View Single Post
  #1  
Old 08-17-2012, 05:56 PM
jianpan jianpan is offline
Junior Member
 
Join Date: Jul 2012
Posts: 9
Default backpropagation at the final layer

since \delta_1^{(L)}=\frac{\partial e(w)}{\partial s_1^{(L)}}, and e(w)=(x_1^{(L)}-y_n)^2, and x_1^{(L)}=\theta(s_1^{(L)}), I got \delta_1^{(L)}=2*(x_1^{(L)}-y_n)*(1-\theta^2(s)). is this correct? I found a python program at wikipedia about backpropagation using the \delta_1^{(L)}=(x_1^{(L)}-y_n)*(1-\theta^2(s)), and couldn't figure out where the 2 was dropped. Can someone confirm my derivation is correct? Thanks.
Reply With Quote