gradient formulation for unit normalized Image embedding

[nitneh]

bottom[0].diff[i] = self.a*((x_n - x_p)/((bottom[0]).num))
bottom[1].diff[i] = self.a*((x_p - x_a)/((bottom[0]).num))
bottom[2].diff[i] = self.a*((x_a - x_n)/((bottom[0]).num))

If we differentiate, loss function with respect to x_a,x_p,x_n, then, the gradients should be:
(If we are assuming embedding of an image as unit normalized)

bottom[0].diff[i] = self.a*((x_n - x_p)/((bottom[0]).num))
bottom[1].diff[i] = self.a*((- x_a)/((bottom[0]).num))
bottom[2].diff[i] = self.a*((x_a)/((bottom[0]).num))

Please correct me, if I am wrong...