tf.transpose如何在tensorflow中工作？

Question

tf.transpose(a, perm=None, name='transpose')

transposes a. It permutes the dimensions according to perm. So if I am using this matrix to transform:

import tensorflow as tt
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"
import numpy as bb
ab=([[[1,2,3],[6,5,4]],[[4,5,6],[3,6,3]]])
v=bb.array(ab)
fg=tt.transpose(v)
print(v)

with tt.Session() as df:
    print("\n New tranformed matrix is: \n\n{}".format(df.run(fg)))

Result is :

[[[1 2 3]
  [6 5 4]]

 [[4 5 6]
  [3 6 3]]]

 New tranformed matrix is: 

[[[1 4]
  [6 3]]

 [[2 5]
  [5 6]]

 [[3 6]
  [4 3]]]

Process finished with exit code 0

now if i use perm argument then :

import tensorflow as tt
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"
import numpy as bb
ab=([[[1,2,3],[6,5,4]],[[4,5,6],[3,6,3]]])
v=bb.array(ab)
fg=tt.transpose(v,perm=[0,2,1])
print(v)

with tt.Session() as df:
    print("\n New tranformed matrix is: \n\n{}".format(df.run(fg)))

Result is :

[[[1 2 3]
  [6 5 4]]

 [[4 5 6]
  [3 6 3]]]

 New tranformed matrix is: 

[[[1 6]
  [2 5]
  [3 4]]

 [[4 3]
  [5 6]
  [6 3]]]

Process finished with exit code 0

Due to this, I am confused and I have two questions :

• Whenever I want to transpose a matrix I have to give perm[0,2,1] as default ?
• What is 0,2,1 here ?

Answer 1

Looking at the numpy.transpose documentation, we find that transpose takes the argument

axes : list of ints, optional
By default, reverse the dimensions, otherwise permute the axes according to the values given.

So the default call to transpose translates into np.transpose(a, axes=[1,0]) for the 2D case, or np.transpose(a, axes=[2,1,0]) .

The operation you want to have here, is one that leaves the "depth" dimension unchanged. Therefore in the axes argument, the depth axes, which is the 0 th axes, needs to stay unchanged. The axes 1 and 2 (where 1 is the vertical axis), need to change positions. So you change the axes order from the initial [0,1,2] to [0,2,1] ( [stays the same, changes with other, changes with other] ).

In tensorflow, they have for some reason renamed axes to perm . The argument from above stays the same.

images

Concerning images, they differ from the arrays in the question. Images normally have their x and y stored in the first two dimensions and the channel in the last, [y,x,channel] .

In order to "transpose" an image in the sense of a 2D transposition, where horizontal and vertical axes are exchanged, you would need to use

np.transpose(a, axes=[1,0,2])

(channel stays the same, x and y are exchanged).