先感受一下队列之美

import tensorflow as tf

q = tf.FIFOQueue(2, "int32")  # 创建一个先进先出队列
# 队列中最多可以保存两个元素，并指定类型为整数
init = q.enqueue_many(([0, 10],))
x = q.dequeue()
y = x + 1
q_inc = q.enqueue([y])
with tf.Session() as sess:
    init.run()
    for _ in range(5):
        v, _ = sess.run([x, q_inc])
        print(v)

一个多线程的示例

import tensorflow as tf
import numpy as np
import threading
import time


def MyLoop(coord, worker_id):
    while not coord.should_stop():
        if np.random.rand() < 0.1:
            print("Stoping from id: %d\n" % worker_id,)
            coord.request_stop()
        else:
            print("Working on id: %d\n" % worker_id, )
        time.sleep(1)


coord = tf.train.Coordinator()  # 这玩意用来协同多个线程
# 声明创建5个线程
threads = [threading.Thread(target=MyLoop,
            args=(coord, i, )) for i in range(5)]
# 启动所有线程
for t in threads:
    t.start()
coord.join(threads)  # 等待所有线程退出

启动多线程

import tensorflow as tf

queue = tf.FIFOQueue(100, "float")
enqueue_op = queue.enqueue([tf.random_normal([1])])
qr = tf.train.QueueRunner(queue, [enqueue_op] * 5)
# 这表示启动5个线程
tf.train.add_queue_runner(qr)
out_tensor = queue.dequeue()

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess,
                                            coord=coord)
    for _ in range(6):
        print(sess.run(out_tensor)[0])
    coord.request_stop()
    coord.join(threads)