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math.cross-entropy-loss Calculator
Calculates cross-entropy loss from predicted probabilities and true class labels, showing per-class contribution and total loss. Cross-entropy is the standard loss function for classification neural networks — it penalises confident wrong predictions more harshly than uncertain ones.
Inputs
True Label
Reference formula or conversion factor shown for context.
Predicted Prob
Reference formula or conversion factor shown for context.
N Classes
Count of items or occurrences.
Results
binary cross-entropy loss
The decrease or degradation from the baseline.
prediction correct?
Sample size or count used in the calculation.
BCE = -y·log(p) - (1-y)·log(1-p)
Reference formula or conversion factor shown for context.