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.
\[
Cross Entropy = -\sum_{c=1}^My_{o,c}\log(p_{o,c})
\]
Parameters#
This cost function does not have any parameters.
Example#
use Rubix\ML\NeuralNet\CostFunctions\CrossEntropy;
$costFunction = new CrossEntropy();