PyTorch torch.conj() method “returns a view of input with a flipped conjugate bit.” If the input has a non-complex dtype, this function just returns input.
Syntax
torch.conj(input)
Parameters
input (Tensor): The input tensor.
Example 1: Basic usage with a 1D complex tensor
import torch
tensor_1 = torch.tensor([1 + 2j, 3 - 4j, -1 + 5j])
conjugate_1 = torch.conj(tensor_1)
print("Original Tensor:")
print(tensor_1)
print("\nConjugate Tensor:")
print(conjugate_1)
Output
Original Tensor:
tensor([ 1.+2.j, 3.-4.j, -1.+5.j])
Conjugate Tensor:
tensor([ 1.-2.j, 3.+4.j, -1.-5.j])
Example 2: Using the method with a 2D complex tensor
import torch
tensor_2 = torch.tensor([
[1 + 1j, 2 - 2j],
[3 + 3j, 4 - 4j]
])
conjugate_2 = torch.conj(tensor_2)
print("Original Tensor:")
print(tensor_2)
print("\nConjugate Tensor:")
print(conjugate_2)
Output
Example 3: Using the method with real numbers
import torch
tensor_3 = torch.tensor([1, 2, 3, 4])
conjugate_3 = torch.conj(tensor_3)
print("Original Tensor:")
print(tensor_3)
print("\nConjugate Tensor (same as original for real numbers):")
print(conjugate_3)
Output
Original Tensor:
tensor([1, 2, 3, 4])
Conjugate Tensor (same as original for real numbers):
tensor([1, 2, 3, 4])That’s it!
Related posts
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Machine Learning frameworks like PyTorch and Tensorflow is a testament to his versatility and commitment to the craft.