我已经编写了一些代码来将数据推送到tensorflow中的队列，我的队列处理程序的init和所有线程运行的main函数如下：
def __init__(self):
self.X = tf.placeholder(tf.int64)
self.Y = tf.placeholder(tf.int64)
self.queue = tf.RandomShuffleQueue(dtypes=[tf.int64, tf.int64],
capacity=100,
min_after_dequeue=20)
self.enqueue_op = self.queue.enqueue([self.X, self.Y])
def thread_main(self, sess, coord):
"""Cycle through the dataset until the main process says stop."""
train_fs = open(data_train, 'r')
while not coord.should_stop():
X_, Y_ = get_batch(train_fs)
if not Y: #We're at the end of the file
train_fs = open(data_train, 'r')
X, Y = get_batch(train_fs)
sess.run(self.enqueue_op, feed_dict={self.X:X_, self.Y:Y_})
我在训练期间监控队列的大小。由于某些原因，当我增加向其推送数据的线程数时，我的队列填充速度会变慢。知道为什么吗？是因为我同时读取python文件吗？
编辑：
这是我正在使用的代码，除了数据和图表之外，它完全相同。代码在此虚拟数据上的行为与预期相同。我有两个观察结果：
首先，生成一个虚拟数据集：
data_train = "./test.txt"
with open(data_train, 'w') as out_stream:
out_stream.write("""[1,2,3,4,5,6]|1\n[1,2,3,4]|2\n[1,2,3,4,5,6]|0\n[1,2,3,4,5,6]|1\n[1,2,5,6]|1\n[1,2,5,6]|0""")
def get_batch(fs):
line = fs.readline()
X, Y = line.split('|')
X = eval(X)
Y = eval(Y)
return X, Y
然后是队列控制器：
import tensorflow as tf
import numpy as np
import threading
tf.reset_default_graph()#Reset the graph essential to use with jupyter else variable conflicts
class QueueCtrl(object):
def __init__(self):
self.X = tf.placeholder(tf.int64)
self.Y = tf.placeholder(tf.int64)
self.queue = tf.RandomShuffleQueue(dtypes=[tf.int64, tf.int64],
capacity=100,
min_after_dequeue=20)
self.enqueue_op = self.queue.enqueue([self.X, self.Y])
def thread_main(self, sess, coord):
"""Cycle through the dataset until the main process says stop."""
train_fs = open(data_train, 'r')
while not coord.should_stop():
X_, Y_ = get_batch(train_fs)
if not Y_: #We're at the end of the file
train_fs = open(data_train, 'r')
X_, Y_ = get_batch(train_fs)
sess.run(self.enqueue_op, feed_dict={self.X:X_, self.Y:Y_})
def get_batch_from_queue(self):
"""
Return one batch
"""
return self.queue.dequeue()
def start_threads(self, sess, coord, num_threads=2):
"""Start the threads"""
threads = []
for _ in range(num_threads):
t = threading.Thread(target=self.thread_main, args=(sess, coord))
t.daemon = True
t.start()
threads.append(t)
return threads
然后我们构建一个虚拟图：
queue_ctrl = QueueCtrl()
X_, Y_ = queue_ctrl.get_batch_from_queue()
output = Y_ * tf.reduce_sum(X_)
init = tf.initialize_all_variables()
最后，我们迭代数据：
sess = tf.Session()
sess.run(init)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess=sess, coord=coord)
my_thread = queue_ctrl.start_threads(sess, coord, num_threads=6)
for i in range(100):
out = sess.run(output)
print("Iter: %d, output: %d, Element in queue: %d"
% (i, out, sess.run(queue_ctrl.queue.size())))
coord.request_stop()
for _ in range(len(my_thread)): #if the queue is full at that time then the threads won't see the coord.should_stop
_ = sess.run([output])
coord.join(my_thread, stop_grace_period_secs=10)
sess.close()
以下是包含五个主题的25个第一个输出：
Iter: 0, output: 21, Element in queue: 27
Iter: 1, output: 21, Element in queue: 37
Iter: 2, output: 20, Element in queue: 51
Iter: 3, output: 21, Element in queue: 67
Iter: 4, output: 20, Element in queue: 81
Iter: 5, output: 20, Element in queue: 89
Iter: 6, output: 21, Element in queue: 100
Iter: 7, output: 20, Element in queue: 100
Iter: 8, output: 20, Element in queue: 100
Iter: 9, output: 21, Element in queue: 100
Iter: 10, output: 20, Element in queue: 100
Iter: 11, output: 20, Element in queue: 100
Iter: 12, output: 21, Element in queue: 100
Iter: 13, output: 21, Element in queue: 100
Iter: 14, output: 20, Element in queue: 100
Iter: 15, output: 20, Element in queue: 100
Iter: 16, output: 21, Element in queue: 100
Iter: 17, output: 21, Element in queue: 100
Iter: 18, output: 20, Element in queue: 100
Iter: 19, output: 21, Element in queue: 100
Iter: 20, output: 21, Element in queue: 100
Iter: 21, output: 21, Element in queue: 100
Iter: 22, output: 20, Element in queue: 100
Iter: 23, output: 21, Element in queue: 100
Iter: 24, output: 21, Element in queue: 100
Iter: 25, output: 21, Element in queue: 100
有一个帖子：
Iter: 0, output: 21, Element in queue: 22
Iter: 1, output: 20, Element in queue: 25
Iter: 2, output: 20, Element in queue: 27
Iter: 3, output: 20, Element in queue: 29
Iter: 4, output: 21, Element in queue: 31
Iter: 5, output: 20, Element in queue: 32
Iter: 6, output: 20, Element in queue: 34
Iter: 7, output: 21, Element in queue: 35
Iter: 8, output: 21, Element in queue: 36
Iter: 9, output: 21, Element in queue: 38
Iter: 10, output: 20, Element in queue: 40
Iter: 11, output: 20, Element in queue: 42
Iter: 12, output: 20, Element in queue: 43
Iter: 13, output: 21, Element in queue: 46
Iter: 14, output: 20, Element in queue: 47
Iter: 15, output: 21, Element in queue: 48
Iter: 16, output: 20, Element in queue: 53
Iter: 17, output: 20, Element in queue: 56
Iter: 18, output: 21, Element in queue: 57
Iter: 19, output: 21, Element in queue: 61
Iter: 20, output: 21, Element in queue: 63
Iter: 21, output: 20, Element in queue: 67
Iter: 22, output: 21, Element in queue: 70
Iter: 23, output: 21, Element in queue: 73
Iter: 24, output: 20, Element in queue: 76
Iter: 25, output: 20, Element in queue: 78
答案 0 :(得分：4)
我想在这里添加一些东西，我实现了一个基于多进程的数据馈送管道，用于多任务学习。它可以达到平均水平。 GPU利用率> 90％，四核CPU利用率> 95％。不易发生内存泄漏，特别适合长达数日的培训。不是说它很完美，但至少比我目前的TF队列API（1.1）更好。