reading from files

如果有图会很好理解，最近太忙，以后再加吧

1. #首先有一个需要读取的文件名列表
   #然后将文件名列表通过函数string_input_producer放进文件名队列。
   #有时候因为数据量太大，需要把他们放进不同的tfrecord文件中
   filename_queue = tf.train.string_input_producer(["file0.csv","file1.csv"])
   #对不同格式的文件有不同的reader
   reader = tf.TextLineReader()
   #通过reader的read函数extract a record from a file whose name is in the queue,
   #如果该文件中所有记录都被抽取完，dequeue这个filename，参考readerbase
   #read（）返回下一个record
   key, value = reader.read(filename_queue)
   # decoded record，decode方式和文件内部record格式相关，然后拼接成需要的格式
   record_defaults =[[1],[1],[1],[1],[1]]
   col1, col2, col3, col4, col5 = tf.decode_csv(
   value, record_defaults=record_defaults)
   features = tf.stack([col1, col2, col3, col4])
   with tf.Session()as sess:
   # Start populating the filename queue.
   coord = tf.train.Coordinator()
   threads = tf.train.start_queue_runners(coord=coord)
   for i in range(1200):
   # Retrieve a single instance:
   example, label = sess.run([features, col5])
   coord.request_stop()
   coord.join(threads)

   参考：https://www.tensorflow.org/programmers_guide/reading_data

---

提到queue就不得不提两个帮助多线程异步的类:tf.train.Coordinator和tf.train.QueueRunner；

• tf.train.Coordinator：控制多线程，使其同时结束。
• tf.train.QueueRunner：包含一些enqueue op，为其create一些线程，每一个op都在一个线程上运行。

coordinator

Coordinator方法：should_stop,request_stop,join

1.  # Thread body: loop until the coordinator indicates a stop was requested.
    # If some condition becomes true, ask the coordinator to stop.
    defMyLoop(coord):
    whilenot coord.should_stop():#should_stop返回true or false，表示线程是否该结束
    ...do something...
    if...some condition...:
    coord.request_stop()#当某些条件发生时，一个进程request_stop，其他进程因为should_stop返回true而终止
    # Main thread: create a coordinator.
    coord = tf.train.Coordinator()
    # Create 10 threads that run 'MyLoop()'
    threads =[threading.Thread(target=MyLoop, args=(coord,))for i in xrange(10)]
    # Start the threads and wait for all of them to stop.
    for t in threads:
    t.start()
    coord.join(threads)

QueueRunner

1.  example =...ops to create one example...
    # Create a queue, and an op that enqueues examples one at a time in the queue.
    #区别于filename queue，这是example queue。可以是接着上面读数据解析然后放进这个queue
    queue = tf.RandomShuffleQueue(...)
    enqueue_op = queue.enqueue(example)#定义入队操作
    # Create a training graph that starts by dequeuing a batch of examples.
    inputs = queue.dequeue_many(batch_size)
    train_op =...use 'inputs' to build the training part of the graph...
    # Create a queue runner that will run 4 threads in parallel to enqueue
    # examples.
    #QueueRunner的构造函数，queuerunner是为一个queue的入队操作多线程化服务的，
    #第二个参数是入队操作列表
    qr = tf.train.QueueRunner(queue,[enqueue_op]*4)
    # Launch the graph.
    sess = tf.Session()
    # Create a coordinator, launch the queue runner threads.
    coord = tf.train.Coordinator()
    #queuerunner为queue创造多线程，并且把这些线程的结束交由coordinator管理
    enqueue_threads = qr.create_threads(sess, coord=coord, start=True)
    # Run the training loop, controlling termination with the coordinator.
    for step in xrange(1000000):
    if coord.should_stop():
    break
    sess.run(train_op)
    # When done, ask the threads to stop.
    coord.request_stop()
    # And wait for them to actually do it.
    coord.join(enqueue_threads)

未完待续。。。

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