https://blog.csdn.net/he_min/article/details/78694383
在tensorflow中经常见到reducemean这个api,到底有什么用，到底是对谁求均值？
api中是这样写的：
tf.reduce_mean(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)
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Computes the mean of elements across dimensions of a tensor.Reduces input_tensor along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.
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直观的翻译就是
根据给出的axis在input_tensor上求平均值。除非keep_dims为真，axis中的每个的张量秩会减少1。如果keep_dims为真，求平均值的维度的长度都会保持为1.如果不设置axis，所有维度上的元素都会被求平均值，并且只会返回一个只有一个元素的张量。
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为了更加清楚的理解其含义，给出一个简单的例子：
import tensorflow as tf import numpy as np ''' x = [[1.,2.],[3.,4.]] print(tf.reduce_mean(x)) print(tf.reduce_mean(x,0)) print(tf.reduce_mean(x,1)) ''' x = np.array([[1.,2.,3.],[4.,5.,6.]]) with tf.Session() as sess: #返回所有元素的平均值 mean_none = sess.run(tf.reduce_mean(x)) #返回各列的平均值 mean_0 = sess.run(tf.reduce_mean(x, 0)) #返回各行的平均值向量 mean_1 = sess.run(tf.reduce_mean(x, 1)) print(x) print(mean_none) print(mean_0) print(mean_1)