循环评估Tensorflow操作非常慢 [英] Evaluating Tensorflow operation is very slow in a loop

问题描述

我正在尝试通过编写一些简单的问题来学习张量流:我正在尝试使用直接采样蒙特卡洛方法来查找pi的值.

I'm trying to learn tensorflow by coding up some simple problems: I was trying to find the value of pi using a direct sampling Monte Carlo method.

运行时间比使用for loop执行此操作的时间要长得多.我看过其他有关类似事情的文章，并且尝试遵循解决方案，但我认为我仍然必须做错什么.

The run time is much longer than I thought it would be when using a for loop to do this. I've seen other posts about similar things and I've tried to follow the solutions, but I think I still must be doing something wrong.

下面是我的代码:

import tensorflow as tf
import numpy as np
import time

n_trials = 50000

tf.reset_default_graph()


x = tf.random_uniform(shape=(), name='x')
y = tf.random_uniform(shape=(), name='y')
r = tf.sqrt(x**2 + y**2)

hit = tf.Variable(0, name='hit')

# perform the monte carlo step
is_inside = tf.cast(tf.less(r, 1), tf.int32)
hit_op = hit.assign_add(is_inside) 

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)

    # Make sure no new nodes are added to the graph
    sess.graph.finalize()

    start = time.time()   

    # Run monte carlo trials  -- This is very slow
    for _ in range(n_trials):
        sess.run(hit_op)

    hits = hit.eval()
    print("Pi is {}".format(4*hits/n_trials))
    print("Tensorflow operation took {:.2f} s".format((time.time()-start)))

>>> Pi is 3.15208
>>> Tensorflow operation took 8.98 s

相比之下，在numpy中执行for loop类型的解决方案要快一个数量级

In comparison, doing a for loop type solution in numpy is an order of magnitude faster

start = time.time()   
hits = [ 1 if np.sqrt(np.sum(np.square(np.random.uniform(size=2)))) < 1 else 0 for _ in range(n_trials) ]
a = 0
for hit in hits:
    a+=hit
print("numpy operation took {:.2f} s".format((time.time()-start)))
print("Pi is {}".format(4*a/n_trials))

>>> Pi is 3.14032
>>> numpy operation took 0.75 s

以下是各种试验的总执行时间差异的图表.

Attached below is a plot of the difference in overall executioin times for various numbers of trials.

请注意:我的问题不是关于如何最快地执行此任务"，我认识到有更多更有效的计算Pi的方法.我仅将其用作基准测试工具来对照我熟悉的(numpy)检查tensorflow的性能.

Please note: my question is not about "how to perform this task the fastest", I recognize there are much more effective ways of calculating Pi. I've only used this as a benchmarking tool to check the performance of tensorflow against something I'm familiar with (numpy).

推荐答案

速度下降与sess.run中的Python和Tensorflow之间的一些通信开销有关，该开销在循环中多次执行.我建议使用tf.while_loop在Tensorflow中执行计算.与numpy相比，这会更好.

The slow in speed has got to do with some communication overhead between Python and Tensorflow in sess.run, which is executed multiple times inside your loop. I would suggest using tf.while_loop to execute the computations within Tensorflow. That would be a better comparison over numpy.

import tensorflow as tf
import numpy as np
import time

n_trials = 50000

tf.reset_default_graph()

hit = tf.Variable(0, name='hit')

def body(ctr):
    x = tf.random_uniform(shape=[2], name='x')
    r = tf.sqrt(tf.reduce_sum(tf.square(x))
    is_inside = tf.cond(tf.less(r,1), lambda: tf.constant(1), lambda: tf.constant(0))
    hit_op = hit.assign_add(is_inside)
    with tf.control_dependencies([hit_op]):
        return ctr + 1

def condition(ctr):
    return ctr < n_trials

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    result = tf.while_loop(condition, body, [tf.constant(0)])

    start = time.time()
    sess.run(result)

    hits = hit.eval()
    print("Pi is {}".format(4.*hits/n_trials))
    print("Tensorflow operation took {:.2f} s".format((time.time()-start)))

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