Transposing a High-dimensional Tensor
Here, we create a 4D tensor named x. The tensor contains two sets of 2×2 matrices, arranged in a 2x2x2x2 structure. This means we have two sets of 2×2 matrices, where each set is arranged along two dimensions.
Then, we use the tf.transpose() function to transpose the tensor and we use the permutation [0, 2, 1, 3], which means we’re rearranging the dimensions of the tensor. The first and third dimensions are swapped, and the second dimension remains unchanged.
Python3
import tensorflow as tf# Define a 4D tensorx = tf.constant([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]])# Transpose the tensor to change the order of dimensionstransposed_x = tf.transpose(x, perm=[0, 2, 1, 3])print("Transposed 4D Tensor:")print(transposed_x.numpy()) |
Output:
Transposed 4D Tensor:
[[[[ 1 2]
[ 5 6]]
[[ 3 4]
[ 7 8]]]
[[[ 9 10]
[13 14]]
[[11 12]
[15 16]]]]
Tensor Transpose in Tensorflow With Example
Tensor transpose is a fundamental operation in TensorFlow that rearranges the dimensions of a tensor according to a specified permutation. This operation is crucial in various machine learning algorithms and data manipulation tasks.
Tensor is useful when dealing with multidimensional data, such as images, time series, and sequences. Transposing a tensor changes the order of its dimensions, providing flexibility in data manipulation and computation.
In this article, we will learn Tensor Transpose in TensorFlow with Example.
Syntax of tf.transpose()
tf.transpose(
a, perm=None, conjugate=False, name=’transpose’
)
Parameters
- a: Input tensor.
- perm: Permutation of dimensions. If not provided, the default permutation is set to (n-1…0), where n is the rank of the input tensor.
- conjugate: Optional parameter for complex tensors. The values are conjugated and transposed if set to True and the tensor dtype is either complex64 or complex128.
- name: Optional parameter for operation name.
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