tensorflow中tf.argmax和tf.reduce_max

import tensorflow as tf

import numpy as np
d_scores = {}
d_scores[0] = [[[1,2],[3,4],[5,6]],[[7,8],[9,10],[11,12]]]

classes = tf.argmax(d_scores[0],axis=1)
scores = tf.reduce_max(d_scores[0],axis=1)

with tf.Session() as sess:

    print(classes.eval())
    print(scores.eval())

结果

[[2 2]
 [2 2]]
[[ 5  6]

 [11 12]]

import tensorflow as tf
import numpy as np
d_scores = {}
d_scores[0] = [[[1,2],[3,4],[5,6]],[[7,8],[9,10],[11,12]]]

classes = tf.argmax(d_scores[0],axis=2)
scores = tf.reduce_max(d_scores[0],axis=2)

with tf.Session() as sess:

    print(classes.eval())
    print(scores.eval())

[[1 1 1]
 [1 1 1]]
[[ 2  4  6]
 [ 8 10 12]]

可以看出tf.argmax和tf.reduce_max会把指定维度降掉