perf = sse(net,t,y,ew)
takes a network net, targets T, outputs
Y, and optionally error weights EW, and returns
network performance calculated as the sum squared error.
sse is a network performance function. It measures performance
according to the sum of squared errors.
perf = sse(net,t,y,ew,Name,Value)
has two optional function parameters that set the regularization of the errors and the
normalizations of the outputs and targets.
sse is a network performance function. It measures performance
according to the sum of squared errors.
Input network, specified as a network object. To create a network object, use for
example, feedforwardnet or narxnet.
Network targets, specified as a matrix or cell array.
Network outputs, specified as a matrix or cell array.
Error weights, specified as a vector, matrix, or cell array.
Error weights can be defined by sample, output element, time step, or network
output:
ew = [1.0 0.5 0.7 0.2]; % Across 4 samples
ew = [0.1; 0.5; 1.0]; % Across 3 elements
ew = {0.1 0.2 0.3 0.5 1.0}; % Across 5 timesteps
ew = {1.0; 0.5}; % Across 2 outputs
The error weights can also be defined across any combination, such as across two
time-series (i.e., two samples) over four timesteps.
ew = {[0.5 0.4],[0.3 0.5],[1.0 1.0],[0.7 0.5]};
In the general case, error weights may have exactly the same dimensions as targets,
in which case each target value will have an associated error weight.
The default error weight treats all errors the same.
Specify optional
comma-separated pairs of Name,Value arguments. Name
is the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as Name1,Value1,...,NameN,ValueN.
Proportion of performance attributed to weight and bias values, specified as the
comma-separated pair consisting of 'regularization' and an integer
between 0 and 1. The larger this value is, the
more the network is penalized for larger weights, and the more likely the network
function avoids overfitting.
Output and target normalization, specified as the comma-separated pair consisting
of 'normalization' and either:
'none' — performs no normalization.
'standard' — normalizes outputs and targets to
[-1, +1], and therefore normalizes errors to [-2,
+2].
'percent' — normalizes outputs and targets to
[-0.5, +0.5], and therefore normalizes errors to
[-1, +1].
To prepare a custom network to be trained with sse, set
net.performFcn to 'sse'. This automatically sets
net.performParam to the default function parameters.
Then calling train, adapt or perform will result in sse
being used to calculate performance.
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