Mean absolute error performance function
perf = mae(E,Y,X,FP)
mae is a network performance function. It measures network performance
as the mean of absolute errors.
perf = mae(E,Y,X,FP) takes E and optional function
parameters,
E | Matrix or cell array of error vectors |
Y | Matrix or cell array of output vectors (ignored) |
X | Vector of all weight and bias values (ignored) |
FP | Function parameters (ignored) |
and returns the mean absolute error.
dPerf_dx = mae('dx',E,Y,X,perf,FP) returns the derivative of
perf with respect to X.
info = mae(' returns useful information
for each code')code character vector:
mae('name') returns the name of this function.
mae('pnames') returns the names of the training parameters.
mae('pdefaults') returns the default function parameters.
Create and configure a perceptron to have one input and one neuron:
net = perceptron; net = configure(net,0,0);
The network is given a batch of inputs P. The error is calculated by
subtracting the output A from target T. Then the mean
absolute error is calculated.
p = [-10 -5 0 5 10]; t = [0 0 1 1 1]; y = net(p) e = t-y perf = mae(e)
Note that mae can be called with only one argument because the other
arguments are ignored. mae supports those arguments to conform to the
standard performance function argument list.
You can create a standard network that uses mae with
perceptron.
To prepare a custom network to be trained with mae, set
net.performFcn to 'mae'. This automatically sets
net.performParam to the empty matrix [], because
mae has no performance parameters.
In either case, calling train or adapt, results in
mae being used to calculate performance.