python – 恢复使用迭代器的Tensorflow模型

我读了tfrecord文件,将数据提供给网络,训练得很好,一切顺利,我在训练结束时保存了我的模型,所以我可以在以后运行推理.代码的简化版本如下：

""" Training and saving """

training_dataset = tf.contrib.data.TFRecordDataset(training_record)
training_dataset = training_dataset.map(ds._path_records_parser)
training_dataset = training_dataset.batch(BATCH_SIZE)
with tf.name_scope("iterators"):
  training_iterator = Iterator.from_structure(training_dataset.output_types,training_dataset.output_shapes)
  next_training_element = training_iterator.get_next()
  training_init_op = training_iterator.make_initializer(training_dataset)

def train(num_epochs):
  # compute for the number of epochs
  for e in range(1,num_epochs+1):
    session.run(training_init_op) #initializing iterator here
    while True:
      try:
        images,labels = session.run(next_training_element)
        session.run(optimizer,Feed_dict={x: images,y_true: labels})
      except tf.errors.OutOfRangeError:
        saver_name = './saved_models/ucf-model'
        print("Finished Training Epoch {}".format(e))
        break



    """ Restoring """
# restoring the saved model and its variables
session = tf.Session()
saver = tf.train.import_Meta_graph(r'saved_models\ucf-model.Meta')
saver.restore(session,tf.train.latest_checkpoint('.\saved_models'))
graph = tf.get_default_graph()

# restoring relevant tensors/ops
accuracy = graph.get_tensor_by_name("accuracy/Mean:0") #the tensor that when evaluated returns the mean accuracy of the batch
testing_iterator = graph.get_operation_by_name("iterators/Iterator") #my iterator used in testing.
next_testing_element = graph.get_operation_by_name("iterators/IteratorGetNext") #the GetNext operator for my iterator
# loading my testing set tfrecords
testing_dataset = tf.contrib.data.TFRecordDataset(testing_record_path)
testing_dataset = testing_dataset.map(ds._path_records_parser,num_threads=4,output_buffer_size=BATCH_SIZE*20)
testing_dataset = testing_dataset.batch(BATCH_SIZE)

testing_init_op = testing_iterator.make_initializer(testing_dataset) #to initialize the dataset

with tf.Session() as session:
  session.run(testing_init_op)
  while True:
    try:
      images,labels = session.run(next_testing_element)
      accuracy = session.run(accuracy,Feed_dict={x: test_images,y_true: test_labels}) #error here,x,y_true not defined
    except tf.errors.OutOfRangeError:
      break

我的问题主要是我恢复模型.如何将测试数据提供给网络？

>当我使用testing_iterator = graph.get_operation_by_name(“iterators / Iterator”),next_testing_element = graph.get_operation_by_name(“iterators / IteratorGetNext”)恢复我的Iterator时,出现以下错误：
GetNext()失败,因为迭代器尚未初始化.确保在获取下一个元素之前已为此迭代器运行初始化程序操作.
>所以我尝试使用以下方法初始化我的数据集：testing_init_op = testing_iterator.make_initializer(testing_dataset)).我收到此错误：AttributeError：’Operation’对象没有属性’make_initializer’

另一个问题是,由于正在使用迭代器,因此不需要在training_model中使用占位符,因为迭代器直接将数据提供给图形.但是这样,当我将数据提供给“准确度”操作时,如何在第3行到最后一行恢复我的Feed_dict键？

编辑：如果有人可以建议在迭代器和网络输入之间添加占位符的方法,那么我可以尝试通过评估“准确性”张量来运行图形,同时将数据提供给占位符并完全忽略迭代器.