Tensorflow细节-P186-队列与多线程

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)