tensorflow reduce_mean VS numpy的意思

Question

据我了解tensorflow reduce_mean和numpy的平均值应返回相同的值，但下面的例子返回的值不同：

import numpy as np 
import tensorflow as tf 

t_1 = tf.constant([1,3,4,5]) 
t_2 = tf.constant([7,8,9,0]) 
list_t = [t_1, t_2] 
reduced_t_list = tf.reduce_mean(list_t) 
sess= tf.Session() 
print(sess.run(reduced_t_list)) 
print(np.mean([1,3,4,5,7,8,9,0])) 

output: 
4 
4.625 

任何猜测，为什么？

Answer 1

从tf.constant docs：

If the argument dtype is not specified, then the type is inferred from the type of value. 

的的[1, 2, 3, 4]dtype是int，而np.mean([1, 2, 3])它转换到默认的的float列数组。

尝试tf.constant(np.arange(3.0))。