loss for multi label classification.py

def contrastive_loss(self, y, d, batch_size):
        tmp = y * tf.square(d)
        # tmp= tf.mul(y,tf.square(d))
        tmp2 = (1 - y) * tf.square(tf.maximum((1 - d), 0))
        return tf.reduce_sum(tmp + tmp2) / batch_size / 2

    
       #  self.scores = logits

        with tf.name_scope("loss"):
            self.loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=self.input_y, logits=self.scores)
            self.cost = tf.reduce_mean(self.loss)