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Cross-entropy builds upon this idea to compute the number of bits required to represent or transmit an average event from one distribution compared to another distribution. if we consider a target distribution P and an approximation of the target distribution Q, the cross-entropy of Q from P is the number of additional bits to represent an event using Q instead of P: Dec 14, 2020 · x - x * z + log (1 + exp (-x)) = log (exp (x)) - x * z + log (1 + exp (-x)) = - x * z + log (1 + exp (x)) Hence, to ensure stability and avoid overflow, the implementation uses this equivalent formulation. max (x, 0) - x * z + log (1 + exp (-abs (x))) logits and labels must have the same type and shape.