import tensorflow as tf

        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]
        )

        
        trainable_variables = tf.trainable_variables()
        
        self.d_variables = [var for var in trainable_variables if 'discriminator' in var.name]
        self.g_variables = [var for var in trainable_variables if 'generator' in var.name]
      
        print ('Variable printing start :'  )
        for var in self.d_variables: 
            print(var.name)
            
        self.test_image_A = tf.placeholder(tf.float32,[None, self.image_size,self.image_size,self.input_dim], name='test_A')
