import tensorflow as tf

        self.D_B_loss = (self.D_B_loss_real + self.D_B_loss_fake) / 2.0
        
        
        self.D_A_loss_real = binary_cross_entropy_loss(tf.ones_like(self.D_A_real),self.D_A_real)
        self.D_A_loss_fake = binary_cross_entropy_loss(tf.zeros_like(self.D_A_fake),self.D_A_fake)
        self.D_A_loss = (self.D_A_loss_real + self.D_A_loss_fake) / 2.0
        
        self.discriminator_loss = self.D_B_loss + self.D_A_loss
        
        self.loss_GABA_sum = tf.summary.scalar("g_loss_a2b", self.loss_GABA)
        self.loss_GBAB_sum = tf.summary.scalar("g_loss_b2a", self.loss_GBAB)
        self.g_total_loss_sum = tf.summary.scalar("g_loss", self.generator_loss)
        self.g_sum = tf.summary.merge([self.loss_GABA_sum,self.loss_GBAB_sum,self.g_total_loss_sum])
        
        self.loss_db_sum = tf.summary.scalar("db_loss", self.D_B_loss)
        self.loss_da_sum = tf.summary.scalar("da_loss", self.D_A_loss)
        self.loss_d_sum = tf.summary.scalar("d_loss",self.discriminator_loss)
        
        self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.D_B_loss_real)
        self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.D_B_loss_fake)
        self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.D_A_loss_real)
        self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.D_A_loss_fake)
        self.d_sum = tf.summary.merge(
            [self.loss_da_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
             self.loss_db_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
             self.loss_d_sum]
        )

