Question

使用Tensorflow时，我正在尝试使用检查点文件恢复CIFAR10培训。引用其他一些文章，我尝试了tf.train.Saver（）。恢复没有成功。有人可以告诉我如何继续吗？

来自Tensorflow CIFAR10的代码段

def train():
  # methods to build graph from the cifar10_train.py
  global_step = tf.Variable(0, trainable=False)
  images, labels = cifar10.distorted_inputs()
  logits = cifar10.inference(images)
  loss = cifar10.loss(logits, labels)
  train_op = cifar10.train(loss, global_step)
  saver = tf.train.Saver(tf.all_variables())
  summary_op = tf.merge_all_summaries()

  init = tf.initialize_all_variables() 
  sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))
  sess.run(init)


  print("FLAGS.checkpoint_dir is %s" % FLAGS.checkpoint_dir)

  if FLAGS.checkpoint_dir is None:
    # Start the queue runners.
    tf.train.start_queue_runners(sess=sess)
    summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
  else:
    # restoring from the checkpoint file
    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
    tf.train.Saver().restore(sess, ckpt.model_checkpoint_path)

  # cur_step prints out well with the checkpointed variable value
  cur_step = sess.run(global_step);
  print("current step is %s" % cur_step)

  for step in xrange(cur_step, FLAGS.max_steps):
    start_time = time.time()
    # **It stucks at this call **
    _, loss_value = sess.run([train_op, loss])
    # below same as original

Answer 1

问题似乎是这一行：

tf.train.start_queue_runners(sess=sess)

...仅在FLAGS.checkpoint_dir is None时执行。如果从检查点恢复，则仍需要启动队列运行程序。

请注意，我建议您在创建tf.train.Saver之后启动队列运行程序（由于代码的已发布版本中的竞争条件），因此更好的结构将是：

if FLAGS.checkpoint_dir is not None: # restoring from the checkpoint file ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir) tf.train.Saver().restore(sess, ckpt.model_checkpoint_path) # Start the queue runners. tf.train.start_queue_runners(sess=sess) # ... for step in xrange(cur_step, FLAGS.max_steps): start_time = time.time() _, loss_value = sess.run([train_op, loss]) # ...

tensorflow cifar10从检查点文件恢复训练

1 个答案: