Mean squared normalized error performance function
This function has two optional parameters, which are associated with networks whose
net.trainFcn is set to this function:
'regularization' can be set to any value between 0 and 1. The greater
the regularization value, the more squared weights and biases are included in the performance
calculation relative to errors. The default is 0, corresponding to no regularization.
'normalization' can be set to
'standard', which normalizes errors between -2 and 2,
corresponding to normalizing outputs and targets between -1 and 1; and
'percent', which normalizes errors between -1 and 1. This feature is
useful for networks with multi-element outputs. It ensures that the relative accuracy of
output elements with differing target value ranges are treated as equally important, instead
of prioritizing the relative accuracy of the output element with the largest target value
You can create a standard network that uses
cascadeforwardnet. To prepare a custom
network to be trained with
'mse'. This automatically sets
net.performParam to a
structure with the default optional parameter values.
mse is a network performance function. It measures the network’s
performance according to the mean of squared errors.
This example shows shows how to train a neural network using the
mse performance function.
Here a two-layer feedforward network is created and trained to estimate body fat percentage using the
mse performance function and a regularization value of 0.01.
[x, t] = bodyfat_dataset; net = feedforwardnet(10); net.performParam.regularization = 0.01;
MSE is the default performance function for
ans = 'mse'
Train the network and evaluate performance.
net = train(net, x, t); y = net(x); perf = perform(net, t, y)
perf = 20.7769
Alternatively, you can call
perf = mse(net, t, y, 'regularization', 0.01)
perf = 20.7769
Targets, specified as a matrix or a cell array.
Outputs, specified as a matrix or a cell array.
ew— Error weights
1(default) | scalar
Error weights, specified as a scalar.
perf— Network performance
Performance of the network as the mean squared errors.