import tensorflow as tf

            print(var.name)
            
        self.test_image_A = tf.placeholder(tf.float32,[None, self.image_size,self.image_size,self.input_dim], name='test_A')
        self.test_image_B = tf.placeholder(tf.float32,[None, self.image_size, self.image_size,self.output_c_dim], name='test_B')
        self.saver = tf.train.Saver()
        
        
    def train_network(self):
        
        self.learning_rate = tf.placeholder(tf.float32)
        self.d_optimizer = tf.train.AdamOptimizer(self.learning_rate,beta1=self.beta1,beta2=self.beta2).minimize(self.discriminator_loss,var_list=self.d_variables)
        self.g_optimizer = tf.train.AdamOptimizer(self.learning_rate,beta1=self.beta1,beta2=self.beta2).minimize(self.generator_loss,var_list=self.g_variables)      
        
        self.init_op = tf.global_variables_initializer()
        self.sess = tf.Session()
        self.sess.run(self.init_op)
        #self.dataset_dir = '/home/santanu/Downloads/DiscoGAN/edges2handbags/train/'
        self.writer = tf.summary.FileWriter("./logs", self.sess.graph)
        count = 1
        start_time = time.time()
        
        for epoch in range(self.epoch):
            data_A = os.listdir(self.dataset_dir + 'trainA/')
            data_B = os.listdir(self.dataset_dir + 'trainB/')
            data_A = [ (self.dataset_dir + 'trainA/' + str(file_name)) for file_name in data_A ] 

