Mean squared normalized error performance function
perf = mse(net,t,y,ew)
mse is a network performance function. It measures the network’s
performance according to the mean of squared errors.
perf = mse(net,t,y,ew) takes these arguments:
Matrix or cell array of targets
Matrix or cell array of outputs
Error weights (optional)
and returns the mean squared error.
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
'none' (the default);
'standard', which normalizes errors between -2 and 2, corresponding to
normalizing outputs and targets between -1 and 1; and
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 range.
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.
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