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Neural network performance

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`

)