Cross Entropy, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. So predicting a probability of .012 when the actual observation label is 1 would be bad and result in a high loss value. A perfect score would have a log loss of 0.
This cost function does not have any parameters.
use Rubix\ML\NeuralNet\CostFunctions\CrossEntropy; $costFunction = new CrossEntropy();