Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How to compute the inverse hyperbolic sine in PyTorch?
The torch.asinh() method computes the inverse hyperbolic sine of each element of the input tensor. It supports both real and complex-valued inputs. It supports any dimension of the input tensor.
Syntax
torch.asinh(input)
where input is the input tensor.
Output
It returns a tensor inverse hyperbolic sine of each element.
Steps
To compute the inverse hyperbolic sine of each element in the input tensor, you could follow the steps given below −
Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.
import torch
Create a torch tensor and print it.
input = torch.randn(3,4)
print("Input Tensor:
", input)Compute the inverse hyperbolic sine of each element in the input tensor using torch.asinh(input). Here input is the input tensor .
inv_hsi = torch.asinh(input)
Display the computed tensor with inverse hyperbolic sine values.
print("Inverse Hyperbolic Sine Tensor:
", inv_hsin)Now, let's take a couple of examples to demonstrate how to compute the inverse hyperbolic sine.
Example 1
# Import the required library
import torch
# define an input tensor
input = torch.tensor([1.2, 3., 4., 4.2, -3.2])
# print the above defined tensor
print("Input Tensor:
", input)
# compute the inverse hyperbolic sine
inv_hsin = torch.asinh(input)
# print the above computed tensor
print("Inverse Hyperbolic Sine Tensor:
", inv_hsin)
print("............................")
# define a complex input tensor
input = torch.tensor([1.2+2j, 3.+4.j, 4.2-3.2j])
# print the above defined tensor
print("Input Tensor:
", input)
# compute the inverse hyperbolic sine
inv_hsin = torch.asinh(input)
# print the above computed tensor
print("Inverse Hyperbolic Sine Tensor:
", inv_hsin)Output
Input Tensor: tensor([ 1.2000, 3.0000, 4.0000, 4.2000, -3.2000]) Inverse Hyperbolic Sine Tensor: tensor([ 1.0160, 1.8184, 2.0947, 2.1421, -1.8799]) ............................ Input Tensor: tensor([1.2000+2.0000j, 3.0000+4.0000j, 4.2000-3.2000j]) Inverse Hyperbolic Sine Tensor: tensor([1.5205+0.9873j, 2.2999+0.9176j, 2.3596-0.6425j])
In the above program, we computed the inverse hyperbolic sine of each element of the both real and complex-valued input tensors.
Example 2
# Import the required library
import torch
# define an input tensor
input = torch.randn(4,4)
# print the above defined tensor
print("Input Tensor:
", input)
# compute the inverse hyperbolic sine
inv_hsin = torch.asinh(input)
# print the above computed tensor
print("Inverse Hyperbolic Sine Tensor:
", inv_hsin)
print("............................")
# define a complex input tensor
real = torch.randn(2,3)
imag = torch.randn(2,3)
input = torch.complex(real, imag)
# print the above defined tensor
print("Input Tensor:
", input)
# compute the inverse hyperbolic sine
inv_hsin = torch.asinh(input)
# print the above computed tensor
print("Inverse Hyperbolic Sine Tensor:
", inv_hsin)Output
Input Tensor: tensor([[ 0.4057, -1.8063, -0.5133, 0.3540], [-0.7180, -1.0896, 0.1832, 1.9867], [-0.6352, -0.1913, -0.0541, -0.3637], [-0.6229, 0.5518, -0.8876, 2.8466]]) Inverse Hyperbolic Sine Tensor: tensor([[ 0.3953, -1.3535, -0.4931, 0.3470], [-0.6673, -0.9433, 0.1822, 1.4377], [-0.5988, -0.1901, -0.0541, -0.3561], [-0.5884, 0.5270, -0.7996, 1.7688]]) ............................ Input Tensor: tensor([[-0.7072+0.6690j, 0.2434-1.0732j, 1.2196-0.7483j], [-1.2849+0.1874j, -0.7717+1.3786j, 0.6163-0.0782j]]) Inverse Hyperbolic Sine Tensor: tensor([[-0.7525+0.5421j, 0.5764-1.1596j, 1.1148-0.4592j], [-1.0744+0.1149j, -1.1086+0.9624j, 0.5839-0.0666j]])
Note − In the above example, we have taken input tensors of randomly generated numbers. You may notice getting different elements.
116 Views
