本文深入探讨了多种深度学习中常用的损失函数，包括Multinomial Logistic Loss、InfoGain Loss、Softmax with Loss、Sum-of-Squares/Euclidean Loss、Hinge/Margin Loss、Sigmoid Cross-Entropy Loss以及Contrastive Loss等，解析了它们的概念、计算方式及应用场景。
1. Multinomial Logistic Loss
2. Infogain Loss - a generalization of MultinomialLogisticLossLayer.
3. Softmax with Loss - computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
4. Sum-of-Squares / Euclidean - computes the sum of squares of differences of its two inputs, 12N∑Ni=1∥x1i-x2i∥22.
5. Hinge / Margin - The hinge loss layer computes a one-vsall hinge (L1) or squared hinge loss (L2).
6. Sigmoid Cross-Entropy Loss - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
7. Accuracy / Top-k layer - scores the output as an accuracy with respect to target – it is not actually a loss and has no backward step.
8. Contrastive Loss
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