小编sdn*_*dnr的帖子

更新:仍然发生在Tensorflow 1.7.0中

更新:我写了一个协同合作的笔记本再现谷歌的GPU硬件这个错误:https://drive.google.com/file/d/13V87kSTyyFVMM7NoJNk9QTsCYS7FRbyz/view?usp=sharing

更新:在tf.gather对此问题的第一次修订中错误地指责之后,我现在将其缩小为tf.reduce_sum与占位符一起形成:

tf.reduce_sum 为大型张量生成零(仅在GPU上),其形状取决于占位符.

在向占位符提供大整数时运行以下代码batch_size(在我的情况下> 700000):

import tensorflow as tf
import numpy as np

graph = tf.Graph()
with graph.as_default():
    batch_size = tf.placeholder(tf.int32,shape=[])
    ones_with_placeholder = tf.ones([batch_size,256,4])
    sum_out = tf.reduce_sum(ones_with_placeholder,axis=2)
    min_sum_out = tf.reduce_min(sum_out)

sess = tf.Session(graph=graph)

sum_result,min_sum_result = sess.run([sum_out,min_sum_out],feed_dict={batch_size: 1000000})
print("Min value in sum_out processed on host with numpy:", np.min(sum_result))
print("Min value in sum_out tensor processed in graph with tf:", min_sum_result)

显示以下错误结果:

Min value in sum_out processed on host with …