Relative Entropy#

Relative Entropy (or Kullback-Leibler divergence) is a measure of how the expectation and activation of the network diverge. It is different from Cross Entropy in that it is asymmetric and thus does not qualify as a statistical measure of error.

\[ KL(\hat{y} || y) = \sum_{c=1}^{M}\hat{y}_c \log{\frac{\hat{y}_c}{y_c}} \]

Parameters#

This cost function does not have any parameters.

Example#

use Rubix\ML\NeuralNet\CostFunctions\RelativeEntropy;

$costFunction = new RelativeEntropy();