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Cross-entropy

Information theory · Formula · Appears in 3 lessons

H(P,Q)=−∑xP(x)log⁡Q(x)H(P, Q) = -\sum_{x} P(x) \log Q(x)

The average number of bits needed to encode data from P using a code optimised for Q. It splits into the entropy of P plus the divergence from P to Q, which is why minimising it over a model is the same as minimising that divergence.

Symbols

PP
The true distribution, usually a one-hot label in practice.
QQ
The predicted distribution the model outputs.
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