tf.reduce_mean()
tf.reduce_mean( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None )
tf.reduce_mean函数的作用是求平均值。第一个参数是一个集合,可以是列表、二维数组和多维数组。第二个参数指定在哪个维度上面求平均值。默认对所有的元素求平均。
示例
import tensorflow as tf x = tf.constant([[1., 1.], [2., 2.]]) tf.reduce_mean(x) # 1.5 m1 = tf.reduce_mean(x, axis=0) # [1.5, 1.5] m2 = tf.reduce_mean(x, 1) # [1., 2.] xx = tf.constant([[[1., 1, 1], [2., 2, 2]], [[3, 3, 3], [4, 4, 4]]]) m3 = tf.reduce_mean(xx, [0, 1]) # [2.5 2.5 2.5] with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print(sess.run(m1)) print(sess.run(m2)) print(xx.get_shape()) print(sess.run(m3))执行结果:
[1.5 1.5]
[1. 2.]
(2, 2, 3)
[2.5 2.5 2.5]
参考:https://blog.csdn.net/liangyihuai/article/details/79050018