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Cross Entropy#

Cross Entropy (or log loss) measures the performance of a classification model whose output is a joint probability distribution over the possible classes. Entropy increases as the predicted probability distribution diverges from the actual distribution.

\[ Cross Entropy = -\sum_{c=1}^My_{o,c}\log(p_{o,c}) \]


This cost function does not have any parameters.


use Rubix\ML\NeuralNet\CostFunctions\CrossEntropy;

$costFunction = new CrossEntropy();