tf.transpose() is a function provided in TensorFlow. This function is used to transpose the input tensor.
Syntax: tf.transpose(input_tensor, perm, conjugate)
Parameters:
input_tensor: as the name suggests it is the tensor which is to be transposed.
Type: Tensorperm: This parameters specifies the permutation according to which the input_tensor is to be transposed.
Type: Vectorconjugate: This parameters is set to True if the input_tensor is of type complex.
Type: Boolean
Example 1:
import tensorflow as geek x = geek.constant([[1, 2, 3, 4], [5, 6, 7, 8]]) transposed_tensor = geek.transpose(x) |
Output :
array([[1, 5],
[2, 6],
[3, 7],
[4, 8]])
Example 2: With using perm parameter:
When this parameter is passes the tensor is transposed along the given axis. In simple words it defines the output shape of the transposed tensor.
import tensorflow as geek x = geek.constant([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [ 10, 11, 12]], [[ 13, 14, 15], [ 16, 17, 18]], [[ 19, 20, 21], [ 22, 23, 24]]]) transposed_tensor = geek.transpose(x, perm = [0, 2, 1]) |
Output:
array([[[ 1, 4],
[ 2, 5],
[ 3, 6]],
[[ 7, 10],
[ 8, 11],
[ 9, 12]],
[[13, 16],
[14, 17],
[15, 18]],
[[19, 22],
[20, 23],
[21, 24]]])
shape (4, 3, 2)
The shape is (4, 3, 2) because our perm was [0, 2, 1]. The following is the mapping from perm to input tensor shape.
0 => 4 2 => 3 1 => 2
Example 3: Now we will study the conjugate parameter
It is set to True when we have complex variables in our tensor.
import tensorflow as geek x = geek.constant([[1 + 1j, 2 + 2j, 3 + 3j], [4 + 4j, 5 + 5j, 6 + 6j]]) transposed_tensor = geek.transpose(x) |
Output:
array([[1 + 1j, 4 + 4j],
[2 + 2j, 5 + 5j],
[3 + 3j, 6 + 6j]])
