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Neural network performance

- example
`perf = crossentropy(net,targets,outputs,perfWeights)`

`perf = crossentropy(___,Name,Value)`

calculates
a network performance given targets and outputs, with optional performance
weights and other parameters. The function returns a result that heavily
penalizes outputs that are extremely inaccurate (`perf`

= crossentropy(`net`

,`targets`

,`outputs`

,`perfWeights`

)`y`

near `1-t`

),
with very little penalty for fairly correct classifications (`y`

near `t`

).
Minimizing cross-entropy leads to good classifiers.

The cross-entropy for each pair of output-target elements is
calculated as: `ce = -t .* log(y)`

.

The aggregate cross-entropy performance is the mean of the individual
values: `perf = sum(ce(:))/numel(ce)`

.

Special case (N = 1): If an output consists of only one element,
then the outputs and targets are interpreted as binary encoding. That
is, there are two classes with targets of 0 and 1, whereas in 1-of-N
encoding, there are two or more classes. The binary cross-entropy
expression is: `ce = -t .* log(y) - (1-t) .* log(1-y) `

.

supports
customization according to the specified name-value pair arguments.`perf`

= crossentropy(___,`Name,Value`

)

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