX_old*Z;
where gX
is the gradient. The parameter Z
can be
computed in several different ways. For the PolakRibiére variation of conjugate gradient,
it is computed according to
Z = ((gX  gX_old)'*gX)/norm_sqr;
where norm_sqr
is the norm square of the previous gradient, and
gX_old
is the gradient on the previous iteration. See page 78 of Scales
(Introduction to NonLinear Optimization, 1985) for a more detailed
discussion of the 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).
Scales, L.E., Introduction to NonLinear Optimization, New York, SpringerVerlag, 1985
trainbfg
 traincgb
 traincgf
 traingda
 traingdm
 traingdx
 trainlm
 trainoss
 trainrp
 trainscg