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Multiclass Cross Entropy

Multiclass Cross Entropy measures the performance of a multiclass classification model whose output is a probability distribution over the possible classes. Cross-entropy loss increases as the predicted probability distribution diverges from the actual distribution.

\[ Multiclass\ Cross\ Entropy = -\frac{1}{N}\sum_{i=1}^N\sum_{c=1}^C y_{i,c}\log(p_{i,c}) \]

Parameters

This cost function does not have any parameters.

Example

use Rubix\ML\NeuralNet\CostFunctions\MulticlassCrossEntropy;

$costFunction = new MulticlassCrossEntropy();