Tensorflow: How to tile a tensor that duplicate in certain order? [duplicate]

Question

For example, I have a tensor A = tf.Variable([a, b, c, d, e]) and through tf.tile(), it can give a tensor like [a, b, c, d, e, a, b, c, d, e]

But I want to reform A into something like: [a, a, b, b, c, c, d, d, e], where the elements are duplicated at the original place.

What is the most efficient way (less operations) to achieve that (through different-able ops)?

Answer 1

You can do it by adding a dimension, tiling along that dimension, and removing it:

import tensorflow as tf

A = tf.constant([1, 2, 3, 4, 5])

B = tf.expand_dims(A, axis=-1)
C = tf.tile(B, multiples=[1,2])
D = tf.reshape(C, shape=[-1])

with tf.Session() as sess:
    print('A:\n{}'.format(A.eval()))
    print('B:\n{}'.format(B.eval()))
    print('C:\n{}'.format(C.eval()))
    print('D:\n{}'.format(D.eval()))

gives

A:
[1 2 3 4 5]
B: # Add inner dimension
[[1]
 [2]
 [3]
 [4]
 [5]]
C: # Tile along inner dimension
[[1 1]
 [2 2]
 [3 3]
 [4 4]
 [5 5]]
D: # Remove innermost dimension
[1 1 2 2 3 3 4 4 5 5]

Edit: as pointed out in the comments, using tf.stack allows to specify the additional dimension on the go:

F = tf.stack([A, A], axis=1)
F = tf.reshape(F, shape=[-1])

with tf.Session() as sess:
    print(F.eval())

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