使用不同的输入和相同的占位符运行并行运算 [英] Run parallel op with different inputs and same placeholder

问题描述

我必须同时计算多个精度.

I have the necessity to calculate more then one accuracy in the same time, concurrently.

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

该代码段与 TensorFlow 教程中的 mnist 示例相同，但没有:

The piece of code is the same of the mnist example in the tutorial of TensorFlow but instead of having:

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

我有两个Placeolder，因为我已经计算并存储了它们.

I have two placeolder because I already calculated and stored them.

W = tf.placeholder(tf.float32, [784, 10])
b = tf.placeholder(tf.float32, [10])

我想用我Aready拥有的值填充网络，然后计算精度，这对于我加载的每个网络都必须发生.

I want to fill the network with the values I aready have and then calculate the accuracy and this have to happen for each network I loaded.

因此，如果我加载20个网络，则我想并行计算每个网络的准确性.会话运行是否可以通过不同的输入执行相同的操作?

So if I load 20 networks I want to calculate in parallel the accuracy for each one. There is a way with the session run to execute the same operation with different input?

推荐答案

您有多种选择可以使事情并行发生:

You have multiple options to make things happen in parallel:

• 使用多个python线程/子进程进行并行化.(请参阅Python的多处理"库.)
• 将这些操作分成多个较大的操作.(例如，类似于同时对一批图像进行操作的图像操作 https://www.tensorflow.org/api_docs/python/image/resizing#resize_bilinear .)
• 制作一个具有20个网络精度计算结果的图形.

我认为最后一个是最简单的，因此我在下面提供了一些示例代码来帮助您入门:

I think the last one is the easiest, so I've included a bit of sample code below to get you started:

import tensorflow as tf

def construct_accuracy_calculation(i):
  W = tf.placeholder(tf.float32, [784, 10], name=("%d_W" % i))
  b = tf.placeholder(tf.float32, [10], name=("%d_b" % i))
  # ...
  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  return (W, b, accuracy)


def main():
  accuracy_computations = []
  feed_dict={}
  for i in xrange(NUM_NETWORKS):
    (W, b) = load_network(i)
    (W_op, b_op, accuracy) = construct_accuracy_calculation(i)
    feed_dict[W_op] = W
    feed_dict[b_op] = b
    accuracy_computations.append(accuracy)

  # sess = ...
  accuracy_values = sess.run(accuracy_computations, feed_dict=feed_dict)

if __name__ == "__main__":
  main()

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