In this article, we are going to cover how to compute the error function of a tensor in Python using PyTorch.
torch.special.erf() method
We can compute the error function of a tensor by using torch.special.erf() method. This method accepts the input tensor of any dimension and it returns a tensor with a computed error function with the same dimension as the input tensor. The below syntax is used to compute the error function of a tensor.
Syntax: torch.special.erf(input)
Parameters:
- input: This is our input tensor.
Return: This method returns a tensor with computed error function of input tensor.
Example 1:
The following program is to understand how to compute the error function of the 1D tensor.
# import required libraries
import torch
# creating a 1D tensor
tens = torch.tensor([-0.7336, -0.9200, -0.4742,
-0.4470, -0.3472])
# print above created tensor
print("\n Input Tensor:", tens)
# compute the error function
er = torch.special.erf(tens)
# Display result
print("\n After Computed Error function :", er)
Output:
Example 2:
The following program is to know how to compute the error function of a batch of tensors.
# import required libraries
import torch
# creating a batch of tensor
tens = torch.tensor([[[0.8636, -0.4195, -0.4681],
[0.1265, 1.2233, 0.1978],
[1.1389, 0.3686, 1.2339]],
[[1.6362, 0.6235, 1.2631],
[0.3336, 1.5336, 1.3677],
[0.5637, 1.3236, 0.2696]]])
# print above created tensor
print("\n\n Input Tensor: \n", tens)
# compute the error function
er = torch.special.erf(tens)
# Display result
print("\n\n After Computed Error function: \n", er)
Output:
