TensorFlow学习－－tf.reduce_mean()

def reduce_mean(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None):

# !/usr/bin/python
# coding:utf-8

import tensorflow as tf

t0 = tf.Variable([[1., 9.], [2., 0.]], name='t')
t1 = tf.Variable([[[1., 9.], [2., 0.], [6., 3.]], [[1., 9.], [2., 0.], [6., 3.]]], name='t')
# 初始化变量
init = tf.initialize_all_variables()
# 启动默认图
with tf.Session() as sess:
sess.run(init)

print "t0:\n", t0.eval()
# 默认为求全部元素的均值,即所有维度都会减少,最终返回一个单个元素的张量
print "reduce_mean(t0):\n", tf.reduce_mean(t0).eval()
# 若　keep_dims=True　则返回一个单个元素且张量维度与原张量一致的张量
print "reduce_mean(t0, keep_dims=True):\n", tf.reduce_mean(t0, keep_dims=True).eval()
# axis=0即沿列方向求均值,即张量的等级沿列方向降低为1
print "reduce_mean(t0, 0):\n", tf.reduce_mean(t0, 0).eval()
# axis=0即沿行方向求均值,即张量的等级沿行方向降低为1
print "reduce_mean(t0, 1):\n", tf.reduce_mean(t0, 1).eval()

print "t1:\n", t1.eval()
# reduction_indices：轴的旧名称（不推荐使用）
print "reduce_mean(t1, 0):\n", tf.reduce_mean(t1, 0).eval()
print "reduce_mean(t1, 1):\n", tf.reduce_mean(t1, 1).eval()
# reduction_indices=[0]/[1]　即　axis=0/1
print "reduce_mean(t1, reduction_indices=[0]):\n", tf.reduce_mean(t1, reduction_indices=[0]).eval()
print "reduce_mean(t1, reduction_indices=[1]):\n", tf.reduce_mean(t1, reduction_indices=[1]).eval()

t0:
[[ 1.  9.]
[ 2.  0.]]
reduce_mean(t0):
3.0
reduce_mean(t0, keep_dims=True):
[[ 3.]]
reduce_mean(t0, 0):
[ 1.5  4.5]
reduce_mean(t0, 1):
[ 5.  1.]
t1:
[[[ 1.  9.]
[ 2.  0.]
[ 6.  3.]]

[[ 1.  9.]
[ 2.  0.]
[ 6.  3.]]]
reduce_mean(t1, 0):
[[ 1.  9.]
[ 2.  0.]
[ 6.  3.]]
reduce_mean(t1, 1):
[[ 3.  4.]
[ 3.  4.]]
reduce_mean(t1, reduction_indices=[0]):
[[ 1.  9.]
[ 2.  0.]
[ 6.  3.]]
reduce_mean(t1, reduction_indices=[1]):
[[ 3.  4.]
[ 3.  4.]]
reduce_mean(t1):
[[[ 3.5]]]