perf = crossentropy(net,targets,outputs,perfWeights) calculates
a network performance given targets, outputs, performance weights,
and optional parameters with a measure that heavily penalizes outputs
that are extremely inaccurate (y near 1-t),
with very little penalty for fairly correct classifications (y near t).
Minimizing cross-entropy leads to good classifiers.

Neural network target values, specified as a vector or cell
array of numeric values. Network target values define the desired
outputs, and can be specified as an N-by-1 column
vector of numeric values, an N-by-Q matrix
of QN-element vectors, or an M-by-TS cell
array where each element is an Ni-by-Q matrix.
In each of these cases, N or Ni indicates
a vector length, Q the number of samples, M the
number of signals for neural networks with multiple outputs, and TS is
the number of time steps for time series data. targets must
have the same dimensions as outputs.

targets can include NaN values to indicate
unknown or don't-care output values. The performance of NaN target
values is ignored.

Neural network output values, specified as a vector or cell
array of numeric values. Network output values can be specified as
an N-by-1 column vector of numeric
values, an N-by-Q matrix of QN-element
vectors, or an M-by-TS cell
array where each element is an Ni-by-Q matrix.
In each of these cases, N or Ni indicates
a vector length, Q the number of samples, M the
number of signals for neural networks with multiple outputs and TS is
the number of time steps for time series data. outputs must
have the same dimensions as targets.

Outputs can include NaN values to indicate
unknown output values, presumably produced as a result of NaN input
values (also representing unknown or don't-care values). The performance
of NaN output values is ignored.

Performance weights, specified as a vector or cell array of
numeric values. Performance weights are an optional argument defining
the importance of each performance value, associated with each target
value, using values between 0 and 1. Performance values of 0 indicate
targets to ignore, values of 1 indicate targets to be treated with
normal importance. Values between 0 and 1 allow targets to be treated
with relative importance.

Performance weights have many uses. They are helpful for classification
problems, to indicate which classifications (or misclassifications)
have relatively greater benefits (or costs). They can be useful in
time series problems where obtaining a correct output on some time
steps, such as the last time step, is more important than others.
Performance weights can also be used to encourage a neural network
to best fit samples whose targets are known most accurately, while
giving less importance to targets which are known to be less accurate.

perfWeights can have the same dimensions
as targets and outputs. Alternately,
each dimension of the performance weights can either match the dimension
of targets and outputs, or be
1. For instance, if targets is an N-by-Q matrix
defining Q samples of N-element
vectors, the performance weights can be N-by-Q indicating
a different importance for each target value, or N-by-1 defining
a different importance for each row of the targets, or 1-by-Q indicating
a different importance for each sample, or be the scalar 1 (i.e. 1-by-1)
indicating the same importance for all target values.

Similarly, if outputs and targets are
cell arrays of matrices, the perfWeights can be
a cell array of the same size, a row cell array (indicating the relative
importance of each time step), a column cell array (indicating the
relative importance of each neural network output), or a cell array
of a single matrix or just the matrix (both cases indicating that
all matrices have the same importance values). For any problem, a perfWeights value
of {1} or the scalar 1 indicates all performances
have equal importance.

Example:

Data Types: double | cell

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments.
Name is the argument
name and Value is the corresponding
value. Name must appear
inside single quotes (' ').
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN.

Proportion of performance attributed to weight/bias values,
specified as a double between 0 (the default) and 1. A larger value
penalizes the network for large weights, and the more likely the network
function will avoid overfitting.

Normalization mode for outputs, targets, and errors, specified
as 'none', 'standard', or 'percent'. 'none' performs
no normalization. 'standard' results in outputs
and targets being normalized to (-1, +1), and therefore errors in
the range (-2, +2).'percent' normalizes outputs
and targets to (-0.5, 0.5) and errors to (-1, 1).