tensorflow 程序挂起的原因,即整个进程不报错又不执行的原因

在测试集上对训练好的模型进行实验的时候，发现写好的代码没有报错但是会卡在session.run()那里不动。查了好久终于明白是开启线程的问题。

tf的数据线程没有启动,导致数据流图没办法计算,整个程序就卡在那里。

更深层次的原因是tensorflow的计算和数据读入是异步的,合理的方式是主线程进行模型的训练,然后开一个数据读入线程异步读入数据.tensorflow会在内存中维护一个队列,然后数据线程异步从磁盘中将样本推入队列当中。并且,因为tensorflow的训练和读数据是异步的,故即使当前没有数据进来,tensorflow也没办法报错,因为可能接下来会有数据进队列,所以,tensorflow就一直处于等待的状态
下面以别人的例子说明：

错误情况：

在Session当中,没有启动数据读入线程。所以,sess.run(train_input.input_data)就是无数据可取,程序就处于一种挂起的状态。

#-*- coding:utf-8 -*-
import numpy as np
import tensorflow as tf
 
from tensorflow.models.rnn.ptb import reader
 
class PTBInput(object):
  """The input data."""
  def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    #为何要进行-1操作
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
 
class SmallConfig(object):
  """Small config."""
  init_scale = 0.1
  learning_rate = 1.0
  max_grad_norm = 5
  num_layers = 2
  num_steps = 20
  hidden_size = 200
  max_epoch = 4
  max_max_epoch = 13
  keep_prob = 1.0
  lr_decay = 0.5
  batch_size = 20
  vocab_size = 10000
 
if __name__ == '__main__':
	config = SmallConfig()
        data_path = '/home/jdlu/jdluTensor/data/simple-examples/data'       
	raw_data = reader.ptb_raw_data(data_path)
	train_data, valid_data, test_data, _ = raw_data
	train_input = PTBInput(config=config, data=train_data, name="TrainInput")
        print "end--------------------------------"
        
	#wrong,使用session就会出现读不出数据的错误,读不出数据,整个数据流图就无法计算,整个程序就处于挂起的状态
	#使用session会出错
	with tf.Session() as sess:
		for step in range(1):
			print sess.run(train_input.input_data)	

解决方法：

有两种办法：

1. 使用tf.train.range_input_producer(epoch_size, shuffle=False),会默认将QueueRunner添加到全局图中，我们必须使用tf.train.start_queue_runners(sess=sess)，去启动该线程。然后使用coord = tf.train.Coordinator()去做一些线程的同步工作。
2. 第二种方法比较简单，使用sv = tf.train.Supervisor(),文档上说,The Supervisor is a small wrapper around a Coordinator, a Saver, and a SessionManager
   也即使用了Supervisor(),那么保存模型，线程同步的事情都不用我们去干涉了。
    

#-*- coding:utf-8 -*-
import numpy as np
import tensorflow as tf
 
from tensorflow.models.rnn.ptb import reader
 
class PTBInput(object):
  """The input data."""
  def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    #为何要进行-1操作
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
 
class SmallConfig(object):
  """Small config."""
  init_scale = 0.1
  learning_rate = 1.0
  max_grad_norm = 5
  num_layers = 2
  num_steps = 20
  hidden_size = 200
  max_epoch = 4
  max_max_epoch = 13
  keep_prob = 1.0
  lr_decay = 0.5
  batch_size = 20
  vocab_size = 10000
 
if __name__ == '__main__':
	config = SmallConfig()
        data_path = '/home/jdlu/jdluTensor/data/simple-examples/data'       
	raw_data = reader.ptb_raw_data(data_path)
	train_data, valid_data, test_data, _ = raw_data
	train_input = PTBInput(config=config, data=train_data, name="TrainInput")
        print "end--------------------------------"
        
 
	#right,使用Supervisor() 方法二
	#sv = tf.train.Supervisor()
        #with sv.managed_session() as sess:
	#	for step in range(1):
	#		print sess.run(train_input.input_data)	
        
	#right 方法一
	# Create a session for running operations in the Graph.
	sess = tf.Session()
	# Start input enqueue threads.
	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(sess=sess, coord=coord)
	# Run training steps or whatever
	try:
		for step in range(2):
			print sess.run(train_input.input_data)
	except Exception,e:
		#Report exceptions to the coordinator
		coord.request_stop(e)
	coord.request_stop()
	# Terminate as usual.  It is innocuous to request stop twice.
	coord.join(threads)
	sess.close()