Resume training about gan HOT 2 CLOSED

...
	with tf.name_scope('optimizer'):
		gan_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
		dis_vars = [var for var in gan_vars if 'dis' in var.name]
		gen_vars = [var for var in gan_vars if 'gen' in var.name]
		# dis_step = tf.train.RMSPropOptimizer(learning_rate=LEARNING_RATE, name='dis_rmsprop').minimize(dis_loss, var_list=dis_vars)
		# gen_step = tf.train.RMSPropOptimizer(learning_rate=LEARNING_RATE, name='gen_rmsprop').minimize(gen_loss, var_list=gen_vars)
	
	with tf.name_scope('dis_clip'):
		dis_clip = [var.assign(tf.clip_by_value(var, -0.01, 0.01)) for var in dis_vars]

	sess = tf.Session()
	saver = tf.train.Saver()
	sess.run(tf.global_variables_initializer())

	saver = tf.train.import_meta_graph('./model/gan-1412.meta')
	saver.restore(sess,tf.train.latest_checkpoint('./model'), )
	graph = tf.get_default_graph()
	dis_step = graph.get_operation_by_name('dis/conv1/w/dis_rmsprop')
	gen_step = graph.get_operation_by_name('gen/fc1/w/gen_rmsprop')

	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(sess=sess, coord=coord)

	for i in range(TRAIN_ITERS):
....

	with tf.name_scope('optimizer'):
		gan_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
		dis_vars = [var for var in gan_vars if 'dis' in var.name]
		gen_vars = [var for var in gan_vars if 'gen' in var.name]
		
		dis_step = tf.train.RMSPropOptimizer(learning_rate=LEARNING_RATE, name='dis_rmsprop').minimize(dis_loss, var_list=dis_vars)
		gen_step = tf.train.RMSPropOptimizer(learning_rate=LEARNING_RATE, name='gen_rmsprop').minimize(gen_loss, var_list=gen_vars)
	

	with tf.name_scope('dis_clip'):
		dis_clip = [var.assign(tf.clip_by_value(var, -0.01, 0.01)) for var in dis_vars]

	sess = tf.Session()
	saver = tf.train.Saver()
	sess.run(tf.global_variables_initializer())

	saver.restore(sess,'./model/gan-1412',)
	graph = tf.get_default_graph()


	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(sess=sess, coord=coord)

	for i in range(TRAIN_ITERS):