[tf]使用tf.data制作模型输入的Pipeline的使用数据的方式

dataset = tf.data.Dataset.range(5)
iterator = dataset.make_initializable_iterator()
# iterator = dataset.make_one_shot_iterator()  一般是使用这个，但是现在数据是从range中获取的所以要先初始化所以使用make_initializable_iterator()
next_element = iterator.get_next()

# Typically `result` will be the output of a model, or an optimizer's
# training operation.
result = tf.add(next_element, next_element)

sess.run(iterator.initializer)
print(sess.run(result))  # ==> "0"
print(sess.run(result))  # ==> "2"
print(sess.run(result))  # ==> "4"
print(sess.run(result))  # ==> "6"
print(sess.run(result))  # ==> "8"
try:
  sess.run(result)
except tf.errors.OutOfRangeError:
  print("End of dataset")  # ==> "End of dataset"