I was trying to implement YOLO with 5 anchor boxes. I wanted to use keras.backend.max as described here 1 but it’s not functioning properly or I am misunderstanding it.
The code looks something like this:
`
   from keras import backend as K

   with tf.compat.v1.Session() as test_a:
    example =  tf.random.normal([4, 4, 2, 2], mean=1, stddev=4, seed = 1)
    print(example.eval())
    # test = K.argmax(example, axis=-1)
    # print(test.eval())
    another_test = K.max(example, axis = -1)
    print(another_test.eval())
`

The output is this:
`
  [[[[-2.2452729   6.938395  ]
   [ 1.2613175  -8.770817  ]]

  [[ 1.3969936   3.3648973 ]
   [ 3.3712919  -7.4917183 ]]

  [[-1.8915889   0.7749185 ]
  [ 3.5741792  -0.05729628]]

  [[ 8.426533    3.2713668 ]
   [-0.5313436  -4.9413733 ]]]


 [[[ 6.0470843   0.8987757 ]
   [-0.05851877  7.131255  ]]

  [[-5.9719086  -0.7515718 ]
   [-1.26404     2.2826772 ]]

  [[ 5.531324   -8.113029  ]
    [ 2.9312482  -4.250835  ]]

  [[ 2.4274013  -5.9211335 ]
    [ 0.83932906  4.5986476 ]]]


 [[[-4.5223565   6.9258494 ]
   [ 0.01802081 -1.9305887 ]]

  [[ 0.21641421  1.2868321 ]
   [ 3.5319235  -5.284763  ]]

  [[ 6.317525   -3.693468  ]
   [ 1.1261784   2.9082098 ]]

  [[ 2.747768   -0.26723564]
   [-0.8030013  -6.2242627 ]]]


 [[[ 1.4995985  -2.0826168 ]
   [-1.9849663  -0.12781298]]

  [[-6.835262   -0.35044277]
   [ 5.1207933   7.053607  ]]

  [[ 1.9006321  -0.14264834]
   [ 2.0753016   7.984844  ]]

  [[ 4.695484   -7.2363987 ]
   [-0.25753224  5.841353  ]]]]
[[[-0.0205164   5.3679433 ]
  [ 3.9440122   4.684675  ]
  [ 4.1706614   1.3993862 ]
  [ 9.333472    3.0931065 ]]

 [[ 6.7779894   5.3317556 ]
  [-3.0122952  -1.5482914 ]
  [-0.5402719  -5.03914   ]
  [ 3.0354424   2.9042442 ]]

 [[ 4.730604    2.9970727 ]
  [ 3.8026135   0.04406112]
  [ 5.1083784   9.928441  ]
  [-1.8228705   1.4313807 ]]

 [[ 7.075215   -4.0160027 ]
  [ 2.046008    0.94906276]
  [ 0.6468721   1.6242697 ]
  [-0.2219665   5.4110713 ]]] `

As far as I can comprehend it should return the max value but what is it returning?