tensorflowで一つ足りないbatchを取り除くデータを実現

#-*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
 
value1 = tf.placeholder(dtype=tf.float32)
value2 = tf.placeholder(dtype=tf.float32)
value3 = value1 + value2
 
#   dataset   ，          
dataset = tf.data.Dataset.range(10)
#         
dataset = dataset.shuffle(100)
dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(3)) #  batch3   ，  3   
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
 
with tf.Session() as sess:
  #            
  for i in range(2):
    sess.run(iterator.initializer)
    while True:
      try:
        value = sess.run(next_element)
        result = sess.run(value3,feed_dict={value1:value,value2:value})
        print(result)
      except tf.errors.OutOfRangeError:
        print("End of epoch %d" % i)
        break