the loss function seems deferent from orignal one. #2

def loss_with_spring(self):
        margin = 5.0
        labels_t = self.y_
        labels_f = tf.subtract(1.0, self.y_, name="1-yi")          # labels_ = !labels;
        eucd2 = tf.pow(tf.subtract(self.o1, self.o2), 2)
        eucd2 = tf.reduce_sum(eucd2, 1)
        eucd = tf.sqrt(eucd2+1e-6, name="eucd")
        C = tf.constant(margin, name="C")
        # yi*||CNN(p1i)-CNN(p2i)||^2 + (1-yi)*max(0, C-||CNN(p1i)-CNN(p2i)||^2)
        pos = tf.multiply(labels_t, eucd2, name="yi_x_eucd2")
        # neg = tf.multiply(labels_f, tf.sub(0.0,eucd2), name="yi_x_eucd2")
        # neg = tf.multiply(labels_f, tf.maximum(0.0, tf.sub(C,eucd2)), name="Nyi_x_C-eucd_xx_2")
        neg = tf.multiply(labels_f, tf.pow(tf.maximum(tf.subtract(C, eucd), 0), 2), name="Nyi_x_C-eucd_xx_2")
        losses = tf.add(pos, neg, name="losses")
        loss = tf.reduce_mean(losses, name="loss")
        return loss