#caffe examples
#edition 1 #Add a new loss layer, #L1 norm: L = abs(x1) + abs(x2) + abs(x3) + ... + abs(xn), L = L / N #L2 norm: L = x1^2 + x2^2 + x3^2 + ... + xn^2, L = L / N
#edition 2 #Add a new loss layer, #L1 norm: #L_positive = (x1 + x2 + ... + xnp) / Np #L_negative = (x1 + x2 + ... + xnn) / Nn #L_weakly = (x1 + x2 + ... + xnw) / Nw #L = alpha * L_positive + beta * L_negative + gamma * L_weakly
#L2 norm #L_positive = (x1^2 + x2^2 + ... + xnp^2) / Np #L_negative = (x1^2 + x2^2 + ... + xnn^2) / Nn #L_weakly = (x1^2 + x2^2 + ... + xnw^2) / Nw #L = alpha * L_positive + beta * L_negative + gamma * L_weakly
#edition 3 #remove L1 norm and L2 norm #L_positive = (fp1 + fp2 + ... + fpnp) / Np #L_negative = (fn1 + fn2 + ... + fnnn) / Nn #L_weakly = (fw1 + fw2 + ... + fwnw) / Nw #fp(x) = -log(sigmoid(x)) #fn(x) = -log(sigmoid(-x)) #fw(x) = -log(sigmoid(max(x))) #L = alpha * L_positive + beta * L_negative + gamma * L_weakly