Main Content


One-step secant backpropagation


net.trainFcn = 'trainoss'
[net,tr] = train(net,...)


trainoss is a network training function that updates weight and bias values according to the one-step secant method.

net.trainFcn = 'trainoss' sets the network trainFcn property.

[net,tr] = train(net,...) trains the network with trainoss.

Training occurs according to trainoss training parameters, shown here with their default values:


Maximum number of epochs to train


Performance goal


Maximum validation failures


Minimum performance gradient


Name of line search routine to use


Epochs between displays (NaN for no displays)


Generate command-line output


Show training GUI


Maximum time to train in seconds

Parameters related to line search methods (not all used for all methods):


Divide into delta to determine tolerance for linear search.


Scale factor that determines sufficient reduction in perf


Scale factor that determines sufficiently large step size


Initial step size in interval location step


Parameter to avoid small reductions in performance, usually set to 0.1 (see srch_cha)


Lower limit on change in step size

net.trainParam.up_lim 0.5

Upper limit on change in step size


Maximum step length


Minimum step length


Maximum step size

Network Use

You can create a standard network that uses trainoss with feedforwardnet or cascadeforwardnet. To prepare a custom network to be trained with trainoss:

  1. Set net.trainFcn to 'trainoss'. This sets net.trainParam to trainoss’s default parameters.

  2. Set net.trainParam properties to desired values.

In either case, calling train with the resulting network trains the network with trainoss.


collapse all

This example shows how to train a neural network using the trainoss train function.

Here a neural network is trained to predict body fat percentages.

[x, t] = bodyfat_dataset;
net = feedforwardnet(10, 'trainoss');
net = train(net, x, t);

y = net(x);

More About

collapse all

One Step Secant Method

Because the BFGS algorithm requires more storage and computation in each iteration than the conjugate gradient algorithms, there is need for a secant approximation with smaller storage and computation requirements. The one step secant (OSS) method is an attempt to bridge the gap between the conjugate gradient algorithms and the quasi-Newton (secant) algorithms. This algorithm does not store the complete Hessian matrix; it assumes that at each iteration, the previous Hessian was the identity matrix. This has the additional advantage that the new search direction can be calculated without computing a matrix inverse.

The one step secant method is described in [Batt92]. This algorithm requires less storage and computation per epoch than the BFGS algorithm. It requires slightly more storage and computation per epoch than the conjugate gradient algorithms. It can be considered a compromise between full quasi-Newton algorithms and conjugate gradient algorithms.


trainoss 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 steps and gradients, according to the following formula:

dX = -gX + Ac*X_step + Bc*dgX;

where gX is the gradient, X_step is the change in the weights on the previous iteration, and dgX is the change in the gradient from the last iteration. See Battiti (Neural Computation, Vol. 4, 1992, pp. 141–166) for a more detailed discussion of the one-step secant 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 (validation error) has increased more than max_fail times since the last time it decreased (when using validation).


Battiti, R., “First and second order methods for learning: Between steepest descent and Newton’s method,” Neural Computation, Vol. 4, No. 2, 1992, pp. 141–166

Version History

Introduced before R2006a