tf是封装的真好，我是真菜——导入数据

with tf.Session() as sess:
    # The `Iterator.string_handle()` method returns a tensor that can be evaluated
    # and used to feed the `handle` placeholder.
    training_handle = sess.run(training_iterator.string_handle())
    validation_handle = sess.run(validation_iterator.string_handle())

    # Loop forever, alternating between training and validation.
    while True:
      # Run 200 steps using the training dataset. Note that the training dataset is
      # infinite, and we resume from where we left off in the previous `while` loop
      # iteration.
      sess.run(training_iterator.initializer)
      for _ in range(5):
        batch_images_tf_value,batch_labels_tf_value,_ = sess.run( iterator.get_next(), feed_dict={handle: training_handle})
        print("train:",batch_images_tf_value.shape)
      # Run one pass over the validation dataset.
      sess.run(validation_iterator.initializer)
      for _ in range(5):
        batch_images_tf_value,batch_labels_tf_value,_  = sess.run(iterator.get_next(), feed_dict={handle: validation_handle})
        print("val:",batch_images_tf_value.shape) 

ref：https://blog.csdn.net/weixin_39506322/article/details/82455860