NLP(二)

交叉熵误差 \(H(p,q)=-\sum^{C}_{c=1}{p(c)logq(c)}\) p(c)为类别概率 q(c)为SoftMax概率\(J(\theta)={\frac{1}{N}}\)\(\sum^{N}_{i=1}{-log(\frac{e^{f_{yi}}}{e^{f_c}})}+M\sum_{K}{{\theta}k^2}\)\(M\sum_{K}{{\theta}k^2}\)是正

>>阅读原文<<